fix: delete error boudle

fix: open debuger
fix: add hydrate debug
2026-06-19 13:54:10 +00:00 · 2025-11-19 17:54:58 +08:00 · 2025-11-19 16:23:41 +08:00 · 2025-11-19 15:44:17 +08:00 · 2025-11-19 15:29:22 +08:00 · 2025-11-19 13:30:30 +08:00
1608 changed files with 110690 additions and 25638 deletions
@@ -13,6 +13,17 @@
 # Default is '0' (enabled)
 # ENABLED_CSP=1

+# SSRF Protection Settings
+# Set to '1' to allow connections to private IP addresses (disable SSRF protection)
+# WARNING: Only enable this in trusted environments
+# Default is '0' (SSRF protection enabled)
+# SSRF_ALLOW_PRIVATE_IP_ADDRESS=0
+
+# Whitelist of allowed private IP addresses (comma-separated)
+# Only takes effect when SSRF_ALLOW_PRIVATE_IP_ADDRESS is '0'
+# Example: Allow specific internal servers while keeping SSRF protection
+# SSRF_ALLOW_IP_ADDRESS_LIST=192.168.1.100,10.0.0.50
+
 ########################################
 ########## AI Provider Service #########
 ########################################
@@ -21,6 +21,7 @@ jobs:
      (github.event_name == 'pull_request_review' && github.event.sender.type != 'Bot') ||
      (github.event_name == 'pull_request_review_comment' && github.event.sender.type != 'Bot')
    runs-on: ubuntu-latest
+    timeout-minutes: 5
    permissions:
      contents: read
      # update issues/comments
@@ -1,7 +1,7 @@
 name: Desktop PR Build

 on:
-  pull_request_target:
+  pull_request:
    types: [synchronize, labeled, unlabeled] # PR 更新或标签变化时触发

 # 确保同一 PR 同一时间只运行一个相同的 workflow，取消正在进行的旧的运行
@@ -126,6 +126,7 @@ jobs:
        run: npm run workflow:set-desktop-version ${{ needs.version.outputs.version }} nightly

      # macOS 构建处理
+      # 注意：fork 的 PR 无法访问 secrets，会构建未签名版本
      - name: Build artifact on macOS
        if: runner.os == 'macOS'
        run: npm run desktop:build
@@ -136,7 +137,7 @@ jobs:
          DATABASE_URL: "postgresql://postgres@localhost:5432/postgres"
          # 默认添加一个加密 SECRET
          KEY_VAULTS_SECRET: "oLXWIiR/AKF+rWaqy9lHkrYgzpATbW3CtJp3UfkVgpE="
-          # macOS 签名和公证配置
+          # macOS 签名和公证配置（fork 的 PR 访问不到 secrets，会跳过签名）
          CSC_LINK: ${{ secrets.APPLE_CERTIFICATE_BASE64 }}
          CSC_KEY_PASSWORD: ${{ secrets.APPLE_CERTIFICATE_PASSWORD }}
          NEXT_PUBLIC_DESKTOP_PROJECT_ID: ${{ secrets.UMAMI_NIGHTLY_DESKTOP_PROJECT_ID }}
@@ -148,7 +149,8 @@ jobs:
          APPLE_APP_SPECIFIC_PASSWORD: ${{ secrets.APPLE_APP_SPECIFIC_PASSWORD }}
          APPLE_TEAM_ID: ${{ secrets.APPLE_TEAM_ID }}

-      #  Windows 平台构建处理
+      # Windows 平台构建处理
+      # 注意：fork 的 PR 无法访问 secrets，会构建未签名版本
      - name: Build artifact on Windows
        if: runner.os == 'Windows'
        run: npm run desktop:build
@@ -275,6 +277,8 @@ jobs:
  publish-pr:
    needs: [merge-mac-files, version]
    name: Publish PR Build
+    # 只为非 fork 的 PR 发布（fork 的 PR 没有写权限）
+    if: github.event.pull_request.head.repo.full_name == github.repository
    runs-on: ubuntu-latest
    # Grant write permissions for creating release and commenting on PR
    permissions:
@@ -21,6 +21,7 @@ jobs:
          - python-interpreter
          - context-engine
          - agent-runtime
+          - conversation-flow

    name: Test package ${{ matrix.package }}

@@ -1,272 +0,0 @@
-## Set global build ENV
-ARG NODEJS_VERSION="24"
-
-## Base image for all building stages
-FROM node:${NODEJS_VERSION}-slim AS base
-
-ARG USE_CN_MIRROR
-
-ENV DEBIAN_FRONTEND="noninteractive"
-
-RUN \
-    # If you want to build docker in China, build with --build-arg USE_CN_MIRROR=true
-    if [ "${USE_CN_MIRROR:-false}" = "true" ]; then \
-        sed -i "s/deb.debian.org/mirrors.ustc.edu.cn/g" "/etc/apt/sources.list.d/debian.sources"; \
-    fi \
-    # Add required package
-    && apt update \
-    && apt install ca-certificates proxychains-ng -qy \
-    # Prepare required package to distroless
-    && mkdir -p /distroless/bin /distroless/etc /distroless/etc/ssl/certs /distroless/lib \
-    # Copy proxychains to distroless
-    && cp /usr/lib/$(arch)-linux-gnu/libproxychains.so.4 /distroless/lib/libproxychains.so.4 \
-    && cp /usr/lib/$(arch)-linux-gnu/libdl.so.2 /distroless/lib/libdl.so.2 \
-    && cp /usr/bin/proxychains4 /distroless/bin/proxychains \
-    && cp /etc/proxychains4.conf /distroless/etc/proxychains4.conf \
-    # Copy node to distroless
-    && cp /usr/lib/$(arch)-linux-gnu/libstdc++.so.6 /distroless/lib/libstdc++.so.6 \
-    && cp /usr/lib/$(arch)-linux-gnu/libgcc_s.so.1 /distroless/lib/libgcc_s.so.1 \
-    && cp /usr/local/bin/node /distroless/bin/node \
-    # Copy CA certificates to distroless
-    && cp /etc/ssl/certs/ca-certificates.crt /distroless/etc/ssl/certs/ca-certificates.crt \
-    # Cleanup temp files
-    && rm -rf /tmp/* /var/lib/apt/lists/* /var/tmp/*
-
-## Builder image, install all the dependencies and build the app
-FROM base AS builder
-
-ARG USE_CN_MIRROR
-ARG NEXT_PUBLIC_BASE_PATH
-ARG NEXT_PUBLIC_SENTRY_DSN
-ARG NEXT_PUBLIC_ANALYTICS_POSTHOG
-ARG NEXT_PUBLIC_POSTHOG_HOST
-ARG NEXT_PUBLIC_POSTHOG_KEY
-ARG NEXT_PUBLIC_ANALYTICS_UMAMI
-ARG NEXT_PUBLIC_UMAMI_SCRIPT_URL
-ARG NEXT_PUBLIC_UMAMI_WEBSITE_ID
-ARG FEATURE_FLAGS
-
-ENV NEXT_PUBLIC_CLIENT_DB="pglite"
-ENV NEXT_PUBLIC_BASE_PATH="${NEXT_PUBLIC_BASE_PATH}" \
-    FEATURE_FLAGS="${FEATURE_FLAGS}"
-
-# Sentry
-ENV NEXT_PUBLIC_SENTRY_DSN="${NEXT_PUBLIC_SENTRY_DSN}" \
-    SENTRY_ORG="" \
-    SENTRY_PROJECT=""
-
-ENV APP_URL="http://app.com"
-
-# Posthog
-ENV NEXT_PUBLIC_ANALYTICS_POSTHOG="${NEXT_PUBLIC_ANALYTICS_POSTHOG}" \
-    NEXT_PUBLIC_POSTHOG_HOST="${NEXT_PUBLIC_POSTHOG_HOST}" \
-    NEXT_PUBLIC_POSTHOG_KEY="${NEXT_PUBLIC_POSTHOG_KEY}"
-
-# Umami
-ENV NEXT_PUBLIC_ANALYTICS_UMAMI="${NEXT_PUBLIC_ANALYTICS_UMAMI}" \
-    NEXT_PUBLIC_UMAMI_SCRIPT_URL="${NEXT_PUBLIC_UMAMI_SCRIPT_URL}" \
-    NEXT_PUBLIC_UMAMI_WEBSITE_ID="${NEXT_PUBLIC_UMAMI_WEBSITE_ID}"
-
-# Node
-ENV NODE_OPTIONS="--max-old-space-size=6144"
-
-WORKDIR /app
-
-COPY package.json pnpm-workspace.yaml ./
-COPY .npmrc ./
-COPY packages ./packages
-
-RUN \
-    # If you want to build docker in China, build with --build-arg USE_CN_MIRROR=true
-    if [ "${USE_CN_MIRROR:-false}" = "true" ]; then \
-        export SENTRYCLI_CDNURL="https://npmmirror.com/mirrors/sentry-cli"; \
-        npm config set registry "https://registry.npmmirror.com/"; \
-        echo 'canvas_binary_host_mirror=https://npmmirror.com/mirrors/canvas' >> .npmrc; \
-    fi \
-    # Set the registry for corepack
-    && export COREPACK_NPM_REGISTRY=$(npm config get registry | sed 's/\/$//') \
-    # Update corepack to latest (nodejs/corepack#612)
-    && npm i -g corepack@latest \
-    # Enable corepack
-    && corepack enable \
-    # Use pnpm for corepack
-    && corepack use $(sed -n 's/.*"packageManager": "\(.*\)".*/\1/p' package.json) \
-    # Install the dependencies
-    && pnpm i
-
-COPY . .
-
-# run build standalone for docker version
-RUN npm run build:docker
-
-## Application image, copy all the files for production
-FROM busybox:latest AS app
-
-COPY --from=base /distroless/ /
-
-# Automatically leverage output traces to reduce image size
-# https://nextjs.org/docs/advanced-features/output-file-tracing
-COPY --from=builder /app/.next/standalone /app/
-
-# Copy server launcher
-COPY --from=builder /app/scripts/serverLauncher/startServer.js /app/startServer.js
-
-RUN \
-    # Add nextjs:nodejs to run the app
-    addgroup -S -g 1001 nodejs \
-    && adduser -D -G nodejs -H -S -h /app -u 1001 nextjs \
-    # Set permission for nextjs:nodejs
-    && chown -R nextjs:nodejs /app /etc/proxychains4.conf
-
-## Production image, copy all the files and run next
-FROM scratch
-
-# Copy all the files from app, set the correct permission for prerender cache
-COPY --from=app / /
-
-ENV NODE_ENV="production" \
-    NODE_OPTIONS="--dns-result-order=ipv4first --use-openssl-ca" \
-    NODE_EXTRA_CA_CERTS="" \
-    NODE_TLS_REJECT_UNAUTHORIZED="" \
-    SSL_CERT_FILE="/etc/ssl/certs/ca-certificates.crt"
-
-# Make the middleware rewrite through local as default
-# refs: https://github.com/lobehub/lobe-chat/issues/5876
-ENV MIDDLEWARE_REWRITE_THROUGH_LOCAL="1"
-
-# set hostname to localhost
-ENV HOSTNAME="0.0.0.0" \
-    PORT="3210"
-
-# General Variables
-ENV ACCESS_CODE="" \
-    API_KEY_SELECT_MODE="" \
-    DEFAULT_AGENT_CONFIG="" \
-    SYSTEM_AGENT="" \
-    FEATURE_FLAGS="" \
-    PROXY_URL="" \
-    ENABLE_AUTH_PROTECTION=""
-
-# Model Variables
-ENV \
-    # AI21
-    AI21_API_KEY="" AI21_MODEL_LIST="" \
-    # Ai360
-    AI360_API_KEY="" AI360_MODEL_LIST="" \
-    # AiHubMix
-    AIHUBMIX_API_KEY="" AIHUBMIX_MODEL_LIST="" \
-    # Anthropic
-    ANTHROPIC_API_KEY="" ANTHROPIC_MODEL_LIST="" ANTHROPIC_PROXY_URL="" \
-    # Amazon Bedrock
-    ENABLED_AWS_BEDROCK="" AWS_ACCESS_KEY_ID="" AWS_SECRET_ACCESS_KEY="" AWS_REGION="" AWS_BEDROCK_MODEL_LIST="" \
-    # Azure OpenAI
-    AZURE_API_KEY="" AZURE_API_VERSION="" AZURE_ENDPOINT="" AZURE_MODEL_LIST="" \
-    # Baichuan
-    BAICHUAN_API_KEY="" BAICHUAN_MODEL_LIST="" \
-    # Cloudflare
-    CLOUDFLARE_API_KEY="" CLOUDFLARE_BASE_URL_OR_ACCOUNT_ID="" CLOUDFLARE_MODEL_LIST="" \
-    # Cohere
-    COHERE_API_KEY="" COHERE_MODEL_LIST="" COHERE_PROXY_URL="" \
-    # ComfyUI
-    ENABLED_COMFYUI="" COMFYUI_BASE_URL="" COMFYUI_AUTH_TYPE="" \
-    COMFYUI_API_KEY="" COMFYUI_USERNAME="" COMFYUI_PASSWORD="" COMFYUI_CUSTOM_HEADERS="" \
-    # DeepSeek
-    DEEPSEEK_API_KEY="" DEEPSEEK_MODEL_LIST="" \
-    # Fireworks AI
-    FIREWORKSAI_API_KEY="" FIREWORKSAI_MODEL_LIST="" \
-    # Gitee AI
-    GITEE_AI_API_KEY="" GITEE_AI_MODEL_LIST="" \
-    # GitHub
-    GITHUB_TOKEN="" GITHUB_MODEL_LIST="" \
-    # Google
-    GOOGLE_API_KEY="" GOOGLE_MODEL_LIST="" GOOGLE_PROXY_URL="" \
-    # Groq
-    GROQ_API_KEY="" GROQ_MODEL_LIST="" GROQ_PROXY_URL="" \
-    # Higress
-    HIGRESS_API_KEY="" HIGRESS_MODEL_LIST="" HIGRESS_PROXY_URL="" \
-    # HuggingFace
-    HUGGINGFACE_API_KEY="" HUGGINGFACE_MODEL_LIST="" HUGGINGFACE_PROXY_URL="" \
-    # Hunyuan
-    HUNYUAN_API_KEY="" HUNYUAN_MODEL_LIST="" \
-    # InternLM
-    INTERNLM_API_KEY="" INTERNLM_MODEL_LIST="" \
-    # Jina
-    JINA_API_KEY="" JINA_MODEL_LIST="" JINA_PROXY_URL="" \
-    # Minimax
-    MINIMAX_API_KEY="" MINIMAX_MODEL_LIST="" \
-    # Mistral
-    MISTRAL_API_KEY="" MISTRAL_MODEL_LIST="" \
-    # ModelScope
-    MODELSCOPE_API_KEY="" MODELSCOPE_MODEL_LIST="" MODELSCOPE_PROXY_URL="" \
-    # Moonshot
-    MOONSHOT_API_KEY="" MOONSHOT_MODEL_LIST="" MOONSHOT_PROXY_URL="" \
-    # Nebius
-    NEBIUS_API_KEY="" NEBIUS_MODEL_LIST="" NEBIUS_PROXY_URL="" \
-    # NewAPI
-    NEWAPI_API_KEY="" NEWAPI_PROXY_URL="" \
-    # Novita
-    NOVITA_API_KEY="" NOVITA_MODEL_LIST="" \
-    # Nvidia NIM
-    NVIDIA_API_KEY="" NVIDIA_MODEL_LIST="" NVIDIA_PROXY_URL="" \
-    # Ollama
-    ENABLED_OLLAMA="" OLLAMA_MODEL_LIST="" OLLAMA_PROXY_URL="" \
-    # OpenAI
-    ENABLED_OPENAI="" OPENAI_API_KEY="" OPENAI_MODEL_LIST="" OPENAI_PROXY_URL="" \
-    # OpenRouter
-    OPENROUTER_API_KEY="" OPENROUTER_MODEL_LIST="" \
-    # Perplexity
-    PERPLEXITY_API_KEY="" PERPLEXITY_MODEL_LIST="" PERPLEXITY_PROXY_URL="" \
-    # Qiniu
-    QINIU_API_KEY="" QINIU_MODEL_LIST="" QINIU_PROXY_URL="" \
-    # Qwen
-    QWEN_API_KEY="" QWEN_MODEL_LIST="" QWEN_PROXY_URL="" \
-    # SambaNova
-    SAMBANOVA_API_KEY="" SAMBANOVA_MODEL_LIST="" \
-    # SenseNova
-    SENSENOVA_API_KEY="" SENSENOVA_MODEL_LIST="" \
-    # SiliconCloud
-    SILICONCLOUD_API_KEY="" SILICONCLOUD_MODEL_LIST="" SILICONCLOUD_PROXY_URL="" \
-    # Spark
-    SPARK_API_KEY="" SPARK_MODEL_LIST="" SPARK_PROXY_URL="" SPARK_SEARCH_MODE="" \
-    # Stepfun
-    STEPFUN_API_KEY="" STEPFUN_MODEL_LIST="" \
-    # Taichu
-    TAICHU_API_KEY="" TAICHU_MODEL_LIST="" \
-    # TogetherAI
-    TOGETHERAI_API_KEY="" TOGETHERAI_MODEL_LIST="" \
-    # Upstage
-    UPSTAGE_API_KEY="" UPSTAGE_MODEL_LIST="" \
-    # v0 (Vercel)
-    V0_API_KEY="" V0_MODEL_LIST="" \
-    # vLLM
-    VLLM_API_KEY="" VLLM_MODEL_LIST="" VLLM_PROXY_URL="" \
-    # Wenxin
-    WENXIN_API_KEY="" WENXIN_MODEL_LIST="" \
-    # xAI
-    XAI_API_KEY="" XAI_MODEL_LIST="" XAI_PROXY_URL="" \
-    # Xinference
-    XINFERENCE_API_KEY="" XINFERENCE_MODEL_LIST="" XINFERENCE_PROXY_URL="" \
-    # 01.AI
-    ZEROONE_API_KEY="" ZEROONE_MODEL_LIST="" \
-    # Zhipu
-    ZHIPU_API_KEY="" ZHIPU_MODEL_LIST="" \
-    # Tencent Cloud
-    TENCENT_CLOUD_API_KEY="" TENCENT_CLOUD_MODEL_LIST="" \
-    # Infini-AI
-    INFINIAI_API_KEY="" INFINIAI_MODEL_LIST="" \
-    # 302.AI
-    AI302_API_KEY="" AI302_MODEL_LIST="" \
-    # FAL
-    ENABLED_FAL="" FAL_API_KEY="" FAL_MODEL_LIST="" \
-    # BFL
-    BFL_API_KEY="" BFL_MODEL_LIST="" \
-    # Vercel AI Gateway
-    VERCELAIGATEWAY_API_KEY="" VERCELAIGATEWAY_MODEL_LIST=""
-
-USER nextjs
-
-EXPOSE 3210/tcp
-
-ENTRYPOINT ["/bin/node"]
-
-CMD ["/app/startServer.js"]
@@ -345,12 +345,12 @@ In addition, these plugins are not limited to news aggregation, but can also ext

 <!-- PLUGIN LIST -->

-| Recent Submits                                                                                                               | Description                                                                                                                               |
-| ---------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
-| [Shopping tools](https://lobechat.com/discover/plugin/ShoppingTools)<br/><sup>By **shoppingtools** on **2025-10-27**</sup>   | Search for products on eBay & AliExpress, find eBay events & coupons. Get prompt examples.<br/>`shopping` `e-bay` `ali-express` `coupons` |
-| [PortfolioMeta](https://lobechat.com/discover/plugin/StockData)<br/><sup>By **portfoliometa** on **2025-09-27**</sup>        | Analyze stocks and get comprehensive real-time investment data and analytics.<br/>`stock`                                                 |
-| [Web](https://lobechat.com/discover/plugin/web)<br/><sup>By **Proghit** on **2025-01-24**</sup>                              | Smart web search that reads and analyzes pages to deliver comprehensive answers from Google results.<br/>`web` `search`                   |
-| [Bing_websearch](https://lobechat.com/discover/plugin/Bingsearch-identifier)<br/><sup>By **FineHow** on **2024-12-22**</sup> | Search for information from the internet base BingApi<br/>`bingsearch`                                                                    |
+| Recent Submits                                                                                                             | Description                                                                                                                               |
+| -------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
+| [SEO](https://lobechat.com/discover/plugin/SEO)<br/><sup>By **orrenprunckun** on **2025-11-14**</sup>                      | Enter any URL and keyword and get an On-Page SEO analysis & insights!<br/>`seo`                                                           |
+| [Shopping tools](https://lobechat.com/discover/plugin/ShoppingTools)<br/><sup>By **shoppingtools** on **2025-10-27**</sup> | Search for products on eBay & AliExpress, find eBay events & coupons. Get prompt examples.<br/>`shopping` `e-bay` `ali-express` `coupons` |
+| [PortfolioMeta](https://lobechat.com/discover/plugin/StockData)<br/><sup>By **portfoliometa** on **2025-09-27**</sup>      | Analyze stocks and get comprehensive real-time investment data and analytics.<br/>`stock`                                                 |
+| [Web](https://lobechat.com/discover/plugin/web)<br/><sup>By **Proghit** on **2025-01-24**</sup>                            | Smart web search that reads and analyzes pages to deliver comprehensive answers from Google results.<br/>`web` `search`                   |

 > 📊 Total plugins: [<kbd>**42**</kbd>](https://lobechat.com/discover/plugins)

@@ -338,12 +338,12 @@ LobeChat 的插件生态系统是其核心功能的重要扩展，它极大地

 <!-- PLUGIN LIST -->

-| 最近新增                                                                                                                   | 描述                                                                                                               |
-| -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ |
-| [购物工具](https://lobechat.com/discover/plugin/ShoppingTools)<br/><sup>By **shoppingtools** on **2025-10-27**</sup>       | 在 eBay 和 AliExpress 上搜索产品，查找 eBay 活动和优惠券。获取快速示例。<br/>`购物` `e-bay` `ali-express` `优惠券` |
-| [PortfolioMeta](https://lobechat.com/discover/plugin/StockData)<br/><sup>By **portfoliometa** on **2025-09-27**</sup>      | 分析股票并获取全面的实时投资数据和分析。<br/>`股票`                                                                |
-| [网页](https://lobechat.com/discover/plugin/web)<br/><sup>By **Proghit** on **2025-01-24**</sup>                           | 智能网页搜索，读取和分析页面，以提供来自 Google 结果的全面答案。<br/>`网页` `搜索`                                 |
-| [必应网页搜索](https://lobechat.com/discover/plugin/Bingsearch-identifier)<br/><sup>By **FineHow** on **2024-12-22**</sup> | 通过 BingApi 搜索互联网上的信息<br/>`bingsearch`                                                                   |
+| 最近新增                                                                                                              | 描述                                                                                                               |
+| --------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ |
+| [SEO](https://lobechat.com/discover/plugin/SEO)<br/><sup>By **orrenprunckun** on **2025-11-14**</sup>                 | 输入任何 URL 和关键词，获取页面 SEO 分析和见解！<br/>`seo`                                                         |
+| [购物工具](https://lobechat.com/discover/plugin/ShoppingTools)<br/><sup>By **shoppingtools** on **2025-10-27**</sup>  | 在 eBay 和 AliExpress 上搜索产品，查找 eBay 活动和优惠券。获取快速示例。<br/>`购物` `e-bay` `ali-express` `优惠券` |
+| [PortfolioMeta](https://lobechat.com/discover/plugin/StockData)<br/><sup>By **portfoliometa** on **2025-09-27**</sup> | 分析股票并获取全面的实时投资数据和分析。<br/>`股票`                                                                |
+| [网页](https://lobechat.com/discover/plugin/web)<br/><sup>By **Proghit** on **2025-01-24**</sup>                      | 智能网页搜索，读取和分析页面，以提供来自 Google 结果的全面答案。<br/>`网页` `搜索`                                 |

 > 📊 Total plugins: [<kbd>**42**</kbd>](https://lobechat.com/discover/plugins)

@@ -32,33 +32,34 @@
    "electron-updater": "^6.6.2",
    "electron-window-state": "^5.0.3",
    "fetch-socks": "^1.3.2",
-    "get-port-please": "^3.1.2",
+    "get-port-please": "^3.2.0",
    "pdfjs-dist": "4.10.38"
  },
  "devDependencies": {
    "@electron-toolkit/eslint-config-prettier": "^3.0.0",
-    "@electron-toolkit/eslint-config-ts": "^3.0.0",
-    "@electron-toolkit/preload": "^3.0.1",
+    "@electron-toolkit/eslint-config-ts": "^3.1.0",
+    "@electron-toolkit/preload": "^3.0.2",
    "@electron-toolkit/tsconfig": "^2.0.0",
    "@electron-toolkit/utils": "^4.0.0",
    "@lobechat/electron-client-ipc": "workspace:*",
    "@lobechat/electron-server-ipc": "workspace:*",
    "@lobechat/file-loaders": "workspace:*",
-    "@lobehub/i18n-cli": "^1.20.3",
-    "@types/lodash": "^4.17.0",
+    "@lobehub/i18n-cli": "^1.25.1",
+    "@types/lodash": "^4.17.20",
    "@types/resolve": "^1.20.6",
-    "@types/semver": "^7.7.0",
+    "@types/semver": "^7.7.1",
    "@types/set-cookie-parser": "^2.4.10",
    "@typescript/native-preview": "7.0.0-dev.20250711.1",
-    "consola": "^3.1.0",
+    "consola": "^3.4.2",
    "cookie": "^1.0.2",
-    "electron": "^38.0.0",
+    "diff": "^8.0.2",
+    "electron": "^38.7.0",
    "electron-builder": "^26.0.12",
    "electron-is": "^3.0.0",
-    "electron-log": "^5.3.3",
+    "electron-log": "^5.4.3",
    "electron-store": "^8.2.0",
-    "electron-vite": "^3.0.0",
-    "execa": "^9.5.2",
+    "electron-vite": "^3.1.0",
+    "execa": "^9.6.0",
    "fast-glob": "^3.3.3",
    "fix-path": "^5.0.0",
    "http-proxy-agent": "^7.0.2",
@@ -66,13 +67,13 @@
    "just-diff": "^6.0.2",
    "lodash": "^4.17.21",
    "lodash-es": "^4.17.21",
-    "resolve": "^1.22.8",
-    "semver": "^7.5.4",
-    "set-cookie-parser": "^2.7.1",
-    "tsx": "^4.19.3",
-    "typescript": "^5.7.3",
-    "undici": "^7.9.0",
-    "vite": "^6.3.5",
+    "resolve": "^1.22.11",
+    "semver": "^7.7.3",
+    "set-cookie-parser": "^2.7.2",
+    "tsx": "^4.20.6",
+    "typescript": "^5.9.3",
+    "undici": "^7.16.0",
+    "vite": "^6.4.1",
    "vitest": "^3.2.4"
  },
  "pnpm": {
@@ -1,4 +1,4 @@
-import { DataSyncConfig } from '@lobechat/electron-client-ipc';
+import { DataSyncConfig, MarketAuthorizationParams } from '@lobechat/electron-client-ipc';
 import { BrowserWindow, shell } from 'electron';
 import crypto from 'node:crypto';
 import querystring from 'node:querystring';
@@ -117,7 +117,32 @@ export default class AuthCtr extends ControllerModule {
  }

  /**
-   * Start polling mechanism to retrieve credentials
+   * Request Market OAuth authorization (desktop)
+   */
+  @ipcClientEvent('requestMarketAuthorization')
+  async requestMarketAuthorization(params: MarketAuthorizationParams) {
+    const { authUrl } = params;
+
+    if (!authUrl) {
+      const errorMessage = 'Market authorization URL is required';
+      logger.error(errorMessage);
+      return { error: errorMessage, success: false };
+    }
+
+    logger.info(`Requesting market authorization via: ${authUrl}`);
+    try {
+      await shell.openExternal(authUrl);
+      logger.debug('Opening market authorization URL in default browser');
+      return { success: true };
+    } catch (error) {
+      const message = error instanceof Error ? error.message : String(error);
+      logger.error('Market authorization request failed:', error);
+      return { error: message, success: false };
+    }
+  }
+
+  /**
+   * 启动轮询机制获取凭证
   */
  private startPolling() {
    if (!this.authRequestState) {
@@ -18,6 +18,7 @@ import {
  WriteLocalFileParams,
 } from '@lobechat/electron-client-ipc';
 import { SYSTEM_FILES_TO_IGNORE, loadFile } from '@lobechat/file-loaders';
+import { createPatch } from 'diff';
 import { shell } from 'electron';
 import fg from 'fast-glob';
 import { Stats, constants } from 'node:fs';
@@ -94,26 +95,45 @@ export default class LocalFileCtr extends ControllerModule {
  }

  @ipcClientEvent('readLocalFile')
-  async readFile({ path: filePath, loc }: LocalReadFileParams): Promise<LocalReadFileResult> {
-    const effectiveLoc = loc ?? [0, 200];
-    logger.debug('Starting to read file:', { filePath, loc: effectiveLoc });
+  async readFile({
+    path: filePath,
+    loc,
+    fullContent,
+  }: LocalReadFileParams): Promise<LocalReadFileResult> {
+    const effectiveLoc = fullContent ? undefined : (loc ?? [0, 200]);
+    logger.debug('Starting to read file:', { filePath, fullContent, loc: effectiveLoc });

    try {
      const fileDocument = await loadFile(filePath);

-      const [startLine, endLine] = effectiveLoc;
      const lines = fileDocument.content.split('\n');
      const totalLineCount = lines.length;
      const totalCharCount = fileDocument.content.length;

-      // Adjust slice indices to be 0-based and inclusive/exclusive
-      const selectedLines = lines.slice(startLine, endLine);
-      const content = selectedLines.join('\n');
-      const charCount = content.length;
-      const lineCount = selectedLines.length;
+      let content: string;
+      let charCount: number;
+      let lineCount: number;
+      let actualLoc: [number, number];
+
+      if (effectiveLoc === undefined) {
+        // Return full content
+        content = fileDocument.content;
+        charCount = totalCharCount;
+        lineCount = totalLineCount;
+        actualLoc = [0, totalLineCount];
+      } else {
+        // Return specified range
+        const [startLine, endLine] = effectiveLoc;
+        const selectedLines = lines.slice(startLine, endLine);
+        content = selectedLines.join('\n');
+        charCount = content.length;
+        lineCount = selectedLines.length;
+        actualLoc = effectiveLoc;
+      }

      logger.debug('File read successfully:', {
        filePath,
+        fullContent,
        selectedLineCount: lineCount,
        totalCharCount,
        totalLineCount,
@@ -128,7 +148,7 @@ export default class LocalFileCtr extends ControllerModule {
        fileType: fileDocument.fileType,
        filename: fileDocument.filename,
        lineCount,
-        loc: effectiveLoc,
+        loc: actualLoc,
        // Line count for the selected range
        modifiedTime: fileDocument.modifiedTime,

@@ -467,15 +487,35 @@ export default class LocalFileCtr extends ControllerModule {
   */
  @ipcClientEvent('searchLocalFiles')
  async handleLocalFilesSearch(params: LocalSearchFilesParams): Promise<FileResult[]> {
-    logger.debug('Received file search request:', { keywords: params.keywords });
+    logger.debug('Received file search request:', {
+      directory: params.directory,
+      keywords: params.keywords,
+    });

-    const options: Omit<SearchOptions, 'keywords'> = {
-      limit: 30,
+    // Build search options from params, mapping directory to onlyIn
+    const options: SearchOptions = {
+      contentContains: params.contentContains,
+      createdAfter: params.createdAfter ? new Date(params.createdAfter) : undefined,
+      createdBefore: params.createdBefore ? new Date(params.createdBefore) : undefined,
+      detailed: params.detailed,
+      exclude: params.exclude,
+      fileTypes: params.fileTypes,
+      keywords: params.keywords,
+      limit: params.limit || 30,
+      liveUpdate: params.liveUpdate,
+      modifiedAfter: params.modifiedAfter ? new Date(params.modifiedAfter) : undefined,
+      modifiedBefore: params.modifiedBefore ? new Date(params.modifiedBefore) : undefined,
+      onlyIn: params.directory, // Map directory param to onlyIn option
+      sortBy: params.sortBy,
+      sortDirection: params.sortDirection,
    };

    try {
-      const results = await this.searchService.search(params.keywords, options);
-      logger.debug('File search completed', { count: results.length });
+      const results = await this.searchService.search(options.keywords, options);
+      logger.debug('File search completed', {
+        count: results.length,
+        directory: params.directory,
+      });
      return results;
    } catch (error) {
      logger.error('File search failed:', error);
@@ -691,8 +731,32 @@ export default class LocalFileCtr extends ControllerModule {
      // Write back to file
      await writeFile(filePath, newContent, 'utf8');

-      logger.info(`${logPrefix} File edited successfully`, { replacements });
+      // Generate diff for UI display
+      const patch = createPatch(filePath, content, newContent, '', '');
+      const diffText = `diff --git a${filePath} b${filePath}\n${patch}`;
+
+      // Calculate lines added and deleted from patch
+      const patchLines = patch.split('\n');
+      let linesAdded = 0;
+      let linesDeleted = 0;
+
+      for (const line of patchLines) {
+        if (line.startsWith('+') && !line.startsWith('+++')) {
+          linesAdded++;
+        } else if (line.startsWith('-') && !line.startsWith('---')) {
+          linesDeleted++;
+        }
+      }
+
+      logger.info(`${logPrefix} File edited successfully`, {
+        linesAdded,
+        linesDeleted,
+        replacements,
+      });
      return {
+        diffText,
+        linesAdded,
+        linesDeleted,
        replacements,
        success: true,
      };
@@ -0,0 +1,242 @@
+import {
+  GetCommandOutputParams,
+  GetCommandOutputResult,
+  KillCommandParams,
+  KillCommandResult,
+  RunCommandParams,
+  RunCommandResult,
+} from '@lobechat/electron-client-ipc';
+import { ChildProcess, spawn } from 'node:child_process';
+import { randomUUID } from 'node:crypto';
+
+import { createLogger } from '@/utils/logger';
+
+import { ControllerModule, ipcClientEvent } from './index';
+
+const logger = createLogger('controllers:ShellCommandCtr');
+
+interface ShellProcess {
+  lastReadStderr: number;
+  lastReadStdout: number;
+  process: ChildProcess;
+  stderr: string[];
+  stdout: string[];
+}
+
+export default class ShellCommandCtr extends ControllerModule {
+  // Shell process management
+  private shellProcesses = new Map<string, ShellProcess>();
+
+  @ipcClientEvent('runCommand')
+  async handleRunCommand({
+    command,
+    description,
+    run_in_background,
+    timeout = 120_000,
+  }: RunCommandParams): Promise<RunCommandResult> {
+    const logPrefix = `[runCommand: ${description || command.slice(0, 50)}]`;
+    logger.debug(`${logPrefix} Starting command execution`, {
+      background: run_in_background,
+      timeout,
+    });
+
+    // Validate timeout
+    const effectiveTimeout = Math.min(Math.max(timeout, 1000), 600_000);
+
+    // Cross-platform shell selection
+    const shellConfig =
+      process.platform === 'win32'
+        ? { args: ['/c', command], cmd: 'cmd.exe' }
+        : { args: ['-c', command], cmd: '/bin/sh' };
+
+    try {
+      if (run_in_background) {
+        // Background execution
+        const shellId = randomUUID();
+        const childProcess = spawn(shellConfig.cmd, shellConfig.args, {
+          env: process.env,
+          shell: false,
+        });
+
+        const shellProcess: ShellProcess = {
+          lastReadStderr: 0,
+          lastReadStdout: 0,
+          process: childProcess,
+          stderr: [],
+          stdout: [],
+        };
+
+        // Capture output
+        childProcess.stdout?.on('data', (data) => {
+          shellProcess.stdout.push(data.toString());
+        });
+
+        childProcess.stderr?.on('data', (data) => {
+          shellProcess.stderr.push(data.toString());
+        });
+
+        childProcess.on('exit', (code) => {
+          logger.debug(`${logPrefix} Background process exited`, { code, shellId });
+        });
+
+        this.shellProcesses.set(shellId, shellProcess);
+
+        logger.info(`${logPrefix} Started background execution`, { shellId });
+        return {
+          shell_id: shellId,
+          success: true,
+        };
+      } else {
+        // Synchronous execution with timeout
+        return new Promise((resolve) => {
+          const childProcess = spawn(shellConfig.cmd, shellConfig.args, {
+            env: process.env,
+            shell: false,
+          });
+
+          let stdout = '';
+          let stderr = '';
+          let killed = false;
+
+          const timeoutHandle = setTimeout(() => {
+            killed = true;
+            childProcess.kill();
+            resolve({
+              error: `Command timed out after ${effectiveTimeout}ms`,
+              stderr,
+              stdout,
+              success: false,
+            });
+          }, effectiveTimeout);
+
+          childProcess.stdout?.on('data', (data) => {
+            stdout += data.toString();
+          });
+
+          childProcess.stderr?.on('data', (data) => {
+            stderr += data.toString();
+          });
+
+          childProcess.on('exit', (code) => {
+            if (!killed) {
+              clearTimeout(timeoutHandle);
+              const success = code === 0;
+              logger.info(`${logPrefix} Command completed`, { code, success });
+              resolve({
+                exit_code: code || 0,
+                output: stdout + stderr,
+                stderr,
+                stdout,
+                success,
+              });
+            }
+          });
+
+          childProcess.on('error', (error) => {
+            clearTimeout(timeoutHandle);
+            logger.error(`${logPrefix} Command failed:`, error);
+            resolve({
+              error: error.message,
+              stderr,
+              stdout,
+              success: false,
+            });
+          });
+        });
+      }
+    } catch (error) {
+      logger.error(`${logPrefix} Failed to execute command:`, error);
+      return {
+        error: (error as Error).message,
+        success: false,
+      };
+    }
+  }
+
+  @ipcClientEvent('getCommandOutput')
+  async handleGetCommandOutput({
+    filter,
+    shell_id,
+  }: GetCommandOutputParams): Promise<GetCommandOutputResult> {
+    const logPrefix = `[getCommandOutput: ${shell_id}]`;
+    logger.debug(`${logPrefix} Retrieving output`);
+
+    const shellProcess = this.shellProcesses.get(shell_id);
+    if (!shellProcess) {
+      logger.error(`${logPrefix} Shell process not found`);
+      return {
+        error: `Shell ID ${shell_id} not found`,
+        output: '',
+        running: false,
+        stderr: '',
+        stdout: '',
+        success: false,
+      };
+    }
+
+    const { lastReadStderr, lastReadStdout, process: childProcess, stderr, stdout } = shellProcess;
+
+    // Get new output since last read
+    const newStdout = stdout.slice(lastReadStdout).join('');
+    const newStderr = stderr.slice(lastReadStderr).join('');
+    let output = newStdout + newStderr;
+
+    // Apply filter if provided
+    if (filter) {
+      try {
+        const regex = new RegExp(filter, 'gm');
+        const lines = output.split('\n');
+        output = lines.filter((line) => regex.test(line)).join('\n');
+      } catch (error) {
+        logger.error(`${logPrefix} Invalid filter regex:`, error);
+      }
+    }
+
+    // Update last read positions separately
+    shellProcess.lastReadStdout = stdout.length;
+    shellProcess.lastReadStderr = stderr.length;
+
+    const running = childProcess.exitCode === null;
+
+    logger.debug(`${logPrefix} Output retrieved`, {
+      outputLength: output.length,
+      running,
+    });
+
+    return {
+      output,
+      running,
+      stderr: newStderr,
+      stdout: newStdout,
+      success: true,
+    };
+  }
+
+  @ipcClientEvent('killCommand')
+  async handleKillCommand({ shell_id }: KillCommandParams): Promise<KillCommandResult> {
+    const logPrefix = `[killCommand: ${shell_id}]`;
+    logger.debug(`${logPrefix} Attempting to kill shell`);
+
+    const shellProcess = this.shellProcesses.get(shell_id);
+    if (!shellProcess) {
+      logger.error(`${logPrefix} Shell process not found`);
+      return {
+        error: `Shell ID ${shell_id} not found`,
+        success: false,
+      };
+    }
+
+    try {
+      shellProcess.process.kill();
+      this.shellProcesses.delete(shell_id);
+      logger.info(`${logPrefix} Shell killed successfully`);
+      return { success: true };
+    } catch (error) {
+      logger.error(`${logPrefix} Failed to kill shell:`, error);
+      return {
+        error: (error as Error).message,
+        success: false,
+      };
+    }
+  }
+}
@@ -183,6 +183,26 @@ describe('LocalFileCtr', () => {
      expect(result.totalLineCount).toBe(5);
    });

+    it('should read full file content when fullContent is true', async () => {
+      const mockFileContent = 'line1\nline2\nline3\nline4\nline5';
+      vi.mocked(mockLoadFile).mockResolvedValue({
+        content: mockFileContent,
+        filename: 'test.txt',
+        fileType: 'txt',
+        createdTime: new Date('2024-01-01'),
+        modifiedTime: new Date('2024-01-02'),
+      });
+
+      const result = await localFileCtr.readFile({ path: '/test/file.txt', fullContent: true });
+
+      expect(result.content).toBe(mockFileContent);
+      expect(result.lineCount).toBe(5);
+      expect(result.charCount).toBe(mockFileContent.length);
+      expect(result.totalLineCount).toBe(5);
+      expect(result.totalCharCount).toBe(mockFileContent.length);
+      expect(result.loc).toEqual([0, 5]);
+    });
+
    it('should handle file read error', async () => {
      vi.mocked(mockLoadFile).mockRejectedValue(new Error('File not found'));

@@ -345,7 +365,10 @@ describe('LocalFileCtr', () => {
      const result = await localFileCtr.handleLocalFilesSearch({ keywords: 'test' });

      expect(result).toEqual(mockResults);
-      expect(mockSearchService.search).toHaveBeenCalledWith('test', { limit: 30 });
+      expect(mockSearchService.search).toHaveBeenCalledWith('test', {
+        keywords: 'test',
+        limit: 30,
+      });
    });

    it('should return empty array on search error', async () => {
@@ -389,4 +412,137 @@ describe('LocalFileCtr', () => {
      });
    });
  });
+
+  describe('handleEditFile', () => {
+    it('should replace first occurrence successfully', async () => {
+      const originalContent = 'Hello world\nHello again\nGoodbye world';
+      vi.mocked(mockFsPromises.readFile).mockResolvedValue(originalContent);
+      vi.mocked(mockFsPromises.writeFile).mockResolvedValue(undefined);
+
+      const result = await localFileCtr.handleEditFile({
+        file_path: '/test/file.txt',
+        old_string: 'Hello',
+        new_string: 'Hi',
+        replace_all: false,
+      });
+
+      expect(result.success).toBe(true);
+      expect(result.replacements).toBe(1);
+      expect(result.linesAdded).toBe(1);
+      expect(result.linesDeleted).toBe(1);
+      expect(result.diffText).toContain('diff --git a/test/file.txt b/test/file.txt');
+      expect(mockFsPromises.writeFile).toHaveBeenCalledWith(
+        '/test/file.txt',
+        'Hi world\nHello again\nGoodbye world',
+        'utf8',
+      );
+    });
+
+    it('should replace all occurrences when replace_all is true', async () => {
+      const originalContent = 'Hello world\nHello again\nHello there';
+      vi.mocked(mockFsPromises.readFile).mockResolvedValue(originalContent);
+      vi.mocked(mockFsPromises.writeFile).mockResolvedValue(undefined);
+
+      const result = await localFileCtr.handleEditFile({
+        file_path: '/test/file.txt',
+        old_string: 'Hello',
+        new_string: 'Hi',
+        replace_all: true,
+      });
+
+      expect(result.success).toBe(true);
+      expect(result.replacements).toBe(3);
+      expect(result.linesAdded).toBe(3);
+      expect(result.linesDeleted).toBe(3);
+      expect(mockFsPromises.writeFile).toHaveBeenCalledWith(
+        '/test/file.txt',
+        'Hi world\nHi again\nHi there',
+        'utf8',
+      );
+    });
+
+    it('should handle multiline replacement correctly', async () => {
+      const originalContent = 'function test() {\n  console.log("old");\n}';
+      vi.mocked(mockFsPromises.readFile).mockResolvedValue(originalContent);
+      vi.mocked(mockFsPromises.writeFile).mockResolvedValue(undefined);
+
+      const result = await localFileCtr.handleEditFile({
+        file_path: '/test/file.js',
+        old_string: 'console.log("old");',
+        new_string: 'console.log("new");\n  console.log("added");',
+        replace_all: false,
+      });
+
+      expect(result.success).toBe(true);
+      expect(result.replacements).toBe(1);
+      expect(result.linesAdded).toBe(2);
+      expect(result.linesDeleted).toBe(1);
+    });
+
+    it('should return error when old_string is not found', async () => {
+      const originalContent = 'Hello world';
+      vi.mocked(mockFsPromises.readFile).mockResolvedValue(originalContent);
+
+      const result = await localFileCtr.handleEditFile({
+        file_path: '/test/file.txt',
+        old_string: 'NonExistent',
+        new_string: 'New',
+        replace_all: false,
+      });
+
+      expect(result.success).toBe(false);
+      expect(result.error).toBe('The specified old_string was not found in the file');
+      expect(result.replacements).toBe(0);
+      expect(mockFsPromises.writeFile).not.toHaveBeenCalled();
+    });
+
+    it('should handle file read error', async () => {
+      vi.mocked(mockFsPromises.readFile).mockRejectedValue(new Error('Permission denied'));
+
+      const result = await localFileCtr.handleEditFile({
+        file_path: '/test/file.txt',
+        old_string: 'Hello',
+        new_string: 'Hi',
+        replace_all: false,
+      });
+
+      expect(result.success).toBe(false);
+      expect(result.error).toBe('Permission denied');
+      expect(result.replacements).toBe(0);
+    });
+
+    it('should handle file write error', async () => {
+      const originalContent = 'Hello world';
+      vi.mocked(mockFsPromises.readFile).mockResolvedValue(originalContent);
+      vi.mocked(mockFsPromises.writeFile).mockRejectedValue(new Error('Disk full'));
+
+      const result = await localFileCtr.handleEditFile({
+        file_path: '/test/file.txt',
+        old_string: 'Hello',
+        new_string: 'Hi',
+        replace_all: false,
+      });
+
+      expect(result.success).toBe(false);
+      expect(result.error).toBe('Disk full');
+    });
+
+    it('should generate correct diff format', async () => {
+      const originalContent = 'line 1\nline 2\nline 3';
+      vi.mocked(mockFsPromises.readFile).mockResolvedValue(originalContent);
+      vi.mocked(mockFsPromises.writeFile).mockResolvedValue(undefined);
+
+      const result = await localFileCtr.handleEditFile({
+        file_path: '/test/file.txt',
+        old_string: 'line 2',
+        new_string: 'modified line 2',
+        replace_all: false,
+      });
+
+      expect(result.success).toBe(true);
+      expect(result.diffText).toContain('diff --git a/test/file.txt b/test/file.txt');
+      expect(result.diffText).toContain('-line 2');
+      expect(result.diffText).toContain('+modified line 2');
+    });
+  });
 });
@@ -0,0 +1,499 @@
+import { beforeEach, describe, expect, it, vi } from 'vitest';
+
+import type { App } from '@/core/App';
+
+import ShellCommandCtr from '../ShellCommandCtr';
+
+// Mock logger
+vi.mock('@/utils/logger', () => ({
+  createLogger: () => ({
+    debug: vi.fn(),
+    info: vi.fn(),
+    warn: vi.fn(),
+    error: vi.fn(),
+  }),
+}));
+
+// Mock child_process
+vi.mock('node:child_process', () => ({
+  spawn: vi.fn(),
+}));
+
+// Mock crypto
+vi.mock('node:crypto', () => ({
+  randomUUID: vi.fn(() => 'test-uuid-123'),
+}));
+
+const mockApp = {} as unknown as App;
+
+describe('ShellCommandCtr', () => {
+  let shellCommandCtr: ShellCommandCtr;
+  let mockSpawn: any;
+  let mockChildProcess: any;
+
+  beforeEach(async () => {
+    vi.clearAllMocks();
+
+    // Import mocks
+    const childProcessModule = await import('node:child_process');
+    mockSpawn = vi.mocked(childProcessModule.spawn);
+
+    // Create mock child process
+    mockChildProcess = {
+      stdout: {
+        on: vi.fn(),
+      },
+      stderr: {
+        on: vi.fn(),
+      },
+      on: vi.fn(),
+      kill: vi.fn(),
+      exitCode: null,
+    };
+
+    mockSpawn.mockReturnValue(mockChildProcess);
+
+    shellCommandCtr = new ShellCommandCtr(mockApp);
+  });
+
+  describe('handleRunCommand', () => {
+    describe('synchronous mode', () => {
+      it('should execute command successfully', async () => {
+        let exitCallback: (code: number) => void;
+        let stdoutCallback: (data: Buffer) => void;
+
+        mockChildProcess.on.mockImplementation((event: string, callback: any) => {
+          if (event === 'exit') {
+            exitCallback = callback;
+            // Simulate successful exit
+            setTimeout(() => exitCallback(0), 10);
+          }
+          return mockChildProcess;
+        });
+
+        mockChildProcess.stdout.on.mockImplementation((event: string, callback: any) => {
+          if (event === 'data') {
+            stdoutCallback = callback;
+            // Simulate output
+            setTimeout(() => stdoutCallback(Buffer.from('test output\n')), 5);
+          }
+          return mockChildProcess.stdout;
+        });
+
+        mockChildProcess.stderr.on.mockImplementation(() => mockChildProcess.stderr);
+
+        const result = await shellCommandCtr.handleRunCommand({
+          command: 'echo "test"',
+          description: 'test command',
+        });
+
+        expect(result.success).toBe(true);
+        expect(result.stdout).toBe('test output\n');
+        expect(result.exit_code).toBe(0);
+      });
+
+      it('should handle command timeout', async () => {
+        mockChildProcess.on.mockImplementation(() => mockChildProcess);
+        mockChildProcess.stdout.on.mockImplementation(() => mockChildProcess.stdout);
+        mockChildProcess.stderr.on.mockImplementation(() => mockChildProcess.stderr);
+
+        const result = await shellCommandCtr.handleRunCommand({
+          command: 'sleep 10',
+          description: 'long running command',
+          timeout: 100,
+        });
+
+        expect(result.success).toBe(false);
+        expect(result.error).toContain('timed out');
+        expect(mockChildProcess.kill).toHaveBeenCalled();
+      });
+
+      it('should handle command execution error', async () => {
+        let errorCallback: (error: Error) => void;
+
+        mockChildProcess.on.mockImplementation((event: string, callback: any) => {
+          if (event === 'error') {
+            errorCallback = callback;
+            setTimeout(() => errorCallback(new Error('Command not found')), 10);
+          }
+          return mockChildProcess;
+        });
+
+        mockChildProcess.stdout.on.mockImplementation(() => mockChildProcess.stdout);
+        mockChildProcess.stderr.on.mockImplementation(() => mockChildProcess.stderr);
+
+        const result = await shellCommandCtr.handleRunCommand({
+          command: 'invalid-command',
+          description: 'invalid command',
+        });
+
+        expect(result.success).toBe(false);
+        expect(result.error).toBe('Command not found');
+      });
+
+      it('should handle non-zero exit code', async () => {
+        let exitCallback: (code: number) => void;
+
+        mockChildProcess.on.mockImplementation((event: string, callback: any) => {
+          if (event === 'exit') {
+            exitCallback = callback;
+            setTimeout(() => exitCallback(1), 10);
+          }
+          return mockChildProcess;
+        });
+
+        mockChildProcess.stdout.on.mockImplementation(() => mockChildProcess.stdout);
+        mockChildProcess.stderr.on.mockImplementation(() => mockChildProcess.stderr);
+
+        const result = await shellCommandCtr.handleRunCommand({
+          command: 'exit 1',
+          description: 'failing command',
+        });
+
+        expect(result.success).toBe(false);
+        expect(result.exit_code).toBe(1);
+      });
+
+      it('should capture stderr output', async () => {
+        let exitCallback: (code: number) => void;
+        let stderrCallback: (data: Buffer) => void;
+
+        mockChildProcess.on.mockImplementation((event: string, callback: any) => {
+          if (event === 'exit') {
+            exitCallback = callback;
+            setTimeout(() => exitCallback(1), 10);
+          }
+          return mockChildProcess;
+        });
+
+        mockChildProcess.stdout.on.mockImplementation(() => mockChildProcess.stdout);
+        mockChildProcess.stderr.on.mockImplementation((event: string, callback: any) => {
+          if (event === 'data') {
+            stderrCallback = callback;
+            setTimeout(() => stderrCallback(Buffer.from('error message\n')), 5);
+          }
+          return mockChildProcess.stderr;
+        });
+
+        const result = await shellCommandCtr.handleRunCommand({
+          command: 'command-with-error',
+          description: 'command with stderr',
+        });
+
+        expect(result.stderr).toBe('error message\n');
+      });
+
+      it('should enforce timeout limits', async () => {
+        mockChildProcess.on.mockImplementation(() => mockChildProcess);
+        mockChildProcess.stdout.on.mockImplementation(() => mockChildProcess.stdout);
+        mockChildProcess.stderr.on.mockImplementation(() => mockChildProcess.stderr);
+
+        // Test minimum timeout
+        const minResult = await shellCommandCtr.handleRunCommand({
+          command: 'sleep 5',
+          timeout: 500, // Below 1000ms minimum
+        });
+
+        expect(minResult.success).toBe(false);
+        expect(minResult.error).toContain('1000ms'); // Should use 1000ms minimum
+      });
+    });
+
+    describe('background mode', () => {
+      it('should start command in background', async () => {
+        mockChildProcess.on.mockImplementation(() => mockChildProcess);
+        mockChildProcess.stdout.on.mockImplementation(() => mockChildProcess.stdout);
+        mockChildProcess.stderr.on.mockImplementation(() => mockChildProcess.stderr);
+
+        const result = await shellCommandCtr.handleRunCommand({
+          command: 'long-running-task',
+          description: 'background task',
+          run_in_background: true,
+        });
+
+        expect(result.success).toBe(true);
+        expect(result.shell_id).toBe('test-uuid-123');
+      });
+
+      it('should use correct shell on Windows', async () => {
+        const originalPlatform = process.platform;
+        Object.defineProperty(process, 'platform', { value: 'win32' });
+
+        mockChildProcess.on.mockImplementation(() => mockChildProcess);
+        mockChildProcess.stdout.on.mockImplementation(() => mockChildProcess.stdout);
+        mockChildProcess.stderr.on.mockImplementation(() => mockChildProcess.stderr);
+
+        await shellCommandCtr.handleRunCommand({
+          command: 'dir',
+          description: 'windows command',
+          run_in_background: true,
+        });
+
+        expect(mockSpawn).toHaveBeenCalledWith('cmd.exe', ['/c', 'dir'], expect.any(Object));
+
+        Object.defineProperty(process, 'platform', { value: originalPlatform });
+      });
+
+      it('should use correct shell on Unix', async () => {
+        const originalPlatform = process.platform;
+        Object.defineProperty(process, 'platform', { value: 'darwin' });
+
+        mockChildProcess.on.mockImplementation(() => mockChildProcess);
+        mockChildProcess.stdout.on.mockImplementation(() => mockChildProcess.stdout);
+        mockChildProcess.stderr.on.mockImplementation(() => mockChildProcess.stderr);
+
+        await shellCommandCtr.handleRunCommand({
+          command: 'ls',
+          description: 'unix command',
+          run_in_background: true,
+        });
+
+        expect(mockSpawn).toHaveBeenCalledWith('/bin/sh', ['-c', 'ls'], expect.any(Object));
+
+        Object.defineProperty(process, 'platform', { value: originalPlatform });
+      });
+    });
+  });
+
+  describe('handleGetCommandOutput', () => {
+    beforeEach(async () => {
+      mockChildProcess.on.mockImplementation(() => mockChildProcess);
+      mockChildProcess.stdout.on.mockImplementation((event: string, callback: any) => {
+        if (event === 'data') {
+          // Simulate some output
+          setTimeout(() => callback(Buffer.from('line 1\n')), 5);
+          setTimeout(() => callback(Buffer.from('line 2\n')), 10);
+        }
+        return mockChildProcess.stdout;
+      });
+      mockChildProcess.stderr.on.mockImplementation((event: string, callback: any) => {
+        if (event === 'data') {
+          setTimeout(() => callback(Buffer.from('error line\n')), 7);
+        }
+        return mockChildProcess.stderr;
+      });
+
+      // Start a background process first
+      await shellCommandCtr.handleRunCommand({
+        command: 'test-command',
+        run_in_background: true,
+      });
+    });
+
+    it('should retrieve command output', async () => {
+      // Wait for output to be captured
+      await new Promise((resolve) => setTimeout(resolve, 20));
+
+      const result = await shellCommandCtr.handleGetCommandOutput({
+        shell_id: 'test-uuid-123',
+      });
+
+      expect(result.success).toBe(true);
+      expect(result.stdout).toContain('line 1');
+      expect(result.stderr).toContain('error line');
+    });
+
+    it('should return error for non-existent shell_id', async () => {
+      const result = await shellCommandCtr.handleGetCommandOutput({
+        shell_id: 'non-existent-id',
+      });
+
+      expect(result.success).toBe(false);
+      expect(result.error).toContain('not found');
+    });
+
+    it('should filter output with regex', async () => {
+      // Wait for output to be captured
+      await new Promise((resolve) => setTimeout(resolve, 20));
+
+      const result = await shellCommandCtr.handleGetCommandOutput({
+        shell_id: 'test-uuid-123',
+        filter: 'line 1',
+      });
+
+      expect(result.success).toBe(true);
+      expect(result.output).toContain('line 1');
+      expect(result.output).not.toContain('line 2');
+    });
+
+    it('should only return new output since last read', async () => {
+      // Wait for initial output
+      await new Promise((resolve) => setTimeout(resolve, 20));
+
+      // First read
+      const firstResult = await shellCommandCtr.handleGetCommandOutput({
+        shell_id: 'test-uuid-123',
+      });
+
+      expect(firstResult.stdout).toContain('line 1');
+
+      // Second read should return empty (no new output)
+      const secondResult = await shellCommandCtr.handleGetCommandOutput({
+        shell_id: 'test-uuid-123',
+      });
+
+      expect(secondResult.stdout).toBe('');
+      expect(secondResult.stderr).toBe('');
+    });
+
+    it('should handle invalid regex filter gracefully', async () => {
+      await new Promise((resolve) => setTimeout(resolve, 20));
+
+      const result = await shellCommandCtr.handleGetCommandOutput({
+        shell_id: 'test-uuid-123',
+        filter: '[invalid(regex',
+      });
+
+      expect(result.success).toBe(true);
+      // Should return unfiltered output when filter is invalid
+    });
+
+    it('should report running status correctly', async () => {
+      mockChildProcess.exitCode = null;
+
+      const runningResult = await shellCommandCtr.handleGetCommandOutput({
+        shell_id: 'test-uuid-123',
+      });
+
+      expect(runningResult.running).toBe(true);
+
+      // Simulate process exit
+      mockChildProcess.exitCode = 0;
+
+      const exitedResult = await shellCommandCtr.handleGetCommandOutput({
+        shell_id: 'test-uuid-123',
+      });
+
+      expect(exitedResult.running).toBe(false);
+    });
+
+    it('should track stdout and stderr offsets separately when streaming output', async () => {
+      // Create a new background process with manual control over stdout/stderr
+      let stdoutCallback: (data: Buffer) => void;
+      let stderrCallback: (data: Buffer) => void;
+
+      mockChildProcess.stdout.on.mockImplementation((event: string, callback: any) => {
+        if (event === 'data') {
+          stdoutCallback = callback;
+        }
+        return mockChildProcess.stdout;
+      });
+
+      mockChildProcess.stderr.on.mockImplementation((event: string, callback: any) => {
+        if (event === 'data') {
+          stderrCallback = callback;
+        }
+        return mockChildProcess.stderr;
+      });
+
+      // Start a new background process
+      await shellCommandCtr.handleRunCommand({
+        command: 'test-interleaved',
+        run_in_background: true,
+      });
+
+      // Simulate stderr output first
+      stderrCallback(Buffer.from('error 1\n'));
+      await new Promise((resolve) => setTimeout(resolve, 5));
+
+      // First read - should get stderr
+      const firstRead = await shellCommandCtr.handleGetCommandOutput({
+        shell_id: 'test-uuid-123',
+      });
+      expect(firstRead.stderr).toBe('error 1\n');
+      expect(firstRead.stdout).toBe('');
+
+      // Simulate stdout output after stderr
+      stdoutCallback(Buffer.from('output 1\n'));
+      await new Promise((resolve) => setTimeout(resolve, 5));
+
+      // Second read - should get stdout without losing data
+      const secondRead = await shellCommandCtr.handleGetCommandOutput({
+        shell_id: 'test-uuid-123',
+      });
+      expect(secondRead.stdout).toBe('output 1\n');
+      expect(secondRead.stderr).toBe('');
+
+      // Simulate more stderr
+      stderrCallback(Buffer.from('error 2\n'));
+      await new Promise((resolve) => setTimeout(resolve, 5));
+
+      // Third read - should get new stderr
+      const thirdRead = await shellCommandCtr.handleGetCommandOutput({
+        shell_id: 'test-uuid-123',
+      });
+      expect(thirdRead.stderr).toBe('error 2\n');
+      expect(thirdRead.stdout).toBe('');
+
+      // Simulate more stdout
+      stdoutCallback(Buffer.from('output 2\n'));
+      await new Promise((resolve) => setTimeout(resolve, 5));
+
+      // Fourth read - should get new stdout
+      const fourthRead = await shellCommandCtr.handleGetCommandOutput({
+        shell_id: 'test-uuid-123',
+      });
+      expect(fourthRead.stdout).toBe('output 2\n');
+      expect(fourthRead.stderr).toBe('');
+    });
+  });
+
+  describe('handleKillCommand', () => {
+    beforeEach(async () => {
+      mockChildProcess.on.mockImplementation(() => mockChildProcess);
+      mockChildProcess.stdout.on.mockImplementation(() => mockChildProcess.stdout);
+      mockChildProcess.stderr.on.mockImplementation(() => mockChildProcess.stderr);
+
+      // Start a background process
+      await shellCommandCtr.handleRunCommand({
+        command: 'test-command',
+        run_in_background: true,
+      });
+    });
+
+    it('should kill command successfully', async () => {
+      const result = await shellCommandCtr.handleKillCommand({
+        shell_id: 'test-uuid-123',
+      });
+
+      expect(result.success).toBe(true);
+      expect(mockChildProcess.kill).toHaveBeenCalled();
+    });
+
+    it('should return error for non-existent shell_id', async () => {
+      const result = await shellCommandCtr.handleKillCommand({
+        shell_id: 'non-existent-id',
+      });
+
+      expect(result.success).toBe(false);
+      expect(result.error).toContain('not found');
+    });
+
+    it('should remove process from map after killing', async () => {
+      await shellCommandCtr.handleKillCommand({
+        shell_id: 'test-uuid-123',
+      });
+
+      // Try to get output from killed process
+      const outputResult = await shellCommandCtr.handleGetCommandOutput({
+        shell_id: 'test-uuid-123',
+      });
+
+      expect(outputResult.success).toBe(false);
+      expect(outputResult.error).toContain('not found');
+    });
+
+    it('should handle kill error gracefully', async () => {
+      mockChildProcess.kill.mockImplementation(() => {
+        throw new Error('Kill failed');
+      });
+
+      const result = await shellCommandCtr.handleKillCommand({
+        shell_id: 'test-uuid-123',
+      });
+
+      expect(result.success).toBe(false);
+      expect(result.error).toBe('Kill failed');
+    });
+  });
+});
@@ -50,7 +50,9 @@ export class ProtocolManager {

    // Check if already registered
    const isCurrentlyRegistered = app.isDefaultProtocolClient(this.protocolScheme);
-    logger.debug(`🔗 [Protocol] Is currently default protocol client: ${isCurrentlyRegistered}`);
+    logger.debug(
+      `🔗 [Protocol] ${this.protocolScheme}:// is currently registered: ${isCurrentlyRegistered}`,
+    );

    // Register as default protocol client
    let registrationResult: boolean;
@@ -71,7 +73,9 @@ export class ProtocolManager {
      registrationResult = app.setAsDefaultProtocolClient(this.protocolScheme);
    }

-    logger.debug(`🔗 [Protocol] Registration result: ${registrationResult}`);
+    logger.debug(
+      `🔗 [Protocol] Registration result for ${this.protocolScheme}://: ${registrationResult}`,
+    );

    if (!registrationResult) {
      logger.error(
@@ -83,7 +87,9 @@ export class ProtocolManager {

    // Verify registration
    const isRegisteredAfter = app.isDefaultProtocolClient(this.protocolScheme);
-    logger.debug(`🔗 [Protocol] Final registration status: ${isRegisteredAfter}`);
+    logger.debug(
+      `🔗 [Protocol] Final registration status for ${this.protocolScheme}://: ${isRegisteredAfter}`,
+    );
  }

  /**
@@ -123,7 +129,6 @@ export class ProtocolManager {
   */
  private getProtocolUrlFromArgs(args: string[]): string | null {
    const protocolPrefix = `${this.protocolScheme}://`;
-
    logger.debug(`🔗 [Protocol] Searching for protocol URLs in args: ${JSON.stringify(args)}`);
    logger.debug(`🔗 [Protocol] Looking for prefix: ${protocolPrefix}`);

@@ -141,8 +141,29 @@ export class UpdaterManager {
    // Mark application for exit
    this.app.isQuiting = true;

-    // Delay installation by 1 second to ensure window is closed
-    autoUpdater.quitAndInstall();
+    // Close all windows first to ensure clean exit
+    logger.info('Closing all windows before update installation...');
+    const { BrowserWindow, app } = require('electron');
+    const allWindows = BrowserWindow.getAllWindows();
+    allWindows.forEach((window) => {
+      if (!window.isDestroyed()) {
+        window.close();
+      }
+    });
+
+    // Release single instance lock before quitting
+    // This ensures the new instance can acquire the lock
+    logger.info('Releasing single instance lock...');
+    app.releaseSingleInstanceLock();
+
+    // Small delay to ensure windows are closed and lock is released
+    setTimeout(() => {
+      // quitAndInstall parameters:
+      // - isSilent: true (don't show installation UI)
+      // - isForceRunAfter: true (force start app after installation)
+      logger.info('Calling autoUpdater.quitAndInstall...');
+      autoUpdater.quitAndInstall(true, true);
+    }, 100);
  };

  /**
@@ -0,0 +1,357 @@
+import path from 'node:path';
+import { describe, expect, it } from 'vitest';
+
+import { MacOSSearchServiceImpl } from '../impl/macOS';
+
+/**
+ * macOS File Search Integration Tests
+ *
+ * These tests run against the real macOS Spotlight service
+ * using files in the current repository.
+ *
+ * Run with: bunx vitest run 'macOS.integration.test'
+ */
+
+// Get repository root path (assumes test runs from apps/desktop)
+const repoRoot = path.resolve(__dirname, '../../../../..');
+
+describe.skipIf(process.platform !== 'darwin')('MacOSSearchServiceImpl Integration', () => {
+  const searchService = new MacOSSearchServiceImpl();
+
+  describe('checkSearchServiceStatus', () => {
+    it('should verify Spotlight is available on macOS', async () => {
+      const isAvailable = await searchService.checkSearchServiceStatus();
+
+      expect(isAvailable).toBe(true);
+    });
+  });
+
+  describe('search for known repository files', () => {
+    it('should find package.json in repo root', async () => {
+      const results = await searchService.search({
+        keywords: 'package.json',
+        limit: 10,
+        onlyIn: repoRoot,
+      });
+
+      expect(results.length).toBeGreaterThan(0);
+
+      // Should find at least one package.json
+      const packageJson = results.find((r) => r.name === 'package.json');
+      expect(packageJson).toBeDefined();
+      expect(packageJson!.type).toBe('json');
+      expect(packageJson!.path).toContain(repoRoot);
+    });
+
+    it('should find README files', async () => {
+      const results = await searchService.search({
+        keywords: 'README',
+        limit: 10,
+        onlyIn: repoRoot,
+      });
+      expect(results.length).toBeGreaterThan(0);
+
+      // Should contain markdown files
+      const mdFile = results.find((r) => r.type === 'md');
+      expect(mdFile).toBeDefined();
+      expect(mdFile!.name).toMatch(/README/i);
+    });
+
+    it('should find TypeScript files', async () => {
+      const results = await searchService.search({
+        keywords: 'macOS',
+        limit: 10,
+        onlyIn: repoRoot,
+      });
+
+      expect(results.length).toBeGreaterThan(0);
+
+      // Should find the macOS.ts implementation file
+      const macOSFile = results.find((r) => r.name.includes('macOS') && r.type === 'ts');
+      expect(macOSFile).toBeDefined();
+      expect(macOSFile!.contentType).toBe('code');
+    });
+
+    it('should find files in apps/desktop directory', async () => {
+      const desktopPath = path.join(repoRoot, 'apps/desktop');
+
+      const results = await searchService.search({
+        keywords: 'src',
+        limit: 20,
+        onlyIn: desktopPath,
+      });
+
+      // Spotlight indexing may not be complete for this directory
+      // so we make the test lenient
+      if (results.length > 0) {
+        // All results should be within apps/desktop
+        results.forEach((result) => {
+          expect(result.path).toContain('apps/desktop');
+        });
+      } else {
+        // eslint-disable-next-line no-console
+        console.warn(
+          '⚠️  No results found in apps/desktop - Spotlight indexing may not be complete',
+        );
+      }
+
+      // At minimum, verify the search completed without error
+      expect(Array.isArray(results)).toBe(true);
+    });
+
+    it('should find test files', async () => {
+      const results = await searchService.search({
+        keywords: 'test.ts',
+        limit: 10,
+        onlyIn: repoRoot,
+      });
+
+      expect(results.length).toBeGreaterThan(0);
+
+      // Should find test files
+      const testFile = results.find((r) => r.name.endsWith('.test.ts'));
+      expect(testFile).toBeDefined();
+      expect(testFile!.path).toContain('__tests__');
+    });
+  });
+
+  describe('search with filters', () => {
+    it('should respect limit parameter', async () => {
+      const limit = 3;
+      const results = await searchService.search({
+        keywords: 'src',
+        limit,
+        onlyIn: repoRoot,
+      });
+
+      expect(results.length).toBeLessThanOrEqual(limit);
+    });
+
+    it('should search in specific subdirectory only', async () => {
+      const srcPath = path.join(repoRoot, 'apps/desktop/src');
+
+      const results = await searchService.search({
+        keywords: 'index',
+        limit: 10,
+        onlyIn: srcPath,
+      });
+
+      // All results should be within the specified directory
+      results.forEach((result) => {
+        expect(result.path).toContain('apps/desktop/src');
+      });
+    });
+
+    it('should return empty array for non-existent keywords', async () => {
+      const results = await searchService.search({
+        keywords: 'xyzabc123unlikely-keyword-that-does-not-exist-12345',
+        limit: 5,
+        onlyIn: repoRoot,
+      });
+
+      expect(results).toEqual([]);
+    });
+  });
+
+  describe('file type detection', () => {
+    it('should correctly identify TypeScript files', async () => {
+      const results = await searchService.search({
+        keywords: 'LocalFileCtr',
+        limit: 5,
+        onlyIn: repoRoot,
+      });
+
+      const tsFile = results.find((r) => r.name === 'LocalFileCtr.ts');
+      if (tsFile) {
+        expect(tsFile.type).toBe('ts');
+        expect(tsFile.contentType).toBe('code');
+        expect(tsFile.isDirectory).toBe(false);
+      }
+    });
+
+    it('should correctly identify JSON files', async () => {
+      const results = await searchService.search({
+        keywords: 'tsconfig',
+        limit: 5,
+        onlyIn: repoRoot,
+      });
+
+      const jsonFile = results.find((r) => r.name.includes('tsconfig') && r.type === 'json');
+      if (jsonFile) {
+        expect(jsonFile.type).toBe('json');
+        expect(jsonFile.contentType).toBe('code');
+        expect(jsonFile.size).toBeGreaterThan(0);
+      }
+    });
+
+    it('should correctly identify directories', async () => {
+      const results = await searchService.search({
+        keywords: '__tests__',
+        limit: 10,
+        onlyIn: repoRoot,
+      });
+
+      const testDir = results.find((r) => r.name === '__tests__' && r.isDirectory);
+      if (testDir) {
+        expect(testDir.isDirectory).toBe(true);
+        expect(testDir.type).toBe('');
+      }
+    });
+
+    it('should correctly identify markdown files', async () => {
+      const results = await searchService.search({
+        keywords: 'CLAUDE.md',
+        limit: 5,
+        onlyIn: repoRoot,
+      });
+
+      const mdFile = results.find((r) => r.name === 'CLAUDE.md');
+      if (mdFile) {
+        expect(mdFile.type).toBe('md');
+        expect(mdFile.contentType).toBe('text');
+      }
+    });
+  });
+
+  describe('file metadata', () => {
+    it('should return valid file metadata', async () => {
+      const results = await searchService.search({
+        keywords: 'package.json',
+        limit: 1,
+        onlyIn: repoRoot,
+      });
+
+      expect(results.length).toBeGreaterThan(0);
+
+      const file = results[0];
+
+      // Verify all metadata fields are present
+      expect(file.path).toBeTruthy();
+      expect(file.name).toBeTruthy();
+      expect(typeof file.isDirectory).toBe('boolean');
+      expect(typeof file.size).toBe('number');
+      expect(file.size).toBeGreaterThanOrEqual(0);
+      expect(file.type).toBeDefined();
+      expect(file.contentType).toBeDefined();
+      expect(file.modifiedTime).toBeInstanceOf(Date);
+      expect(file.createdTime).toBeInstanceOf(Date);
+      expect(file.lastAccessTime).toBeInstanceOf(Date);
+
+      // Dates should be valid
+      expect(file.modifiedTime.getTime()).toBeGreaterThan(0);
+      expect(file.createdTime.getTime()).toBeGreaterThan(0);
+    });
+
+    it('should handle files with different extensions', async () => {
+      const testCases = [
+        { keyword: '.ts', expectedType: 'ts', expectedContentType: 'code' },
+        { keyword: '.json', expectedType: 'json', expectedContentType: 'code' },
+        { keyword: '.txt', expectedType: 'txt', expectedContentType: 'text' },
+      ];
+
+      for (const { keyword, expectedType, expectedContentType } of testCases) {
+        const results = await searchService.search({
+          keywords: keyword,
+          limit: 5,
+          onlyIn: repoRoot,
+        });
+
+        if (results.length > 0) {
+          const file = results.find((r) => r.type === expectedType);
+          if (file) {
+            expect(file.type).toBe(expectedType);
+            expect(file.contentType).toBe(expectedContentType);
+          }
+        }
+      }
+    });
+  });
+
+  describe('search accuracy after fix', () => {
+    it('should use fuzzy matching instead of exact phrase', async () => {
+      // Test the fix: keywords should do fuzzy matching, not exact phrase
+      // Before fix: "local file" would only match exact phrase "local file"
+      // After fix: "local file" should match "LocalFileCtr" (contains "local" and "file")
+
+      const results = await searchService.search({
+        keywords: 'LocalFile',
+        limit: 10,
+        onlyIn: repoRoot,
+      });
+
+      expect(results.length).toBeGreaterThan(0);
+
+      // Should find LocalFileCtr.ts or similar files
+      const found = results.some(
+        (r) => r.name.includes('LocalFile') || r.name.includes('localFile'),
+      );
+      expect(found).toBe(true);
+    });
+
+    it('should handle paths with spaces correctly', async () => {
+      // Test the fix: command args should be properly split
+      // This test verifies spawn receives correct arguments array
+
+      const pathWithSpaces = repoRoot; // May contain spaces in CI or certain setups
+      const results = await searchService.search({
+        keywords: 'test',
+        limit: 5,
+        onlyIn: pathWithSpaces,
+      });
+
+      // Should not throw error even if path contains spaces
+      expect(Array.isArray(results)).toBe(true);
+    });
+
+    it('should search case-insensitively', async () => {
+      // The "cd" flag in kMDItemFSName makes it case-insensitive
+
+      const lowerResults = await searchService.search({
+        keywords: 'readme',
+        limit: 5,
+        onlyIn: repoRoot,
+      });
+
+      const upperResults = await searchService.search({
+        keywords: 'README',
+        limit: 5,
+        onlyIn: repoRoot,
+      });
+
+      // Both searches should find similar files
+      expect(lowerResults.length).toBeGreaterThan(0);
+      expect(upperResults.length).toBeGreaterThan(0);
+    });
+  });
+
+  describe('error handling', () => {
+    it('should handle non-existent directory gracefully', async () => {
+      const nonExistentPath = path.join(repoRoot, 'this-directory-does-not-exist-12345');
+
+      const results = await searchService.search({
+        keywords: 'test',
+        limit: 5,
+        onlyIn: nonExistentPath,
+      });
+
+      // Should return empty array instead of throwing
+      expect(results).toEqual([]);
+    });
+  });
+
+  describe('updateSearchIndex', () => {
+    it.skip('should handle index update request', async () => {
+      // Index update requires elevated permissions, may fail in restricted environments
+      const result = await searchService.updateSearchIndex(repoRoot);
+
+      // Should return boolean (true if succeeded, false if failed)
+      expect(typeof result).toBe('boolean');
+    }, 15000); // Index update can take time
+  });
+});
+
+// Skip message for non-macOS platforms
+if (process.platform !== 'darwin') {
+  // eslint-disable-next-line no-console
+  console.log('⏭️  Skipping macOS integration tests on', process.platform, '(only runs on darwin)');
+}
@@ -23,12 +23,11 @@ export class MacOSSearchServiceImpl extends FileSearchImpl {
   */
  async search(options: SearchOptions): Promise<FileResult[]> {
    // Build the command first, regardless of execution method
-    const command = this.buildSearchCommand(options);
-    logger.debug(`Executing command: ${command}`);
+    const { cmd, args, commandString } = this.buildSearchCommand(options);
+    logger.debug(`Executing command: ${commandString}`);

    // Use spawn for both live and non-live updates to handle large outputs
    return new Promise((resolve, reject) => {
-      const [cmd, ...args] = command.split(' ');
      const childProcess = spawn(cmd, args);

      let results: string[] = []; // Store raw file paths
@@ -137,31 +136,39 @@ export class MacOSSearchServiceImpl extends FileSearchImpl {
  /**
   * Build mdfind command string
   * @param options Search options
-   * @returns Complete command string
+   * @returns Command components (cmd, args array, and command string for logging)
   */
-  private buildSearchCommand(options: SearchOptions): string {
-    // Basic command
-    let command = 'mdfind';
-
-    // Add options
-    const mdFindOptions: string[] = [];
+  private buildSearchCommand(options: SearchOptions): {
+    args: string[];
+    cmd: string;
+    commandString: string;
+  } {
+    // Command and arguments array
+    const cmd = 'mdfind';
+    const args: string[] = [];

    // macOS mdfind doesn't support -limit parameter, we'll limit results in post-processing

    // Search in specific directory
    if (options.onlyIn) {
-      mdFindOptions.push(`-onlyin "${options.onlyIn}"`);
+      args.push('-onlyin', options.onlyIn);
    }

    // Live update
    if (options.liveUpdate) {
-      mdFindOptions.push('-live');
+      args.push('-live');
    }

    // Detailed metadata
    if (options.detailed) {
-      mdFindOptions.push(
-        '-attr kMDItemDisplayName kMDItemContentType kMDItemKind kMDItemFSSize kMDItemFSCreationDate kMDItemFSContentChangeDate',
+      args.push(
+        '-attr',
+        'kMDItemDisplayName',
+        'kMDItemContentType',
+        'kMDItemKind',
+        'kMDItemFSSize',
+        'kMDItemFSCreationDate',
+        'kMDItemFSContentChangeDate',
      );
    }

@@ -171,9 +178,10 @@ export class MacOSSearchServiceImpl extends FileSearchImpl {
    // Basic query
    if (options.keywords) {
      // If the query string doesn't use Spotlight query syntax (doesn't contain kMDItem properties),
-      // treat it as plain text search
+      // treat it as a flexible name search rather than exact phrase match
      if (!options.keywords.includes('kMDItem')) {
-        queryExpression = `"${options.keywords.replaceAll('"', '\\"')}"`;
+        // Use kMDItemFSName for filename matching with wildcards for better flexibility
+        queryExpression = `kMDItemFSName == "*${options.keywords.replaceAll('"', '\\"')}*"cd`;
      } else {
        queryExpression = options.keywords;
      }
@@ -244,15 +252,15 @@ export class MacOSSearchServiceImpl extends FileSearchImpl {
      }
    }

-    // Combine complete command
-    if (mdFindOptions.length > 0) {
-      command += ' ' + mdFindOptions.join(' ');
+    // Add query expression to args
+    if (queryExpression) {
+      args.push(queryExpression);
    }

-    // Finally add query expression
-    command += ` ${queryExpression}`;
+    // Build command string for logging
+    const commandString = `${cmd} ${args.map((arg) => (arg.includes(' ') || arg.includes('*') ? `"${arg}"` : arg)).join(' ')}`;

-    return command;
+    return { args, cmd, commandString };
  }

  /**
@@ -0,0 +1,401 @@
+import { NetworkProxySettings } from '@lobechat/electron-client-ipc';
+import { beforeEach, describe, expect, it, vi } from 'vitest';
+
+import { ProxyDispatcherManager } from '../dispatcher';
+
+// Mock logger
+vi.mock('@/utils/logger', () => ({
+  createLogger: () => ({
+    debug: vi.fn(),
+    info: vi.fn(),
+    warn: vi.fn(),
+    error: vi.fn(),
+  }),
+}));
+
+// Mock undici
+vi.mock('undici', () => ({
+  Agent: vi.fn(),
+  ProxyAgent: vi.fn(),
+  getGlobalDispatcher: vi.fn(),
+  setGlobalDispatcher: vi.fn(),
+}));
+
+// Mock fetch-socks
+vi.mock('fetch-socks', () => ({
+  socksDispatcher: vi.fn(),
+}));
+
+// Mock ProxyUrlBuilder
+vi.mock('../urlBuilder', () => ({
+  ProxyUrlBuilder: {
+    build: vi.fn(),
+  },
+}));
+
+describe('ProxyDispatcherManager', () => {
+  let mockDispatcher: any;
+  let mockAgent: any;
+  let mockProxyAgent: any;
+  let mockGetGlobalDispatcher: any;
+  let mockSetGlobalDispatcher: any;
+  let mockSocksDispatcher: any;
+  let mockProxyUrlBuilder: any;
+
+  beforeEach(async () => {
+    vi.clearAllMocks();
+
+    // Import mocked modules
+    const undici = await import('undici');
+    const fetchSocks = await import('fetch-socks');
+    const urlBuilder = await import('../urlBuilder');
+
+    mockAgent = vi.mocked(undici.Agent);
+    mockProxyAgent = vi.mocked(undici.ProxyAgent);
+    mockGetGlobalDispatcher = vi.mocked(undici.getGlobalDispatcher);
+    mockSetGlobalDispatcher = vi.mocked(undici.setGlobalDispatcher);
+    mockSocksDispatcher = vi.mocked(fetchSocks.socksDispatcher);
+    mockProxyUrlBuilder = vi.mocked(urlBuilder.ProxyUrlBuilder.build);
+
+    // Setup mock dispatcher with destroy method
+    mockDispatcher = {
+      destroy: vi.fn().mockResolvedValue(undefined),
+    };
+
+    mockGetGlobalDispatcher.mockReturnValue(mockDispatcher);
+    mockAgent.mockReturnValue({ destroy: vi.fn().mockResolvedValue(undefined) });
+    mockProxyAgent.mockReturnValue({ destroy: vi.fn().mockResolvedValue(undefined) });
+    mockSocksDispatcher.mockReturnValue({ destroy: vi.fn().mockResolvedValue(undefined) });
+
+    // Setup ProxyUrlBuilder mock to return properly formatted URLs
+    mockProxyUrlBuilder.mockImplementation((config: NetworkProxySettings) => {
+      if (config.proxyRequireAuth && config.proxyUsername && config.proxyPassword) {
+        return `${config.proxyType}://${config.proxyUsername}:${config.proxyPassword}@${config.proxyServer}:${config.proxyPort}`;
+      }
+      return `${config.proxyType}://${config.proxyServer}:${config.proxyPort}`;
+    });
+  });
+
+  describe('createProxyAgent', () => {
+    describe('HTTP/HTTPS proxy', () => {
+      it('should create ProxyAgent for http proxy', () => {
+        const proxyUrl = 'http://proxy.example.com:8080';
+
+        ProxyDispatcherManager.createProxyAgent('http', proxyUrl);
+
+        expect(mockProxyAgent).toHaveBeenCalledWith({ uri: proxyUrl });
+      });
+
+      it('should create ProxyAgent for https proxy', () => {
+        const proxyUrl = 'https://proxy.example.com:8080';
+
+        ProxyDispatcherManager.createProxyAgent('https', proxyUrl);
+
+        expect(mockProxyAgent).toHaveBeenCalledWith({ uri: proxyUrl });
+      });
+
+      it('should create ProxyAgent with authentication', () => {
+        const proxyUrl = 'http://user:pass@proxy.example.com:8080';
+
+        ProxyDispatcherManager.createProxyAgent('http', proxyUrl);
+
+        expect(mockProxyAgent).toHaveBeenCalledWith({ uri: proxyUrl });
+      });
+    });
+
+    describe('SOCKS5 proxy', () => {
+      it('should create socksDispatcher for socks5 proxy without auth', () => {
+        const proxyUrl = 'socks5://proxy.example.com:1080';
+
+        ProxyDispatcherManager.createProxyAgent('socks5', proxyUrl);
+
+        expect(mockSocksDispatcher).toHaveBeenCalledWith([
+          {
+            host: 'proxy.example.com',
+            port: 1080,
+            type: 5,
+          },
+        ]);
+      });
+
+      it('should create socksDispatcher for socks5 proxy with auth', () => {
+        const proxyUrl = 'socks5://user:pass@proxy.example.com:1080';
+
+        ProxyDispatcherManager.createProxyAgent('socks5', proxyUrl);
+
+        expect(mockSocksDispatcher).toHaveBeenCalledWith([
+          {
+            host: 'proxy.example.com',
+            port: 1080,
+            type: 5,
+            userId: 'user',
+            password: 'pass',
+          },
+        ]);
+      });
+
+      it('should create socksDispatcher with IPv4 address', () => {
+        const proxyUrl = 'socks5://192.168.1.1:1080';
+
+        ProxyDispatcherManager.createProxyAgent('socks5', proxyUrl);
+
+        expect(mockSocksDispatcher).toHaveBeenCalledWith([
+          {
+            host: '192.168.1.1',
+            port: 1080,
+            type: 5,
+          },
+        ]);
+      });
+
+      it('should create socksDispatcher with different port', () => {
+        const proxyUrl = 'socks5://proxy.example.com:9050';
+
+        ProxyDispatcherManager.createProxyAgent('socks5', proxyUrl);
+
+        expect(mockSocksDispatcher).toHaveBeenCalledWith([
+          {
+            host: 'proxy.example.com',
+            port: 9050,
+            type: 5,
+          },
+        ]);
+      });
+    });
+
+    describe('error handling', () => {
+      it('should throw error when ProxyAgent creation fails', () => {
+        mockProxyAgent.mockImplementationOnce(() => {
+          throw new Error('ProxyAgent creation failed');
+        });
+
+        expect(() => {
+          ProxyDispatcherManager.createProxyAgent('http', 'http://invalid');
+        }).toThrow('Failed to create proxy agent: ProxyAgent creation failed');
+      });
+
+      it('should throw error when socksDispatcher creation fails', () => {
+        mockSocksDispatcher.mockImplementationOnce(() => {
+          throw new Error('SOCKS dispatcher creation failed');
+        });
+
+        expect(() => {
+          ProxyDispatcherManager.createProxyAgent('socks5', 'socks5://invalid');
+        }).toThrow('Failed to create proxy agent: SOCKS dispatcher creation failed');
+      });
+
+      it('should throw error with unknown error type', () => {
+        mockProxyAgent.mockImplementationOnce(() => {
+          throw 'String error';
+        });
+
+        expect(() => {
+          ProxyDispatcherManager.createProxyAgent('http', 'http://invalid');
+        }).toThrow('Failed to create proxy agent: Unknown error');
+      });
+    });
+  });
+
+  describe('applyProxySettings', () => {
+    const validConfig: NetworkProxySettings = {
+      enableProxy: true,
+      proxyType: 'http',
+      proxyServer: 'proxy.example.com',
+      proxyPort: '8080',
+      proxyRequireAuth: false,
+      proxyBypass: 'localhost,127.0.0.1,::1',
+    };
+
+    describe('disable proxy', () => {
+      it('should reset to direct connection when proxy is disabled', async () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          enableProxy: false,
+        };
+
+        await ProxyDispatcherManager.applyProxySettings(config);
+
+        expect(mockDispatcher.destroy).toHaveBeenCalled();
+        expect(mockAgent).toHaveBeenCalled();
+        expect(mockSetGlobalDispatcher).toHaveBeenCalled();
+      });
+
+      it('should handle dispatcher destruction failure gracefully', async () => {
+        mockDispatcher.destroy.mockRejectedValueOnce(new Error('Destroy failed'));
+
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          enableProxy: false,
+        };
+
+        // Should not throw even if destroy fails
+        await expect(ProxyDispatcherManager.applyProxySettings(config)).resolves.not.toThrow();
+      });
+
+      it('should handle dispatcher without destroy method', async () => {
+        mockGetGlobalDispatcher.mockReturnValueOnce({});
+
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          enableProxy: false,
+        };
+
+        await expect(ProxyDispatcherManager.applyProxySettings(config)).resolves.not.toThrow();
+      });
+    });
+
+    describe('enable proxy', () => {
+      it('should apply http proxy settings', async () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyType: 'http',
+        };
+
+        await ProxyDispatcherManager.applyProxySettings(config);
+
+        expect(mockDispatcher.destroy).toHaveBeenCalled();
+        expect(mockProxyAgent).toHaveBeenCalledWith({
+          uri: 'http://proxy.example.com:8080',
+        });
+        expect(mockSetGlobalDispatcher).toHaveBeenCalled();
+      });
+
+      it('should apply https proxy settings', async () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyType: 'https',
+        };
+
+        await ProxyDispatcherManager.applyProxySettings(config);
+
+        expect(mockProxyAgent).toHaveBeenCalledWith({
+          uri: 'https://proxy.example.com:8080',
+        });
+      });
+
+      it('should apply socks5 proxy settings', async () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyType: 'socks5',
+        };
+
+        await ProxyDispatcherManager.applyProxySettings(config);
+
+        expect(mockSocksDispatcher).toHaveBeenCalled();
+        expect(mockSetGlobalDispatcher).toHaveBeenCalled();
+      });
+
+      it('should apply proxy with authentication', async () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyRequireAuth: true,
+          proxyUsername: 'testuser',
+          proxyPassword: 'testpass',
+        };
+
+        await ProxyDispatcherManager.applyProxySettings(config);
+
+        expect(mockProxyAgent).toHaveBeenCalledWith({
+          uri: 'http://testuser:testpass@proxy.example.com:8080',
+        });
+      });
+
+      it('should destroy old dispatcher before applying new proxy', async () => {
+        const destroySpy = vi.fn().mockResolvedValue(undefined);
+        mockGetGlobalDispatcher.mockReturnValue({ destroy: destroySpy });
+
+        await ProxyDispatcherManager.applyProxySettings(validConfig);
+
+        expect(destroySpy).toHaveBeenCalled();
+        expect(mockSetGlobalDispatcher).toHaveBeenCalled();
+      });
+    });
+
+    describe('concurrent proxy changes', () => {
+      it('should queue concurrent proxy setting changes', async () => {
+        const config1: NetworkProxySettings = {
+          ...validConfig,
+          proxyPort: '8080',
+        };
+
+        const config2: NetworkProxySettings = {
+          ...validConfig,
+          proxyPort: '8081',
+        };
+
+        // Start both operations concurrently
+        const promise1 = ProxyDispatcherManager.applyProxySettings(config1);
+        const promise2 = ProxyDispatcherManager.applyProxySettings(config2);
+
+        await Promise.all([promise1, promise2]);
+
+        // Both operations should complete successfully
+        expect(mockSetGlobalDispatcher).toHaveBeenCalled();
+      });
+
+      it('should process queued operations sequentially', async () => {
+        const operations: Promise<void>[] = [];
+
+        // Queue multiple operations
+        for (let i = 0; i < 5; i++) {
+          const config: NetworkProxySettings = {
+            ...validConfig,
+            proxyPort: `${8080 + i}`,
+          };
+          operations.push(ProxyDispatcherManager.applyProxySettings(config));
+        }
+
+        await Promise.all(operations);
+
+        // All operations should complete
+        expect(mockSetGlobalDispatcher).toHaveBeenCalledTimes(5);
+      });
+
+      it('should handle errors in queued operations', async () => {
+        mockProxyAgent.mockReturnValueOnce({ destroy: vi.fn() }).mockImplementationOnce(() => {
+          throw new Error('Agent creation failed');
+        });
+
+        const config1: NetworkProxySettings = {
+          ...validConfig,
+          proxyPort: '8080',
+        };
+
+        const config2: NetworkProxySettings = {
+          ...validConfig,
+          proxyPort: '8081',
+        };
+
+        const promise1 = ProxyDispatcherManager.applyProxySettings(config1);
+        const promise2 = ProxyDispatcherManager.applyProxySettings(config2);
+
+        await expect(promise1).resolves.not.toThrow();
+        await expect(promise2).rejects.toThrow();
+      });
+    });
+
+    describe('error handling', () => {
+      it('should propagate error when agent creation fails', async () => {
+        mockProxyAgent.mockImplementationOnce(() => {
+          throw new Error('Agent creation failed');
+        });
+
+        await expect(ProxyDispatcherManager.applyProxySettings(validConfig)).rejects.toThrow(
+          'Failed to create proxy agent',
+        );
+      });
+
+      it('should handle null dispatcher gracefully', async () => {
+        mockGetGlobalDispatcher.mockReturnValueOnce(null);
+
+        await expect(ProxyDispatcherManager.applyProxySettings(validConfig)).resolves.not.toThrow();
+      });
+
+      it('should handle undefined dispatcher gracefully', async () => {
+        mockGetGlobalDispatcher.mockReturnValueOnce(undefined);
+
+        await expect(ProxyDispatcherManager.applyProxySettings(validConfig)).resolves.not.toThrow();
+      });
+    });
+  });
+});
@@ -0,0 +1,531 @@
+import { NetworkProxySettings } from '@lobechat/electron-client-ipc';
+import { beforeEach, describe, expect, it, vi } from 'vitest';
+
+import { ProxyConnectionTester } from '../tester';
+
+// Mock logger
+vi.mock('@/utils/logger', () => ({
+  createLogger: () => ({
+    debug: vi.fn(),
+    info: vi.fn(),
+    warn: vi.fn(),
+    error: vi.fn(),
+  }),
+}));
+
+// Mock undici
+vi.mock('undici', () => ({
+  fetch: vi.fn(),
+  getGlobalDispatcher: vi.fn(),
+  setGlobalDispatcher: vi.fn(),
+}));
+
+// Mock ProxyConfigValidator
+vi.mock('../validator', () => ({
+  ProxyConfigValidator: {
+    validate: vi.fn(),
+  },
+}));
+
+// Mock ProxyUrlBuilder
+vi.mock('../urlBuilder', () => ({
+  ProxyUrlBuilder: {
+    build: vi.fn(),
+  },
+}));
+
+// Mock ProxyDispatcherManager
+vi.mock('../dispatcher', () => ({
+  ProxyDispatcherManager: {
+    createProxyAgent: vi.fn(),
+  },
+}));
+
+describe('ProxyConnectionTester', () => {
+  let mockAgent: any;
+  let mockOriginalDispatcher: any;
+  let mockFetch: any;
+  let mockGetGlobalDispatcher: any;
+  let mockSetGlobalDispatcher: any;
+  let mockProxyDispatcherManager: any;
+  let mockProxyConfigValidator: any;
+  let mockProxyUrlBuilder: any;
+
+  beforeEach(async () => {
+    vi.clearAllMocks();
+
+    // Import mocked modules
+    const undici = await import('undici');
+    const dispatcher = await import('../dispatcher');
+    const validator = await import('../validator');
+    const urlBuilder = await import('../urlBuilder');
+
+    mockFetch = vi.mocked(undici.fetch);
+    mockGetGlobalDispatcher = vi.mocked(undici.getGlobalDispatcher);
+    mockSetGlobalDispatcher = vi.mocked(undici.setGlobalDispatcher);
+    mockProxyDispatcherManager = vi.mocked(dispatcher.ProxyDispatcherManager);
+    mockProxyConfigValidator = vi.mocked(validator.ProxyConfigValidator);
+    mockProxyUrlBuilder = vi.mocked(urlBuilder.ProxyUrlBuilder.build);
+
+    // Setup mock agent
+    mockAgent = {
+      destroy: vi.fn().mockResolvedValue(undefined),
+    };
+
+    mockOriginalDispatcher = {
+      destroy: vi.fn().mockResolvedValue(undefined),
+    };
+
+    mockGetGlobalDispatcher.mockReturnValue(mockOriginalDispatcher);
+    mockProxyDispatcherManager.createProxyAgent.mockReturnValue(mockAgent);
+    mockProxyConfigValidator.validate.mockReturnValue({ isValid: true, errors: [] });
+    mockProxyUrlBuilder.mockImplementation((config: NetworkProxySettings) => {
+      return `${config.proxyType}://${config.proxyServer}:${config.proxyPort}`;
+    });
+  });
+
+  describe('testConnection', () => {
+    describe('successful connection', () => {
+      it('should return success for successful HTTP request', async () => {
+        mockFetch.mockResolvedValueOnce({
+          ok: true,
+          status: 200,
+          statusText: 'OK',
+        });
+
+        const result = await ProxyConnectionTester.testConnection();
+
+        expect(result.success).toBe(true);
+        expect(result.responseTime).toBeGreaterThanOrEqual(0);
+        expect(result.message).toBeUndefined();
+        expect(mockFetch).toHaveBeenCalledWith(
+          'https://www.google.com',
+          expect.objectContaining({
+            headers: expect.objectContaining({
+              'User-Agent': 'LobeChat-Desktop/1.0.0',
+            }),
+            signal: expect.any(AbortSignal),
+          }),
+        );
+      });
+
+      it('should return success with custom URL', async () => {
+        mockFetch.mockResolvedValueOnce({
+          ok: true,
+          status: 200,
+          statusText: 'OK',
+        });
+
+        const customUrl = 'https://api.example.com';
+        const result = await ProxyConnectionTester.testConnection(customUrl);
+
+        expect(result.success).toBe(true);
+        expect(mockFetch).toHaveBeenCalledWith(customUrl, expect.any(Object));
+      });
+
+      it('should return success with custom timeout', async () => {
+        mockFetch.mockResolvedValueOnce({
+          ok: true,
+          status: 200,
+          statusText: 'OK',
+        });
+
+        const result = await ProxyConnectionTester.testConnection('https://www.google.com', 5000);
+
+        expect(result.success).toBe(true);
+      });
+
+      it('should include response time in result', async () => {
+        mockFetch.mockResolvedValueOnce({
+          ok: true,
+          status: 200,
+          statusText: 'OK',
+        });
+
+        const result = await ProxyConnectionTester.testConnection();
+
+        expect(result.responseTime).toBeDefined();
+        expect(typeof result.responseTime).toBe('number');
+        expect(result.responseTime).toBeGreaterThanOrEqual(0);
+      });
+    });
+
+    describe('connection failures', () => {
+      it('should return failure for HTTP error status', async () => {
+        mockFetch.mockResolvedValueOnce({
+          ok: false,
+          status: 404,
+          statusText: 'Not Found',
+        });
+
+        const result = await ProxyConnectionTester.testConnection();
+
+        expect(result.success).toBe(false);
+        expect(result.message).toContain('HTTP 404');
+        expect(result.message).toContain('Not Found');
+        expect(result.responseTime).toBeGreaterThanOrEqual(0);
+      });
+
+      it('should return failure for HTTP 500 error', async () => {
+        mockFetch.mockResolvedValueOnce({
+          ok: false,
+          status: 500,
+          statusText: 'Internal Server Error',
+        });
+
+        const result = await ProxyConnectionTester.testConnection();
+
+        expect(result.success).toBe(false);
+        expect(result.message).toContain('HTTP 500');
+      });
+
+      it('should return failure for network error', async () => {
+        mockFetch.mockRejectedValueOnce(new Error('Network error'));
+
+        const result = await ProxyConnectionTester.testConnection();
+
+        expect(result.success).toBe(false);
+        expect(result.message).toBe('Network error');
+        expect(result.responseTime).toBeGreaterThanOrEqual(0);
+      });
+
+      it('should return failure for timeout', async () => {
+        mockFetch.mockImplementationOnce(() => {
+          return new Promise((_, reject) => {
+            const error = new Error('Request aborted');
+            error.name = 'AbortError';
+            setTimeout(() => reject(error), 50);
+          });
+        });
+
+        const result = await ProxyConnectionTester.testConnection('https://www.google.com', 100);
+
+        expect(result.success).toBe(false);
+        expect(result.message).toBeTruthy();
+      });
+
+      it('should return failure for connection refused', async () => {
+        mockFetch.mockRejectedValueOnce(new Error('ECONNREFUSED'));
+
+        const result = await ProxyConnectionTester.testConnection();
+
+        expect(result.success).toBe(false);
+        expect(result.message).toBe('ECONNREFUSED');
+      });
+
+      it('should handle unknown error type', async () => {
+        mockFetch.mockRejectedValueOnce('String error');
+
+        const result = await ProxyConnectionTester.testConnection();
+
+        expect(result.success).toBe(false);
+        expect(result.message).toBe('Unknown error');
+      });
+    });
+  });
+
+  describe('testProxyConfig', () => {
+    const validConfig: NetworkProxySettings = {
+      enableProxy: true,
+      proxyType: 'http',
+      proxyServer: 'proxy.example.com',
+      proxyPort: '8080',
+      proxyRequireAuth: false,
+      proxyBypass: 'localhost,127.0.0.1,::1',
+    };
+
+    describe('config validation', () => {
+      it('should return failure for invalid config', async () => {
+        mockProxyConfigValidator.validate.mockReturnValueOnce({
+          isValid: false,
+          errors: ['Proxy server is required', 'Invalid port'],
+        });
+
+        const result = await ProxyConnectionTester.testProxyConfig(validConfig);
+
+        expect(result.success).toBe(false);
+        expect(result.message).toContain('Invalid proxy configuration');
+        expect(result.message).toContain('Proxy server is required');
+        expect(result.message).toContain('Invalid port');
+      });
+
+      it('should validate config before testing', async () => {
+        mockFetch.mockResolvedValueOnce({
+          ok: true,
+          status: 200,
+          statusText: 'OK',
+        });
+
+        await ProxyConnectionTester.testProxyConfig(validConfig);
+
+        expect(mockProxyConfigValidator.validate).toHaveBeenCalledWith(validConfig);
+      });
+    });
+
+    describe('disabled proxy', () => {
+      it('should test direct connection when proxy is disabled', async () => {
+        const disabledConfig: NetworkProxySettings = {
+          ...validConfig,
+          enableProxy: false,
+        };
+
+        mockFetch.mockResolvedValueOnce({
+          ok: true,
+          status: 200,
+          statusText: 'OK',
+        });
+
+        const result = await ProxyConnectionTester.testProxyConfig(disabledConfig);
+
+        expect(result.success).toBe(true);
+        expect(mockFetch).toHaveBeenCalled();
+      });
+
+      it('should use custom test URL for disabled proxy', async () => {
+        const disabledConfig: NetworkProxySettings = {
+          ...validConfig,
+          enableProxy: false,
+        };
+
+        mockFetch.mockResolvedValueOnce({
+          ok: true,
+          status: 200,
+          statusText: 'OK',
+        });
+
+        const customUrl = 'https://api.example.com';
+        await ProxyConnectionTester.testProxyConfig(disabledConfig, customUrl);
+
+        expect(mockFetch).toHaveBeenCalledWith(customUrl, expect.any(Object));
+      });
+    });
+
+    describe('enabled proxy', () => {
+      it('should test proxy connection successfully', async () => {
+        mockFetch.mockResolvedValueOnce({
+          ok: true,
+          status: 200,
+          statusText: 'OK',
+        });
+
+        const result = await ProxyConnectionTester.testProxyConfig(validConfig);
+
+        expect(result.success).toBe(true);
+        expect(result.responseTime).toBeGreaterThanOrEqual(0);
+      });
+
+      it('should create temporary proxy agent for testing', async () => {
+        mockFetch.mockResolvedValueOnce({
+          ok: true,
+          status: 200,
+          statusText: 'OK',
+        });
+
+        await ProxyConnectionTester.testProxyConfig(validConfig);
+
+        expect(mockProxyDispatcherManager.createProxyAgent).toHaveBeenCalledWith(
+          'http',
+          'http://proxy.example.com:8080',
+        );
+      });
+
+      it('should restore original dispatcher after test', async () => {
+        mockFetch.mockResolvedValueOnce({
+          ok: true,
+          status: 200,
+          statusText: 'OK',
+        });
+
+        await ProxyConnectionTester.testProxyConfig(validConfig);
+
+        expect(mockSetGlobalDispatcher).toHaveBeenCalledWith(mockOriginalDispatcher);
+      });
+
+      it('should destroy temporary agent after test', async () => {
+        mockFetch.mockResolvedValueOnce({
+          ok: true,
+          status: 200,
+          statusText: 'OK',
+        });
+
+        await ProxyConnectionTester.testProxyConfig(validConfig);
+
+        expect(mockAgent.destroy).toHaveBeenCalled();
+      });
+
+      it('should restore dispatcher even if test fails', async () => {
+        mockFetch.mockRejectedValueOnce(new Error('Connection failed'));
+
+        await ProxyConnectionTester.testProxyConfig(validConfig);
+
+        expect(mockSetGlobalDispatcher).toHaveBeenCalledWith(mockOriginalDispatcher);
+      });
+
+      it('should destroy agent even if test fails', async () => {
+        mockFetch.mockRejectedValueOnce(new Error('Connection failed'));
+
+        await ProxyConnectionTester.testProxyConfig(validConfig);
+
+        expect(mockAgent.destroy).toHaveBeenCalled();
+      });
+
+      it('should handle agent destroy failure gracefully', async () => {
+        mockAgent.destroy.mockRejectedValueOnce(new Error('Destroy failed'));
+        mockFetch.mockResolvedValueOnce({
+          ok: true,
+          status: 200,
+          statusText: 'OK',
+        });
+
+        const result = await ProxyConnectionTester.testProxyConfig(validConfig);
+
+        expect(result.success).toBe(true);
+      });
+
+      it('should test with custom URL', async () => {
+        mockFetch.mockResolvedValueOnce({
+          ok: true,
+          status: 200,
+          statusText: 'OK',
+        });
+
+        const customUrl = 'https://httpbin.org/ip';
+        await ProxyConnectionTester.testProxyConfig(validConfig, customUrl);
+
+        expect(mockFetch).toHaveBeenCalledWith(
+          customUrl,
+          expect.objectContaining({
+            dispatcher: mockAgent,
+          }),
+        );
+      });
+
+      it('should test socks5 proxy', async () => {
+        const socks5Config: NetworkProxySettings = {
+          ...validConfig,
+          proxyType: 'socks5',
+        };
+
+        mockFetch.mockResolvedValueOnce({
+          ok: true,
+          status: 200,
+          statusText: 'OK',
+        });
+
+        await ProxyConnectionTester.testProxyConfig(socks5Config);
+
+        expect(mockProxyDispatcherManager.createProxyAgent).toHaveBeenCalledWith(
+          'socks5',
+          expect.any(String),
+        );
+      });
+
+      it('should test proxy with authentication', async () => {
+        const authConfig: NetworkProxySettings = {
+          ...validConfig,
+          proxyRequireAuth: true,
+          proxyUsername: 'user',
+          proxyPassword: 'pass',
+        };
+
+        mockFetch.mockResolvedValueOnce({
+          ok: true,
+          status: 200,
+          statusText: 'OK',
+        });
+
+        await ProxyConnectionTester.testProxyConfig(authConfig);
+
+        expect(mockProxyUrlBuilder).toHaveBeenCalledWith(authConfig);
+      });
+    });
+
+    describe('error handling', () => {
+      it('should return failure when agent creation fails', async () => {
+        mockProxyDispatcherManager.createProxyAgent.mockImplementationOnce(() => {
+          throw new Error('Agent creation failed');
+        });
+
+        const result = await ProxyConnectionTester.testProxyConfig(validConfig);
+
+        expect(result.success).toBe(false);
+        expect(result.message).toContain('Proxy test failed');
+        expect(result.message).toContain('Agent creation failed');
+      });
+
+      it('should return failure when fetch fails', async () => {
+        mockFetch.mockRejectedValueOnce(new Error('Connection timeout'));
+
+        const result = await ProxyConnectionTester.testProxyConfig(validConfig);
+
+        expect(result.success).toBe(false);
+        expect(result.message).toContain('Connection timeout');
+      });
+
+      it('should return failure for HTTP error response', async () => {
+        mockFetch.mockResolvedValueOnce({
+          ok: false,
+          status: 407,
+          statusText: 'Proxy Authentication Required',
+        });
+
+        const result = await ProxyConnectionTester.testProxyConfig(validConfig);
+
+        expect(result.success).toBe(false);
+        expect(result.message).toContain('HTTP 407');
+      });
+
+      it('should handle timeout correctly', async () => {
+        mockFetch.mockImplementationOnce(() => {
+          return new Promise((_, reject) => {
+            setTimeout(() => reject(new Error('Timeout')), 50);
+          });
+        });
+
+        const result = await ProxyConnectionTester.testProxyConfig(validConfig);
+
+        expect(result.success).toBe(false);
+      });
+
+      it('should handle unknown error type', async () => {
+        mockProxyDispatcherManager.createProxyAgent.mockImplementationOnce(() => {
+          throw 'String error';
+        });
+
+        const result = await ProxyConnectionTester.testProxyConfig(validConfig);
+
+        expect(result.success).toBe(false);
+        expect(result.message).toContain('Unknown error');
+      });
+
+      it('should handle null agent', async () => {
+        mockProxyDispatcherManager.createProxyAgent.mockReturnValueOnce(null);
+
+        mockFetch.mockResolvedValueOnce({
+          ok: true,
+          status: 200,
+          statusText: 'OK',
+        });
+
+        const result = await ProxyConnectionTester.testProxyConfig(validConfig);
+
+        // Should handle gracefully
+        expect(result).toBeDefined();
+      });
+
+      it('should handle agent without destroy method', async () => {
+        mockProxyDispatcherManager.createProxyAgent.mockReturnValueOnce({});
+
+        mockFetch.mockResolvedValueOnce({
+          ok: true,
+          status: 200,
+          statusText: 'OK',
+        });
+
+        const result = await ProxyConnectionTester.testProxyConfig(validConfig);
+
+        expect(result.success).toBe(true);
+      });
+    });
+  });
+});
@@ -0,0 +1,349 @@
+import { NetworkProxySettings } from '@lobechat/electron-client-ipc';
+import { describe, expect, it } from 'vitest';
+
+import { ProxyUrlBuilder } from '../urlBuilder';
+
+describe('ProxyUrlBuilder', () => {
+  const baseConfig: NetworkProxySettings = {
+    enableProxy: true,
+    proxyType: 'http',
+    proxyServer: 'proxy.example.com',
+    proxyPort: '8080',
+    proxyRequireAuth: false,
+    proxyBypass: 'localhost,127.0.0.1,::1',
+  };
+
+  describe('build', () => {
+    describe('without authentication', () => {
+      it('should build URL with http proxy type', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyType: 'http',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://proxy.example.com:8080');
+      });
+
+      it('should build URL with https proxy type', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyType: 'https',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('https://proxy.example.com:8080');
+      });
+
+      it('should build URL with socks5 proxy type', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyType: 'socks5',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('socks5://proxy.example.com:8080');
+      });
+
+      it('should build URL with IPv4 address', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyServer: '192.168.1.1',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://192.168.1.1:8080');
+      });
+
+      it('should build URL with localhost', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyServer: 'localhost',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://localhost:8080');
+      });
+
+      it('should build URL with different port', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyPort: '3128',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://proxy.example.com:3128');
+      });
+    });
+
+    describe('with authentication', () => {
+      it('should build URL with username and password', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyRequireAuth: true,
+          proxyUsername: 'testuser',
+          proxyPassword: 'testpass',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://testuser:testpass@proxy.example.com:8080');
+      });
+
+      it('should build URL with encoded username containing @ symbol', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyRequireAuth: true,
+          proxyUsername: 'user@domain.com',
+          proxyPassword: 'password',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://user%40domain.com:password@proxy.example.com:8080');
+        // Verify encoding
+        expect(url).toContain('user%40domain.com');
+      });
+
+      it('should build URL with encoded password containing colon', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyRequireAuth: true,
+          proxyUsername: 'user',
+          proxyPassword: 'pass:word',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://user:pass%3Aword@proxy.example.com:8080');
+        // Verify encoding
+        expect(url).toContain('pass%3Aword');
+      });
+
+      it('should build URL with encoded special characters in username', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyRequireAuth: true,
+          proxyUsername: 'user name',
+          proxyPassword: 'password',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://user%20name:password@proxy.example.com:8080');
+        // Verify encoding
+        expect(url).toContain('user%20name');
+      });
+
+      it('should build URL with encoded special characters in password', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyRequireAuth: true,
+          proxyUsername: 'user',
+          proxyPassword: 'p@ss w0rd!',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        // Verify encoding of special characters
+        expect(url).toContain(encodeURIComponent('p@ss w0rd!'));
+        expect(url).toContain('user:');
+        expect(url).toContain('@proxy.example.com:8080');
+      });
+
+      it('should build URL with encoded slash in credentials', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyRequireAuth: true,
+          proxyUsername: 'domain/user',
+          proxyPassword: 'pass/word',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://domain%2Fuser:pass%2Fword@proxy.example.com:8080');
+        // Verify encoding
+        expect(url).toContain('domain%2Fuser');
+        expect(url).toContain('pass%2Fword');
+      });
+
+      it('should build URL with encoded hash in credentials', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyRequireAuth: true,
+          proxyUsername: 'user#123',
+          proxyPassword: 'pass#word',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://user%23123:pass%23word@proxy.example.com:8080');
+        // Verify encoding
+        expect(url).toContain('user%23123');
+        expect(url).toContain('pass%23word');
+      });
+
+      it('should build URL with encoded question mark in credentials', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyRequireAuth: true,
+          proxyUsername: 'user?name',
+          proxyPassword: 'pass?word',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://user%3Fname:pass%3Fword@proxy.example.com:8080');
+        // Verify encoding
+        expect(url).toContain('user%3Fname');
+        expect(url).toContain('pass%3Fword');
+      });
+
+      it('should build URL with https proxy type and auth', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyType: 'https',
+          proxyRequireAuth: true,
+          proxyUsername: 'testuser',
+          proxyPassword: 'testpass',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('https://testuser:testpass@proxy.example.com:8080');
+      });
+
+      it('should build URL with socks5 proxy type and auth', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyType: 'socks5',
+          proxyRequireAuth: true,
+          proxyUsername: 'sockuser',
+          proxyPassword: 'sockpass',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('socks5://sockuser:sockpass@proxy.example.com:8080');
+      });
+    });
+
+    describe('edge cases', () => {
+      it('should not include auth when proxyRequireAuth is false but credentials are provided', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyRequireAuth: false,
+          proxyUsername: 'testuser',
+          proxyPassword: 'testpass',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://proxy.example.com:8080');
+        expect(url).not.toContain('testuser');
+        expect(url).not.toContain('testpass');
+      });
+
+      it('should not include auth when username is empty', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyRequireAuth: true,
+          proxyUsername: '',
+          proxyPassword: 'testpass',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://proxy.example.com:8080');
+        expect(url).not.toContain('@');
+      });
+
+      it('should not include auth when password is empty', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyRequireAuth: true,
+          proxyUsername: 'testuser',
+          proxyPassword: '',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://proxy.example.com:8080');
+        expect(url).not.toContain('@');
+      });
+
+      it('should not include auth when username is undefined', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyRequireAuth: true,
+          proxyUsername: undefined,
+          proxyPassword: 'testpass',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://proxy.example.com:8080');
+        expect(url).not.toContain('@');
+      });
+
+      it('should not include auth when password is undefined', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyRequireAuth: true,
+          proxyUsername: 'testuser',
+          proxyPassword: undefined,
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://proxy.example.com:8080');
+        expect(url).not.toContain('@');
+      });
+
+      it('should handle minimum port number', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyPort: '1',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://proxy.example.com:1');
+      });
+
+      it('should handle maximum port number', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyPort: '65535',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        expect(url).toBe('http://proxy.example.com:65535');
+      });
+
+      it('should handle complex URL encoding scenario', () => {
+        const config: NetworkProxySettings = {
+          ...baseConfig,
+          proxyRequireAuth: true,
+          proxyUsername: 'user@example.com:admin',
+          proxyPassword: 'p@ss:w0rd#123',
+        };
+
+        const url = ProxyUrlBuilder.build(config);
+
+        // Verify all special characters are encoded
+        const expectedUsername = encodeURIComponent('user@example.com:admin');
+        const expectedPassword = encodeURIComponent('p@ss:w0rd#123');
+
+        expect(url).toBe(`http://${expectedUsername}:${expectedPassword}@proxy.example.com:8080`);
+      });
+    });
+  });
+});
@@ -0,0 +1,492 @@
+import { NetworkProxySettings } from '@lobechat/electron-client-ipc';
+import { describe, expect, it } from 'vitest';
+
+import { ProxyConfigValidator } from '../validator';
+
+describe('ProxyConfigValidator', () => {
+  const validConfig: NetworkProxySettings = {
+    enableProxy: true,
+    proxyType: 'http',
+    proxyServer: 'proxy.example.com',
+    proxyPort: '8080',
+    proxyRequireAuth: false,
+    proxyBypass: 'localhost,127.0.0.1,::1',
+  };
+
+  describe('validate', () => {
+    describe('disabled proxy', () => {
+      it('should validate successfully when proxy is disabled', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          enableProxy: false,
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(true);
+        expect(result.errors).toHaveLength(0);
+      });
+
+      it('should skip validation for disabled proxy even with invalid fields', () => {
+        const config: NetworkProxySettings = {
+          enableProxy: false,
+          proxyType: 'invalid' as any,
+          proxyServer: '',
+          proxyPort: 'invalid',
+          proxyRequireAuth: false,
+          proxyBypass: 'localhost',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(true);
+        expect(result.errors).toHaveLength(0);
+      });
+    });
+
+    describe('proxy type validation', () => {
+      it('should accept http proxy type', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyType: 'http',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(true);
+        expect(result.errors).toHaveLength(0);
+      });
+
+      it('should accept https proxy type', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyType: 'https',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(true);
+        expect(result.errors).toHaveLength(0);
+      });
+
+      it('should accept socks5 proxy type', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyType: 'socks5',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(true);
+        expect(result.errors).toHaveLength(0);
+      });
+
+      it('should reject unsupported proxy type', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyType: 'socks4' as any,
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toHaveLength(1);
+        expect(result.errors[0]).toContain('Unsupported proxy type');
+        expect(result.errors[0]).toContain('socks4');
+      });
+
+      it('should reject invalid proxy type', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyType: 'ftp' as any,
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors[0]).toContain('Supported types: http, https, socks5');
+      });
+    });
+
+    describe('proxy server validation', () => {
+      it('should accept valid domain name', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyServer: 'proxy.example.com',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(true);
+        expect(result.errors).toHaveLength(0);
+      });
+
+      it('should accept valid IPv4 address', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyServer: '192.168.1.1',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(true);
+        expect(result.errors).toHaveLength(0);
+      });
+
+      it('should accept localhost', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyServer: 'localhost',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(true);
+        expect(result.errors).toHaveLength(0);
+      });
+
+      it('should accept subdomain', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyServer: 'proxy.subdomain.example.com',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(true);
+        expect(result.errors).toHaveLength(0);
+      });
+
+      it('should reject empty proxy server', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyServer: '',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toContain('Proxy server is required when proxy is enabled');
+      });
+
+      it('should reject whitespace-only proxy server', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyServer: '   ',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toContain('Proxy server is required when proxy is enabled');
+      });
+
+      it('should reject invalid domain format', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyServer: 'invalid..domain',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toContain('Invalid proxy server format');
+      });
+
+      it('should reject domain starting with hyphen', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyServer: '-proxy.com',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toContain('Invalid proxy server format');
+      });
+
+      it('should reject domain with invalid characters', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyServer: 'proxy@example.com',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toContain('Invalid proxy server format');
+      });
+    });
+
+    describe('proxy port validation', () => {
+      it('should accept valid port 1', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyPort: '1',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(true);
+        expect(result.errors).toHaveLength(0);
+      });
+
+      it('should accept valid port 65535', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyPort: '65535',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(true);
+        expect(result.errors).toHaveLength(0);
+      });
+
+      it('should accept common proxy port 8080', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyPort: '8080',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(true);
+        expect(result.errors).toHaveLength(0);
+      });
+
+      it('should reject empty proxy port', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyPort: '',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toContain('Proxy port is required when proxy is enabled');
+      });
+
+      it('should reject whitespace-only proxy port', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyPort: '   ',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toContain('Proxy port is required when proxy is enabled');
+      });
+
+      it('should reject port 0', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyPort: '0',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toContain('Proxy port must be a valid number between 1 and 65535');
+      });
+
+      it('should reject port above 65535', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyPort: '65536',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toContain('Proxy port must be a valid number between 1 and 65535');
+      });
+
+      it('should reject negative port', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyPort: '-1',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toContain('Proxy port must be a valid number between 1 and 65535');
+      });
+
+      it('should reject non-numeric port', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyPort: 'abc',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toContain('Proxy port must be a valid number between 1 and 65535');
+      });
+    });
+
+    describe('authentication validation', () => {
+      it('should validate successfully with auth disabled', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyRequireAuth: false,
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(true);
+        expect(result.errors).toHaveLength(0);
+      });
+
+      it('should validate successfully with auth enabled and credentials provided', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyRequireAuth: true,
+          proxyUsername: 'testuser',
+          proxyPassword: 'testpass',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(true);
+        expect(result.errors).toHaveLength(0);
+      });
+
+      it('should reject when auth is enabled but username is missing', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyRequireAuth: true,
+          proxyUsername: '',
+          proxyPassword: 'testpass',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toContain(
+          'Proxy username is required when authentication is enabled',
+        );
+      });
+
+      it('should reject when auth is enabled but username is whitespace', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyRequireAuth: true,
+          proxyUsername: '   ',
+          proxyPassword: 'testpass',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toContain(
+          'Proxy username is required when authentication is enabled',
+        );
+      });
+
+      it('should reject when auth is enabled but password is missing', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyRequireAuth: true,
+          proxyUsername: 'testuser',
+          proxyPassword: '',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toContain(
+          'Proxy password is required when authentication is enabled',
+        );
+      });
+
+      it('should reject when auth is enabled but password is whitespace', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyRequireAuth: true,
+          proxyUsername: 'testuser',
+          proxyPassword: '   ',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toContain(
+          'Proxy password is required when authentication is enabled',
+        );
+      });
+
+      it('should reject when auth is enabled but both username and password are missing', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyRequireAuth: true,
+          proxyUsername: '',
+          proxyPassword: '',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toHaveLength(2);
+        expect(result.errors).toContain(
+          'Proxy username is required when authentication is enabled',
+        );
+        expect(result.errors).toContain(
+          'Proxy password is required when authentication is enabled',
+        );
+      });
+
+      it('should allow missing credentials when auth is disabled', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyRequireAuth: false,
+          proxyUsername: undefined,
+          proxyPassword: undefined,
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(true);
+        expect(result.errors).toHaveLength(0);
+      });
+    });
+
+    describe('multiple validation errors', () => {
+      it('should collect all validation errors', () => {
+        const config: NetworkProxySettings = {
+          enableProxy: true,
+          proxyType: 'invalid' as any,
+          proxyServer: '',
+          proxyPort: 'abc',
+          proxyRequireAuth: true,
+          proxyUsername: '',
+          proxyPassword: '',
+          proxyBypass: 'localhost',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors.length).toBeGreaterThan(1);
+      });
+
+      it('should collect errors for invalid server and port', () => {
+        const config: NetworkProxySettings = {
+          ...validConfig,
+          proxyServer: 'invalid..domain',
+          proxyPort: '99999',
+        };
+
+        const result = ProxyConfigValidator.validate(config);
+
+        expect(result.isValid).toBe(false);
+        expect(result.errors).toHaveLength(2);
+        expect(result.errors).toContain('Invalid proxy server format');
+        expect(result.errors).toContain('Proxy port must be a valid number between 1 and 65535');
+      });
+    });
+  });
+});
@@ -10,14 +10,14 @@ import { ProxyUrlBuilder } from './urlBuilder';
 const logger = createLogger('modules:networkProxy:dispatcher');

 /**
- * 代理管理器
+ * Proxy dispatcher manager
 */
 export class ProxyDispatcherManager {
  private static isChanging = false;
  private static changeQueue: Array<() => Promise<void>> = [];

  /**
-   * 应用代理设置（带并发控制）
+   * Apply proxy settings (with concurrency control)
   */
  static async applyProxySettings(config: NetworkProxySettings): Promise<void> {
    return new Promise((resolve, reject) => {
@@ -31,17 +31,17 @@ export class ProxyDispatcherManager {
      };

      if (this.isChanging) {
-        // 如果正在切换，加入队列
+        // If currently switching, add to queue
        this.changeQueue.push(operation);
      } else {
-        // 立即执行
+        // Execute immediately
        operation();
      }
    });
  }

  /**
-   * 执行代理设置应用
+   * Execute proxy settings application
   */
  private static async doApplyProxySettings(config: NetworkProxySettings): Promise<void> {
    this.isChanging = true;
@@ -49,22 +49,22 @@ export class ProxyDispatcherManager {
    try {
      const currentDispatcher = getGlobalDispatcher();

-      // 禁用代理，恢复默认连接
+      // Disable proxy, restore default connection
      if (!config.enableProxy) {
        await this.safeDestroyDispatcher(currentDispatcher);
-        // 创建一个新的默认 Agent 来替代代理
+        // Create a new default Agent to replace the proxy
        setGlobalDispatcher(new Agent());
        logger.debug('Proxy disabled, reset to direct connection mode');
        return;
      }

-      // 构建代理 URL
+      // Build proxy URL
      const proxyUrl = ProxyUrlBuilder.build(config);

-      // 创建代理 agent
+      // Create proxy agent
      const agent = this.createProxyAgent(config.proxyType, proxyUrl);

-      // 切换代理前销毁旧 dispatcher
+      // Destroy old dispatcher before switching proxy
      await this.safeDestroyDispatcher(currentDispatcher);
      setGlobalDispatcher(agent);

@@ -77,7 +77,7 @@ export class ProxyDispatcherManager {
    } finally {
      this.isChanging = false;

-      // 处理队列中的下一个操作
+      // Process next operation in queue
      if (this.changeQueue.length > 0) {
        const nextOperation = this.changeQueue.shift();
        if (nextOperation) {
@@ -88,12 +88,12 @@ export class ProxyDispatcherManager {
  }

  /**
-   * 创建代理 agent
+   * Create proxy agent
   */
  static createProxyAgent(proxyType: string, proxyUrl: string) {
    try {
      if (proxyType === 'socks5') {
-        // 解析 SOCKS5 代理 URL
+        // Parse SOCKS5 proxy URL
        const url = new URL(proxyUrl);
        const socksProxies: SocksProxies = [
          {
@@ -109,10 +109,10 @@ export class ProxyDispatcherManager {
          },
        ];

-        // 使用 fetch-socks 处理 SOCKS5 代理
+        // Use fetch-socks to handle SOCKS5 proxy
        return socksDispatcher(socksProxies);
      } else {
-        // undici 的 ProxyAgent 支持 http, https
+        // undici's ProxyAgent supports http, https
        return new ProxyAgent({ uri: proxyUrl });
      }
    } catch (error) {
@@ -124,7 +124,7 @@ export class ProxyDispatcherManager {
  }

  /**
-   * 安全销毁 dispatcher
+   * Safely destroy dispatcher
   */
  private static async safeDestroyDispatcher(dispatcher: any): Promise<void> {
    try {
@@ -11,7 +11,7 @@ import { ProxyConfigValidator } from './validator';
 const logger = createLogger('modules:networkProxy:tester');

 /**
- * 代理连接测试结果
+ * Proxy connection test result
 */
 export interface ProxyTestResult {
  message?: string;
@@ -20,14 +20,14 @@ export interface ProxyTestResult {
 }

 /**
- * 代理连接测试器
+ * Proxy connection tester
 */
 export class ProxyConnectionTester {
-  private static readonly DEFAULT_TIMEOUT = 10_000; // 10秒超时
+  private static readonly DEFAULT_TIMEOUT = 10_000; // 10 seconds timeout
  private static readonly DEFAULT_TEST_URL = 'https://www.google.com';

  /**
-   * 测试代理连接
+   * Test proxy connection
   */
  static async testConnection(
    url: string = this.DEFAULT_TEST_URL,
@@ -77,13 +77,13 @@ export class ProxyConnectionTester {
  }

  /**
-   * 测试指定代理配置的连接
+   * Test connection with specified proxy configuration
   */
  static async testProxyConfig(
    config: NetworkProxySettings,
    testUrl: string = this.DEFAULT_TEST_URL,
  ): Promise<ProxyTestResult> {
-    // 验证配置
+    // Validate configuration
    const validation = ProxyConfigValidator.validate(config);
    if (!validation.isValid) {
      return {
@@ -92,12 +92,12 @@ export class ProxyConnectionTester {
      };
    }

-    // 如果未启用代理，直接测试
+    // If proxy is not enabled, test directly
    if (!config.enableProxy) {
      return this.testConnection(testUrl);
    }

-    // 创建临时代理 agent 进行测试
+    // Create temporary proxy agent for testing
    try {
      const proxyUrl = ProxyUrlBuilder.build(config);
      logger.debug(`Testing proxy with URL: ${proxyUrl}`);
@@ -108,7 +108,7 @@ export class ProxyConnectionTester {
      const controller = new AbortController();
      const timeoutId = setTimeout(() => controller.abort(), this.DEFAULT_TIMEOUT);

-      // 临时设置代理进行测试
+      // Temporarily set proxy for testing
      const originalDispatcher = getGlobalDispatcher();
      setGlobalDispatcher(agent);

@@ -138,9 +138,9 @@ export class ProxyConnectionTester {
        clearTimeout(timeoutId);
        throw fetchError;
      } finally {
-        // 恢复原来的 dispatcher
+        // Restore original dispatcher
        setGlobalDispatcher(originalDispatcher);
-        // 清理临时创建的代理 agent
+        // Clean up temporary proxy agent
        if (agent && typeof agent.destroy === 'function') {
          try {
            await agent.destroy();
@@ -1,11 +1,11 @@
 import { NetworkProxySettings } from '@lobechat/electron-client-ipc';

 /**
- * 代理 URL 构建器
+ * Proxy URL builder
 */
 export const ProxyUrlBuilder = {
  /**
-   * 构建代理 URL
+   * Build proxy URL
   */
  build(config: NetworkProxySettings): string {
    const { proxyType, proxyServer, proxyPort, proxyRequireAuth, proxyUsername, proxyPassword } =
@@ -13,7 +13,7 @@ export const ProxyUrlBuilder = {

    let proxyUrl = `${proxyType}://${proxyServer}:${proxyPort}`;

-    // 添加认证信息
+    // Add authentication information
    if (proxyRequireAuth && proxyUsername && proxyPassword) {
      const encodedUsername = encodeURIComponent(proxyUsername);
      const encodedPassword = encodeURIComponent(proxyPassword);
@@ -1,7 +1,7 @@
 import { NetworkProxySettings } from '@lobechat/electron-client-ipc';

 /**
- * 代理配置验证结果
+ * Proxy configuration validation result
 */
 export interface ProxyValidationResult {
  errors: string[];
@@ -9,38 +9,38 @@ export interface ProxyValidationResult {
 }

 /**
- * 代理配置验证器
+ * Proxy configuration validator
 */
 export class ProxyConfigValidator {
  private static readonly SUPPORTED_TYPES = ['http', 'https', 'socks5'] as const;
  private static readonly DEFAULT_BYPASS = 'localhost,127.0.0.1,::1';

  /**
-   * 验证代理配置
+   * Validate proxy configuration
   */
  static validate(config: NetworkProxySettings): ProxyValidationResult {
    const errors: string[] = [];

-    // 如果未启用代理，跳过验证
+    // If proxy is not enabled, skip validation
    if (!config.enableProxy) {
      return { errors: [], isValid: true };
    }

-    // 验证代理类型
+    // Validate proxy type
    if (!this.SUPPORTED_TYPES.includes(config.proxyType as any)) {
      errors.push(
        `Unsupported proxy type: ${config.proxyType}. Supported types: ${this.SUPPORTED_TYPES.join(', ')}`,
      );
    }

-    // 验证代理服务器
+    // Validate proxy server
    if (!config.proxyServer?.trim()) {
      errors.push('Proxy server is required when proxy is enabled');
    } else if (!this.isValidHost(config.proxyServer)) {
      errors.push('Invalid proxy server format');
    }

-    // 验证代理端口
+    // Validate proxy port
    if (!config.proxyPort?.trim()) {
      errors.push('Proxy port is required when proxy is enabled');
    } else {
@@ -50,7 +50,7 @@ export class ProxyConfigValidator {
      }
    }

-    // 验证认证信息
+    // Validate authentication information
    if (config.proxyRequireAuth) {
      if (!config.proxyUsername?.trim()) {
        errors.push('Proxy username is required when authentication is enabled');
@@ -67,10 +67,10 @@ export class ProxyConfigValidator {
  }

  /**
-   * 验证主机名格式
+   * Validate host format
   */
  private static isValidHost(host: string): boolean {
-    // 简单的主机名验证（IP 地址或域名）
+    // Simple host validation (IP address or domain name)
    const ipRegex = /^(\d{1,3}\.){3}\d{1,3}$/;
    const domainRegex =
      /^[\dA-Za-z]([\dA-Za-z-]*[\dA-Za-z])?(\.[\dA-Za-z]([\dA-Za-z-]*[\dA-Za-z])?)*$/;
@@ -1,4 +1,407 @@
 [
+  {
+    "children": {
+      "features": ["Support user abort in the agent runtime."]
+    },
+    "date": "2025-11-19",
+    "version": "2.0.0-next.86"
+  },
+  {
+    "children": {
+      "fixes": ["Slove discover pagination router."]
+    },
+    "date": "2025-11-19",
+    "version": "2.0.0-next.85"
+  },
+  {
+    "children": {
+      "improvements": ["Add Gemini 3.0 Pro Preview to Google Provider."]
+    },
+    "date": "2025-11-19",
+    "version": "2.0.0-next.84"
+  },
+  {
+    "children": {
+      "features": ["New API support switch Responses API mode."],
+      "improvements": ["Update i18n."]
+    },
+    "date": "2025-11-19",
+    "version": "2.0.0-next.83"
+  },
+  {
+    "children": {
+      "fixes": ["Fix noisy error notification."]
+    },
+    "date": "2025-11-18",
+    "version": "2.0.0-next.82"
+  },
+  {
+    "children": {
+      "fixes": ["Slove when logout always show loading."]
+    },
+    "date": "2025-11-18",
+    "version": "2.0.0-next.81"
+  },
+  {
+    "children": {},
+    "date": "2025-11-18",
+    "version": "2.0.0-next.80"
+  },
+  {
+    "children": {
+      "fixes": ["Fixed the discover page categray sider link error."]
+    },
+    "date": "2025-11-18",
+    "version": "2.0.0-next.79"
+  },
+  {
+    "children": {},
+    "date": "2025-11-18",
+    "version": "2.0.0-next.78"
+  },
+  {
+    "children": {
+      "improvements": ["Delete /settings/newapi pages in nextjs build."]
+    },
+    "date": "2025-11-18",
+    "version": "2.0.0-next.77"
+  },
+  {
+    "children": {
+      "features": ["Support Interleaved thinking in MiniMax."]
+    },
+    "date": "2025-11-18",
+    "version": "2.0.0-next.76"
+  },
+  {
+    "children": {
+      "improvements": ["Update i18n."]
+    },
+    "date": "2025-11-18",
+    "version": "2.0.0-next.75"
+  },
+  {
+    "children": {
+      "features": ["Edit local file render & intervention."]
+    },
+    "date": "2025-11-17",
+    "version": "2.0.0-next.74"
+  },
+  {
+    "children": {
+      "features": ["Support parallel topic agent runtime."]
+    },
+    "date": "2025-11-17",
+    "version": "2.0.0-next.73"
+  },
+  {
+    "children": {
+      "improvements": ["Add model information for the Qiniu provider."]
+    },
+    "date": "2025-11-17",
+    "version": "2.0.0-next.72"
+  },
+  {
+    "children": {
+      "fixes": ["Fix desktop user panel."]
+    },
+    "date": "2025-11-17",
+    "version": "2.0.0-next.71"
+  },
+  {
+    "children": {},
+    "date": "2025-11-17",
+    "version": "2.0.0-next.70"
+  },
+  {
+    "children": {
+      "improvements": ["Remove language_model_settings and remove isDeprecatedEdition."]
+    },
+    "date": "2025-11-17",
+    "version": "2.0.0-next.69"
+  },
+  {
+    "children": {
+      "fixes": ["The tool to fail execution on ollama when a message contains b…."]
+    },
+    "date": "2025-11-16",
+    "version": "2.0.0-next.68"
+  },
+  {
+    "children": {
+      "improvements": ["Refactor to virtua."]
+    },
+    "date": "2025-11-16",
+    "version": "2.0.0-next.67"
+  },
+  {
+    "children": {
+      "features": ["Support to collapse message."]
+    },
+    "date": "2025-11-16",
+    "version": "2.0.0-next.66"
+  },
+  {
+    "children": {
+      "improvements": ["Update i18n."]
+    },
+    "date": "2025-11-16",
+    "version": "2.0.0-next.65"
+  },
+  {
+    "children": {
+      "improvements": ["Refactor package types."]
+    },
+    "date": "2025-11-15",
+    "version": "2.0.0-next.64"
+  },
+  {
+    "children": {
+      "features": ["Show orphaned tool message and support delete tool message."]
+    },
+    "date": "2025-11-15",
+    "version": "2.0.0-next.63"
+  },
+  {
+    "children": {},
+    "date": "2025-11-15",
+    "version": "2.0.0-next.62"
+  },
+  {
+    "children": {
+      "improvements": ["Update i18n."]
+    },
+    "date": "2025-11-15",
+    "version": "2.0.0-next.61"
+  },
+  {
+    "children": {
+      "fixes": ["Reduce threshold."]
+    },
+    "date": "2025-11-14",
+    "version": "2.0.0-next.60"
+  },
+  {
+    "children": {
+      "improvements": ["Update i18n."]
+    },
+    "date": "2025-11-14",
+    "version": "2.0.0-next.59"
+  },
+  {
+    "children": {
+      "features": ["Support DeepSeek Interleaved thinking."]
+    },
+    "date": "2025-11-14",
+    "version": "2.0.0-next.58"
+  },
+  {
+    "children": {
+      "improvements": ["Revert background style."]
+    },
+    "date": "2025-11-14",
+    "version": "2.0.0-next.57"
+  },
+  {
+    "children": {
+      "features": ["Add folder creation UI and clean up debug code."]
+    },
+    "date": "2025-11-14",
+    "version": "2.0.0-next.56"
+  },
+  {
+    "children": {
+      "features": ["Create Pages in Knowledge Base."]
+    },
+    "date": "2025-11-14",
+    "version": "2.0.0-next.55"
+  },
+  {
+    "children": {
+      "improvements": ["Refactor and support move locale file intervention."]
+    },
+    "date": "2025-11-14",
+    "version": "2.0.0-next.54"
+  },
+  {
+    "children": {
+      "features": ["Add GPT-5.1 models."],
+      "improvements": ["Fix approving render and improve Conversation style."]
+    },
+    "date": "2025-11-14",
+    "version": "2.0.0-next.53"
+  },
+  {
+    "children": {
+      "fixes": ["Filter out reasoning fields from messages in ChatCompletion API."]
+    },
+    "date": "2025-11-13",
+    "version": "2.0.0-next.52"
+  },
+  {
+    "children": {
+      "improvements": ["Update ERNIE-5.0-Thinking-Preview model."]
+    },
+    "date": "2025-11-13",
+    "version": "2.0.0-next.51"
+  },
+  {
+    "children": {
+      "fixes": ["Fix oidc accountId mismatch."]
+    },
+    "date": "2025-11-13",
+    "version": "2.0.0-next.50"
+  },
+  {
+    "children": {
+      "features": ["Support tool invention."],
+      "fixes": ["Update lost i18n files."]
+    },
+    "date": "2025-11-13",
+    "version": "2.0.0-next.49"
+  },
+  {
+    "children": {},
+    "date": "2025-11-12",
+    "version": "2.0.0-next.48"
+  },
+  {
+    "children": {
+      "fixes": ["Fix mcp server return image error."]
+    },
+    "date": "2025-11-11",
+    "version": "2.0.0-next.47"
+  },
+  {
+    "children": {
+      "improvements": ["Fix thread display."]
+    },
+    "date": "2025-11-11",
+    "version": "2.0.0-next.46"
+  },
+  {
+    "children": {
+      "improvements": ["Edge to node runtime."]
+    },
+    "date": "2025-11-10",
+    "version": "2.0.0-next.45"
+  },
+  {
+    "children": {
+      "fixes": ["Fix reasoning issue with claude and Response API thinking."]
+    },
+    "date": "2025-11-10",
+    "version": "2.0.0-next.44"
+  },
+  {
+    "children": {
+      "fixes": ["Abnormal animation of tokens."]
+    },
+    "date": "2025-11-09",
+    "version": "2.0.0-next.43"
+  },
+  {
+    "children": {
+      "fixes": ["Fix missing messages when finish runtime."]
+    },
+    "date": "2025-11-09",
+    "version": "2.0.0-next.42"
+  },
+  {
+    "children": {
+      "improvements": ["Update i18n."]
+    },
+    "date": "2025-11-09",
+    "version": "2.0.0-next.41"
+  },
+  {
+    "children": {},
+    "date": "2025-11-08",
+    "version": "2.0.0-next.40"
+  },
+  {
+    "children": {},
+    "date": "2025-11-08",
+    "version": "2.0.0-next.39"
+  },
+  {
+    "children": {
+      "improvements": ["Update i18n."]
+    },
+    "date": "2025-11-08",
+    "version": "2.0.0-next.38"
+  },
+  {
+    "children": {
+      "fixes": ["Don't include runtimeProvider in JWT for non-image operations."]
+    },
+    "date": "2025-11-07",
+    "version": "2.0.0-next.37"
+  },
+  {
+    "children": {
+      "features": ["Refactor to use agent runtime as the generation core and support branch mode."]
+    },
+    "date": "2025-11-07",
+    "version": "2.0.0-next.36"
+  },
+  {
+    "children": {
+      "improvements": ["Use react-router-dom change /chat page to spa mode."]
+    },
+    "date": "2025-11-07",
+    "version": "2.0.0-next.35"
+  },
+  {
+    "children": {
+      "improvements": [
+        "Add sorting functionality for disabled models and model providers with tooltip support."
+      ]
+    },
+    "date": "2025-11-07",
+    "version": "2.0.0-next.34"
+  },
+  {
+    "children": {
+      "improvements": ["Refactor message create name."],
+      "fixes": ["Model name display in the assistant panel disappears."]
+    },
+    "date": "2025-11-06",
+    "version": "2.0.0-next.33"
+  },
+  {
+    "children": {
+      "fixes": ["Should install new version after quit this instance."]
+    },
+    "date": "2025-11-05",
+    "version": "2.0.0-next.32"
+  },
+  {
+    "children": {},
+    "date": "2025-11-05",
+    "version": "2.0.0-next.31"
+  },
+  {
+    "children": {
+      "improvements": ["Enhance message router with service layer and comprehensive tests."]
+    },
+    "date": "2025-11-05",
+    "version": "2.0.0-next.30"
+  },
+  {
+    "children": {
+      "improvements": ["Refactor chat message model to speed up."]
+    },
+    "date": "2025-11-04",
+    "version": "2.0.0-next.29"
+  },
+  {
+    "children": {
+      "features": ["Support install sreamable http mcp server on web."]
+    },
+    "date": "2025-11-04",
+    "version": "2.0.0-next.28"
+  },
  {
    "children": {
      "improvements": ["Refactor services to a more clean structure."]
@@ -17,6 +17,9 @@ LOBE_PORT=3210
 CASDOOR_PORT=8000
 MINIO_PORT=9000
 APP_URL=http://localhost:3210
+# INTERNAL_APP_URL is optional, used for server-to-server calls
+# to bypass CDN/proxy. If not set, defaults to APP_URL.
+# Example: INTERNAL_APP_URL=http://localhost:3210
 AUTH_URL=http://localhost:3210/api/auth

 # Postgres related, which are the necessary environment variables for DB
@@ -6,6 +6,7 @@ table agents {
  tags jsonb [default: `[]`]
  avatar text
  background_color text
+  market_identifier text
  plugins jsonb [default: `[]`]
  client_id text
  user_id text [not null]
@@ -169,20 +170,8 @@ table chat_groups_agents {
  }
 }

-table document_chunks {
-  document_id varchar(30) [not null]
-  chunk_id uuid [not null]
-  page_index integer
-  user_id text [not null]
-  created_at "timestamp with time zone" [not null, default: `now()`]
-
-  indexes {
-    (document_id, chunk_id) [pk]
-  }
-}
-
 table documents {
-  id varchar(30) [pk, not null]
+  id varchar(255) [pk, not null]
  title text
  content text
  file_type varchar(255) [not null]
@@ -194,6 +183,7 @@ table documents {
  source_type text [not null]
  source text [not null]
  file_id text
+  parent_id varchar(255)
  user_id text [not null]
  client_id text
  editor_data jsonb
@@ -205,6 +195,7 @@ table documents {
    source [name: 'documents_source_idx']
    file_type [name: 'documents_file_type_idx']
    file_id [name: 'documents_file_id_idx']
+    parent_id [name: 'documents_parent_id_idx']
    (client_id, user_id) [name: 'documents_client_id_user_id_unique', unique]
  }
 }
@@ -218,6 +209,7 @@ table files {
  size integer [not null]
  url text [not null]
  source text
+  parent_id varchar(255)
  client_id text
  metadata jsonb
  chunk_task_id uuid
@@ -228,6 +220,7 @@ table files {

  indexes {
    file_hash [name: 'file_hash_idx']
+    parent_id [name: 'files_parent_id_idx']
    (client_id, user_id) [name: 'files_client_id_user_id_unique', unique]
  }
 }
@@ -345,6 +338,7 @@ table message_plugins {
  id text [pk, not null]
  tool_call_id text
  type text [default: 'default']
+  intervention jsonb
  api_name text
  arguments text
  identifier text
@@ -658,6 +652,18 @@ table chunks {
  }
 }

+table document_chunks {
+  document_id varchar(30) [not null]
+  chunk_id uuid [not null]
+  page_index integer
+  user_id text [not null]
+  created_at "timestamp with time zone" [not null, default: `now()`]
+
+  indexes {
+    (document_id, chunk_id) [pk]
+  }
+}
+
 table embeddings {
  id uuid [pk, not null, default: `gen_random_uuid()`]
  chunk_id uuid [unique]
@@ -127,16 +127,62 @@ For specific content, please refer to the [Feature Flags](/docs/self-hosting/adv
 ### `SSRF_ALLOW_PRIVATE_IP_ADDRESS`

 - Type: Optional
- Description: Allow to connect private IP address. In a trusted environment, it can be set to true to turn off SSRF protection.
+- Description: Controls whether to allow connections to private IP addresses. Set to `1` to disable SSRF protection and allow all private IP addresses. In a trusted environment (e.g., internal network), this can be enabled to allow access to internal resources.
 - Default: `0`
 - Example: `1` or `0`

+<Callout type="warning">
+  **Security Notice**: Enabling this option will disable SSRF protection and allow connections to private
+  IP addresses (127.0.0.0/8, 10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16, etc.). Only enable this in
+  trusted environments where you need to access internal network resources.
+</Callout>
+
+**Use Cases**:
+
+LobeChat performs SSRF security checks in the following scenarios:
+
+1. **Image/Video URL to Base64 Conversion**: When processing media messages (e.g., vision models, multimodal models), LobeChat converts image and video URLs to base64 format. This check prevents malicious users from accessing internal network resources.
+
+   Examples:
+
+   - Image: A user sends an image message with URL `http://192.168.1.100/admin/secrets.png`
+   - Video: A user sends a video message with URL `http://10.0.0.50/internal/meeting.mp4`
+
+   Without SSRF protection, these requests could expose internal network resources.
+
+2. **Web Crawler**: When using web crawling features to fetch external content.
+
+3. **Proxy Requests**: When proxying external API requests.
+
+**Configuration Examples**:
+
+```bash
+# Scenario 1: Public deployment (recommended)
+# Block all private IP addresses for security
+SSRF_ALLOW_PRIVATE_IP_ADDRESS=0
+
+# Scenario 2: Internal deployment
+# Allow all private IP addresses to access internal image servers
+SSRF_ALLOW_PRIVATE_IP_ADDRESS=1
+
+# Scenario 3: Hybrid deployment (most common)
+# Block private IPs by default, but allow specific trusted internal servers
+SSRF_ALLOW_PRIVATE_IP_ADDRESS=0
+SSRF_ALLOW_IP_ADDRESS_LIST=192.168.1.100,10.0.0.50
+```
+
 ### `SSRF_ALLOW_IP_ADDRESS_LIST`

 - Type: Optional
- Description: Allow private IP address list, multiple IP addresses are separated by commas. Only when `SSRF_ALLOW_PRIVATE_IP_ADDRESS` is `0`, it takes effect.
+- Description: Whitelist of allowed IP addresses, separated by commas. Only takes effect when `SSRF_ALLOW_PRIVATE_IP_ADDRESS` is `0`. Use this to allow specific internal IP addresses while keeping SSRF protection enabled for other private IPs.
 - Default: -
- Example: `198.18.1.62,224.0.0.3`
+- Example: `192.168.1.100,10.0.0.50,172.16.0.10`
+
+**Common Use Cases**:
+
+- Allow access to internal image storage server: `192.168.1.100`
+- Allow access to internal API gateway: `10.0.0.50`
+- Allow access to internal documentation server: `172.16.0.10`

 ### `ENABLE_AUTH_PROTECTION`

@@ -123,16 +123,61 @@ LobeChat 在部署时提供了一些额外的配置项，你可以使用环境
 ### `SSRF_ALLOW_PRIVATE_IP_ADDRESS`

 - 类型：可选
- 描述：是否允许连接私有 IP 地址。在可信环境中可以设置为 true 来关闭 SSRF 防护。
+- 描述：控制是否允许连接私有 IP 地址。设置为 `1` 时将关闭 SSRF 防护并允许所有私有 IP 地址。在可信环境（如内网部署）中，可以启用此选项以访问内部资源。
 - 默认值：`0`
- 示例：`1` or `0`
+- 示例：`1` 或 `0`
+
+<Callout type="warning">
+  **安全提示**：启用此选项将关闭 SSRF 防护，允许连接私有 IP 地址段（127.0.0.0/8、10.0.0.0/8、172.16.0.0/12、192.168.0.0/16
+  等）。仅在需要访问内网资源的可信环境中启用。
+</Callout>
+
+**应用场景**：
+
+LobeChat 会在以下场景执行 SSRF 安全检查：
+
+1. **图片 / 视频 URL 转 Base64**：在处理媒体消息时（例如视觉模型、多模态模型），LobeChat 会将图片和视频 URL 转换为 base64 格式。此检查可防止恶意用户通过媒体 URL 访问内网资源。
+
+   举例：
+
+   - 图片：用户发送图片消息，URL 为 `http://192.168.1.100/admin/secrets.png`
+   - 视频：用户发送视频消息，URL 为 `http://10.0.0.50/internal/meeting.mp4`
+
+   若无 SSRF 防护，这些请求可能导致内网资源泄露。
+
+2. **网页爬取**：使用网页爬取功能获取外部内容时。
+
+3. **代理请求**：代理外部 API 请求时。
+
+**配置示例**：
+
+```bash
+# 场景 1：公网部署（推荐）
+# 阻止所有私有 IP 访问，保证安全
+SSRF_ALLOW_PRIVATE_IP_ADDRESS=0
+
+# 场景 2：内网部署
+# 允许所有私有 IP，可访问内网图片服务器等资源
+SSRF_ALLOW_PRIVATE_IP_ADDRESS=1
+
+# 场景 3：混合部署（最常见）
+# 默认阻止私有 IP，但允许特定可信的内网服务器
+SSRF_ALLOW_PRIVATE_IP_ADDRESS=0
+SSRF_ALLOW_IP_ADDRESS_LIST=192.168.1.100,10.0.0.50
+```

 ### `SSRF_ALLOW_IP_ADDRESS_LIST`

 - 类型：可选
- 说明：允许的私有 IP 地址列表，多个 IP 地址用逗号分隔。仅在 `SSRF_ALLOW_PRIVATE_IP_ADDRESS` 为 `0` 时生效。
+- 描述：允许访问的 IP 地址白名单，多个 IP 地址用逗号分隔。仅在 `SSRF_ALLOW_PRIVATE_IP_ADDRESS` 为 `0` 时生效。使用此选项可以在保持 SSRF 防护的同时，允许访问特定的内网 IP 地址。
 - 默认值：-
- 示例：`198.18.1.62,224.0.0.3`
+- 示例：`192.168.1.100,10.0.0.50,172.16.0.10`
+
+**常见使用场景**：
+
+- 允许访问内网图片存储服务器：`192.168.1.100`
+- 允许访问内网 API 网关：`10.0.0.50`
+- 允许访问内网文档服务器：`172.16.0.10`

 ### `ENABLE_AUTH_PROTECTION`

@@ -675,6 +675,35 @@ You first need to access the WebUI for configuration:

 At this point, you have successfully deployed the LobeChat database version, and you can access your LobeChat service at `https://lobe.example.com`.

+#### Configuring Internal Server Communication with `INTERNAL_APP_URL`
+
+<Callout type="info">
+  If you are deploying LobeChat behind a CDN (like Cloudflare) or reverse proxy, you may want to configure internal server-to-server communication to bypass the CDN/proxy layer for better performance.
+</Callout>
+
+You can configure the `INTERNAL_APP_URL` environment variable:
+
+```yaml
+environment:
+  - 'APP_URL=https://lobe.example.com'  # Public URL for browser access
+  - 'INTERNAL_APP_URL=http://localhost:3210'  # Internal URL for server-to-server calls
+```
+
+**How it works:**
+- `APP_URL`: Used for browser/client access, OAuth callbacks, webhooks, etc. (goes through CDN/proxy)
+- `INTERNAL_APP_URL`: Used for internal server-to-server communication (bypasses CDN/proxy)
+
+If `INTERNAL_APP_URL` is not set, it defaults to `APP_URL`.
+
+**Configuration options:**
+- `http://localhost:3210` - If using Docker with host network mode
+- `http://lobe:3210` - If using Docker network with service name
+- `http://127.0.0.1:3210` - Alternative localhost address
+
+<Callout type="tip">
+  For Docker Compose deployments with `network_mode: 'service:network-service'`, use `http://localhost:3210` as the `INTERNAL_APP_URL`.
+</Callout>
+
 #### Configuration Files

 For convenience, here is a summary of example configuration files required for the production deployment using the Casdoor authentication scheme:
@@ -651,6 +651,35 @@ docker compose up -d # 重新启动

 至此，你已经成功部署了 LobeChat 数据库版本，你可以通过 `https://lobe.example.com` 访问你的 LobeChat 服务。

+#### 使用 `INTERNAL_APP_URL` 配置内部服务器通信
+
+<Callout type="info">
+  如果你在 CDN（如 Cloudflare）或反向代理后部署 LobeChat，你可以配置内部服务器到服务器通信以绕过 CDN/代理层，以获得更好的性能。
+</Callout>
+
+你可以配置 `INTERNAL_APP_URL` 环境变量：
+
+```yaml
+environment:
+  - 'APP_URL=https://lobe.example.com'  # 浏览器访问的公开 URL
+  - 'INTERNAL_APP_URL=http://localhost:3210'  # 服务器到服务器调用的内部 URL
+```
+
+**工作原理：**
+- `APP_URL`：用于浏览器/客户端访问、OAuth 回调、webhook 等（通过 CDN/代理）
+- `INTERNAL_APP_URL`：用于内部服务器到服务器通信（绕过 CDN/代理）
+
+如果未设置 `INTERNAL_APP_URL`，它将默认为 `APP_URL`。
+
+**配置选项：**
+- `http://localhost:3210` - 如果使用 Docker 主机网络模式
+- `http://lobe:3210` - 如果使用 Docker 网络与服务名称
+- `http://127.0.0.1:3210` - 备用本地主机地址
+
+<Callout type="tip">
+  对于使用 `network_mode: 'service:network-service'` 的 Docker Compose 部署，请使用 `http://localhost:3210` 作为 `INTERNAL_APP_URL`。
+</Callout>
+
 #### 配置文件

 为方便一键复制，在此汇总基于 casdoor 鉴权方案的域名方式下生产部署配置服务端数据库所需要的示例配置文件。
@@ -11,7 +11,7 @@ tags:

 # Using ComfyUI in LobeChat

-<Image alt={'Using ComfyUI in LobeChat'} cover src={'https://github.com/lobehub/lobe-chat/assets/17870709/c9e5eafc-ca22-496b-a88d-cc0ae53bf720'} />
+<Image alt={'Using ComfyUI in LobeChat'} cover src={'https://hub-apac-1.lobeobjects.space/docs/e9b811f248a1db2bd1be1af888cf9b9d.png'} />

 This documentation will guide you on how to use [ComfyUI](https://github.com/comfyanonymous/ComfyUI) in LobeChat for high-quality AI image generation and editing.

@@ -11,7 +11,7 @@ tags:

 # 在 LobeChat 中使用 ComfyUI

-<Image alt={'在 LobeChat 中使用 ComfyUI'} cover src={'https://github.com/lobehub/lobe-chat/assets/17870709/c9e5eafc-ca22-496b-a88d-cc0ae53bf720'} />
+<Image alt={'在 LobeChat 中使用 ComfyUI'} cover src={'https://hub-apac-1.lobeobjects.space/docs/e9b811f248a1db2bd1be1af888cf9b9d.png'} />

 本文档将指导你如何在 LobeChat 中使用 [ComfyUI](https://github.com/comfyanonymous/ComfyUI) 进行高质量的 AI 图像生成和编辑。

@@ -17,8 +17,8 @@
    "playwright": "^1.56.1"
  },
  "devDependencies": {
-    "@types/node": "^22.10.5",
+    "@types/node": "^22.19.1",
    "tsx": "^4.20.6",
-    "typescript": "^5.7.3"
+    "typescript": "^5.9.3"
  }
 }
@@ -145,6 +145,50 @@
    "apikey": "إدارة مفاتيح API",
    "profile": "الملف الشخصي",
    "security": "الأمان",
-    "stats": "الإحصائيات"
+    "stats": "الإحصائيات",
+    "usage": "إحصاءات الاستخدام"
+  },
+  "usage": {
+    "activeModels": {
+      "modelTable": "قائمة النماذج",
+      "models": "النماذج النشطة",
+      "providerTable": "قائمة المزودين",
+      "providers": "المزودون النشطون",
+      "table": {
+        "calls": "عدد الاستدعاءات",
+        "model": "النموذج",
+        "provider": "المزود",
+        "spend": "التكلفة"
+      }
+    },
+    "cards": {
+      "month": {
+        "modelCalls": "استدعاءات النموذج",
+        "title": "إنفاق هذا الشهر"
+      },
+      "today": {
+        "title": "إنفاق اليوم",
+        "yesterday": "أمس"
+      }
+    },
+    "table": {
+      "actions": "إجراءات",
+      "createdAt": "وقت الاستخدام",
+      "inputTokens": "رموز الإدخال",
+      "model": "النموذج",
+      "outputTokens": "رموز الإخراج",
+      "spend": "التكلفة",
+      "tps": "TPS",
+      "ttft": "TTFT",
+      "type": "نوع الاستدعاء"
+    },
+    "trends": {
+      "spend": "المبلغ",
+      "tokens": "الرموز"
+    },
+    "welcome": {
+      "model": "النموذج",
+      "provider": "المزود"
+    }
  }
 }
@@ -17,6 +17,7 @@
  "availableAgents": "المساعدون المتاحون",
  "backToBottom": "العودة إلى الأسفل",
  "chatList": {
+    "expandMessage": "عرض الرسائل",
    "longMessageDetail": "عرض التفاصيل"
  },
  "clearCurrentMessages": "مسح رسائل الجلسة الحالية",
@@ -173,8 +174,11 @@
    "title": "الإشارة إلى الأعضاء"
  },
  "messageAction": {
+    "collapse": "إخفاء الرسائل",
+    "continueGeneration": "متابعة التوليد",
    "delAndRegenerate": "حذف وإعادة الإنشاء",
    "deleteDisabledByThreads": "يوجد موضوعات فرعية، لا يمكن الحذف",
+    "expand": "عرض الرسائل",
    "regenerate": "إعادة الإنشاء"
  },
  "messages": {
@@ -239,6 +243,7 @@
  "noMatchingAgents": "لا يوجد أعضاء مطابقون",
  "noMembersYet": "لا يوجد أعضاء في هذه المجموعة بعد. انقر على زر + لدعوة المساعدين.",
  "noSelectedAgents": "لم يتم اختيار أي أعضاء بعد",
+  "openInNewWindow": "افتح الصفحة في نافذة جديدة",
  "owner": "مالك المجموعة",
  "pin": "تثبيت",
  "pinOff": "إلغاء التثبيت",
@@ -325,6 +330,11 @@
    "screenshot": "لقطة شاشة",
    "settings": "إعدادات التصدير",
    "text": "نص",
+    "widthMode": {
+      "label": "وضع العرض",
+      "narrow": "وضع الشاشة الضيقة",
+      "wide": "وضع الشاشة الواسعة"
+    },
    "withBackground": "تضمين صورة الخلفية",
    "withFooter": "تضمين تذييل",
    "withPluginInfo": "تضمين معلومات البرنامج المساعد",
@@ -367,6 +377,28 @@
    "remained": "متبقي",
    "used": "مستخدم"
  },
+  "tool": {
+    "intervention": {
+      "approve": "الموافقة",
+      "approveAndRemember": "الموافقة والتذكر",
+      "approveOnce": "الموافقة لمرة واحدة فقط",
+      "mode": {
+        "allowList": "قائمة السماح",
+        "allowListDesc": "تنفيذ الأدوات المعتمدة فقط تلقائيًا",
+        "autoRun": "الموافقة التلقائية",
+        "autoRunDesc": "الموافقة تلقائيًا على تنفيذ جميع الأدوات",
+        "manual": "يدوي",
+        "manualDesc": "يتطلب الموافقة اليدوية في كل مرة يتم فيها الاستدعاء"
+      },
+      "reject": "رفض",
+      "rejectAndContinue": "رفض ثم إعادة المحاولة",
+      "rejectOnly": "رفض",
+      "rejectReasonPlaceholder": "إدخال سبب الرفض سيساعد الوكيل على الفهم وتحسين الإجراءات المستقبلية",
+      "rejectTitle": "رفض استدعاء الأداة هذه المرة",
+      "rejectedWithReason": "تم رفض استدعاء الأداة هذه المرة بشكل يدوي: {{reason}}",
+      "toolRejected": "تم رفض استدعاء الأداة هذه المرة بشكل يدوي"
+    }
+  },
  "topic": {
    "checkOpenNewTopic": "هل ترغب في فتح موضوع جديد؟",
    "checkSaveCurrentMessages": "هل ترغب في حفظ الدردشة الحالية كموضوع؟",
@@ -135,6 +135,7 @@
    }
  },
  "close": "إغلاق",
+  "confirm": "تأكيد",
  "contact": "اتصل بنا",
  "copy": "نسخ",
  "copyFail": "فشل في النسخ",
@@ -285,6 +286,7 @@
  "oauth": "تسجيل الدخول SSO",
  "officialSite": "الموقع الرسمي",
  "ok": "موافق",
+  "or": "أو",
  "password": "كلمة المرور",
  "pin": "تثبيت في الأعلى",
  "pinOff": "إلغاء التثبيت",
@@ -106,6 +106,12 @@
    "keyPlaceholder": "المفتاح",
    "valuePlaceholder": "القيمة"
  },
+  "LocalFile": {
+    "action": {
+      "open": "فتح",
+      "showInFolder": "عرض في المجلد"
+    }
+  },
  "MaxTokenSlider": {
    "unlimited": "غير محدود"
  },
@@ -41,9 +41,29 @@
        "openingMessage": "رسالة الافتتاح",
        "openingQuestions": "أسئلة الافتتاح",
        "title": "إعدادات المساعد"
+      },
+      "version": {
+        "empty": "لا توجد إصدارات سابقة",
+        "status": {
+          "archived": "مؤرشف",
+          "deprecated": "مرفوض",
+          "unpublished": "قيد المراجعة"
+        },
+        "table": {
+          "isLatest": "أحدث إصدار",
+          "isValidated": "تم التحقق منه",
+          "publishAt": "تاريخ النشر",
+          "version": "رقم الإصدار"
+        },
+        "title": "سجل الإصدارات"
      }
    },
    "list": "قائمة المساعدين",
+    "marketSource": {
+      "label": "تبديل مصدر السوق",
+      "legacy": "السوق القديم",
+      "new": "السوق الجديد"
+    },
    "more": "المزيد",
    "plugins": "دمج الإضافات",
    "recentSubmits": "آخر التحديثات",
@@ -51,10 +71,35 @@
      "createdAt": "تم النشر مؤخراً",
      "identifier": "معرف المساعد",
      "knowledgeCount": "عدد قواعد المعرفة",
+      "myown": "عرض مساعدي",
      "pluginCount": "عدد الإضافات",
      "title": "اسم المساعد",
      "tokenUsage": "استهلاك التوكن"
    },
+    "status": {
+      "archived": {
+        "reasons": {
+          "official": "تمت إزالة المساعد من قبل الإدارة لأسباب أمنية أو سياسية",
+          "owner": "قام مالك المساعد بإزالته أو أرشفته طوعًا"
+        },
+        "subtitle": "المساعد الذي تحاول الوصول إليه تم أرشفته للأسباب التالية المحتملة:",
+        "title": "تم أرشفة المساعد"
+      },
+      "backToMarket": "العودة إلى سوق المساعدين",
+      "deprecated": {
+        "reasons": {
+          "official": "تمت إزالة المساعد من قبل الإدارة لأسباب أمنية أو سياسية",
+          "owner": "قام مالك المساعد بإزالته أو رفضه طوعًا"
+        },
+        "subtitle": "المساعد الذي تحاول الوصول إليه تم رفضه للأسباب التالية المحتملة:",
+        "title": "تم رفض المساعد"
+      },
+      "support": "إذا واجهت أي مشاكل، يرجى نسخ الرابط وإرساله إلى <1>support@lobehub.com</1> للاستفسار.",
+      "unpublished": {
+        "subtitle": "المساعد الذي تحاول الوصول إليه قيد المراجعة حاليًا. إذا كان لديك أي استفسار، يرجى نسخ الرابط وإرساله إلى <1>support@lobehub.com</1>.",
+        "title": "المساعد قيد المراجعة"
+      }
+    },
    "suggestions": "اقتراحات ذات صلة",
    "systemRole": "إعدادات المساعد",
    "tokenUsage": "استهلاك توكنات تعليمات المساعد",
@@ -102,7 +102,7 @@
      "SPII": "قد يحتوي المحتوى على معلومات شخصية حساسة. لحماية الخصوصية، يرجى إزالة المعلومات الحساسة ثم المحاولة مرة أخرى.",
      "default": "تم حظر المحتوى: {{blockReason}}. يرجى تعديل طلبك ثم المحاولة مرة أخرى."
    },
-    "InsufficientQuota": "عذرًا، لقد reached الحد الأقصى للحصة (quota) لهذه المفتاح، يرجى التحقق من رصيد الحساب الخاص بك أو زيادة حصة المفتاح ثم المحاولة مرة أخرى",
+    "InsufficientQuota": "عذرًا، لقد تم الوصول إلى الحد الأقصى لحصة المفتاح (quota). يرجى التحقق من رصيد الحساب أو زيادة حصة المفتاح ثم المحاولة مرة أخرى.",
    "InvalidAccessCode": "كلمة المرور غير صحيحة أو فارغة، يرجى إدخال كلمة مرور الوصول الصحيحة أو إضافة مفتاح API مخصص",
    "InvalidBedrockCredentials": "فشلت مصادقة Bedrock، يرجى التحقق من AccessKeyId/SecretAccessKey وإعادة المحاولة",
    "InvalidClerkUser": "عذرًا، لم تقم بتسجيل الدخول بعد، يرجى تسجيل الدخول أو التسجيل للمتابعة",
@@ -131,7 +131,7 @@
    "PluginServerError": "خطأ في استجابة الخادم لطلب الإضافة، يرجى التحقق من ملف وصف الإضافة وتكوين الإضافة وتنفيذ الخادم وفقًا لمعلومات الخطأ أدناه",
    "PluginSettingsInvalid": "تحتاج هذه الإضافة إلى تكوين صحيح قبل الاستخدام، يرجى التحقق من صحة تكوينك",
    "ProviderBizError": "طلب خدمة {{provider}} خاطئ، يرجى التحقق من المعلومات التالية أو إعادة المحاولة",
-    "QuotaLimitReached": "عذرًا، لقد reached الحد الأقصى من استخدام الرموز أو عدد الطلبات لهذا المفتاح. يرجى زيادة حصة هذا المفتاح أو المحاولة لاحقًا.",
+    "QuotaLimitReached": "عذرًا، لقد تم الوصول إلى الحد الأقصى لاستخدام الرموز (Token) أو عدد الطلبات لهذا المفتاح. يرجى زيادة حصة المفتاح أو المحاولة لاحقًا.",
    "StreamChunkError": "خطأ في تحليل كتلة الرسالة لطلب التدفق، يرجى التحقق مما إذا كانت واجهة برمجة التطبيقات الحالية تتوافق مع المعايير، أو الاتصال بمزود واجهة برمجة التطبيقات الخاصة بك للاستفسار.",
    "SubscriptionKeyMismatch": "نعتذر، بسبب عطل عرضي في النظام، فإن استخدام الاشتراك الحالي غير فعال مؤقتًا. يرجى النقر على الزر أدناه لاستعادة الاشتراك، أو مراسلتنا عبر البريد الإلكتروني للحصول على الدعم.",
    "SubscriptionPlanLimit": "لقد استنفدت نقاط اشتراكك، ولا يمكنك استخدام هذه الميزة. يرجى الترقية إلى خطة أعلى، أو تكوين واجهة برمجة التطبيقات للنموذج المخصص للاستمرار في الاستخدام",
@@ -1,5 +1,8 @@
 {
-  "desc": "إدارة معرفتك",
+  "addFolder": "إنشاء مجلد",
+  "addKnowledge": "إضافة معرفة",
+  "addPage": "إنشاء مستند",
+  "desc": "نظّم معرفتك في العمل، الدراسة والحياة.",
  "detail": {
    "basic": {
      "createdAt": "تاريخ الإنشاء",
@@ -21,15 +24,89 @@
      "embeddingStatus": "تحويل إلى متجهات"
    }
  },
+  "documentEditor": {
+    "addIcon": "إضافة أيقونة",
+    "autoSaveMessage": "يتم حفظ المستند تلقائيًا، لا حاجة للحفظ اليدوي",
+    "chooseIcon": "اختر أيقونة",
+    "deleteConfirm": {
+      "content": "سيتم حذف هذا المستند، ولا يمكن استعادته بعد الحذف. يرجى توخي الحذر.",
+      "title": "حذف المستند"
+    },
+    "deleteError": "فشل في حذف المستند",
+    "deleteSuccess": "تم حذف المستند بنجاح",
+    "editedAt": "آخر تعديل في {{time}}",
+    "editedBy": "آخر من عدّل: {{name}}",
+    "editorPlaceholder": "أدخل محتوى المستند، اضغط / لفتح قائمة الأوامر",
+    "empty": {
+      "createNewDocument": "إنشاء مستند جديد",
+      "title": "اختر مستندًا للبدء",
+      "uploadMarkdown": "رفع ملف Markdown"
+    },
+    "linkCopied": "تم نسخ الرابط",
+    "menu": {
+      "copyLink": "نسخ الرابط",
+      "exportDocument": "تصدير المستند",
+      "importDocument": "استيراد مستند",
+      "pin": "تثبيت المستند"
+    },
+    "saving": "جارٍ الحفظ...",
+    "titlePlaceholder": "بدون عنوان",
+    "wordCount": "{{wordCount}} كلمة"
+  },
+  "documentList": {
+    "copyContent": "نسخ المحتوى الكامل",
+    "documentCount": "إجمالي {{count}} مستند",
+    "duplicate": "إنشاء نسخة",
+    "empty": "لا توجد مستندات حاليًا، انقر على الزر أعلاه لإنشاء أول مستند لك",
+    "noResults": "لم يتم العثور على مستندات مطابقة",
+    "selectNote": "اختر مستندًا للبدء في التحرير",
+    "untitled": "بدون عنوان"
+  },
  "empty": "لا توجد ملفات/مجلدات تم تحميلها بعد",
  "header": {
    "actions": {
      "newFolder": "إنشاء مجلد جديد",
+      "newPage": "مستند جديد",
      "uploadFile": "رفع ملف",
      "uploadFolder": "رفع مجلد"
    },
+    "newDocumentButton": "مستند جديد",
+    "newNoteDialog": {
+      "cancel": "إلغاء",
+      "editTitle": "تحرير المستند",
+      "emptyContent": "لا يمكن أن يكون محتوى المستند فارغًا",
+      "loadError": "فشل في تحميل المستند، يرجى المحاولة مرة أخرى",
+      "loading": "جارٍ التحميل...",
+      "save": "حفظ",
+      "saveError": "فشل في حفظ المستند، يرجى المحاولة مرة أخرى",
+      "saveSuccess": "تم حفظ المستند بنجاح",
+      "title": "مستند جديد",
+      "updateSuccess": "تم تحديث المستند بنجاح"
+    },
    "uploadButton": "رفع"
  },
+  "home": {
+    "getStarted": "ابدأ الآن",
+    "greeting": "ابدأ",
+    "quickActions": "إجراءات سريعة",
+    "recentDocuments": "المستندات الأخيرة",
+    "recentFiles": "الملفات الأخيرة",
+    "subtitle": "مرحبًا بك في قاعدة المعرفة، ابدأ من هنا لإدارة مستنداتك وملاحظاتك",
+    "uploadEntries": {
+      "files": {
+        "title": "رفع ملفات"
+      },
+      "folder": {
+        "title": "رفع مجلد"
+      },
+      "knowledgeBase": {
+        "title": "قاعدة معرفة جديدة"
+      },
+      "newDocument": {
+        "title": "مستند جديد"
+      }
+    }
+  },
  "knowledgeBase": {
    "list": {
      "confirmRemoveKnowledgeBase": "سيتم حذف هذه المكتبة المعرفية، ولن يتم حذف الملفات الموجودة بها، بل ستنتقل إلى جميع الملفات. بعد حذف المكتبة المعرفية، لن يمكن استعادتها، يرجى توخي الحذر.",
@@ -38,6 +115,10 @@
    "new": "إنشاء مكتبة معرفية جديدة",
    "title": "المكتبة المعرفية"
  },
+  "menu": {
+    "allDocuments": "جميع المستندات",
+    "allFiles": "جميع الملفات"
+  },
  "networkError": "فشل في الحصول على قاعدة المعرفة، يرجى التحقق من اتصال الشبكة ثم إعادة المحاولة",
  "notSupportGuide": {
    "desc": "الوضع الحالي للنشر هو وضع قاعدة بيانات العميل، ولا يمكن استخدام وظيفة إدارة الملفات. يرجى التبديل إلى <1>وضع نشر قاعدة بيانات الخادم</1>، أو استخدام <3>LobeChat Cloud</3> مباشرة.",
@@ -61,12 +142,16 @@
    "downloadFile": "تحميل الملف",
    "unsupportedFileAndContact": "هذا التنسيق من الملفات غير مدعوم للمعاينة عبر الإنترنت، إذا كان لديك طلب للمعاينة، فلا تتردد في <1>إبلاغنا</1>"
  },
+  "searchDocumentPlaceholder": "ابحث في المستندات",
  "searchFilePlaceholder": "بحث عن ملف",
  "tab": {
-    "all": "جميع الملفات",
+    "all": "الكل",
    "audios": "الصوتيات",
    "documents": "المستندات",
+    "home": "الرئيسية",
    "images": "الصور",
+    "moreTypes": "أنواع أخرى",
+    "pages": "المستندات",
    "videos": "الفيديوهات",
    "websites": "المواقع"
  },
@@ -0,0 +1,42 @@
+{
+  "callback": {
+    "buttons": {
+      "close": "إغلاق النافذة"
+    },
+    "messages": {
+      "authFailed": "فشل التفويض: {{error}}",
+      "missingParams": "معلمات التفويض مفقودة",
+      "processing": "جارٍ معالجة التفويض...",
+      "successWithCountdown": "{{message}} سيتم إغلاق النافذة تلقائيًا خلال {{countdown}} ثانية",
+      "successWithRedirect": "تم التفويض بنجاح! جارٍ إعادة التوجيه..."
+    },
+    "titles": {
+      "error": "فشل التفويض",
+      "loading": "تفويض LobeHub Market",
+      "success": "تم التفويض بنجاح"
+    }
+  },
+  "errors": {
+    "authorizationFailed": "فشل التفويض، يرجى المحاولة مرة أخرى.",
+    "browserOnly": "يمكن بدء عملية التفويض من خلال المتصفح فقط.",
+    "codeConsumed": "تم استخدام رمز التفويض، يرجى المحاولة مرة أخرى.",
+    "codeVerifierMissing": "جلسة التفويض غير صالحة، يرجى إعادة تسجيل الدخول.",
+    "general": "حدث خطأ أثناء التفويض، يرجى المحاولة مرة أخرى.",
+    "handoffFailed": "تعذر الحصول على نتيجة التفويض، يرجى المحاولة مرة أخرى.",
+    "handoffTimeout": "انتهت مهلة التفويض، يرجى إكمال العملية في المتصفح ثم المحاولة مرة أخرى.",
+    "oidcNotReady": "خدمة التفويض غير جاهزة بعد، يرجى المحاولة لاحقًا.",
+    "openBrowserFailed": "تعذر فتح متصفح النظام، يرجى المحاولة مرة أخرى.",
+    "openPopupFailed": "تعذر فتح نافذة التفويض، يرجى التحقق من إعدادات حظر النوافذ المنبثقة في المتصفح.",
+    "popupClosed": "تم إغلاق نافذة التفويض قبل إتمام العملية.",
+    "sessionExpired": "انتهت صلاحية جلسة التفويض، يرجى تسجيل الدخول مرة أخرى.",
+    "stateMismatch": "حالة التفويض غير متطابقة، يرجى المحاولة مرة أخرى.",
+    "stateMissing": "لم يتم العثور على حالة التفويض، يرجى المحاولة مرة أخرى."
+  },
+  "messages": {
+    "loading": "جارٍ بدء عملية التفويض...",
+    "success": {
+      "submit": "تم التفويض بنجاح! يمكنك الآن نشر المساعد.",
+      "upload": "تم التفويض بنجاح! يمكنك الآن نشر إصدار جديد."
+    }
+  }
+}
@@ -199,6 +199,12 @@
    "all": "الكل",
    "list": {
      "disabled": "غير مفعل",
+      "disabledActions": {
+        "sort": "طريقة الترتيب",
+        "sortAlphabetical": "ترتيب أبجديًا",
+        "sortAlphabeticalDesc": "ترتيب أبجدي عكسي",
+        "sortDefault": "الترتيب الافتراضي"
+      },
      "enabled": "مفعل"
    },
    "notFound": "لم يتم العثور على نتائج البحث",
@@ -289,7 +295,7 @@
      },
      "helpDoc": "دليل التكوين",
      "responsesApi": {
-        "desc": "استخدام معيار طلبات الجيل الجديد من OpenAI، لفتح ميزات متقدمة مثل سلسلة التفكير",
+        "desc": "يعتمد تنسيق طلب الجيل الجديد من OpenAI، لتمكين ميزات متقدمة مثل سلسلة التفكير (مدعومة فقط من نماذج OpenAI)",
        "title": "استخدام معيار Responses API"
      },
      "waitingForMore": "المزيد من النماذج قيد <1>التخطيط للإدماج</1>، يرجى الانتظار"
@@ -391,7 +397,13 @@
      "addNew": "إضافة نموذج",
      "disabled": "غير مفعل",
      "disabledActions": {
-        "showMore": "عرض الكل"
+        "showMore": "عرض الكل",
+        "sort": "طريقة الترتيب",
+        "sortAlphabetical": "ترتيب أبجديًا",
+        "sortAlphabeticalDesc": "ترتيب أبجدي عكسي",
+        "sortDefault": "الترتيب الافتراضي",
+        "sortReleasedAt": "ترتيب حسب أقدم تاريخ إصدار",
+        "sortReleasedAtDesc": "ترتيب حسب أحدث تاريخ إصدار"
      },
      "empty": {
        "desc": "يرجى إنشاء نموذج مخصص أو سحب نموذج للبدء في الاستخدام",
@@ -236,6 +236,9 @@
  "MiniMaxAI/MiniMax-M1-80k": {
    "description": "MiniMax-M1 هو نموذج استدلال كبير الحجم مفتوح المصدر يعتمد على الانتباه المختلط، يحتوي على 456 مليار معلمة، حيث يمكن لكل رمز تفعيل حوالي 45.9 مليار معلمة. يدعم النموذج أصلاً سياقًا فائق الطول يصل إلى مليون رمز، ومن خلال آلية الانتباه السريع، يوفر 75% من العمليات الحسابية العائمة في مهام التوليد التي تصل إلى 100 ألف رمز مقارنة بـ DeepSeek R1. بالإضافة إلى ذلك، يعتمد MiniMax-M1 على بنية MoE (الخبراء المختلطون)، ويجمع بين خوارزمية CISPO وتصميم الانتباه المختلط لتدريب تعلم معزز فعال، محققًا أداءً رائدًا في الصناعة في استدلال الإدخالات الطويلة وسيناريوهات هندسة البرمجيات الحقيقية."
  },
+  "MiniMaxAI/MiniMax-M2": {
+    "description": "MiniMax-M2 يعيد تعريف الكفاءة للوكيل الذكي. إنه نموذج MoE مدمج وسريع وفعّال من حيث التكلفة، يحتوي على 230 مليار معلمة إجمالية و10 مليارات معلمة نشطة، وقد صُمم لتحقيق أداء رفيع المستوى في مهام الترميز والوكالة، مع الحفاظ على ذكاء عام قوي. بفضل 10 مليارات معلمة نشطة فقط، يقدم MiniMax-M2 أداءً يُضاهي النماذج الضخمة، مما يجعله خيارًا مثاليًا للتطبيقات عالية الكفاءة."
+  },
  "Moonshot-Kimi-K2-Instruct": {
    "description": "يحتوي على 1 تريليون معلمة و32 مليار معلمة مفعلة. من بين النماذج غير المعتمدة على التفكير، يحقق مستويات متقدمة في المعرفة الحديثة، الرياضيات والبرمجة، ويتفوق في مهام الوكيل العامة. تم تحسينه بعناية لمهام الوكيل، لا يجيب فقط على الأسئلة بل يتخذ إجراءات. مثالي للدردشة العفوية، التجارب العامة والوكيل، وهو نموذج سريع الاستجابة لا يتطلب تفكيرًا طويلًا."
  },
@@ -1049,6 +1052,9 @@
  "deepseek-r1-0528": {
    "description": "نموذج كامل القوة بحجم 685 مليار، صدر في 28 مايو 2025. استخدم DeepSeek-R1 تقنيات التعلم المعزز على نطاق واسع في مرحلة ما بعد التدريب، مما عزز بشكل كبير قدرات الاستدلال للنموذج مع وجود بيانات تعليمية قليلة جدًا. يتمتع بأداء عالي وقدرات قوية في المهام المتعلقة بالرياضيات، البرمجة، والاستدلال اللغوي الطبيعي."
  },
+  "deepseek-r1-250528": {
+    "description": "DeepSeek R1 250528، النسخة الكاملة من نموذج الاستدلال DeepSeek-R1، مناسب للمهام الرياضية والمنطقية المعقدة."
+  },
  "deepseek-r1-70b-fast-online": {
    "description": "DeepSeek R1 70B النسخة السريعة، تدعم البحث المتصل في الوقت الحقيقي، وتوفر سرعة استجابة أسرع مع الحفاظ على أداء النموذج."
  },
@@ -1059,31 +1065,34 @@
    "description": "deepseek-r1-distill-llama هو نموذج مستخلص من DeepSeek-R1 بناءً على Llama."
  },
  "deepseek-r1-distill-llama-70b": {
-    "description": "DeepSeek R1 - النموذج الأكبر والأذكى في مجموعة DeepSeek - تم تقطيره إلى بنية Llama 70B. بناءً على اختبارات المعايير والتقييمات البشرية، يظهر هذا النموذج ذكاءً أكبر من Llama 70B الأصلي، خاصة في المهام التي تتطلب دقة رياضية وحقائق."
+    "description": "DeepSeek R1 Distill Llama 70B، نموذج تقطير يجمع بين قدرات الاستدلال العامة لـ R1 ونظام Llama البيئي."
  },
  "deepseek-r1-distill-llama-8b": {
-    "description": "نموذج DeepSeek-R1-Distill تم تطويره من خلال تقنية تقطير المعرفة، حيث تم تعديل عينات تم إنشاؤها بواسطة DeepSeek-R1 على نماذج مفتوحة المصدر مثل Qwen وLlama."
+    "description": "DeepSeek-R1-Distill-Llama-8B هو نموذج لغوي كبير مقطر مبني على Llama-3.1-8B، يستخدم مخرجات DeepSeek R1."
+  },
+  "deepseek-r1-distill-qianfan-70b": {
+    "description": "DeepSeek R1 Distill Qianfan 70B، نموذج تقطير R1 مبني على Qianfan-70B، يتميز بكفاءة عالية من حيث التكلفة."
+  },
+  "deepseek-r1-distill-qianfan-8b": {
+    "description": "DeepSeek R1 Distill Qianfan 8B، نموذج تقطير R1 مبني على Qianfan-8B، مناسب للتطبيقات المتوسطة والصغيرة."
  },
  "deepseek-r1-distill-qianfan-llama-70b": {
-    "description": "تم إصداره لأول مرة في 14 فبراير 2025، تم استخلاصه بواسطة فريق تطوير نموذج Qianfan باستخدام Llama3_70B كنموذج أساسي (مبني على Meta Llama)، وتم إضافة نصوص Qianfan إلى بيانات الاستخلاص."
-  },
-  "deepseek-r1-distill-qianfan-llama-8b": {
-    "description": "تم إصداره لأول مرة في 14 فبراير 2025، تم استخلاصه بواسطة فريق تطوير نموذج Qianfan باستخدام Llama3_8B كنموذج أساسي (مبني على Meta Llama)، وتم إضافة نصوص Qianfan إلى بيانات الاستخلاص."
+    "description": "DeepSeek R1 Distill Qianfan Llama 70B، نموذج تقطير R1 مبني على Llama-70B."
  },
  "deepseek-r1-distill-qwen": {
    "description": "deepseek-r1-distill-qwen هو نموذج مستخلص من DeepSeek-R1 بناءً على Qwen."
  },
  "deepseek-r1-distill-qwen-1.5b": {
-    "description": "نموذج DeepSeek-R1-Distill تم تطويره من خلال تقنية تقطير المعرفة، حيث تم تعديل عينات تم إنشاؤها بواسطة DeepSeek-R1 على نماذج مفتوحة المصدر مثل Qwen وLlama."
+    "description": "DeepSeek R1 Distill Qwen 1.5B، نموذج تقطير R1 فائق الخفة، مناسب للبيئات ذات الموارد المحدودة جداً."
  },
  "deepseek-r1-distill-qwen-14b": {
-    "description": "نموذج DeepSeek-R1-Distill تم تطويره من خلال تقنية تقطير المعرفة، حيث تم تعديل عينات تم إنشاؤها بواسطة DeepSeek-R1 على نماذج مفتوحة المصدر مثل Qwen وLlama."
+    "description": "DeepSeek R1 Distill Qwen 14B، نموذج تقطير R1 متوسط الحجم، مناسب للنشر في سيناريوهات متعددة."
  },
  "deepseek-r1-distill-qwen-32b": {
-    "description": "نموذج DeepSeek-R1-Distill تم تطويره من خلال تقنية تقطير المعرفة، حيث تم تعديل عينات تم إنشاؤها بواسطة DeepSeek-R1 على نماذج مفتوحة المصدر مثل Qwen وLlama."
+    "description": "DeepSeek R1 Distill Qwen 32B، نموذج تقطير R1 مبني على Qwen-32B، يوازن بين الأداء والتكلفة."
  },
  "deepseek-r1-distill-qwen-7b": {
-    "description": "نموذج DeepSeek-R1-Distill تم تطويره من خلال تقنية تقطير المعرفة، حيث تم تعديل عينات تم إنشاؤها بواسطة DeepSeek-R1 على نماذج مفتوحة المصدر مثل Qwen وLlama."
+    "description": "DeepSeek R1 Distill Qwen 7B، نموذج تقطير R1 خفيف الوزن، مناسب للبيئات الطرفية والخاصة بالمؤسسات."
  },
  "deepseek-r1-fast-online": {
    "description": "DeepSeek R1 النسخة السريعة الكاملة، تدعم البحث المتصل في الوقت الحقيقي، تجمع بين القدرات القوية لـ 671 مليار معلمة وسرعة استجابة أسرع."
@@ -1112,12 +1121,24 @@
  "deepseek-v3.1-terminus": {
    "description": "DeepSeek-V3.1-Terminus هو إصدار محسن من نموذج اللغة الكبير أطلقته DeepSeek، ومُصمم خصيصًا للأجهزة الطرفية."
  },
+  "deepseek-v3.1-think-250821": {
+    "description": "DeepSeek V3.1 Think 250821، نموذج تفكير عميق بإصدار Terminus، مناسب لسيناريوهات الاستدلال عالية الأداء."
+  },
  "deepseek-v3.1:671b": {
    "description": "DeepSeek V3.1: نموذج استدلال من الجيل التالي يعزز القدرات على الاستدلال المعقد والتفكير التسلسلي، مناسب للمهام التي تتطلب تحليلاً عميقًا."
  },
  "deepseek-v3.2-exp": {
    "description": "deepseek-v3.2-exp يُدخل آلية الانتباه المتفرق، بهدف تحسين كفاءة التدريب والاستدلال عند معالجة النصوص الطويلة، بسعر أقل من deepseek-v3.1."
  },
+  "deepseek-v3.2-think": {
+    "description": "DeepSeek V3.2 Think، النسخة الكاملة من نموذج التفكير العميق، معزّز بقدرات استدلال طويلة السلسلة."
+  },
+  "deepseek-vl2": {
+    "description": "DeepSeek VL2، نموذج متعدد الوسائط يدعم فهم الصور والنصوص والإجابات البصرية الدقيقة."
+  },
+  "deepseek-vl2-small": {
+    "description": "DeepSeek VL2 Small، نسخة خفيفة متعددة الوسائط، مناسبة للبيئات ذات الموارد المحدودة وسيناريوهات الحمل العالي."
+  },
  "deepseek/deepseek-chat-v3-0324": {
    "description": "DeepSeek V3 هو نموذج مختلط خبير يحتوي على 685B من المعلمات، وهو أحدث إصدار من سلسلة نماذج الدردشة الرائدة لفريق DeepSeek.\n\nيستفيد من نموذج [DeepSeek V3](/deepseek/deepseek-chat-v3) ويظهر أداءً ممتازًا في مجموعة متنوعة من المهام."
  },
@@ -1137,7 +1158,7 @@
    "description": "DeepSeek-R1 يعزز بشكل كبير قدرة الاستدلال للنموذج حتى مع وجود بيانات تعليمية قليلة جدًا. قبل إخراج الإجابة النهائية، يقوم النموذج أولاً بإخراج سلسلة من التفكير لتحسين دقة الإجابة النهائية."
  },
  "deepseek/deepseek-r1-distill-llama-70b": {
-    "description": "DeepSeek-R1-Distill-Llama-70B هو نسخة مكثفة وأكثر كفاءة من نموذج Llama 70B. يحافظ على أداء قوي في مهام توليد النصوص مع تقليل استهلاك الحوسبة لتسهيل النشر والبحث. يتم تشغيله بواسطة Groq باستخدام وحدة معالجة اللغة المخصصة (LPU) لتوفير استدلال سريع وفعال."
+    "description": "DeepSeek R1 Distill Llama 70B هو نموذج لغوي ضخم مبني على Llama3.3 70B، وقد تم تحسينه باستخدام نتائج DeepSeek R1، ليحقق أداءً تنافسيًا يعادل النماذج الرائدة الكبيرة."
  },
  "deepseek/deepseek-r1-distill-llama-8b": {
    "description": "DeepSeek R1 Distill Llama 8B هو نموذج لغوي كبير مكرر يعتمد على Llama-3.1-8B-Instruct، تم تدريبه باستخدام مخرجات DeepSeek R1."
@@ -1253,83 +1274,89 @@
  "emohaa": {
    "description": "Emohaa هو نموذج نفسي، يتمتع بقدرات استشارية متخصصة، يساعد المستخدمين في فهم القضايا العاطفية."
  },
-  "ernie-3.5-128k": {
-    "description": "نموذج اللغة الكبير الرائد الذي طورته بايدو، يغطي كمية هائلة من البيانات باللغة الصينية والإنجليزية، ويتميز بقدرات عامة قوية، تلبي متطلبات معظم حالات الحوار، والإجابة، والتوليد، وتطبيقات المكونات الإضافية؛ يدعم الاتصال التلقائي بمكونات البحث من بايدو، مما يضمن تحديث معلومات الإجابة."
-  },
-  "ernie-3.5-8k": {
-    "description": "نموذج اللغة الكبير الرائد الذي طورته بايدو، يغطي كمية هائلة من البيانات باللغة الصينية والإنجليزية، ويتميز بقدرات عامة قوية، تلبي متطلبات معظم حالات الحوار، والإجابة، والتوليد، وتطبيقات المكونات الإضافية؛ يدعم الاتصال التلقائي بمكونات البحث من بايدو، مما يضمن تحديث معلومات الإجابة."
-  },
-  "ernie-3.5-8k-preview": {
-    "description": "نموذج اللغة الكبير الرائد الذي طورته بايدو، يغطي كمية هائلة من البيانات باللغة الصينية والإنجليزية، ويتميز بقدرات عامة قوية، تلبي متطلبات معظم حالات الحوار، والإجابة، والتوليد، وتطبيقات المكونات الإضافية؛ يدعم الاتصال التلقائي بمكونات البحث من بايدو، مما يضمن تحديث معلومات الإجابة."
-  },
-  "ernie-4.0-8k-latest": {
-    "description": "نموذج اللغة الكبير الرائد الذي طورته بايدو، والذي حقق ترقية شاملة في القدرات مقارنةً بـ ERNIE 3.5، ويستخدم على نطاق واسع في مشاهد المهام المعقدة في مختلف المجالات؛ يدعم الاتصال التلقائي بمكونات البحث من بايدو، مما يضمن تحديث معلومات الإجابة."
-  },
-  "ernie-4.0-8k-preview": {
-    "description": "نموذج اللغة الكبير الرائد الذي طورته بايدو، والذي حقق ترقية شاملة في القدرات مقارنةً بـ ERNIE 3.5، ويستخدم على نطاق واسع في مشاهد المهام المعقدة في مختلف المجالات؛ يدعم الاتصال التلقائي بمكونات البحث من بايدو، مما يضمن تحديث معلومات الإجابة."
-  },
-  "ernie-4.0-turbo-128k": {
-    "description": "نموذج اللغة الكبير الرائد الذي طورته بايدو، والذي يظهر أداءً ممتازًا بشكل شامل، ويستخدم على نطاق واسع في مشاهد المهام المعقدة في مختلف المجالات؛ يدعم الاتصال التلقائي بمكونات البحث من بايدو، مما يضمن تحديث معلومات الإجابة. مقارنةً بـ ERNIE 4.0، يظهر أداءً أفضل."
-  },
-  "ernie-4.0-turbo-8k-latest": {
-    "description": "نموذج اللغة الكبير الرائد الذي طورته بايدو، والذي يظهر أداءً ممتازًا بشكل شامل، ويستخدم على نطاق واسع في مشاهد المهام المعقدة في مختلف المجالات؛ يدعم الاتصال التلقائي بمكونات البحث من بايدو، مما يضمن تحديث معلومات الإجابة. مقارنةً بـ ERNIE 4.0، يظهر أداءً أفضل."
-  },
-  "ernie-4.0-turbo-8k-preview": {
-    "description": "نموذج اللغة الكبير الرائد الذي طورته بايدو، والذي يظهر أداءً ممتازًا بشكل شامل، ويستخدم على نطاق واسع في مشاهد المهام المعقدة في مختلف المجالات؛ يدعم الاتصال التلقائي بمكونات البحث من بايدو، مما يضمن تحديث معلومات الإجابة. مقارنةً بـ ERNIE 4.0، يظهر أداءً أفضل."
+  "ernie-4.5-0.3b": {
+    "description": "ERNIE 4.5 0.3B، نموذج مفتوح المصدر وخفيف الوزن، مناسب للنشر المحلي والمخصص."
  },
  "ernie-4.5-21b-a3b": {
-    "description": "ERNIE 4.5 21B A3B هو نموذج خبراء هجين أطلقته Baidu Wenxin، يتمتع بقدرات قوية في الاستدلال ودعم متعدد اللغات."
+    "description": "ERNIE 4.5 21B A3B، نموذج كبير مفتوح المصدر، يتميز بأداء قوي في مهام الفهم والتوليد."
  },
  "ernie-4.5-300b-a47b": {
    "description": "ERNIE 4.5 300B A47B هو نموذج خبراء هجين فائق الحجم أطلقته Baidu Wenxin، يتميز بقدرات استدلال فائقة."
  },
  "ernie-4.5-8k-preview": {
-    "description": "نموذج ونسين 4.5 هو نموذج أساسي جديد متعدد الوسائط تم تطويره ذاتيًا بواسطة بايدو، من خلال نمذجة متعددة الوسائط لتحقيق تحسين متزامن، ويظهر قدرة ممتازة على الفهم متعدد الوسائط؛ يتمتع بقدرات لغوية متقدمة، مع تحسين شامل في الفهم، والتوليد، والمنطق، والذاكرة، مع تحسين كبير في إزالة الأوهام، والاستدلال المنطقي، وقدرات البرمجة."
+    "description": "ERNIE 4.5 8K Preview، نموذج معاينة بسياق 8K، مخصص لتجربة واختبار قدرات Wenxin 4.5."
  },
  "ernie-4.5-turbo-128k": {
-    "description": "تم تعزيز Wenxin 4.5 Turbo بشكل ملحوظ في مجالات مثل تقليل الهلوسة، والاستدلال المنطقي، وقدرات البرمجة. مقارنةً بـ Wenxin 4.5، فهو أسرع وأقل تكلفة. تم تحسين قدرات النموذج بشكل شامل لتلبية احتياجات معالجة المحادثات الطويلة متعددة الجولات، ومهام فهم الأسئلة والأجوبة للنصوص الطويلة."
+    "description": "ERNIE 4.5 Turbo 128K، نموذج عام عالي الأداء، يدعم البحث المعزز واستدعاء الأدوات، مناسب لمهام مثل الأسئلة والأجوبة، البرمجة، والوكلاء الذكيين."
+  },
+  "ernie-4.5-turbo-128k-preview": {
+    "description": "ERNIE 4.5 Turbo 128K Preview، نسخة معاينة توفر تجربة مماثلة للنسخة الرسمية، مناسبة للاختبار والتكامل المرحلي."
  },
  "ernie-4.5-turbo-32k": {
-    "description": "تم تعزيز Wenxin 4.5 Turbo بشكل ملحوظ في مجالات مثل تقليل الهلوسة، والاستدلال المنطقي، وقدرات البرمجة. مقارنةً بـ Wenxin 4.5، فهو أسرع وأقل تكلفة. تم تحسين قدرات الإبداع النصي، والأسئلة والأجوبة بشكل ملحوظ. زادت مدة الإخراج وتأخير الجمل الكاملة مقارنةً بـ ERNIE 4.5."
+    "description": "ERNIE 4.5 Turbo 32K، نسخة بسياق متوسط إلى طويل، مناسبة للأسئلة والأجوبة، استرجاع المعرفة، والحوار متعدد الجولات."
+  },
+  "ernie-4.5-turbo-latest": {
+    "description": "ERNIE 4.5 Turbo Latest، نسخة محسّنة شاملة، مناسبة كنموذج رئيسي عام في بيئات الإنتاج."
+  },
+  "ernie-4.5-turbo-vl": {
+    "description": "ERNIE 4.5 Turbo VL، نموذج متعدد الوسائط ناضج، مناسب لمهام فهم الصور والنصوص في بيئات الإنتاج."
  },
  "ernie-4.5-turbo-vl-32k": {
-    "description": "إصدار جديد من نموذج Wenxin Yiyan، مع تحسينات ملحوظة في فهم الصور، والإبداع، والترجمة، والبرمجة، ويدعم لأول مرة طول سياق يصل إلى 32K، مع تقليل ملحوظ في تأخير أول توكن."
+    "description": "ERNIE 4.5 Turbo VL 32K، نسخة متعددة الوسائط بسياق متوسط إلى طويل، مناسبة لفهم مشترك للوثائق الطويلة والصور."
+  },
+  "ernie-4.5-turbo-vl-32k-preview": {
+    "description": "ERNIE 4.5 Turbo VL 32K Preview، نسخة معاينة متعددة الوسائط بسياق 32K، لتقييم قدرات الفهم البصري في السياقات الطويلة."
+  },
+  "ernie-4.5-turbo-vl-latest": {
+    "description": "ERNIE 4.5 Turbo VL Latest، أحدث نسخة متعددة الوسائط، تقدم أداءً محسّناً في فهم الصور والنصوص والاستدلال."
+  },
+  "ernie-4.5-turbo-vl-preview": {
+    "description": "ERNIE 4.5 Turbo VL Preview، نموذج معاينة متعدد الوسائط، يدعم فهم وتوليد الصور والنصوص، مناسب لتجربة الأسئلة البصرية وفهم المحتوى."
+  },
+  "ernie-4.5-vl-28b-a3b": {
+    "description": "ERNIE 4.5 VL 28B A3B، نموذج متعدد الوسائط مفتوح المصدر، يدعم مهام الفهم والاستدلال بين الصور والنصوص."
+  },
+  "ernie-5.0-thinking-preview": {
+    "description": "Wenxin 5.0 Thinking Preview، نموذج رائد شامل متعدد الوسائط، يدعم النصوص، الصور، الصوت، والفيديو، مع قدرات متقدمة مناسبة للأسئلة المعقدة، الإبداع، والوكلاء الذكيين."
  },
  "ernie-char-8k": {
-    "description": "نموذج اللغة الكبير المخصص الذي طورته بايدو، مناسب لتطبيقات مثل NPC في الألعاب، محادثات خدمة العملاء، وأدوار الحوار، حيث يتميز بأسلوب شخصيات واضح ومتسق، وقدرة قوية على اتباع التعليمات، وأداء استدلال ممتاز."
+    "description": "ERNIE Character 8K، نموذج حواري بشخصيات، مناسب لبناء شخصيات IP وحوارات طويلة الأمد."
  },
  "ernie-char-fiction-8k": {
-    "description": "نموذج اللغة الكبير المخصص الذي طورته بايدو، مناسب لتطبيقات مثل NPC في الألعاب، محادثات خدمة العملاء، وأدوار الحوار، حيث يتميز بأسلوب شخصيات واضح ومتسق، وقدرة قوية على اتباع التعليمات، وأداء استدلال ممتاز."
+    "description": "ERNIE Character Fiction 8K، نموذج شخصيات مخصص لكتابة الروايات والقصص، مناسب لتوليد نصوص طويلة."
+  },
+  "ernie-char-fiction-8k-preview": {
+    "description": "ERNIE Character Fiction 8K Preview، نسخة معاينة لنموذج الشخصيات والقصص، مخصصة لتجربة الوظائف والاختبار."
  },
  "ernie-irag-edit": {
-    "description": "نموذج تحرير الصور ERNIE iRAG المطور ذاتيًا من Baidu يدعم عمليات مثل المسح (إزالة الكائنات)، إعادة الرسم (إعادة رسم الكائنات)، والتنوع (توليد متغيرات) بناءً على الصور."
+    "description": "ERNIE iRAG Edit، نموذج تحرير الصور يدعم المسح، إعادة الرسم، وتوليد المتغيرات."
  },
  "ernie-lite-8k": {
-    "description": "ERNIE Lite هو نموذج اللغة الكبير الخفيف الذي طورته بايدو، يجمع بين أداء النموذج الممتاز وأداء الاستدلال، مناسب للاستخدام مع بطاقات تسريع الذكاء الاصطناعي ذات القدرة الحاسوبية المنخفضة."
+    "description": "ERNIE Lite 8K، نموذج عام خفيف الوزن، مناسب للأسئلة اليومية وتوليد المحتوى بتكلفة منخفضة."
  },
  "ernie-lite-pro-128k": {
-    "description": "نموذج اللغة الكبير الخفيف الذي طورته بايدو، يجمع بين أداء النموذج الممتاز وأداء الاستدلال، ويظهر أداءً أفضل من ERNIE Lite، مناسب للاستخدام مع بطاقات تسريع الذكاء الاصطناعي ذات القدرة الحاسوبية المنخفضة."
+    "description": "ERNIE Lite Pro 128K، نموذج خفيف عالي الأداء، مناسب للمهام الحساسة من حيث التأخير والتكلفة."
  },
  "ernie-novel-8k": {
-    "description": "نموذج اللغة الكبير العام الذي طورته بايدو، يظهر مزايا واضحة في القدرة على كتابة روايات، ويمكن استخدامه أيضًا في مشاهد مثل المسرحيات القصيرة والأفلام."
+    "description": "ERNIE Novel 8K، نموذج مخصص لكتابة الروايات الطويلة وسيناريوهات IP، بارع في السرد متعدد الشخصيات والخطوط."
  },
  "ernie-speed-128k": {
-    "description": "نموذج اللغة الكبير عالي الأداء الذي طورته بايدو، والذي تم إصداره في عام 2024، يتمتع بقدرات عامة ممتازة، مناسب كنموذج أساسي للتعديل، مما يساعد على معالجة مشكلات المشاهد المحددة بشكل أفضل، ويظهر أداءً ممتازًا في الاستدلال."
+    "description": "ERNIE Speed 128K، نموذج كبير بدون تكلفة إدخال/إخراج، مناسب لفهم النصوص الطويلة والتجارب واسعة النطاق."
+  },
+  "ernie-speed-8k": {
+    "description": "ERNIE Speed 8K، نموذج مجاني وسريع، مناسب للحوار اليومي والمهام النصية الخفيفة."
  },
  "ernie-speed-pro-128k": {
-    "description": "نموذج اللغة الكبير عالي الأداء الذي طورته بايدو، والذي تم إصداره في عام 2024، يتمتع بقدرات عامة ممتازة، ويظهر أداءً أفضل من ERNIE Speed، مناسب كنموذج أساسي للتعديل، مما يساعد على معالجة مشكلات المشاهد المحددة بشكل أفضل، ويظهر أداءً ممتازًا في الاستدلال."
+    "description": "ERNIE Speed Pro 128K، نموذج عالي التوافر وكفاءة التكلفة، مناسب للخدمات عبر الإنترنت واسعة النطاق وتطبيقات المؤسسات."
  },
  "ernie-tiny-8k": {
-    "description": "ERNIE Tiny هو نموذج اللغة الكبير عالي الأداء الذي طورته بايدو، وتكاليف النشر والتعديل هي الأدنى بين نماذج سلسلة Wenxin."
-  },
-  "ernie-x1-32k": {
-    "description": "يمتلك قدرة أقوى على الفهم والتخطيط والتفكير والتطور. كنموذج تفكير عميق شامل، يتميز Wenxin X1 بالدقة والإبداع والبلاغة، ويظهر أداءً متميزًا في مجالات مثل الأسئلة والأجوبة باللغة الصينية، والإبداع الأدبي، وكتابة النصوص، والحوار اليومي، والاستدلال المنطقي، والحسابات المعقدة، واستخدام الأدوات."
-  },
-  "ernie-x1-32k-preview": {
-    "description": "نموذج Ernie X1 الكبير يتمتع بقدرات أقوى في الفهم، التخطيط، التفكير النقدي، والتطور. كنموذج تفكير عميق أكثر شمولاً، يجمع Ernie X1 بين الدقة، الإبداع، والبلاغة، ويتميز بشكل خاص في أسئلة المعرفة باللغة الصينية، الإبداع الأدبي، كتابة النصوص، المحادثات اليومية، الاستدلال المنطقي، الحسابات المعقدة، واستدعاء الأدوات."
+    "description": "ERNIE Tiny 8K، نموذج فائق الخفة، مناسب للأسئلة البسيطة، التصنيف، وسيناريوهات الاستدلال منخفضة التكلفة."
  },
  "ernie-x1-turbo-32k": {
-    "description": "يتميز هذا النموذج بأداء أفضل مقارنةً بـ ERNIE-X1-32K."
+    "description": "ERNIE X1 Turbo 32K، نموذج تفكير سريع بسياق طويل 32K، مناسب للاستدلال المعقد والحوار متعدد الجولات."
+  },
+  "ernie-x1.1-preview": {
+    "description": "ERNIE X1.1 Preview، نسخة معاينة من نموذج التفكير ERNIE X1.1، مناسبة لاختبار القدرات والتحقق منها."
  },
  "fal-ai/bytedance/seedream/v4": {
    "description": "نموذج توليد الصور Seedream 4.0 من فريق Seed في ByteDance، يدعم إدخال النص والصورة، ويوفر تجربة توليد صور عالية الجودة وقابلة للتحكم بدرجة كبيرة. يعتمد على أوامر نصية لتوليد الصور."
@@ -1389,7 +1416,7 @@
    "description": "FLUX.1 [schnell] هو النموذج المفتوح المصدر الأكثر تقدمًا حاليًا في فئة النماذج قليلة الخطوات، متفوقًا على المنافسين وحتى على نماذج غير مكررة قوية مثل Midjourney v6.0 وDALL·E 3 (HD). تم ضبط النموذج خصيصًا للحفاظ على تنوع المخرجات الكامل من مرحلة ما قبل التدريب، ويحقق تحسينات ملحوظة في جودة الصورة، الالتزام بالتعليمات، التغيرات في الحجم/النسبة، معالجة الخطوط وتنوع المخرجات مقارنة بأحدث النماذج في السوق، مما يوفر تجربة توليد صور إبداعية أكثر ثراءً وتنوعًا للمستخدمين."
  },
  "flux.1-schnell": {
-    "description": "محول تدفق مصحح يحتوي على 12 مليار معلمة، قادر على توليد الصور بناءً على الوصف النصي."
+    "description": "FLUX.1-schnell، نموذج توليد صور عالي الأداء، مناسب لإنشاء صور متعددة الأنماط بسرعة."
  },
  "gemini-1.0-pro-001": {
    "description": "Gemini 1.0 Pro 001 (تعديل) يوفر أداءً مستقرًا وقابلًا للتعديل، وهو الخيار المثالي لحلول المهام المعقدة."
@@ -1538,6 +1565,9 @@
  "glm-4-0520": {
    "description": "GLM-4-0520 هو أحدث إصدار من النموذج، مصمم للمهام المعقدة والمتنوعة، ويظهر أداءً ممتازًا."
  },
+  "glm-4-32b-0414": {
+    "description": "GLM-4 32B 0414، إصدار من سلسلة GLM للنماذج العامة الكبيرة، يدعم توليد النصوص وفهمها في مهام متعددة."
+  },
  "glm-4-9b-chat": {
    "description": "يُظهر GLM-4-9B-Chat أداءً عاليًا في مجالات الدلالة، والرياضيات، والاستدلال، والبرمجة، والمعرفة. كما يدعم تصفح الويب، وتنفيذ الأكواد، واستدعاء الأدوات المخصصة، والاستدلال على النصوص الطويلة. يدعم 26 لغة من بينها اليابانية والكورية والألمانية."
  },
@@ -1826,6 +1856,18 @@
  "gpt-5-pro": {
    "description": "يستخدم GPT-5 pro قدرة حسابية أكبر للتفكير بشكل أعمق، ويواصل تقديم إجابات أفضل باستمرار."
  },
+  "gpt-5.1": {
+    "description": "GPT-5.1 — نموذج رائد مُحسَّن لمهام البرمجة والوكلاء، يدعم قوة استدلال قابلة للتخصيص وسياقًا أطول."
+  },
+  "gpt-5.1-chat-latest": {
+    "description": "GPT-5.1 Chat: إصدار GPT-5.1 مخصص لـ ChatGPT، مثالي لسيناريوهات المحادثة."
+  },
+  "gpt-5.1-codex": {
+    "description": "GPT-5.1 Codex: إصدار من GPT-5.1 مُحسَّن لمهام البرمجة القائمة على الوكلاء، يمكن استخدامه في واجهة Responses API لتدفقات عمل أكثر تعقيدًا في البرمجة والوكلاء."
+  },
+  "gpt-5.1-codex-mini": {
+    "description": "GPT-5.1 Codex mini: إصدار مصغر ومنخفض التكلفة من Codex، مُحسَّن لمهام البرمجة القائمة على الوكلاء."
+  },
  "gpt-audio": {
    "description": "GPT Audio هو نموذج دردشة عام موجه لإدخال وإخراج الصوت، ويدعم استخدام الصوت في واجهة برمجة تطبيقات Chat Completions."
  },
@@ -2001,13 +2043,13 @@
    "description": "سلسلة نماذج Imagen لتحويل النص إلى صورة من الجيل الرابع"
  },
  "imagen-4.0-generate-preview-06-06": {
-    "description": "سلسلة نموذج Imagen للجيل الرابع لتحويل النص إلى صورة"
+    "description": "سلسلة نماذج Imagen من الجيل الرابع لتحويل النص إلى صورة"
  },
  "imagen-4.0-ultra-generate-001": {
    "description": "سلسلة نماذج Imagen لتحويل النص إلى صورة من الجيل الرابع — إصدار Ultra"
  },
  "imagen-4.0-ultra-generate-preview-06-06": {
-    "description": "نسخة ألترا من سلسلة نموذج Imagen للجيل الرابع لتحويل النص إلى صورة"
+    "description": "النسخة Ultra من سلسلة نماذج Imagen من الجيل الرابع لتحويل النص إلى صورة"
  },
  "inception/mercury-coder-small": {
    "description": "Mercury Coder Small هو الخيار المثالي لمهام توليد الكود، وتصحيح الأخطاء، وإعادة الهيكلة، مع أدنى تأخير."
@@ -2036,14 +2078,26 @@
  "internlm3-latest": {
    "description": "سلسلة نماذجنا الأحدث، تتمتع بأداء استدلال ممتاز، تتصدر نماذج المصدر المفتوح من نفس الفئة. تشير بشكل افتراضي إلى أحدث نماذج سلسلة InternLM3 التي تم إصدارها."
  },
+  "internvl2.5-38b-mpo": {
+    "description": "InternVL2.5 38B MPO، نموذج ما قبل التدريب متعدد الوسائط، يدعم مهام الاستدلال المعقدة بين الصور والنصوص."
+  },
  "internvl2.5-latest": {
    "description": "نحن لا نزال ندعم إصدار InternVL2.5، الذي يتمتع بأداء ممتاز ومستقر. يشير بشكل افتراضي إلى أحدث نموذج من سلسلة InternVL2.5، الحالي هو internvl2.5-78b."
  },
+  "internvl3-14b": {
+    "description": "InternVL3 14B، نموذج متعدد الوسائط متوسط الحجم، يوازن بين الأداء والتكلفة."
+  },
+  "internvl3-1b": {
+    "description": "InternVL3 1B، نموذج متعدد الوسائط خفيف الوزن، مناسب للنشر في البيئات ذات الموارد المحدودة."
+  },
+  "internvl3-38b": {
+    "description": "InternVL3 38B، نموذج مفتوح المصدر كبير متعدد الوسائط، مناسب لمهام فهم الصور والنصوص عالية الدقة."
+  },
  "internvl3-latest": {
    "description": "أحدث نموذج متعدد الوسائط تم إصداره، يتمتع بقدرات فهم أقوى للنصوص والصور، وفهم الصور على المدى الطويل، وأدائه يتساوى مع النماذج المغلقة الرائدة. يشير بشكل افتراضي إلى أحدث نموذج من سلسلة InternVL، الحالي هو internvl3-78b."
  },
  "irag-1.0": {
-    "description": "نموذج iRAG (استرجاع معزز بالصور) المطور ذاتيًا من Baidu، يجمع بين موارد صور بحث Baidu الضخمة وقدرات النموذج الأساسي القوية لتوليد صور فائقة الواقعية، متفوقًا بشكل كبير على أنظمة توليد الصور النصية الأصلية، مع إزالة الطابع الاصطناعي وتقليل التكلفة. يتميز iRAG بعدم وجود هلوسة، واقعية فائقة، وسرعة في الحصول على النتائج."
+    "description": "ERNIE iRAG، نموذج توليد معزز باسترجاع الصور، يدعم البحث بالصور، استرجاع الصور والنصوص، وتوليد المحتوى."
  },
  "jamba-large": {
    "description": "أقوى وأحدث نموذج لدينا، مصمم لمعالجة المهام المعقدة على مستوى المؤسسات، ويتميز بأداء استثنائي."
@@ -2064,7 +2118,7 @@
    "description": "نموذج kimi-k2-0905-preview يدعم طول سياق 256k، يتمتع بقدرات ترميز وكيل أقوى، وجمالية وعملية أفضل في الشيفرة الأمامية، وفهم سياق محسن."
  },
  "kimi-k2-instruct": {
-    "description": "Kimi K2 Instruct هو نموذج لغة كبير أطلقته Moonshot AI، يتمتع بقدرة فائقة على معالجة السياقات الطويلة."
+    "description": "Kimi K2 Instruct، نموذج الاستدلال الرسمي من Kimi، يدعم السياق الطويل، البرمجة، الأسئلة والأجوبة، وغيرها من السيناريوهات."
  },
  "kimi-k2-turbo-preview": {
    "description": "kimi-k2 هو نموذج أساسي بمعمارية MoE يتمتع بقدرات قوية للغاية في البرمجة وقدرات الوكيل (Agent)، بإجمالي معلمات يبلغ 1 تريليون والمعلمات المُفعَّلة 32 مليار. في اختبارات الأداء المعيارية للفئات الرئيسية مثل الاستدلال المعرفي العام والبرمجة والرياضيات والوكلاء (Agent)، تفوق أداء نموذج K2 على النماذج المفتوحة المصدر السائدة الأخرى."
@@ -2174,6 +2228,9 @@
  "megrez-3b-instruct": {
    "description": "Megrez 3B Instruct هو نموذج صغير الحجم وعالي الكفاءة أطلقته شركة Wuwen Xinqiong."
  },
+  "meituan/longcat-flash-chat": {
+    "description": "نموذج أساسي غير تأملي مفتوح المصدر من Meituan، مُحسَّن للتفاعل الحواري ومهام الوكلاء الذكيين، ويتميز في استدعاء الأدوات وسيناريوهات التفاعل المعقدة متعددة الجولات."
+  },
  "meta-llama-3-70b-instruct": {
    "description": "نموذج قوي بحجم 70 مليار معلمة يتفوق في التفكير، والترميز، وتطبيقات اللغة الواسعة."
  },
@@ -2405,6 +2462,12 @@
  "minimax-m2": {
    "description": "MiniMax M2 هو نموذج لغوي كبير وفعّال، تم تطويره خصيصًا لتلبية احتياجات الترميز وتدفقات عمل الوكلاء."
  },
+  "minimax/minimax-m2": {
+    "description": "مصمم خصيصًا للترميز الفعّال وتدفقات عمل الوكلاء."
+  },
+  "minimaxai/minimax-m2": {
+    "description": "MiniMax-M2 هو نموذج خبراء مختلط (MoE) مدمج وسريع وفعّال من حيث التكلفة، يحتوي على 230 مليار معلمة إجمالية و10 مليارات معلمة نشطة، صُمم لتحقيق أداء فائق في مهام الترميز والوكالة، مع الحفاظ على ذكاء عام قوي. يتميز هذا النموذج بأداء ممتاز في تحرير الملفات المتعددة، ودورة الترميز-التنفيذ-الإصلاح، والتحقق من الاختبارات والإصلاح، وسلاسل الأدوات المعقدة ذات الروابط الطويلة، مما يجعله خيارًا مثاليًا لسير عمل المطورين."
+  },
  "ministral-3b-latest": {
    "description": "Ministral 3B هو نموذج حافة عالمي المستوى من Mistral."
  },
@@ -2735,6 +2798,54 @@
  "pro-deepseek-v3": {
    "description": "نموذج مخصص لخدمات المؤسسات، يشمل خدمات متزامنة."
  },
+  "qianfan-70b": {
+    "description": "Qianfan 70B، نموذج صيني كبير المعلمات، مناسب لإنشاء محتوى عالي الجودة ومهام الاستدلال المعقدة."
+  },
+  "qianfan-8b": {
+    "description": "Qianfan 8B، نموذج عام متوسط الحجم، مناسب لتوليد النصوص والإجابة على الأسئلة بتوازن بين التكلفة والأداء."
+  },
+  "qianfan-agent-intent-32k": {
+    "description": "Qianfan Agent Intent 32K، نموذج مخصص للتعرف على النوايا وتنسيق الوكلاء، يدعم السياقات الطويلة."
+  },
+  "qianfan-agent-lite-8k": {
+    "description": "Qianfan Agent Lite 8K، نموذج وكيل خفيف الوزن، مناسب للحوارات متعددة الجولات منخفضة التكلفة وتنسيق الأعمال."
+  },
+  "qianfan-agent-speed-32k": {
+    "description": "Qianfan Agent Speed 32K، نموذج وكيل عالي التحكم في التدفق، مناسب لتطبيقات الوكلاء واسعة النطاق ومتعددة المهام."
+  },
+  "qianfan-agent-speed-8k": {
+    "description": "Qianfan Agent Speed 8K، نموذج وكيل عالي التوازي مخصص للحوارات القصيرة والمتوسطة والاستجابة السريعة."
+  },
+  "qianfan-check-vl": {
+    "description": "Qianfan Check VL، نموذج مراجعة واكتشاف متعدد الوسائط، يدعم التحقق من توافق الصور والنصوص والتعرف عليها."
+  },
+  "qianfan-composition": {
+    "description": "Qianfan Composition، نموذج إبداعي متعدد الوسائط، يدعم الفهم والتوليد المدمج للنصوص والصور."
+  },
+  "qianfan-engcard-vl": {
+    "description": "Qianfan EngCard VL، نموذج تعرف متعدد الوسائط مخصص للسيناريوهات الإنجليزية."
+  },
+  "qianfan-lightning-128b-a19b": {
+    "description": "Qianfan Lightning 128B A19B، نموذج صيني عام عالي الأداء، مناسب للأسئلة المعقدة ومهام الاستدلال واسعة النطاق."
+  },
+  "qianfan-llama-vl-8b": {
+    "description": "Qianfan Llama VL 8B، نموذج متعدد الوسائط مبني على Llama، مخصص لمهام الفهم العام للنصوص والصور."
+  },
+  "qianfan-multipicocr": {
+    "description": "Qianfan MultiPicOCR، نموذج OCR متعدد الصور، يدعم اكتشاف وتعرف النصوص في صور متعددة."
+  },
+  "qianfan-qi-vl": {
+    "description": "Qianfan QI VL، نموذج سؤال وجواب متعدد الوسائط، يدعم الاسترجاع الدقيق والإجابة في سيناريوهات الصور والنصوص المعقدة."
+  },
+  "qianfan-singlepicocr": {
+    "description": "Qianfan SinglePicOCR، نموذج OCR لصورة واحدة، يدعم التعرف عالي الدقة على الأحرف."
+  },
+  "qianfan-vl-70b": {
+    "description": "Qianfan VL 70B، نموذج لغة بصرية كبير المعلمات، مناسب لفهم الصور والنصوص المعقدة."
+  },
+  "qianfan-vl-8b": {
+    "description": "Qianfan VL 8B، نموذج لغة بصرية خفيف الوزن، مناسب للأسئلة اليومية حول الصور والنصوص والتحليل."
+  },
  "qvq-72b-preview": {
    "description": "نموذج QVQ هو نموذج بحث تجريبي تم تطويره بواسطة فريق Qwen، يركز على تعزيز قدرات الاستدلال البصري، خاصة في مجال الاستدلال الرياضي."
  },
@@ -2886,7 +2997,7 @@
    "description": "نموذج Qwen 2.5 مفتوح المصدر بحجم 72B."
  },
  "qwen2.5-7b-instruct": {
-    "description": "نموذج Qwen 2.5 مفتوح المصدر بحجم 7B."
+    "description": "Qwen2.5 7B Instruct، نموذج تعليمات مفتوح المصدر ناضج، مناسب للحوار والتوليد في سيناريوهات متعددة."
  },
  "qwen2.5-coder-1.5b-instruct": {
    "description": "نموذج كود تونغي، النسخة مفتوحة المصدر."
@@ -2919,13 +3030,13 @@
    "description": "تدعم نماذج سلسلة Qwen-Omni إدخال بيانات متعددة الأنماط، بما في ذلك الفيديو والصوت والصور والنصوص، وتخرج الصوت والنص."
  },
  "qwen2.5-vl-32b-instruct": {
-    "description": "سلسلة نماذج Qwen2.5-VL تعزز مستوى الذكاء والفعّالية والملاءمة للنماذج، مما يجعل أداءها أفضل في سيناريوهات مثل المحادثات الطبيعية، وإنشاء المحتوى، وتقديم الخدمات المتخصصة، وتطوير الأكواد. يستخدم الإصدار 32B تقنية التعلم المعزز لتحسين النموذج، مقارنةً بنماذج سلسلة Qwen2.5 VL الأخرى، حيث يقدم أسلوب إخراج أكثر توافقًا مع تفضيلات البشر، وقدرة على استنتاج المسائل الرياضية المعقدة، بالإضافة إلى فهم واستدلال دقيق للصور."
+    "description": "Qwen2.5 VL 32B Instruct، نموذج متعدد الوسائط مفتوح المصدر، مناسب للنشر الخاص والتطبيقات المتنوعة."
  },
  "qwen2.5-vl-72b-instruct": {
    "description": "تحسين شامل في اتباع التعليمات، الرياضيات، حل المشكلات، والبرمجة، وزيادة قدرة التعرف على العناصر البصرية، يدعم تنسيقات متعددة لتحديد العناصر البصرية بدقة، ويدعم فهم ملفات الفيديو الطويلة (حتى 10 دقائق) وتحديد اللحظات الزمنية بدقة، قادر على فهم التسلسل الزمني والسرعة، يدعم التحكم في أنظمة التشغيل أو الوكلاء المحمولة بناءً على قدرات التحليل والتحديد، قوي في استخراج المعلومات الرئيسية وإخراج البيانات بتنسيق Json، هذه النسخة هي النسخة 72B، وهي الأقوى في هذه السلسلة."
  },
  "qwen2.5-vl-7b-instruct": {
-    "description": "تحسين شامل في اتباع التعليمات، الرياضيات، حل المشكلات، والبرمجة، وزيادة قدرة التعرف على العناصر البصرية، يدعم تنسيقات متعددة لتحديد العناصر البصرية بدقة، ويدعم فهم ملفات الفيديو الطويلة (حتى 10 دقائق) وتحديد اللحظات الزمنية بدقة، قادر على فهم التسلسل الزمني والسرعة، يدعم التحكم في أنظمة التشغيل أو الوكلاء المحمولة بناءً على قدرات التحليل والتحديد، قوي في استخراج المعلومات الرئيسية وإخراج البيانات بتنسيق Json، هذه النسخة هي النسخة 72B، وهي الأقوى في هذه السلسلة."
+    "description": "Qwen2.5 VL 7B Instruct، نموذج متعدد الوسائط خفيف الوزن، يوازن بين تكلفة النشر وقدرات التعرف."
  },
  "qwen2.5-vl-instruct": {
    "description": "Qwen2.5-VL هو أحدث إصدار من نماذج الرؤية واللغة في عائلة نماذج Qwen."
@@ -2952,46 +3063,46 @@
    "description": "Qwen3 هو الجيل الجديد من نموذج اللغة واسع النطاق من علي بابا، يدعم مجموعة متنوعة من احتياجات التطبيقات بأداء ممتاز."
  },
  "qwen3-0.6b": {
-    "description": "Qwen3 هو نموذج جديد من الجيل التالي مع تحسينات كبيرة في القدرات، حيث يصل إلى مستويات رائدة في الصناعة في الاستدلال، والعموم، والوكلاء، واللغات المتعددة، ويدعم التبديل بين أنماط التفكير."
+    "description": "Qwen3 0.6B، نموذج للمبتدئين، مناسب للاستدلال البسيط والبيئات ذات الموارد المحدودة للغاية."
  },
  "qwen3-1.7b": {
-    "description": "Qwen3 هو نموذج جديد من الجيل التالي مع تحسينات كبيرة في القدرات، حيث يصل إلى مستويات رائدة في الصناعة في الاستدلال، والعموم، والوكلاء، واللغات المتعددة، ويدعم التبديل بين أنماط التفكير."
+    "description": "Qwen3 1.7B، نموذج فائق الخفة، سهل النشر على الحواف والأجهزة الطرفية."
  },
  "qwen3-14b": {
-    "description": "Qwen3 هو نموذج جديد من الجيل التالي مع تحسينات كبيرة في القدرات، حيث يصل إلى مستويات رائدة في الصناعة في الاستدلال، والعموم، والوكلاء، واللغات المتعددة، ويدعم التبديل بين أنماط التفكير."
+    "description": "Qwen3 14B، نموذج متوسط الحجم، مناسب للأسئلة متعددة اللغات وتوليد النصوص."
  },
  "qwen3-235b-a22b": {
-    "description": "Qwen3 هو نموذج جديد من الجيل التالي مع تحسينات كبيرة في القدرات، حيث يصل إلى مستويات رائدة في الصناعة في الاستدلال، والعموم، والوكلاء، واللغات المتعددة، ويدعم التبديل بين أنماط التفكير."
+    "description": "Qwen3 235B A22B، نموذج عام كبير، مخصص لمهام معقدة متعددة."
  },
  "qwen3-235b-a22b-instruct-2507": {
-    "description": "نموذج مفتوح المصدر غير تفكيري مبني على Qwen3، مع تحسينات طفيفة في القدرات الإبداعية والسلامة مقارنة بالإصدار السابق (Tongyi Qianwen 3-235B-A22B)."
+    "description": "Qwen3 235B A22B Instruct 2507، نموذج تعليمات عام رائد، مناسب لمهام التوليد والاستدلال المتنوعة."
  },
  "qwen3-235b-a22b-thinking-2507": {
-    "description": "نموذج مفتوح المصدر تفكيري مبني على Qwen3، مع تحسينات كبيرة في القدرات المنطقية، العامة، تعزيز المعرفة والإبداع مقارنة بالإصدار السابق (Tongyi Qianwen 3-235B-A22B)، مناسب للمهام المعقدة التي تتطلب استدلالًا قويًا."
+    "description": "Qwen3 235B A22B Thinking 2507، نموذج تفكير واسع النطاق، مخصص للاستدلال عالي الصعوبة."
  },
  "qwen3-30b-a3b": {
-    "description": "Qwen3 هو نموذج جديد من الجيل التالي مع تحسينات كبيرة في القدرات، حيث يصل إلى مستويات رائدة في الصناعة في الاستدلال، والعموم، والوكلاء، واللغات المتعددة، ويدعم التبديل بين أنماط التفكير."
+    "description": "Qwen3 30B A3B، نموذج عام متوسط إلى كبير الحجم، يوازن بين التكلفة والأداء."
  },
  "qwen3-30b-a3b-instruct-2507": {
-    "description": "تحسنت القدرات العامة للنموذج بشكل كبير في اللغتين الصينية والإنجليزية واللغات المتعددة مقارنة بالإصدار السابق (Qwen3-30B-A3B). تم تحسين المهام المفتوحة الذاتية بشكل خاص لتتوافق بشكل أفضل مع تفضيلات المستخدم، مما يمكنه من تقديم ردود أكثر فائدة."
+    "description": "Qwen3 30B A3B Instruct 2507، نموذج تعليمات متوسط إلى كبير الحجم، مناسب للتوليد عالي الجودة والإجابة على الأسئلة."
  },
  "qwen3-30b-a3b-thinking-2507": {
-    "description": "نموذج مفتوح المصدر لوضع التفكير مبني على Qwen3، مع تحسينات كبيرة في القدرات المنطقية، والقدرات العامة، وتعزيز المعرفة، والقدرة الإبداعية مقارنة بالإصدار السابق (Tongyi Qianwen 3-30B-A3B)، مناسب للسيناريوهات التي تتطلب استدلالًا عالي الصعوبة."
+    "description": "Qwen3 30B A3B Thinking 2507، نموذج تفكير متوسط إلى كبير الحجم، يوازن بين الدقة والتكلفة."
  },
  "qwen3-32b": {
-    "description": "Qwen3 هو نموذج جديد من الجيل التالي مع تحسينات كبيرة في القدرات، حيث يصل إلى مستويات رائدة في الصناعة في الاستدلال، والعموم، والوكلاء، واللغات المتعددة، ويدعم التبديل بين أنماط التفكير."
+    "description": "Qwen3 32B، مناسب للمهام العامة التي تتطلب قدرات فهم أقوى."
  },
  "qwen3-4b": {
-    "description": "Qwen3 هو نموذج جديد من الجيل التالي مع تحسينات كبيرة في القدرات، حيث يصل إلى مستويات رائدة في الصناعة في الاستدلال، والعموم، والوكلاء، واللغات المتعددة، ويدعم التبديل بين أنماط التفكير."
+    "description": "Qwen3 4B، مناسب للتطبيقات الصغيرة والمتوسطة وسيناريوهات الاستدلال المحلي."
  },
  "qwen3-8b": {
-    "description": "Qwen3 هو نموذج جديد من الجيل التالي مع تحسينات كبيرة في القدرات، حيث يصل إلى مستويات رائدة في الصناعة في الاستدلال، والعموم، والوكلاء، واللغات المتعددة، ويدعم التبديل بين أنماط التفكير."
+    "description": "Qwen3 8B، نموذج خفيف الوزن، مرن في النشر، مناسب للأعمال عالية التوازي."
  },
  "qwen3-coder-30b-a3b-instruct": {
    "description": "النسخة مفتوحة المصدر من نموذج Qwen3 للبرمجة. النموذج الأحدث qwen3-coder-30b-a3b-instruct مبني على Qwen3، ويتميز بقدرات قوية كوكيل برمجي، بارع في استخدام الأدوات والتفاعل مع البيئات، ويجمع بين مهارات البرمجة الذاتية والقدرات العامة."
  },
  "qwen3-coder-480b-a35b-instruct": {
-    "description": "نسخة مفتوحة المصدر من نموذج كود Tongyi Qianwen. أحدث نموذج qwen3-coder-480b-a35b-instruct مبني على Qwen3 لتوليد الكود، يتمتع بقدرات قوية كوكيل برمجي، بارع في استدعاء الأدوات والتفاعل مع البيئة، قادر على البرمجة الذاتية مع أداء برمجي ممتاز وقدرات عامة."
+    "description": "Qwen3 Coder 480B A35B Instruct، نموذج برمجة رائد، يدعم البرمجة متعددة اللغات وفهم الشيفرات المعقدة."
  },
  "qwen3-coder-flash": {
    "description": "نموذج كود Tongyi Qianwen. أحدث سلسلة نماذج Qwen3-Coder مبنية على Qwen3 لتوليد الأكواد، تتمتع بقدرات وكيل ترميز قوية، بارعة في استدعاء الأدوات والتفاعل مع البيئة، قادرة على البرمجة الذاتية، وتجمع بين مهارات برمجية ممتازة وقدرات عامة."
@@ -3005,32 +3116,41 @@
  "qwen3-max": {
    "description": "سلسلة نماذج Tongyi Qianwen 3 Max، التي تحسنت بشكل كبير مقارنة بسلسلة 2.5 في القدرات العامة، فهم النصوص باللغتين الصينية والإنجليزية، اتباع التعليمات المعقدة، المهام المفتوحة الذاتية، القدرات متعددة اللغات، واستدعاء الأدوات؛ مع تقليل الأوهام المعرفية للنموذج. النسخة الأحدث من qwen3-max: مقارنةً بنسخة qwen3-max-preview، تم ترقية خاصة في برمجة الوكلاء واستدعاء الأدوات. النسخة الرسمية المنشورة وصلت إلى مستوى SOTA في المجال، وتلبي احتياجات الوكلاء في سيناريوهات أكثر تعقيدًا."
  },
+  "qwen3-max-preview": {
+    "description": "أفضل نموذج في سلسلة Tongyi Qianwen، مناسب للمهام المعقدة ومتعددة الخطوات. يدعم التفكير في الإصدار التجريبي."
+  },
  "qwen3-next-80b-a3b-instruct": {
    "description": "نموذج مفتوح المصدر من الجيل الجديد لوضع عدم التفكير مبني على Qwen3، يتميز بفهم أفضل للنصوص الصينية مقارنة بالإصدار السابق (Tongyi Qianwen 3-235B-A22B-Instruct-2507)، مع تعزيز في قدرات الاستدلال المنطقي وأداء أفضل في مهام توليد النصوص."
  },
  "qwen3-next-80b-a3b-thinking": {
-    "description": "نموذج مفتوح المصدر من الجيل الجديد لوضع التفكير مبني على Qwen3، يتميز بتحسين في الالتزام بالتعليمات مقارنة بالإصدار السابق (Tongyi Qianwen 3-235B-A22B-Thinking-2507)، مع ردود ملخصة وأكثر إيجازًا من النموذج."
+    "description": "Qwen3 Next 80B A3B Thinking، إصدار استدلال رائد مخصص للمهام المعقدة."
  },
  "qwen3-omni-flash": {
    "description": "نموذج Qwen-Omni قادر على استقبال مدخلات متعددة الوسائط مثل النصوص، الصور، الصوت، والفيديو، ويولّد ردودًا على شكل نص أو صوت. يوفر أصواتًا بشرية متعددة، ويدعم إخراج الصوت بعدة لغات ولهجات، ويمكن استخدامه في مجالات مثل إنشاء النصوص، التعرف البصري، والمساعدات الصوتية."
  },
  "qwen3-vl-235b-a22b-instruct": {
-    "description": "Qwen3 VL 235B A22B في وضع غير التفكير (Instruct)، مصمم لسيناريوهات الأوامر غير المعتمدة على التفكير، مع الحفاظ على قدرات قوية في الفهم البصري."
+    "description": "Qwen3 VL 235B A22B Instruct، نموذج متعدد الوسائط رائد، مخصص للفهم والإبداع عالي المتطلبات."
  },
  "qwen3-vl-235b-a22b-thinking": {
-    "description": "Qwen3 VL 235B A22B في وضع التفكير (نسخة مفتوحة المصدر)، مخصص للمهام المعقدة التي تتطلب استدلالًا عاليًا وفهمًا لمقاطع الفيديو الطويلة، ويقدم قدرات رائدة في الاستدلال البصري والنصي."
+    "description": "Qwen3 VL 235B A22B Thinking، إصدار تفكير رائد، مخصص للاستدلال والتخطيط متعدد الوسائط المعقد."
  },
  "qwen3-vl-30b-a3b-instruct": {
-    "description": "Qwen3 VL 30B في وضع غير التفكير (Instruct)، موجه لسيناريوهات متابعة الأوامر العامة، مع الحفاظ على مستوى عالٍ من الفهم والتوليد متعدد الوسائط."
+    "description": "Qwen3 VL 30B A3B Instruct، نموذج كبير متعدد الوسائط، يوازن بين الدقة وأداء الاستدلال."
  },
  "qwen3-vl-30b-a3b-thinking": {
-    "description": "Qwen-VL (نسخة مفتوحة المصدر) يوفر قدرات في الفهم البصري وتوليد النصوص، ويدعم التفاعل الذكي، الترميز البصري، الإدراك المكاني، فهم الفيديوهات الطويلة والتفكير العميق، مع دعم قوي للتعرف على النصوص المتقدمة وتعدد اللغات في البيئات المعقدة."
+    "description": "Qwen3 VL 30B A3B Thinking، إصدار تفكير مخصص للمهام متعددة الوسائط المعقدة."
+  },
+  "qwen3-vl-32b-instruct": {
+    "description": "Qwen3 VL 32B Instruct، نموذج تعليمات متعدد الوسائط، مناسب للأسئلة والإبداع عالي الجودة في الصور والنصوص."
+  },
+  "qwen3-vl-32b-thinking": {
+    "description": "Qwen3 VL 32B Thinking، إصدار تفكير متعدد الوسائط، يعزز الاستدلال المعقد والتحليل طويل السلسلة."
  },
  "qwen3-vl-8b-instruct": {
-    "description": "Qwen3 VL 8B في وضع غير التفكير (Instruct)، مناسب لمهام التوليد والتعرف متعدد الوسائط الروتينية."
+    "description": "Qwen3 VL 8B Instruct، نموذج متعدد الوسائط خفيف الوزن، مناسب للأسئلة البصرية اليومية وتكامل التطبيقات."
  },
  "qwen3-vl-8b-thinking": {
-    "description": "Qwen3 VL 8B في وضع التفكير، مخصص لسيناريوهات الاستدلال والتفاعل متعدد الوسائط الخفيفة، مع الحفاظ على قدرة فهم السياق الطويل."
+    "description": "Qwen3 VL 8B Thinking، نموذج سلسلة تفكير متعدد الوسائط، مناسب للاستدلال الدقيق على المعلومات البصرية."
  },
  "qwen3-vl-flash": {
    "description": "Qwen3 VL Flash: نسخة خفيفة وسريعة للاستدلال، مناسبة للسيناريوهات الحساسة للزمن أو التي تتطلب معالجة عدد كبير من الطلبات."
@@ -3263,6 +3383,12 @@
  "wizardlm2:8x22b": {
    "description": "WizardLM 2 هو نموذج لغوي تقدمه Microsoft AI، يتميز بأداء ممتاز في الحوار المعقد، واللغات المتعددة، والاستدلال، والمساعدين الذكيين."
  },
+  "x-ai/grok-4-fast": {
+    "description": "يسعدنا أن نعلن عن إصدار Grok 4 Fast، وهو أحدث تقدم لنا في نماذج الاستدلال الفعّالة من حيث التكلفة."
+  },
+  "x-ai/grok-code-fast-1": {
+    "description": "يسعدنا إطلاق grok-code-fast-1، وهو نموذج استدلال سريع وفعّال من حيث التكلفة يتميز في ترميز الوكلاء."
+  },
  "x1": {
    "description": "سيتم ترقية نموذج Spark X1 بشكل أكبر، حيث ستحقق المهام العامة مثل الاستدلال، وتوليد النصوص، وفهم اللغة نتائج تتماشى مع OpenAI o1 و DeepSeek R1."
  },
@@ -3323,6 +3449,9 @@
  "yi-vision-v2": {
    "description": "نموذج مهام بصرية معقدة، يوفر فهمًا عالي الأداء وقدرات تحليلية بناءً على صور متعددة."
  },
+  "z-ai/glm-4.6": {
+    "description": "GLM-4.6 هو النموذج الرائد الأحدث من Zhipu، ويتفوق على الجيل السابق في الترميز المتقدم، ومعالجة النصوص الطويلة، والاستدلال، وقدرات الوكلاء الذكيين."
+  },
  "zai-org/GLM-4.5": {
    "description": "GLM-4.5 هو نموذج أساسي مصمم لتطبيقات الوكلاء الذكية، يستخدم بنية Mixture-of-Experts (MoE). تم تحسينه بعمق في مجالات استدعاء الأدوات، تصفح الويب، هندسة البرمجيات، وبرمجة الواجهة الأمامية، ويدعم التكامل السلس مع وكلاء الكود مثل Claude Code وRoo Code. يستخدم وضع استدلال مختلط ليتكيف مع سيناريوهات الاستدلال المعقدة والاستخدام اليومي."
  },
@@ -236,6 +236,8 @@
  },
  "inspector": {
    "args": "عرض قائمة المعلمات",
+    "delete": "حذف استدعاء الأداة",
+    "orphanedToolCall": "قد تكون رسالة استدعاء الأداة هذه معزولة بسبب ظروف غير طبيعية، مما قد يؤثر على تنفيذ الوكيل بشكل صحيح. يُرجى إزالتها.",
    "pluginRender": "عرض واجهة الإضافة"
  },
  "list": {
@@ -251,14 +253,20 @@
  },
  "localSystem": {
    "apiName": {
+      "editLocalFile": "تحرير الملف",
+      "getCommandOutput": "الحصول على مخرجات الأوامر",
+      "globLocalFiles": "البحث عن الملفات المطابقة",
+      "grepContent": "البحث في المحتوى",
+      "killCommand": "إيقاف تنفيذ الأمر",
      "listLocalFiles": "عرض قائمة الملفات",
      "moveLocalFiles": "نقل الملفات",
      "readLocalFile": "قراءة محتوى الملف",
      "renameLocalFile": "إعادة تسمية",
+      "runCommand": "تشغيل الأمر",
      "searchLocalFiles": "بحث في الملفات",
      "writeLocalFile": "كتابة في الملف"
    },
-    "title": "الملفات المحلية"
+    "title": "النظام المحلي"
  },
  "mcpInstall": {
    "CHECKING_INSTALLATION": "جارٍ فحص بيئة التثبيت...",
@@ -2,6 +2,45 @@
  "about": {
    "title": "حول"
  },
+  "agentInfoDescription": {
+    "basic": {
+      "avatar": "الصورة الرمزية",
+      "description": "الوصف",
+      "name": "الاسم",
+      "tags": "الوسوم",
+      "title": "معلومات المساعد"
+    },
+    "chat": {
+      "displayMode": "وضع العرض",
+      "enableHistoryCount": "تمكين عداد الرسائل السابقة",
+      "historyCount": "عدد الرسائل السابقة",
+      "no": "لا",
+      "searchMode": "وضع البحث",
+      "title": "تفضيلات الدردشة",
+      "yes": "نعم"
+    },
+    "model": {
+      "maxTokens": "الحد الأقصى للرموز",
+      "model": "النموذج",
+      "provider": "المزود",
+      "temperature": "درجة الحرارة",
+      "title": "إعدادات النموذج",
+      "topP": "قيمة Top P"
+    },
+    "plugins": {
+      "count": "إعدادات الإضافات ({{count}})",
+      "empty": "لم يتم تثبيت أي إضافات بعد",
+      "title": "الإضافات المثبتة"
+    },
+    "role": {
+      "systemRole": "تعليمات النظام",
+      "title": "إعدادات الدور"
+    },
+    "value": {
+      "unset": "غير مُعيّن",
+      "untitled": "مساعد بدون عنوان"
+    }
+  },
  "agentTab": {
    "chat": "تفضيلات الدردشة",
    "meta": "معلومات المساعد",
@@ -18,6 +57,8 @@
    },
    "title": "تحليلات"
  },
+  "checking": "جارٍ التحقق...",
+  "checkingPermissions": "جارٍ التحقق من الأذونات...",
  "danger": {
    "clear": {
      "action": "مسح الآن",
@@ -146,6 +187,58 @@
    },
    "waitingForMore": "يتم <1>التخطيط لتوفير</1> المزيد من النماذج، ترقبوا المزيد"
  },
+  "marketPublish": {
+    "modal": {
+      "changelog": {
+        "extra": "صف التغييرات والتحسينات الرئيسية في هذا الإصدار",
+        "label": "سجل التغييرات",
+        "maxLengthError": "لا يمكن أن يتجاوز سجل التغييرات 500 حرف",
+        "placeholder": "يرجى إدخال سجل التغييرات",
+        "required": "يرجى إدخال سجل التغييرات"
+      },
+      "comparison": {
+        "local": "الإصدار المحلي الحالي",
+        "remote": "الإصدار المنشور الحالي"
+      },
+      "identifier": {
+        "extra": "سيكون المعرف هو الهوية الفريدة للمساعد، يُفضل استخدام أحرف صغيرة وأرقام وشرطات",
+        "label": "معرف المساعد",
+        "lengthError": "يجب أن يتراوح طول المعرف بين 3 و50 حرفًا",
+        "patternError": "يمكن أن يحتوي المعرف فقط على أحرف صغيرة وأرقام وشرطات",
+        "placeholder": "يرجى إدخال معرف فريد للمساعد، مثل: web-development",
+        "required": "يرجى إدخال معرف المساعد"
+      },
+      "loading": {
+        "fetchingRemote": "جارٍ تحميل البيانات البعيدة...",
+        "submit": "جارٍ نشر المساعد...",
+        "upload": "جارٍ نشر الإصدار الجديد..."
+      },
+      "messages": {
+        "createVersionFailed": "فشل إنشاء الإصدار: {{message}}",
+        "fetchRemoteFailed": "فشل في جلب بيانات المساعد من السوق",
+        "missingIdentifier": "لا يحتوي هذا المساعد على معرف سوق حتى الآن",
+        "notAuthenticated": "يرجى تسجيل الدخول إلى حساب السوق أولاً",
+        "publishFailed": "فشل النشر: {{message}}"
+      },
+      "submitButton": "نشر",
+      "title": {
+        "submit": "مشاركة في سوق المساعدين",
+        "upload": "نشر إصدار جديد"
+      }
+    },
+    "resultModal": {
+      "message": "تم إرسال المساعد للمراجعة، وسيتم نشره تلقائيًا بعد الموافقة. انقر على \"عرض في السوق\" لرؤية المساعد المنشور.",
+      "view": "عرض في السوق"
+    },
+    "submit": {
+      "button": "مشاركة في السوق",
+      "tooltip": "شارك المساعد في سوق المساعدين"
+    },
+    "upload": {
+      "button": "نشر إصدار جديد",
+      "tooltip": "نشر إصدار جديد في سوق المساعدين"
+    }
+  },
  "message": {
    "success": "تم التحديث بنجاح"
  },
@@ -561,7 +654,7 @@
  },
  "submitAgentModal": {
    "button": "تقديم المساعد",
-    "identifier": "معرف المساعد",
+    "identifier": "معرف المساعد (identifier)",
    "metaMiss": "يرجى استكمال معلومات المساعد قبل التقديم، يجب أن تتضمن الاسم والوصف والعلامة",
    "placeholder": "الرجاء إدخال معرف المساعد، يجب أن يكون فريدًا، مثل تطوير الويب",
    "tooltips": "مشاركة في سوق المساعدين"
@@ -15,8 +15,22 @@
    "prompt": "كلمة تلميح"
  },
  "localFiles": {
+    "editFile": {
+      "newString": "استبدال بـ",
+      "oldString": "البحث عن",
+      "replaceAll": "استبدال جميع المطابقات",
+      "replaceFirst": "استبدال أول مطابقة فقط"
+    },
    "file": "ملف",
    "folder": "مجلد",
+    "moveFiles": {
+      "itemsMoved": "تم نقل {{count}} عنصر:",
+      "itemsMoved_one": "تم نقل {{count}} عنصر:",
+      "itemsMoved_other": "تم نقل {{count}} عنصر:",
+      "itemsToMove": "{{count}} عنصر في انتظار النقل:",
+      "itemsToMove_one": "{{count}} عنصر في انتظار النقل:",
+      "itemsToMove_other": "{{count}} عنصر في انتظار النقل:"
+    },
    "open": "فتح",
    "openFile": "فتح ملف",
    "openFolder": "فتح مجلد",
@@ -26,7 +40,12 @@
    "readFile": "قراءة الملف",
    "readFileError": "فشل في قراءة الملف، يرجى التحقق من صحة مسار الملف",
    "readFiles": "قراءة الملفات",
-    "readFilesError": "فشل في قراءة الملفات، يرجى التحقق من صحة مسار الملف"
+    "readFilesError": "فشل في قراءة الملفات، يرجى التحقق من صحة مسار الملف",
+    "writeFile": {
+      "characters": "أحرف",
+      "preview": "معاينة المحتوى",
+      "truncated": "تم الاقتطاع"
+    }
  },
  "search": {
    "createNewSearch": "إنشاء سجل بحث جديد",
@@ -6,6 +6,7 @@
    "confirmRemoveUnstarred": "سيتم حذف المواضيع غير المفضلة، ولن يمكن استعادتها بعد الحذف، يرجى توخي الحذر.",
    "duplicate": "إنشاء نسخة",
    "export": "تصدير الموضوع",
+    "openInNewWindow": "افتح الصفحة في نافذة جديدة",
    "removeAll": "حذف جميع المواضيع",
    "removeUnstarred": "حذف المواضيع غير المفضلة"
  },
@@ -145,6 +145,50 @@
    "apikey": "Управление на API ключове",
    "profile": "Профил",
    "security": "Сигурност",
-    "stats": "Статистика"
+    "stats": "Статистика",
+    "usage": "Статистика за използване"
+  },
+  "usage": {
+    "activeModels": {
+      "modelTable": "Списък с модели",
+      "models": "Активни модели",
+      "providerTable": "Списък с доставчици",
+      "providers": "Активни доставчици",
+      "table": {
+        "calls": "Брой извиквания",
+        "model": "Модел",
+        "provider": "Доставчик",
+        "spend": "Разходи"
+      }
+    },
+    "cards": {
+      "month": {
+        "modelCalls": "Извиквания на модела",
+        "title": "Разходи за този месец"
+      },
+      "today": {
+        "title": "Разходи за днес",
+        "yesterday": "Вчера"
+      }
+    },
+    "table": {
+      "actions": "Действия",
+      "createdAt": "Време на използване",
+      "inputTokens": "Входни токени",
+      "model": "Модел",
+      "outputTokens": "Изходни токени",
+      "spend": "Разходи",
+      "tps": "TPS",
+      "ttft": "TTFT",
+      "type": "Тип извикване"
+    },
+    "trends": {
+      "spend": "Сума",
+      "tokens": "Токени"
+    },
+    "welcome": {
+      "model": "Модел",
+      "provider": "Доставчик"
+    }
  }
 }
@@ -17,6 +17,7 @@
  "availableAgents": "Налични асистенти",
  "backToBottom": "Върни се в началото",
  "chatList": {
+    "expandMessage": "Разгъни съобщението",
    "longMessageDetail": "Вижте детайлите"
  },
  "clearCurrentMessages": "Изчисти съобщенията от текущата сесия",
@@ -173,8 +174,11 @@
    "title": "Споменаване на членове"
  },
  "messageAction": {
+    "collapse": "Скрий съобщението",
+    "continueGeneration": "Продължи генерирането",
    "delAndRegenerate": "Изтрий и прегенерирай",
    "deleteDisabledByThreads": "Съществуват подтеми, не можете да изтриете.",
+    "expand": "Разгъни съобщението",
    "regenerate": "Прегенерирай"
  },
  "messages": {
@@ -239,6 +243,7 @@
  "noMatchingAgents": "Няма съвпадащи членове",
  "noMembersYet": "В тази група все още няма членове. Щракнете върху бутона +, за да поканите асистенти.",
  "noSelectedAgents": "Все още не са избрани членове",
+  "openInNewWindow": "Отвори в нов прозорец",
  "owner": "Собственик на групата",
  "pin": "Закачи",
  "pinOff": "Откачи",
@@ -325,6 +330,11 @@
    "screenshot": "Екранна снимка",
    "settings": "Настройки за експортиране",
    "text": "Текст",
+    "widthMode": {
+      "label": "Режим на ширина",
+      "narrow": "Режим за тесен екран",
+      "wide": "Режим за широк екран"
+    },
    "withBackground": "Включи фоново изображение",
    "withFooter": "Включи долен колонтитул",
    "withPluginInfo": "Включи информация за плъгина",
@@ -367,6 +377,28 @@
    "remained": "Оставащи",
    "used": "Използвани"
  },
+  "tool": {
+    "intervention": {
+      "approve": "Одобряване",
+      "approveAndRemember": "Одобряване и запомняне",
+      "approveOnce": "Одобряване само този път",
+      "mode": {
+        "allowList": "Бял списък",
+        "allowListDesc": "Автоматично се изпълняват само одобрените инструменти",
+        "autoRun": "Автоматично одобрение",
+        "autoRunDesc": "Автоматично одобряване на всички изпълнения на инструменти",
+        "manual": "Ръчно",
+        "manualDesc": "Необходимо е ръчно одобрение при всяко извикване"
+      },
+      "reject": "Отхвърляне",
+      "rejectAndContinue": "Откажи и опитай отново",
+      "rejectOnly": "Откажи",
+      "rejectReasonPlaceholder": "Въведете причина за отхвърляне, за да помогнете на агента да разбере и подобри бъдещите действия",
+      "rejectTitle": "Отхвърляне на това извикване на инструмент",
+      "rejectedWithReason": "Това извикване на инструмент беше умишлено отхвърлено: {{reason}}",
+      "toolRejected": "Това извикване на инструмент беше умишлено отхвърлено"
+    }
+  },
  "topic": {
    "checkOpenNewTopic": "Да се отвори ли нова тема?",
    "checkSaveCurrentMessages": "Искате ли да запазите текущата сесия като тема?",
@@ -135,6 +135,7 @@
    }
  },
  "close": "Затвори",
+  "confirm": "Потвърди",
  "contact": "Свържете се с нас",
  "copy": "Копирай",
  "copyFail": "Копирането не е успешно",
@@ -285,6 +286,7 @@
  "oauth": "SSO Вход",
  "officialSite": "Официален сайт",
  "ok": "Добре",
+  "or": "или",
  "password": "Парола",
  "pin": "Закачи",
  "pinOff": "Откачи",
@@ -106,6 +106,12 @@
    "keyPlaceholder": "Ключ",
    "valuePlaceholder": "Стойност"
  },
+  "LocalFile": {
+    "action": {
+      "open": "Отвори",
+      "showInFolder": "Показване в папката"
+    }
+  },
  "MaxTokenSlider": {
    "unlimited": "Неограничено"
  },
@@ -41,9 +41,29 @@
        "openingMessage": "Начално съобщение",
        "openingQuestions": "Начални въпроси",
        "title": "Настройки на асистента"
+      },
+      "version": {
+        "empty": "Няма налични предишни версии",
+        "status": {
+          "archived": "Архивиран",
+          "deprecated": "Отхвърлен",
+          "unpublished": "В процес на преглед"
+        },
+        "table": {
+          "isLatest": "Последна версия",
+          "isValidated": "Потвърдена",
+          "publishAt": "Дата на публикуване",
+          "version": "Версия"
+        },
+        "title": "История на версиите"
      }
    },
    "list": "Списък с асистенти",
+    "marketSource": {
+      "label": "Превключване на източник на пазара",
+      "legacy": "Стар пазар",
+      "new": "Нов пазар"
+    },
    "more": "Още",
    "plugins": "Интегрирани плъгини",
    "recentSubmits": "Наскоро обновено",
@@ -51,10 +71,35 @@
      "createdAt": "Последно публикуван",
      "identifier": "ID на асистента",
      "knowledgeCount": "Брой бази знания",
+      "myown": "Виж моите",
      "pluginCount": "Брой плъгини",
      "title": "Име на асистента",
      "tokenUsage": "Използване на токени"
    },
+    "status": {
+      "archived": {
+        "reasons": {
+          "official": "Асистентът е премахнат от официалните лица поради проблеми със сигурността/политиката",
+          "owner": "Собственикът на асистента доброволно го е премахнал/архивирал"
+        },
+        "subtitle": "Асистентът, който се опитвате да достъпите, е архивиран поради една от следните възможни причини:",
+        "title": "Асистентът е архивиран"
+      },
+      "backToMarket": "Обратно към пазара на асистенти",
+      "deprecated": {
+        "reasons": {
+          "official": "Асистентът е премахнат от официалните лица поради проблеми със сигурността/политиката",
+          "owner": "Собственикът на асистента доброволно го е премахнал/отхвърлил"
+        },
+        "subtitle": "Асистентът, който се опитвате да достъпите, е отхвърлен поради една от следните възможни причини:",
+        "title": "Асистентът е отхвърлен"
+      },
+      "support": "Ако имате въпроси, моля копирайте линка и го изпратете на <1>support@lobehub.com</1> за съдействие.",
+      "unpublished": {
+        "subtitle": "Асистентът, който се опитвате да достъпите, е в процес на преглед. Ако имате въпроси, копирайте линка и го изпратете на <1>support@lobehub.com</1> за съдействие.",
+        "title": "Асистентът е в процес на преглед"
+      }
+    },
    "suggestions": "Свързани предложения",
    "systemRole": "Настройки на асистента",
    "tokenUsage": "Използване на токени в подсказките на асистента",
@@ -102,7 +102,7 @@
      "SPII": "Вашето съдържание може да съдържа чувствителна лична информация. За да защитите поверителността, моля, премахнете съответната чувствителна информация и опитайте отново.",
      "default": "Съдържанието е блокирано: {{blockReason}}。请调整您的请求内容后重试。"
    },
-    "InsufficientQuota": "Съжаляваме, квотата за този ключ е достигнала лимита. Моля, проверете баланса на акаунта си или увеличете квотата на ключа и опитайте отново.",
+    "InsufficientQuota": "Съжаляваме, но квотата за този ключ е изчерпана. Моля, проверете дали имате достатъчен баланс в акаунта си или увеличете квотата на ключа и опитайте отново.",
    "InvalidAccessCode": "Невалиден или празен код за достъп. Моля, въведете правилния код за достъп или добавете персонализиран API ключ.",
    "InvalidBedrockCredentials": "Удостоверяването на Bedrock е неуспешно. Моля, проверете AccessKeyId/SecretAccessKey и опитайте отново.",
    "InvalidClerkUser": "很抱歉，你当前尚未登录，请先登录或注册账号后继续操作",
@@ -131,7 +131,7 @@
    "PluginServerError": "Заявката към сървъра на плъгина върна грешка. Моля, проверете файла на манифеста на плъгина, конфигурацията на плъгина или изпълнението на сървъра въз основа на информацията за грешката по-долу",
    "PluginSettingsInvalid": "Този плъгин трябва да бъде конфигуриран правилно, преди да може да се използва. Моля, проверете дали конфигурацията ви е правилна",
    "ProviderBizError": "Грешка в услугата на {{provider}}, моля проверете следната информация или опитайте отново",
-    "QuotaLimitReached": "Съжаляваме, но текущото използване на токени или брой на заявките е достигнало лимита на квотата за този ключ. Моля, увеличете квотата на ключа или опитайте отново по-късно.",
+    "QuotaLimitReached": "Съжаляваме, но текущото използване на токени или броят на заявките е достигнало лимита на квотата за този ключ. Моля, увеличете квотата на ключа или опитайте отново по-късно.",
    "StreamChunkError": "Грешка при парсирането на съобщение от потокова заявка. Моля, проверете дали текущият API интерфейс отговаря на стандартите или се свържете с вашия доставчик на API за консултация.",
    "SubscriptionKeyMismatch": "Съжаляваме, но поради случайна системна грешка, текущото използване на абонамента временно е невалидно. Моля, кликнете върху бутона по-долу, за да възстановите абонамента, или се свържете с нас по имейл за поддръжка.",
    "SubscriptionPlanLimit": "Вашият абонаментен план е изчерпан, не можете да използвате тази функция. Моля, надстройте до по-висок план или конфигурирайте персонализиран модел API, за да продължите да използвате.",
@@ -1,5 +1,8 @@
 {
-  "desc": "Управлявайте своите знания",
+  "addFolder": "Създаване на папка",
+  "addKnowledge": "Добавяне на знание",
+  "addPage": "Създаване на документ",
+  "desc": "Управлявайте знанията си за работа, учене и живот.",
  "detail": {
    "basic": {
      "createdAt": "Дата на създаване",
@@ -21,15 +24,89 @@
      "embeddingStatus": "Векторизация"
    }
  },
+  "documentEditor": {
+    "addIcon": "Добавяне на икона",
+    "autoSaveMessage": "Документът се запазва автоматично, не е необходимо ръчно запазване",
+    "chooseIcon": "Избор на икона",
+    "deleteConfirm": {
+      "content": "Документът ще бъде изтрит и няма да може да бъде възстановен. Моля, бъдете внимателни.",
+      "title": "Изтриване на документ"
+    },
+    "deleteError": "Неуспешно изтриване на документа",
+    "deleteSuccess": "Документът беше изтрит успешно",
+    "editedAt": "Последна редакция на {{time}}",
+    "editedBy": "Последно редактиран от {{name}}",
+    "editorPlaceholder": "Въведете съдържанието на документа, натиснете / за меню с команди",
+    "empty": {
+      "createNewDocument": "Създаване на нов документ",
+      "title": "Изберете документ, за да започнете",
+      "uploadMarkdown": "Качване на Markdown файл"
+    },
+    "linkCopied": "Връзката е копирана",
+    "menu": {
+      "copyLink": "Копиране на връзка",
+      "exportDocument": "Експортиране на документ",
+      "importDocument": "Импортиране на документ",
+      "pin": "Закачане на документа"
+    },
+    "saving": "Запазване...",
+    "titlePlaceholder": "Без заглавие",
+    "wordCount": "{{wordCount}} думи"
+  },
+  "documentList": {
+    "copyContent": "Копиране на цялото съдържание",
+    "documentCount": "Общо {{count}} документа",
+    "duplicate": "Създаване на копие",
+    "empty": "Все още няма документи. Натиснете бутона по-горе, за да създадете първия си документ",
+    "noResults": "Няма намерени съвпадащи документи",
+    "selectNote": "Изберете документ, за да започнете редактиране",
+    "untitled": "Без заглавие"
+  },
  "empty": "Няма качени файлове/папки",
  "header": {
    "actions": {
      "newFolder": "Нова папка",
+      "newPage": "Създаване на нов документ",
      "uploadFile": "Качване на файл",
      "uploadFolder": "Качване на папка"
    },
+    "newDocumentButton": "Нов документ",
+    "newNoteDialog": {
+      "cancel": "Отказ",
+      "editTitle": "Редактиране на документ",
+      "emptyContent": "Съдържанието на документа не може да бъде празно",
+      "loadError": "Неуспешно зареждане на документа, моля опитайте отново",
+      "loading": "Зареждане...",
+      "save": "Запази",
+      "saveError": "Неуспешно запазване на документа, моля опитайте отново",
+      "saveSuccess": "Документът беше запазен успешно",
+      "title": "Нов документ",
+      "updateSuccess": "Документът беше обновен успешно"
+    },
    "uploadButton": "Качване"
  },
+  "home": {
+    "getStarted": "Започнете",
+    "greeting": "Начало",
+    "quickActions": "Бързи действия",
+    "recentDocuments": "Скорошни документи",
+    "recentFiles": "Скорошни файлове",
+    "subtitle": "Добре дошли в базата знания. Започнете да управлявате вашите документи оттук",
+    "uploadEntries": {
+      "files": {
+        "title": "Качване на файлове"
+      },
+      "folder": {
+        "title": "Качване на папка"
+      },
+      "knowledgeBase": {
+        "title": "Създай база знания"
+      },
+      "newDocument": {
+        "title": "Създай нов документ"
+      }
+    }
+  },
  "knowledgeBase": {
    "list": {
      "confirmRemoveKnowledgeBase": "Сигурни ли сте, че искате да изтриете тази база знания? Файловете в нея няма да бъдат изтрити, а ще бъдат преместени в общите файлове. След изтриването на базата знания, тя не може да бъде възстановена, моля, действайте внимателно.",
@@ -38,6 +115,10 @@
    "new": "Нова база знания",
    "title": "База знания"
  },
+  "menu": {
+    "allDocuments": "Всички документи",
+    "allFiles": "Всички файлове"
+  },
  "networkError": "Неуспешно получаване на базата от знания, моля, проверете интернет връзката и опитайте отново",
  "notSupportGuide": {
    "desc": "Текущият инстанс е в режим на клиентска база данни и не поддържа функцията за управление на файлове. Моля, превключете на <1>режим на сървърна база данни</1> или използвайте директно <3>LobeChat Cloud</3>",
@@ -61,12 +142,16 @@
    "downloadFile": "Изтеглете файла",
    "unsupportedFileAndContact": "Този формат на файла не поддържа онлайн преглед. Ако имате нужда от преглед, моля, <1>свържете се с нас</1>."
  },
+  "searchDocumentPlaceholder": "Търсене на документи",
  "searchFilePlaceholder": "Търсене на файл",
  "tab": {
-    "all": "Всички файлове",
+    "all": "Всички",
    "audios": "Аудио",
    "documents": "Документи",
+    "home": "Начало",
    "images": "Снимки",
+    "moreTypes": "Още типове",
+    "pages": "Документи",
    "videos": "Видеа",
    "websites": "Уебсайтове"
  },
@@ -0,0 +1,42 @@
+{
+  "callback": {
+    "buttons": {
+      "close": "Затвори прозореца"
+    },
+    "messages": {
+      "authFailed": "Неуспешно упълномощаване: {{error}}",
+      "missingParams": "Липсват параметри за упълномощаване",
+      "processing": "Обработва се упълномощаването...",
+      "successWithCountdown": "{{message}} Прозорецът ще се затвори автоматично след {{countdown}} секунди",
+      "successWithRedirect": "Упълномощаването е успешно! Пренасочване..."
+    },
+    "titles": {
+      "error": "Неуспешно упълномощаване",
+      "loading": "Упълномощаване в LobeHub Market",
+      "success": "Упълномощаването е успешно"
+    }
+  },
+  "errors": {
+    "authorizationFailed": "Упълномощаването не бе успешно, моля опитайте отново.",
+    "browserOnly": "Процесът на упълномощаване може да бъде стартиран само в браузър.",
+    "codeConsumed": "Кодът за упълномощаване вече е използван, моля опитайте отново.",
+    "codeVerifierMissing": "Сесията за упълномощаване е невалидна, моля започнете отново процеса на вход.",
+    "general": "Възникна грешка при упълномощаването, моля опитайте отново.",
+    "handoffFailed": "Неуспешно получаване на резултат от упълномощаването, моля опитайте отново.",
+    "handoffTimeout": "Времето за упълномощаване изтече, моля завършете процеса в браузъра и опитайте отново.",
+    "oidcNotReady": "Услугата за упълномощаване все още не е готова, моля опитайте по-късно.",
+    "openBrowserFailed": "Неуспешно отваряне на системния браузър, моля опитайте отново.",
+    "openPopupFailed": "Неуспешно отваряне на изскачащ прозорец за упълномощаване, моля проверете настройките за блокиране на изскачащи прозорци в браузъра.",
+    "popupClosed": "Прозорецът за упълномощаване бе затворен преди завършване на процеса.",
+    "sessionExpired": "Сесията за упълномощаване е изтекла, моля влезте отново.",
+    "stateMismatch": "Несъответствие в състоянието на упълномощаване, моля опитайте отново.",
+    "stateMissing": "Състоянието на упълномощаване не бе намерено, моля опитайте отново."
+  },
+  "messages": {
+    "loading": "Стартиране на процеса по упълномощаване...",
+    "success": {
+      "submit": "Упълномощаването е успешно! Вече можете да публикувате помощник.",
+      "upload": "Упълномощаването е успешно! Вече можете да публикувате нова версия."
+    }
+  }
+}
@@ -199,6 +199,12 @@
    "all": "Всички",
    "list": {
      "disabled": "Неактивиран",
+      "disabledActions": {
+        "sort": "Сортиране",
+        "sortAlphabetical": "Сортиране по азбучен ред",
+        "sortAlphabeticalDesc": "Сортиране по обратен азбучен ред",
+        "sortDefault": "Сортиране по подразбиране"
+      },
      "enabled": "Активиран"
    },
    "notFound": "Не са намерени резултати от търсенето",
@@ -289,7 +295,7 @@
      },
      "helpDoc": "Ръководство за конфигуриране",
      "responsesApi": {
-        "desc": "Използва новия формат на заявките на OpenAI, отключващ функции като вериги на мислене и други усъвършенствани възможности",
+        "desc": "Използва новия формат за заявки на OpenAI, отключвайки разширени функции като вериги на мисълта (поддържа се само от моделите на OpenAI)",
        "title": "Използване на Responses API стандарта"
      },
      "waitingForMore": "Още модели са в <1>планиране</1>, моля, очаквайте"
@@ -391,7 +397,13 @@
      "addNew": "Добавяне на модел",
      "disabled": "Неактивен",
      "disabledActions": {
-        "showMore": "Покажи всичко"
+        "showMore": "Покажи всичко",
+        "sort": "Сортиране",
+        "sortAlphabetical": "Сортиране по азбучен ред",
+        "sortAlphabeticalDesc": "Сортиране по обратен азбучен ред",
+        "sortDefault": "Сортиране по подразбиране",
+        "sortReleasedAt": "Сортиране по най-ранна дата на публикуване",
+        "sortReleasedAtDesc": "Сортиране по най-нова дата на публикуване"
      },
      "empty": {
        "desc": "Моля, създайте персонализиран модел или изтеглете модел, за да започнете да го използвате",
@@ -236,6 +236,9 @@
  "MiniMaxAI/MiniMax-M1-80k": {
    "description": "MiniMax-M1 е мащабен модел за разсъждение с отворени тегла и смесено внимание, с 456 милиарда параметри, като всеки токен активира около 45.9 милиарда параметри. Моделът поддържа естествено контекст с дължина до 1 милион токена и чрез механизма за светкавично внимание спестява 75% от изчисленията при задачи с генериране на 100 хиляди токена в сравнение с DeepSeek R1. Освен това MiniMax-M1 използва MoE (смесен експертен) архитектура, комбинирайки CISPO алгоритъм и ефективно обучение с подсилване с дизайн на смесено внимание, постигащи водещи в индустрията резултати при дълги входни разсъждения и реални софтуерни инженерни сценарии."
  },
+  "MiniMaxAI/MiniMax-M2": {
+    "description": "MiniMax-M2 преосмисля ефективността на интелигентните агенти. Това е компактен, бърз и икономичен MoE модел с общо 230 милиарда параметъра и 10 милиарда активни параметъра, създаден за постигане на върхова производителност при кодиране и задачи, свързани с интелигентни агенти, като същевременно поддържа силен общ интелект. Със само 10 милиарда активни параметъра, MiniMax-M2 предлага производителност, сравнима с тази на мащабни модели, което го прави идеален избор за приложения с висока ефективност."
+  },
  "Moonshot-Kimi-K2-Instruct": {
    "description": "Общ брой параметри 1 трилион, активирани параметри 32 милиарда. Сред немисловните модели постига водещи резултати в областта на актуални знания, математика и кодиране, с по-добри възможности за универсални агентски задачи. Специално оптимизиран за агентски задачи, не само отговаря на въпроси, но и може да предприема действия. Най-подходящ за импровизирани, универсални разговори и агентски преживявания, модел с рефлексна скорост без нужда от дълго мислене."
  },
@@ -1049,6 +1052,9 @@
  "deepseek-r1-0528": {
    "description": "Пълноценен модел с 685 милиарда параметри, пуснат на 28 май 2025 г. DeepSeek-R1 използва мащабно обучение с подсилване в последващия етап на обучение, значително подобрявайки способността за разсъждение с минимални анотирани данни. Отличава се с висока производителност и способности в задачи по математика, кодиране и естествен езиков разсъждения."
  },
+  "deepseek-r1-250528": {
+    "description": "DeepSeek R1 250528, пълнофункционален модел за дедукция DeepSeek-R1, подходящ за сложни математически и логически задачи."
+  },
  "deepseek-r1-70b-fast-online": {
    "description": "DeepSeek R1 70B бърза версия, поддържаща търсене в реално време, предлагаща по-бърза скорост на отговор, без да компрометира производителността на модела."
  },
@@ -1059,31 +1065,34 @@
    "description": "deepseek-r1-distill-llama е модел, дестилиран от DeepSeek-R1 на базата на Llama."
  },
  "deepseek-r1-distill-llama-70b": {
-    "description": "DeepSeek R1 - по-голям и по-интелигентен модел в комплекта DeepSeek - е дестилиран в архитектурата Llama 70B. На базата на бенчмаркове и човешка оценка, този модел е по-интелигентен от оригиналния Llama 70B, особено в задачи, изискващи математическа и фактическа точност."
+    "description": "DeepSeek R1 Distill Llama 70B, дистилиран модел, съчетаващ универсалните дедуктивни способности на R1 с екосистемата на Llama."
  },
  "deepseek-r1-distill-llama-8b": {
-    "description": "Моделите от серията DeepSeek-R1-Distill са получени чрез техника на знание дестилация, като се фино настройват образците, генерирани от DeepSeek-R1, спрямо отворени модели като Qwen и Llama."
+    "description": "DeepSeek-R1-Distill-Llama-8B е дистилиран голям езиков модел, базиран на Llama-3.1-8B, използващ изхода на DeepSeek R1."
+  },
+  "deepseek-r1-distill-qianfan-70b": {
+    "description": "DeepSeek R1 Distill Qianfan 70B, дистилиран модел R1, базиран на Qianfan-70B, с високо съотношение цена/качество."
+  },
+  "deepseek-r1-distill-qianfan-8b": {
+    "description": "DeepSeek R1 Distill Qianfan 8B, дистилиран модел R1, базиран на Qianfan-8B, подходящ за малки и средни приложения."
  },
  "deepseek-r1-distill-qianfan-llama-70b": {
-    "description": "Първоначално пуснат на 14 февруари 2025 г., дестилиран от екипа за разработка на модела Qianfan с базов модел Llama3_70B (създаден с Meta Llama), в дестилираните данни също е добавен корпус от Qianfan."
-  },
-  "deepseek-r1-distill-qianfan-llama-8b": {
-    "description": "Първоначално пуснат на 14 февруари 2025 г., дестилиран от екипа за разработка на модела Qianfan с базов модел Llama3_8B (създаден с Meta Llama), в дестилираните данни също е добавен корпус от Qianfan."
+    "description": "DeepSeek R1 Distill Qianfan Llama 70B, дистилиран модел R1, базиран на Llama-70B."
  },
  "deepseek-r1-distill-qwen": {
    "description": "deepseek-r1-distill-qwen е модел, базиран на Qwen, дестилиран от DeepSeek-R1."
  },
  "deepseek-r1-distill-qwen-1.5b": {
-    "description": "Моделите от серията DeepSeek-R1-Distill са получени чрез техника на знание дестилация, като се фино настройват образците, генерирани от DeepSeek-R1, спрямо отворени модели като Qwen и Llama."
+    "description": "DeepSeek R1 Distill Qwen 1.5B, ултралек дистилиран модел R1, подходящ за среди с изключително ограничени ресурси."
  },
  "deepseek-r1-distill-qwen-14b": {
-    "description": "Моделите от серията DeepSeek-R1-Distill са получени чрез техника на знание дестилация, като се фино настройват образците, генерирани от DeepSeek-R1, спрямо отворени модели като Qwen и Llama."
+    "description": "DeepSeek R1 Distill Qwen 14B, дистилиран модел R1 със среден мащаб, подходящ за разгръщане в различни сценарии."
  },
  "deepseek-r1-distill-qwen-32b": {
-    "description": "Моделите от серията DeepSeek-R1-Distill са получени чрез техника на знание дестилация, като се фино настройват образците, генерирани от DeepSeek-R1, спрямо отворени модели като Qwen и Llama."
+    "description": "DeepSeek R1 Distill Qwen 32B, дистилиран модел R1, базиран на Qwen-32B, балансиращ между производителност и разходи."
  },
  "deepseek-r1-distill-qwen-7b": {
-    "description": "Моделите от серията DeepSeek-R1-Distill са получени чрез техника на знание дестилация, като се фино настройват образците, генерирани от DeepSeek-R1, спрямо отворени модели като Qwen и Llama."
+    "description": "DeepSeek R1 Distill Qwen 7B, лек дистилиран модел R1, подходящ за edge устройства и частни корпоративни среди."
  },
  "deepseek-r1-fast-online": {
    "description": "DeepSeek R1 пълна бърза версия, поддържаща търсене в реално време, комбинираща мощността на 671B параметри с по-бърза скорост на отговор."
@@ -1112,12 +1121,24 @@
  "deepseek-v3.1-terminus": {
    "description": "DeepSeek-V3.1-Terminus е оптимизирана версия на голям езиков модел от DeepSeek, създаден специално за крайни устройства."
  },
+  "deepseek-v3.1-think-250821": {
+    "description": "DeepSeek V3.1 Think 250821, модел за дълбоко мислене, съответстващ на версията Terminus, подходящ за високопроизводителни дедуктивни задачи."
+  },
  "deepseek-v3.1:671b": {
    "description": "DeepSeek V3.1: следващо поколение модел за разсъждение, подобряващ способностите за сложни разсъждения и свързано мислене, подходящ за задачи, изискващи задълбочен анализ."
  },
  "deepseek-v3.2-exp": {
    "description": "deepseek-v3.2-exp въвежда механизъм за разредено внимание, с цел подобряване на ефективността при обучение и извод при обработка на дълги текстове, като цената е по-ниска от тази на deepseek-v3.1."
  },
+  "deepseek-v3.2-think": {
+    "description": "DeepSeek V3.2 Think, пълнофункционален модел за дълбоко мислене, с подсилени способности за дълговерижна дедукция."
+  },
+  "deepseek-vl2": {
+    "description": "DeepSeek VL2, мултимодален модел, поддържащ разбиране на изображения и текст, както и фино визуално въпроси-отговори."
+  },
+  "deepseek-vl2-small": {
+    "description": "DeepSeek VL2 Small, олекотена мултимодална версия, подходяща за среди с ограничени ресурси и висока едновременност."
+  },
  "deepseek/deepseek-chat-v3-0324": {
    "description": "DeepSeek V3 е експертен смесен модел с 685B параметри, последната итерация на флагманската серия чат модели на екипа DeepSeek.\n\nТой наследява модела [DeepSeek V3](/deepseek/deepseek-chat-v3) и показва отлични резултати в различни задачи."
  },
@@ -1137,7 +1158,7 @@
    "description": "DeepSeek-R1 значително подобрява способността за разсъждение на модела дори с много малко анотирани данни. Преди да изведе окончателния отговор, моделът първо генерира мисловна верига, за да повиши точността на крайния отговор."
  },
  "deepseek/deepseek-r1-distill-llama-70b": {
-    "description": "DeepSeek-R1-Distill-Llama-70B е дистилиран и по-ефективен вариант на 70B Llama модела. Той запазва силна производителност при генериране на текст, намалявайки изчислителните разходи за по-лесно внедряване и изследване. Обслужва се от Groq с помощта на техния персонализиран хардуер за езикова обработка (LPU), осигурявайки бързо и ефективно разсъждение."
+    "description": "DeepSeek R1 Distill Llama 70B е голям езиков модел, базиран на Llama3.3 70B, който използва фино настройване, извлечено от DeepSeek R1, за да постигне конкурентна производителност, съпоставима с водещите мащабни модели."
  },
  "deepseek/deepseek-r1-distill-llama-8b": {
    "description": "DeepSeek R1 Distill Llama 8B е дестилиран голям езиков модел, базиран на Llama-3.1-8B-Instruct, обучен с изхода на DeepSeek R1."
@@ -1253,83 +1274,89 @@
  "emohaa": {
    "description": "Emohaa е психологически модел с професионални консултантски способности, помагащ на потребителите да разберат емоционалните проблеми."
  },
-  "ernie-3.5-128k": {
-    "description": "Флагманският голям езиков модел, разработен от Baidu, обхваща огромно количество китайски и английски текстове, притежаващ силни общи способности, способен да отговори на повечето изисквания за диалогови въпроси и отговори, генериране на съдържание и приложения на плъгини; поддържа автоматично свързване с плъгина за търсене на Baidu, осигурявайки актуалност на информацията."
-  },
-  "ernie-3.5-8k": {
-    "description": "Флагманският голям езиков модел, разработен от Baidu, обхваща огромно количество китайски и английски текстове, притежаващ силни общи способности, способен да отговори на повечето изисквания за диалогови въпроси и отговори, генериране на съдържание и приложения на плъгини; поддържа автоматично свързване с плъгина за търсене на Baidu, осигурявайки актуалност на информацията."
-  },
-  "ernie-3.5-8k-preview": {
-    "description": "Флагманският голям езиков модел, разработен от Baidu, обхваща огромно количество китайски и английски текстове, притежаващ силни общи способности, способен да отговори на повечето изисквания за диалогови въпроси и отговори, генериране на съдържание и приложения на плъгини; поддържа автоматично свързване с плъгина за търсене на Baidu, осигурявайки актуалност на информацията."
-  },
-  "ernie-4.0-8k-latest": {
-    "description": "Флагманският голям езиков модел, разработен от Baidu, с изключителни подобрения в сравнение с ERNIE 3.5, широко приложим в сложни задачи в различни области; поддържа автоматично свързване с плъгина за търсене на Baidu, осигурявайки актуалност на информацията."
-  },
-  "ernie-4.0-8k-preview": {
-    "description": "Флагманският голям езиков модел, разработен от Baidu, с изключителни подобрения в сравнение с ERNIE 3.5, широко приложим в сложни задачи в различни области; поддържа автоматично свързване с плъгина за търсене на Baidu, осигурявайки актуалност на информацията."
-  },
-  "ernie-4.0-turbo-128k": {
-    "description": "Флагманският голям езиков модел, разработен от Baidu, с отлични общи резултати, широко приложим в сложни задачи в различни области; поддържа автоматично свързване с плъгина за търсене на Baidu, осигурявайки актуалност на информацията. В сравнение с ERNIE 4.0, показва по-добри резултати."
-  },
-  "ernie-4.0-turbo-8k-latest": {
-    "description": "Флагманският голям езиков модел, разработен от Baidu, с отлични общи резултати, широко приложим в сложни задачи в различни области; поддържа автоматично свързване с плъгина за търсене на Baidu, осигурявайки актуалност на информацията. В сравнение с ERNIE 4.0, показва по-добри резултати."
-  },
-  "ernie-4.0-turbo-8k-preview": {
-    "description": "Флагманският голям езиков модел, разработен от Baidu, с отлични общи резултати, широко приложим в сложни задачи в различни области; поддържа автоматично свързване с плъгина за търсене на Baidu, осигурявайки актуалност на информацията. В сравнение с ERNIE 4.0, показва по-добри резултати."
+  "ernie-4.5-0.3b": {
+    "description": "ERNIE 4.5 0.3B, отворен и лек модел, подходящ за локално и персонализирано внедряване."
  },
  "ernie-4.5-21b-a3b": {
-    "description": "ERNIE 4.5 21B A3B е хибриден експертен модел, разработен от Baidu Wenxin, с мощни способности за извеждане на заключения и поддръжка на множество езици."
+    "description": "ERNIE 4.5 21B A3B, отворен модел с голям брой параметри, с по-добра производителност при задачи за разбиране и генериране."
  },
  "ernie-4.5-300b-a47b": {
    "description": "ERNIE 4.5 300B A47B е мащабен хибриден експертен модел от Baidu Wenxin, отличаващ се с изключителни способности за извеждане на заключения."
  },
  "ernie-4.5-8k-preview": {
-    "description": "Моделът Ernie 4.5 е ново поколение оригинален много модален основен модел, разработен от Baidu, който постига съвместна оптимизация чрез многомодално моделиране, с отлични способности за разбиране на много модалности; предлага усъвършенствани езикови способности, с подобрено разбиране, генериране, логика и памет, значително подобрени способности за избягване на халюцинации, логическо разсъждение и код."
+    "description": "ERNIE 4.5 8K Preview, модел с 8K контекст за предварителен преглед, предназначен за тестване на възможностите на Wenxin 4.5."
  },
  "ernie-4.5-turbo-128k": {
-    "description": "Wenxin 4.5 Turbo показва значителни подобрения в областите на елиминиране на илюзии, логическо разсъждение и кодиране. В сравнение с Wenxin 4.5, е по-бърз и по-евтин. Моделът е с цялостно подобрени способности, по-добре отговарящи на задачите за обработка на многократни дълги исторически разговори и разбиране на дълги документи."
+    "description": "ERNIE 4.5 Turbo 128K, високопроизводителен универсален модел, поддържащ търсене с подобрение и използване на инструменти, подходящ за въпроси-отговори, код, агенти и други бизнес сценарии."
+  },
+  "ernie-4.5-turbo-128k-preview": {
+    "description": "ERNIE 4.5 Turbo 128K Preview, предварителна версия с възможности, идентични на официалната, подходяща за интеграционно тестване и поетапно внедряване."
  },
  "ernie-4.5-turbo-32k": {
-    "description": "Wenxin 4.5 Turbo показва значителни подобрения в областите на елиминиране на илюзии, логическо разсъждение и кодиране. В сравнение с Wenxin 4.5, е по-бърз и по-евтин. Способностите за текстово творчество и знания са значително подобрени. Дължината на изхода и времето за забавяне на цялото изречение са увеличени в сравнение с ERNIE 4.5."
+    "description": "ERNIE 4.5 Turbo 32K, версия с междинен контекст, подходяща за въпроси-отговори, търсене в бази знания и многократни диалози."
+  },
+  "ernie-4.5-turbo-latest": {
+    "description": "ERNIE 4.5 Turbo Latest, оптимизиран за цялостна производителност, подходящ като основен универсален модел за продукционна среда."
+  },
+  "ernie-4.5-turbo-vl": {
+    "description": "ERNIE 4.5 Turbo VL, зрял мултимодален модел, подходящ за задачи по разбиране и разпознаване на изображения и текст в продукционна среда."
  },
  "ernie-4.5-turbo-vl-32k": {
-    "description": "Нова версия на големия модел Wenxin, с значително подобрени способности за разбиране на изображения, творчество, превод и кодиране, за първи път поддържа контекстна дължина от 32K, значително намалено забавяне при първия токен."
+    "description": "ERNIE 4.5 Turbo VL 32K, мултимодална версия с междинен контекст, подходяща за съвместно разбиране на дълги документи и изображения."
+  },
+  "ernie-4.5-turbo-vl-32k-preview": {
+    "description": "ERNIE 4.5 Turbo VL 32K Preview, предварителна мултимодална версия с 32K контекст, улесняваща оценката на визуалните способности при дълъг контекст."
+  },
+  "ernie-4.5-turbo-vl-latest": {
+    "description": "ERNIE 4.5 Turbo VL Latest, най-новата мултимодална версия, предлагаща по-добро разбиране и дедукция на изображения и текст."
+  },
+  "ernie-4.5-turbo-vl-preview": {
+    "description": "ERNIE 4.5 Turbo VL Preview, предварителен мултимодален модел, поддържащ разбиране и генериране на изображения и текст, подходящ за визуални въпроси-отговори и разбиране на съдържание."
+  },
+  "ernie-4.5-vl-28b-a3b": {
+    "description": "ERNIE 4.5 VL 28B A3B, отворен мултимодален модел, поддържащ разбиране и дедукция на изображения и текст."
+  },
+  "ernie-5.0-thinking-preview": {
+    "description": "Wenxin 5.0 Thinking Preview, флагмански модел с пълна мултимодалност, поддържащ унифицирано моделиране на текст, изображения, аудио и видео, с цялостно подобрени способности, подходящ за сложни въпроси, творчество и интелигентни агенти."
  },
  "ernie-char-8k": {
-    "description": "Специализиран голям езиков модел, разработен от Baidu, подходящ за приложения като NPC в игри, диалози на клиентска поддръжка и ролеви игри, с по-изразителен и последователен стил на персонажите, по-силна способност за следване на инструкции и по-добра производителност на разсъжденията."
+    "description": "ERNIE Character 8K, модел за диалог с характер и личност, подходящ за изграждане на IP персонажи и дългосрочни разговори."
  },
  "ernie-char-fiction-8k": {
-    "description": "Специализиран голям езиков модел, разработен от Baidu, подходящ за приложения като NPC в игри, диалози на клиентска поддръжка и ролеви игри, с по-изразителен и последователен стил на персонажите, по-силна способност за следване на инструкции и по-добра производителност на разсъжденията."
+    "description": "ERNIE Character Fiction 8K, персонализиран модел за създаване на романи и сюжетни линии, подходящ за генериране на дълги истории."
+  },
+  "ernie-char-fiction-8k-preview": {
+    "description": "ERNIE Character Fiction 8K Preview, предварителна версия на модел за създаване на персонажи и сюжети, предназначена за тестване и демонстрация."
  },
  "ernie-irag-edit": {
-    "description": "Собствен модел за редактиране на изображения ERNIE iRAG на Baidu поддържа операции като изтриване (erase), прерисуване (repaint) и вариации (variation) върху изображения."
+    "description": "ERNIE iRAG Edit, модел за редактиране на изображения, поддържащ изтриване, прерисуване и генериране на варианти."
  },
  "ernie-lite-8k": {
-    "description": "ERNIE Lite е лек голям езиков модел, разработен от Baidu, който съчетава отлични резултати с производителност на разсъжденията, подходящ за използване с AI ускорителни карти с ниска изчислителна мощ."
+    "description": "ERNIE Lite 8K, лек универсален модел, подходящ за ежедневни въпроси и генериране на съдържание с ограничен бюджет."
  },
  "ernie-lite-pro-128k": {
-    "description": "Лек голям езиков модел, разработен от Baidu, който съчетава отлични резултати с производителност на разсъжденията, с по-добри резултати в сравнение с ERNIE Lite, подходящ за използване с AI ускорителни карти с ниска изчислителна мощ."
+    "description": "ERNIE Lite Pro 128K, лек и високопроизводителен модел, подходящ за бизнес сценарии, чувствителни към закъснение и разходи."
  },
  "ernie-novel-8k": {
-    "description": "Общ голям езиков модел, разработен от Baidu, с очевидни предимства в продължаването на разкази, подходящ и за кратки пиеси и филми."
+    "description": "ERNIE Novel 8K, модел за създаване на дълги романи и IP сюжети, с умения за многоперсонажно и многолинейно повествование."
  },
  "ernie-speed-128k": {
-    "description": "Най-новият високопроизводителен голям езиков модел, разработен от Baidu през 2024 г., с отлични общи способности, подходящ за финализиране на специфични проблеми, с отлична производителност на разсъжденията."
+    "description": "ERNIE Speed 128K, голям модел без разходи за вход/изход, подходящ за разбиране на дълги текстове и мащабно тестване."
+  },
+  "ernie-speed-8k": {
+    "description": "ERNIE Speed 8K, безплатен и бърз модел, подходящ за ежедневни разговори и леки текстови задачи."
  },
  "ernie-speed-pro-128k": {
-    "description": "Най-новият високопроизводителен голям езиков модел, разработен от Baidu през 2024 г., с отлични общи способности, с по-добри резултати в сравнение с ERNIE Speed, подходящ за финализиране на специфични проблеми, с отлична производителност на разсъжденията."
+    "description": "ERNIE Speed Pro 128K, модел с висока едновременност и отлична цена/производителност, подходящ за мащабни онлайн услуги и корпоративни приложения."
  },
  "ernie-tiny-8k": {
-    "description": "ERNIE Tiny е модел с изключителна производителност, разработен от Baidu, с най-ниски разходи за внедряване и фина настройка сред моделите от серията Wenxin."
-  },
-  "ernie-x1-32k": {
-    "description": "Разполага с по-силни способности за разбиране, планиране, размисъл и еволюция. Като модел за дълбоко мислене с по-пълни способности, Wenxin X1 съчетава точност, креативност и изящество, и се представя особено добре в области като китайски знания, литературно творчество, писане на документи, ежедневни разговори, логическо разсъждение, сложни изчисления и извикване на инструменти."
-  },
-  "ernie-x1-32k-preview": {
-    "description": "Моделът Wenxin X1 притежава по-силни способности за разбиране, планиране, размисъл и еволюция. Като модел за дълбоко мислене с по-широки възможности, Wenxin X1 съчетава точност, креативност и изящество, особено в области като китайски знания и отговори, литературно творчество, писане на документи, ежедневни разговори, логическо разсъждение, сложни изчисления и извикване на инструменти."
+    "description": "ERNIE Tiny 8K, ултралек модел, подходящ за прости въпроси, класификация и други нискобюджетни дедуктивни задачи."
  },
  "ernie-x1-turbo-32k": {
-    "description": "В сравнение с ERNIE-X1-32K, моделът предлага по-добри резултати и производителност."
+    "description": "ERNIE X1 Turbo 32K, модел за бързо мислене с 32K дълъг контекст, подходящ за сложна дедукция и многократни диалози."
+  },
+  "ernie-x1.1-preview": {
+    "description": "ERNIE X1.1 Preview, предварителна версия на модела за мислене ERNIE X1.1, подходяща за тестване и валидиране на способности."
  },
  "fal-ai/bytedance/seedream/v4": {
    "description": "Seedream 4.0 е модел за генериране на изображения, разработен от екипа Seed на ByteDance, поддържа вход от текст и изображения, предоставя висококонтролирано и качествено генериране на изображения. Генерира изображения на базата на текстови подсказки."
@@ -1389,7 +1416,7 @@
    "description": "FLUX.1 [schnell] е най-напредналият отворен модел с малък брой стъпки, който надминава конкурентите си и дори превъзхожда мощни нефино настроени модели като Midjourney v6.0 и DALL·E 3 (HD). Моделът е специално фино настроен, за да запази пълното разнообразие на изхода от предварителното обучение и значително подобрява визуалното качество, следването на инструкции, промяната на размери/пропорции, обработката на шрифтове и разнообразието на изхода в сравнение с най-съвременните модели на пазара, предоставяйки по-богато и разнообразно творческо генериране на изображения."
  },
  "flux.1-schnell": {
-    "description": "Коригиран потоков трансформър с 12 милиарда параметри, способен да генерира изображения въз основа на текстово описание."
+    "description": "FLUX.1-schnell, високопроизводителен модел за генериране на изображения, подходящ за бързо създаване на изображения в различни стилове."
  },
  "gemini-1.0-pro-001": {
    "description": "Gemini 1.0 Pro 001 (Тунинг) предлага стабилна и настройваема производителност, идеален избор за решения на сложни задачи."
@@ -1538,6 +1565,9 @@
  "glm-4-0520": {
    "description": "GLM-4-0520 е най-новата версия на модела, проектирана за високо сложни и разнообразни задачи, с отлични резултати."
  },
+  "glm-4-32b-0414": {
+    "description": "GLM-4 32B 0414, универсален голям езиков модел от серията GLM, поддържащ многозадачно генериране и разбиране на текст."
+  },
  "glm-4-9b-chat": {
    "description": "GLM-4-9B-Chat показва висока производителност в области като семантика, математика, логическо мислене, програмиране и общи знания. Поддържа също така уеб браузване, изпълнение на код, извикване на персонализирани инструменти и извеждане на заключения от дълги текстове. Поддържа 26 езика, включително японски, корейски и немски."
  },
@@ -1826,6 +1856,18 @@
  "gpt-5-pro": {
    "description": "GPT-5 pro използва повече изчислителна мощност за по-задълбочено мислене и постоянно предоставя по-добри отговори."
  },
+  "gpt-5.1": {
+    "description": "GPT-5.1 — флагмански модел, оптимизиран за кодиране и задачи с агенти, поддържа конфигурируема интензивност на разсъждение и по-дълъг контекст."
+  },
+  "gpt-5.1-chat-latest": {
+    "description": "GPT-5.1 Chat: вариант на GPT-5.1 за ChatGPT, подходящ за чат сценарии."
+  },
+  "gpt-5.1-codex": {
+    "description": "GPT-5.1 Codex: версия на GPT-5.1, оптимизирана за агентски задачи по кодиране, използваема в Responses API за по-сложни работни потоци с код и агенти."
+  },
+  "gpt-5.1-codex-mini": {
+    "description": "GPT-5.1 Codex mini: по-компактен и икономичен вариант на Codex, оптимизиран за агентски задачи по кодиране."
+  },
  "gpt-audio": {
    "description": "GPT Audio е универсален чат модел, ориентиран към аудио вход и изход, поддържащ използване на аудио I/O в Chat Completions API."
  },
@@ -2001,13 +2043,13 @@
    "description": "Imagen: серия от модели от 4-то поколение за генериране на изображения от текст"
  },
  "imagen-4.0-generate-preview-06-06": {
-    "description": "Imagen 4-то поколение текст-към-изображение модел серия"
+    "description": "Четвърто поколение модели Imagen за генериране на изображения от текст."
  },
  "imagen-4.0-ultra-generate-001": {
    "description": "Imagen, 4-то поколение модел за преобразуване на текст в изображение, серия Ultra"
  },
  "imagen-4.0-ultra-generate-preview-06-06": {
-    "description": "Imagen 4-то поколение текст-към-изображение модел серия Ултра версия"
+    "description": "Ultra версия на четвъртото поколение модели Imagen за генериране на изображения от текст."
  },
  "inception/mercury-coder-small": {
    "description": "Mercury Coder Small е идеален за задачи по генериране, отстраняване на грешки и рефакториране на код с минимална латентност."
@@ -2036,14 +2078,26 @@
  "internlm3-latest": {
    "description": "Нашата най-нова серия модели с изключителна производителност на разсъжденията, водеща в категорията на отворените модели. По подразбиране сочи към най-ново публикуваната серия модели InternLM3."
  },
+  "internvl2.5-38b-mpo": {
+    "description": "InternVL2.5 38B MPO, мултимодален предварително обучен модел, способен на сложни задачи за визуално-текстово разсъждение."
+  },
  "internvl2.5-latest": {
    "description": "Версията InternVL2.5, която все още поддържаме, предлага отлична и стабилна производителност. По подразбиране сочи към нашата най-нова версия на серията InternVL2.5, текущо сочи към internvl2.5-78b."
  },
+  "internvl3-14b": {
+    "description": "InternVL3 14B, мултимодален модел със среден мащаб, постигащ баланс между производителност и разходи."
+  },
+  "internvl3-1b": {
+    "description": "InternVL3 1B, лек мултимодален модел, подходящ за внедряване в среди с ограничени ресурси."
+  },
+  "internvl3-38b": {
+    "description": "InternVL3 38B, мащабен мултимодален модел с отворен код, предназначен за задачи с висока точност на визуално-текстово разбиране."
+  },
  "internvl3-latest": {
    "description": "Нашият най-нов мултимодален голям модел, с по-силни способности за разбиране на текст и изображения, дългосрочно разбиране на изображения, производителност, сравнима с водещи затворени модели. По подразбиране сочи към нашата най-нова версия на серията InternVL, текущо сочи към internvl3-78b."
  },
  "irag-1.0": {
-    "description": "Собствената технология iRAG (image based RAG) на Baidu за генериране на изображения с подсилено търсене, комбинираща милиарди изображения от търсачката на Baidu с мощни основни модели, позволява създаването на изключително реалистични изображения, далеч надминаващи родните системи за генериране на изображения от текст, без изкуствен вид и с ниски разходи. iRAG се характеризира с липса на халюцинации, изключителна реалистичност и незабавна готовност."
+    "description": "ERNIE iRAG, модел за генериране, подсилен с визуално търсене, поддържащ търсене по изображение, визуално-текстово търсене и създаване на съдържание."
  },
  "jamba-large": {
    "description": "Нашият най-мощен и напреднал модел, проектиран за справяне с комплексни задачи на корпоративно ниво, с изключителна производителност."
@@ -2064,7 +2118,7 @@
    "description": "Моделът kimi-k2-0905-preview има контекстна дължина от 256k, с по-силни способности за агентно кодиране, по-изразителна естетика и практичност на фронтенд кода, както и по-добро разбиране на контекста."
  },
  "kimi-k2-instruct": {
-    "description": "Kimi K2 Instruct е голям езиков модел, разработен от Moonshot AI, с изключителна способност за обработка на дълъг контекст."
+    "description": "Kimi K2 Instruct, официален модел за извеждане от Kimi, поддържащ дълъг контекст, програмиране, въпроси и отговори и други сценарии."
  },
  "kimi-k2-turbo-preview": {
    "description": "Kimi-k2 е базов модел с MoE архитектура, който притежава изключителни възможности за работа с код и агентни функции. Общият брой параметри е 1T, а активните параметри са 32B. В бенчмарковете за основни категории като общо знание и разсъждение, програмиране, математика и агентни задачи, моделът K2 превъзхожда другите водещи отворени модели."
@@ -2174,6 +2228,9 @@
  "megrez-3b-instruct": {
    "description": "Megrez 3B Instruct е ефективен модел с малък брой параметри, разработен от Wuwen Xinqiong."
  },
+  "meituan/longcat-flash-chat": {
+    "description": "Longcat Flash Chat е с отворен код от Meituan и представлява базов модел без мисловни процеси, оптимизиран за диалогови взаимодействия и задачи на интелигентни агенти, с изключителна ефективност при използване на инструменти и в сложни многократни взаимодействия."
+  },
  "meta-llama-3-70b-instruct": {
    "description": "Мощен модел с 70 милиарда параметри, отличаващ се в разсъждения, кодиране и широки езикови приложения."
  },
@@ -2405,6 +2462,12 @@
  "minimax-m2": {
    "description": "MiniMax M2 е ефективен голям езиков модел, създаден специално за кодиране и работни процеси с агенти."
  },
+  "minimax/minimax-m2": {
+    "description": "Създаден специално за ефективно кодиране и работни потоци с агенти."
+  },
+  "minimaxai/minimax-m2": {
+    "description": "MiniMax-M2 е компактен, бърз и икономичен хибриден експертен (MoE) модел с общо 230 милиарда параметъра и 10 милиарда активни параметъра, създаден за постигане на върхова производителност при кодиране и задачи, свързани с интелигентни агенти, като същевременно поддържа силен общ интелект. Моделът се отличава с отлична работа при редактиране на множество файлове, затворен цикъл кодиране-изпълнение-поправка, тестване и валидиране на поправки, както и при сложни дълговерижни инструментални процеси, което го прави идеален избор за работния процес на разработчиците."
+  },
  "ministral-3b-latest": {
    "description": "Ministral 3B е световен лидер сред моделите на Mistral."
  },
@@ -2735,6 +2798,54 @@
  "pro-deepseek-v3": {
    "description": "Специализиран модел за корпоративни услуги, включващ паралелна обработка."
  },
+  "qianfan-70b": {
+    "description": "Qianfan 70B, голям китайски езиков модел, подходящ за създаване на висококачествено съдържание и сложни разсъждения."
+  },
+  "qianfan-8b": {
+    "description": "Qianfan 8B, универсален модел със среден мащаб, балансиращ между разходи и ефективност за генериране на текст и въпроси и отговори."
+  },
+  "qianfan-agent-intent-32k": {
+    "description": "Qianfan Agent Intent 32K, модел за разпознаване на намерения и оркестрация на интелигентни агенти, поддържащ дълъг контекст."
+  },
+  "qianfan-agent-lite-8k": {
+    "description": "Qianfan Agent Lite 8K, лек модел за интелигентни агенти, подходящ за нискобюджетни многократни диалози и бизнес оркестрация."
+  },
+  "qianfan-agent-speed-32k": {
+    "description": "Qianfan Agent Speed 32K, високопроизводителен модел за интелигентни агенти, подходящ за мащабни и многозадачни приложения."
+  },
+  "qianfan-agent-speed-8k": {
+    "description": "Qianfan Agent Speed 8K, модел за интелигентни агенти с висока едновременност, предназначен за кратки диалози и бързи отговори."
+  },
+  "qianfan-check-vl": {
+    "description": "Qianfan Check VL, мултимодален модел за проверка и откриване на съдържание, поддържащ съответствие и разпознаване на визуално-текстово съдържание."
+  },
+  "qianfan-composition": {
+    "description": "Qianfan Composition, мултимодален творчески модел, поддържащ разбиране и генериране на комбинирано визуално и текстово съдържание."
+  },
+  "qianfan-engcard-vl": {
+    "description": "Qianfan EngCard VL, мултимодален модел, фокусиран върху англоезични сценарии за разпознаване."
+  },
+  "qianfan-lightning-128b-a19b": {
+    "description": "Qianfan Lightning 128B A19B, високопроизводителен универсален китайски езиков модел, подходящ за сложни въпроси и мащабни разсъждения."
+  },
+  "qianfan-llama-vl-8b": {
+    "description": "Qianfan Llama VL 8B, мултимодален модел, базиран на Llama, предназначен за общо визуално-текстово разбиране."
+  },
+  "qianfan-multipicocr": {
+    "description": "Qianfan MultiPicOCR, OCR модел за множество изображения, поддържащ откриване и разпознаване на текст от няколко изображения."
+  },
+  "qianfan-qi-vl": {
+    "description": "Qianfan QI VL, мултимодален въпросно-отговорен модел, поддържащ прецизно търсене и отговори в сложни визуално-текстови сценарии."
+  },
+  "qianfan-singlepicocr": {
+    "description": "Qianfan SinglePicOCR, OCR модел за едно изображение, поддържащ високоточна разпознаваемост на символи."
+  },
+  "qianfan-vl-70b": {
+    "description": "Qianfan VL 70B, голям визуално-езиков модел, подходящ за сложни визуално-текстови задачи."
+  },
+  "qianfan-vl-8b": {
+    "description": "Qianfan VL 8B, лек визуално-езиков модел, подходящ за ежедневни визуално-текстови въпроси и анализи."
+  },
  "qvq-72b-preview": {
    "description": "QVQ моделът е експериментален изследователски модел, разработен от екипа на Qwen, фокусиран върху повишаване на визуалните способности за разсъждение, особено в областта на математическото разсъждение."
  },
@@ -2886,7 +2997,7 @@
    "description": "Модел с мащаб 72B, отворен за обществеността от Qwen 2.5."
  },
  "qwen2.5-7b-instruct": {
-    "description": "Модел с мащаб 7B, отворен за обществеността от Qwen 2.5."
+    "description": "Qwen2.5 7B Instruct, зрял отворен модел с инструкции, подходящ за диалози и генериране в различни сценарии."
  },
  "qwen2.5-coder-1.5b-instruct": {
    "description": "通义千问（Qwen） е отворен код модел за програмиране."
@@ -2919,13 +3030,13 @@
    "description": "Моделите от серията Qwen-Omni поддържат входни данни от множество модалности, включително видео, аудио, изображения и текст, и изходят аудио и текст."
  },
  "qwen2.5-vl-32b-instruct": {
-    "description": "Моделите от серията Qwen2.5-VL подобряват интелигентността, практичността и приложимостта на модела, като ги правят по-ефективни в сценарии като естествени разговори, създаване на съдържание, професионални услуги и разработка на код. Версията 32B използва технологии за обучение с подсилване за оптимизиране на модела, предлагайки в сравнение с другите модели от серията Qwen2.5 VL по-съответстващ на човешките предпочитания стил на изход, способност за разсъждение върху сложни математически проблеми, както и фино разбиране и разсъждение на изображения."
+    "description": "Qwen2.5 VL 32B Instruct, мултимодален отворен модел, подходящ за частно внедряване и разнообразни приложения."
  },
  "qwen2.5-vl-72b-instruct": {
    "description": "Подобрение на следването на инструкции, математика, решаване на проблеми и код, повишаване на способността за разпознаване на обекти, поддържа директно точно локализиране на визуални елементи в различни формати, поддържа разбиране на дълги видео файлове (до 10 минути) и локализиране на събития в секунда, може да разбира времеви последователности и скорости, базирано на способности за анализ и локализация, поддържа управление на OS или Mobile агенти, силна способност за извличане на ключова информация и изход в JSON формат, тази версия е 72B, най-силната версия в серията."
  },
  "qwen2.5-vl-7b-instruct": {
-    "description": "Подобрение на следването на инструкции, математика, решаване на проблеми и код, повишаване на способността за разпознаване на обекти, поддържа директно точно локализиране на визуални елементи в различни формати, поддържа разбиране на дълги видео файлове (до 10 минути) и локализиране на събития в секунда, може да разбира времеви последователности и скорости, базирано на способности за анализ и локализация, поддържа управление на OS или Mobile агенти, силна способност за извличане на ключова информация и изход в JSON формат, тази версия е 72B, най-силната версия в серията."
+    "description": "Qwen2.5 VL 7B Instruct, лек мултимодален модел, балансиращ между разходи за внедряване и разпознаваемост."
  },
  "qwen2.5-vl-instruct": {
    "description": "Qwen2.5-VL е най-новата версия на визуално-езиковия модел от семейството Qwen."
@@ -2952,46 +3063,46 @@
    "description": "Qwen3 е новото поколение на Alibaba голям езиков модел, който предлага отлична производителност, за да отговори на разнообразни приложения."
  },
  "qwen3-0.6b": {
-    "description": "Qwen3 е ново поколение модел с значително подобрени способности, който достига водещо ниво в индустрията в области като разсъждение, общо използване, агенти и многоезичност, и поддържа превключване на режимите на разсъждение."
+    "description": "Qwen3 0.6B, начален модел, подходящ за прости разсъждения и среди с изключително ограничени ресурси."
  },
  "qwen3-1.7b": {
-    "description": "Qwen3 е ново поколение модел с значително подобрени способности, който достига водещо ниво в индустрията в области като разсъждение, общо използване, агенти и многоезичност, и поддържа превключване на режимите на разсъждение."
+    "description": "Qwen3 1.7B, ултралек модел, удобен за внедряване на крайни и гранични устройства."
  },
  "qwen3-14b": {
-    "description": "Qwen3 е ново поколение модел с значително подобрени способности, който достига водещо ниво в индустрията в области като разсъждение, общо използване, агенти и многоезичност, и поддържа превключване на режимите на разсъждение."
+    "description": "Qwen3 14B, модел със среден мащаб, подходящ за многоезични въпроси и генериране на текст."
  },
  "qwen3-235b-a22b": {
-    "description": "Qwen3 е ново поколение модел с значително подобрени способности, който достига водещо ниво в индустрията в области като разсъждение, общо използване, агенти и многоезичност, и поддържа превключване на режимите на разсъждение."
+    "description": "Qwen3 235B A22B, универсален голям модел, предназначен за различни сложни задачи."
  },
  "qwen3-235b-a22b-instruct-2507": {
-    "description": "Отворен модел в не-мисловен режим, базиран на Qwen3, с леки подобрения в субективните творчески способности и безопасността на модела спрямо предишната версия (Tongyi Qianwen 3-235B-A22B)."
+    "description": "Qwen3 235B A22B Instruct 2507, флагмански универсален модел с инструкции, подходящ за генериране и разсъждение."
  },
  "qwen3-235b-a22b-thinking-2507": {
-    "description": "Отворен модел в мисловен режим, базиран на Qwen3, с големи подобрения в логическите способности, общите умения, обогатяването на знания и творческите способности спрямо предишната версия (Tongyi Qianwen 3-235B-A22B), подходящ за сложни задачи с високи изисквания за разсъждение."
+    "description": "Qwen3 235B A22B Thinking 2507, ултрамащабен мисловен модел, предназначен за сложни разсъждения."
  },
  "qwen3-30b-a3b": {
-    "description": "Qwen3 е ново поколение модел с значително подобрени способности, който достига водещо ниво в индустрията в области като разсъждение, общо използване, агенти и многоезичност, и поддържа превключване на режимите на разсъждение."
+    "description": "Qwen3 30B A3B, универсален модел със средно голям мащаб, балансиращ между разходи и ефективност."
  },
  "qwen3-30b-a3b-instruct-2507": {
-    "description": "В сравнение с предишната версия (Qwen3-30B-A3B), общите способности на английски, китайски и многоезични задачи са значително подобрени. Специализирана оптимизация за субективни и отворени задачи, значително по-добре съобразена с предпочитанията на потребителите, което позволява предоставяне на по-полезни отговори."
+    "description": "Qwen3 30B A3B Instruct 2507, модел със среден мащаб с инструкции, подходящ за висококачествено генериране и въпроси и отговори."
  },
  "qwen3-30b-a3b-thinking-2507": {
-    "description": "Базиран на отворения модел в режим мислене на Qwen3, в сравнение с предишната версия (Tongyi Qianwen 3-30B-A3B) логическите способности, общите умения, знанията и творческите способности са значително подобрени, подходящ за сложни сценарии с интензивно разсъждение."
+    "description": "Qwen3 30B A3B Thinking 2507, мисловен модел със среден мащаб, балансиращ между точност и разходи."
  },
  "qwen3-32b": {
-    "description": "Qwen3 е ново поколение модел с значително подобрени способности, който достига водещо ниво в индустрията в области като разсъждение, общо използване, агенти и многоезичност, и поддържа превключване на режимите на разсъждение."
+    "description": "Qwen3 32B, подходящ за универсални задачи, изискващи по-силни способности за разбиране."
  },
  "qwen3-4b": {
-    "description": "Qwen3 е ново поколение модел с значително подобрени способности, който достига водещо ниво в индустрията в области като разсъждение, общо използване, агенти и многоезичност, и поддържа превключване на режимите на разсъждение."
+    "description": "Qwen3 4B, подходящ за малки и средни приложения и локални сценарии за извеждане."
  },
  "qwen3-8b": {
-    "description": "Qwen3 е ново поколение модел с значително подобрени способности, който достига водещо ниво в индустрията в области като разсъждение, общо използване, агенти и многоезичност, и поддържа превключване на режимите на разсъждение."
+    "description": "Qwen3 8B, лек модел с гъвкаво внедряване, подходящ за приложения с висока едновременност."
  },
  "qwen3-coder-30b-a3b-instruct": {
    "description": "Откритият кодов модел на Tongyi Qianwen. Най-новият qwen3-coder-30b-a3b-instruct е модел за генериране на код, базиран на Qwen3, с мощни способности като Coding Agent, умело използва инструменти и взаимодейства с околната среда, способен на автономно програмиране и отлични кодови умения, съчетани с общи способности."
  },
  "qwen3-coder-480b-a35b-instruct": {
-    "description": "Отворена версия на кодовия модел Tongyi Qianwen. Най-новият qwen3-coder-480b-a35b-instruct е кодов модел, базиран на Qwen3, с мощни Coding Agent способности, умения за използване на инструменти и взаимодействие с околната среда, способен на автономно програмиране с отлични кодови и общи умения."
+    "description": "Qwen3 Coder 480B A35B Instruct, флагмански модел за програмиране, поддържащ многoезично кодиране и сложен анализ на код."
  },
  "qwen3-coder-flash": {
    "description": "Кодиращ модел на Tongyi Qianwen. Най-новата серия модели Qwen3-Coder е базирана на Qwen3 и е модел за генериране на код с мощни възможности на Coding Agent, умеещ да използва инструменти и да взаимодейства с околната среда, способен на автономно програмиране, с изключителни кодови умения и същевременно общи способности."
@@ -3005,32 +3116,41 @@
  "qwen3-max": {
    "description": "Серията Max на Tongyi Qianwen 3 предлага значително подобрена обща способност в сравнение с серия 2.5, с подобрено разбиране на текст на китайски и английски, способност за следване на сложни инструкции, умения за субективни отворени задачи, многоезични възможности и повишена способност за извикване на инструменти; моделът демонстрира по-малко халюцинации на знания. Последният модел qwen3-max включва специални подобрения в програмирането на агенти и извикването на инструменти в сравнение с версията qwen3-max-preview. Официалната версия, публикувана сега, достига SOTA ниво в своята област и е адаптирана за по-сложни изисквания на интелигентни агенти."
  },
+  "qwen3-max-preview": {
+    "description": "Най-ефективният модел от серията Tongyi Qianwen, подходящ за сложни и многоетапни задачи. Прегледната версия вече поддържа разсъждение."
+  },
  "qwen3-next-80b-a3b-instruct": {
    "description": "Базирано на Qwen3, ново поколение отворен модел без мисловен режим, който предлага по-добро разбиране на китайски текстове, подобрени логически умения и по-добри резултати при задачи за генериране на текст в сравнение с предишната версия (Tongyi Qianwen 3-235B-A22B-Instruct-2507)."
  },
  "qwen3-next-80b-a3b-thinking": {
-    "description": "Базирано на Qwen3, ново поколение отворен модел с мисловен режим, който подобрява спазването на инструкции и предоставя по-кратки и точни обобщения в сравнение с предишната версия (Tongyi Qianwen 3-235B-A22B-Thinking-2507)."
+    "description": "Qwen3 Next 80B A3B Thinking, флагманска версия за разсъждение, предназначена за сложни задачи."
  },
  "qwen3-omni-flash": {
    "description": "Моделът Qwen-Omni приема комбинирани входове от текст, изображения, аудио и видео, и генерира отговори под формата на текст или реч. Предлага разнообразни хуманизирани гласове, поддържа много езици и диалекти, и е приложим в сценарии като текстово творчество, визуално разпознаване и гласови асистенти."
  },
  "qwen3-vl-235b-a22b-instruct": {
-    "description": "Qwen3 VL 235B A22B в non-thinking режим (Instruct), подходящ за инструкции без необходимост от дълбоко разсъждение, като същевременно запазва силни визуални разбирания."
+    "description": "Qwen3 VL 235B A22B Instruct, флагмански мултимодален модел, предназначен за взискателни задачи по разбиране и творчество."
  },
  "qwen3-vl-235b-a22b-thinking": {
-    "description": "Qwen3 VL 235B A22B в мисловен режим (отворен код), предназначен за сложни задачи с интензивно разсъждение и разбиране на дълги видеа, предоставяйки водещи способности за визуално и текстово разсъждение."
+    "description": "Qwen3 VL 235B A22B Thinking, флагманска мисловна версия, използвана за сложни мултимодални разсъждения и планиране."
  },
  "qwen3-vl-30b-a3b-instruct": {
-    "description": "Qwen3 VL 30B в non-thinking режим (Instruct), насочен към обичайни сценарии за следване на инструкции, като същевременно поддържа високо ниво на мултимодално разбиране и генериране."
+    "description": "Qwen3 VL 30B A3B Instruct, голям мултимодален модел, балансиращ между точност и производителност при разсъждение."
  },
  "qwen3-vl-30b-a3b-thinking": {
-    "description": "Qwen-VL (отворен код) предлага способности за визуално разбиране и генериране на текст, поддържа взаимодействие с интелигентни агенти, визуално кодиране, пространствено възприятие, разбиране на дълги видеа и дълбоко мислене, с подобрено разпознаване на текст и многоезична поддръжка в сложни сценарии."
+    "description": "Qwen3 VL 30B A3B Thinking, дълбока мисловна версия за сложни мултимодални задачи."
+  },
+  "qwen3-vl-32b-instruct": {
+    "description": "Qwen3 VL 32B Instruct, мултимодален модел с фино настройване по инструкции, подходящ за висококачествени визуално-текстови въпроси и творчество."
+  },
+  "qwen3-vl-32b-thinking": {
+    "description": "Qwen3 VL 32B Thinking, мултимодална дълбока мисловна версия, подсилваща сложни разсъждения и анализ на дълги вериги."
  },
  "qwen3-vl-8b-instruct": {
-    "description": "Qwen3 VL 8B в non-thinking режим (Instruct), подходящ за стандартни задачи по мултимодално генериране и разпознаване."
+    "description": "Qwen3 VL 8B Instruct, лек мултимодален модел, подходящ за ежедневни визуални въпроси и интеграция в приложения."
  },
  "qwen3-vl-8b-thinking": {
-    "description": "Qwen3 VL 8B в мисловен режим, предназначен за леки мултимодални задачи по разсъждение и взаимодействие, като същевременно запазва способността за разбиране на дълъг контекст."
+    "description": "Qwen3 VL 8B Thinking, мултимодален модел с мисловна верига, подходящ за детайлно разсъждение върху визуална информация."
  },
  "qwen3-vl-flash": {
    "description": "Qwen3 VL Flash: олекотена версия за високоскоростно разсъждение, подходяща за сценарии, чувствителни към закъснение или с голям обем заявки."
@@ -3263,6 +3383,12 @@
  "wizardlm2:8x22b": {
    "description": "WizardLM 2 е езиков модел, предоставен от Microsoft AI, който се отличава в сложни диалози, многоезичност, разсъждение и интелигентни асистенти."
  },
+  "x-ai/grok-4-fast": {
+    "description": "С радост представяме Grok 4 Fast — нашият най-нов напредък в модели за ефективно и икономично извеждане."
+  },
+  "x-ai/grok-code-fast-1": {
+    "description": "С гордост представяме grok-code-fast-1 — бърз и икономичен модел за извеждане, който се отличава в агентно кодиране."
+  },
  "x1": {
    "description": "Моделът Spark X1 ще бъде допълнително обновен, като на базата на водещите в страната резултати в математически задачи, ще постигне ефекти в общи задачи като разсъждение, генериране на текст и разбиране на език, сравними с OpenAI o1 и DeepSeek R1."
  },
@@ -3323,6 +3449,9 @@
  "yi-vision-v2": {
    "description": "Модел за сложни визуални задачи, предлагащ висока производителност в разбирането и анализа на базата на множество изображения."
  },
+  "z-ai/glm-4.6": {
+    "description": "GLM-4.6 е най-новият флагмански модел на Zhipu, който значително надминава предшествениците си в напреднало кодиране, обработка на дълги текстове, извеждане и способности на интелигентни агенти."
+  },
  "zai-org/GLM-4.5": {
    "description": "GLM-4.5 е базов модел, специално създаден за интелигентни агенти, използващ архитектура с микс от експерти (Mixture-of-Experts). Той е дълбоко оптимизиран за използване на инструменти, уеб браузване, софтуерно инженерство и фронтенд програмиране, и поддържа безпроблемна интеграция с кодови агенти като Claude Code и Roo Code. GLM-4.5 използва смесен режим на разсъждение, подходящ за сложни и ежедневни приложения."
  },
@@ -236,6 +236,8 @@
  },
  "inspector": {
    "args": "Преглед на списъка с параметри",
+    "delete": "Изтриване на извикване на инструмент",
+    "orphanedToolCall": "Това извикване на инструмент може да е станало изолирано поради необичайни причини, което може да повлияе на нормалното изпълнение на агента. Моля, премахнете го.",
    "pluginRender": "Преглед на интерфейса на плъгина"
  },
  "list": {
@@ -251,14 +253,20 @@
  },
  "localSystem": {
    "apiName": {
+      "editLocalFile": "Редактиране на файл",
+      "getCommandOutput": "Получаване на изход от командата",
+      "globLocalFiles": "Търсене на съвпадащи файлове",
+      "grepContent": "Търсене на съдържание",
+      "killCommand": "Прекратяване на изпълнението на командата",
      "listLocalFiles": "Преглед на списък с файлове",
      "moveLocalFiles": "Преместване на файлове",
      "readLocalFile": "Четене на съдържание на файл",
      "renameLocalFile": "Преименуване",
+      "runCommand": "Изпълни код",
      "searchLocalFiles": "Търсене на файлове",
      "writeLocalFile": "Запис в файл"
    },
-    "title": "Локални файлове"
+    "title": "Локална система"
  },
  "mcpInstall": {
    "CHECKING_INSTALLATION": "Проверка на инсталационната среда...",
@@ -2,6 +2,45 @@
  "about": {
    "title": "Относно"
  },
+  "agentInfoDescription": {
+    "basic": {
+      "avatar": "Аватар",
+      "description": "Описание",
+      "name": "Име",
+      "tags": "Етикети",
+      "title": "Информация за асистента"
+    },
+    "chat": {
+      "displayMode": "Режим на показване",
+      "enableHistoryCount": "Разреши броене на историята",
+      "historyCount": "Брой съобщения в историята",
+      "no": "Не",
+      "searchMode": "Режим на търсене",
+      "title": "Предпочитания за чат",
+      "yes": "Да"
+    },
+    "model": {
+      "maxTokens": "Максимален брой токени",
+      "model": "Модел",
+      "provider": "Доставчик",
+      "temperature": "Температура",
+      "title": "Настройки на модела",
+      "topP": "Стойност на Top P"
+    },
+    "plugins": {
+      "count": "Настройки на плъгини ({{count}})",
+      "empty": "Все още няма инсталирани плъгини",
+      "title": "Инсталирани плъгини"
+    },
+    "role": {
+      "systemRole": "Системна подсказка",
+      "title": "Настройки на роля"
+    },
+    "value": {
+      "unset": "Не е зададено",
+      "untitled": "Безименен асистент"
+    }
+  },
  "agentTab": {
    "chat": "Предпочитания за чат",
    "meta": "Информация за асистента",
@@ -18,6 +57,8 @@
    },
    "title": "Анализи"
  },
+  "checking": "Проверка...",
+  "checkingPermissions": "Проверка на разрешения...",
  "danger": {
    "clear": {
      "action": "Изчисти сега",
@@ -146,6 +187,58 @@
    },
    "waitingForMore": "Още модели са <1>планирани да бъдат добавени</1>, очаквайте"
  },
+  "marketPublish": {
+    "modal": {
+      "changelog": {
+        "extra": "Опишете основните промени и подобрения в тази версия",
+        "label": "Дневник на промените",
+        "maxLengthError": "Дневникът на промените не може да надвишава 500 знака",
+        "placeholder": "Моля, въведете дневник на промените",
+        "required": "Моля, въведете дневник на промените"
+      },
+      "comparison": {
+        "local": "Текуща локална версия",
+        "remote": "Текуща публикувана версия"
+      },
+      "identifier": {
+        "extra": "Идентификаторът ще бъде уникален за асистента. Препоръчва се използване на малки букви, цифри и тирета",
+        "label": "Идентификатор на асистента",
+        "lengthError": "Дължината на идентификатора трябва да е между 3 и 50 знака",
+        "patternError": "Идентификаторът може да съдържа само малки букви, цифри и тирета",
+        "placeholder": "Моля, въведете уникален идентификатор, напр.: web-development",
+        "required": "Моля, въведете идентификатор на асистента"
+      },
+      "loading": {
+        "fetchingRemote": "Зареждане на отдалечени данни...",
+        "submit": "Публикуване на асистента...",
+        "upload": "Публикуване на нова версия..."
+      },
+      "messages": {
+        "createVersionFailed": "Неуспешно създаване на версия: {{message}}",
+        "fetchRemoteFailed": "Неуспешно извличане на отдалечени данни за асистента",
+        "missingIdentifier": "Текущият асистент няма идентификатор за пазара",
+        "notAuthenticated": "Моля, влезте в акаунта си в пазара",
+        "publishFailed": "Публикуването не бе успешно: {{message}}"
+      },
+      "submitButton": "Публикувай",
+      "title": {
+        "submit": "Сподели в пазара за асистенти",
+        "upload": "Публикувай нова версия"
+      }
+    },
+    "resultModal": {
+      "message": "Асистентът е изпратен за преглед. След одобрение ще бъде автоматично публикуван. Кликнете върху „Преглед в пазара“, за да видите публикувания асистент.",
+      "view": "Преглед в пазара"
+    },
+    "submit": {
+      "button": "Сподели в пазара",
+      "tooltip": "Сподели асистента в пазара"
+    },
+    "upload": {
+      "button": "Публикувай нова версия",
+      "tooltip": "Публикувай нова версия в пазара за асистенти"
+    }
+  },
  "message": {
    "success": "Актуализацията беше успешна"
  },
@@ -561,7 +654,7 @@
  },
  "submitAgentModal": {
    "button": "Изпрати агент",
-    "identifier": "Идентификатор на агент",
+    "identifier": "Идентификатор на асистента (identifier)",
    "metaMiss": "Моля, попълнете информацията за агента, преди да го изпратите. Тя трябва да включва име, описание и тагове",
    "placeholder": "Въведете уникален идентификатор за агента, напр. web-development",
    "tooltips": "Споделяне на пазара на агенти"
@@ -15,8 +15,22 @@
    "prompt": "подсказка"
  },
  "localFiles": {
+    "editFile": {
+      "newString": "Замени с",
+      "oldString": "Търсене на съдържание",
+      "replaceAll": "Замени всички съвпадения",
+      "replaceFirst": "Замени само първото съвпадение"
+    },
    "file": "Файл",
    "folder": "Папка",
+    "moveFiles": {
+      "itemsMoved": "Преместени {{count}} елемента:",
+      "itemsMoved_one": "Преместен {{count}} елемент:",
+      "itemsMoved_other": "Преместени {{count}} елемента:",
+      "itemsToMove": "{{count}} елемента за преместване:",
+      "itemsToMove_one": "{{count}} елемент за преместване:",
+      "itemsToMove_other": "{{count}} елемента за преместване:"
+    },
    "open": "Отвори",
    "openFile": "Отвори файл",
    "openFolder": "Отвори папка",
@@ -26,7 +40,12 @@
    "readFile": "Прочети файл",
    "readFileError": "Неуспешно четене на файла, моля, проверете дали пътят към файла е правилен",
    "readFiles": "Прочети файлове",
-    "readFilesError": "Неуспешно четене на файловете, моля, проверете дали пътят към файловете е правилен"
+    "readFilesError": "Неуспешно четене на файловете, моля, проверете дали пътят към файловете е правилен",
+    "writeFile": {
+      "characters": "Знаци",
+      "preview": "Преглед на съдържанието",
+      "truncated": "Съкратено"
+    }
  },
  "search": {
    "createNewSearch": "Създаване на нова търсене",
@@ -6,6 +6,7 @@
    "confirmRemoveUnstarred": "Ще бъдат изтрити темите, които не са запазени. След изтриването им не може да се възстановят. Моля, действайте внимателно.",
    "duplicate": "Създаване на копие",
    "export": "Експортиране на темата",
+    "openInNewWindow": "Отвори страницата в нов прозорец",
    "removeAll": "Изтриване на всички теми",
    "removeUnstarred": "Изтриване на незапазените теми"
  },
@@ -145,6 +145,50 @@
    "apikey": "API-Schlüssel Verwaltung",
    "profile": "Profil",
    "security": "Sicherheit",
-    "stats": "Statistiken"
+    "stats": "Statistiken",
+    "usage": "Nutzungsstatistik"
+  },
+  "usage": {
+    "activeModels": {
+      "modelTable": "Modellliste",
+      "models": "Aktive Modelle",
+      "providerTable": "Anbieterliste",
+      "providers": "Aktive Anbieter",
+      "table": {
+        "calls": "Anzahl der Aufrufe",
+        "model": "Modell",
+        "provider": "Anbieter",
+        "spend": "Kosten"
+      }
+    },
+    "cards": {
+      "month": {
+        "modelCalls": "Modellaufrufe",
+        "title": "Ausgaben dieses Monats"
+      },
+      "today": {
+        "title": "Heutige Ausgaben",
+        "yesterday": "Gestern"
+      }
+    },
+    "table": {
+      "actions": "Aktionen",
+      "createdAt": "Nutzungszeit",
+      "inputTokens": "Eingabe-Token",
+      "model": "Modell",
+      "outputTokens": "Ausgabe-Token",
+      "spend": "Kosten",
+      "tps": "TPS",
+      "ttft": "TTFT",
+      "type": "Aufruftyp"
+    },
+    "trends": {
+      "spend": "Betrag",
+      "tokens": "Token"
+    },
+    "welcome": {
+      "model": "Modell",
+      "provider": "Anbieter"
+    }
  }
 }
@@ -17,6 +17,7 @@
  "availableAgents": "Verfügbare Assistenten",
  "backToBottom": "Zurück zum Ende",
  "chatList": {
+    "expandMessage": "Nachricht anzeigen",
    "longMessageDetail": "Details anzeigen"
  },
  "clearCurrentMessages": "Aktuelle Nachrichten löschen",
@@ -173,8 +174,11 @@
    "title": "Mitglieder erwähnen"
  },
  "messageAction": {
+    "collapse": "Nachricht ausblenden",
+    "continueGeneration": "Generierung fortsetzen",
    "delAndRegenerate": "Löschen und neu generieren",
    "deleteDisabledByThreads": "Es gibt Unterthemen, die Löschung ist nicht möglich.",
+    "expand": "Nachricht anzeigen",
    "regenerate": "Neu generieren"
  },
  "messages": {
@@ -239,6 +243,7 @@
  "noMatchingAgents": "Keine passenden Mitglieder gefunden",
  "noMembersYet": "Diese Gruppe hat noch keine Mitglieder. Klicken Sie auf die +-Schaltfläche, um Assistenten einzuladen.",
  "noSelectedAgents": "Noch keine Mitglieder ausgewählt",
+  "openInNewWindow": "In neuem Fenster öffnen",
  "owner": "Gruppeninhaber",
  "pin": "Anheften",
  "pinOff": "Anheften aufheben",
@@ -325,6 +330,11 @@
    "screenshot": "Screenshot",
    "settings": "Exporteinstellungen",
    "text": "Text",
+    "widthMode": {
+      "label": "Breitenmodus",
+      "narrow": "Schmalbildmodus",
+      "wide": "Breitbildmodus"
+    },
    "withBackground": "Mit Hintergrundbild",
    "withFooter": "Mit Fußzeile",
    "withPluginInfo": "Mit Plugin-Informationen",
@@ -367,6 +377,28 @@
    "remained": "Verbleibend",
    "used": "Verwendet"
  },
+  "tool": {
+    "intervention": {
+      "approve": "Genehmigen",
+      "approveAndRemember": "Genehmigen und merken",
+      "approveOnce": "Nur dieses Mal genehmigen",
+      "mode": {
+        "allowList": "Positivliste",
+        "allowListDesc": "Nur automatisch genehmigte Tools ausführen",
+        "autoRun": "Automatisch genehmigen",
+        "autoRunDesc": "Alle Tool-Ausführungen automatisch genehmigen",
+        "manual": "Manuell",
+        "manualDesc": "Jede Ausführung muss manuell genehmigt werden"
+      },
+      "reject": "Ablehnen",
+      "rejectAndContinue": "Ablehnen und erneut ausführen",
+      "rejectOnly": "Ablehnen",
+      "rejectReasonPlaceholder": "Die Angabe eines Ablehnungsgrundes hilft dem Agenten, zukünftige Aktionen zu verbessern",
+      "rejectTitle": "Tool-Ausführung ablehnen",
+      "rejectedWithReason": "Die Tool-Ausführung wurde abgelehnt: {{reason}}",
+      "toolRejected": "Die Tool-Ausführung wurde abgelehnt"
+    }
+  },
  "topic": {
    "checkOpenNewTopic": "Soll ein neues Thema eröffnet werden?",
    "checkSaveCurrentMessages": "Möchten Sie die aktuelle Konversation als Thema speichern?",
@@ -135,6 +135,7 @@
    }
  },
  "close": "Schließen",
+  "confirm": "Bestätigen",
  "contact": "Kontakt",
  "copy": "Kopieren",
  "copyFail": "Kopieren fehlgeschlagen",
@@ -285,6 +286,7 @@
  "oauth": "SSO-Anmeldung",
  "officialSite": "Offizielle Website",
  "ok": "OK",
+  "or": "oder",
  "password": "Passwort",
  "pin": "Anheften",
  "pinOff": "Anheften aufheben",
@@ -106,6 +106,12 @@
    "keyPlaceholder": "Schlüssel",
    "valuePlaceholder": "Wert"
  },
+  "LocalFile": {
+    "action": {
+      "open": "Öffnen",
+      "showInFolder": "Im Ordner anzeigen"
+    }
+  },
  "MaxTokenSlider": {
    "unlimited": "Unbegrenzt"
  },
@@ -41,9 +41,29 @@
        "openingMessage": "Eröffnungsnachricht",
        "openingQuestions": "Eröffnungsfragen",
        "title": "Assistenteneinstellungen"
+      },
+      "version": {
+        "empty": "Keine früheren Versionen verfügbar",
+        "status": {
+          "archived": "Archiviert",
+          "deprecated": "Abgelehnt",
+          "unpublished": "In Überprüfung"
+        },
+        "table": {
+          "isLatest": "Neueste Version",
+          "isValidated": "Verifiziert",
+          "publishAt": "Veröffentlichungsdatum",
+          "version": "Versionsnummer"
+        },
+        "title": "Versionsverlauf"
      }
    },
    "list": "Assistentenliste",
+    "marketSource": {
+      "label": "Marktquelle wechseln",
+      "legacy": "Alter Markt",
+      "new": "Neuer Markt"
+    },
    "more": "Mehr",
    "plugins": "Integrations-Plugins",
    "recentSubmits": "Neueste Aktualisierungen",
@@ -51,10 +71,35 @@
      "createdAt": "Zuletzt veröffentlicht",
      "identifier": "Assistenten-ID",
      "knowledgeCount": "Anzahl der Wissensdatenbanken",
+      "myown": "Meine anzeigen",
      "pluginCount": "Anzahl der Plugins",
      "title": "Assistentenname",
      "tokenUsage": "Token-Verbrauch"
    },
+    "status": {
+      "archived": {
+        "reasons": {
+          "official": "Der Assistent wurde aufgrund von Sicherheits- oder politischen Problemen offiziell entfernt.",
+          "owner": "Der Entwickler des Assistenten hat ihn freiwillig entfernt oder archiviert."
+        },
+        "subtitle": "Der aktuell aufgerufene Assistent wurde aus einem der folgenden Gründe archiviert:",
+        "title": "Assistent archiviert"
+      },
+      "backToMarket": "Zurück zum Assistenten-Markt",
+      "deprecated": {
+        "reasons": {
+          "official": "Der Assistent wurde aufgrund von Sicherheits- oder politischen Problemen offiziell entfernt.",
+          "owner": "Der Entwickler des Assistenten hat ihn freiwillig entfernt oder abgelehnt."
+        },
+        "subtitle": "Der aktuell aufgerufene Assistent wurde aus einem der folgenden Gründe abgelehnt:",
+        "title": "Assistent abgelehnt"
+      },
+      "support": "Bei Fragen senden Sie bitte den Link an <1>support@lobehub.com</1>.",
+      "unpublished": {
+        "subtitle": "Der aktuell aufgerufene Assistent befindet sich in der Versionsprüfung. Bei Fragen senden Sie bitte den Link an <1>support@lobehub.com</1>.",
+        "title": "Assistent in Überprüfung"
+      }
+    },
    "suggestions": "Ähnliche Empfehlungen",
    "systemRole": "Assistenteneinstellungen",
    "tokenUsage": "Token-Verbrauch der Assistenten-Prompt",
@@ -102,7 +102,7 @@
      "SPII": "Ihr Inhalt könnte sensible personenbezogene Daten enthalten. Zum Schutz der Privatsphäre entfernen Sie bitte diese Informationen und versuchen Sie es erneut.",
      "default": "Inhalt blockiert: {{blockReason}}. Bitte passen Sie Ihre Anfrage an und versuchen Sie es erneut."
    },
-    "InsufficientQuota": "Es tut uns leid, das Kontingent (Quota) für diesen Schlüssel ist erreicht. Bitte überprüfen Sie Ihr Kontoguthaben oder erhöhen Sie das Kontingent des Schlüssels und versuchen Sie es erneut.",
+    "InsufficientQuota": "Es tut uns leid, das Kontingent dieses Schlüssels wurde erreicht. Bitte überprüfen Sie, ob Ihr Kontostand ausreichend ist, oder erhöhen Sie das Kontingent des Schlüssels und versuchen Sie es erneut.",
    "InvalidAccessCode": "Das Passwort ist ungültig oder leer. Bitte geben Sie das richtige Zugangspasswort ein oder fügen Sie einen benutzerdefinierten API-Schlüssel hinzu.",
    "InvalidBedrockCredentials": "Die Bedrock-Authentifizierung ist fehlgeschlagen. Bitte überprüfen Sie AccessKeyId/SecretAccessKey und versuchen Sie es erneut.",
    "InvalidClerkUser": "Entschuldigung, du bist derzeit nicht angemeldet. Bitte melde dich an oder registriere ein Konto, um fortzufahren.",
@@ -131,7 +131,7 @@
    "PluginServerError": "Fehler bei der Serveranfrage des Plugins. Bitte überprüfen Sie die Fehlerinformationen unten in Ihrer Plugin-Beschreibungsdatei, Plugin-Konfiguration oder Serverimplementierung",
    "PluginSettingsInvalid": "Das Plugin muss korrekt konfiguriert werden, um verwendet werden zu können. Bitte überprüfen Sie Ihre Konfiguration auf Richtigkeit",
    "ProviderBizError": "Fehler bei der Anforderung des {{provider}}-Dienstes. Bitte überprüfen Sie die folgenden Informationen oder versuchen Sie es erneut.",
-    "QuotaLimitReached": "Es tut uns leid, die aktuelle Token-Nutzung oder die Anzahl der Anfragen hat das Kontingent (Quota) für diesen Schlüssel erreicht. Bitte erhöhen Sie das Kontingent für diesen Schlüssel oder versuchen Sie es später erneut.",
+    "QuotaLimitReached": "Es tut uns leid, die Anzahl der Token oder Anfragen hat das Kontingent dieses Schlüssels erreicht. Bitte erhöhen Sie das Kontingent des Schlüssels oder versuchen Sie es später erneut.",
    "StreamChunkError": "Fehler beim Parsen des Nachrichtenchunks der Streaming-Anfrage. Bitte überprüfen Sie, ob die aktuelle API-Schnittstelle den Standards entspricht, oder wenden Sie sich an Ihren API-Anbieter.",
    "SubscriptionKeyMismatch": "Es tut uns leid, aufgrund eines vorübergehenden Systemfehlers ist das aktuelle Abonnement vorübergehend ungültig. Bitte klicken Sie auf die Schaltfläche unten, um das Abonnement wiederherzustellen, oder kontaktieren Sie uns per E-Mail für Unterstützung.",
    "SubscriptionPlanLimit": "Ihr Abonnementspunktestand ist erschöpft, Sie können diese Funktion nicht nutzen. Bitte upgraden Sie auf einen höheren Plan oder konfigurieren Sie die benutzerdefinierte Modell-API, um weiterhin zu verwenden.",
@@ -1,5 +1,8 @@
 {
-  "desc": "Verwalte dein Wissen",
+  "addFolder": "Ordner erstellen",
+  "addKnowledge": "Wissen hinzufügen",
+  "addPage": "Dokument erstellen",
+  "desc": "Verwalte dein Wissen für Arbeit, Studium und Alltag.",
  "detail": {
    "basic": {
      "createdAt": "Erstellungszeit",
@@ -21,15 +24,89 @@
      "embeddingStatus": "Vektorisierung"
    }
  },
+  "documentEditor": {
+    "addIcon": "Symbol hinzufügen",
+    "autoSaveMessage": "Das Dokument wird automatisch gespeichert, ein manuelles Speichern ist nicht erforderlich.",
+    "chooseIcon": "Symbol auswählen",
+    "deleteConfirm": {
+      "content": "Dieses Dokument wird gelöscht und kann danach nicht wiederhergestellt werden. Bitte seien Sie vorsichtig.",
+      "title": "Dokument löschen"
+    },
+    "deleteError": "Löschen des Dokuments fehlgeschlagen",
+    "deleteSuccess": "Dokument erfolgreich gelöscht",
+    "editedAt": "Zuletzt bearbeitet am {{time}}",
+    "editedBy": "Zuletzt bearbeitet von {{name}}",
+    "editorPlaceholder": "Geben Sie den Dokumentinhalt ein, drücken Sie / für das Befehlsmenü",
+    "empty": {
+      "createNewDocument": "Neues Dokument erstellen",
+      "title": "Wählen Sie ein Dokument, um zu beginnen",
+      "uploadMarkdown": "Markdown-Datei hochladen"
+    },
+    "linkCopied": "Link wurde kopiert",
+    "menu": {
+      "copyLink": "Link kopieren",
+      "exportDocument": "Dokument exportieren",
+      "importDocument": "Dokument importieren",
+      "pin": "Dokument anheften"
+    },
+    "saving": "Speichern...",
+    "titlePlaceholder": "Ohne Titel",
+    "wordCount": "{{wordCount}} Wörter"
+  },
+  "documentList": {
+    "copyContent": "Gesamten Inhalt kopieren",
+    "documentCount": "Insgesamt {{count}} Dokumente",
+    "duplicate": "Kopie erstellen",
+    "empty": "Noch keine Dokumente vorhanden. Klicken Sie oben, um Ihr erstes Dokument zu erstellen.",
+    "noResults": "Keine passenden Dokumente gefunden",
+    "selectNote": "Wählen Sie ein Dokument zum Bearbeiten",
+    "untitled": "Ohne Titel"
+  },
  "empty": "Keine hochgeladenen Dateien/Ordner vorhanden",
  "header": {
    "actions": {
      "newFolder": "Neuen Ordner erstellen",
+      "newPage": "Neues Dokument",
      "uploadFile": "Datei hochladen",
      "uploadFolder": "Ordner hochladen"
    },
+    "newDocumentButton": "Neues Dokument",
+    "newNoteDialog": {
+      "cancel": "Abbrechen",
+      "editTitle": "Dokument bearbeiten",
+      "emptyContent": "Der Dokumentinhalt darf nicht leer sein",
+      "loadError": "Fehler beim Laden des Dokuments. Bitte versuchen Sie es erneut.",
+      "loading": "Wird geladen...",
+      "save": "Speichern",
+      "saveError": "Fehler beim Speichern des Dokuments. Bitte versuchen Sie es erneut.",
+      "saveSuccess": "Dokument erfolgreich gespeichert",
+      "title": "Neues Dokument",
+      "updateSuccess": "Dokument erfolgreich aktualisiert"
+    },
    "uploadButton": "Hochladen"
  },
+  "home": {
+    "getStarted": "Loslegen",
+    "greeting": "Loslegen",
+    "quickActions": "Schnellaktionen",
+    "recentDocuments": "Kürzlich verwendete Dokumente",
+    "recentFiles": "Kürzlich verwendete Dateien",
+    "subtitle": "Willkommen im Wissensspeicher. Beginnen Sie hier mit der Verwaltung Ihrer Dokumente.",
+    "uploadEntries": {
+      "files": {
+        "title": "Dateien hochladen"
+      },
+      "folder": {
+        "title": "Ordner hochladen"
+      },
+      "knowledgeBase": {
+        "title": "Neue Wissensdatenbank"
+      },
+      "newDocument": {
+        "title": "Neues Dokument"
+      }
+    }
+  },
  "knowledgeBase": {
    "list": {
      "confirmRemoveKnowledgeBase": "Die Wissensdatenbank wird gelöscht, die darin enthaltenen Dateien werden nicht gelöscht, sondern in den gesamten Dateien verschoben. Nach dem Löschen der Wissensdatenbank kann sie nicht wiederhergestellt werden, bitte vorsichtig vorgehen.",
@@ -38,6 +115,10 @@
    "new": "Neue Wissensdatenbank",
    "title": "Wissensdatenbank"
  },
+  "menu": {
+    "allDocuments": "Alle Dokumente",
+    "allFiles": "Alle Dateien"
+  },
  "networkError": "Fehler beim Abrufen der Wissensdatenbank. Bitte überprüfen Sie Ihre Netzwerkverbindung und versuchen Sie es erneut.",
  "notSupportGuide": {
    "desc": "Die aktuelle Bereitstellung ist im Client-Datenbankmodus und unterstützt keine Dateiverwaltungsfunktionen. Bitte wechseln Sie zu <1>Server-Datenbank-Bereitstellungsmodus</1> oder verwenden Sie direkt <3>LobeChat Cloud</3>",
@@ -61,12 +142,16 @@
    "downloadFile": "Datei herunterladen",
    "unsupportedFileAndContact": "Dieses Dateiformat wird derzeit nicht für die Online-Vorschau unterstützt. Wenn Sie eine Vorschau wünschen, können Sie uns gerne <1>Feedback geben</1>."
  },
+  "searchDocumentPlaceholder": "Dokumente durchsuchen",
  "searchFilePlaceholder": "Datei suchen",
  "tab": {
-    "all": "Alle Dateien",
+    "all": "Alle",
    "audios": "Audio",
    "documents": "Dokumente",
+    "home": "Startseite",
    "images": "Bilder",
+    "moreTypes": "Weitere Typen",
+    "pages": "Dokumente",
    "videos": "Videos",
    "websites": "Webseiten"
  },
@@ -0,0 +1,42 @@
+{
+  "callback": {
+    "buttons": {
+      "close": "Fenster schließen"
+    },
+    "messages": {
+      "authFailed": "Autorisierung fehlgeschlagen: {{error}}",
+      "missingParams": "Autorisierungsparameter fehlen",
+      "processing": "Autorisierung wird verarbeitet...",
+      "successWithCountdown": "{{message}} Das Fenster wird in {{countdown}} Sekunden automatisch geschlossen",
+      "successWithRedirect": "Autorisierung erfolgreich! Weiterleitung läuft..."
+    },
+    "titles": {
+      "error": "Autorisierung fehlgeschlagen",
+      "loading": "LobeHub Market Autorisierung",
+      "success": "Autorisierung erfolgreich"
+    }
+  },
+  "errors": {
+    "authorizationFailed": "Autorisierung fehlgeschlagen, bitte erneut versuchen.",
+    "browserOnly": "Der Autorisierungsvorgang kann nur im Browser gestartet werden.",
+    "codeConsumed": "Autorisierungscode wurde bereits verwendet, bitte erneut versuchen.",
+    "codeVerifierMissing": "Autorisierungssitzung ungültig, bitte den Anmeldevorgang erneut starten.",
+    "general": "Ein Fehler ist bei der Autorisierung aufgetreten, bitte erneut versuchen.",
+    "handoffFailed": "Autorisierungsergebnis konnte nicht abgerufen werden, bitte erneut versuchen.",
+    "handoffTimeout": "Autorisierung zeitüberschritten, bitte nach Abschluss im Browser erneut versuchen.",
+    "oidcNotReady": "Autorisierungsdienst ist noch nicht bereit, bitte später erneut versuchen.",
+    "openBrowserFailed": "Systembrowser konnte nicht geöffnet werden, bitte erneut versuchen.",
+    "openPopupFailed": "Autorisierungs-Popup konnte nicht geöffnet werden, bitte die Popup-Blocker-Einstellungen im Browser überprüfen.",
+    "popupClosed": "Autorisierungsfenster wurde geschlossen, bevor der Vorgang abgeschlossen war.",
+    "sessionExpired": "Autorisierungssitzung ist abgelaufen, bitte erneut anmelden.",
+    "stateMismatch": "Autorisierungsstatus stimmt nicht überein, bitte erneut versuchen.",
+    "stateMissing": "Autorisierungsstatus nicht gefunden, bitte erneut versuchen."
+  },
+  "messages": {
+    "loading": "Autorisierungsvorgang wird gestartet...",
+    "success": {
+      "submit": "Autorisierung erfolgreich! Du kannst jetzt einen Assistenten veröffentlichen.",
+      "upload": "Autorisierung erfolgreich! Du kannst jetzt eine neue Version veröffentlichen."
+    }
+  }
+}
@@ -199,6 +199,12 @@
    "all": "Alle",
    "list": {
      "disabled": "Nicht aktiviert",
+      "disabledActions": {
+        "sort": "Sortieroptionen",
+        "sortAlphabetical": "Alphabetisch sortieren",
+        "sortAlphabeticalDesc": "In umgekehrter alphabetischer Reihenfolge sortieren",
+        "sortDefault": "Standard-Sortierung"
+      },
      "enabled": "Aktiviert"
    },
    "notFound": "Keine Suchergebnisse gefunden",
@@ -289,7 +295,7 @@
      },
      "helpDoc": "Konfigurationsanleitung",
      "responsesApi": {
-        "desc": "Verwendet das neue Anforderungsformat von OpenAI, um fortgeschrittene Funktionen wie Chain-of-Thought freizuschalten",
+        "desc": "Verwendet das neue Anforderungsformat von OpenAI, um erweiterte Funktionen wie Chain-of-Thought freizuschalten (nur mit OpenAI-Modellen kompatibel)",
        "title": "Verwendung des Responses API-Standards"
      },
      "waitingForMore": "Weitere Modelle werden <1>geplant</1>, bitte warten Sie"
@@ -391,7 +397,13 @@
      "addNew": "Modell hinzufügen",
      "disabled": "Nicht aktiviert",
      "disabledActions": {
-        "showMore": "Alle anzeigen"
+        "showMore": "Alle anzeigen",
+        "sort": "Sortieroptionen",
+        "sortAlphabetical": "Alphabetisch sortieren",
+        "sortAlphabeticalDesc": "In umgekehrter alphabetischer Reihenfolge sortieren",
+        "sortDefault": "Standard-Sortierung",
+        "sortReleasedAt": "Nach frühestem Veröffentlichungsdatum sortieren",
+        "sortReleasedAtDesc": "Nach neuestem Veröffentlichungsdatum sortieren"
      },
      "empty": {
        "desc": "Bitte erstellen Sie ein benutzerdefiniertes Modell oder ziehen Sie ein Modell, um zu beginnen.",
@@ -236,6 +236,9 @@
  "MiniMaxAI/MiniMax-M1-80k": {
    "description": "MiniMax-M1 ist ein groß angelegtes hybrides Aufmerksamkeits-Inferenzmodell mit offenen Gewichten, das 456 Milliarden Parameter umfasst und etwa 45,9 Milliarden Parameter pro Token aktiviert. Das Modell unterstützt nativ einen ultralangen Kontext von 1 Million Tokens und spart durch den Blitz-Attention-Mechanismus bei Aufgaben mit 100.000 Tokens im Vergleich zu DeepSeek R1 75 % der Fließkommaoperationen ein. Gleichzeitig verwendet MiniMax-M1 eine MoE-Architektur (Mixture of Experts) und kombiniert den CISPO-Algorithmus mit einem hybriden Aufmerksamkeitsdesign für effizientes verstärkendes Lernen, was in der Langzeiteingabe-Inferenz und realen Software-Engineering-Szenarien branchenführende Leistung erzielt."
  },
+  "MiniMaxAI/MiniMax-M2": {
+    "description": "MiniMax-M2 definiert Effizienz für Agenten neu. Es handelt sich um ein kompaktes, schnelles und kosteneffizientes MoE-Modell mit insgesamt 230 Milliarden Parametern und 10 Milliarden aktiven Parametern. Es wurde für Spitzenleistungen bei Codierungs- und Agentenaufgaben entwickelt und bewahrt gleichzeitig eine starke allgemeine Intelligenz. Mit nur 10 Milliarden aktiven Parametern bietet MiniMax-M2 eine Leistung, die mit groß angelegten Modellen vergleichbar ist, und ist damit die ideale Wahl für Anwendungen mit hohen Effizienzanforderungen."
+  },
  "Moonshot-Kimi-K2-Instruct": {
    "description": "Mit insgesamt 1 Billion Parametern und 32 Milliarden aktivierten Parametern erreicht dieses nicht-denkende Modell Spitzenleistungen in den Bereichen aktuelles Wissen, Mathematik und Programmierung und ist besonders für allgemeine Agentenaufgaben optimiert. Es wurde speziell für Agentenaufgaben verfeinert, kann nicht nur Fragen beantworten, sondern auch Aktionen ausführen. Ideal für spontane, allgemeine Gespräche und Agentenerfahrungen, ist es ein reflexartiges Modell ohne lange Denkzeiten."
  },
@@ -1049,6 +1052,9 @@
  "deepseek-r1-0528": {
    "description": "Das voll ausgestattete 685B-Modell, veröffentlicht am 28. Mai 2025. DeepSeek-R1 nutzt im Nachtrainingsprozess umfangreiche Verstärkungslernverfahren und verbessert die Modell-Inferenzfähigkeit erheblich, selbst bei minimalen annotierten Daten. Es zeigt hohe Leistung und starke Fähigkeiten in Mathematik, Programmierung und natürlicher Sprachlogik."
  },
+  "deepseek-r1-250528": {
+    "description": "DeepSeek R1 250528, die Vollversion des DeepSeek-R1-Inferenzmodells, geeignet für anspruchsvolle Mathematik- und Logikaufgaben."
+  },
  "deepseek-r1-70b-fast-online": {
    "description": "DeepSeek R1 70B Schnellversion, die Echtzeit-Online-Suche unterstützt und eine schnellere Reaktionszeit bei gleichbleibender Modellleistung bietet."
  },
@@ -1059,31 +1065,34 @@
    "description": "deepseek-r1-distill-llama ist ein Modell, das auf der Grundlage von Llama aus DeepSeek-R1 destilliert wurde."
  },
  "deepseek-r1-distill-llama-70b": {
-    "description": "DeepSeek R1 – das größere und intelligentere Modell im DeepSeek-Paket – wurde in die Llama 70B-Architektur destilliert. Basierend auf Benchmark-Tests und menschlicher Bewertung ist dieses Modell intelligenter als das ursprüngliche Llama 70B, insbesondere bei Aufgaben, die mathematische und faktische Genauigkeit erfordern."
+    "description": "DeepSeek R1 Distill Llama 70B, ein distilliertes Modell, das die allgemeine R1-Inferenzfähigkeit mit dem Llama-Ökosystem kombiniert."
  },
  "deepseek-r1-distill-llama-8b": {
-    "description": "Das DeepSeek-R1-Distill Modell wurde durch Wissensdistillationstechniken entwickelt, indem Proben, die von DeepSeek-R1 generiert wurden, auf Qwen, Llama und andere Open-Source-Modelle feinabgestimmt wurden."
+    "description": "DeepSeek-R1-Distill-Llama-8B ist ein distilliertes großes Sprachmodell auf Basis von Llama-3.1-8B unter Verwendung der Ausgaben von DeepSeek R1."
+  },
+  "deepseek-r1-distill-qianfan-70b": {
+    "description": "DeepSeek R1 Distill Qianfan 70B, ein kosteneffizientes R1-Distillationsmodell basierend auf Qianfan-70B."
+  },
+  "deepseek-r1-distill-qianfan-8b": {
+    "description": "DeepSeek R1 Distill Qianfan 8B, ein R1-Distillationsmodell auf Basis von Qianfan-8B, geeignet für mittelgroße und kleinere Anwendungen."
  },
  "deepseek-r1-distill-qianfan-llama-70b": {
-    "description": "Erstmals veröffentlicht am 14. Februar 2025, destilliert vom Qianfan-Modellteam auf Basis des Llama3_70B Modells (gebaut mit Meta Llama), wobei auch die Qianfan-Korpora in die Destillationsdaten aufgenommen wurden."
-  },
-  "deepseek-r1-distill-qianfan-llama-8b": {
-    "description": "Erstmals veröffentlicht am 14. Februar 2025, destilliert vom Qianfan-Modellteam auf Basis des Llama3_8B Modells (gebaut mit Meta Llama), wobei auch die Qianfan-Korpora in die Destillationsdaten aufgenommen wurden."
+    "description": "DeepSeek R1 Distill Qianfan Llama 70B, ein R1-Distillationsmodell basierend auf Llama-70B."
  },
  "deepseek-r1-distill-qwen": {
    "description": "deepseek-r1-distill-qwen ist ein Modell, das auf der Grundlage von Qwen durch Distillierung aus DeepSeek-R1 erstellt wurde."
  },
  "deepseek-r1-distill-qwen-1.5b": {
-    "description": "Das DeepSeek-R1-Distill Modell wurde durch Wissensdistillationstechniken entwickelt, indem Proben, die von DeepSeek-R1 generiert wurden, auf Qwen, Llama und andere Open-Source-Modelle feinabgestimmt wurden."
+    "description": "DeepSeek R1 Distill Qwen 1.5B, ein ultraleichtes R1-Distillationsmodell, ideal für Umgebungen mit sehr begrenzten Ressourcen."
  },
  "deepseek-r1-distill-qwen-14b": {
-    "description": "Das DeepSeek-R1-Distill Modell wurde durch Wissensdistillationstechniken entwickelt, indem Proben, die von DeepSeek-R1 generiert wurden, auf Qwen, Llama und andere Open-Source-Modelle feinabgestimmt wurden."
+    "description": "DeepSeek R1 Distill Qwen 14B, ein mittelgroßes R1-Distillationsmodell, geeignet für den Einsatz in verschiedenen Szenarien."
  },
  "deepseek-r1-distill-qwen-32b": {
-    "description": "Das DeepSeek-R1-Distill Modell wurde durch Wissensdistillationstechniken entwickelt, indem Proben, die von DeepSeek-R1 generiert wurden, auf Qwen, Llama und andere Open-Source-Modelle feinabgestimmt wurden."
+    "description": "DeepSeek R1 Distill Qwen 32B, ein R1-Distillationsmodell basierend auf Qwen-32B, das Leistung und Kosten ausbalanciert."
  },
  "deepseek-r1-distill-qwen-7b": {
-    "description": "Das DeepSeek-R1-Distill Modell wurde durch Wissensdistillationstechniken entwickelt, indem Proben, die von DeepSeek-R1 generiert wurden, auf Qwen, Llama und andere Open-Source-Modelle feinabgestimmt wurden."
+    "description": "DeepSeek R1 Distill Qwen 7B, ein leichtgewichtiges R1-Distillationsmodell, geeignet für Edge-Computing und unternehmensinterne Umgebungen."
  },
  "deepseek-r1-fast-online": {
    "description": "DeepSeek R1 Vollschnellversion, die Echtzeit-Online-Suche unterstützt und die leistungsstarken Fähigkeiten von 671B Parametern mit einer schnelleren Reaktionszeit kombiniert."
@@ -1112,12 +1121,24 @@
  "deepseek-v3.1-terminus": {
    "description": "DeepSeek-V3.1-Terminus ist eine optimierte Version des großen Sprachmodells von DeepSeek, speziell für Endgeräte entwickelt."
  },
+  "deepseek-v3.1-think-250821": {
+    "description": "DeepSeek V3.1 Think 250821, das Deep-Thinking-Modell der Terminus-Version, geeignet für leistungsstarke Inferenzszenarien."
+  },
  "deepseek-v3.1:671b": {
    "description": "DeepSeek V3.1: Ein Inferenzmodell der nächsten Generation, das komplexe Schlussfolgerungen und verknüpfte Denkfähigkeiten verbessert und sich für Aufgaben eignet, die tiefgehende Analysen erfordern."
  },
  "deepseek-v3.2-exp": {
    "description": "deepseek-v3.2-exp führt einen sparsamen Aufmerksamkeitsmechanismus ein, um die Effizienz beim Training und der Inferenz bei der Verarbeitung langer Texte zu verbessern. Der Preis liegt unter dem von deepseek-v3.1."
  },
+  "deepseek-v3.2-think": {
+    "description": "DeepSeek V3.2 Think, die Vollversion des Deep-Thinking-Modells mit verbesserter Fähigkeit zur Langketteninferenz."
+  },
+  "deepseek-vl2": {
+    "description": "DeepSeek VL2, ein multimodales Modell mit Unterstützung für Bild-Text-Verständnis und fein abgestimmte visuelle Fragebeantwortung."
+  },
+  "deepseek-vl2-small": {
+    "description": "DeepSeek VL2 Small, eine leichte multimodale Version, geeignet für ressourcenbeschränkte und hochparallele Szenarien."
+  },
  "deepseek/deepseek-chat-v3-0324": {
    "description": "DeepSeek V3 ist ein Experten-Mischmodell mit 685B Parametern und die neueste Iteration der Flaggschiff-Chatmodellreihe des DeepSeek-Teams.\n\nEs erbt das [DeepSeek V3](/deepseek/deepseek-chat-v3) Modell und zeigt hervorragende Leistungen in verschiedenen Aufgaben."
  },
@@ -1137,7 +1158,7 @@
    "description": "DeepSeek-R1 verbessert die Modellschlussfolgerungsfähigkeit erheblich, selbst bei sehr begrenzten annotierten Daten. Vor der Ausgabe der endgültigen Antwort generiert das Modell eine Denkprozesskette, um die Genauigkeit der Antwort zu erhöhen."
  },
  "deepseek/deepseek-r1-distill-llama-70b": {
-    "description": "DeepSeek-R1-Distill-Llama-70B ist eine destillierte, effizientere Variante des 70B Llama Modells. Es behält starke Leistung bei Textgenerierungsaufgaben bei und reduziert den Rechenaufwand für einfachere Bereitstellung und Forschung. Betrieben von Groq mit deren maßgeschneiderter Language Processing Unit (LPU) Hardware für schnelle und effiziente Inferenz."
+    "description": "DeepSeek R1 Distill Llama 70B ist ein großes Sprachmodell auf Basis von Llama3.3 70B. Durch Feintuning mit den Ausgaben von DeepSeek R1 erreicht es eine konkurrenzfähige Leistung, die mit führenden Großmodellen vergleichbar ist."
  },
  "deepseek/deepseek-r1-distill-llama-8b": {
    "description": "DeepSeek R1 Distill Llama 8B ist ein distilliertes großes Sprachmodell, das auf Llama-3.1-8B-Instruct basiert und durch Training mit den Ausgaben von DeepSeek R1 erstellt wurde."
@@ -1253,83 +1274,89 @@
  "emohaa": {
    "description": "Emohaa ist ein psychologisches Modell mit professionellen Beratungsfähigkeiten, das den Nutzern hilft, emotionale Probleme zu verstehen."
  },
-  "ernie-3.5-128k": {
-    "description": "Das von Baidu entwickelte Flaggschiff-Modell für große Sprachmodelle deckt eine riesige Menge an chinesischen und englischen Korpora ab und bietet starke allgemeine Fähigkeiten, die die meisten Anforderungen an Dialogfragen, kreative Generierung und Plugin-Anwendungen erfüllen; es unterstützt die automatische Anbindung an das Baidu-Suchplugin, um die Aktualität der Antwortinformationen zu gewährleisten."
-  },
-  "ernie-3.5-8k": {
-    "description": "Das von Baidu entwickelte Flaggschiff-Modell für große Sprachmodelle deckt eine riesige Menge an chinesischen und englischen Korpora ab und bietet starke allgemeine Fähigkeiten, die die meisten Anforderungen an Dialogfragen, kreative Generierung und Plugin-Anwendungen erfüllen; es unterstützt die automatische Anbindung an das Baidu-Suchplugin, um die Aktualität der Antwortinformationen zu gewährleisten."
-  },
-  "ernie-3.5-8k-preview": {
-    "description": "Das von Baidu entwickelte Flaggschiff-Modell für große Sprachmodelle deckt eine riesige Menge an chinesischen und englischen Korpora ab und bietet starke allgemeine Fähigkeiten, die die meisten Anforderungen an Dialogfragen, kreative Generierung und Plugin-Anwendungen erfüllen; es unterstützt die automatische Anbindung an das Baidu-Suchplugin, um die Aktualität der Antwortinformationen zu gewährleisten."
-  },
-  "ernie-4.0-8k-latest": {
-    "description": "Das von Baidu entwickelte Flaggschiff-Modell für große Sprachmodelle hat im Vergleich zu ERNIE 3.5 eine umfassende Verbesserung der Modellfähigkeiten erreicht und ist weit verbreitet in komplexen Aufgabenbereichen anwendbar; es unterstützt die automatische Anbindung an das Baidu-Suchplugin, um die Aktualität der Antwortinformationen zu gewährleisten."
-  },
-  "ernie-4.0-8k-preview": {
-    "description": "Das von Baidu entwickelte Flaggschiff-Modell für große Sprachmodelle hat im Vergleich zu ERNIE 3.5 eine umfassende Verbesserung der Modellfähigkeiten erreicht und ist weit verbreitet in komplexen Aufgabenbereichen anwendbar; es unterstützt die automatische Anbindung an das Baidu-Suchplugin, um die Aktualität der Antwortinformationen zu gewährleisten."
-  },
-  "ernie-4.0-turbo-128k": {
-    "description": "Das von Baidu entwickelte Flaggschiff-Modell für große Sprachmodelle zeigt hervorragende Gesamtergebnisse und ist weit verbreitet in komplexen Aufgabenbereichen anwendbar; es unterstützt die automatische Anbindung an das Baidu-Suchplugin, um die Aktualität der Antwortinformationen zu gewährleisten. Im Vergleich zu ERNIE 4.0 bietet es eine bessere Leistung."
-  },
-  "ernie-4.0-turbo-8k-latest": {
-    "description": "Das von Baidu entwickelte Flaggschiff-Modell für große Sprachmodelle zeigt hervorragende Gesamtergebnisse und ist weit verbreitet in komplexen Aufgabenbereichen anwendbar; es unterstützt die automatische Anbindung an das Baidu-Suchplugin, um die Aktualität der Antwortinformationen zu gewährleisten. Im Vergleich zu ERNIE 4.0 bietet es eine bessere Leistung."
-  },
-  "ernie-4.0-turbo-8k-preview": {
-    "description": "Das von Baidu entwickelte Flaggschiff-Modell für große Sprachmodelle zeigt hervorragende Gesamtergebnisse und ist weit verbreitet in komplexen Aufgabenbereichen anwendbar; es unterstützt die automatische Anbindung an das Baidu-Suchplugin, um die Aktualität der Antwortinformationen zu gewährleisten. Im Vergleich zu ERNIE 4.0 bietet es eine bessere Leistung."
+  "ernie-4.5-0.3b": {
+    "description": "ERNIE 4.5 0.3B, ein leichtgewichtiges Open-Source-Modell, ideal für lokale und maßgeschneiderte Bereitstellungen."
  },
  "ernie-4.5-21b-a3b": {
-    "description": "ERNIE 4.5 21B A3B ist ein hybrides Expertenmodell von Baidu Wenxin mit herausragenden Fähigkeiten im logischen Denken und in der Mehrsprachigkeit."
+    "description": "ERNIE 4.5 21B A3B, ein Open-Source-Modell mit großer Parameteranzahl, leistungsstark bei Verständnis- und Generierungsaufgaben."
  },
  "ernie-4.5-300b-a47b": {
    "description": "ERNIE 4.5 300B A47B ist ein großskaliges hybrides Expertenmodell von Baidu Wenxin mit exzellenten Fähigkeiten im logischen Schlussfolgern."
  },
  "ernie-4.5-8k-preview": {
-    "description": "Das ERNIE 4.5 Modell ist ein neu entwickeltes, natives multimodales Basis-Modell von Baidu, das durch die gemeinsame Modellierung mehrerer Modalitäten eine synergistische Optimierung erreicht und über hervorragende multimodale Verständnisfähigkeiten verfügt; es bietet verbesserte Sprachfähigkeiten, umfassende Verbesserungen in Verständnis, Generierung, Logik und Gedächtnis, sowie signifikante Verbesserungen in der Vermeidung von Halluzinationen, logischen Schlussfolgerungen und Programmierfähigkeiten."
+    "description": "ERNIE 4.5 8K Preview, ein Vorschau-Modell mit 8K-Kontext, zur Erprobung und zum Testen der Fähigkeiten von Wenxin 4.5."
  },
  "ernie-4.5-turbo-128k": {
-    "description": "Wenxin 4.5 Turbo hat deutliche Verbesserungen in den Bereichen Halluzinationen reduzieren, logisches Denken und Programmierfähigkeiten. Im Vergleich zu Wenxin 4.5 ist es schneller und kostengünstiger. Die Modellfähigkeiten wurden umfassend verbessert, um besser mit mehrstufigen, langen historischen Dialogen und der Beantwortung von Fragen zu langen Dokumenten umzugehen."
+    "description": "ERNIE 4.5 Turbo 128K, ein leistungsstarkes Allzweckmodell mit Unterstützung für suchbasierte Erweiterung und Tool-Nutzung, geeignet für QA, Code, Agenten und mehr."
+  },
+  "ernie-4.5-turbo-128k-preview": {
+    "description": "ERNIE 4.5 Turbo 128K Preview, eine Vorschauversion mit denselben Fähigkeiten wie die finale Version, ideal für Integrationstests und schrittweise Einführung."
  },
  "ernie-4.5-turbo-32k": {
-    "description": "Wenxin 4.5 Turbo hat deutliche Verbesserungen in den Bereichen Halluzinationen reduzieren, logisches Denken und Programmierfähigkeiten. Im Vergleich zu Wenxin 4.5 ist es schneller und kostengünstiger. Die Fähigkeiten in der Textkreation und Wissensfragen haben sich erheblich verbessert. Die Ausgabelänge und die Verzögerung bei vollständigen Sätzen sind im Vergleich zu ERNIE 4.5 gestiegen."
+    "description": "ERNIE 4.5 Turbo 32K, eine Version mit mittellangem Kontext, geeignet für QA, Wissensdatenbankabfragen und mehrstufige Dialoge."
+  },
+  "ernie-4.5-turbo-latest": {
+    "description": "ERNIE 4.5 Turbo Latest, die neueste Version mit umfassender Leistungsoptimierung, ideal als Hauptmodell für Produktionsumgebungen."
+  },
+  "ernie-4.5-turbo-vl": {
+    "description": "ERNIE 4.5 Turbo VL, ein ausgereiftes multimodales Modell für Bild-Text-Verständnis und Erkennung in Produktionsumgebungen."
  },
  "ernie-4.5-turbo-vl-32k": {
-    "description": "Die neueste Version des Wenxin Yi Yan Modells hat signifikante Verbesserungen in den Bereichen Bildverständnis, Kreation, Übersetzung und Programmierung. Es unterstützt erstmals eine Kontextlänge von 32K, und die Verzögerung beim ersten Token wurde erheblich reduziert."
+    "description": "ERNIE 4.5 Turbo VL 32K, eine multimodale Version mit mittellangem Textkontext, geeignet für das kombinierte Verständnis von langen Dokumenten und Bildern."
+  },
+  "ernie-4.5-turbo-vl-32k-preview": {
+    "description": "ERNIE 4.5 Turbo VL 32K Preview, eine Vorschauversion des multimodalen 32K-Modells zur Bewertung der Langkontext-Bildverarbeitungsfähigkeiten."
+  },
+  "ernie-4.5-turbo-vl-latest": {
+    "description": "ERNIE 4.5 Turbo VL Latest, die neueste multimodale Version mit verbesserter Bild-Text-Verständnis- und Inferenzleistung."
+  },
+  "ernie-4.5-turbo-vl-preview": {
+    "description": "ERNIE 4.5 Turbo VL Preview, ein multimodales Vorschau-Modell mit Unterstützung für Bild-Text-Verständnis und -Generierung, ideal für visuelle QA und Inhaltsverständnis."
+  },
+  "ernie-4.5-vl-28b-a3b": {
+    "description": "ERNIE 4.5 VL 28B A3B, ein Open-Source-Multimodalmodell für Bild-Text-Verständnis und Inferenzaufgaben."
+  },
+  "ernie-5.0-thinking-preview": {
+    "description": "Wenxin 5.0 Thinking Preview, ein natives, multimodales Flaggschiffmodell mit einheitlicher Modellierung von Text, Bild, Audio und Video. Umfassend verbesserte Fähigkeiten für komplexe QA, kreative Aufgaben und Agentenszenarien."
  },
  "ernie-char-8k": {
-    "description": "Das von Baidu entwickelte große Sprachmodell für vertikale Szenarien eignet sich für Anwendungen wie NPCs in Spielen, Kundenservice-Dialoge und Rollenspiele, mit einem klareren und konsistenteren Charakterstil, einer stärkeren Befolgung von Anweisungen und besserer Inferenzleistung."
+    "description": "ERNIE Character 8K, ein dialogorientiertes Modell mit Charakterpersönlichkeit, ideal für IP-Charakterentwicklung und langfristige Begleitdialoge."
  },
  "ernie-char-fiction-8k": {
-    "description": "Das von Baidu entwickelte große Sprachmodell für vertikale Szenarien eignet sich für Anwendungen wie NPCs in Spielen, Kundenservice-Dialoge und Rollenspiele, mit einem klareren und konsistenteren Charakterstil, einer stärkeren Befolgung von Anweisungen und besserer Inferenzleistung."
+    "description": "ERNIE Character Fiction 8K, ein Persönlichkeitsmodell für Roman- und Storytelling, geeignet für die Generierung langer Geschichten."
+  },
+  "ernie-char-fiction-8k-preview": {
+    "description": "ERNIE Character Fiction 8K Preview, eine Vorschauversion für Charakter- und Storytelling-Modelle zur Funktionsbewertung und zum Testen."
  },
  "ernie-irag-edit": {
-    "description": "Das von Baidu entwickelte ERNIE iRAG Edit Bildbearbeitungsmodell unterstützt Operationen wie Löschen (erase), Neumalen (repaint) und Variationserzeugung (variation) basierend auf Bildern."
+    "description": "ERNIE iRAG Edit, ein Bildbearbeitungsmodell mit Unterstützung für Bildlöschung, Neuzeichnung und Varianten-Generierung."
  },
  "ernie-lite-8k": {
-    "description": "ERNIE Lite ist ein leichtgewichtiges großes Sprachmodell, das von Baidu entwickelt wurde und sowohl hervorragende Modellleistung als auch Inferenzleistung bietet, geeignet für die Verwendung mit AI-Beschleunigungskarten mit geringer Rechenleistung."
+    "description": "ERNIE Lite 8K, ein leichtgewichtiges Allzweckmodell, ideal für kostensensitive Alltags-QA- und Content-Generierungsszenarien."
  },
  "ernie-lite-pro-128k": {
-    "description": "Das von Baidu entwickelte leichtgewichtige große Sprachmodell bietet sowohl hervorragende Modellleistung als auch Inferenzleistung, die besser ist als die von ERNIE Lite, und ist geeignet für die Verwendung mit AI-Beschleunigungskarten mit geringer Rechenleistung."
+    "description": "ERNIE Lite Pro 128K, ein leichtes Hochleistungsmodell, geeignet für latenz- und kostensensitive Geschäftsanwendungen."
  },
  "ernie-novel-8k": {
-    "description": "Das von Baidu entwickelte allgemeine große Sprachmodell hat deutliche Vorteile in der Fähigkeit zur Fortsetzung von Romanen und kann auch in Szenarien wie Kurzdramen und Filmen eingesetzt werden."
+    "description": "ERNIE Novel 8K, ein Modell für Romane und IP-Storytelling, spezialisiert auf Mehrcharakter- und Multistrang-Erzählungen."
  },
  "ernie-speed-128k": {
-    "description": "Das neueste hochleistungsfähige große Sprachmodell von Baidu, das 2024 veröffentlicht wurde, bietet hervorragende allgemeine Fähigkeiten und eignet sich gut als Basismodell für Feinabstimmungen, um spezifische Szenarien besser zu bewältigen, während es auch hervorragende Inferenzleistungen bietet."
+    "description": "ERNIE Speed 128K, ein großes Modell ohne Ein-/Ausgabegebühren, ideal für Langtextverständnis und großflächige Testszenarien."
+  },
+  "ernie-speed-8k": {
+    "description": "ERNIE Speed 8K, ein kostenloses Schnellmodell, geeignet für alltägliche Dialoge und leichte Textaufgaben."
  },
  "ernie-speed-pro-128k": {
-    "description": "Das neueste hochleistungsfähige große Sprachmodell von Baidu, das 2024 veröffentlicht wurde, bietet hervorragende allgemeine Fähigkeiten und ist besser als ERNIE Speed, geeignet als Basismodell für Feinabstimmungen, um spezifische Szenarien besser zu bewältigen, während es auch hervorragende Inferenzleistungen bietet."
+    "description": "ERNIE Speed Pro 128K, ein hochgradig paralleles und kosteneffizientes Modell, ideal für großflächige Online-Dienste und Unternehmensanwendungen."
  },
  "ernie-tiny-8k": {
-    "description": "ERNIE Tiny ist ein hochleistungsfähiges großes Sprachmodell, dessen Bereitstellungs- und Feinabstimmungskosten die niedrigsten unter den Wenshin-Modellen sind."
-  },
-  "ernie-x1-32k": {
-    "description": "Verfügt über stärkere Fähigkeiten in Verständnis, Planung, Reflexion und Evolution. Als umfassenderes tiefes Denkmodell kombiniert Wenxin X1 Genauigkeit, Kreativität und Ausdruckskraft und zeigt herausragende Leistungen in den Bereichen chinesische Wissensfragen, literarische Kreation, Textverfassung, alltägliche Gespräche, logisches Denken, komplexe Berechnungen und Werkzeugnutzung."
-  },
-  "ernie-x1-32k-preview": {
-    "description": "Das große Modell ERNIE X1 verfügt über verbesserte Fähigkeiten in Verständnis, Planung, Reflexion und Evolution. Als umfassenderes tiefes Denkmodell kombiniert ERNIE X1 Genauigkeit, Kreativität und Ausdruckskraft und zeigt herausragende Leistungen in den Bereichen chinesische Wissensabfragen, literarisches Schaffen, Textverfassung, alltägliche Gespräche, logisches Denken, komplexe Berechnungen und Werkzeugnutzung."
+    "description": "ERNIE Tiny 8K, ein extrem leichtes Modell, geeignet für einfache QA-, Klassifizierungs- und kostengünstige Inferenzszenarien."
  },
  "ernie-x1-turbo-32k": {
-    "description": "Im Vergleich zu ERNIE-X1-32K bietet dieses Modell bessere Leistung und Effizienz."
+    "description": "ERNIE X1 Turbo 32K, ein Hochgeschwindigkeits-Denkmodell mit 32K-Kontext, geeignet für komplexe Inferenz und mehrstufige Dialoge."
+  },
+  "ernie-x1.1-preview": {
+    "description": "ERNIE X1.1 Preview, eine Vorschauversion des Denkmodells ERNIE X1.1, geeignet für Fähigkeitsvalidierung und Tests."
  },
  "fal-ai/bytedance/seedream/v4": {
    "description": "Seedream 4.0 Bildgenerierungsmodell vom Seed-Team von ByteDance, unterstützt Texteingaben und Bilder und bietet eine hochgradig kontrollierbare, qualitativ hochwertige Bildgenerierung. Bilder werden basierend auf Textanweisungen erzeugt."
@@ -1389,7 +1416,7 @@
    "description": "FLUX.1 [schnell] ist das derzeit fortschrittlichste Open-Source-Modell mit wenigen Schritten, das nicht nur Konkurrenten übertrifft, sondern auch leistungsstärkere nicht-feinabgestimmte Modelle wie Midjourney v6.0 und DALL·E 3 (HD) übertrifft. Das Modell wurde speziell feinabgestimmt, um die gesamte Vielfalt der Vortrainingsausgaben zu bewahren. Im Vergleich zu den aktuell besten Modellen auf dem Markt bietet FLUX.1 [schnell] erhebliche Verbesserungen in visueller Qualität, Instruktionsbefolgung, Größen- und Proportionsänderungen, Schriftartenverarbeitung und Ausgabediversität, was den Nutzern eine reichhaltigere und vielfältigere kreative Bildgenerierung ermöglicht."
  },
  "flux.1-schnell": {
-    "description": "Ein Rectified Flow Transformer mit 12 Milliarden Parametern, der Bilder basierend auf Textbeschreibungen generieren kann."
+    "description": "FLUX.1-schnell, ein leistungsstarkes Bildgenerierungsmodell, ideal für die schnelle Erstellung von Bildern in verschiedenen Stilen."
  },
  "gemini-1.0-pro-001": {
    "description": "Gemini 1.0 Pro 001 (Tuning) bietet stabile und anpassbare Leistung und ist die ideale Wahl für Lösungen komplexer Aufgaben."
@@ -1538,6 +1565,9 @@
  "glm-4-0520": {
    "description": "GLM-4-0520 ist die neueste Modellversion, die für hochkomplexe und vielfältige Aufgaben konzipiert wurde und hervorragende Leistungen zeigt."
  },
+  "glm-4-32b-0414": {
+    "description": "GLM-4 32B 0414, eine Version des allgemeinen GLM-Großmodells mit Unterstützung für Textgenerierung und -verständnis in mehreren Aufgaben."
+  },
  "glm-4-9b-chat": {
    "description": "GLM-4-9B-Chat bietet hohe Leistung in Bereichen wie Semantik, Mathematik, logisches Denken, Programmierung und Wissen. Es unterstützt Web-Browsing, Code-Ausführung, benutzerdefinierte Tool-Nutzung und Langtext-Inferenz. Unterstützt 26 Sprachen, darunter Japanisch, Koreanisch und Deutsch."
  },
@@ -1826,6 +1856,18 @@
  "gpt-5-pro": {
    "description": "GPT-5 Pro nutzt mehr Rechenleistung für tiefgreifendere Überlegungen und liefert kontinuierlich bessere Antworten."
  },
+  "gpt-5.1": {
+    "description": "GPT-5.1 – Flaggschiffmodell, optimiert für Programmier- und Agentenaufgaben, unterstützt konfigurierbare Rechenintensität und längere Kontexte."
+  },
+  "gpt-5.1-chat-latest": {
+    "description": "GPT-5.1 Chat: Eine Variante von GPT-5.1 für ChatGPT, ideal für Konversationsszenarien."
+  },
+  "gpt-5.1-codex": {
+    "description": "GPT-5.1 Codex: Eine für agentenbasierte Programmieraufgaben optimierte Version von GPT-5.1, geeignet für komplexe Code- und Agenten-Workflows über die Responses API."
+  },
+  "gpt-5.1-codex-mini": {
+    "description": "GPT-5.1 Codex mini: Eine kompaktere und kostengünstigere Codex-Variante, optimiert für agentenbasierte Programmieraufgaben."
+  },
  "gpt-audio": {
    "description": "GPT Audio ist ein universelles Chatmodell für Audioeingabe und -ausgabe, das Audio-I/O in der Chat Completions API unterstützt."
  },
@@ -2001,13 +2043,13 @@
    "description": "Imagen, Text-zu-Bild-Modellreihe der 4. Generation"
  },
  "imagen-4.0-generate-preview-06-06": {
-    "description": "Imagen 4. Generation Text-zu-Bild Modellserie"
+    "description": "Vierte Generation der Imagen-Modelle zur Text-zu-Bild-Generierung."
  },
  "imagen-4.0-ultra-generate-001": {
    "description": "Imagen, Text-zu-Bild-Modell der 4. Generation (Ultra-Version)"
  },
  "imagen-4.0-ultra-generate-preview-06-06": {
-    "description": "Imagen 4. Generation Text-zu-Bild Modellserie Ultra-Version"
+    "description": "Ultra-Version der vierten Generation der Imagen-Modelle zur Text-zu-Bild-Generierung."
  },
  "inception/mercury-coder-small": {
    "description": "Mercury Coder Small ist ideal für Codegenerierung, Debugging und Refactoring-Aufgaben mit minimaler Latenz."
@@ -2036,14 +2078,26 @@
  "internlm3-latest": {
    "description": "Unsere neueste Modellreihe bietet herausragende Inferenzleistungen und führt die Open-Source-Modelle in ihrer Gewichtsklasse an. Standardmäßig verweist sie auf unser neuestes veröffentlichtes InternLM3-Modell."
  },
+  "internvl2.5-38b-mpo": {
+    "description": "InternVL2.5 38B MPO, ein multimodales vortrainiertes Modell, das komplexe Bild-Text-Inferenzaufgaben unterstützt."
+  },
  "internvl2.5-latest": {
    "description": "Die von uns weiterhin unterstützte Version InternVL2.5 bietet hervorragende und stabile Leistungen. Standardmäßig verweist es auf unser neuestes veröffentlichtes InternVL2.5-Modell, derzeit auf internvl2.5-78b."
  },
+  "internvl3-14b": {
+    "description": "InternVL3 14B, ein mittelgroßes multimodales Modell mit ausgewogenem Verhältnis zwischen Leistung und Kosten."
+  },
+  "internvl3-1b": {
+    "description": "InternVL3 1B, ein leichtgewichtiges multimodales Modell, geeignet für den Einsatz in ressourcenbeschränkten Umgebungen."
+  },
+  "internvl3-38b": {
+    "description": "InternVL3 38B, ein großskaliges Open-Source-Multimodalmodell, geeignet für hochpräzises Bild-Text-Verständnis."
+  },
  "internvl3-latest": {
    "description": "Unser neuestes multimodales Großmodell bietet verbesserte Fähigkeiten im Verständnis von Text und Bildern sowie im langfristigen Verständnis von Bildern und erreicht eine Leistung, die mit führenden proprietären Modellen vergleichbar ist. Standardmäßig verweist es auf unser neuestes veröffentlichtes InternVL-Modell, derzeit auf internvl3-78b."
  },
  "irag-1.0": {
-    "description": "Das von Baidu entwickelte iRAG (image based RAG) ist eine durch Suche verstärkte Text-zu-Bild-Technologie, die Baidus Milliarden von Bildressourcen mit leistungsstarken Basismodellen kombiniert, um ultra-realistische Bilder zu erzeugen. Das Gesamtergebnis übertrifft native Text-zu-Bild-Systeme deutlich, wirkt weniger künstlich und ist kostengünstig. iRAG zeichnet sich durch keine Halluzinationen, hohe Realitätsnähe und sofortige Verfügbarkeit aus."
+    "description": "ERNIE iRAG, ein bildgestütztes Retrieval-Augmented-Generation-Modell mit Unterstützung für Bildsuche, Bild-Text-Retrieval und Inhaltserzeugung."
  },
  "jamba-large": {
    "description": "Unser leistungsstärkstes und fortschrittlichstes Modell, das speziell für die Bewältigung komplexer Aufgaben auf Unternehmensebene entwickelt wurde und herausragende Leistung bietet."
@@ -2064,7 +2118,7 @@
    "description": "Das Modell kimi-k2-0905-preview hat eine Kontextlänge von 256k, verfügt über stärkere Agentic-Coding-Fähigkeiten, eine herausragendere Ästhetik und Praktikabilität von Frontend-Code sowie ein besseres Kontextverständnis."
  },
  "kimi-k2-instruct": {
-    "description": "Kimi K2 Instruct ist ein großes Sprachmodell von Moonshot AI mit der Fähigkeit zur Verarbeitung extrem langer Kontexte."
+    "description": "Kimi K2 Instruct, das offizielle Inferenzmodell von Kimi mit Unterstützung für Langkontext, Code, QA und mehr."
  },
  "kimi-k2-turbo-preview": {
    "description": "kimi-k2 ist ein Basis-Modell mit MoE-Architektur und besonders starken Fähigkeiten im Bereich Code und Agenten. Es verfügt über insgesamt 1T Parameter und 32B aktivierte Parameter. In Benchmark-Tests der wichtigsten Kategorien – allgemeines Wissens-Reasoning, Programmierung, Mathematik und Agenten – übertrifft das K2-Modell die Leistung anderer gängiger Open‑Source‑Modelle."
@@ -2174,6 +2228,9 @@
  "megrez-3b-instruct": {
    "description": "Megrez 3B Instruct ist ein effizientes Modell mit geringer Parameteranzahl, entwickelt von Wuwen Xinqiong."
  },
+  "meituan/longcat-flash-chat": {
+    "description": "Ein von Meituan entwickeltes Open-Source-Basismodell, das speziell für dialogorientierte Interaktionen und agentenbasierte Aufgaben optimiert wurde und sich besonders bei Werkzeugaufrufen und komplexen mehrstufigen Dialogszenarien auszeichnet."
+  },
  "meta-llama-3-70b-instruct": {
    "description": "Ein leistungsstarkes Modell mit 70 Milliarden Parametern, das in den Bereichen Schlussfolgerungen, Programmierung und breiten Sprachanwendungen herausragt."
  },
@@ -2405,6 +2462,12 @@
  "minimax-m2": {
    "description": "MiniMax M2 ist ein leistungsstarkes, effizientes Sprachmodell, das speziell für Programmier- und Agenten-Workflows entwickelt wurde."
  },
+  "minimax/minimax-m2": {
+    "description": "Speziell entwickelt für effizientes Codieren und Agenten-Workflows."
+  },
+  "minimaxai/minimax-m2": {
+    "description": "MiniMax-M2 ist ein kompaktes, schnelles und kosteneffizientes Mixture-of-Experts (MoE)-Modell mit 230 Milliarden Gesamtparametern und 10 Milliarden aktiven Parametern. Es wurde für höchste Leistung bei Codierungs- und Agentenaufgaben entwickelt und bietet gleichzeitig eine starke allgemeine Intelligenz. Das Modell überzeugt bei Aufgaben wie der Bearbeitung mehrerer Dateien, dem Code-Ausführen-Fehlerbeheben-Zyklus, Testverifikation und -korrektur sowie bei komplexen, lang verknüpften Toolchains – und ist damit die ideale Wahl für Entwickler-Workflows."
+  },
  "ministral-3b-latest": {
    "description": "Ministral 3B ist das weltbeste Edge-Modell von Mistral."
  },
@@ -2735,6 +2798,54 @@
  "pro-deepseek-v3": {
    "description": "Modell für exklusive Unternehmensdienste, inklusive paralleler Serviceunterstützung."
  },
+  "qianfan-70b": {
+    "description": "Qianfan 70B, ein großparametrisches chinesisches Modell, geeignet für hochwertige Inhaltserstellung und komplexe Schlussfolgerungsaufgaben."
+  },
+  "qianfan-8b": {
+    "description": "Qianfan 8B, ein mittelgroßes Allzweckmodell, ideal für Textgenerierung und Frage-Antwort-Szenarien mit ausgewogenem Kosten-Nutzen-Verhältnis."
+  },
+  "qianfan-agent-intent-32k": {
+    "description": "Qianfan Agent Intent 32K, ein Modell für Absichtserkennung und Agenten-Orchestrierung, unterstützt Szenarien mit langem Kontext."
+  },
+  "qianfan-agent-lite-8k": {
+    "description": "Qianfan Agent Lite 8K, ein leichtgewichtiges Agentenmodell, geeignet für kostengünstige Mehrfachdialoge und Geschäftsprozesse."
+  },
+  "qianfan-agent-speed-32k": {
+    "description": "Qianfan Agent Speed 32K, ein hochperformantes Agentenmodell mit hoher Durchsatzrate, ideal für groß angelegte, mehrfache Aufgabenanwendungen."
+  },
+  "qianfan-agent-speed-8k": {
+    "description": "Qianfan Agent Speed 8K, ein hochgradig paralleles Agentenmodell für mittlere bis kurze Dialoge und schnelle Reaktionen."
+  },
+  "qianfan-check-vl": {
+    "description": "Qianfan Check VL, ein multimodales Modell zur Inhaltsprüfung und -erkennung, unterstützt Aufgaben zur Einhaltung von Bild-Text-Richtlinien."
+  },
+  "qianfan-composition": {
+    "description": "Qianfan Composition, ein multimodales Kreativmodell, unterstützt integriertes Verständnis und Generierung von Bild und Text."
+  },
+  "qianfan-engcard-vl": {
+    "description": "Qianfan EngCard VL, ein multimodales Erkennungsmodell, spezialisiert auf englischsprachige Szenarien."
+  },
+  "qianfan-lightning-128b-a19b": {
+    "description": "Qianfan Lightning 128B A19B, ein leistungsstarkes chinesisches Allzweckmodell, geeignet für komplexe Frage-Antwort- und groß angelegte Schlussfolgerungsaufgaben."
+  },
+  "qianfan-llama-vl-8b": {
+    "description": "Qianfan Llama VL 8B, ein auf Llama basierendes multimodales Modell für allgemeines Bild-Text-Verständnis."
+  },
+  "qianfan-multipicocr": {
+    "description": "Qianfan MultiPicOCR, ein OCR-Modell für mehrere Bilder, unterstützt Texterkennung und -extraktion aus mehreren Bildern."
+  },
+  "qianfan-qi-vl": {
+    "description": "Qianfan QI VL, ein multimodales Frage-Antwort-Modell, ermöglicht präzise Suche und Beantwortung in komplexen Bild-Text-Szenarien."
+  },
+  "qianfan-singlepicocr": {
+    "description": "Qianfan SinglePicOCR, ein OCR-Modell für Einzelbilder, unterstützt hochpräzise Zeichenerkennung."
+  },
+  "qianfan-vl-70b": {
+    "description": "Qianfan VL 70B, ein großparametrisches visuell-sprachliches Modell, geeignet für komplexe Bild-Text-Verständnisaufgaben."
+  },
+  "qianfan-vl-8b": {
+    "description": "Qianfan VL 8B, ein leichtgewichtiges visuell-sprachliches Modell, ideal für alltägliche Bild-Text-Fragen und Analysen."
+  },
  "qvq-72b-preview": {
    "description": "Das QVQ-Modell ist ein experimentelles Forschungsmodell, das vom Qwen-Team entwickelt wurde und sich auf die Verbesserung der visuellen Schlussfolgerungsfähigkeiten konzentriert, insbesondere im Bereich der mathematischen Schlussfolgerungen."
  },
@@ -2886,7 +2997,7 @@
    "description": "Das 72B-Modell von Tongyi Qianwen 2.5 ist öffentlich zugänglich."
  },
  "qwen2.5-7b-instruct": {
-    "description": "Das 7B-Modell von Tongyi Qianwen 2.5 ist öffentlich zugänglich."
+    "description": "Qwen2.5 7B Instruct, ein ausgereiftes Open-Source-Instruktionsmodell, geeignet für Dialoge und Generierung in verschiedenen Szenarien."
  },
  "qwen2.5-coder-1.5b-instruct": {
    "description": "Die Open-Source-Version des Qwen-Codemodells."
@@ -2919,13 +3030,13 @@
    "description": "Das Qwen-Omni-Modell der Serie unterstützt die Eingabe verschiedener Modalitäten, einschließlich Video, Audio, Bilder und Text, und gibt Audio und Text aus."
  },
  "qwen2.5-vl-32b-instruct": {
-    "description": "Die Qwen2.5-VL-Modellreihe verbessert die Intelligenz, Praktikabilität und Anwendbarkeit des Modells, sodass es in Szenarien wie natürlichen Dialogen, Inhaltserstellung, Fachwissensdiensten und Codeentwicklung besser abschneidet. Die 32B-Version verwendet Techniken des verstärkenden Lernens zur Optimierung des Modells. Im Vergleich zu anderen Modellen der Qwen2.5-VL-Reihe bietet sie einen für Menschen präferierten Ausgabe-Stil, Fähigkeiten zur Inferenz komplexer mathematischer Probleme sowie die Fähigkeit zur feingranularen Bildverarbeitung und -inferenz."
+    "description": "Qwen2.5 VL 32B Instruct, ein multimodales Open-Source-Modell, ideal für private Bereitstellung und vielseitige Anwendungen."
  },
  "qwen2.5-vl-72b-instruct": {
    "description": "Verbesserte Befolgung von Anweisungen, Mathematik, Problemlösung und Programmierung, gesteigerte Erkennungsfähigkeiten für alle Arten von visuellen Elementen, Unterstützung für die präzise Lokalisierung visueller Elemente in verschiedenen Formaten, Verständnis von langen Videodateien (maximal 10 Minuten) und sekundengenauer Ereigniszeitpunktlokalisierung, Fähigkeit zur zeitlichen Einordnung und Geschwindigkeitsverständnis, Unterstützung für die Steuerung von OS- oder Mobile-Agenten basierend auf Analyse- und Lokalisierungsfähigkeiten, starke Fähigkeit zur Extraktion von Schlüsselinformationen und JSON-Format-Ausgabe. Diese Version ist die leistungsstärkste Version der 72B-Serie."
  },
  "qwen2.5-vl-7b-instruct": {
-    "description": "Verbesserte Befolgung von Anweisungen, Mathematik, Problemlösung und Programmierung, gesteigerte Erkennungsfähigkeiten für alle Arten von visuellen Elementen, Unterstützung für die präzise Lokalisierung visueller Elemente in verschiedenen Formaten, Verständnis von langen Videodateien (maximal 10 Minuten) und sekundengenauer Ereigniszeitpunktlokalisierung, Fähigkeit zur zeitlichen Einordnung und Geschwindigkeitsverständnis, Unterstützung für die Steuerung von OS- oder Mobile-Agenten basierend auf Analyse- und Lokalisierungsfähigkeiten, starke Fähigkeit zur Extraktion von Schlüsselinformationen und JSON-Format-Ausgabe. Diese Version ist die leistungsstärkste Version der 72B-Serie."
+    "description": "Qwen2.5 VL 7B Instruct, ein leichtgewichtiges multimodales Modell, das Kosten und Erkennungsleistung ausbalanciert."
  },
  "qwen2.5-vl-instruct": {
    "description": "Qwen2.5-VL ist die neueste Version des visuellen Sprachmodells in der Qwen-Modellfamilie."
@@ -2952,46 +3063,46 @@
    "description": "Qwen3 ist das neue, großangelegte Sprachmodell von Alibaba, das mit hervorragender Leistung vielfältige Anwendungsbedürfnisse unterstützt."
  },
  "qwen3-0.6b": {
-    "description": "Qwen3 ist ein neues, leistungsstarkes Modell der nächsten Generation, das in den Bereichen Inferenz, Allgemeinwissen, Agenten und Mehrsprachigkeit erhebliche Fortschritte erzielt hat und den Wechsel zwischen Denkmodi unterstützt."
+    "description": "Qwen3 0.6B, ein Einstiegsmodell, geeignet für einfache Schlussfolgerungen und stark ressourcenbeschränkte Umgebungen."
  },
  "qwen3-1.7b": {
-    "description": "Qwen3 ist ein neues, leistungsstarkes Modell der nächsten Generation, das in den Bereichen Inferenz, Allgemeinwissen, Agenten und Mehrsprachigkeit erhebliche Fortschritte erzielt hat und den Wechsel zwischen Denkmodi unterstützt."
+    "description": "Qwen3 1.7B, ein ultraleichtes Modell, ideal für Edge- und Endgerätebereitstellung."
  },
  "qwen3-14b": {
-    "description": "Qwen3 ist ein neues, leistungsstarkes Modell der nächsten Generation, das in den Bereichen Inferenz, Allgemeinwissen, Agenten und Mehrsprachigkeit erhebliche Fortschritte erzielt hat und den Wechsel zwischen Denkmodi unterstützt."
+    "description": "Qwen3 14B, ein mittelgroßes Modell, geeignet für mehrsprachige Frage-Antwort- und Textgenerierungsaufgaben."
  },
  "qwen3-235b-a22b": {
-    "description": "Qwen3 ist ein neues, leistungsstarkes Modell der nächsten Generation, das in den Bereichen Inferenz, Allgemeinwissen, Agenten und Mehrsprachigkeit erhebliche Fortschritte erzielt hat und den Wechsel zwischen Denkmodi unterstützt."
+    "description": "Qwen3 235B A22B, ein universelles Großmodell für eine Vielzahl komplexer Aufgaben."
  },
  "qwen3-235b-a22b-instruct-2507": {
-    "description": "Open-Source-Modell im nicht-denkenden Modus basierend auf Qwen3, mit leichten Verbesserungen in subjektiver Kreativität und Modellsicherheit gegenüber der Vorgängerversion (Tongyi Qianwen 3-235B-A22B)."
+    "description": "Qwen3 235B A22B Instruct 2507, ein universelles Flaggschiff-Instruktionsmodell, geeignet für vielfältige Generierungs- und Schlussfolgerungsaufgaben."
  },
  "qwen3-235b-a22b-thinking-2507": {
-    "description": "Open-Source-Modell im Denkmodus basierend auf Qwen3, mit erheblichen Verbesserungen in Logik, allgemeinen Fähigkeiten, Wissensabdeckung und Kreativität gegenüber der Vorgängerversion (Tongyi Qianwen 3-235B-A22B). Geeignet für anspruchsvolle und stark schlussfolgernde Szenarien."
+    "description": "Qwen3 235B A22B Thinking 2507, ein extrem großskaliges Denkmodell, ideal für hochkomplexe Schlussfolgerungen."
  },
  "qwen3-30b-a3b": {
-    "description": "Qwen3 ist ein neues, leistungsstarkes Modell der nächsten Generation, das in den Bereichen Inferenz, Allgemeinwissen, Agenten und Mehrsprachigkeit erhebliche Fortschritte erzielt hat und den Wechsel zwischen Denkmodi unterstützt."
+    "description": "Qwen3 30B A3B, ein mittelgroßes bis großes Allzweckmodell mit ausgewogenem Verhältnis zwischen Kosten und Leistung."
  },
  "qwen3-30b-a3b-instruct-2507": {
-    "description": "Im Vergleich zur vorherigen Version (Qwen3-30B-A3B) wurde die allgemeine Leistungsfähigkeit in Chinesisch, Englisch und mehreren Sprachen deutlich verbessert. Spezielle Optimierungen für subjektive und offene Aufgaben führen zu einer deutlich besseren Übereinstimmung mit den Nutzerpräferenzen und ermöglichen hilfreichere Antworten."
+    "description": "Qwen3 30B A3B Instruct 2507, ein mittelgroßes bis großes Instruktionsmodell, geeignet für hochwertige Generierung und Frage-Antwort-Aufgaben."
  },
  "qwen3-30b-a3b-thinking-2507": {
-    "description": "Basierend auf dem Denkmodus-Open-Source-Modell von Qwen3 wurden im Vergleich zur vorherigen Version (Tongyi Qianwen 3-30B-A3B) die logischen Fähigkeiten, die allgemeine Leistungsfähigkeit, das Wissen und die Kreativität erheblich verbessert. Es eignet sich für anspruchsvolle Szenarien mit starker Argumentation."
+    "description": "Qwen3 30B A3B Thinking 2507, ein mittelgroßes bis großes Denkmodell mit ausgewogener Genauigkeit und Effizienz."
  },
  "qwen3-32b": {
-    "description": "Qwen3 ist ein neues, leistungsstarkes Modell der nächsten Generation, das in den Bereichen Inferenz, Allgemeinwissen, Agenten und Mehrsprachigkeit erhebliche Fortschritte erzielt hat und den Wechsel zwischen Denkmodi unterstützt."
+    "description": "Qwen3 32B, geeignet für allgemeine Aufgaben mit erhöhtem Verständnisbedarf."
  },
  "qwen3-4b": {
-    "description": "Qwen3 ist ein neues, leistungsstarkes Modell der nächsten Generation, das in den Bereichen Inferenz, Allgemeinwissen, Agenten und Mehrsprachigkeit erhebliche Fortschritte erzielt hat und den Wechsel zwischen Denkmodi unterstützt."
+    "description": "Qwen3 4B, ideal für mittelgroße bis kleine Anwendungen und lokale Inferenzszenarien."
  },
  "qwen3-8b": {
-    "description": "Qwen3 ist ein neues, leistungsstarkes Modell der nächsten Generation, das in den Bereichen Inferenz, Allgemeinwissen, Agenten und Mehrsprachigkeit erhebliche Fortschritte erzielt hat und den Wechsel zwischen Denkmodi unterstützt."
+    "description": "Qwen3 8B, ein leichtgewichtiges Modell mit flexibler Bereitstellung, geeignet für hochparallele Anwendungen."
  },
  "qwen3-coder-30b-a3b-instruct": {
    "description": "Open-Source-Version des Qwen-Codegenerierungsmodells. Das neueste qwen3-coder-30b-a3b-instruct basiert auf Qwen3 und bietet leistungsstarke Coding-Agent-Fähigkeiten. Es ist spezialisiert auf Tool-Nutzung und Interaktion mit Umgebungen, ermöglicht autonomes Programmieren und kombiniert herausragende Programmierfähigkeiten mit allgemeinen Fähigkeiten."
  },
  "qwen3-coder-480b-a35b-instruct": {
-    "description": "Open-Source-Code-Modell von Tongyi Qianwen. Das neueste qwen3-coder-480b-a35b-instruct basiert auf Qwen3, verfügt über starke Coding-Agent-Fähigkeiten, ist versiert im Werkzeugaufruf und in der Umgebungskommunikation und ermöglicht selbstständiges Programmieren mit hervorragender Codequalität und allgemeinen Fähigkeiten."
+    "description": "Qwen3 Coder 480B A35B Instruct, ein Flaggschiff-Code-Modell, unterstützt mehrsprachige Programmierung und komplexes Codeverständnis."
  },
  "qwen3-coder-flash": {
    "description": "Tongyi Qianwen Code-Modell. Die neueste Qwen3-Coder Modellreihe basiert auf Qwen3 und ist ein Code-Generierungsmodell mit starker Coding-Agent-Fähigkeit, spezialisiert auf Werkzeugaufrufe und Umgebungsinteraktion, das selbstständiges Programmieren ermöglicht und neben hervorragenden Code-Fähigkeiten auch allgemeine Kompetenzen besitzt."
@@ -3005,32 +3116,41 @@
  "qwen3-max": {
    "description": "Tongyi Qianwen 3 Max Modellserie, die im Vergleich zur 2.5 Serie eine deutliche Verbesserung der allgemeinen Fähigkeiten bietet, einschließlich verbesserter Textverständnisfähigkeiten in Chinesisch und Englisch, komplexer Befolgung von Anweisungen, subjektiver offener Aufgaben, Mehrsprachigkeit und Tool-Integration; das Modell zeigt weniger Wissenshalluzinationen. Die neueste qwen3-max Version wurde speziell im Bereich Agentenprogrammierung und Tool-Integration weiterentwickelt. Die offizielle Veröffentlichung erreicht SOTA-Niveau in Fachgebieten und ist für komplexere Agentenanforderungen optimiert."
  },
+  "qwen3-max-preview": {
+    "description": "Das leistungsstärkste Modell der Tongyi Qianwen-Serie, geeignet für komplexe, mehrstufige Aufgaben. Vorschauversion mit Denkfähigkeit."
+  },
  "qwen3-next-80b-a3b-instruct": {
    "description": "Ein neues Open-Source-Modell der nächsten Generation im Nicht-Denk-Modus basierend auf Qwen3. Im Vergleich zur vorherigen Version (Tongyi Qianwen 3-235B-A22B-Instruct-2507) bietet es eine verbesserte chinesische Textverständnisfähigkeit, verstärkte logische Schlussfolgerungen und bessere Leistung bei textgenerierenden Aufgaben."
  },
  "qwen3-next-80b-a3b-thinking": {
-    "description": "Ein neues Open-Source-Modell der nächsten Generation im Denkmodus basierend auf Qwen3. Im Vergleich zur vorherigen Version (Tongyi Qianwen 3-235B-A22B-Thinking-2507) wurde die Befehlsbefolgung verbessert und die Modellantworten sind prägnanter zusammengefasst."
+    "description": "Qwen3 Next 80B A3B Thinking, eine Flaggschiff-Version für komplexe Schlussfolgerungsaufgaben."
  },
  "qwen3-omni-flash": {
    "description": "Das Qwen-Omni-Modell kann kombinierte Eingaben aus Text, Bildern, Audio und Video verarbeiten und Antworten in Text- oder Sprachform generieren. Es bietet verschiedene menschenähnliche Sprachstile, unterstützt mehrsprachige und dialektale Sprachausgabe und eignet sich für Anwendungen wie Textgenerierung, visuelle Erkennung und Sprachassistenten."
  },
  "qwen3-vl-235b-a22b-instruct": {
-    "description": "Qwen3 VL 235B A22B im Non-Thinking-Modus (Instruct), geeignet für Anwendungsfälle mit einfachen Anweisungen, bei gleichzeitig starker visueller Verständnisfähigkeit."
+    "description": "Qwen3 VL 235B A22B Instruct, ein Flaggschiff-Multimodalmodell für anspruchsvolle Verständnis- und Kreativaufgaben."
  },
  "qwen3-vl-235b-a22b-thinking": {
-    "description": "Qwen3 VL 235B A22B im Thinking-Modus (Open-Source-Version), bietet erstklassige visuelle und textbasierte Schlussfolgerungsfähigkeiten für komplexe Aufgaben mit hoher kognitiver Anforderung und Langvideo-Verständnis."
+    "description": "Qwen3 VL 235B A22B Thinking, die Denkversion des Flaggschiffs für komplexe multimodale Schlussfolgerungs- und Planungsaufgaben."
  },
  "qwen3-vl-30b-a3b-instruct": {
-    "description": "Qwen3 VL 30B im Non-Thinking-Modus (Instruct), konzipiert für allgemeine Anweisungsfolgeszenarien mit starker multimodaler Verständnis- und Generierungsfähigkeit."
+    "description": "Qwen3 VL 30B A3B Instruct, ein großes multimodales Modell mit ausgewogener Genauigkeit und Schlussfolgerungsleistung."
  },
  "qwen3-vl-30b-a3b-thinking": {
-    "description": "Qwen-VL (Open-Source-Version) bietet visuelles Verständnis und Textgenerierung, unterstützt Agenteninteraktion, visuelle Kodierung, räumliches Bewusstsein, Langvideo-Verständnis und tiefes Denken. Es verfügt über starke Texterkennungs- und Mehrsprachenfähigkeiten in komplexen Szenarien."
+    "description": "Qwen3 VL 30B A3B Thinking, eine tiefgreifende Denkversion für komplexe multimodale Aufgaben."
+  },
+  "qwen3-vl-32b-instruct": {
+    "description": "Qwen3 VL 32B Instruct, ein multimodales Instruktionsmodell, geeignet für hochwertige Bild-Text-Fragen und kreative Aufgaben."
+  },
+  "qwen3-vl-32b-thinking": {
+    "description": "Qwen3 VL 32B Thinking, eine multimodale Denkversion mit Fokus auf komplexe Schlussfolgerungen und Langkettenanalysen."
  },
  "qwen3-vl-8b-instruct": {
-    "description": "Qwen3 VL 8B im Non-Thinking-Modus (Instruct), geeignet für Standardaufgaben der multimodalen Generierung und Erkennung."
+    "description": "Qwen3 VL 8B Instruct, ein leichtgewichtiges multimodales Modell, ideal für alltägliche visuelle Fragen und Anwendungsintegration."
  },
  "qwen3-vl-8b-thinking": {
-    "description": "Qwen3 VL 8B im Thinking-Modus, konzipiert für leichte multimodale Schlussfolgerungs- und Interaktionsszenarien, mit erhaltener Fähigkeit zum Verständnis langer Kontexte."
+    "description": "Qwen3 VL 8B Thinking, ein multimodales Denkmodell, geeignet für detaillierte Schlussfolgerungen aus visuellen Informationen."
  },
  "qwen3-vl-flash": {
    "description": "Qwen3 VL Flash: eine leichtgewichtige, hochperformante Version für schnelle Inferenz, ideal für latenzkritische oder großvolumige Anfragen."
@@ -3263,6 +3383,12 @@
  "wizardlm2:8x22b": {
    "description": "WizardLM 2 ist ein Sprachmodell von Microsoft AI, das in komplexen Dialogen, mehrsprachigen Anwendungen, Schlussfolgerungen und intelligenten Assistenten besonders gut abschneidet."
  },
+  "x-ai/grok-4-fast": {
+    "description": "Wir freuen uns, Grok 4 Fast vorzustellen – unseren neuesten Fortschritt im Bereich kosteneffizienter Inferenzmodelle."
+  },
+  "x-ai/grok-code-fast-1": {
+    "description": "Wir freuen uns, grok-code-fast-1 zu präsentieren – ein schnelles und kosteneffizientes Inferenzmodell mit hervorragender Leistung im Bereich Agenten-Codierung."
+  },
  "x1": {
    "description": "Das Spark X1 Modell wird weiter verbessert und erreicht in allgemeinen Aufgaben wie Schlussfolgerungen, Textgenerierung und Sprachverständnis Ergebnisse, die mit OpenAI o1 und DeepSeek R1 vergleichbar sind, basierend auf der bereits führenden Leistung in mathematischen Aufgaben."
  },
@@ -3323,6 +3449,9 @@
  "yi-vision-v2": {
    "description": "Ein Modell für komplexe visuelle Aufgaben, das leistungsstarke Verständnis- und Analysefähigkeiten auf der Grundlage mehrerer Bilder bietet."
  },
+  "z-ai/glm-4.6": {
+    "description": "Das neueste Flaggschiffmodell von Zhipu, GLM-4.6, übertrifft seine Vorgänger deutlich in den Bereichen fortgeschrittenes Codieren, Verarbeitung langer Texte, logisches Schließen und agentenbasierte Fähigkeiten."
+  },
  "zai-org/GLM-4.5": {
    "description": "GLM-4.5 ist ein speziell für Agentenanwendungen entwickeltes Basismodell mit Mixture-of-Experts-Architektur. Es ist tief optimiert für Werkzeugaufrufe, Web-Browsing, Softwareentwicklung und Frontend-Programmierung und unterstützt nahtlos die Integration in Code-Agenten wie Claude Code und Roo Code. GLM-4.5 verwendet einen hybriden Inferenzmodus und ist für komplexe Schlussfolgerungen sowie den Alltagsgebrauch geeignet."
  },
@@ -236,6 +236,8 @@
  },
  "inspector": {
    "args": "Parameterliste anzeigen",
+    "delete": "Werkzeugaufruf löschen",
+    "orphanedToolCall": "Dieser Werkzeugaufruf könnte aufgrund eines Fehlers verwaist sein, was den normalen Ablauf des Agenten beeinträchtigen kann. Bitte entfernen Sie ihn.",
    "pluginRender": "Plugin-Oberfläche anzeigen"
  },
  "list": {
@@ -251,14 +253,20 @@
  },
  "localSystem": {
    "apiName": {
+      "editLocalFile": "Datei bearbeiten",
+      "getCommandOutput": "Codeausgabe abrufen",
+      "globLocalFiles": "Dateien durchsuchen",
+      "grepContent": "Inhalt durchsuchen",
+      "killCommand": "Codeausführung beenden",
      "listLocalFiles": "Dateiliste anzeigen",
      "moveLocalFiles": "Dateien verschieben",
      "readLocalFile": "Dateiinhalt lesen",
      "renameLocalFile": "Datei umbenennen",
+      "runCommand": "Befehl ausführen",
      "searchLocalFiles": "Dateien suchen",
      "writeLocalFile": "Datei schreiben"
    },
-    "title": "Lokale Dateien"
+    "title": "Lokales System"
  },
  "mcpInstall": {
    "CHECKING_INSTALLATION": "Installationsumgebung wird geprüft...",
@@ -2,6 +2,45 @@
  "about": {
    "title": "Über"
  },
+  "agentInfoDescription": {
+    "basic": {
+      "avatar": "Profilbild",
+      "description": "Beschreibung",
+      "name": "Name",
+      "tags": "Stichworte",
+      "title": "Assistenteninformationen"
+    },
+    "chat": {
+      "displayMode": "Anzeigemodus",
+      "enableHistoryCount": "Anzahl vergangener Nachrichten aktivieren",
+      "historyCount": "Anzahl vergangener Nachrichten",
+      "no": "Nein",
+      "searchMode": "Suchmodus",
+      "title": "Chat-Einstellungen",
+      "yes": "Ja"
+    },
+    "model": {
+      "maxTokens": "Maximale Token-Anzahl",
+      "model": "Modell",
+      "provider": "Anbieter",
+      "temperature": "Temperatur",
+      "title": "Modelleinstellungen",
+      "topP": "Top-P-Wert"
+    },
+    "plugins": {
+      "count": "Plugin-Einstellungen ({{count}})",
+      "empty": "Noch keine Plugins installiert",
+      "title": "Installierte Plugins"
+    },
+    "role": {
+      "systemRole": "System-Prompt",
+      "title": "Rollen-Einstellungen"
+    },
+    "value": {
+      "unset": "Nicht festgelegt",
+      "untitled": "Unbenannter Assistent"
+    }
+  },
  "agentTab": {
    "chat": "Chat-Präferenz",
    "meta": "Assistenteninformation",
@@ -18,6 +57,8 @@
    },
    "title": "Analytics"
  },
+  "checking": "Überprüfung läuft...",
+  "checkingPermissions": "Berechtigungen werden überprüft...",
  "danger": {
    "clear": {
      "action": "Alle löschen",
@@ -146,6 +187,58 @@
    },
    "waitingForMore": "Weitere Modelle werden <1>geplant</1>, bitte freuen Sie sich auf weitere Updates"
  },
+  "marketPublish": {
+    "modal": {
+      "changelog": {
+        "extra": "Beschreiben Sie die wichtigsten Änderungen und Verbesserungen in dieser Version",
+        "label": "Änderungsprotokoll",
+        "maxLengthError": "Das Änderungsprotokoll darf maximal 500 Zeichen enthalten",
+        "placeholder": "Bitte geben Sie das Änderungsprotokoll ein",
+        "required": "Bitte geben Sie das Änderungsprotokoll ein"
+      },
+      "comparison": {
+        "local": "Lokale aktuelle Version",
+        "remote": "Veröffentlichte Version"
+      },
+      "identifier": {
+        "extra": "Der Bezeichner dient als eindeutige Kennung des Assistenten. Verwenden Sie Kleinbuchstaben, Zahlen und Bindestriche.",
+        "label": "Assistenten-Bezeichner",
+        "lengthError": "Die Länge des Bezeichners muss zwischen 3 und 50 Zeichen liegen",
+        "patternError": "Der Bezeichner darf nur Kleinbuchstaben, Zahlen und Bindestriche enthalten",
+        "placeholder": "Bitte geben Sie einen eindeutigen Bezeichner ein, z. B.: web-entwicklung",
+        "required": "Bitte geben Sie den Bezeichner des Assistenten ein"
+      },
+      "loading": {
+        "fetchingRemote": "Lade entfernte Daten...",
+        "submit": "Assistent wird veröffentlicht...",
+        "upload": "Neue Version wird veröffentlicht..."
+      },
+      "messages": {
+        "createVersionFailed": "Versionserstellung fehlgeschlagen: {{message}}",
+        "fetchRemoteFailed": "Fehler beim Abrufen der entfernten Assistentendaten",
+        "missingIdentifier": "Der aktuelle Assistent hat noch keinen Markt-Bezeichner",
+        "notAuthenticated": "Bitte melden Sie sich zuerst mit Ihrem Marktkonto an",
+        "publishFailed": "Veröffentlichung fehlgeschlagen: {{message}}"
+      },
+      "submitButton": "Veröffentlichen",
+      "title": {
+        "submit": "Im Assistenten-Markt teilen",
+        "upload": "Neue Version veröffentlichen"
+      }
+    },
+    "resultModal": {
+      "message": "Der Assistent wurde zur Überprüfung eingereicht. Nach erfolgreicher Prüfung wird er automatisch veröffentlicht. Klicken Sie auf „Im Markt ansehen“, um den veröffentlichten Assistenten zu sehen.",
+      "view": "Im Markt ansehen"
+    },
+    "submit": {
+      "button": "Im Markt teilen",
+      "tooltip": "Assistent im Markt veröffentlichen"
+    },
+    "upload": {
+      "button": "Neue Version veröffentlichen",
+      "tooltip": "Neue Version im Assistenten-Markt veröffentlichen"
+    }
+  },
  "message": {
    "success": "Erfolgreich aktualisiert"
  },
@@ -561,7 +654,7 @@
  },
  "submitAgentModal": {
    "button": "Assistent einreichen",
-    "identifier": "Assistenten-Kennung",
+    "identifier": "Assistenten-Bezeichner (identifier)",
    "metaMiss": "Bitte vervollständigen Sie die Assistenteninformationen, einschließlich Name, Beschreibung und Tags, bevor Sie sie einreichen.",
    "placeholder": "Geben Sie die Kennung des Assistenten ein, die eindeutig sein muss, z. B. Web-Entwicklung",
    "tooltips": "Auf dem Assistentenmarkt teilen"
@@ -15,8 +15,22 @@
    "prompt": "Hinweiswort"
  },
  "localFiles": {
+    "editFile": {
+      "newString": "Ersetzen durch",
+      "oldString": "Suchbegriff",
+      "replaceAll": "Alle Vorkommen ersetzen",
+      "replaceFirst": "Nur erstes Vorkommen ersetzen"
+    },
    "file": "Datei",
    "folder": "Ordner",
+    "moveFiles": {
+      "itemsMoved": "{{count}} Elemente wurden verschoben:",
+      "itemsMoved_one": "{{count}} Element wurde verschoben:",
+      "itemsMoved_other": "{{count}} Elemente wurden verschoben:",
+      "itemsToMove": "{{count}} Elemente zum Verschieben:",
+      "itemsToMove_one": "{{count}} Element zum Verschieben:",
+      "itemsToMove_other": "{{count}} Elemente zum Verschieben:"
+    },
    "open": "Öffnen",
    "openFile": "Datei öffnen",
    "openFolder": "Ordner öffnen",
@@ -26,7 +40,12 @@
    "readFile": "Datei lesen",
    "readFileError": "Fehler beim Lesen der Datei, bitte überprüfen Sie den Dateipfad",
    "readFiles": "Dateien lesen",
-    "readFilesError": "Fehler beim Lesen der Dateien, bitte überprüfen Sie den Dateipfad"
+    "readFilesError": "Fehler beim Lesen der Dateien, bitte überprüfen Sie den Dateipfad",
+    "writeFile": {
+      "characters": "Zeichen",
+      "preview": "Vorschau des Inhalts",
+      "truncated": "Abgeschnitten"
+    }
  },
  "search": {
    "createNewSearch": "Neue Suchanfrage erstellen",
@@ -6,6 +6,7 @@
    "confirmRemoveUnstarred": "Nicht markierte Themen werden gelöscht. Nach dem Löschen können sie nicht wiederhergestellt werden. Bitte vorsichtig handeln.",
    "duplicate": "Kopie erstellen",
    "export": "Thema exportieren",
+    "openInNewWindow": "Seite in einem neuen Fenster öffnen",
    "removeAll": "Alle Themen löschen",
    "removeUnstarred": "Nicht markierte Themen löschen"
  },
@@ -145,6 +145,50 @@
    "apikey": "API Key Management",
    "profile": "Profile",
    "security": "Security",
-    "stats": "Statistics"
+    "stats": "Statistics",
+    "usage": "Usage Statistics"
+  },
+  "usage": {
+    "activeModels": {
+      "modelTable": "Model List",
+      "models": "Active Models",
+      "providerTable": "Provider List",
+      "providers": "Active Providers",
+      "table": {
+        "calls": "Calls",
+        "model": "Model",
+        "provider": "Provider",
+        "spend": "Spend"
+      }
+    },
+    "cards": {
+      "month": {
+        "modelCalls": "Model Calls",
+        "title": "This Month's Spend"
+      },
+      "today": {
+        "title": "Today's Spend",
+        "yesterday": "Yesterday"
+      }
+    },
+    "table": {
+      "actions": "Actions",
+      "createdAt": "Usage Time",
+      "inputTokens": "Input Tokens",
+      "model": "Model",
+      "outputTokens": "Output Tokens",
+      "spend": "Spend",
+      "tps": "TPS",
+      "ttft": "TTFT",
+      "type": "Call Type"
+    },
+    "trends": {
+      "spend": "Amount",
+      "tokens": "Tokens"
+    },
+    "welcome": {
+      "model": "Model",
+      "provider": "Provider"
+    }
  }
 }
@@ -17,6 +17,7 @@
  "availableAgents": "Available assistants",
  "backToBottom": "Back to bottom",
  "chatList": {
+    "expandMessage": "Expand Message",
    "longMessageDetail": "View Details"
  },
  "clearCurrentMessages": "Clear current session messages",
@@ -173,8 +174,11 @@
    "title": "Mention Members"
  },
  "messageAction": {
+    "collapse": "Collapse Message",
+    "continueGeneration": "Continue Generating",
    "delAndRegenerate": "Delete and Regenerate",
    "deleteDisabledByThreads": "There are subtopics, deletion is not allowed",
+    "expand": "Expand Message",
    "regenerate": "Regenerate"
  },
  "messages": {
@@ -239,6 +243,7 @@
  "noMatchingAgents": "No matching members found",
  "noMembersYet": "This group doesn't have any members yet. Click the + button to invite assistants.",
  "noSelectedAgents": "No members selected yet",
+  "openInNewWindow": "Open in a new window",
  "owner": "Group Owner",
  "pin": "Pin",
  "pinOff": "Unpin",
@@ -325,6 +330,11 @@
    "screenshot": "Screenshot",
    "settings": "Export Settings",
    "text": "Text",
+    "widthMode": {
+      "label": "Width Mode",
+      "narrow": "Narrow",
+      "wide": "Wide"
+    },
    "withBackground": "Include Background Image",
    "withFooter": "Include Footer",
    "withPluginInfo": "Include Plugin Information",
@@ -367,6 +377,28 @@
    "remained": "Remaining",
    "used": "Used"
  },
+  "tool": {
+    "intervention": {
+      "approve": "Approve",
+      "approveAndRemember": "Approve and Remember",
+      "approveOnce": "Approve This Time Only",
+      "mode": {
+        "allowList": "Allow List",
+        "allowListDesc": "Only automatically execute approved tools",
+        "autoRun": "Auto Approve",
+        "autoRunDesc": "Automatically approve all tool executions",
+        "manual": "Manual",
+        "manualDesc": "Manual approval required for each invocation"
+      },
+      "reject": "Reject",
+      "rejectAndContinue": "Reject and Retry",
+      "rejectOnly": "Reject",
+      "rejectReasonPlaceholder": "Providing a reason will help the Agent understand and improve future actions",
+      "rejectTitle": "Reject This Tool Invocation",
+      "rejectedWithReason": "This tool invocation was actively rejected: {{reason}}",
+      "toolRejected": "This tool invocation was actively rejected"
+    }
+  },
  "topic": {
    "checkOpenNewTopic": "Enable new topic?",
    "checkSaveCurrentMessages": "Do you want to save the current conversation as a topic?",
@@ -135,6 +135,7 @@
    }
  },
  "close": "Close",
+  "confirm": "Confirm",
  "contact": "Contact Us",
  "copy": "Copy",
  "copyFail": "Copy failed",
@@ -285,6 +286,7 @@
  "oauth": "SSO Login",
  "officialSite": "Official Website",
  "ok": "OK",
+  "or": "or",
  "password": "Password",
  "pin": "Pin",
  "pinOff": "Unpin",
@@ -106,6 +106,12 @@
    "keyPlaceholder": "Key",
    "valuePlaceholder": "Value"
  },
+  "LocalFile": {
+    "action": {
+      "open": "Open",
+      "showInFolder": "Show in Folder"
+    }
+  },
  "MaxTokenSlider": {
    "unlimited": "Unlimited"
  },
@@ -41,9 +41,29 @@
        "openingMessage": "Opening Message",
        "openingQuestions": "Opening Questions",
        "title": "Assistant Settings"
+      },
+      "version": {
+        "empty": "No historical versions yet",
+        "status": {
+          "archived": "Archived",
+          "deprecated": "Deprecated",
+          "unpublished": "Under Review"
+        },
+        "table": {
+          "isLatest": "Latest Version",
+          "isValidated": "Validated",
+          "publishAt": "Published Date",
+          "version": "Version"
+        },
+        "title": "Version History"
      }
    },
    "list": "Assistant List",
+    "marketSource": {
+      "label": "Market Source",
+      "legacy": "Legacy",
+      "new": "New"
+    },
    "more": "More",
    "plugins": "Integrated Plugins",
    "recentSubmits": "Recent Updates",
@@ -51,10 +71,35 @@
      "createdAt": "Recently Published",
      "identifier": "Assistant ID",
      "knowledgeCount": "Number of Knowledge Bases",
+      "myown": "View My Assistants",
      "pluginCount": "Number of Plugins",
      "title": "Assistant Name",
      "tokenUsage": "Token Usage"
    },
+    "status": {
+      "archived": {
+        "reasons": {
+          "official": "The assistant was removed by the platform due to security, political, or other concerns.",
+          "owner": "The assistant was voluntarily removed or archived by its developer."
+        },
+        "subtitle": "The assistant you're trying to access has been archived for one of the following reasons:",
+        "title": "Assistant Archived"
+      },
+      "backToMarket": "Back to Assistant Marketplace",
+      "deprecated": {
+        "reasons": {
+          "official": "The assistant was removed by the platform due to security, political, or other concerns.",
+          "owner": "The assistant was voluntarily removed or rejected by its developer."
+        },
+        "subtitle": "The assistant you're trying to access has been rejected for one of the following reasons:",
+        "title": "Assistant Rejected"
+      },
+      "support": "If you have any questions, please copy the link and contact us at <1>support@lobehub.com</1>.",
+      "unpublished": {
+        "subtitle": "The assistant you're trying to access is currently under review. If you have any questions, please copy the link and contact us at <1>support@lobehub.com</1>.",
+        "title": "Assistant Under Review"
+      }
+    },
    "suggestions": "Related Recommendations",
    "systemRole": "Assistant Settings",
    "tokenUsage": "Assistant Prompt Token Usage",
@@ -102,7 +102,7 @@
      "SPII": "Your content may contain sensitive personally identifiable information (PII). To protect privacy, please remove any sensitive details and try again.",
      "default": "Content blocked: {{blockReason}}. Please adjust your request and try again."
    },
-    "InsufficientQuota": "Sorry, the quota for this key has been reached. Please check your account balance or increase the key quota and try again.",
+    "InsufficientQuota": "Sorry, the quota for this key has been reached. Please check if your account balance is sufficient or try again after increasing the key's quota.",
    "InvalidAccessCode": "Invalid access code or empty. Please enter the correct access code or add a custom API Key.",
    "InvalidBedrockCredentials": "Bedrock authentication failed. Please check the AccessKeyId/SecretAccessKey and retry.",
    "InvalidClerkUser": "Sorry, you are not currently logged in. Please log in or register an account to continue.",
@@ -131,7 +131,7 @@
    "PluginServerError": "Plugin server request returned an error. Please check your plugin manifest file, plugin configuration, or server implementation based on the error information below",
    "PluginSettingsInvalid": "This plugin needs to be correctly configured before it can be used. Please check if your configuration is correct",
    "ProviderBizError": "Error requesting {{provider}} service, please troubleshoot or retry based on the following information",
-    "QuotaLimitReached": "We apologize, but the current token usage or number of requests has reached the quota limit for this key. Please increase the quota for this key or try again later.",
+    "QuotaLimitReached": "Sorry, the token usage or request count has reached the quota limit for this key. Please increase the key's quota or try again later.",
    "StreamChunkError": "Error parsing the message chunk of the streaming request. Please check if the current API interface complies with the standard specifications, or contact your API provider for assistance.",
    "SubscriptionKeyMismatch": "We apologize for the inconvenience. Due to a temporary system malfunction, your current subscription usage is inactive. Please click the button below to restore your subscription, or contact us via email for support.",
    "SubscriptionPlanLimit": "Your subscription points have been exhausted, and you cannot use this feature. Please upgrade to a higher plan or configure a custom model API to continue using it.",
@@ -1,5 +1,8 @@
 {
-  "desc": "Manage your knowledge",
+  "addFolder": "Create Folder",
+  "addKnowledge": "Add Knowledge",
+  "addPage": "Create Document",
+  "desc": "Manage your knowledge for work, study, and life.",
  "detail": {
    "basic": {
      "createdAt": "Creation Time",
@@ -21,15 +24,89 @@
      "embeddingStatus": "embedding"
    }
  },
+  "documentEditor": {
+    "addIcon": "Add Icon",
+    "autoSaveMessage": "Document auto-saves, no manual save needed",
+    "chooseIcon": "Choose Icon",
+    "deleteConfirm": {
+      "content": "This document will be deleted permanently and cannot be recovered. Please proceed with caution.",
+      "title": "Delete Document"
+    },
+    "deleteError": "Failed to delete document",
+    "deleteSuccess": "Document deleted successfully",
+    "editedAt": "Edited {{time}}",
+    "editedBy": "Edited by {{name}}",
+    "editorPlaceholder": "Type document content, press / to open command menu",
+    "empty": {
+      "createNewDocument": "Create New Document",
+      "title": "Select a document to start",
+      "uploadMarkdown": "Upload Markdown File"
+    },
+    "linkCopied": "Link copied",
+    "menu": {
+      "copyLink": "Copy Link",
+      "exportDocument": "Export Document",
+      "importDocument": "Import Document",
+      "pin": "Pin Document"
+    },
+    "saving": "Saving...",
+    "titlePlaceholder": "Untitled",
+    "wordCount": "{{wordCount}} words"
+  },
+  "documentList": {
+    "copyContent": "Copy All",
+    "documentCount": "Total {{count}} documents",
+    "duplicate": "Duplicate",
+    "empty": "No documents yet. Click the button above to create your first one.",
+    "noResults": "No matching documents found.",
+    "selectNote": "Select a document to start editing.",
+    "untitled": "Untitled"
+  },
  "empty": "No files or folders have been uploaded yet.",
  "header": {
    "actions": {
      "newFolder": "New Folder",
+      "newPage": "New Document",
      "uploadFile": "Upload File",
      "uploadFolder": "Upload Folder"
    },
+    "newDocumentButton": "New Document",
+    "newNoteDialog": {
+      "cancel": "Cancel",
+      "editTitle": "Edit Document",
+      "emptyContent": "Document content cannot be empty.",
+      "loadError": "Failed to load the document. Please try again.",
+      "loading": "Loading...",
+      "save": "Save",
+      "saveError": "Failed to save the document. Please try again.",
+      "saveSuccess": "Document saved successfully.",
+      "title": "New Document",
+      "updateSuccess": "Document updated successfully."
+    },
    "uploadButton": "Upload"
  },
+  "home": {
+    "getStarted": "Get Started",
+    "greeting": "Get Started",
+    "quickActions": "Quick Actions",
+    "recentDocuments": "Recent Documents",
+    "recentFiles": "Recent Files",
+    "subtitle": "Welcome to your knowledge base. Start managing your documents here.",
+    "uploadEntries": {
+      "files": {
+        "title": "Upload Files"
+      },
+      "folder": {
+        "title": "Upload Folder"
+      },
+      "knowledgeBase": {
+        "title": "Create Knowledge Base"
+      },
+      "newDocument": {
+        "title": "Create Document"
+      }
+    }
+  },
  "knowledgeBase": {
    "list": {
      "confirmRemoveKnowledgeBase": "You are about to delete this knowledge base. The files within will not be deleted and will be moved to All Files. Once deleted, the knowledge base cannot be recovered. Please proceed with caution.",
@@ -38,6 +115,10 @@
    "new": "New Knowledge Base",
    "title": "Knowledge Base"
  },
+  "menu": {
+    "allDocuments": "All Documents",
+    "allFiles": "All Files"
+  },
  "networkError": "Failed to retrieve the knowledge base. Please check your network connection and try again.",
  "notSupportGuide": {
    "desc": "The current deployment instance is in client database mode, and file management features are not available. Please switch to <1>server database deployment mode</1>, or use <3>LobeChat Cloud</3> directly.",
@@ -61,12 +142,16 @@
    "downloadFile": "Download File",
    "unsupportedFileAndContact": "This file format is not currently supported for online preview. If you have a request for previewing, feel free to <1>contact us</1>."
  },
+  "searchDocumentPlaceholder": "Search documents",
  "searchFilePlaceholder": "Search Files",
  "tab": {
-    "all": "All Files",
+    "all": "All",
    "audios": "Audio",
    "documents": "Documents",
+    "home": "Home",
    "images": "Images",
+    "moreTypes": "More Types",
+    "pages": "Pages",
    "videos": "Videos",
    "websites": "Websites"
  },
@@ -0,0 +1,42 @@
+{
+  "callback": {
+    "buttons": {
+      "close": "Close Window"
+    },
+    "messages": {
+      "authFailed": "Authorization failed: {{error}}",
+      "missingParams": "Missing authorization parameters",
+      "processing": "Processing authorization...",
+      "successWithCountdown": "{{message}} The window will close automatically in {{countdown}} seconds",
+      "successWithRedirect": "Authorization successful! Redirecting..."
+    },
+    "titles": {
+      "error": "Authorization Failed",
+      "loading": "LobeHub Market Authorization",
+      "success": "Authorization Successful"
+    }
+  },
+  "errors": {
+    "authorizationFailed": "Authorization failed, please try again.",
+    "browserOnly": "The authorization process can only be initiated in a browser.",
+    "codeConsumed": "The authorization code has already been used. Please try again.",
+    "codeVerifierMissing": "Invalid authorization session. Please restart the login process.",
+    "general": "An error occurred during authorization. Please try again.",
+    "handoffFailed": "Failed to retrieve authorization result. Please try again.",
+    "handoffTimeout": "Authorization timed out. Please complete the process in your browser and try again.",
+    "oidcNotReady": "Authorization service is not ready. Please try again later.",
+    "openBrowserFailed": "Failed to open the system browser. Please try again.",
+    "openPopupFailed": "Failed to open authorization popup. Please check your browser's popup blocker settings.",
+    "popupClosed": "The authorization window was closed before completion.",
+    "sessionExpired": "Authorization session has expired. Please log in again.",
+    "stateMismatch": "Authorization state mismatch. Please try again.",
+    "stateMissing": "Authorization state not found. Please try again."
+  },
+  "messages": {
+    "loading": "Starting authorization process...",
+    "success": {
+      "submit": "Authorization successful! You can now publish your assistant.",
+      "upload": "Authorization successful! You can now publish a new version."
+    }
+  }
+}
@@ -199,6 +199,12 @@
    "all": "All",
    "list": {
      "disabled": "Disabled",
+      "disabledActions": {
+        "sort": "Sort By",
+        "sortAlphabetical": "Sort Alphabetically",
+        "sortAlphabeticalDesc": "Sort by Reverse Alphabetical Order",
+        "sortDefault": "Default Sort"
+      },
      "enabled": "Enabled"
    },
    "notFound": "No search results found",
@@ -289,7 +295,7 @@
      },
      "helpDoc": "Configuration Guide",
      "responsesApi": {
-        "desc": "Utilizes OpenAI's next-generation request format specification to unlock advanced features like chain of thought",
+        "desc": "Uses OpenAI's next-generation request format specification to unlock advanced features like chain-of-thought (supported by OpenAI models only)",
        "title": "Use Responses API Specification"
      },
      "waitingForMore": "More models are currently <1>planned for integration</1>, please stay tuned"
@@ -391,7 +397,13 @@
      "addNew": "Add Model",
      "disabled": "Disabled",
      "disabledActions": {
-        "showMore": "Show All"
+        "showMore": "Show All",
+        "sort": "Sort By",
+        "sortAlphabetical": "Sort Alphabetically",
+        "sortAlphabeticalDesc": "Sort by Reverse Alphabetical Order",
+        "sortDefault": "Default Sort",
+        "sortReleasedAt": "Sort by Earliest Release Date",
+        "sortReleasedAtDesc": "Sort by Latest Release Date"
      },
      "empty": {
        "desc": "Please create a custom model or pull a model to get started.",
@@ -236,6 +236,9 @@
  "MiniMaxAI/MiniMax-M1-80k": {
    "description": "MiniMax-M1 is a large-scale hybrid attention inference model with open-source weights, featuring 456 billion parameters, with approximately 45.9 billion parameters activated per token. The model natively supports ultra-long contexts of up to 1 million tokens and, through lightning attention mechanisms, reduces floating-point operations by 75% compared to DeepSeek R1 in tasks generating 100,000 tokens. Additionally, MiniMax-M1 employs a Mixture of Experts (MoE) architecture, combining the CISPO algorithm with an efficient reinforcement learning training design based on hybrid attention, achieving industry-leading performance in long-input inference and real-world software engineering scenarios."
  },
+  "MiniMaxAI/MiniMax-M2": {
+    "description": "MiniMax-M2 redefines efficiency for intelligent agents. It is a compact, fast, and cost-effective Mixture of Experts (MoE) model with 230 billion total parameters and 10 billion active parameters. Designed for top-tier performance in coding and agent tasks, it also maintains strong general intelligence. With only 10 billion active parameters, MiniMax-M2 delivers performance comparable to large-scale models, making it an ideal choice for high-efficiency applications."
+  },
  "Moonshot-Kimi-K2-Instruct": {
    "description": "With a total of 1 trillion parameters and 32 billion activated parameters, this non-thinking model achieves top-tier performance in cutting-edge knowledge, mathematics, and coding, excelling in general agent tasks. It is carefully optimized for agent tasks, capable not only of answering questions but also taking actions. Ideal for improvisational, general chat, and agent experiences, it is a reflex-level model requiring no prolonged thinking."
  },
@@ -1049,6 +1052,9 @@
  "deepseek-r1-0528": {
    "description": "The full-capacity 685B model released on May 28, 2025. DeepSeek-R1 extensively employs reinforcement learning during post-training, significantly enhancing reasoning capabilities with minimal labeled data. It demonstrates strong performance in mathematics, coding, and natural language reasoning tasks."
  },
+  "deepseek-r1-250528": {
+    "description": "DeepSeek R1 250528, the full-performance DeepSeek-R1 inference model, ideal for complex mathematical and logical tasks."
+  },
  "deepseek-r1-70b-fast-online": {
    "description": "DeepSeek R1 70B fast version, supporting real-time online search, providing faster response times while maintaining model performance."
  },
@@ -1059,31 +1065,34 @@
    "description": "deepseek-r1-distill-llama is a model distilled from DeepSeek-R1 based on Llama."
  },
  "deepseek-r1-distill-llama-70b": {
-    "description": "DeepSeek R1—the larger and smarter model in the DeepSeek suite—has been distilled into the Llama 70B architecture. Based on benchmark tests and human evaluations, this model is smarter than the original Llama 70B, especially excelling in tasks requiring mathematical and factual accuracy."
+    "description": "DeepSeek R1 Distill Llama 70B, a distilled model combining general R1 inference capabilities with the Llama ecosystem."
  },
  "deepseek-r1-distill-llama-8b": {
-    "description": "The DeepSeek-R1-Distill series models are fine-tuned versions of samples generated by DeepSeek-R1, using knowledge distillation techniques on open-source models like Qwen and Llama."
+    "description": "DeepSeek-R1-Distill-Llama-8B is a distilled large language model based on Llama-3.1-8B, utilizing outputs from DeepSeek R1."
+  },
+  "deepseek-r1-distill-qianfan-70b": {
+    "description": "DeepSeek R1 Distill Qianfan 70B, a cost-effective R1 distilled model based on Qianfan-70B."
+  },
+  "deepseek-r1-distill-qianfan-8b": {
+    "description": "DeepSeek R1 Distill Qianfan 8B, an R1 distilled model based on Qianfan-8B, suitable for small to medium-scale applications."
  },
  "deepseek-r1-distill-qianfan-llama-70b": {
-    "description": "First released on February 14, 2025, distilled by the Qianfan model development team using Llama3_70B as the base model (Built with Meta Llama), with Qianfan's corpus also added to the distilled data."
-  },
-  "deepseek-r1-distill-qianfan-llama-8b": {
-    "description": "First released on February 14, 2025, distilled by the Qianfan model development team using Llama3_8B as the base model (Built with Meta Llama), with Qianfan's corpus also added to the distilled data."
+    "description": "DeepSeek R1 Distill Qianfan Llama 70B, an R1 distilled model based on Llama-70B."
  },
  "deepseek-r1-distill-qwen": {
    "description": "deepseek-r1-distill-qwen is a model distilled from DeepSeek-R1 based on Qwen."
  },
  "deepseek-r1-distill-qwen-1.5b": {
-    "description": "The DeepSeek-R1-Distill series models are fine-tuned versions of samples generated by DeepSeek-R1, using knowledge distillation techniques on open-source models like Qwen and Llama."
+    "description": "DeepSeek R1 Distill Qwen 1.5B, an ultra-lightweight R1 distilled model designed for extremely low-resource environments."
  },
  "deepseek-r1-distill-qwen-14b": {
-    "description": "The DeepSeek-R1-Distill series models are fine-tuned versions of samples generated by DeepSeek-R1, using knowledge distillation techniques on open-source models like Qwen and Llama."
+    "description": "DeepSeek R1 Distill Qwen 14B, a mid-sized R1 distilled model suitable for multi-scenario deployment."
  },
  "deepseek-r1-distill-qwen-32b": {
-    "description": "The DeepSeek-R1-Distill series models are fine-tuned versions of samples generated by DeepSeek-R1, using knowledge distillation techniques on open-source models like Qwen and Llama."
+    "description": "DeepSeek R1 Distill Qwen 32B, an R1 distilled model based on Qwen-32B, balancing performance and cost."
  },
  "deepseek-r1-distill-qwen-7b": {
-    "description": "The DeepSeek-R1-Distill series models are fine-tuned versions of samples generated by DeepSeek-R1, using knowledge distillation techniques on open-source models like Qwen and Llama."
+    "description": "DeepSeek R1 Distill Qwen 7B, a lightweight R1 distilled model ideal for edge computing and enterprise private deployments."
  },
  "deepseek-r1-fast-online": {
    "description": "DeepSeek R1 full fast version, supporting real-time online search, combining the powerful capabilities of 671B parameters with faster response times."
@@ -1112,12 +1121,24 @@
  "deepseek-v3.1-terminus": {
    "description": "DeepSeek-V3.1-Terminus is an optimized large language model developed by DeepSeek, specifically tailored for terminal devices."
  },
+  "deepseek-v3.1-think-250821": {
+    "description": "DeepSeek V3.1 Think 250821, a deep reasoning model aligned with the Terminus version, suitable for high-performance inference scenarios."
+  },
  "deepseek-v3.1:671b": {
    "description": "DeepSeek V3.1: The next-generation reasoning model that enhances complex reasoning and chain-of-thought capabilities, suitable for tasks requiring in-depth analysis."
  },
  "deepseek-v3.2-exp": {
    "description": "deepseek-v3.2-exp introduces a sparse attention mechanism designed to enhance training and inference efficiency when processing long texts, priced lower than deepseek-v3.1."
  },
+  "deepseek-v3.2-think": {
+    "description": "DeepSeek V3.2 Think, a full-performance deep reasoning model with enhanced long-chain reasoning capabilities."
+  },
+  "deepseek-vl2": {
+    "description": "DeepSeek VL2, a multimodal model supporting image-text understanding and fine-grained visual question answering."
+  },
+  "deepseek-vl2-small": {
+    "description": "DeepSeek VL2 Small, a lightweight multimodal version designed for resource-constrained and high-concurrency scenarios."
+  },
  "deepseek/deepseek-chat-v3-0324": {
    "description": "DeepSeek V3 is a 685B parameter expert mixture model, the latest iteration in the DeepSeek team's flagship chat model series.\n\nIt inherits from the [DeepSeek V3](/deepseek/deepseek-chat-v3) model and performs excellently across various tasks."
  },
@@ -1137,7 +1158,7 @@
    "description": "DeepSeek-R1 greatly improves model reasoning capabilities with minimal labeled data. Before outputting the final answer, the model first generates a chain of thought to enhance answer accuracy."
  },
  "deepseek/deepseek-r1-distill-llama-70b": {
-    "description": "DeepSeek-R1-Distill-Llama-70B is a distilled, more efficient variant of the 70B Llama model. It maintains strong performance on text generation tasks while reducing computational overhead for easier deployment and research. Served by Groq using its custom Language Processing Unit (LPU) hardware for fast, efficient inference."
+    "description": "DeepSeek R1 Distill Llama 70B is a large language model based on Llama3.3 70B. Fine-tuned using outputs from DeepSeek R1, it achieves competitive performance on par with leading-edge large models."
  },
  "deepseek/deepseek-r1-distill-llama-8b": {
    "description": "DeepSeek R1 Distill Llama 8B is a distilled large language model based on Llama-3.1-8B-Instruct, trained using outputs from DeepSeek R1."
@@ -1253,83 +1274,89 @@
  "emohaa": {
    "description": "Emohaa is a psychological model with professional counseling capabilities, helping users understand emotional issues."
  },
-  "ernie-3.5-128k": {
-    "description": "Baidu's flagship large-scale language model, covering a vast amount of Chinese and English corpus, possesses strong general capabilities to meet the requirements of most dialogue Q&A, creative generation, and plugin application scenarios; it supports automatic integration with Baidu search plugins to ensure the timeliness of Q&A information."
-  },
-  "ernie-3.5-8k": {
-    "description": "Baidu's flagship large-scale language model, covering a vast amount of Chinese and English corpus, possesses strong general capabilities to meet the requirements of most dialogue Q&A, creative generation, and plugin application scenarios; it supports automatic integration with Baidu search plugins to ensure the timeliness of Q&A information."
-  },
-  "ernie-3.5-8k-preview": {
-    "description": "Baidu's flagship large-scale language model, covering a vast amount of Chinese and English corpus, possesses strong general capabilities to meet the requirements of most dialogue Q&A, creative generation, and plugin application scenarios; it supports automatic integration with Baidu search plugins to ensure the timeliness of Q&A information."
-  },
-  "ernie-4.0-8k-latest": {
-    "description": "Baidu's flagship ultra-large-scale language model, which has achieved a comprehensive upgrade in model capabilities compared to ERNIE 3.5, widely applicable to complex task scenarios across various fields; it supports automatic integration with Baidu search plugins to ensure the timeliness of Q&A information."
-  },
-  "ernie-4.0-8k-preview": {
-    "description": "Baidu's flagship ultra-large-scale language model, which has achieved a comprehensive upgrade in model capabilities compared to ERNIE 3.5, widely applicable to complex task scenarios across various fields; it supports automatic integration with Baidu search plugins to ensure the timeliness of Q&A information."
-  },
-  "ernie-4.0-turbo-128k": {
-    "description": "Baidu's flagship ultra-large-scale language model, demonstrating outstanding overall performance, widely applicable to complex task scenarios across various fields; it supports automatic integration with Baidu search plugins to ensure the timeliness of Q&A information. It performs better than ERNIE 4.0 in terms of performance."
-  },
-  "ernie-4.0-turbo-8k-latest": {
-    "description": "Baidu's flagship ultra-large-scale language model, demonstrating outstanding overall performance, widely applicable to complex task scenarios across various fields; it supports automatic integration with Baidu search plugins to ensure the timeliness of Q&A information. It performs better than ERNIE 4.0 in terms of performance."
-  },
-  "ernie-4.0-turbo-8k-preview": {
-    "description": "Baidu's flagship ultra-large-scale language model, demonstrating outstanding overall performance, widely applicable to complex task scenarios across various fields; it supports automatic integration with Baidu search plugins to ensure the timeliness of Q&A information. It performs better than ERNIE 4.0 in terms of performance."
+  "ernie-4.5-0.3b": {
+    "description": "ERNIE 4.5 0.3B, an open-source lightweight model suitable for local and customized deployments."
  },
  "ernie-4.5-21b-a3b": {
-    "description": "ERNIE 4.5 21B A3B is a Mixture of Experts model from Baidu's Wenxin series, offering strong reasoning and multilingual capabilities."
+    "description": "ERNIE 4.5 21B A3B, an open-source large-parameter model with stronger performance in understanding and generation tasks."
  },
  "ernie-4.5-300b-a47b": {
    "description": "ERNIE 4.5 300B A47B is a large-scale Mixture of Experts model from Baidu's Wenxin series, delivering exceptional reasoning performance."
  },
  "ernie-4.5-8k-preview": {
-    "description": "ERNIE 4.5 is Baidu's self-developed next-generation native multimodal foundational model, achieving collaborative optimization through joint modeling of multiple modalities, with excellent multimodal understanding capabilities; it features enhanced language abilities, with significant improvements in understanding, generation, logic, and memory, as well as reduced hallucinations and improved logical reasoning and coding capabilities."
+    "description": "ERNIE 4.5 8K Preview, an 8K context preview model for experiencing and testing ERNIE 4.5 capabilities."
  },
  "ernie-4.5-turbo-128k": {
-    "description": "Wenxin 4.5 Turbo shows significant enhancements in reducing hallucinations, logical reasoning, and coding capabilities. Compared to Wenxin 4.5, it is faster and more cost-effective. The model's capabilities have been comprehensively improved to better meet the needs of multi-turn long history dialogue processing and long document understanding Q&A tasks."
+    "description": "ERNIE 4.5 Turbo 128K, a high-performance general-purpose model supporting search augmentation and tool invocation, suitable for Q&A, coding, agents, and more."
+  },
+  "ernie-4.5-turbo-128k-preview": {
+    "description": "ERNIE 4.5 Turbo 128K Preview, offering the same capabilities as the official version, ideal for integration testing and staging."
  },
  "ernie-4.5-turbo-32k": {
-    "description": "Wenxin 4.5 Turbo has also shown significant enhancements in reducing hallucinations, logical reasoning, and coding capabilities. Compared to Wenxin 4.5, it is faster and more cost-effective. There are notable improvements in text creation and knowledge Q&A capabilities. The output length and sentence delay have increased compared to ERNIE 4.5."
+    "description": "ERNIE 4.5 Turbo 32K, a medium-to-long context version suitable for Q&A, knowledge retrieval, and multi-turn conversations."
+  },
+  "ernie-4.5-turbo-latest": {
+    "description": "ERNIE 4.5 Turbo Latest, optimized for overall performance, ideal as a general-purpose production model."
+  },
+  "ernie-4.5-turbo-vl": {
+    "description": "ERNIE 4.5 Turbo VL, a mature multimodal model for image-text understanding and recognition in production environments."
  },
  "ernie-4.5-turbo-vl-32k": {
-    "description": "A brand new version of the Wenxin large model, with significant improvements in image understanding, creation, translation, and coding capabilities, now supports a context length of 32K for the first time, with a significant reduction in first token delay."
+    "description": "ERNIE 4.5 Turbo VL 32K, a medium-to-long text multimodal version for joint understanding of long documents and images."
+  },
+  "ernie-4.5-turbo-vl-32k-preview": {
+    "description": "ERNIE 4.5 Turbo VL 32K Preview, a 32K multimodal preview version for evaluating long-context visual capabilities."
+  },
+  "ernie-4.5-turbo-vl-latest": {
+    "description": "ERNIE 4.5 Turbo VL Latest, the latest multimodal version offering improved image-text understanding and reasoning."
+  },
+  "ernie-4.5-turbo-vl-preview": {
+    "description": "ERNIE 4.5 Turbo VL Preview, a multimodal preview model supporting image-text understanding and generation, ideal for visual Q&A and content comprehension."
+  },
+  "ernie-4.5-vl-28b-a3b": {
+    "description": "ERNIE 4.5 VL 28B A3B, an open-source multimodal model supporting image-text understanding and reasoning tasks."
+  },
+  "ernie-5.0-thinking-preview": {
+    "description": "ERNIE 5.0 Thinking Preview, a native all-modality flagship model supporting unified modeling of text, image, audio, and video, with comprehensive capability upgrades for complex Q&A, creative tasks, and agent scenarios."
  },
  "ernie-char-8k": {
-    "description": "Baidu's vertical scene large language model, suitable for applications such as game NPCs, customer service dialogues, and role-playing conversations, with a more distinct and consistent character style, stronger instruction-following capabilities, and superior inference performance."
+    "description": "ERNIE Character 8K, a persona dialogue model ideal for IP character building and long-term companion conversations."
  },
  "ernie-char-fiction-8k": {
-    "description": "Baidu's vertical scene large language model, suitable for applications such as game NPCs, customer service dialogues, and role-playing conversations, with a more distinct and consistent character style, stronger instruction-following capabilities, and superior inference performance."
+    "description": "ERNIE Character Fiction 8K, a persona model for novel and story creation, suitable for generating long-form narratives."
+  },
+  "ernie-char-fiction-8k-preview": {
+    "description": "ERNIE Character Fiction 8K Preview, a preview model for character and story creation, designed for feature testing and experience."
  },
  "ernie-irag-edit": {
-    "description": "Baidu's self-developed ERNIE iRAG Edit image editing model supports operations such as erase (object removal), repaint (object redrawing), and variation (variant generation) based on images."
+    "description": "ERNIE iRAG Edit, an image editing model supporting image erasure, redrawing, and variant generation."
  },
  "ernie-lite-8k": {
-    "description": "ERNIE Lite is Baidu's lightweight large language model, balancing excellent model performance with inference efficiency, suitable for low-power AI acceleration card inference."
+    "description": "ERNIE Lite 8K, a lightweight general-purpose model suitable for cost-sensitive daily Q&A and content generation."
  },
  "ernie-lite-pro-128k": {
-    "description": "Baidu's lightweight large language model, balancing excellent model performance with inference efficiency, offering better performance than ERNIE Lite, suitable for low-power AI acceleration card inference."
+    "description": "ERNIE Lite Pro 128K, a lightweight high-performance model ideal for latency- and cost-sensitive business scenarios."
  },
  "ernie-novel-8k": {
-    "description": "Baidu's general-purpose large language model, which has a significant advantage in novel continuation capabilities and can also be used in short plays, movies, and other scenarios."
+    "description": "ERNIE Novel 8K, a model for long-form novel and IP story creation, skilled in multi-character and multi-threaded storytelling."
  },
  "ernie-speed-128k": {
-    "description": "Baidu's latest self-developed high-performance large language model released in 2024, with excellent general capabilities, suitable as a base model for fine-tuning to better address specific scenario issues while also demonstrating excellent inference performance."
+    "description": "ERNIE Speed 128K, a large model with no input/output cost, suitable for long-text understanding and large-scale trials."
+  },
+  "ernie-speed-8k": {
+    "description": "ERNIE Speed 8K, a free and fast model ideal for daily conversations and lightweight text tasks."
  },
  "ernie-speed-pro-128k": {
-    "description": "Baidu's latest self-developed high-performance large language model released in 2024, with excellent general capabilities, offering better performance than ERNIE Speed, suitable as a base model for fine-tuning to better address specific scenario issues while also demonstrating excellent inference performance."
+    "description": "ERNIE Speed Pro 128K, a high-concurrency, cost-effective model suitable for large-scale online services and enterprise applications."
  },
  "ernie-tiny-8k": {
-    "description": "ERNIE Tiny is Baidu's ultra-high-performance large language model, with the lowest deployment and fine-tuning costs among the Wenxin series models."
-  },
-  "ernie-x1-32k": {
-    "description": "Possesses stronger abilities in understanding, planning, reflection, and evolution. As a more comprehensive deep thinking model, Wenxin X1 combines accuracy, creativity, and eloquence, excelling in areas such as Chinese knowledge Q&A, literary creation, document writing, daily conversation, logical reasoning, complex calculations, and tool invocation."
-  },
-  "ernie-x1-32k-preview": {
-    "description": "The ERNIE X1 model possesses stronger understanding, planning, reflection, and evolution capabilities. As a more comprehensive deep thinking model, ERNIE X1 excels in accuracy, creativity, and eloquence, particularly in Chinese knowledge Q&A, literary creation, document writing, daily conversation, logical reasoning, complex calculations, and tool invocation."
+    "description": "ERNIE Tiny 8K, an ultra-lightweight model for simple Q&A, classification, and other low-cost inference scenarios."
  },
  "ernie-x1-turbo-32k": {
-    "description": "The model performs better in terms of effectiveness and performance compared to ERNIE-X1-32K."
+    "description": "ERNIE X1 Turbo 32K, a high-speed reasoning model with 32K long context, ideal for complex reasoning and multi-turn dialogue."
+  },
+  "ernie-x1.1-preview": {
+    "description": "ERNIE X1.1 Preview, a preview version of the ERNIE X1.1 reasoning model, suitable for capability validation and testing."
  },
  "fal-ai/bytedance/seedream/v4": {
    "description": "Seedream 4.0 image generation model developed by ByteDance Seed team supports text and image inputs, providing a highly controllable and high-quality image generation experience. Generates images based on text prompts."
@@ -1389,7 +1416,7 @@
    "description": "FLUX.1 [schnell], currently the most advanced open-source few-step model, surpasses competitors and even powerful non-distilled models like Midjourney v6.0 and DALL·E 3 (HD). Finely tuned to retain the full output diversity from pretraining, FLUX.1 [schnell] significantly enhances visual quality, instruction compliance, size/aspect ratio variation, font handling, and output diversity compared to state-of-the-art models on the market, offering users a richer and more diverse creative image generation experience."
  },
  "flux.1-schnell": {
-    "description": "A 12-billion-parameter rectified flow transformer capable of generating images based on text descriptions."
+    "description": "FLUX.1-schnell, a high-performance image generation model for fast creation of multi-style images."
  },
  "gemini-1.0-pro-001": {
    "description": "Gemini 1.0 Pro 001 (Tuning) offers stable and tunable performance, making it an ideal choice for complex task solutions."
@@ -1538,6 +1565,9 @@
  "glm-4-0520": {
    "description": "GLM-4-0520 is the latest model version designed for highly complex and diverse tasks, demonstrating outstanding performance."
  },
+  "glm-4-32b-0414": {
+    "description": "GLM-4 32B 0414, a general-purpose large model from the GLM series, supporting multi-task text generation and understanding."
+  },
  "glm-4-9b-chat": {
    "description": "GLM-4-9B-Chat demonstrates high performance across semantics, mathematics, reasoning, coding, and knowledge. It also supports web browsing, code execution, custom tool invocation, and long-text reasoning. Supports 26 languages including Japanese, Korean, and German."
  },
@@ -1826,6 +1856,18 @@
  "gpt-5-pro": {
    "description": "GPT-5 Pro leverages greater computational power for deeper reasoning and consistently delivers improved answers."
  },
+  "gpt-5.1": {
+    "description": "GPT-5.1 — A flagship model optimized for coding and agent tasks, featuring configurable reasoning strength and extended context support."
+  },
+  "gpt-5.1-chat-latest": {
+    "description": "GPT-5.1 Chat: A variant of GPT-5.1 tailored for ChatGPT, ideal for conversational scenarios."
+  },
+  "gpt-5.1-codex": {
+    "description": "GPT-5.1 Codex: A GPT-5.1 variant optimized for agentic coding tasks, designed for more complex code/agent workflows via the Responses API."
+  },
+  "gpt-5.1-codex-mini": {
+    "description": "GPT-5.1 Codex mini: A smaller, more cost-effective Codex variant optimized for agentic coding tasks."
+  },
  "gpt-audio": {
    "description": "GPT Audio is a general-purpose chat model designed for audio input and output, supporting audio I/O in the Chat Completions API."
  },
@@ -2001,13 +2043,13 @@
    "description": "Imagen 4th-generation text-to-image model series"
  },
  "imagen-4.0-generate-preview-06-06": {
-    "description": "Imagen 4th generation text-to-image model series"
+    "description": "Imagen 4th-generation text-to-image model series."
  },
  "imagen-4.0-ultra-generate-001": {
    "description": "Imagen 4th-generation text-to-image model, Ultra version"
  },
  "imagen-4.0-ultra-generate-preview-06-06": {
-    "description": "Imagen 4th generation text-to-image model series Ultra version"
+    "description": "Ultra version of the 4th-generation Imagen text-to-image model series."
  },
  "inception/mercury-coder-small": {
    "description": "Mercury Coder Small is ideal for code generation, debugging, and refactoring tasks, offering minimal latency."
@@ -2036,14 +2078,26 @@
  "internlm3-latest": {
    "description": "Our latest model series boasts exceptional inference performance, leading the pack among open-source models of similar scale. It defaults to our most recently released InternLM3 series models."
  },
+  "internvl2.5-38b-mpo": {
+    "description": "InternVL2.5 38B MPO, a multimodal pre-trained model supporting complex image-text reasoning tasks."
+  },
  "internvl2.5-latest": {
    "description": "The InternVL2.5 version we continue to maintain, offering excellent and stable performance. It defaults to our latest released InternVL2.5 series model, currently pointing to internvl2.5-78b."
  },
+  "internvl3-14b": {
+    "description": "InternVL3 14B, a mid-sized multimodal model balancing performance and cost."
+  },
+  "internvl3-1b": {
+    "description": "InternVL3 1B, a lightweight multimodal model suitable for deployment in resource-constrained environments."
+  },
+  "internvl3-38b": {
+    "description": "InternVL3 38B, a large-scale open-source multimodal model for high-precision image-text understanding tasks."
+  },
  "internvl3-latest": {
    "description": "Our latest released multimodal large model, featuring enhanced image-text understanding capabilities and long-sequence image comprehension, performs on par with top proprietary models. It defaults to our latest released InternVL series model, currently pointing to internvl3-78b."
  },
  "irag-1.0": {
-    "description": "Baidu's self-developed iRAG (image-based Retrieval-Augmented Generation) technology combines Baidu Search's hundreds of millions of image resources with powerful foundational model capabilities to generate ultra-realistic images. The overall effect far surpasses native text-to-image systems, eliminating the AI-generated feel while maintaining low cost. iRAG features hallucination-free, ultra-realistic, and instant retrieval characteristics."
+    "description": "ERNIE iRAG, an image retrieval-augmented generation model supporting image search, image-text retrieval, and content generation."
  },
  "jamba-large": {
    "description": "Our most powerful and advanced model, designed for handling complex enterprise-level tasks with exceptional performance."
@@ -2064,7 +2118,7 @@
    "description": "The kimi-k2-0905-preview model has a context length of 256k, featuring stronger Agentic Coding capabilities, more outstanding aesthetics and practicality of frontend code, and better context understanding."
  },
  "kimi-k2-instruct": {
-    "description": "Kimi K2 Instruct is a large language model developed by Moonshot AI, featuring ultra-long context processing capabilities."
+    "description": "Kimi K2 Instruct, the official Kimi inference model supporting long context, code, Q&A, and more."
  },
  "kimi-k2-turbo-preview": {
    "description": "Kimi-K2 is a Mixture-of-Experts (MoE) foundation model with exceptional coding and agent capabilities, featuring 1T total parameters and 32B activated parameters. In benchmark evaluations across core categories — general knowledge reasoning, programming, mathematics, and agent tasks — the K2 model outperforms other leading open-source models."
@@ -2174,6 +2228,9 @@
  "megrez-3b-instruct": {
    "description": "Megrez 3B Instruct is a compact and efficient model developed by Wuwen Xinqiong."
  },
+  "meituan/longcat-flash-chat": {
+    "description": "An open-source foundational model from Meituan, optimized for conversational interactions and agent-based tasks. Excels in tool usage and complex multi-turn dialogue scenarios."
+  },
  "meta-llama-3-70b-instruct": {
    "description": "A powerful 70-billion parameter model excelling in reasoning, coding, and broad language applications."
  },
@@ -2405,6 +2462,12 @@
  "minimax-m2": {
    "description": "MiniMax M2 is a high-efficiency large language model built for coding and agent-based workflows."
  },
+  "minimax/minimax-m2": {
+    "description": "Purpose-built for efficient coding and agent workflows."
+  },
+  "minimaxai/minimax-m2": {
+    "description": "MiniMax-M2 is a compact, fast, and cost-efficient Mixture of Experts (MoE) model with 230 billion total parameters and 10 billion active parameters. It is engineered for top performance in coding and agent tasks while maintaining robust general intelligence. Excelling in multi-file editing, code-run-debug loops, test validation and repair, and complex long-chain tool integrations, it is an ideal choice for developer workflows."
+  },
  "ministral-3b-latest": {
    "description": "Ministral 3B is Mistral's top-tier edge model."
  },
@@ -2735,6 +2798,54 @@
  "pro-deepseek-v3": {
    "description": "Enterprise-exclusive service model with concurrent service support."
  },
+  "qianfan-70b": {
+    "description": "Qianfan 70B, a large-parameter Chinese model suitable for high-quality content generation and complex reasoning tasks."
+  },
+  "qianfan-8b": {
+    "description": "Qianfan 8B, a medium-sized general-purpose model balancing cost and performance for text generation and Q&A."
+  },
+  "qianfan-agent-intent-32k": {
+    "description": "Qianfan Agent Intent 32K, a model for intent recognition and agent orchestration, supporting long-context scenarios."
+  },
+  "qianfan-agent-lite-8k": {
+    "description": "Qianfan Agent Lite 8K, a lightweight agent model for low-cost multi-turn dialogue and business orchestration."
+  },
+  "qianfan-agent-speed-32k": {
+    "description": "Qianfan Agent Speed 32K, a high-throughput agent model for large-scale, multi-task agent applications."
+  },
+  "qianfan-agent-speed-8k": {
+    "description": "Qianfan Agent Speed 8K, a high-concurrency agent model for short-to-medium conversations and fast response."
+  },
+  "qianfan-check-vl": {
+    "description": "Qianfan Check VL, a multimodal content moderation and detection model supporting image-text compliance and recognition tasks."
+  },
+  "qianfan-composition": {
+    "description": "Qianfan Composition, a multimodal creative model supporting integrated image-text understanding and generation."
+  },
+  "qianfan-engcard-vl": {
+    "description": "Qianfan EngCard VL, a multimodal recognition model focused on English-language scenarios."
+  },
+  "qianfan-lightning-128b-a19b": {
+    "description": "Qianfan Lightning 128B A19B, a high-performance Chinese general-purpose model for complex Q&A and large-scale reasoning tasks."
+  },
+  "qianfan-llama-vl-8b": {
+    "description": "Qianfan Llama VL 8B, a multimodal model based on Llama for general image-text understanding tasks."
+  },
+  "qianfan-multipicocr": {
+    "description": "Qianfan MultiPicOCR, a multi-image OCR model supporting text detection and recognition across multiple images."
+  },
+  "qianfan-qi-vl": {
+    "description": "Qianfan QI VL, a multimodal Q&A model supporting accurate retrieval and question answering in complex image-text scenarios."
+  },
+  "qianfan-singlepicocr": {
+    "description": "Qianfan SinglePicOCR, a single-image OCR model supporting high-precision character recognition."
+  },
+  "qianfan-vl-70b": {
+    "description": "Qianfan VL 70B, a large-parameter vision-language model for complex image-text understanding scenarios."
+  },
+  "qianfan-vl-8b": {
+    "description": "Qianfan VL 8B, a lightweight vision-language model suitable for daily image-text Q&A and analysis."
+  },
  "qvq-72b-preview": {
    "description": "The QVQ model is an experimental research model developed by the Qwen team, focusing on enhancing visual reasoning capabilities, particularly in the field of mathematical reasoning."
  },
@@ -2886,7 +2997,7 @@
    "description": "The 72B model of Tongyi Qianwen 2.5 is open-sourced."
  },
  "qwen2.5-7b-instruct": {
-    "description": "The 7B model of Tongyi Qianwen 2.5 is open-sourced."
+    "description": "Qwen2.5 7B Instruct, a mature open-source instruction model designed for versatile dialogue and content generation."
  },
  "qwen2.5-coder-1.5b-instruct": {
    "description": "Open-source version of the Qwen coding model."
@@ -2919,13 +3030,13 @@
    "description": "The Qwen-Omni series models support input of various modalities, including video, audio, images, and text, and output audio and text."
  },
  "qwen2.5-vl-32b-instruct": {
-    "description": "The Qwen2.5-VL model series enhances the model's intelligence level, practicality, and applicability, delivering superior performance in scenarios such as natural conversations, content creation, professional knowledge services, and code development. The 32B version employs reinforcement learning techniques to optimize the model, offering more human-preferred output styles, enhanced reasoning capabilities for complex mathematical problems, and fine-grained image understanding and reasoning compared to other models in the Qwen2.5-VL series."
+    "description": "Qwen2.5 VL 32B Instruct, a multimodal open-source model ideal for private deployment and diverse applications."
  },
  "qwen2.5-vl-72b-instruct": {
    "description": "This version enhances instruction following, mathematics, problem-solving, and coding capabilities, improving the ability to recognize various formats and accurately locate visual elements. It supports understanding long video files (up to 10 minutes) and pinpointing events in seconds, comprehending the sequence and speed of time, and based on parsing and locating capabilities, it supports controlling OS or Mobile agents. It has strong key information extraction and JSON output capabilities, and this version is the most powerful in the series at 72B."
  },
  "qwen2.5-vl-7b-instruct": {
-    "description": "This version enhances instruction following, mathematics, problem-solving, and coding capabilities, improving the ability to recognize various formats and accurately locate visual elements. It supports understanding long video files (up to 10 minutes) and pinpointing events in seconds, comprehending the sequence and speed of time, and based on parsing and locating capabilities, it supports controlling OS or Mobile agents. It has strong key information extraction and JSON output capabilities, and this version is the most powerful in the series at 72B."
+    "description": "Qwen2.5 VL 7B Instruct, a lightweight multimodal model balancing deployment cost and recognition capabilities."
  },
  "qwen2.5-vl-instruct": {
    "description": "Qwen2.5-VL is the latest version of the visual language model in the Qwen model family."
@@ -2952,46 +3063,46 @@
    "description": "Qwen3 is Alibaba's next-generation large-scale language model, designed to support diverse application needs with outstanding performance."
  },
  "qwen3-0.6b": {
-    "description": "Qwen3 is a next-generation model with significantly enhanced capabilities, achieving industry-leading levels in reasoning, general tasks, agent functionality, and multilingual support, while also supporting mode switching."
+    "description": "Qwen3 0.6B, an entry-level model suitable for basic reasoning and highly resource-constrained environments."
  },
  "qwen3-1.7b": {
-    "description": "Qwen3 is a next-generation model with significantly enhanced capabilities, achieving industry-leading levels in reasoning, general tasks, agent functionality, and multilingual support, while also supporting mode switching."
+    "description": "Qwen3 1.7B, an ultra-lightweight model optimized for edge and on-device deployment."
  },
  "qwen3-14b": {
-    "description": "Qwen3 is a next-generation model with significantly enhanced capabilities, achieving industry-leading levels in reasoning, general tasks, agent functionality, and multilingual support, while also supporting mode switching."
+    "description": "Qwen3 14B, a mid-sized model ideal for multilingual Q&A and text generation tasks."
  },
  "qwen3-235b-a22b": {
-    "description": "Qwen3 is a next-generation model with significantly enhanced capabilities, achieving industry-leading levels in reasoning, general tasks, agent functionality, and multilingual support, while also supporting mode switching."
+    "description": "Qwen3 235B A22B, a general-purpose large model built for a wide range of complex tasks."
  },
  "qwen3-235b-a22b-instruct-2507": {
-    "description": "An open-source non-thinking mode model based on Qwen3, with slight improvements in subjective creativity and model safety compared to the previous version (Tongyi Qianwen 3-235B-A22B)."
+    "description": "Qwen3 235B A22B Instruct 2507, a flagship general-purpose instruction model for diverse generation and reasoning tasks."
  },
  "qwen3-235b-a22b-thinking-2507": {
-    "description": "An open-source thinking mode model based on Qwen3, with significant improvements in logical ability, general capabilities, knowledge enhancement, and creativity compared to the previous version (Tongyi Qianwen 3-235B-A22B), suitable for high-difficulty and strong reasoning scenarios."
+    "description": "Qwen3 235B A22B Thinking 2507, an ultra-large-scale reasoning model tailored for high-difficulty inference."
  },
  "qwen3-30b-a3b": {
-    "description": "Qwen3 is a next-generation model with significantly enhanced capabilities, achieving industry-leading levels in reasoning, general tasks, agent functionality, and multilingual support, while also supporting mode switching."
+    "description": "Qwen3 30B A3B, a medium-to-large general-purpose model offering a balance between cost and performance."
  },
  "qwen3-30b-a3b-instruct-2507": {
-    "description": "Compared to the previous version (Qwen3-30B-A3B), this version shows substantial improvements in overall general capabilities in both Chinese and multilingual contexts. It features specialized optimizations for subjective and open-ended tasks, aligning significantly better with user preferences and providing more helpful responses."
+    "description": "Qwen3 30B A3B Instruct 2507, a medium-to-large instruction model designed for high-quality generation and Q&A."
  },
  "qwen3-30b-a3b-thinking-2507": {
-    "description": "An open-source thinking mode model based on Qwen3, this version shows significant enhancements over the previous release (Tongyi Qianwen 3-30B-A3B) in logical ability, general capability, knowledge augmentation, and creative capacity. It is suitable for challenging scenarios requiring strong reasoning."
+    "description": "Qwen3 30B A3B Thinking 2507, a medium-to-large reasoning model balancing accuracy and cost."
  },
  "qwen3-32b": {
-    "description": "Qwen3 is a next-generation model with significantly enhanced capabilities, achieving industry-leading levels in reasoning, general tasks, agent functionality, and multilingual support, while also supporting mode switching."
+    "description": "Qwen3 32B, a general-purpose model suited for tasks requiring stronger comprehension capabilities."
  },
  "qwen3-4b": {
-    "description": "Qwen3 is a next-generation model with significantly enhanced capabilities, achieving industry-leading levels in reasoning, general tasks, agent functionality, and multilingual support, while also supporting mode switching."
+    "description": "Qwen3 4B, ideal for small to medium-scale applications and local inference scenarios."
  },
  "qwen3-8b": {
-    "description": "Qwen3 is a next-generation model with significantly enhanced capabilities, achieving industry-leading levels in reasoning, general tasks, agent functionality, and multilingual support, while also supporting mode switching."
+    "description": "Qwen3 8B, a lightweight model with flexible deployment, suitable for high-concurrency workloads."
  },
  "qwen3-coder-30b-a3b-instruct": {
    "description": "The open-source version of the Tongyi Qianwen code model. The latest qwen3-coder-30b-a3b-instruct is a code generation model based on Qwen3, featuring powerful Coding Agent capabilities. It excels at tool usage and environment interaction, enabling autonomous programming with outstanding coding and general abilities."
  },
  "qwen3-coder-480b-a35b-instruct": {
-    "description": "Open-source version of Tongyi Qianwen's code model. The latest qwen3-coder-480b-a35b-instruct is a code generation model based on Qwen3, featuring powerful Coding Agent capabilities, proficient in tool invocation and environment interaction, enabling autonomous programming with excellent coding and general capabilities."
+    "description": "Qwen3 Coder 480B A35B Instruct, a flagship code model supporting multilingual programming and complex code understanding."
  },
  "qwen3-coder-flash": {
    "description": "Tongyi Qianwen code model. The latest Qwen3-Coder series models are code generation models based on Qwen3, equipped with powerful Coding Agent capabilities, proficient in tool invocation and environment interaction, enabling autonomous programming with excellent coding skills alongside general capabilities."
@@ -3005,32 +3116,41 @@
  "qwen3-max": {
    "description": "Tongyi Qianwen 3 series Max model, which shows significant overall improvements compared to the 2.5 series, including enhanced bilingual (Chinese and English) text understanding, complex instruction following, subjective open-domain task capabilities, multilingual support, and tool invocation abilities; the model also exhibits fewer hallucinations. The latest qwen3-max model features specialized upgrades in agent programming and tool invocation compared to the qwen3-max-preview version. The officially released model achieves state-of-the-art performance in its domain and is adapted to more complex agent scenarios."
  },
+  "qwen3-max-preview": {
+    "description": "The most advanced model in the Qwen series, ideal for complex, multi-step tasks. Preview version now supports reasoning."
+  },
  "qwen3-next-80b-a3b-instruct": {
    "description": "A new generation of non-thinking mode open-source model based on Qwen3. Compared to the previous version (Tongyi Qianwen 3-235B-A22B-Instruct-2507), it offers better Chinese text comprehension, enhanced logical reasoning abilities, and improved performance in text generation tasks."
  },
  "qwen3-next-80b-a3b-thinking": {
-    "description": "A new generation of thinking mode open-source model based on Qwen3. Compared to the previous version (Tongyi Qianwen 3-235B-A22B-Thinking-2507), it features improved instruction-following capabilities and more concise model-generated summaries."
+    "description": "Qwen3 Next 80B A3B Thinking, a flagship reasoning model version designed for complex tasks."
  },
  "qwen3-omni-flash": {
    "description": "The Qwen-Omni model accepts multimodal input including text, images, audio, and video, and generates responses in text or speech. It offers a variety of human-like voice tones, supports multilingual and dialectal speech output, and is applicable to scenarios such as text creation, visual recognition, and voice assistants."
  },
  "qwen3-vl-235b-a22b-instruct": {
-    "description": "Qwen3 VL 235B A22B Instruct Mode (non-thinking), designed for instruction-following scenarios without deep reasoning, while maintaining strong visual understanding capabilities."
+    "description": "Qwen3 VL 235B A22B Instruct, a flagship multimodal model built for high-demand comprehension and creative tasks."
  },
  "qwen3-vl-235b-a22b-thinking": {
-    "description": "Qwen3 VL 235B A22B Thinking Mode (open-source version), tailored for complex reasoning and long video understanding tasks, offering top-tier visual and textual reasoning performance."
+    "description": "Qwen3 VL 235B A22B Thinking, a flagship reasoning edition for complex multimodal inference and planning."
  },
  "qwen3-vl-30b-a3b-instruct": {
-    "description": "Qwen3 VL 30B Instruct Mode (non-thinking), designed for general instruction-following scenarios, with strong multimodal understanding and generation capabilities."
+    "description": "Qwen3 VL 30B A3B Instruct, a large multimodal model balancing accuracy and inference performance."
  },
  "qwen3-vl-30b-a3b-thinking": {
-    "description": "The open-source Qwen-VL model provides visual understanding and text generation capabilities. It supports agent interaction, visual encoding, spatial awareness, long video comprehension, and deep reasoning, with enhanced text recognition and multilingual support in complex scenarios."
+    "description": "Qwen3 VL 30B A3B Thinking, a deep reasoning version tailored for complex multimodal tasks."
+  },
+  "qwen3-vl-32b-instruct": {
+    "description": "Qwen3 VL 32B Instruct, a multimodal instruction-tuned model for high-quality visual-text Q&A and creative tasks."
+  },
+  "qwen3-vl-32b-thinking": {
+    "description": "Qwen3 VL 32B Thinking, a multimodal deep reasoning model enhanced for complex inference and long-chain analysis."
  },
  "qwen3-vl-8b-instruct": {
-    "description": "Qwen3 VL 8B Instruct Mode (non-thinking), suitable for standard multimodal generation and recognition tasks."
+    "description": "Qwen3 VL 8B Instruct, a lightweight multimodal model ideal for everyday visual Q&A and application integration."
  },
  "qwen3-vl-8b-thinking": {
-    "description": "Qwen3 VL 8B Thinking Mode, designed for lightweight multimodal reasoning and interaction scenarios, while retaining long-context understanding capabilities."
+    "description": "Qwen3 VL 8B Thinking, a multimodal chain-of-thought model designed for detailed reasoning over visual information."
  },
  "qwen3-vl-flash": {
    "description": "Qwen3 VL Flash: a lightweight, high-speed inference version ideal for latency-sensitive or high-volume request scenarios."
@@ -3263,6 +3383,12 @@
  "wizardlm2:8x22b": {
    "description": "WizardLM 2 is a language model provided by Microsoft AI, excelling in complex dialogues, multilingual capabilities, reasoning, and intelligent assistant applications."
  },
+  "x-ai/grok-4-fast": {
+    "description": "We’re excited to introduce Grok 4 Fast, our latest advancement in cost-effective reasoning models."
+  },
+  "x-ai/grok-code-fast-1": {
+    "description": "We’re proud to launch grok-code-fast-1, a fast and cost-efficient reasoning model that excels in agent-based coding tasks."
+  },
  "x1": {
    "description": "The Spark X1 model will undergo further upgrades, achieving results in reasoning, text generation, and language understanding tasks that match OpenAI o1 and DeepSeek R1, building on its leading position in domestic mathematical tasks."
  },
@@ -3323,6 +3449,9 @@
  "yi-vision-v2": {
    "description": "A complex visual task model that provides high-performance understanding and analysis capabilities based on multiple images."
  },
+  "z-ai/glm-4.6": {
+    "description": "GLM-4.6, the latest flagship model from Zhipu AI, delivers significant improvements over its predecessor in advanced coding, long-form text processing, reasoning, and agent capabilities."
+  },
  "zai-org/GLM-4.5": {
    "description": "GLM-4.5 is a foundational model designed specifically for agent applications, using a Mixture-of-Experts (MoE) architecture. It is deeply optimized for tool invocation, web browsing, software engineering, and front-end programming, supporting seamless integration with code agents like Claude Code and Roo Code. GLM-4.5 employs a hybrid inference mode, adaptable to complex reasoning and everyday use scenarios."
  },
@@ -236,6 +236,8 @@
  },
  "inspector": {
    "args": "View Argument List",
+    "delete": "Delete Tool Call",
+    "orphanedToolCall": "This tool call message may have become orphaned due to an unexpected issue, which could affect the Agent's normal operation. Please remove it.",
    "pluginRender": "View Plugin Interface"
  },
  "list": {
@@ -251,14 +253,20 @@
  },
  "localSystem": {
    "apiName": {
+      "getCommandOutput": "Get Code Output",
+      "globLocalFiles": "Match Files",
+      "grepContent": "Search Content",
+      "killCommand": "Kill Code Execution",
      "listLocalFiles": "View File List",
      "moveLocalFiles": "Move Files",
      "readLocalFile": "Read File Content",
      "renameLocalFile": "Rename",
+      "runCommand": "Run Code",
      "searchLocalFiles": "Search Files",
-      "writeLocalFile": "Write File"
+      "writeLocalFile": "Write File",
+      "editLocalFile": "Edit File"
    },
-    "title": "Local Files"
+    "title": "Local System"
  },
  "mcpInstall": {
    "CHECKING_INSTALLATION": "Checking installation environment...",
@@ -2,6 +2,45 @@
  "about": {
    "title": "About"
  },
+  "agentInfoDescription": {
+    "basic": {
+      "avatar": "Avatar",
+      "description": "Description",
+      "name": "Name",
+      "tags": "Tags",
+      "title": "Agent Info"
+    },
+    "chat": {
+      "displayMode": "Display Mode",
+      "enableHistoryCount": "Enable Message History Count",
+      "historyCount": "Message History Count",
+      "no": "No",
+      "searchMode": "Search Mode",
+      "title": "Chat Preferences",
+      "yes": "Yes"
+    },
+    "model": {
+      "maxTokens": "Max Token Count",
+      "model": "Model",
+      "provider": "Provider",
+      "temperature": "Temperature",
+      "title": "Model Settings",
+      "topP": "Top P Value"
+    },
+    "plugins": {
+      "count": "Plugin Settings ({{count}})",
+      "empty": "No plugins installed yet",
+      "title": "Installed Plugins"
+    },
+    "role": {
+      "systemRole": "System Prompt",
+      "title": "Role Settings"
+    },
+    "value": {
+      "unset": "Not Set",
+      "untitled": "Untitled Agent"
+    }
+  },
  "agentTab": {
    "chat": "Chat Preferences",
    "meta": "Assistant Info",
@@ -18,6 +57,8 @@
    },
    "title": "Analytics"
  },
+  "checking": "Checking...",
+  "checkingPermissions": "Checking permissions...",
  "danger": {
    "clear": {
      "action": "Clear Now",
@@ -146,6 +187,58 @@
    },
    "waitingForMore": "More models are <1>planned to be added</1>, stay tuned"
  },
+  "marketPublish": {
+    "modal": {
+      "changelog": {
+        "extra": "Describe the key changes and improvements in this version",
+        "label": "Changelog",
+        "maxLengthError": "Changelog must not exceed 500 characters",
+        "placeholder": "Enter the changelog",
+        "required": "Please enter the changelog"
+      },
+      "comparison": {
+        "local": "Current Local Version",
+        "remote": "Currently Published Version"
+      },
+      "identifier": {
+        "extra": "The identifier serves as the unique ID for the agent. Use lowercase letters, numbers, and hyphens.",
+        "label": "Agent Identifier",
+        "lengthError": "Identifier must be between 3 and 50 characters",
+        "patternError": "Identifier can only contain lowercase letters, numbers, and hyphens",
+        "placeholder": "Enter a unique identifier for the agent, e.g., web-development",
+        "required": "Please enter the agent identifier"
+      },
+      "loading": {
+        "fetchingRemote": "Loading remote data...",
+        "submit": "Publishing agent...",
+        "upload": "Publishing new version..."
+      },
+      "messages": {
+        "createVersionFailed": "Failed to create version: {{message}}",
+        "fetchRemoteFailed": "Failed to fetch remote agent data",
+        "missingIdentifier": "This agent does not have a market identifier yet",
+        "notAuthenticated": "Please log in to your market account first",
+        "publishFailed": "Publish failed: {{message}}"
+      },
+      "submitButton": "Publish",
+      "title": {
+        "submit": "Share to Agent Market",
+        "upload": "Publish New Version"
+      }
+    },
+    "resultModal": {
+      "message": "The agent has been submitted for review. It will go live automatically once approved. Click 'View in Market' to see the published agent.",
+      "view": "View in Market"
+    },
+    "submit": {
+      "button": "Share to Market",
+      "tooltip": "Share the agent to the market"
+    },
+    "upload": {
+      "button": "Publish New Version",
+      "tooltip": "Publish a new version to the agent market"
+    }
+  },
  "message": {
    "success": "Update successful"
  },
@@ -561,7 +654,7 @@
  },
  "submitAgentModal": {
    "button": "Submit Assistant",
-    "identifier": "Assistant Identifier",
+    "identifier": "Agent Identifier",
    "metaMiss": "Please complete the assistant information before submitting. It should include name, description, and tags",
    "placeholder": "Enter a unique identifier for the assistant, e.g. web-development",
    "tooltips": "Share to the assistant marketplace"
@@ -15,8 +15,22 @@
    "prompt": "Prompt"
  },
  "localFiles": {
+    "editFile": {
+      "newString": "Replace with",
+      "oldString": "Find",
+      "replaceAll": "Replace all occurrences",
+      "replaceFirst": "Replace first occurrence only"
+    },
    "file": "File",
    "folder": "Folder",
+    "moveFiles": {
+      "itemsMoved": "{{count}} item(s) moved:",
+      "itemsMoved_one": "{{count}} item moved:",
+      "itemsMoved_other": "{{count}} items moved:",
+      "itemsToMove": "{{count}} item(s) to move:",
+      "itemsToMove_one": "{{count}} item to move:",
+      "itemsToMove_other": "{{count}} items to move:"
+    },
    "open": "Open",
    "openFile": "Open File",
    "openFolder": "Open Folder",
@@ -26,7 +40,12 @@
    "readFile": "Read File",
    "readFileError": "Failed to read file, please check if the file path is correct",
    "readFiles": "Read Files",
-    "readFilesError": "Failed to read files, please check if the file path is correct"
+    "readFilesError": "Failed to read files, please check if the file path is correct",
+    "writeFile": {
+      "characters": "characters",
+      "preview": "Content Preview",
+      "truncated": "truncated"
+    }
  },
  "search": {
    "createNewSearch": "Create a new search record",
@@ -6,6 +6,7 @@
    "confirmRemoveUnstarred": "You are about to delete unstarred topics. This action cannot be undone, please proceed with caution.",
    "duplicate": "Create Copy",
    "export": "Export Topics",
+    "openInNewWindow": "Open in a new window",
    "removeAll": "Delete All Topics",
    "removeUnstarred": "Delete Unstarred Topics"
  },
@@ -145,6 +145,50 @@
    "apikey": "Gestión de Claves API",
    "profile": "Perfil",
    "security": "Seguridad",
-    "stats": "Estadísticas"
+    "stats": "Estadísticas",
+    "usage": "Estadísticas de uso"
+  },
+  "usage": {
+    "activeModels": {
+      "modelTable": "Lista de modelos",
+      "models": "Modelos activos",
+      "providerTable": "Lista de proveedores",
+      "providers": "Proveedores activos",
+      "table": {
+        "calls": "Número de llamadas",
+        "model": "Modelo",
+        "provider": "Proveedor",
+        "spend": "Gasto"
+      }
+    },
+    "cards": {
+      "month": {
+        "modelCalls": "Llamadas del modelo",
+        "title": "Gastos de este mes"
+      },
+      "today": {
+        "title": "Gasto de hoy",
+        "yesterday": "Ayer"
+      }
+    },
+    "table": {
+      "actions": "Acciones",
+      "createdAt": "Fecha de uso",
+      "inputTokens": "Tokens de entrada",
+      "model": "Modelo",
+      "outputTokens": "Tokens de salida",
+      "spend": "Gasto",
+      "tps": "TPS",
+      "ttft": "TTFT",
+      "type": "Tipo de llamada"
+    },
+    "trends": {
+      "spend": "Importe",
+      "tokens": "Tokens"
+    },
+    "welcome": {
+      "model": "Modelo",
+      "provider": "Proveedor"
+    }
  }
 }
@@ -17,6 +17,7 @@
  "availableAgents": "Agentes disponibles",
  "backToBottom": "Volver al fondo",
  "chatList": {
+    "expandMessage": "Expandir mensaje",
    "longMessageDetail": "Ver detalles"
  },
  "clearCurrentMessages": "Borrar mensajes actuales",
@@ -173,8 +174,11 @@
    "title": "Mencionar miembros"
  },
  "messageAction": {
+    "collapse": "Ocultar mensaje",
+    "continueGeneration": "Continuar generando",
    "delAndRegenerate": "Eliminar y Regenerar",
    "deleteDisabledByThreads": "Existen subtemas, no se puede eliminar",
+    "expand": "Expandir mensaje",
    "regenerate": "Regenerar"
  },
  "messages": {
@@ -239,6 +243,7 @@
  "noMatchingAgents": "No se encontraron miembros coincidentes",
  "noMembersYet": "Este grupo aún no tiene miembros. Haz clic en el botón + para invitar asistentes.",
  "noSelectedAgents": "Aún no se han seleccionado miembros",
+  "openInNewWindow": "Abrir en una nueva ventana",
  "owner": "Propietario del grupo",
  "pin": "Fijar",
  "pinOff": "Desfijar",
@@ -325,6 +330,11 @@
    "screenshot": "Captura de pantalla",
    "settings": "Configuración de exportación",
    "text": "Texto",
+    "widthMode": {
+      "label": "Modo de ancho",
+      "narrow": "Modo de pantalla estrecha",
+      "wide": "Modo de pantalla ancha"
+    },
    "withBackground": "Incluir imagen de fondo",
    "withFooter": "Incluir pie de página",
    "withPluginInfo": "Incluir información del plugin",
@@ -367,6 +377,28 @@
    "remained": "Restante",
    "used": "Usado"
  },
+  "tool": {
+    "intervention": {
+      "approve": "Aprobar",
+      "approveAndRemember": "Aprobar y recordar",
+      "approveOnce": "Aprobar solo esta vez",
+      "mode": {
+        "allowList": "Lista blanca",
+        "allowListDesc": "Ejecutar automáticamente solo las herramientas aprobadas",
+        "autoRun": "Aprobación automática",
+        "autoRunDesc": "Aprobar automáticamente la ejecución de todas las herramientas",
+        "manual": "Manual",
+        "manualDesc": "Se requiere aprobación manual para cada ejecución"
+      },
+      "reject": "Rechazar",
+      "rejectAndContinue": "Rechazar y volver a intentar",
+      "rejectOnly": "Rechazar",
+      "rejectReasonPlaceholder": "Ingresar una razón ayudará al agente a comprender y mejorar futuras acciones",
+      "rejectTitle": "Rechazar esta ejecución de herramienta",
+      "rejectedWithReason": "Esta ejecución de herramienta fue rechazada: {{reason}}",
+      "toolRejected": "Esta ejecución de herramienta fue rechazada"
+    }
+  },
  "topic": {
    "checkOpenNewTopic": "¿Abrir un nuevo tema?",
    "checkSaveCurrentMessages": "¿Desea guardar la conversación actual como tema?",
@@ -135,6 +135,7 @@
    }
  },
  "close": "Cerrar",
+  "confirm": "Confirmar",
  "contact": "Contacto",
  "copy": "Copiar",
  "copyFail": "Fallo al copiar",
@@ -285,6 +286,7 @@
  "oauth": "Inicio de sesión SSO",
  "officialSite": "Sitio oficial",
  "ok": "Aceptar",
+  "or": "o",
  "password": "Contraseña",
  "pin": "Fijar",
  "pinOff": "Quitar fijación",
@@ -106,6 +106,12 @@
    "keyPlaceholder": "Clave",
    "valuePlaceholder": "Valor"
  },
+  "LocalFile": {
+    "action": {
+      "open": "Abrir",
+      "showInFolder": "Mostrar en la carpeta"
+    }
+  },
  "MaxTokenSlider": {
    "unlimited": "Sin límite"
  },
@@ -41,9 +41,29 @@
        "openingMessage": "Mensaje de apertura",
        "openingQuestions": "Preguntas iniciales",
        "title": "Configuración del Asistente"
+      },
+      "version": {
+        "empty": "No hay versiones históricas",
+        "status": {
+          "archived": "Archivado",
+          "deprecated": "Rechazado",
+          "unpublished": "En revisión"
+        },
+        "table": {
+          "isLatest": "Última versión",
+          "isValidated": "Validado",
+          "publishAt": "Fecha de publicación",
+          "version": "Número de versión"
+        },
+        "title": "Historial de versiones"
      }
    },
    "list": "Lista de asistentes",
+    "marketSource": {
+      "label": "Cambiar fuente del mercado",
+      "legacy": "Mercado antiguo",
+      "new": "Nuevo mercado"
+    },
    "more": "Más",
    "plugins": "Integrar complementos",
    "recentSubmits": "Actualizaciones recientes",
@@ -51,10 +71,35 @@
      "createdAt": "Publicado recientemente",
      "identifier": "ID del Asistente",
      "knowledgeCount": "Cantidad de bases de conocimiento",
+      "myown": "Ver mis asistentes",
      "pluginCount": "Cantidad de complementos",
      "title": "Nombre del Asistente",
      "tokenUsage": "Uso de Tokens"
    },
+    "status": {
+      "archived": {
+        "reasons": {
+          "official": "El asistente fue retirado por problemas de seguridad/política por parte del equipo oficial",
+          "owner": "El propietario del asistente lo retiró o archivó voluntariamente"
+        },
+        "subtitle": "El asistente al que estás accediendo ha sido archivado por una de las siguientes razones:",
+        "title": "Asistente archivado"
+      },
+      "backToMarket": "Volver al mercado de asistentes",
+      "deprecated": {
+        "reasons": {
+          "official": "El asistente fue retirado por problemas de seguridad/política por parte del equipo oficial",
+          "owner": "El propietario del asistente lo retiró o rechazó voluntariamente"
+        },
+        "subtitle": "El asistente al que estás accediendo ha sido rechazado por una de las siguientes razones:",
+        "title": "Asistente rechazado"
+      },
+      "support": "Si tienes algún problema, copia el enlace y envíalo a <1>support@lobehub.com</1> para recibir asistencia.",
+      "unpublished": {
+        "subtitle": "El asistente al que estás accediendo está en proceso de revisión. Si tienes dudas, copia el enlace y envía tu consulta a <1>support@lobehub.com</1>.",
+        "title": "Asistente en revisión"
+      }
+    },
    "suggestions": "Recomendaciones relacionadas",
    "systemRole": "Configuración del asistente",
    "tokenUsage": "Uso de Tokens en el prompt del asistente",
@@ -102,7 +102,7 @@
      "SPII": "Su contenido podría contener información personal sensible. Para proteger la privacidad, elimine la información sensible y vuelva a intentarlo.",
      "default": "Contenido bloqueado: {{blockReason}}. Ajuste su solicitud y vuelva a intentarlo."
    },
-    "InsufficientQuota": "Lo sentimos, la cuota de esta clave ha alcanzado su límite. Por favor, verifique si el saldo de su cuenta es suficiente o aumente la cuota de la clave y vuelva a intentarlo.",
+    "InsufficientQuota": "Lo sentimos, la cuota de esta clave ha alcanzado su límite. Por favor, verifica si el saldo de tu cuenta es suficiente o aumenta la cuota de la clave antes de intentarlo nuevamente.",
    "InvalidAccessCode": "La contraseña no es válida o está vacía. Por favor, introduce una contraseña de acceso válida o añade una clave API personalizada",
    "InvalidBedrockCredentials": "La autenticación de Bedrock no se ha completado con éxito, por favor, verifica AccessKeyId/SecretAccessKey e inténtalo de nuevo",
    "InvalidClerkUser": "Lo siento mucho, actualmente no has iniciado sesión. Por favor, inicia sesión o regístrate antes de continuar.",
@@ -131,7 +131,7 @@
    "PluginServerError": "Error al recibir la respuesta del servidor del complemento. Verifique el archivo de descripción del complemento, la configuración del complemento o la implementación del servidor según la información de error a continuación",
    "PluginSettingsInvalid": "Este complemento necesita una configuración correcta antes de poder usarse. Verifique si su configuración es correcta",
    "ProviderBizError": "Se produjo un error al solicitar el servicio de {{provider}}, por favor, revise la siguiente información o inténtelo de nuevo",
-    "QuotaLimitReached": "Lo sentimos, el uso actual de tokens o el número de solicitudes ha alcanzado el límite de cuota de esta clave. Por favor, aumenta la cuota de esta clave o intenta de nuevo más tarde.",
+    "QuotaLimitReached": "Lo sentimos, el uso de tokens o el número de solicitudes ha alcanzado el límite de cuota de esta clave. Por favor, aumenta la cuota de la clave o inténtalo más tarde.",
    "StreamChunkError": "Error de análisis del bloque de mensajes de la solicitud en streaming. Por favor, verifica si la API actual cumple con las normas estándar o contacta a tu proveedor de API para más información.",
    "SubscriptionKeyMismatch": "Lo sentimos, debido a un fallo ocasional del sistema, el uso de la suscripción actual ha dejado de ser válido temporalmente. Por favor, haga clic en el botón de abajo para restaurar la suscripción o contáctenos por correo electrónico para obtener soporte.",
    "SubscriptionPlanLimit": "Se han agotado sus puntos de suscripción, no puede utilizar esta función. Por favor, actualice a un plan superior o configure la API del modelo personalizado para continuar.",
@@ -1,5 +1,8 @@
 {
-  "desc": "Gestiona tu conocimiento",
+  "addFolder": "Crear carpeta",
+  "addKnowledge": "Agregar conocimiento",
+  "addPage": "Crear documento",
+  "desc": "Gestiona tus conocimientos de trabajo, estudio y vida.",
  "detail": {
    "basic": {
      "createdAt": "Fecha de creación",
@@ -21,15 +24,89 @@
      "embeddingStatus": "Vectorización"
    }
  },
+  "documentEditor": {
+    "addIcon": "Agregar ícono",
+    "autoSaveMessage": "El documento se guarda automáticamente, no es necesario guardarlo manualmente",
+    "chooseIcon": "Elegir ícono",
+    "deleteConfirm": {
+      "content": "Estás a punto de eliminar este documento. Una vez eliminado, no se podrá recuperar. Procede con precaución.",
+      "title": "Eliminar documento"
+    },
+    "deleteError": "Error al eliminar el documento",
+    "deleteSuccess": "Documento eliminado con éxito",
+    "editedAt": "Última edición el {{time}}",
+    "editedBy": "Editado por última vez por {{name}}",
+    "editorPlaceholder": "Escribe el contenido del documento, pulsa / para abrir el menú de comandos",
+    "empty": {
+      "createNewDocument": "Crear nuevo documento",
+      "title": "Selecciona un documento para comenzar",
+      "uploadMarkdown": "Subir archivo Markdown"
+    },
+    "linkCopied": "Enlace copiado",
+    "menu": {
+      "copyLink": "Copiar enlace",
+      "exportDocument": "Exportar documento",
+      "importDocument": "Importar documento",
+      "pin": "Fijar documento"
+    },
+    "saving": "Guardando...",
+    "titlePlaceholder": "Sin título",
+    "wordCount": "{{wordCount}} palabras"
+  },
+  "documentList": {
+    "copyContent": "Copiar todo el contenido",
+    "documentCount": "Total de {{count}} documentos",
+    "duplicate": "Crear una copia",
+    "empty": "No hay documentos. Haz clic en el botón de arriba para crear tu primer documento",
+    "noResults": "No se encontraron documentos coincidentes",
+    "selectNote": "Selecciona un documento para comenzar a editar",
+    "untitled": "Sin título"
+  },
  "empty": "No hay archivos/carpetas subidos aún",
  "header": {
    "actions": {
      "newFolder": "Nueva carpeta",
+      "newPage": "Nuevo documento",
      "uploadFile": "Subir archivo",
      "uploadFolder": "Subir carpeta"
    },
+    "newDocumentButton": "Nuevo documento",
+    "newNoteDialog": {
+      "cancel": "Cancelar",
+      "editTitle": "Editar documento",
+      "emptyContent": "El contenido del documento no puede estar vacío",
+      "loadError": "Error al cargar el documento, por favor intenta de nuevo",
+      "loading": "Cargando...",
+      "save": "Guardar",
+      "saveError": "Error al guardar el documento, por favor intenta de nuevo",
+      "saveSuccess": "Documento guardado con éxito",
+      "title": "Nuevo documento",
+      "updateSuccess": "Documento actualizado con éxito"
+    },
    "uploadButton": "Subir"
  },
+  "home": {
+    "getStarted": "Comenzar",
+    "greeting": "Comenzar",
+    "quickActions": "Acciones rápidas",
+    "recentDocuments": "Documentos recientes",
+    "recentFiles": "Archivos recientes",
+    "subtitle": "Bienvenido a tu base de conocimientos. Comienza aquí a gestionar tus documentos y notas",
+    "uploadEntries": {
+      "files": {
+        "title": "Subir archivos"
+      },
+      "folder": {
+        "title": "Subir carpeta"
+      },
+      "knowledgeBase": {
+        "title": "Nueva base de conocimientos"
+      },
+      "newDocument": {
+        "title": "Nuevo documento"
+      }
+    }
+  },
  "knowledgeBase": {
    "list": {
      "confirmRemoveKnowledgeBase": "Estás a punto de eliminar esta base de conocimientos. Los archivos no se eliminarán, se moverán a Todos los archivos. Una vez eliminada, la base de conocimientos no se podrá recuperar, por favor actúa con precaución.",
@@ -38,6 +115,10 @@
    "new": "Nueva base de conocimientos",
    "title": "Base de conocimientos"
  },
+  "menu": {
+    "allDocuments": "Todos los documentos",
+    "allFiles": "Todos los archivos"
+  },
  "networkError": "Error al obtener la base de conocimientos, por favor verifica la conexión a internet y vuelve a intentarlo",
  "notSupportGuide": {
    "desc": "La instancia de despliegue actual está en modo de base de datos cliente, no se puede utilizar la función de gestión de archivos. Por favor, cambia a <1>modo de despliegue de base de datos en servidor</1>, o utiliza directamente <3>LobeChat Cloud</3>",
@@ -61,12 +142,16 @@
    "downloadFile": "Descargar archivo",
    "unsupportedFileAndContact": "Este formato de archivo no es compatible con la vista previa en línea. Si desea solicitar una vista previa, no dude en <1>contactarnos</1>."
  },
+  "searchDocumentPlaceholder": "Buscar documentos",
  "searchFilePlaceholder": "Buscar archivo",
  "tab": {
-    "all": "Todos los archivos",
+    "all": "Todo",
    "audios": "Audios",
    "documents": "Documentos",
+    "home": "Inicio",
    "images": "Imágenes",
+    "moreTypes": "Más tipos",
+    "pages": "Documentos",
    "videos": "Videos",
    "websites": "Sitios web"
  },
@@ -0,0 +1,42 @@
+{
+  "callback": {
+    "buttons": {
+      "close": "Cerrar ventana"
+    },
+    "messages": {
+      "authFailed": "Autenticación fallida: {{error}}",
+      "missingParams": "Faltan parámetros de autenticación",
+      "processing": "Procesando autenticación...",
+      "successWithCountdown": "{{message}} La ventana se cerrará automáticamente en {{countdown}} segundos",
+      "successWithRedirect": "¡Autenticación exitosa! Redirigiendo..."
+    },
+    "titles": {
+      "error": "Error de autenticación",
+      "loading": "Autenticación de LobeHub Market",
+      "success": "Autenticación exitosa"
+    }
+  },
+  "errors": {
+    "authorizationFailed": "La autenticación ha fallado, por favor inténtalo de nuevo.",
+    "browserOnly": "El proceso de autenticación solo puede iniciarse en un navegador.",
+    "codeConsumed": "El código de autenticación ya ha sido utilizado, por favor inténtalo de nuevo.",
+    "codeVerifierMissing": "Sesión de autenticación no válida, por favor inicia sesión nuevamente.",
+    "general": "Se produjo un error durante la autenticación, por favor inténtalo de nuevo.",
+    "handoffFailed": "No se pudo obtener el resultado de la autenticación, por favor inténtalo de nuevo.",
+    "handoffTimeout": "Tiempo de espera de autenticación agotado, por favor completa la operación en el navegador e inténtalo de nuevo.",
+    "oidcNotReady": "El servicio de autenticación aún no está listo, por favor inténtalo más tarde.",
+    "openBrowserFailed": "No se pudo abrir el navegador del sistema, por favor inténtalo de nuevo.",
+    "openPopupFailed": "No se pudo abrir la ventana emergente de autenticación, por favor revisa la configuración del bloqueador de ventanas emergentes del navegador.",
+    "popupClosed": "La ventana de autenticación se cerró antes de completarse.",
+    "sessionExpired": "La sesión de autenticación ha expirado, por favor inicia sesión nuevamente.",
+    "stateMismatch": "Estado de autenticación no coincide, por favor inténtalo de nuevo.",
+    "stateMissing": "No se encontró el estado de autenticación, por favor inténtalo de nuevo."
+  },
+  "messages": {
+    "loading": "Iniciando el proceso de autenticación...",
+    "success": {
+      "submit": "¡Autenticación exitosa! Ahora puedes publicar asistentes.",
+      "upload": "¡Autenticación exitosa! Ahora puedes publicar una nueva versión."
+    }
+  }
+}
--- a/Show More
+++ b/Show More