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How to Use Langbase Memory Agents to Make Any LLM a Conversational AI for Your Docs
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...
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 your private context.
Memory agents solve this by securely linking your private data to any LLM in real time. In this tutorial, I’ll walk you through turning an LLM into a conversational AI that chats with your personal documents using Langbase memory agents.
Here’s what we’ll cover:
What are Memory Agents?
Memory is what makes interactions meaningful. It’s how systems can remember what came before, a key aspect for building truly intelligent AI agents.
Here’s the thing: LLMs might seem human-like, but they don’t have memory built in. They’re stateless by design. To make them useful for real-world tasks, you need to add memory. That’s where memory agents step in.
Langbase memory agents (long-term memory solution) are designed to acquire, process, retain, and retrieve information seamlessly. They dynamically attach private data to any LLM, enabling context-aware responses in real time and reducing hallucinations.
These agents combine vector storage, Retrieval-Augmented Generation (RAG), and internet access to create a powerful managed context search API. Developers can use them to build smarter, more capable AI applications.
In a RAG setup, memory – when connected directly to a Langbase Pipe Agent – becomes a memory agent. This pairing gives the LLM the ability to fetch relevant data and deliver precise, contextually accurate answers—addressing the limitations of LLMs when it comes to handling private data.
