* ✨ feat: add cross-platform message tool for AI bot channel operations
Implement a unified message tool (`lobe-message`) that provides AI with
messaging capabilities across Discord, Telegram, Slack, Google Chat,
and IRC through a single interface with platform-specific extensions.
Core APIs: sendMessage, readMessages, editMessage, deleteMessage,
searchMessages, reactToMessage, getReactions, pin/unpin management,
channel/member info, thread operations, and polls.
Architecture follows the established builtin-tool pattern:
- Package: @lobechat/builtin-tool-message (manifest, types, executor,
ExecutionRuntime, client components)
- Registry: registered in builtin-tools (renders, inspectors,
interventions, streamings)
- Server runtime: stub service ready for platform adapter integration
https://claude.ai/code/session_011sHc6R7V4cSYKere9RY1QM
* feat: implement platform specific message service
* chore: add wechat platform
* chore: update wechat api service
* chore: update protocol implementation
* chore: optimize platform api test
* fix: lark domain error
* feat: support bot message cli
* chore: refactor adapter to service
* chore: optimize bot status fetch
* fix: bot status
* fix: channel nav ignore
* feat: message tool support bot manage
* feat: add lobe-message runtime
* feat: support direct message
* feat: add history limit
* chore: update const limit
* feat: optimize server id message history limit
* chore: optimize system role & inject platform environment info
* chore: update readMessages vibe
* fix: form body width 50%
* chore: optimize tool prompt
* chore: update i18n files
* chore: optimize read message system role and update bot message lh
* updage readMessage api rate limit
* chore: comatible for readMessages
* fix: feishu readMessage implementation error
* fix: test case
* chore: update i18n files
* fix: lint error
* chore: add timeout for conversaction case
* fix: message test case
* fix: vite gzip error
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Co-authored-by: Claude <noreply@anthropic.com>
* ✨ feat: add GPT-5.4 model support and fix reasoning payload pruning
- Add GPT-5.4 model card to model-bank
- Update planCardModels to use gpt-5.4
- Add gpt-5.4 to responsesAPIModels
- Fix pruneReasoningPayload to strip logprobs/top_logprobs for reasoning models
- Add logprobs, top_logprobs to ChatStreamPayload type
- Extend reasoning_effort to include none and xhigh
- Add success log for non-fallback requests in RouterRuntime
- Fix log parameter mismatch in RouterRuntime
Fixes LOBE-5735
* 🐛 fix: match gpt-5.4 to gpt5_2ReasoningEffort in openrouter and vercelaigateway
* 🐛 fix: update OpenRouterReasoning effort type to include none and xhigh
* 🐛 fix: use tiered pricing for gpt-5.4 based on 272K token threshold
* 🌐 chore: update i18n translations
* 🐛 fix: update claude-sonnet model version to 4-6 in planCardModels
* ✨ feat: add GPT-5.4 Pro model support
* 🐛 fix: remove dated snapshot for gpt-5.4-pro in responsesAPIModels
* 🐛 fix: add tierBy support for cross-unit tiered pricing threshold
OpenAI charges output at 1.5x when INPUT exceeds 272K tokens.
The tiered strategy previously only checked the unit's own quantity
to select a tier. Added optional tierBy field to TieredPricingUnit
so output/cacheRead tiers can reference input quantity for selection.
* 🐛 fix: use totalInputTokens for tiered pricing tier selection
Tiered pricing tiers should be determined by total prompt size
(totalInputTokens), not each unit's own quantity. This fixes output
and cacheRead being charged at the wrong tier rate when the prompt
exceeds the threshold but the individual unit quantity does not.
Rdmclin2