Mem0 is a universal memory layer for AI agents and assistants, giving applications persistent, personalized memory that improves every interaction.
Mem0 sits between your application and its model, extracting and storing durable facts about users and conversations, then retrieving the right memories at the right moment. Instead of stuffing raw chat logs into context, it keeps a distilled memory store that captures preferences, decisions and project state.
The platform offers a hosted API and an open-source version, with support for user-level, session-level and agent-level memories. Integrations cover popular agent frameworks and vector stores, and the managed service adds dashboards, analytics and evaluation tools so teams can measure how memory changes assistant quality. Retrieval is tuned for production latency, so relevant memories can be added to prompts in milliseconds.
Mem0 is open source and free to self-host. The hosted platform includes a generous free tier for development, with paid tiers based on stored memories and retrieval calls, and custom enterprise pricing for high-volume applications and compliance requirements. Volume discounts apply as memory operations grow into the millions per month.
Mem0 is built for developers shipping chatbots, copilots, agents and companions that need to remember users across sessions. Product teams in education, healthcare, customer support and consumer apps use it to add personalization without building a memory pipeline from scratch.
Yes, the open-source version is free forever, and the hosted service has a free tier suitable for prototypes and small apps. Paid plans are usage-based once you exceed the free memory and retrieval quotas.
Yes, Mem0 is API-first: memories are added, searched, updated and deleted over simple REST endpoints, with SDKs for Python and JavaScript. The same API powers the hosted platform and self-hosted deployments.
Mem0 is more focused than general agent frameworks: it solves only the memory problem and plugs into whatever stack you already use. Versus raw vector databases it adds extraction, deduplication and scoping logic that would otherwise be custom work. If you only need long documents in context, a plain retrieval pipeline is enough; Mem0 earns its place when the agent must remember the user.
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