3 Ways To Build LLMs With Long-Term Memory

Long-term LLM memory enables conversational agents to retain useful information across sessions and threads without repeatedly sending full chat histories, reducing token costs, latency, and irrelevant context. Using a therapy-assistant example, the guide distinguishes short-term session memory from persistent memory and describes semantic facts, episodic events, and procedural habits as useful memory categories. It demonstrates LangGraph’s thread-based checkpoints and cross-thread stores, including an in-memory store that extracts facts, indexes them with embeddings, and retrieves relevant memories through semantic search. For production use, it presents Chroma as a persistent vector database combined with summarized short-term conversation history, while also outlining JSON files for simple fixed user data and knowledge graphs for relationship-heavy domains. The guide concludes by introducing Supermemory as a managed persistence option with metadata, tagging, natural-language retrieval, automatic chunking, multimodal search, and scalable context support, while recommending regular evaluation, pruning, and schema maintenance for reliable memory systems.

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