How To Build An Agentic RAG Chatbot With Memory Using LangGraph And Mem0

A tutorial explains how to build a Streamlit documentation chatbot that combines retrieval-augmented generation with persistent, user-specific memory so users do not need to repeat their setup, preferences, or prior troubleshooting steps across sessions. Using LangChain’s agent interface over LangGraph, GPT-5-mini selects among three tools: Chroma-based documentation search, Mem0 memory search, and Mem0 memory storage, allowing it to retrieve technical material, personalize answers, or save durable information such as goals and experience level. The example application loads local Markdown documentation, chunks and embeds it with OpenAI embeddings, binds memory tools to a specific user ID to isolate data, streams responses and tool activity in Streamlit, and limits tool calls to control loops and cost. It also outlines production considerations including persistent vector storage, authenticated user identities, secrets management, observability, rate limits, and modular code structure, while noting that agentic memory improves personalization but introduces additional latency, cost, relevance-management challenges, and user-data privacy responsibilities.

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