A searchable record of your AI work.
A governed memory that travels between AIs.
Nessie syncs your sessions from ChatGPT, Claude, Codex, Cursor and more than twenty other tools into one place, and lets your team share them. Kemory distils your chats into memory you can recall and continue in any AI, with consent and an audit trail when you share it, including across organisations.
Last verified 7 November 2026 against primary sources
Two good answers.
Different jobs.
- You want every AI session, coding-agent sessions included, kept as full searchable transcripts in one place.
- Your team wants to share sessions, repositories or contexts with simple, revocable permissions.
- A local-first Mac app with a wide list of tools and sources fits how you work.
- You want chats distilled into memory and one synthesised context, recalled in any AI and continued in another.
- You need deny-by-default access, just-in-time consent and a hash-chained audit log, including for reads across organisations.
- You want Counsel, residency in India or Switzerland, or a free plan with Pro at $19.99 a month.
See each one
doing its job.
Screenshots taken 7 November 2026. Nessie 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.
Nessie syncs more than twenty AI tools, coding agents included, plus Gmail, Apple Notes and Obsidian, without a browser extension. Agents can read whole past sessions, which suits coding work, and its team sharing by session, repository or context is easy to reason about.
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 |
| Nessie | None published | — | — | — | — | — |
What changed at Nessie
Products move quickly. These are the dated changes this comparison has been checked against.
- 27 July 2026Connector setup for Claude and ChatGPT added in Settings.
- 31 August 2026macOS release v1.4.3653.
- By 7 November 2026Cross-platform daemon syncs agent work from macOS, Linux and Windows; per-seat team billing.
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 Nessie? You do not have to replace it to try Kemory. Kemory connects to the AIs you use over MCP and Kora for Chrome.
Choosing Nessie 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 Nessie.
- What is the difference between Kemory and Nessie?
- Nessie keeps a searchable record of your AI sessions across more than twenty tools and lets your team share it. Kemory distils your chats into governed memory that you can recall and continue in any AI, and share across organisations with consent and audit.
- Does Nessie work with agents?
- Yes. Nessie offers a hosted MCP server, a CLI and an agent skill, and supports Claude Code, Codex, Cursor, OpenCode, Pi and OpenClaw. Kemory reaches the same kinds of tools over MCP, a REST API, a CLI and Python and TypeScript SDKs.
- How are Nessie and Kemory priced?
- Nessie lists Plus at $28 a month and Pro at $79 a month, billed yearly, and Team at $80 a seat a month, billed yearly; free-tier limits are not documented as of 7 November 2026. 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).
- Can I share memory with people outside my organisation?
- Nessie shares sessions, repositories, integrations and contexts within a team, with public links; sharing across organisations is not documented. Kemory shares namespaces or chosen items across organisations: recipients accept to receive, their contributions are credited, every cross-organisation read is audited and you can revoke access.
- How do Nessie and Kemory handle privacy?
- Nessie describes itself as local-first with optional cloud sync, and says no one at Nessie can read your content or train on it. Kemory strips credentials and private tags before storage, encrypts each user’s data at rest, deletes by crypto-shredding and keeps data in India for Indian users and in Switzerland otherwise.
More comparisons
Credit where it’s due. The real difference, either way.