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Powerful AI Agent Framework\n\n🚀 **Build intelligent, collaborative AI agents with cutting-edge technology**\n\nThis repository provides a complete framework for creating sophisticated AI agents that can work together on complex tasks, powered by modern AI and graph technologies.\n\n## 🛠️ Tech Stack\n\n| Component | Purpose | Why It's Powerful |\n|-----------|---------|-------------------|\n| **[uv](https://docs.astral.sh/uv/)** | Package Manager | Lightning-fast Python package management - 10-100x faster than pip |\n| **[CrewAI](https://crewai.com)** | Agent Framework | Multi-agent orchestration with role-based collaboration |\n| **[Graphiti](https://github.com/graphiti-ai/graphiti)** | Graph RAG | Neo4j-powered knowledge graphs for intelligent information retrieval |\n| **[LlamaIndex](https://www.llamaindex.ai/)** | Document Processing | Best-in-class document parsing and information extraction |\n\n## 🚀 Quick Start\n\n### Prerequisites\n- Python 3.10 - 3.13\n- [uv](https://docs.astral.sh/uv/) package manager\n\n### Installation\n\n1. **Install uv** (if you haven't already):\n   ```bash\n   # Windows\n   powershell -ExecutionPolicy ByPass -c \"irm https://astral.sh/uv/install.ps1 | iex\"\n   \n   # macOS/Linux\n   curl -LsSf https://astral.sh/uv/install.sh | sh\n   ```\n\n2. **Clone and setup the project**:\n   ```bash\n   git clone <your-repo-url>\n   cd jedAi-stack\n   uv sync\n   ```\n\n3. **Configure your environment**:\n   ```bash\n   # Copy the example environment file\n   cp .env.example .env\n   \n   # Add your API keys\n   echo \"OPENAI_API_KEY=your_openai_key_here\" >> .env\n   echo \"NEO4J_URI=your_neo4j_uri\" >> .env\n   echo \"NEO4J_USERNAME=your_username\" >> .env\n   echo \"NEO4J_PASSWORD=your_password\" >> .env\n   ```\n\n## 📚 uv vs Traditional Python\n\n**Why uv?** It's the modern way to manage Python projects - faster, more reliable, and simpler.\n\n| Traditional Python | uv (Modern) |\n|-------------------|-------------|\n| `python -m venv venv` | `uv sync` (automatic) |\n| `venv\\Scripts\\activate` | `uv run` (no activation needed) |\n| `pip install package` | `uv add package` |\n| `requirements.txt` | `uv.lock` (automatic) |\n| `pip freeze > requirements.txt` | Handled automatically |\n\n## 🎯 Core Capabilities\n\n### 1. Multi-Agent Collaboration (CrewAI)\nCreate specialized agents that work together:\n```python\n# Define agents with specific roles\nresearcher = Agent(\n    role='Research Specialist',\n    goal='Find and analyze relevant information',\n    backstory='Expert in data gathering and analysis'\n)\n\nwriter = Agent(\n    role='Content Creator', \n    goal='Transform research into compelling content',\n    backstory='Skilled writer with technical expertise'\n)\n```\n\n### 2. Intelligent Knowledge Graphs (Graphiti)\nStore and retrieve information using graph relationships:\n```python\nfrom graphiti import Graphiti\n\n# Initialize graph memory\ngraph = Graphiti()\n\n# Add knowledge with relationships\ngraph.add_episode(\n    \"The user asked about AI agents. AI agents are autonomous programs.\"\n)\n\n# Query with semantic understanding\nresults = graph.search(\"What are AI agents?\")\n```\n\n### 3. Advanced Document Processing (LlamaIndex)\nExtract insights from any document type:\n```python\nfrom llama_index import SimpleDirectoryReader, VectorStoreIndex\n\n# Load and process documents\ndocuments = SimpleDirectoryReader(\"./knowledge\").load_data()\nindex = VectorStoreIndex.from_documents(documents)\n\n# Query your documents\nquery_engine = index.as_query_engine()\nresponse = query_engine.query(\"What are the key insights?\")\n```\n\n## 🏗️ Project Structure\n\n```\njedAi-stack/\n├── src/jedai_council/           # Main application code\n│   ├── config/                  # Agent and task configurations\n│   │   ├── agents.yaml         # Define your AI agents\n│   │   └── tasks.yaml          # Define agent tasks\n│   ├── crew.py                 # Main crew orchestration\n│   ├── main.py                 # Entry point\n│   └── tools/                  # Custom tools for agents\n├── knowledge/                   # Documents for processing\n├── tests/                      # Test suite\n├── pyproject.toml              # Project configuration\n├── uv.lock                     # Locked dependencies\n└── README.md                   # This file\n```\n\n## 🚀 Running Your Agents\n\n### Basic Usage\n```bash\n# Run the default crew\nuv run crewai run\n\n# Or run with custom parameters\nuv run python -m jedai_council.main\n```\n\n### Development Workflow\n```bash\n# Add new dependencies\nuv add anthropic        # Add Claude AI\nuv add --dev pytest     # Add development dependency\n\n# Install/sync all dependencies\nuv sync\n\n# Run tests\nuv run pytest\n\n# Run specific scripts\nuv run python scripts/your_script.py\n```\n\n## 🔧 Configuration\n\n### 1. Environment Variables (.env)\n```env\n# Required\nOPENAI_API_KEY=your_openai_api_key\n\n# Optional - for Graph RAG\nNEO4J_URI=bolt://localhost:7687\nNEO4J_USERNAME=neo4j\nNEO4J_PASSWORD=your_password\n\n# Optional - for other LLM providers\nANTHROPIC_API_KEY=your_claude_key\nCOHERE_API_KEY=your_cohere_key\n```\n\n### 2. Agent Configuration (config/agents.yaml)\n```yaml\nresearcher:\n  role: \"Senior Research Analyst\"\n  goal: \"Uncover cutting-edge developments in AI and data science\"\n  backstory: |\n    You're a seasoned researcher with a knack for uncovering the latest\n    developments in AI and data science. You have a keen eye for detail.\n  tools:\n    - search_tool\n    - graph_search_tool\n\nwriter:\n  role: \"Tech Content Strategist\"  \n  goal: \"Craft compelling content on complex technical topics\"\n  backstory: |\n    You're a skilled writer specializing in making complex technical\n    topics accessible and engaging for diverse audiences.\n  tools:\n    - document_processor\n```\n\n### 3. Task Configuration (config/tasks.yaml)\n```yaml\nresearch_task:\n  description: |\n    Conduct comprehensive research on {topic}.\n    Focus on recent developments and practical applications.\n  expected_output: |\n    A detailed research report with:\n    - Key findings and insights\n    - Recent developments\n    - Practical applications\n    - Reliable sources\n  agent: researcher\n\nwriting_task:\n  description: |\n    Transform the research findings into an engaging article.\n    Make complex topics accessible to a general audience.\n  expected_output: |\n    A well-structured article (1500+ words) with:\n    - Compelling introduction\n    - Clear explanations\n    - Practical examples\n    - Strong conclusion\n  agent: writer\n```\n\n## 🎨 Advanced Features\n\n### Custom Tools\nCreate specialized tools for your agents:\n```python\nfrom crewai_tools import tool\n\n@tool\ndef knowledge_graph_search(query: str) -> str:\n    \"\"\"Search the knowledge graph for relevant information.\"\"\"\n    # Your custom graph search logic\n    return results\n\n@tool \ndef document_analyzer(file_path: str) -> str:\n    \"\"\"Analyze documents using LlamaIndex.\"\"\"\n    # Your document processing logic\n    return analysis\n```\n\n### Multi-Modal Processing\n```python\n# Process different file types\nfrom llama_index import (\n    SimpleDirectoryReader,\n    PDFReader, \n    DocxReader,\n    ImageReader\n)\n\n# Support for PDFs, Word docs, images, etc.\nreader = SimpleDirectoryReader(\n    input_dir=\"./knowledge\",\n    file_extractor={\n        \".pdf\": PDFReader(),\n        \".docx\": DocxReader(), \n        \".jpg\": ImageReader(),\n        \".png\": ImageReader(),\n    }\n)\n```\n\n## 🧪 Examples\n\n### Example 1: Research Assistant\n```bash\n# Run research on a specific topic\nuv run crewai run --topic \"Latest developments in Large Language Models\"\n```\n\n### Example 2: Document Analysis\n```python\n# Process and analyze documents in knowledge folder\nuv run python examples/document_analysis.py\n```\n\n### Example 3: Interactive Agent Chat\n```python\n# Start interactive session with your agents\nuv run python examples/interactive_chat.py\n```\n\n## 📈 Performance Tips\n\n1. **Use Graph RAG** for better context retention across conversations\n2. **Chunk documents** appropriately for LlamaIndex processing\n3. **Cache frequently used** embeddings and graph queries\n4. **Monitor token usage** across different LLM providers\n5. **Use async operations** for concurrent agent tasks\n\n## 🛠️ Development\n\n### Adding New Agents\n1. Define agent in `config/agents.yaml`\n2. Create corresponding tasks in `config/tasks.yaml`\n3. Add any custom tools in `src/jedai_council/tools/`\n4. Update crew configuration in `crew.py`\n\n### Testing\n```bash\n# Run all tests\nuv run pytest\n\n# Run specific test categories\nuv run pytest tests/agents/\nuv run pytest tests/tools/\n```\n\n### Contributing\n1. Fork the repository\n2. Create a feature branch: `git checkout -b feature/amazing-feature`\n3. Make your changes\n4. Add tests for new functionality\n5. Run tests: `uv run pytest`\n6. Commit changes: `git commit -m 'Add amazing feature'`\n7. Push to branch: `git push origin feature/amazing-feature`\n8. Open a Pull Request\n\n## 🔍 Troubleshooting\n\n### Common Issues\n\n**Dependencies not installing?**\n```bash\n# Clear cache and retry\nuv cache clean\nuv sync --reinstall\n```\n\n**Environment variables not loading?**\n```bash\n# Make sure .env file exists and has correct format\ncp .env.example .env\n# Edit .env with your actual API keys\n```\n\n**Graph database connection issues?**\n```bash\n# Check Neo4j is running\ndocker run -p 7474:7474 -p 7687:7687 neo4j:latest\n```\n\n**Memory issues with large documents?**\n```python\n# Process documents in chunks\nfrom llama_index import SimpleDirectoryReader\nreader = SimpleDirectoryReader(\"./knowledge\", chunk_size=512)\n```\n\n## 📚 Resources\n\n### Documentation\n- [CrewAI Documentation](https://docs.crewai.com)\n- [LlamaIndex Documentation](https://docs.llamaindex.ai)\n- [Graphiti Documentation](https://github.com/graphiti-ai/graphiti)\n- [uv Documentation](https://docs.astral.sh/uv/)\n\n### Community\n- [CrewAI Discord](https://discord.com/invite/X4JWnZnxPb)\n- [LlamaIndex Discord](https://discord.gg/dGcwcsnxhU)\n- [uv GitHub Discussions](https://github.com/astral-sh/uv/discussions)\n\n### Tutorials\n- [Building Multi-Agent Systems](https://docs.crewai.com/how-to/Creating-a-Crew-and-kick-it-off)\n- [Graph RAG with Neo4j](https://neo4j.com/developer/graph-data-science/)\n- [LlamaIndex QuickStart](https://docs.llamaindex.ai/en/stable/getting_started/starter_example.html)\n\n## 📄 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## 🙏 Acknowledgments\n\n- **CrewAI** team for the excellent multi-agent framework\n- **LlamaIndex** community for document processing capabilities  \n- **Graphiti** developers for graph-based RAG implementation\n- **Astral** team for the lightning-fast uv package manager\n\n---\n\n**Ready to build the future with AI agents?** 🤖✨\n\nStart by running `uv sync` and then `uv run crewai run` to see your agents in action!\n","readmeExcerpt":"JedAI Stack - Powerful AI Agent Framework 🚀 **Build intelligent, collaborative AI agents with cutting-edge technology** This repository provides a complete framework for creating sophisticated AI agents that can work together on complex tasks, powered by modern AI and graph technologies. 🛠️ Tech Stack | Component | Purpose | Why It's Powerful | |-----------|---------|-------------------| | **$1** | Package Manager ","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"curl -LsSf https://astral.sh/uv/install.sh | sh"},{"language":"bash","snippet":"# Windows\n   powershell -ExecutionPolicy ByPass -c \"irm https://astral.sh/uv/install.ps1 | iex\"\n   \n   # macOS/Linux\n   curl -LsSf https://astral.sh/uv/install.sh | sh"},{"language":"bash","snippet":"git clone <your-repo-url>\n   cd jedAi-stack\n   uv sync"},{"language":"bash","snippet":"# Copy the example environment file\n   cp .env.example .env\n   \n   # Add your API keys\n   echo \"OPENAI_API_KEY=your_openai_key_here\" >> .env\n   echo \"NEO4J_URI=your_neo4j_uri\" >> .env\n   echo \"NEO4J_USERNAME=your_username\" >> .env\n   echo \"NEO4J_PASSWORD=your_password\" >> .env"},{"language":"python","snippet":"# Define agents with specific roles\nresearcher = Agent(\n    role='Research Specialist',\n    goal='Find and analyze relevant information',\n    backstory='Expert in data gathering and analysis'\n)\n\nwriter = Agent(\n    role='Content Creator', \n    goal='Transform research into compelling content',\n    backstory='Skilled writer with technical expertise'\n)"},{"language":"python","snippet":"from graphiti import Graphiti\n\n# Initialize graph memory\ngraph = Graphiti()\n\n# Add knowledge with relationships\ngraph.add_episode(\n    \"The user asked about AI agents. 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