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Cognee vs Mem0

Two fact sheets from our own research, side by side — pricing, platforms, API access and status re-checked by us rather than quoted from the vendors.

The short answer

Entry price
Cognee from $2.50/credit, Mem0 from $19/mo — different billing units, so no direct winner
Free plan
Both have one
Public API
Both have one
Higher Trust score
Mem0 — 89 vs 83 for Cognee
Best fit
Cognee: Developers building agents that need recall across sessions · Mem0: Developers adding per-user memory to chatbots, support agents or assistants

From our own records as of each tool's last check. Anything we have not confirmed is left out rather than guessed. How we verify

CogneeTurns documents and past sessions into a graph-plus-vector memory that AI agents can query across runs.Mem0Memory layer for AI agents and apps that stores user facts and preferences between sessions and retrieves them on demand.

Side by side

FactCogneeMem0
CategoryVector Databases & RAGLLM Orchestration Frameworks
Pricing modelFreemiumFreemium
Starts at$2.50/credit$19/mo
Public APIYesYes
Trust score83 / 10089 / 100
PlatformsWeb, APIAPI, Web
CompanyTopoteretes UG (haftungsbeschränkt)Embedchain, Inc. (d/b/a Mem0)
Launched20242024
Last verifiedSep 6, 2026Oct 2, 2026

What our research says

Cognee

Cognee's bet is that plain vector search loses the relationships between facts, so it builds a graph on top and lets agents traverse it. Being open source means you can run the whole thing locally and pay only if you want the hosted version, which is the right shape for infrastructure. The catch is that graph construction costs tokens, so a large corpus is not cheap, and you are wiring a library rather than clicking through a product.

Pros

  • Open-source Python SDK you can run entirely on your own machines
  • Builds a knowledge graph alongside embeddings instead of only vectors
  • MCP server plus integrations for Claude Code, Codex and LangGraph
  • Free tier and pay-per-token pricing rather than a seat licence

Cons

  • Ingesting a large corpus burns tokens, and 2.50 dollars per million adds up
  • It is a library first, so expect to write code rather than configure a dashboard
  • Workspaces are billed separately at 5 dollars each beyond the first

Mem0

Mem0 is a reasonable way to add cross-session personalisation to an agent without designing your own memory store, and the open-source library means you can start locally and move to the hosted service later. The hosted tiers are priced by request counts, so chatty agents can burn through the small plans faster than the user counts suggest.

Pros

  • Open-source library under Apache-2.0 with a hosted platform for production
  • Free tier with unlimited end users
  • SDKs plus MCP, CLI and a gateway for common agent setups
  • Compliance posture is published, including SOC 2 Type I

Cons

  • Graph memory, advanced analytics and private Slack support only start at the $249 Pro tier
  • Free and Starter tiers allow few retrieval requests, 1,000 and 5,000 a month
  • Quality depends on what the extraction step decides to store, which you cannot fully control

Which one fits

Choose Cognee if you need

  • Developers building agents that need recall across sessions
  • Teams whose data has structure that flat embeddings lose
  • Anyone who wants to self-host the memory layer rather than rent it

Choose Mem0 if you need

  • Developers adding per-user memory to chatbots, support agents or assistants
  • Teams that want an open-source memory library with a hosted option
  • Agent builders who need memory exposed over MCP

Cognee vs Mem0: the questions people ask

Is Cognee cheaper than Mem0?

They charge in different units, so there is no like-for-like answer: Cognee starts at $2.50/credit, Mem0 at $19/mo. Entry price is the lowest paid plan as we last verified it.

Does Cognee or Mem0 have a free tier?

Both: Cognee has a free tier, and Mem0 has a free tier.

Which has a public API, Cognee or Mem0?

Both ship a public API, so either can be built on; the fact table above shows the platforms each one runs on.

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