It’s 2025, and Large Language Models (LLMs) still can’t access your private data. Ask them something personal, and they’ll either guess or give you the wrong answer. That’s the limitation—they’re trained on public information and don’t have access to...

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Memory agents can securely link private data to large language models (LLMs) in real time, enabling context-aware interactions with personal documents and enhancing AI functionalities. This tutorial guides on creating AI agents using Langbase memory agents to provide context-sensitive responses while ensuring data security. Use cases include customer support, document search, education, healthcare, and legal compliance.

How to Use Langbase Memory Agents to Make Any LLM a Conversational AI for Your Docs

Step 1: Create a Directory and Initialize npm

Step 5: Add Documents to the Memory Agent

Step 8: Integrate the Memory Agent in Node.js

<p>Is LangBase a vector database ? If no, how is it different from vector databases like PineCone ?</p>


<p>Great topic – very excited about the trajectory of RAG.</p>
