Your working day, on your machine.
Your AI conversations, shared under rules.
Pieces runs in the background on your computer and captures what you work on, from code and browser to clipboard, audio and calendar, into an on-device Long-Term Memory. Kemory captures your conversations across AI apps and serves one permissioned, audited memory to every AI, agent and colleague you choose. The two can sit side by side.
Last verified 7 November 2026 against primary sources
Two good answers.
Different jobs.
- You want an on-device record of your whole working day, including code, browser, clipboard and audio.
- You want morning briefs, standups and time breakdowns generated from that record.
- Local-first capture matters more to you than sharing memory with other people.
- You want chats from ChatGPT, Claude, Gemini, Perplexity, Manus and Grok captured whole and recalled in any AI.
- You need to share memory with colleagues or other organisations under deny-by-default permissions, consent and audit.
- You want to continue a conversation in another AI, or get one answer from several models.
See each one
doing its job.
Screenshots taken 7 November 2026. Pieces 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.
Pieces captures across your whole machine, not only AI chats, and forms and stores memory locally, processing audio without keeping the raw recording. Its summaries, standups and IDE plugins fit developers’ daily habits, and it shipped agentic retrieval and one-click MCP setup in 2026.
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 |
| Pieces | None found | — | — | — | — | — |
What changed at Pieces
Products move quickly. These are the dated changes this comparison has been checked against.
- 27 January 20265.0.1: Time Breakdown, timeline search and filters, AWS Bedrock inference profiles.
- February 20265.0.3: LTM Audio in preview, processed locally; expanded MCP server.
- March 20265.1.0: scheduled summaries, wider bring-your-own-key, local LLM engine without Ollama.
- May 20266.0.0: agentic Long-Term Memory, Reflection Mode, Google Calendar, Meeting Prep, one-click MCP setup.
- 5 June 2026PiecesOS versions before 12.4.0 lose cloud services.
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 Pieces? You do not have to replace it to try Kemory. Kemory connects to the AIs you use over MCP and Kora for Chrome.
Choosing Pieces 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 Pieces.
- What is the difference between Kemory and Pieces?
- Pieces keeps an on-device timeline of everything you work on and lets you query it in Pieces Copilot or over MCP. Kemory captures your conversations across AI apps and serves one permissioned, audited memory to the AIs, agents and people you choose.
- Does Pieces support MCP?
- Yes. The Pieces MCP server exposes your Long-Term Memory to Cursor, Claude Code, Goose and other MCP clients, and Pieces lists it as included with Pro. Kemory offers a one-click connector in Claude, ChatGPT, Gemini, Perplexity, Manus and Grok, and MCP recall in about twenty more clients.
- How are Pieces and Kemory priced?
- Pieces describes Free and Pro plans, with Teams and Enterprise by quote; we could not verify current prices on 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 use Pieces and Kemory together?
- Yes. They do different jobs. Pieces keeps a local record of your working day; Kemory keeps governed memory of your AI conversations that you can recall in any AI and share with others.
- Is Pieces only for developers?
- It started as a developer tool and most of its features target developers. Recent releases, such as calendar integration and Meeting Prep, widen it towards general knowledge work.
More comparisons
Credit where it’s due. The real difference, either way.