MCP Coding Assistant with support for OpenAI + other LLM Providers

A powerful Python recreation of Claude Code with enhanced real-time visualization, cost management, and Model Context Protocol (MCP) server capabilities. This tool provides a natural language interface for software development tasks with support for multiple LLM providers.

Key Features

• Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers
• Model Context Protocol Integration:
  • Run as an MCP server for use with Claude Desktop and other clients
  • Connect to any MCP server with the built-in MCP client
  • Multi-agent synchronization for complex problem solving
• Real-Time Tool Visualization: See tool execution progress and results in real-time
• Cost Management: Track token usage and expenses with budget controls
• Comprehensive Tool Suite: File operations, search, command execution, and more
• Enhanced UI: Rich terminal interface with progress indicators and syntax highlighting
• Context Optimization: Smart conversation compaction and memory management
• Agent Coordination: Specialized agents with different roles can collaborate on tasks

Installation

1. Clone this repository
2. Install dependencies:

pip install -r requirements.txt

3. Create a .env file with your API keys:

# Choose one or more providers
OPENAI_API_KEY=your_openai_api_key_here
ANTHROPIC_API_KEY=your_anthropic_api_key_here

# Optional model selection
OPENAI_MODEL=gpt-4o
ANTHROPIC_MODEL=claude-3-opus-20240229

Usage

CLI Mode

Run the CLI with the default provider (determined from available API keys):

python claude.py chat

Specify a provider and model:

python claude.py chat --provider openai --model gpt-4o

Set a budget limit to manage costs:

python claude.py chat --budget 5.00

MCP Server Mode

Run as a Model Context Protocol server:

python claude.py serve

Start in development mode with the MCP Inspector:

python claude.py serve --dev

Configure host and port:

python claude.py serve --host 0.0.0.0 --port 8000

Specify additional dependencies:

python claude.py serve --dependencies pandas numpy

Load environment variables from file:

python claude.py serve --env-file .env

MCP Client Mode

Connect to an MCP server using Claude as the reasoning engine:

python claude.py mcp-client path/to/server.py

Specify a Claude model:

python claude.py mcp-client path/to/server.py --model claude-3-5-sonnet-20241022

Try the included example server:

# In terminal 1 - start the server
python examples/echo_server.py

# In terminal 2 - connect with the client
python claude.py mcp-client examples/echo_server.py

Multi-Agent MCP Mode

Launch a multi-agent client with synchronized agents:

python claude.py mcp-multi-agent path/to/server.py

Use a custom agent configuration file:

python claude.py mcp-multi-agent path/to/server.py --config examples/agents_config.json

Example with the echo server:

# In terminal 1 - start the server
python examples/echo_server.py

# In terminal 2 - launch the multi-agent client
python claude.py mcp-multi-agent examples/echo_server.py --config examples/agents_config.json

Available Tools

• View: Read files with optional line limits
• Edit: Modify files with precise text replacement
• Replace: Create or overwrite files
• GlobTool: Find files by pattern matching
• GrepTool: Search file contents using regex
• LS: List directory contents
• Bash: Execute shell commands

Chat Commands

• /help: Show available commands
• /compact: Compress conversation history to save tokens
• /version: Show version information
• /providers: List available LLM providers
• /cost: Show cost and usage information
• /budget [amount]: Set a budget limit
• /quit, /exit: Exit the application

Architecture

Claude Code Python Edition is built with a modular architecture:

/claude_code/
  /lib/
    /providers/      # LLM provider implementations
    /tools/          # Tool implementations
    /context/        # Context management
    /ui/             # UI components
    /monitoring/     # Cost tracking & metrics
  /commands/         # CLI commands
  /config/           # Configuration management
  /util/             # Utility functions
  claude.py          # Main CLI entry point
  mcp_server.py      # Model Context Protocol server

Using with Model Context Protocol

Using Claude Code as an MCP Server

Once the MCP server is running, you can connect to it from Claude Desktop or other MCP-compatible clients:

1. Install and run the MCP server:

   python claude.py serve
   

2. Open the configuration page in your browser:

   http://localhost:8000
   

3. Follow the instructions to configure Claude Desktop, including:

   • Copy the JSON configuration
   • Download the auto-configured JSON file
   • Step-by-step setup instructions

Using Claude Code as an MCP Client

To connect to any MCP server using Claude Code:

1. Ensure you have your Anthropic API key in the environment or .env file
2. Start the MCP server you want to connect to
3. Connect using the MCP client:

   python claude.py mcp-client path/to/server.py
   

4. Type queries in the interactive chat interface

Using Multi-Agent Mode

For complex tasks, the multi-agent mode allows multiple specialized agents to collaborate:

1. Create an agent configuration file or use the provided example
2. Start your MCP server
3. Launch the multi-agent client:

   python claude.py mcp-multi-agent path/to/server.py --config examples/agents_config.json
   

4. Use the command interface to interact with multiple agents:
   • Type a message to broadcast to all agents
   • Use /talk Agent_Name message for direct communication
   • Use /agents to see all available agents
   • Use /history to view the conversation history

Contributing

1. Fork the repository
2. Create a feature branch
3. Implement your changes with tests
4. Submit a pull request

License

MIT

Acknowledgments

This project is inspired by Anthropic's Claude Code CLI tool, reimplemented in Python with additional features for enhanced visibility, cost management, and MCP server capabilities.# OpenAI Code Assistant

A powerful command-line and API-based coding assistant that uses OpenAI APIs with function calling and streaming.

Features

• Interactive CLI for coding assistance
• Web API for integration with other applications
• Model Context Protocol (MCP) server implementation
• Replication support for high availability
• Tool-based architecture for extensibility
• Reinforcement learning for tool optimization
• Web client for browser-based interaction

Installation

1. Clone the repository
2. Install dependencies:

   pip install -r requirements.txt
   

3. Set your OpenAI API key:

   export OPENAI_API_KEY=your_api_key
   

Usage

CLI Mode

Run the assistant in interactive CLI mode:

python cli.py

Options:

• --model, -m: Specify the model to use (default: gpt-4o)
• --temperature, -t: Set temperature for response generation (default: 0)
• --verbose, -v: Enable verbose output with additional information
• --enable-rl/--disable-rl: Enable/disable reinforcement learning for tool optimization
• --rl-update: Manually trigger an update of the RL model

API Server Mode

Run the assistant as an API server:

python cli.py serve

Options:

• --host: Host address to bind to (default: 127.0.0.1)
• --port, -p: Port to listen on (default: 8000)
• --workers, -w: Number of worker processes (default: 1)
• --enable-replication: Enable replication across instances
• --primary/--secondary: Whether this is a primary or secondary instance
• --peer: Peer instances to replicate with (host:port), can be specified multiple times

MCP Server Mode

Run the assistant as a Model Context Protocol (MCP) server:

python cli.py mcp-serve

Options:

• --host: Host address to bind to (default: 127.0.0.1)
• --port, -p: Port to listen on (default: 8000)
• --dev: Enable development mode with additional logging
• --dependencies: Additional Python dependencies to install
• --env-file: Path to .env file with environment variables

MCP Client Mode

Connect to an MCP server using the assistant as the reasoning engine:

python cli.py mcp-client path/to/server.py

Options:

• --model, -m: Model to use for reasoning (default: gpt-4o)
• --host: Host address for the MCP server (default: 127.0.0.1)
• --port, -p: Port for the MCP server (default: 8000)

Deployment Script

For easier deployment, use the provided script:

./deploy.sh --host 0.0.0.0 --port 8000 --workers 4

To enable replication:

# Primary instance
./deploy.sh --enable-replication --port 8000

# Secondary instance
./deploy.sh --enable-replication --secondary --port 8001 --peer 127.0.0.1:8000

Web Client

To use the web client, open web-client.html in your browser. Make sure the API server is running.

API Endpoints

Standard API Endpoints

• POST /conversation: Create a new conversation
• POST /conversation/{conversation_id}/message: Send a message to a conversation
• POST /conversation/{conversation_id}/message/stream: Stream a message response
• GET /conversation/{conversation_id}: Get conversation details
• DELETE /conversation/{conversation_id}: Delete a conversation
• GET /health: Health check endpoint

MCP Protocol Endpoints

• GET /: Health check (MCP protocol)
• POST /context: Get context for a prompt template
• GET /prompts: List available prompt templates
• GET /prompts/{prompt_id}: Get a specific prompt template
• POST /prompts: Create a new prompt template
• PUT /prompts/{prompt_id}: Update an existing prompt template
• DELETE /prompts/{prompt_id}: Delete a prompt template

Replication

The replication system allows running multiple instances of the assistant with synchronized state. This provides:

• High availability
• Load balancing
• Fault tolerance

To set up replication:

1. Start a primary instance with --enable-replication
2. Start secondary instances with --enable-replication --secondary --peer [primary-host:port]

Tools

The assistant includes various tools:

• Weather: Get current weather for a location
• View: Read files from the filesystem
• Edit: Edit files
• Replace: Write files
• Bash: Execute bash commands
• GlobTool: File pattern matching
• GrepTool: Content search
• LS: List directory contents
• JinaSearch: Web search using Jina.ai
• JinaFactCheck: Fact checking using Jina.ai
• JinaReadURL: Read and summarize webpages

CLI Commands

• /help: Show help message
• /compact: Compact the conversation to reduce token usage
• /status: Show token usage and session information
• /config: Show current configuration settings
• /rl-status: Show RL tool optimizer status (if enabled)
• /rl-update: Update the RL model manually (if enabled)
• /rl-stats: Show tool usage statistics (if enabled)