Mem0

Tier 2: Persistent (per-user)

The fastest path from zero to deployed memory

mem0.ai ↗ · 23,000+ GitHub stars · $24M Series A (YC, Peak XV)

Our take

Mem0 is the safe pick if you need your chatbot to remember users. The API is clean, latency is the fastest we've seen (p50: 148ms), and it handles the messy stuff (deduplication, conflict resolution, decay) so you don't have to. The catch: graph memory is locked behind the $249/mo Pro plan, and the jump from $19 to $249 is steep. If you're building for a team rather than for end users, Mem0 remembers per-person, not per-team. That matters.

How it works

Hybrid datastore: graph + vector + key-value. An LLM decides what to store, handles deduplication, and resolves conflicting facts automatically.

When to use Mem0

  • + Teams that need a memory SaaS running in production this week
  • + User personalization: preferences, conversation history, per-user context
  • + Real-time chat where every millisecond of latency matters

When to skip it

  • − Teams that need the AI to learn behavioral patterns over time (it stores facts but doesn't learn from corrections)
  • − Complex multi-hop reasoning across connected knowledge
  • − Anyone running local LLMs (Ollama integration is unreliable as of early 2026)

What it does better than everything else

Latency. p50 search at 148ms, p95 at 200ms. We haven't found anything faster for real-time applications.

MCP support

Supported. OpenMemory MCP server ships with a local dashboard. All data stays on your machine, no cloud sync. Nine memory tools exposed to any MCP client.

Why MCP matters: 5 MCP servers every engineering team should run →

Pricing

Free

10,000 memories, 1,000 retrievals/month

Paid

$19/mo (Starter) or $249/mo (Pro: unlocks graph memory, advanced analytics)

The gotcha nobody mentions

Memory extraction is hit-or-miss for complex information. In independent testing, Mem0 failed all 5 targeted multi-hop reasoning questions, not because retrieval was bad, but because the relevant information was never captured into a searchable memory in the first place. Simple facts ("user prefers dark mode") work great. Nuanced context ("the reason we chose this architecture was...") often gets lost.

"I needed something that learns user patterns implicitly from behavior over time — when a customer corrects a threshold from 85% to 80% three sessions in a row, the agent should know that next time. Mem0 cannot do this."

Frequently asked

Does Mem0 work with open-source LLMs? +
Partially. It supports OpenAI, Anthropic, and some open-source models, but Ollama integration has known issues. Embeddings with certain models return empty results without error messages.
Can multiple team members share the same Mem0 memory? +
Mem0 is designed as per-user memory. Each user gets their own memory space. There's no built-in team-wide shared memory, that requires a different architecture.
Is the free tier enough to evaluate Mem0 seriously? +
10,000 memories and 1,000 retrievals per month is enough for a proof of concept. But graph memory (arguably the most interesting feature) requires the $249/mo Pro plan.

Related reading

→ The 3 types of AI memory → The amnesia problem

Also in this space

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Letta

Agents that manage their own memory (formerly MemGPT)

Mem0 is Tier 2 — persistent (per-user) memory.

It solves real problems for individual agents and users. But if your team needs shared, always-current memory that works across Cursor, Claude, and every other AI tool simultaneously — that's a different architecture entirely.

We're building that with Knowledge Plane. Join the beta →