README.md

metadata

title: CodeAgent-MCP
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.6.0
python_version: '3.12'
app_file: app.py
pinned: false
license: mit

CodeAgent-MCP

Multi-Agent Code Generation System with MCP Protocol.

Planner (task decomposition) -> Coder (code generation) -> Reviewer (code review) feedback loop.

How to use

1. Enter your DeepSeek / OpenAI API key
2. Choose a provider (default = DeepSeek)
3. Describe the code you want to generate
4. Click "Start" and watch the multi-agent system work

Architecture

• Planner Agent: Decomposes complex requirements into 2-4 subtasks
• Coder Agent: Generates code with optional MCP tool integration
• Reviewer Agent: Scores code quality (1-10) and provides improvement suggestions
• Orchestrator: Manages Coder-Reviewer feedback loop until quality threshold is met

Project Series

1. small-llms-tool-use - Function calling fine-tuning (86-89% exact match)
2. agenttune - Multi-step ReAct reasoning (100% task success rate)
3. smallrag - RAG optimization (chunk_size=512 + MMR + top-k=5)
4. CodeAgent-MCP (this project) - Multi-Agent system integration

GitHub