Last updated: 2026-08-13
Mem0 is a memory layer for AI agents built by YC-backed Mem0, whose open-source repository has passed 63.1k GitHub stars. It combines vector search, a knowledge graph, and key-value caching behind one API, extracting and updating discrete facts so agents keep context across sessions instead of replaying full chat history.
About Mem0
Mem0 is a memory layer for AI agents and apps, built by Y Combinator-backed Mem0 and founded by Taranjeet Singh and Deshraj Yadav in 2023. The company has raised $24 million across seed and Series A rounds from Y Combinator, Peak XV Partners, Basis Set Ventures, and Kindred Ventures, betting that most agent failures come from forgetting rather than reasoning. Mem0 sits between an application and its LLM calls, storing facts extracted from conversations and retrieving only what is relevant to the current turn instead of replaying full chat history. The system combines vector search, a knowledge graph, and key-value caching behind one API, extracting discrete facts from each interaction and merging or updating them instead of appending duplicate entries. Mem0's V3 memory algorithm, released in April 2026, improved retrieval accuracy on the LoCoMo benchmark, with the largest gains coming from temporal-query and multi-hop reasoning tasks compared to the prior algorithm version. It targets backend and AI engineers building customer support bots, coding assistants, and autonomous agents that need to remember user preferences and prior decisions across sessions instead of starting from a blank context window every time. OpenMemory, Mem0's local-first companion, runs as a Model Context Protocol server so tools like Claude Desktop, Cursor, Windsurf, and VS Code can share the same persistent memory store. Pricing starts with a metered free Hobby tier and scales through a Starter plan and a Pro plan that unlocks graph memory and advanced analytics, plus custom Enterprise pricing with SSO and on-premises deployment; exact limits and costs are in the pricing FAQ below. The core SDK is Apache 2.0 licensed and installable via pip or npm, and it can run fully self-hosted or against Mem0's managed Platform API. Mem0 has an exclusive integration with AWS's Agent SDK and native support for LangGraph and CrewAI, and its open-source repository has passed 14 million downloads. The company holds a SOC 2 Type I report, with SOC 2 Type II and GDPR compliance both listed as in progress on its public trust center, and HIPAA status is currently self-attested rather than independently audited.
Pricing
Free Hobby tier: 10,000 memory adds and 1,000 retrieval calls a month, unlimited end users, one project. Starter is $19/month for 50,000 adds and 5,000 retrievals. Pro is $249/month for 500,000 adds, 50,000 retrievals, unlimited projects, graph memory, and advanced analytics. Enterprise is custom pricing with unlimited adds/retrievals, SSO, on-premises deployment, audit logs, and SLA support.
Key Features
- Memory V3 retrieval algorithm: Scores 92.5 on the LoCoMo benchmark following the April 2026 release, with the biggest jump coming from temporal-query and multi-hop reasoning accuracy.
- Unified vector, graph, and key-value memory: Stores memories across a vector index, a knowledge graph, and a key-value cache through one endpoint, so an agent's memory store does not require stitching together three separate databases.
- Adaptive fact deduplication: Extracts discrete facts from each conversation turn and merges or updates existing memories instead of appending duplicate entries, keeping retrieval prompts shorter over time.
- OpenMemory MCP server: Ships a local-first Model Context Protocol server that gives Claude Desktop, Cursor, Windsurf, and VS Code a shared, persistent memory store with no custom integration code.
- Open source core: Released under a permissive open-source license with a large, active GitHub community, so teams can self-host the OSS mode or connect to Mem0's managed Platform API.
- Framework-native integrations: Ships native integrations for LangGraph and CrewAI plus an exclusive AWS Agent SDK integration, so existing multi-agent stacks gain persistent memory without a custom adapter layer.
Pros
- Apache 2.0 licensed core carries 63.1k GitHub stars, letting teams audit or self-host the memory layer instead of trusting a black-box hosted API.
- OpenMemory ships as an MCP server exposing nine memory tools that plug directly into Claude Desktop, Cursor, Windsurf, and VS Code, so agents share one memory store without custom glue code.
- Backed by $24 million in YC, Peak XV Partners, and Basis Set Ventures funding, giving the team runway to keep shipping memory algorithm upgrades like the April 2026 V3 release.
- Holds a SOC 2 Type I report today, with support for self-hosted OSS mode when a customer's compliance posture rules out sending data to a third-party API.
Cons
- No consumer-facing app; every integration goes through the Python or TypeScript SDK or a REST call, so teams need engineering time before end users see any benefit.
- On the independent LongMemEval benchmark, Mem0 scores 49.0% versus 63.8% for Zep, so teams whose agents must resolve conflicting or time-sensitive facts may find a temporal-knowledge-graph competitor more accurate out of the box.
- The free Hobby tier is capped at a low monthly retrieval-call ceiling, and the jump to the Pro plan for graph memory and advanced analytics is a large step up in price for solo developers.
- GDPR compliance and SOC 2 Type II are both still listed as in progress on Mem0's public trust center, and HIPAA status is self-attested rather than independently audited, a gap for healthcare teams with strict vendor-review requirements.
Frequently Asked Questions
How much does Mem0 cost in 2026?
Mem0's free Hobby tier includes 10,000 memory adds and 1,000 retrieval calls a month across one project. The Starter plan is $19/month for 50,000 adds and 5,000 retrievals, and the Pro plan is $249/month for 500,000 adds, 50,000 retrievals, unlimited projects, graph memory, and advanced analytics. Enterprise pricing is custom and adds SSO, on-premises deployment, audit logs, and SLA support.
Is Mem0 free to use?
Yes, the Hobby plan is permanently free and covers unlimited end users, though it caps monthly memory adds and retrieval calls and limits accounts to a single project. It does not include graph memory, advanced analytics, or private support, which are Pro-tier features. There is also a startup program offering three months of free Pro access for qualifying early-stage teams.
What are the best alternatives to Mem0?
Zep is the closest alternative for teams that need a temporal knowledge graph and outperformed Mem0 on the LongMemEval benchmark in independent testing. Letta takes a different approach entirely, giving the agent direct control over its own memory tiers rather than exposing a simple API. LangChain's memory modules are a lighter-weight option for teams already standardized on that framework who do not need a dedicated memory service.
How does Mem0 compare to Zep in 2026?
Zep is built around a temporal knowledge graph designed to track what was true and when, which shows up in its higher LongMemEval accuracy in independent benchmarks. Mem0 favors a simpler fact-extraction model that is faster to integrate and backed by a much larger open-source community and download base. Choose Zep when conflicting or time-sensitive facts matter most; choose Mem0 when speed of integration and ecosystem support matter more.
How do you get started with Mem0?
Install the Python or TypeScript SDK with pip install mem0ai or npm install mem0ai, then create a free account to get an API key for the hosted Platform, or run OSS mode fully self-hosted with your own vector store. Call the add() method to store a memory and search() to retrieve relevant context for a prompt. The OpenMemory MCP server is the fastest path if you just want persistent memory inside Claude Desktop, Cursor, or another MCP-compatible client.
Top Alternatives
- LangChain: Pick LangChain if you need a full orchestration framework with memory as one small piece, not a dedicated memory API.
- LlamaIndex: Pick LlamaIndex if your priority is document indexing and retrieval pipelines rather than agent conversation memory.
- Algolia: Pick Algolia if you need customer-facing search infrastructure, not a memory layer for AI agent reasoning.