Give your agents a real memory

Mem0 is infrastructure, not an app: a memory layer that lets LLM agents remember users, facts, and preferences across sessions. It extracts salient info from conversations, stores it with vector + graph hybrid search, and resolves conflicts as new data arrives — so your assistant gets smarter without bloating the prompt.

Who it's for: AI engineers and product teams building chatbots, copilots, and agents that need durable, per-user memory without hand-rolling a vector DB.

Key features

Long-term Long-term memory

Persist user facts, preferences, and context across sessions instead of resetting every call.

Isolation User & agent isolation

Namespaced memories keep one user's data from leaking into another's — critical for multi-tenant apps.

Hybrid Hybrid search

Combines vector, graph, and recent-memory retrieval for fast, relevant recall.

OSS Open source + managed

Self-host the open-source core or use the managed Platform API with one line of code.

The honest take

✓ What works

Drastically cuts prompt size and token cost
Conflict resolution handles changing user facts
Solid open-source option for self-hosters
Simple API — production memory in an afternoon

✗ What doesn't

Another dependency in your stack to operate
Managed pricing scales with memory volume
Best practices for memory hygiene still emerging
Not a replacement for your own DB for structured data

Verdict

If you are building anything agentic in 2026, Mem0 is the fastest path to durable memory. The open-source core is free to self-host; the Platform tier ($19/mo) is worth it once you need scale and conflict resolution. Skip it only if your agent is stateless by design.

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