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.
Version
Python
Key Features
- Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers
- Model Context Protocol Integration:
- 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
- Clone this repository
- Install dependencies:
- Create a
.envfile with your API keys:
Usage
CLI Mode
Run the CLI with the default provider (determined from available API keys):
Specify a provider and model:
Set a budget limit to manage costs:
MCP Server Mode
Run as a Model Context Protocol server:
Start in development mode with the MCP Inspector:
Configure host and port:
Specify additional dependencies:
Load environment variables from file:
MCP Client Mode
Connect to an MCP server using Claude as the reasoning engine:
Specify a Claude model:
Try the included example server:
Multi-Agent MCP Mode
Launch a multi-agent client with synchronized agents:
Use a custom agent configuration file:
Example with the echo server:
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:
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:
- Install and run the MCP server:
- Open the configuration page in your browser:
- Follow the instructions to configure Claude Desktop, including:
Using Claude Code as an MCP Client
To connect to any MCP server using Claude Code:
- Ensure you have your Anthropic API key in the environment or .env file
- Start the MCP server you want to connect to
- Connect using the MCP client:
- Type queries in the interactive chat interface
Using Multi-Agent Mode
For complex tasks, the multi-agent mode allows multiple specialized agents to collaborate:
- Create an agent configuration file or use the provided example
- Start your MCP server
- Launch the multi-agent client:
- Use the command interface to interact with multiple agents:
Contributing
- Fork the repository
- Create a feature branch
- Implement your changes with tests
- 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
- Clone the repository
- Install dependencies:
- Set your OpenAI API key:
Usage
CLI Mode
Run the assistant in interactive CLI mode:
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:
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:
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:
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:
To enable replication:
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:
- Start a primary instance with
--enable-replication
- 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)
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.
Version
Python
Key Features
- Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers
- Model Context Protocol Integration:
- 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
- Clone this repository
- Install dependencies:
- Create a
.envfile with your API keys:
Usage
CLI Mode
Run the CLI with the default provider (determined from available API keys):
Specify a provider and model:
Set a budget limit to manage costs:
MCP Server Mode
Run as a Model Context Protocol server:
Start in development mode with the MCP Inspector:
Configure host and port:
Specify additional dependencies:
Load environment variables from file:
MCP Client Mode
Connect to an MCP server using Claude as the reasoning engine:
Specify a Claude model:
Try the included example server:
Multi-Agent MCP Mode
Launch a multi-agent client with synchronized agents:
Use a custom agent configuration file:
Example with the echo server:
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:
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:
- Install and run the MCP server:
- Open the configuration page in your browser:
- Follow the instructions to configure Claude Desktop, including:
Using Claude Code as an MCP Client
To connect to any MCP server using Claude Code:
- Ensure you have your Anthropic API key in the environment or .env file
- Start the MCP server you want to connect to
- Connect using the MCP client:
- Type queries in the interactive chat interface
Using Multi-Agent Mode
For complex tasks, the multi-agent mode allows multiple specialized agents to collaborate:
- Create an agent configuration file or use the provided example
- Start your MCP server
- Launch the multi-agent client:
- Use the command interface to interact with multiple agents:
Contributing
- Fork the repository
- Create a feature branch
- Implement your changes with tests
- 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
- Clone the repository
- Install dependencies:
- Set your OpenAI API key:
Usage
CLI Mode
Run the assistant in interactive CLI mode:
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:
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:
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:
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:
To enable replication:
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:
- Start a primary instance with
--enable-replication
- 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)