Persistent Memory for AI Agents With LangChains Deep Agents

▤ Summary

This article explains how LangChain deepagents can give AI agents persistent per-user memory across sessions without using a vector database. It shows how StoreBackend, CompositeBackend, and MemoryMiddleware work together to separate ephemeral conversation state from durable user memory. The piece also walks through setup examples for multi-user isolation and different storage backends such as in-memory, SQLite, and PostgreSQL. It highlights the main benefits, including continuity and inspectable memory, while noting limits around context size and large-scale memory use. The article positions persistent memory as a practical architecture pattern for building better AI assistants.

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LangChain

$50M to $100M