{"id":24401581,"url":"https://github.com/burnycoder/wikipedia-ai-agent","last_synced_at":"2026-04-08T16:01:34.720Z","repository":{"id":272661134,"uuid":"917348723","full_name":"BurnyCoder/wikipedia-ai-agent","owner":"BurnyCoder","description":"Wikipedia AI agent research assistant. 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Offers flexible access through both terminal and web interfaces.\n\nBuilt on a modern stack including LangChain's LangGraph's ReAct agent, Wikipedia API, FAISS vector storage, Microsoft's GraphRAG, support for leading LLM providers (OpenAI, Anthropic, Google), and Streamlit frontend. \n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n![image](https://github.com/user-attachments/assets/1bf6474b-3507-4918-a163-250caed9b5e3)\n\n## Features\n\n- 🤖 Multiple LLM Support from OpenAI, Anthropic, and Google, such as:\n  - OpenAI: gpt-4o, chatgpt-4o-latest, o1-2024-12-17, gpt-4-turbo\n  - Anthropic: claude-3-5-sonnet-latest\n  - Google: gemini-exp-1206, gemini-1.5-pro, gemini-2.0-flash-exp, gemini-2.0-flash-thinking-exp-1219\n- 🔍 Wikipedia search capabilities\n- 📚 RAG (Retrieval Augmented Generation) support, soon finished GraphRAG\n- 💾 Conversation memory and thread management\n- 🖥️ Both terminal and web interfaces\n- 🔄 Streaming responses for real-time interaction\n- 📝 Logging system\n\n## Architecture\n\nThe project uses a modular architecture with:\n- LangGraph's ReAct agent for agent orchestration\n- LangChain for LLM interactions and tool integration\n- Wikipedia API as agent's tool\n- FAISS for vector storage (when using RAG), or soon finished GraphRAG\n- Streamlit for the web interface\n\n## Setup\n\n1. Clone the repository:\n```bash\ngit clone https://github.com/yourusername/wikipedia-agent.git\ncd wikipedia-agent\n```\n\n2. Install dependencies:\n```bash\npip install -r requirements.txt\n```\n\n3. Configure by setting up environment variables in `.env`:\n```bash\nOPENAI_API_KEY=\"your_key_here\" # if you want to use OpenAI\nANTHROPIC_API_KEY=\"your_key_here\" # if you want to use Anthropic\nGOOGLE_API_KEY=\"your_key_here\" # if you want to use Google\nLLM=\"claude-3-5-sonnet-latest\"  # or other supported models\nUSE_RAG=\"false\"  # set to true to enable RAG, is false by default\nSYSTEM_PROMPT=\"your_prompt_here\" # if you want to change the default prompt\n```\n\n## Usage\n\n### Web Interface\n```bash\npython -m streamlit run app.py \n```\nor\n```bash\nstreamlit run app.py\n```\n\nThen visit `http://localhost:8501` in your browser.\n\n### Terminal Interface\n```bash\npython app_terminal.py\n```\n\n## RAG Setup\n\nTo use RAG functionality instead of Wikipedia search:\n1. Place wikipedia pages text files in the `rag_data/` directory (by default includes only artificial_intelligence.txt, physics.txt)\n2. Set `USE_RAG=true` in your `.env` file\n   \nGraphRAG will work soon too.\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a pull request.\n\n## TODO\n\n- [ ] Finish GraphRAG support\n- [ ] Implement automatic Wikipedia scraping for RAG data collection\n- [ ] Integrate text-to-speech capabilities\n- [ ] Add support for more LLM providers (e.g., Mistral, Cohere)\n- [ ] Create a Docker container for easy deployment\n- [ ] Implement rate limiting and API usage tracking\n- [ ] Add support for custom knowledge bases beyond Wikipedia\n- [ ] Create a web API endpoint for programmatic access\n- [ ] Implement caching to reduce API calls\n- [ ] Add support for image and diagram generation\n- [ ] Add benchmarking tools for different LLM models\n- [ ] Add support for concurrent user sessions\n- [ ] Create a configuration UI for easy setup\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fburnycoder%2Fwikipedia-ai-agent","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fburnycoder%2Fwikipedia-ai-agent","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fburnycoder%2Fwikipedia-ai-agent/lists"}