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Written by Max Zeshut
Founder at Agentmelt · Last updated Sep 9, 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.
See it as a workflow
Support Ticket Deflection WorkflowTrigger, steps, n8n nodes, guardrails and an importable template — plus what it costs to have it built.
Or skip the build
Workflows from $197/month, custom agents from $2,000.