DeepCode is an open-source, multi-agent AI system designed to automate the conversion of research papers, natural language descriptions, and URLs into production-ready code. It targets researchers, developers, and product teams seeking to accelerate algorithm implementation, front-end web development, and back-end service generation, thereby reducing development bottlenecks and repetitive coding tasks.

How It Works

DeepCode employs a sophisticated multi-agent architecture orchestrated by a central decision-making system. This system coordinates specialized agents for tasks like document parsing, intent understanding, code planning, reference mining, and code generation. It leverages a "CodeRAG" (Retrieval-Augmented Generation) system for deep code comprehension and context management, enabling it to analyze complex interdependencies across codebases and discover optimal implementation patterns. The system is powered by the Model Context Protocol (MCP) for seamless integration with various tools and services.

Quick Start & Requirements

• Installation: pip install deepcode-hku
• Configuration: Requires API keys for LLM providers (OpenAI, Anthropic) and optionally for search engines (Brave, Bocha-MCP) configured in mcp_agent.secrets.yaml and mcp_agent.config.yaml.
• Launch: deepcode for the web interface (defaulting to http://localhost:8501).
• Development: Requires Python 3.13+ (or 3.11/3.12 for pip) and requirements.txt. Development installation can use uv for dependency management.
• Documentation: README

Highlighted Details

• Paper2Code: Automates the implementation of complex algorithms from research papers.
• Text2Web: Generates front-end web code from textual descriptions.
• Text2Backend: Creates back-end code from simple text inputs.
• Multi-Interface: Offers both a CLI and a responsive web interface.

Maintenance & Community

The project is developed by the Data Intelligence Lab at The University of Hong Kong. Further community engagement details are not explicitly provided in the README.

Licensing & Compatibility

The project is released under the MIT License, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

The system requires significant API key configuration for LLM and search services, which may incur costs. Specific performance benchmarks or detailed comparisons against other code generation tools are not yet available, though a "PaperBench Performance Showcase" is listed as a future feature.