An open memory runtime for agents.
A governed memory for people, teams and agents.
EverOS (formerly EverMemOS), from EverMind, stores agent and user memory as Markdown, indexes it locally and consolidates it between sessions through reflection, with an MCP server and a managed Cloud. Kemory overlaps on agent memory over MCP, and adds chat capture across six AIs and sharing across organisations with consent and audit.
Last verified 7 November 2026 against primary sources
Two good answers.
Different jobs.
- You want an Apache 2.0 memory runtime you can run locally today, with Markdown you can read and version.
- You need separate memory for the user and for the agent’s own skills and past cases.
- You want background reflection that consolidates memory between sessions without building it yourself.
- You want your chats in ChatGPT, Claude, Gemini, Perplexity, Manus and Grok captured automatically, not only agent sessions.
- You need to share memory with people across organisations, with accept-to-receive, credited contributions and audited reads.
- You want governance on every plan and data kept in India or Switzerland.
See each one
doing its job.
Kemory screenshot taken 7 November 2026. EverOS is a trademark of its owner, named 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.
EverOS is open source under Apache 2.0 today, with a large community and a one-command local setup, while Kemory’s Community Edition is still coming. Its distinct agent memory (cases and skills) and offline reflection go further than most memory layers on agents learning from experience, and EverOS Cloud starts free.
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 |
| EverOS | LoCoMo | 93.05% | Overall accuracy, self-reported | Not stated (5 January 2026 post) | Not stated | Not stated |
| EverOS | LongMemEval | 83.00% | Accuracy, self-reported | Split not stated | Not stated | Not stated |
| EverOS | HaluMem | 90.04% | Recall, self-reported | Not stated | Not stated | Not stated |
What changed at EverOS
Products move quickly. These are the dated changes this comparison has been checked against.
- November 2025EverMemOS open-sourced.
- 5 January 2026Benchmark results published for LoCoMo, LongMemEval, HaluMem and PersonaMem v2.
- 2026Renamed from EverMemOS to EverOS.
- 15 October 2026everos-mcp 0.1.1 on PyPI.
- 28 October 2026v1.6.0 in the GitHub changelog (PyPI latest 1.5.1, 22 October).
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 EverOS? You do not have to replace it to try Kemory. Kemory connects to the AIs you use over MCP and Kora for Chrome.
Choosing EverOS 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 EverOS.
- What is the difference between Kemory and EverOS?
- EverOS is an open-source memory runtime that agents and apps write to, with reflection between sessions. Kemory is a memory for people, teams and agents: it captures chats from six AIs, recalls them over MCP, REST, CLI and SDKs, and shares them across organisations with consent and audit.
- Is EverOS the same as EverMemOS?
- Yes. EverMind renamed EverMemOS to EverOS; its README still notes the former name.
- How are EverOS and Kemory priced?
- EverOS is free to self-host. EverOS Cloud has a free tier (3 memory spaces, 50,000 MCU and 100,000 retrieval calls a month), Pro at $25 a month and custom Enterprise plans. 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 (10-seat minimum).
- How does EverOS’s benchmark compare with Kemory’s?
- The numbers cannot be compared directly. EverMind reports LoCoMo 93.05% and LongMemEval 83.00% without stating the split, answer model or judge in its post. Kemory reports 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.
- Can EverOS capture my ChatGPT or Claude web chats?
- Not as documented on 7 November 2026. EverOS records what agents and apps send to it. Kemory’s Kora captures chats in your browser on ChatGPT, Claude, Gemini, Perplexity, Manus and Grok.
More comparisons
Credit where it’s due. The real difference, either way.