Mem0 is a focused memory layer for AI agents with open-source and hosted options.
Persistent memory infrastructure that lets AI applications add, update, and retrieve useful context across sessions.
Persistent memory infrastructure that lets AI applications add, update, and retrieve useful context across sessions.
Mem0 is memory infrastructure for AI agents and applications that need to retain useful context across sessions. Instead of replaying an entire conversation into every model request, an application sends interactions to Mem0, which extracts and updates compact memories associated with a user or other entity. At retrieval time, the app searches for memories relevant to the current request and adds only selected context. The vendor's workflow is described as Add, Learn, Retrieve. Its Memory Compression Engine condenses conversation history to reduce repeated tokens and latency, while Dream performs memory consolidation as the memory set grows.
The homepage shows Python and Node.js integration, including a Python MemoryClient that can add message arrays for a user and search memories with filters. This is developer infrastructure, not a finished personal assistant. The application team remains responsible for deciding what may be remembered, how identities map to memory namespaces, when data expires, and how a user can inspect or delete stored information. Graph memory with entity linking is available on Pro and Enterprise, which is useful when relationships among people, products, places, or organizations matter more than isolated preference snippets. Advanced analytics, multiproject support, and private Slack support also begin on Pro.
Cloud pricing is based on add and retrieval requests. Hobby is free with unlimited end users, 10,000 add requests per month, 1,000 retrieval requests, one project, and community support. Starter costs $19 per month and raises limits to 50,000 adds and 5,000 retrievals while retaining one project and community support. Pro costs $249 per month with 500,000 adds, 50,000 retrievals, unlimited projects, multiproject support, private Slack support, analytics, graph memory, and Dream consolidation. Enterprise is custom-priced with unlimited listed requests and projects plus SLA support, on-premises deployment, audit logs, custom integrations, and SSO. Model API and infrastructure costs should be calculated separately.
A useful pilot compares Mem0 with a simple database plus embedding search. Create a consented test set containing stable preferences, temporary facts, corrections, contradictions, shared devices, and deletion requests. Measure retrieval precision, missed relevant memories, stale or incorrect insertions, added latency, model tokens saved, and cost per active user. Test cross-user isolation aggressively; a memory leak between users is a security incident. Define retention classes so short-lived logistics do not become permanent profile data.
Compare Letta for stateful agent architecture, Zep for agent memory, LangMem for memory workflows, and Contextual Memory Cloud. Mem0 is strongest when developers want a focused, drop-in memory service with clear request limits. Its main tradeoffs are an additional stateful dependency, privacy obligations, and a steep price jump from $19 Starter to $249 Pro when graph memory, consolidation, multiple projects, or production support become necessary. The pricing FAQ also says usage-based pricing is available for teams whose traffic does not fit a fixed tier. Ask for the per-request schedule and model a normal month plus a peak month before comparing that option with Pro.
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Mem0 is a strong option for developers adding persistent memory to AI agents, especially when they want SDKs, hosted infrastructure, an open-source path, and MCP-compatible workflows verified against current documentation.
Free
$19/month
$249/month
Custom
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Horizontal scaling support for large-scale agent deployments with shared memory.
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