DeepCode — a multi-agent code generation platform developed by a research lab at the University of Hong Kong

What is DeepCode？

DeepCode is a multi-agent code generation platform developed by the Data Intelligence Laboratory at the University of Hong Kong. It can transform research papers, natural language descriptions, and other inputs into high-quality, production-ready code, supporting multiple programming languages and frameworks. With features such as Paper2Code, Text2Web, and Text2Backend, DeepCode enables automation from algorithm implementation to full-stack development. Powered by intelligent coordination and efficient memory mechanisms, it improves both the efficiency and quality of code generation, offering developers a powerful tool to accelerate the journey from concept to code.

Key Features of DeepCode

• Paper2Code: Converts complex algorithms from research papers into production-ready code.

• Text2Web: Transforms text descriptions into fully functional and visually appealing front-end web code.

• Text2Backend: Generates efficient, scalable, and feature-rich back-end code from simple text inputs.

• Multi-interface support: Provides both CLI and web interfaces to meet diverse user needs.

• Automated testing & documentation: Automatically generates unit tests and documentation to ensure code quality.

• Intelligent retrieval & recommendation: Uses the CodeRAG system for global code understanding and smart recommendations.

Technical Principles of DeepCode

• Multi-agent architecture:

  • Central Coordinator Agent: Oversees workflow execution and decision-making.

  • Intent Understanding Agent: Parses user requirements and extracts functional specifications and technical constraints.

  • Document Parsing Agent: Processes technical papers and documentation to extract algorithms and methods.

  • Code Planning Agent: Designs system architecture and optimizes technology stacks.

  • Code Mining Agent: Discovers relevant libraries and frameworks, analyzing compatibility and integration potential.

  • Code Indexing Agent: Builds a knowledge graph of codebases for intelligent retrieval and cross-referencing.

  • Code Generation Agent: Synthesizes executable implementations, generates test suites, and creates documentation.

• Intelligent coordination & dynamic task planning: Dynamically selects optimal strategies and adjusts workflows based on input complexity. Supports real-time task allocation and parallel processing to improve efficiency.

• Efficient memory mechanism: Manages large-scale code contexts through intelligent compression and hierarchical memory structures, ensuring consistency and accuracy in generated code.

• Advanced CodeRAG system: Combines semantic vector embeddings with graph-based dependency analysis to automatically identify optimal codebases and implementation patterns, enabling global code understanding and improving generation quality.

• Automated testing & documentation: Generates unit tests and documentation, leveraging static analysis and dynamic testing to detect potential issues and reduce maintenance costs.

Project Links

• GitHub repository: https://github.com/HKUDS/DeepCode

Application Scenarios

• Academic research: Convert algorithms from research papers into code, accelerating validation and application of academic results.

• Software development: Rapidly generate front-end and back-end code, boosting productivity and reducing repetitive work.

• Enterprise applications: Produce runnable prototype code to accelerate product iteration, market validation, and reduce development costs.

• Education & training: Provide students with code generation tools to support teaching and help them better understand programming concepts.

• Data analysis & machine learning: Automatically generate data processing pipelines and machine learning model code, improving efficiency in AI development.