A quick start.
A shared memory with permissions.
Mem0 gives developers a direct way to add and search memories from their own apps and agents. Kemory captures your chats across AIs, recalls them in any MCP client, and puts permissions, consent and an audit trail around everything that is shared.
Last verified 7 November 2026 against primary sources
Two good answers.
Different jobs.
- You are adding memory to your own app or agent and want a focused add-and-search SDK in Python or TypeScript.
- You want to self-host an Apache 2.0 library today, with your own vector store, LLM and embedder.
- You want ready-made memory plugins for coding agents such as Claude Code, Cursor and Codex.
- You want chats in ChatGPT, Claude, Gemini, Perplexity, Manus and Grok captured automatically, then recalled or continued in another AI.
- You need to share memory with other people, across organisations, with accept-to-receive, credited contributions and audited reads.
- You want governance on every plan: a deny-by-default Gatekeeper, just-in-time consent and a hash-chained audit log.
See each one
doing its job.
Screenshots taken 7 November 2026. Mem0 is a trademark of its owner, shown here to identify the product.
At a glance
The same rows on every comparison, so you can read one page against another.
*89.6% end-to-end answer accuracy on LongMemEval-S, open-weight Llama-3.3-70B reader, six runs. See the benchmarks or the full method on docs.sekondbrain.ai. A dash means not documented, not proven absent.
The same job,
done two ways.
Mem0 has a very large open-source community and ships plugins and framework integrations quickly. Its retrieval research is detailed, and you can self-host the Apache 2.0 SDK and server today, while Kemory's Community Edition is still coming.
The numbers,
and how each was run.
These rows use different datasets, splits, readers and retrieval budgets, so they cannot be compared directly. We publish the method, every run including the lower ones, and use an open-weight reader rather than a frontier model.
| Product | Benchmark | Score | What it measures | Setup | Reader model | Runs |
|---|---|---|---|---|---|---|
| Kemory | LongMemEval‑S | 89.6% | End-to-end answer accuracy | 500 questions, oracle split | Llama-3.3-70B (open-weight) | Six, with variance |
| Mem0 | LongMemEval | 94.4 | Self-reported | Managed platform, single-pass, top-200 retrieval | Not stated | Not stated (±1 from judge) |
| Mem0 | LoCoMo | 92.5 | Self-reported | Managed platform, single-pass, top-200 retrieval | Not stated | Not stated |
| Mem0 | BEAM 1M / 10M | 64.1 / 48.6 | Self-reported | Managed platform | Not stated | Not stated |
What changed at Mem0
Products move quickly. These are the dated changes this comparison has been checked against.
- April 2026New memory algorithm (README).
- 13 May 2026Temporal Reasoning on Platform v3.
- June–August 2026Editor plugins, plus n8n and Zapier integrations.
- By 4 August 2026OpenMemory removed from the docs site.
- 26 October 2026New pricing: Starter, Growth and Pro no longer sold to new accounts; free plan on your own LLM key, usage-based above the limits.
Specialists do one job well.
Kemory holds the thread across them all.
Every year brings better models and you will keep trying them. Kemory sits above the models rather than inside any one of them, so what you have worked out goes with you.
Tell one AI something once, and every AI you use just knows it. Start a thought in one and carry on in another, mid-sentence.
Every memory is permissioned and every change is provable. We do not train on your data. Delete anything, and prove it is gone.
Compaction runs on an open-weight model, so the frontier model only ever sees the compacted version — not the pile.
Already using Mem0? You do not have to replace it to try Kemory. Kemory connects to the AIs you use over MCP and Kora for Chrome.
Choosing Mem0 instead?
Good — it fits you better right now.
Come back to Kemory when cross-AI, governed memory is what you need.
Five questions,
Kemory vs Mem0.
- What is the difference between Kemory and Mem0?
- Mem0 is a memory layer for developers to build into their own apps and agents. Kemory is a cross-AI memory for people and teams. It captures chats from the major AIs, recalls them over MCP, and adds sharing, consent and audit. Kemory also has an API, CLI and SDKs for agents.
- Is Kemory a Mem0 alternative?
- For agent memory, yes. Mem0 may suit a quick SDK integration or a self-hosted library. Kemory is worth a look when you need cross-AI capture, sharing between people, or governance on every plan.
- Does Mem0 still offer paid tiers?
- As of 26 October 2026, Mem0's Starter, Growth and Pro plans are no longer sold to new accounts. New users get a free plan, which needs your own LLM key after 30 days, and optional usage-based billing; higher limits, SSO and SLAs go through sales. Kemory is free for 2,000 memories and two AIs, with Pro at $19.99 a month, Team at $30 a seat and Business at $55 a seat.
- Can I self-host Mem0 or Kemory?
- Mem0's SDK is Apache 2.0 and you can self-host its server with Docker Compose today. Kemory offers enterprise self-host, your own cloud or air-gapped deployment; its Apache 2.0 Community Edition is coming.
- Which has the better benchmark score?
- The scores cannot be compared directly. Mem0 publishes LongMemEval 94.4 and LoCoMo 92.5 on its managed platform without stating the split or reader. Kemory publishes 89.6% end-to-end answer accuracy on the LongMemEval-S oracle split with an open-weight Llama-3.3-70B reader, across six runs.
More comparisons
Credit where it’s due. The real difference, either way.