{"data":{"slug":"shyamsaktawat-openalpha-evolve","name":"OpenAlpha_Evolve","tagline":"Framework for autonomous coding agents using evolutionary algorithms","github_url":"https://github.com/shyamsaktawat/OpenAlpha_Evolve","owner":"shyamsaktawat","repo":"OpenAlpha_Evolve","owner_avatar_url":"https://avatars.githubusercontent.com/u/76257252?v=4","primary_language":"Python","stars":1048,"forks":154,"topics":["alphacode","alphafold","coding-agent","discovery","distributed-evolutionary-algorithms","evolution-computing","evolutionary-algorithm","evolutionary-algorithms","genetic-algorithm","google","iterative-methods","iterative-refinement","llm-engineering","llm-ensemble","llm-inference","openevolve","optimize"],"archived":false,"github_pushed_at":"2025-05-31T16:34:33+00:00","maintenance_label":"Dormant","stars_delta_30d":8,"url":"https://www.graphcanon.com/tools/shyamsaktawat-openalpha-evolve","markdown_url":"https://www.graphcanon.com/tools/shyamsaktawat-openalpha-evolve.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/shyamsaktawat-openalpha-evolve","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=shyamsaktawat-openalpha-evolve","description":"OpenAlpha_Evolve is an open-source Python framework inspired by the groundbreaking research on autonomous coding agents like DeepMind's AlphaEvolve.","homepage_url":null,"license":"MIT","open_issues":10,"watchers":17,"ai_summary":"A Python-based framework inspired by DeepMind's AlphaEvolve, aimed at developing autonomous code-generating agents using advanced evolutionary algorithms","readme_excerpt":"## 🏁 Getting Started\n\n1.  **Prerequisites**:\n    *   Python 3.10+\n    *   `pip` for package management\n    *   `git` for cloning\n    *   **Docker**: For sandboxed code evaluation. Ensure Docker Desktop (Windows/Mac) or Docker Engine (Linux) is installed and running. Visit [docker.com](https://www.docker.com/get-started) for installation instructions.\n\n2.  **Clone the Repository**:\n    ```bash\n    git clone https://github.com/shyamsaktawat/OpenAlpha_Evolve.git\n    cd OpenAlpha_Evolve\n    ```\n\n3.  **Set Up a Virtual Environment** (recommended):\n    ```bash\n    python -m venv venv\n    source venv/bin/activate  # On Windows: venv\\Scripts\\activate\n    ```\n\n4.  **Install Dependencies**:\n    ```bash\n    pip install -r requirements.txt\n    ```\n\n5.  **Set Up Environment Variables (Crucial for API Keys)**:\n    *   **This step is essential for the application to function correctly with your API keys.** The `.env` file stores your sensitive credentials and configuration, overriding the default placeholders in `config/settings.py`.\n    *   Create your personal environment file by copying the example:\n        ```bash\n        cp .env_example .env\n        ```\n\n    #### LLM Configuration\n    Google Cloud authentication (e.g., via Application Default Credentials (ADC) or service account keys pointed to by `GOOGLE_APPLICATION_CREDENTIALS`) is a supported method for using Google's LLMs.\n\n    To set up your environment variables for Google Cloud, you can use one of the following methods. These should be added to your `.env` file:\n\n    ```bash\n    # For Google Cloud (Vertex AI / AI Studio)\n    # Option 1: Using Application Default Credentials (ADC)\n    # Ensure you have authenticated via gcloud CLI:\n    # gcloud auth application-default login\n    # Or set the GOOGLE_APPLICATION_CREDENTIALS environment variable:\n    # GOOGLE_APPLICATION_CREDENTIALS=\"/path/to/your/service-account-key.json\"\n\n    # Option 2: Directly using an API Key for specific Google services (e.g., Gemini API)\n    # GEMINI_API_KEY=\"your_gemini_api_key\"\n    ```\n\n    This project uses LiteLLM to interface with various LLM providers. For providers other than Google Cloud (e.g., OpenAI, Anthropic, Cohere), please refer to the [LiteLLM documentation](https://docs.litellm.ai/docs/providers) for the specific environment variables required. Common examples include:\n    ```bash\n    # OPENAI_API_KEY=\"your_openai_api_key\"\n    # ANTHROPIC_API_KEY=\"your_anthropic_api_key\"\n    # COHERE_API_KEY=\"your_cohere_api_key\"\n    ```\n    Add the necessary API key variables for your chosen LLM provider(s) to your `.env` file.\n\n6.  **Run OpenAlpha_Evolve!**\n    Run the example task (Dijkstra's algorithm) with:\n    ```bash\n    python -m main examples/shortest_path.yaml\n    ```\n    Watch the logs in your terminal to see the evolutionary process unfold! Log files are also saved to `alpha_evolve.log` (by default).\n\n7.  **Launch the Gradio Web Interface**\n    Interact with the system via the web UI. To start the Gradio app:\n    ```bash\n    python app.py\n    ```\n    Gradio will display a local URL (e.g., http://127.0.0.1:7860) and a public share link if enabled. Open this in your browser to define custom tasks and run the evolution process interactively.\n\n---\n\n---\n\n## 📜 License\n\nThis project is licensed under the **MIT License**. See the `LICENSE.md` file for details.\n\n---","github_created_at":"2025-05-17T21:04:30+00:00","created_at":"2026-07-11T11:44:03.702755+00:00","updated_at":"2026-08-25T00:01:20.671998+00:00","categories":[{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"alphacode","name":"alphacode"},{"slug":"autonomous-coding-agents","name":"autonomous-coding-agents"},{"slug":"distributed-evolutionary-algorithms","name":"distributed-evolutionary-algorithms"},{"slug":"evolutionary-algorithm","name":"evolutionary-algorithm"},{"slug":"genetic-algorithm","name":"genetic-algorithm"},{"slug":"llm-engineering","name":"llm-engineering"}],"trust":{"provenance":{"is_fork":false,"github_id":985477441,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-25T00:01:19.835Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":450,"last_release_at":null,"stars_delta_30d":8,"open_issues_delta_30d":1},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:44:04.788Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-25T00:01:20.298Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-25T00:01:20.298Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-08-25T00:01:20.298Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["Integration of iterative refinement into code generation processes","Research involving autonomous software development experiments"],"when_not_to_use":["When needing immediate, human-tuned feedback for code improvements","For projects preferring deterministic outcomes over evolved solutions"],"source":"enrich:decision_facts","observed_at":"2026-07-17T03:16:04.875Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Autonomous coding agents evolve via advanced algorithms."},{"label":"License detail","value":"MIT License allows modification and distribution with attribution required"}]}}