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

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.

Written by Max Zeshut

Founder at Agentmelt · Last updated Sep 9, 2026

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.

The pattern behind it

Data extraction

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