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Mem0

Intelligent memory layer for AI agents and assistants

Mem0 is an open-source intelligent memory layer for AI agents with 51K+ GitHub stars providing persistent, adaptive memory across sessions. It manages working, short-term, and long-term memory types, enabling personalized AI experiences that improve over time. Features automatic memory extraction from conversations, semantic search over stored memories, multi-format support, and integration with 100+ frameworks. Simple API for adding memory to any LLM-powered application or agent.

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About Mem0

Mem0 gives AI applications persistent, contextual memory across interactions. With 51K+ GitHub stars, it has become the standard for adding memory to agents, assistants, and chatbots.

Manages working memory, short-term memory, and long-term memory, enabling natural context maintenance. Memory is automatically extracted from conversations and stored with semantic embeddings for efficient retrieval.

Integrates with 100+ frameworks and tools. Both a managed cloud service and self-hosted open-source deployment are available.

Developers add memory to any application with a simple API call, supporting all major LLM providers and vector stores.

Pricing & Platform Specs

Pricing Summary

Open-source core (Apache-2.0) with $0 self-hosting. Mem0 Cloud Free includes 10k memories and 1k retrieval calls/month. Starter is $19/mo for 50k memories and 5k retrievals. Pro ($99-$249/mo) unlocks Graph Memory (Mem0ᵍ), memory consolidation, and 500k memories with 50k retrievals. Enterprise provides custom pricing for VPC/on-premise deployment, SOC 2/HIPAA compliance, SAML SSO, and 24/7 SLA.

full pricing breakdown →

Supported Platforms

Python, API, Self-hosted, Cloud

Explore categories, tags & use cases

Alternatives

All Mem0 alternatives →
Supermemory logo

Supermemory

Memory engine and context API for AI assistants

Supermemory is a memory and context platform for AI assistants and agents. It ranks #1 on LongMemEval, LoCoMo, and ConvoMem, supports MCP for Claude/Cursor-style clients, provides plugins for developer tools, and combines memory extraction, user profiles, hybrid search, connectors, and RAG in one API.

freemiumOpen Source
Graphiti logo

Graphiti

Build real-time temporal knowledge graphs for AI agents

Graphiti is an open-source Python framework by Zep for building temporally-aware knowledge graphs for AI agents. It continuously integrates conversations, business data, and external information into queryable graphs with bi-temporal tracking. The hybrid retrieval combines semantic search, BM25 keywords, and graph traversal for sub-300ms queries without LLM calls at retrieval time.

freemiumOpen Source
Memori logo

Memori

SQL-native memory infrastructure for AI agents and applications

Memori is an AI memory engine that provides persistent, queryable memory for agents and applications using SQL-native storage. It stores structured memories with semantic search, temporal awareness, and relationship tracking, enabling AI systems to remember user preferences, past interactions, and contextual facts across sessions. With 12,900 GitHub stars, it offers a database-native approach to the agent memory problem.

Open Source
agentmemory logo

agentmemory

Persistent memory layer for AI coding agents — keeps Claude Code, Codex, Cursor, and any MCP agent in context across sessions

agentmemory is an open-source MCP server that gives AI coding agents persistent, cross-session memory. Built on hybrid vector-graph search, it achieves 95.2% recall on the LongMemEval-S benchmark while using up to 92% fewer context tokens than naive context injection. Works out of the box with Claude Code, Codex, Cursor, Windsurf, Cline, OpenCode, Kilo Code, Hermes, and any MCP client through 51 MCP tools plus 12 hooks and 4 skills.

Open Source

Side-by-Side Comparisons

Mem0 logo
Mem0
vs
Letta Code logo
Letta Code

Mem0 vs Letta: Memory Layer or Stateful Agent Runtime?

Mem0 and Letta both solve long-term context for AI agents, but they solve it at different architectural layers. Mem0 is a memory service that can be added to an existing agent or application through SDK, REST, or MCP interfaces. Letta is a stateful agent platform in which editable memory blocks, archival memory, tools, model configuration, and agent identity are coordinated by the runtime itself. For most engineering teams comparing the two as infrastructure, Mem0 serves as the more practical daily standard. It adds production memory without forcing a runtime replacement, offers managed and Apache-2.0 self-hosted paths, and integrates across common agent frameworks. Letta is the stronger specialist choice when persistent, agent-editable state is the product requirement and the team wants the runtime—not an external memory layer—to own how the agent remembers, acts, and evolves.

Mem0Letta Code
Mem0 logo
Mem0
vs
Zep logo
Zep

Mem0 vs Zep — AI Agent Memory: Vector-First vs Temporal Knowledge Graph in 2026

Mem0 and Zep are the two most-installed memory layers for AI agents in 2026, but they make opposite architectural bets. Mem0 is a fully open-source vector-first memory framework with optional graph memory, ideal for conversational agents and broad ecosystem coverage. Zep is a commercial platform built on Graphiti, a temporal knowledge graph where every fact has a validity window — the right choice when your agent must reason about state that changes over time. This comparison covers benchmarks, temporal reasoning, self-hosting, pricing, and ecosystem fit.

Mem0Zep
Mem0 logo
Mem0
vs
LangChain logo
LangChain

Mem0 vs LangChain — AI Memory Layer vs LLM Application Framework

Mem0 provides a dedicated memory management layer that gives AI applications persistent user context across sessions. LangChain offers a comprehensive framework for building LLM-powered applications with chains, agents, and retrieval pipelines. Mem0 wins for adding memory to existing apps while LangChain wins as a full application development framework.

Mem0LangChain
View 1 more comparisons

Community experience

Sources & verification

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Content verified

Verification dates are editorial checks. Routine CMS saves and automatic updatedAt timestamps do not advance them.

FAQ

What is Mem0?

Mem0 is an open-source intelligent memory layer for AI agents with 51K+ GitHub stars providing persistent, adaptive memory across sessions. It manages working, short-term, and long-term memory types, enabling personalized AI experiences that improve over time. Features automatic memory extraction from conversations, semantic search over stored memories, multi-format support, and integration with 100+ frameworks. Simple API for adding memory to any LLM-powered application or agent.

Is Mem0 free?

Mem0 offers a free tier alongside paid plans. Open-source core (Apache-2.0) with $0 self-hosting. Mem0 Cloud Free includes 10k memories and 1k retrieval calls/month. Starter is $19/mo for 50k memories and 5k retrievals. Pro ($99-$249/mo) unlocks Graph Memory (Mem0ᵍ), memory consolidation, and 500k memories with 50k retrievals. Enterprise provides custom pricing for VPC/on-premise deployment, SOC 2/HIPAA compliance, SAML SSO, and 24/7 SLA.

Is Mem0 open source?

Yes — Mem0 is open source.

Is Mem0 still maintained?

Yes — Mem0 is active. Its listing was last verified on September 6, 2026.

What are the best Mem0 alternatives?

The first editor-selected Mem0 alternatives are Supermemory, Graphiti, Memori, and more.

How does Mem0 score in our review?

The published editorial review lists Mem0 at 88/100 overall across speed, privacy, and developer experience. Check the review's evidence status and test metadata for its verification level.