Agentic RAG with LangGraph is an advanced, modular framework designed for implementing state-of-the-art Retrieval-Augmented Generation (RAG) systems with flexible agentic workflows. This project seamlessly combines the power of language models with robust retrieval, data processing, and orchestration capabilities—making it the perfect starting point for anyone looking to build production-ready or experimental RAG applications.
- Agentic Workflows: Easily define, compose, and extend agent-driven pipelines for reasoning, retrieval, and generation tasks.
- Plug-and-Play Retrieval: Integrate with multiple data sources—structured or unstructured—and swap retrieval strategies hassle-free.
- Customizable Orchestration: Adapt agents’ behavior, memory, and interaction protocols to suit your use case.
- Scalable & Modular: Built with scalability and maintainability in mind, supporting both quick experiments and robust deployments.
- Data Ingestion: Flexible modules for collecting, cleaning, and indexing data.
- Agent Core: The heart of agentic logic—the reasoning and action framework.
- Retrieval Engine: Pluggable interfaces to connect a wide range of data stores and vector databases.
- Orchestration: Tools to assemble, coordinate, and monitor multi-agent workflows.
- Context-aware conversational AI
- Document question-answering and summarization
- Research assistants that combine reasoning with dynamic knowledge lookup
- Vertical applications (legal, medical, enterprise knowledge bases, etc.)
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Clone the repo
git clone https://github.com/your-username/agentic-rag.git cd agentic-rag -
Install dependencies
uv install
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Run the project
python main.py
Contributions, ideas, and bug reports are very welcome! Please open an issue or submit a pull request.
If you need to build adaptable, multi-agent RAG systems with clarity, speed, and scalability, agentic-rag is for you.
Turn your data into actionable knowledge—powered by intelligent agents!