Mem0: Memory Layer for AI Agents

Mem0 is a managed memory layer for AI agents and applications. It stores facts, preferences, and context from conversations so agents can recall them in later sessions, creating continuity and personalization without building your own memory system.

• Your agent talks to the same users repeatedly and should remember them.
• You want personalized responses without stuffing the entire history into every prompt.
• You are building assistants, coaches, or companion-style agents.
• You need memory that works across multiple models or agent instances.

• Cross-session memory. Facts persist beyond a single conversation.
• Fact extraction. Automatically pulls important details from messages.
• User-scoped retrieval. Recall the right memories for the right user at the right time.
• Drop-in SDK. Add a few lines of code to existing agent frameworks.
• Self-hostable option. Run it yourself if data residency matters.

• Memory quality depends on extraction. Noisy or irrelevant facts can pollute the memory bank.
• Privacy design required. Storing user facts brings consent and deletion obligations.
• Pricing by operation. Costs grow with the volume of memory reads and writes.
• Not a reasoning layer. It stores context; how your agent uses that context is still your logic.

• Free tier for prototyping.
• Paid usage-based pricing from roughly $0.002 per memory operation.
• Enterprise plans for higher volume, SSO, and support.

Agents where relationship and continuity matter: personal assistants, customer support copilots, learning companions, and productivity agents. Less critical for one-shot or stateless workflows.

• OpenAI / Anthropic agents via the Mem0 SDK.
• LangChain / LlamaIndex memory wrappers.
• OpenClaw / Hermes agents for persistent user context.