When should I use a memory framework vs. rolling my own?

Roll your own if your needs are simple — last N conversation summaries per user, no fact extraction, no reconciliation. A Postgres table with (customer_id, timestamp, summary, structured_facts JSONB) covers most support agents. Move to Mem0 / Letta / Zep when your reconciliation logic (which fact wins when two contradict) or dedup logic starts eating engineering hours, or when you need semantic search over episodic memory at scale.

Memory Framework

Written by Max Zeshut

Founder at Agentmelt · Last updated Jul 22, 2026

A dedicated library or service that handles AI agent memory — extraction, storage, reconciliation, expiration, and retrieval — so you don't have to write it yourself. The 2025-26 landscape converged on a handful: [Mem0](https://mem0.ai/) (open source + hosted, personalization-focused), [Letta](https://www.letta.com/) (formerly MemGPT, tiered in-context/archival memory), [Zep](https://www.getzep.com/) (temporal knowledge graph), [Cognee](https://cognee.ai/), and vector-store-native offerings from Redis, Pinecone, and Milvus. LangGraph's checkpoint layer covers working-memory persistence but isn't a full memory framework. See our [memory pillar](/blog/ai-agent-memory-how-it-works/) for the decision matrix.

Frequently asked questions

When should I use a memory framework vs. rolling my own?: Roll your own if your needs are simple — last N conversation summaries per user, no fact extraction, no reconciliation. A Postgres table with (customer_id, timestamp, summary, structured_facts JSONB) covers most support agents. Move to Mem0 / Letta / Zep when your reconciliation logic (which fact wins when two contradict) or dedup logic starts eating engineering hours, or when you need semantic search over episodic memory at scale.

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