Mem0

Provides a long-term memory layer for AI agents that stores, retrieves, and manages structured memories using vector embeddings and an optional knowledge graph, enabling persistent personalization across sessions.

Evaluated Mar 06, 2026 (0d ago) vcurrent
Homepage ↗ Repo ↗ AI & Machine Learning agent-memory long-term-memory embeddings graph-memory rag personalization mcp
⚙ Agent Friendliness
79
/ 100
Can an agent use this?
🔒 Security
82
/ 100
Is it safe for agents?
⚡ Reliability
76
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
78
Documentation
83
Error Messages
79
Auth Simplicity
88
Rate Limits
76

🔒 Security

TLS Enforcement
100
Auth Strength
80
Scope Granularity
68
Dep. Hygiene
80
Secret Handling
82

API key rotation supported; no per-resource scope granularity — single key has full access to all memories for that account.

⚡ Reliability

Uptime/SLA
80
Version Stability
76
Breaking Changes
72
Error Recovery
78
AF Security Reliability

Best When

You need drop-in persistent memory for agents with minimal infra setup and want automatic deduplication and relevance ranking out of the box.

Avoid When

You need fine-grained control over embedding models, storage backends, or have strict data residency requirements that the managed cloud cannot satisfy.

Use Cases

  • Give a customer support agent persistent memory of past tickets, preferences, and resolved issues per user
  • Build a coding assistant that remembers a developer's preferred languages, frameworks, and style conventions
  • Store and retrieve user preferences across sessions in a multi-turn chatbot to avoid repetitive onboarding
  • Maintain agent working memory of facts learned during a long-running research task
  • Implement episodic memory for roleplay or companion agents that recall prior conversation events

Not For

  • Real-time in-context memory within a single prompt window — use context stuffing instead
  • Structured relational data with complex joins and transactional guarantees
  • Bulk document indexing at scale — use a dedicated vector database like Pinecone or Weaviate

Interface

REST API
Yes
GraphQL
No
gRPC
No
MCP Server
Yes
SDK
Yes
Webhooks
No

Authentication

Methods: api_key
OAuth: No Scopes: No

Single API key passed as Authorization Bearer header; separate keys for self-hosted OSS vs. managed cloud platform.

Pricing

Model: freemium
Free tier: Yes
Requires CC: No

Core OSS library is Apache-2.0. Managed cloud has a free tier. Graph memory (Neo4j-backed) available on paid tiers.

Agent Metadata

Pagination
cursor
Idempotent
Partial
Retry Guidance
Documented

Known Gotchas

  • Memory search returns ranked results but no hard relevance cutoff — agents must filter low-score results themselves or retrieve noisy context
  • Graph memory requires Neo4j and is only available on managed cloud or complex self-hosted setup
  • user_id and agent_id scoping must be consistent across calls or memories bleed between agents/users
  • OSS version and managed cloud API have subtle behavioral differences — test against your actual deployment target
  • MCP server requires a running Mem0 Cloud account; OSS self-hosted does not expose MCP natively

Alternatives

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Scores are editorial opinions as of 2026-03-06.

5752
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26151
Need Evaluation
173
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