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Mem0
Drop-in memory layer for AI agents, metered on add and retrieval requests
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https://mem0.ai
About Mem0
Mem0 is drop-in memory infrastructure for AI agents and applications: a hosted layer that stores what a user said and preferred, keeps it across sessions and across separate agents, and returns it as context on the next call. It is one of the most widely adopted open-source projects in this category, with over 62,000 GitHub stars, which matters practically because the self-hosted path is a real option and the API surface has been exercised by a large number of implementations rather than a handful. The billing model reflects how memory systems are actually used and is a useful contrast with per-token pricing: plans meter add requests and retrieval requests separately, with retrieval allowances roughly a tenth of add allowances, because agents write memories far more often than they read them back. End users are unlimited on every tier including the free one, so a consumer app with a large user base and light per-user memory is not penalised for its user count. Higher tiers add graph memory with entity linking, which moves the product from flat fact storage toward relationship-aware retrieval, and Dream, the company's memory consolidation feature that keeps stored memory accurate as it grows rather than accumulating contradictory entries. Multi-project support, advanced analytics and private Slack support arrive at Pro; on-prem deployment, audit logs, SSO and custom integrations at Enterprise.
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Alternatives to Mem0
View all Mem0 alternatives →widemem
An Apache-2.0, local-first memory layer for LLM agents with importance scoring and auditable recall, running on SQLite and FAISS with no services to stand up.
Agent connectivity: not yet verified