Hosts (like KIT) are LLM applications that manage connections and interactions
Clients maintain 1:1 connections with MCP servers
Servers provide context, tools, and capabilities to the LLMs

This architecture allows language models to:

Access external tools and data sources 🛠️
Maintain consistent context across interactions 🔄
Execute commands and retrieve information safely 🔒

Currently supports:

Anthropic Claude models (Claude 3.5 Sonnet, Claude 3.5 Haiku, etc.)
OpenAI models (GPT-4, GPT-4 Turbo, GPT-3.5, etc.)
Google Gemini models (Gemini 2.0 Flash, Gemini 1.5 Pro, etc.)
Any Ollama-compatible model with function calling support
Any OpenAI-compatible API endpoint

Features ✨

Interactive conversations with multiple AI models
Non-interactive mode for scripting and automation
Script mode for executable YAML-based automation scripts
Support for multiple concurrent MCP servers
Tool filtering with allowedTools and excludedTools per server
Dynamic tool discovery and integration
Tool calling capabilities across all supported models
Configurable MCP server locations and arguments
Consistent command interface across model types
Configurable message history window for context management
OAuth authentication support for Anthropic (alternative to API keys)
Hooks system for custom integrations and security policies
Environment variable substitution in configs and scripts
Builtin servers for common functionality (filesystem, bash, todo, http)

Requirements 📋

Go 1.23 or later
For OpenAI/Anthropic: API key for the respective provider
For Ollama: Local Ollama installation with desired models
For Google/Gemini: Google API key (see https://aistudio.google.com/app/apikey)
One or more MCP-compatible tool servers

Environment Setup 🔧

API Keys:

# For all providers (use --provider-api-key flag or these environment variables)
export OPENAI_API_KEY='your-openai-key'        # For OpenAI
export ANTHROPIC_API_KEY='your-anthropic-key'  # For Anthropic
export GOOGLE_API_KEY='your-google-key'        # For Google/Gemini

Ollama Setup:

Install Ollama from https://ollama.ai
Pull your desired model:

ollama pull mistral

Ensure Ollama is running:

ollama serve

You can also configure the Ollama client using standard environment variables, such as OLLAMA_HOST for the Ollama base URL.

Google API Key (for Gemini):

export GOOGLE_API_KEY='your-api-key'

OpenAI Compatible Setup:

Get your API server base URL, API key and model name
Use --provider-url and --provider-api-key flags or set environment variables

Self-Signed Certificates (TLS): If your provider uses self-signed certificates (e.g., local Ollama with HTTPS), you can skip certificate verification:

kit --provider-url https://192.168.1.100:443 --tls-skip-verify

⚠️ WARNING: Only use --tls-skip-verify for development or when connecting to trusted servers with self-signed certificates. This disables TLS certificate verification and is insecure for production use.

Installation 📦

go install github.com/mark3labs/kit/cmd/kit@latest

SDK Usage 🛠️

KIT also provides a Go SDK for programmatic access without spawning OS processes. The SDK maintains identical behavior to the CLI, including configuration loading, environment variables, and defaults.

Quick Example

package main

import (
    "context"
    "fmt"
    kit "github.com/mark3labs/kit/pkg/kit"
)

func main() {
    ctx := context.Background()
    
    // Create Kit instance with default configuration
    host, err := kit.New(ctx, nil)
    if err != nil {
        panic(err)
    }
    defer host.Close()
    
    // Send a prompt and get response
    response, err := host.Prompt(ctx, "What is 2+2?")
    if err != nil {
        panic(err)
    }
    
    fmt.Println(response)
}

SDK Features

✅ Programmatic access without spawning processes
✅ Identical configuration behavior to CLI
✅ Session management (save/load/clear)
✅ Tool execution callbacks for monitoring
✅ Streaming support
✅ Full compatibility with all providers and MCP servers

For detailed SDK documentation, examples, and API reference, see the SDK README.

Configuration ⚙️

MCP Servers

KIT will automatically create a configuration file in your home directory if it doesn't exist. It looks for config files in this order:

.kit.yml or .kit.json

Config file locations by OS:

Linux/macOS: ~/.kit.yml, ~/.kit.json
Windows: %USERPROFILE%\.kit.yml, %USERPROFILE%\.kit.json

You can also specify a custom location using the --config flag.

Environment Variable Substitution

KIT supports environment variable substitution in both config files and script frontmatter using the syntax:

${env://VAR} - Required environment variable (fails if not set)
${env://VAR:-default} - Optional environment variable with default value

This allows you to keep sensitive information like API keys in environment variables while maintaining flexible configuration.

Example:

mcpServers:
  github:
    type: local
    command: ["docker", "run", "-i", "--rm", "-e", "GITHUB_PERSONAL_ACCESS_TOKEN=${env://GITHUB_TOKEN}", "ghcr.io/github/github-mcp-server"]
    environment:
      DEBUG: "${env://DEBUG:-false}"
      LOG_LEVEL: "${env://LOG_LEVEL:-info}"

model: "${env://MODEL:-anthropic/claude-sonnet-4-5-20250929}"
provider-api-key: "${env://OPENAI_API_KEY}"  # Required - will fail if not set

Usage:

# Set required environment variables
export GITHUB_TOKEN="ghp_your_token_here"
export OPENAI_API_KEY="your_openai_key"

# Optionally override defaults
export DEBUG="true"
export MODEL="openai/gpt-4"

# Run kit
kit

Simplified Configuration Schema

KIT now supports a simplified configuration schema with three server types:

Local Servers

For local MCP servers that run commands on your machine:

{
  "mcpServers": {
    "filesystem": {
      "type": "local",
      "command": ["npx", "@modelcontextprotocol/server-filesystem", "${env://WORK_DIR:-/tmp}"],
      "environment": {
        "DEBUG": "${env://DEBUG:-false}",
        "LOG_LEVEL": "${env://LOG_LEVEL:-info}",
        "API_TOKEN": "${env://FS_API_TOKEN}"
      },
      "allowedTools": ["read_file", "write_file"],
      "excludedTools": ["delete_file"]
    },
    "github": {
      "type": "local",
      "command": ["docker", "run", "-i", "--rm", "-e", "GITHUB_PERSONAL_ACCESS_TOKEN=${env://GITHUB_TOKEN}", "ghcr.io/github/github-mcp-server"],
      "environment": {
        "DEBUG": "${env://DEBUG:-false}"
      }
    },
    "sqlite": {
      "type": "local",
      "command": ["uvx", "mcp-server-sqlite", "--db-path", "${env://DB_PATH:-/tmp/foo.db}"],
      "environment": {
        "SQLITE_DEBUG": "${env://DEBUG:-0}",
        "DATABASE_URL": "${env://DATABASE_URL:-sqlite:///tmp/foo.db}"
      }
    }
  }
}

Each local server entry requires:

type: Must be set to "local"
command: Array containing the command and all its arguments
environment: (Optional) Object with environment variables as key-value pairs
allowedTools: (Optional) Array of tool names to include (whitelist)
excludedTools: (Optional) Array of tool names to exclude (blacklist)

Remote Servers

For remote MCP servers accessible via HTTP:

{
  "mcpServers": {
    "websearch": {
      "type": "remote",
      "url": "${env://WEBSEARCH_URL:-https://api.example.com/mcp}",
      "headers": ["Authorization: Bearer ${env://WEBSEARCH_TOKEN}"]
    },
    "weather": {
      "type": "remote", 
      "url": "${env://WEATHER_URL:-https://weather-mcp.example.com}"
    }
  }
}

Each remote server entry requires:

type: Must be set to "remote"
url: The URL where the MCP server is accessible
headers: (Optional) Array of HTTP headers for authentication and custom headers

Remote servers automatically use the StreamableHTTP transport for optimal performance.

Builtin Servers

For builtin MCP servers that run in-process for optimal performance:

{
  "mcpServers": {
    "filesystem": {
      "type": "builtin",
      "name": "fs",
      "options": {
        "allowed_directories": ["${env://WORK_DIR:-/tmp}", "${env://HOME}/documents"]
      },
      "allowedTools": ["read_file", "write_file", "list_directory"]
    },
    "filesystem-cwd": {
      "type": "builtin",
      "name": "fs"
    }
  }
}

Each builtin server entry requires:

type: Must be set to "builtin"
name: Internal name of the builtin server (e.g., "fs" for filesystem)
options: Configuration options specific to the builtin server

Available Builtin Servers:

fs (filesystem): Secure filesystem access with configurable allowed directories
- allowed_directories: Array of directory paths that the server can access (defaults to current working directory if not specified)
bash: Execute bash commands with security restrictions and timeout controls
- No configuration options required
todo: Manage ephemeral todo lists for task tracking during sessions
- No configuration options required (todos are stored in memory and reset on restart)
http: Fetch web content and convert to text, markdown, or HTML formats
- Tools: fetch (fetch and convert web content), fetch_summarize (fetch and summarize web content using AI), fetch_extract (fetch and extract specific data using AI), fetch_filtered_json (fetch JSON and filter using gjson path syntax)
- No configuration options required

Builtin Server Examples

{
  "mcpServers": {
    "filesystem": {
      "type": "builtin",
      "name": "fs",
      "options": {
        "allowed_directories": ["/tmp", "/home/user/documents"]
      }
    },
    "bash-commands": {
      "type": "builtin", 
      "name": "bash"
    },
    "task-manager": {
      "type": "builtin",
      "name": "todo"
    },
    "web-fetcher": {
      "type": "builtin",
      "name": "http"
    }
  }
}

Tool Filtering

All MCP server types support tool filtering to restrict which tools are available:

allowedTools: Whitelist - only specified tools are available from the server
excludedTools: Blacklist - all tools except specified ones are available

{
  "mcpServers": {
    "filesystem-readonly": {
      "type": "builtin",
      "name": "fs",
      "allowedTools": ["read_file", "list_directory"]
    },
    "filesystem-safe": {
      "type": "local", 
      "command": ["npx", "@modelcontextprotocol/server-filesystem", "/tmp"],
      "excludedTools": ["delete_file"]
    }
  }
}

Note: allowedTools and excludedTools are mutually exclusive - you can only use one per server.

Legacy Configuration Support

KIT maintains full backward compatibility with the previous configuration format. Note: A recent bug fix improved legacy stdio transport reliability for external MCP servers (Docker, NPX, etc.).

Legacy STDIO Format

{
  "mcpServers": {
    "sqlite": {
      "command": "uvx",
      "args": ["mcp-server-sqlite", "--db-path", "/tmp/foo.db"],
      "env": {
        "DEBUG": "true"
      }
    }
  }
}

Legacy SSE Format

{
  "mcpServers": {
    "server_name": {
      "url": "http://some_host:8000/sse",
      "headers": ["Authorization: Bearer my-token"]
    }
  }
}

Legacy Docker/Container Format

{
  "mcpServers": {
    "phalcon": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/mark3labs/phalcon-mcp:latest",
        "serve"
      ]
    }
  }
}

Legacy Streamable HTTP Format

{
  "mcpServers": {
    "websearch": {
      "transport": "streamable",
      "url": "https://api.example.com/mcp",
      "headers": ["Authorization: Bearer your-api-token"]
    }
  }
}

Transport Types

KIT supports four transport types:

stdio: Launches a local process and communicates via stdin/stdout (used by "local" servers)
sse: Connects to a server using Server-Sent Events (legacy format)
streamable: Connects to a server using Streamable HTTP protocol (used by "remote" servers)
inprocess: Runs builtin servers in-process for optimal performance (used by "builtin" servers)

The simplified schema automatically maps:

"local" type → stdio transport
"remote" type → streamable transport
"builtin" type → inprocess transport

System Prompt

You can specify a custom system prompt using the --system-prompt flag. You can either:

Pass the prompt directly as text:

kit --system-prompt "You are a helpful assistant that responds in a friendly tone."

Pass a path to a text file containing the prompt:

kit --system-prompt ./prompts/assistant.md

Example assistant.md file:

You are a helpful coding assistant. 

Please:
- Write clean, readable code
- Include helpful comments
- Follow best practices
- Explain your reasoning

Usage 🚀

KIT is a CLI tool that allows you to interact with various AI models through a unified interface. It supports various tools through MCP servers and can run in both interactive and non-interactive modes.

Interactive Mode (Default)

Start an interactive conversation session:

kit

Hooks System

KIT supports a powerful hooks system that allows you to execute custom commands at specific points during execution. This enables security policies, logging, custom integrations, and automated workflows.

Quick Start

Initialize a hooks configuration:
```
kit hooks init
```
View active hooks:
```
kit hooks list
```
Validate your configuration:
```
kit hooks validate
```

Configuration

Hooks are configured in YAML files with the following precedence (highest to lowest):

.kit/hooks.yml (project-specific hooks)
$XDG_CONFIG_HOME/kit/hooks.yml (user global hooks, defaults to ~/.config/kit/hooks.yml)

Example configuration:

hooks:
  PreToolUse:
    - matcher: "bash"
      hooks:
        - type: command
          command: "/usr/local/bin/validate-bash.py"
          timeout: 5
  
  UserPromptSubmit:
    - hooks:
        - type: command
          command: "~/.kit/hooks/log-prompt.sh"

Available Hook Events

PreToolUse: Before any tool execution (bash, fetch, todo, MCP tools)
PostToolUse: After tool execution completes
UserPromptSubmit: When user submits a prompt
Stop: When the agent finishes responding
SubagentStop: When a subagent (Task tool) finishes
Notification: When KIT sends notifications

Security

⚠️ WARNING: Hooks execute arbitrary commands on your system. Only use hooks from trusted sources and always review hook commands before enabling them.

To temporarily disable all hooks, use the --no-hooks flag:

kit --no-hooks

See the example hook scripts in examples/hooks/:

bash-validator.py - Validates and blocks dangerous bash commands
prompt-logger.sh - Logs all user prompts with timestamps
mcp-monitor.py - Monitors and enforces policies on MCP tool usage

Non-Interactive Mode

Run a single prompt and exit - perfect for scripting and automation:

# Basic non-interactive usage
kit -p "What is the weather like today?"

# Quiet mode - only output the AI response (no UI elements)
kit -p "What is 2+2?" --quiet

# Use with different models
kit -m ollama/qwen2.5:3b -p "Explain quantum computing" --quiet

Model Generation Parameters

KIT supports fine-tuning model behavior through various parameters:

# Control response length
kit -p "Explain AI" --max-tokens 1000

# Adjust creativity (0.0 = focused, 1.0 = creative)
kit -p "Write a story" --temperature 0.9

# Control diversity with nucleus sampling
kit -p "Generate ideas" --top-p 0.8

# Limit token choices for more focused responses
kit -p "Answer precisely" --top-k 20

# Set custom stop sequences
kit -p "Generate code" --stop-sequences "```","END"

These parameters work with all supported providers (OpenAI, Anthropic, Google, Ollama) where supported by the underlying model.

Available Models

Models can be specified using the --model (-m) flag:

Anthropic Claude (default): anthropic/claude-sonnet-4-5-20250929, anthropic/claude-3-5-sonnet-latest, anthropic/claude-3-5-haiku-latest
OpenAI: openai/gpt-4, openai/gpt-4-turbo, openai/gpt-3.5-turbo
Google Gemini: google/gemini-2.0-flash, google/gemini-1.5-pro
Ollama models: ollama/llama3.2, ollama/qwen2.5:3b, ollama/mistral
OpenAI-compatible: Any model via custom endpoint with --provider-url

Examples

Interactive Mode

# Use Ollama with Qwen model
kit -m ollama/qwen2.5:3b

# Use OpenAI's GPT-4
kit -m openai/gpt-4

# Use OpenAI-compatible model with custom URL and API key
kit --model openai/<your-model-name> \
--provider-url <your-base-url> \
--provider-api-key <your-api-key>

Non-Interactive Mode

# Single prompt with full UI
kit -p "List files in the current directory"

# Compact mode for cleaner output without fancy styling
kit -p "List files in the current directory" --compact

# Quiet mode for scripting (only AI response output, no UI elements)
kit -p "What is the capital of France?" --quiet

# Use in shell scripts
RESULT=$(kit -p "Calculate 15 * 23" --quiet)
echo "The answer is: $RESULT"

# Pipe to other commands
kit -p "Generate a random UUID" --quiet | tr '[:lower:]' '[:upper:]'

Flags

--provider-url string: Base URL for the provider API (applies to OpenAI, Anthropic, Ollama, and Google)
--provider-api-key string: API key for the provider (applies to OpenAI, Anthropic, and Google)
--tls-skip-verify: Skip TLS certificate verification (WARNING: insecure, use only for self-signed certificates)
--config string: Config file location (default is $HOME/.kit.yml)
--system-prompt string: system-prompt file location
--debug: Enable debug logging
--max-steps int: Maximum number of agent steps (0 for unlimited, default: 0)
-m, --model string: Model to use (format: provider/model) (default "anthropic/claude-sonnet-4-5-20250929")
-p, --prompt string: Run in non-interactive mode with the given prompt
--quiet: Suppress all output except the AI response (only works with --prompt)
--compact: Enable compact output mode without fancy styling (ideal for scripting and automation)
--stream: Enable streaming responses (default: true, use --stream=false to disable)

Authentication Subcommands

kit auth login anthropic: Authenticate with Anthropic using OAuth (alternative to API keys)
kit auth logout anthropic: Remove stored OAuth credentials
kit auth status: Show authentication status

Note: OAuth credentials (when present) take precedence over API keys from environment variables and --provider-api-key flags.

Model Generation Parameters

--max-tokens int: Maximum number of tokens in the response (default: 4096)
--temperature float32: Controls randomness in responses (0.0-1.0, default: 0.7)
--top-p float32: Controls diversity via nucleus sampling (0.0-1.0, default: 0.95)
--top-k int32: Controls diversity by limiting top K tokens to sample from (default: 40)
--stop-sequences strings: Custom stop sequences (comma-separated)

Configuration File Support

All command-line flags can be configured via the config file. KIT will look for configuration in this order:

~/.kit.yml or ~/.kit.json

Example config file (~/.kit.yml):

# MCP Servers - New Simplified Format
mcpServers:
  filesystem-local:
    type: "local"
    command: ["npx", "@modelcontextprotocol/server-filesystem", "/path/to/files"]
    environment:
      DEBUG: "true"
  filesystem-builtin:
    type: "builtin"
    name: "fs"
    options:
      allowed_directories: ["/tmp", "/home/user/documents"]
  websearch:
    type: "remote"
    url: "https://api.example.com/mcp"

# Application settings
model: "anthropic/claude-sonnet-4-5-20250929"
max-steps: 20
debug: false
system-prompt: "/path/to/system-prompt.txt"

# Model generation parameters
max-tokens: 4096
temperature: 0.7
top-p: 0.95
top-k: 40
stop-sequences: ["Human:", "Assistant:"]

# Streaming configuration
stream: false  # Disable streaming (default: true)

# API Configuration
provider-api-key: "your-api-key"      # For OpenAI, Anthropic, or Google
provider-url: "https://api.openai.com/v1"  # Custom base URL
tls-skip-verify: false  # Skip TLS certificate verification (default: false)

Note: Command-line flags take precedence over config file values.

Interactive Commands

While chatting, you can use:

/help: Show available commands
/tools: List all available tools
/servers: List configured MCP servers
/history: Display conversation history
/quit: Exit the application
Ctrl+C: Exit at any time

Authentication Commands

Optional OAuth authentication for Anthropic (alternative to API keys):

kit auth login anthropic: Authenticate using OAuth
kit auth logout anthropic: Remove stored OAuth credentials
kit auth status: Show authentication status

Global Flags

--config: Specify custom config file location

Automation & Scripting 🤖

KIT's non-interactive mode makes it perfect for automation, scripting, and integration with other tools.

Use Cases

Shell Scripts

#!/bin/bash
# Get weather and save to file
kit -p "What's the weather in New York?" --quiet > weather.txt

# Process files with AI
for file in *.txt; do
    summary=$(kit -p "Summarize this file: $(cat $file)" --quiet)
    echo "$file: $summary" >> summaries.txt
done

CI/CD Integration

# Code review automation
DIFF=$(git diff HEAD~1)
kit -p "Review this code diff and suggest improvements: $DIFF" --quiet

# Generate release notes
COMMITS=$(git log --oneline HEAD~10..HEAD)
kit -p "Generate release notes from these commits: $COMMITS" --quiet

Data Processing

# Process CSV data
kit -p "Analyze this CSV data and provide insights: $(cat data.csv)" --quiet

# Generate reports
kit -p "Create a summary report from this JSON: $(cat metrics.json)" --quiet

API Integration

# Use as a microservice
curl -X POST http://localhost:8080/process \
  -d "$(kit -p 'Generate a UUID' --quiet)"

Tips

Use --quiet flag to get clean output suitable for parsing (only AI response, no UI)
Use --compact flag for simplified output without fancy styling (when you want to see UI elements)
Note: --compact and --quiet are mutually exclusive - --compact has no effect with --quiet
Use environment variables for sensitive data like API keys instead of hardcoding them
Use ${env://VAR} syntax in config files for environment variable substitution
Use environment variables for API keys in production

Environment Variable Best Practices

# Set sensitive variables in environment
export GITHUB_TOKEN="ghp_your_token_here"
export OPENAI_API_KEY="your_openai_key"
export DATABASE_URL="postgresql://user:pass@localhost/db"

# Use in config files
mcpServers:
  github:
    environment:
      GITHUB_TOKEN: "${env://GITHUB_TOKEN}"
      DEBUG: "${env://DEBUG:-false}"

MCP Server Compatibility 🔌

KIT can work with any MCP-compliant server. For examples and reference implementations, see the MCP Servers Repository.

Contributing 🤝

Contributions are welcome! Feel free to:

Submit bug reports or feature requests through issues
Create pull requests for improvements
Share your custom MCP servers
Improve documentation

Please ensure your contributions follow good coding practices and include appropriate tests.

License 📄

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments 🙏

Thanks to the Anthropic team for Claude and the MCP specification
Thanks to the Ollama team for their local LLM runtime
Thanks to all contributors who have helped improve this tool

README.md

KIT (Knowledge Inference Tool)

Table of Contents

Overview 🌟

Features ✨

Requirements 📋

Environment Setup 🔧

Installation 📦

SDK Usage 🛠️

Quick Example

SDK Features

Configuration ⚙️

MCP Servers

Environment Variable Substitution

Simplified Configuration Schema

Local Servers

Remote Servers

Builtin Servers

Builtin Server Examples

Tool Filtering

Legacy Configuration Support

Legacy STDIO Format

Legacy SSE Format

Legacy Docker/Container Format

Legacy Streamable HTTP Format

Transport Types

System Prompt

Usage 🚀

Interactive Mode (Default)

Hooks System

Quick Start

Configuration

Available Hook Events

Security

Non-Interactive Mode

Model Generation Parameters

Available Models

Examples

Interactive Mode

Non-Interactive Mode

Flags

Authentication Subcommands

Model Generation Parameters

Configuration File Support

Interactive Commands

Authentication Commands

Global Flags

Automation & Scripting 🤖

Use Cases

Shell Scripts

CI/CD Integration

Data Processing

API Integration

Tips

Environment Variable Best Practices

MCP Server Compatibility 🔌

Contributing 🤝

License 📄

Acknowledgments 🙏