Prometheus
⚡ AI-Powered Software 3.0 Platform ⚡

Redefining Software Productivity Beyond Vibe Coding — There’s More Beyond Lovable and Replit.
Moving beyond unreliable prototype generation, Prometheus turns your ideas into verifiable, affordable software through autonomous code agents.

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• [2025-11] Prometheus achieved TOP 5 and TOP 1 🏆 in the agents using gpt-5 in the swebench leaderboard for automated software engineering using LLMs 🎉!
• [2025-10] We maintain a curated list of code agent projects and research: Awesome Code Agents - explore the cutting-edge developments in autonomous code generation, bug fixing, and software engineering automation.

Prometheus is a research-backed, production-ready platform that leverages unified knowledge graphs and multi-agent systems to perform intelligent operations on multilingual codebases. Built on LangGraph state machines, it orchestrates specialized AI agents to automatically classify issues, reproduce bugs, retrieve relevant context, and generate validated patches.

• Automated Issue Resolution: End-to-end bug fixing with reproduction, patch generation, and multi-level validation
• Feature Implementation Pipeline: Context-aware feature request analysis, implementation planning, and code generation with optional regression testing
• Intelligent Context Retrieval: Graph-based semantic search over codebase structure, AST, and documentation
• Multi-Agent Orchestration: Coordinated workflow between classification, reproduction, and resolution agents
• Knowledge Graph Integration: Neo4j-powered unified representation of code structure and semantics
• Containerized Execution: Docker-isolated testing and validation environment
• Question Answering: Natural language queries with tool-augmented LLM agents

@misc{chen2025prometheusKG,
      title={Prometheus: Unified Knowledge Graphs for Issue Resolution in Multilingual Codebases}, 
      author={Zimin Chen and Yue Pan and Siyu Lu and Jiayi Xu and Claire Le Goues and Martin Monperrus and He Ye},
      year={2025},
      eprint={2507.19942},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2507.19942}, 
}

Prometheus implements a hierarchical multi-agent system:

                              User Issue
                                  |
                                  v
                ┌─────────────────────────────────┐
                │  Issue Classification Agent     │
                │  (bug/question/feature/doc)     │
                └─────────────┬───────────────────┘
                              |
              ┌───────────────┼───────────────┐
              |               |               |
              v               v               v
    ┌──────────────┐  ┌──────────────┐  ┌──────────────┐
    │Bug Pipeline  │  │Feature       │  │Question      │
    │              │  │Pipeline      │  │Pipeline      │
    └──────┬───────┘  └──────┬───────┘  └──────┬───────┘
           |                 |                 |
           v                 v                 v
    ┌──────────────┐  ┌──────────────┐  ┌──────────────┐
    │Bug           │  │Feature       │  │Context       │
    │Reproduction  │  │Analysis      │  │Retrieval     │
    └──────┬───────┘  └──────┬───────┘  └──────┬───────┘
           |                 |                 |
           v                 v                 v
    ┌──────────────┐  ┌──────────────┐  ┌──────────────┐
    │Issue         │  │Patch         │  │Question      │
    │Resolution    │  │Generation    │  │Analysis      │
    └──────┬───────┘  └──────┬───────┘  └──────┬───────┘
           |                 |                 |
           └─────────────────┼─────────────────┘
                             v
                      Response Generation

Core Components:

• Knowledge Graph: Tree-sitter-based AST and semantic code representation in Neo4j
• LangGraph State Machines: Coordinated multi-agent workflows with checkpointing
• Docker Containers: Isolated build and test execution environments
• LLM Integration: Multi-tier model strategy (GPT-4, Claude, Gemini support)

See Architecture Documentation for details.

• Docker and Docker Compose
• Python 3.11+ (for local development)
• API Keys: OpenAI, Anthropic, or Google Gemini

1. Clone the repository

   git clone https://github.com/EuniAI/Prometheus.git
   cd Prometheus

2. Configure environment

   cp example.env .env
   # Edit .env with your API keys

3. Generate JWT secret (required for authentication)

   python -m prometheus.script.generate_jwt_token
   # Copy output to .env as PROMETHEUS_JWT_SECRET_KEY

4. Create working directory

   mkdir -p working_dir

5. Start services

   docker-compose up --build

6. Access the platform

   • API: http://localhost:9002/v1.2
   • Interactive Docs: http://localhost:9002/docs

# Install dependencies
pip install hatchling
pip install .
pip install .[test]

# Run development server
uvicorn prometheus.app.main:app --host 0.0.0.0 --port 9002 --reload

# Run tests (excluding git-dependent tests)
coverage run --source=prometheus -m pytest -v -s -m "not git"

# Generate coverage report
coverage report -m
coverage html
open htmlcov/index.html

PostgreSQL (required for state checkpointing):

docker run -d \
  -p 5432:5432 \
  -e POSTGRES_USER=postgres \
  -e POSTGRES_PASSWORD=password \
  -e POSTGRES_DB=postgres \
  postgres

Neo4j (required for knowledge graph):

docker run -d \
  -p 7474:7474 -p 7687:7687 \
  -e NEO4J_AUTH=neo4j/password \
  -e NEO4J_PLUGINS='["apoc"]' \
  -e NEO4J_dbms_memory_heap_initial__size=4G \
  -e NEO4J_dbms_memory_heap_max__size=8G \
  neo4j

Verify at http://localhost:7474

Prometheus is based on peer-reviewed research on unified knowledge graphs for multilingual code analysis.

@misc{prometheus2025,
  title={Prometheus: Unified Knowledge Graphs for Issue Resolution in Multilingual Codebases},
  author={Zimin Chen and Yue Pan and Siyu Lu and Jiayi Xu and Claire Le Goues and Martin Monperrus and He Ye},
  year={2025},
  eprint={2507.19942},
  archivePrefix={arXiv},
  primaryClass={cs.SE},
  url={https://arxiv.org/abs/2507.19942}
}

We welcome contributions! Please see our Contributing Guidelines for details on how to get started.

Quick Links:

• 📖 Read the full Contributing Guide
• 🐞 Report bugs via GitHub Issues
• ✨ Submit feature requests and improvements via Pull Requests
• 💬 Join discussions on Discord

We're grateful to all our amazing contributors who have made this project what it is today!

If you have any questions or encounter issues, please feel free to reach out. For quick queries, you can also check our Issues page for common questions and solutions.

This project is dual-licensed:

• Community Edition: Licensed under the GNU General Public License v3.0 (GPLv3).
  You are free to use, modify, and redistribute this code, provided that any derivative works are also released under the GPLv3.
  This ensures the project remains open and contributions benefit the community.

• Commercial Edition: For organizations that wish to use this software in proprietary, closed-source, or commercial settings,
  a separate commercial license is available. Please contact EUNI.AI Team to discuss licensing terms.

• Documentation: Multi-Agent Architecture | GitHub Issue Debug Guide
• Community: Discord Server
• Email: business@euni.ai
• Issues: GitHub Issues

We thank Delysium for their support in organizing LLM-related resources, architecture design, and optimization, which greatly strengthened our research infrastructure and capabilities.