Agentic RAG for Dummies is a learning-focused implementation of a Retrieval-Augmented Generation agent built with LangGraph.
The project addresses the challenge of understanding how agentic RAG systems work by providing a modular, hands-on codebase designed for rapid learning. It combines retrieval-augmented generation—where an agent retrieves relevant documents to ground its responses—with agentic decision-making, where the system can reason about when and how to search for information. The implementation uses LangGraph to orchestrate the agent's workflow, allowing it to decide dynamically whether to retrieve additional context or respond based on existing knowledge.
This tool suits developers and researchers who want to understand agentic RAG architectures through working code rather than theory alone. The modular design means you can study individual components and adapt them for your own projects. It integrates with several popular tools in the RAG ecosystem: LangChain for language model interactions, Ollama for running local models, Qdrant for vector storage, and BM25 for keyword-based retrieval. The project includes a Gradio interface for interactive experimentation. If you are building a RAG chatbot or pipeline and want to add agentic capabilities—where the system reasons about retrieval decisions—this provides a concrete reference implementation rather than starting from scratch.
The project is structured as Jupyter Notebooks, making it accessible for exploration and modification in an interactive environment. Development activity shows consistent engagement with the codebase through iterative improvements and refinements to the modular architecture.
