Pieces is a desktop AI tool that captures what you do across every app — code, tabs, chats, docs — into a rolling nine-month searchable memory you can query in plain language. Free tier includes the copilot, snippet manager, and local memory with local models. Pro at $18.99/month adds premium models like Claude Sonnet and Opus. The trade is obvious: it works because it's watching everything.
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This analysis is based on documentation, public user reports, and vendor materials — not yet on our own hands-on testing. How we rate
Pieces is a desktop AI tool that solves a problem most developers have stopped noticing they have: context loss. Not the dramatic kind — the constant micro-losses. Where was that snippet from last week. What did I conclude about that bug before the interruption. What was that link someone dropped in Slack three weeks ago.
Individually those cost seconds. Across a week they add up to real time and real mental overhead, and no amount of disciplined bookmarking captures them, because you don't know at the time which detail will matter later.
Pieces takes a different approach. Its LTM-2 engine captures everything at the OS level — code you open, tabs you visit, conversations, meetings, documents — into a rolling nine-month memory you can query in plain language. No tagging, no saving, no decisions. Then you ask: what was I working on before this meeting, and it tells you.
Free tier with local models and full memory; Pro at $18.99/month for premium cloud models.
Let's be direct about this up front, because it's the deciding factor for most people.
Pieces works because it watches everything. That's not a side effect — it's the mechanism. A memory system that only captured what you deliberately saved would solve nothing, since the whole problem is not knowing in advance what'll matter.
Pieces' mitigation is real: processing runs on-device, local models are supported on the free tier, and your data stays on your machine rather than being shipped to a vendor. For a developer working on client code or anything sensitive, local processing is a meaningful difference from cloud-first tools.
But the honest framing is that you're installing something that records your desktop activity for nine months. Some developers find that entirely reasonable given the local processing. Others find it viscerally uncomfortable regardless. Neither reaction is wrong, and if you're in the second camp, no feature list will change that — skip it.
Pieces fits developers who work across many tools and lose time reconstructing context: people juggling browser research, IDE work, Slack threads, and documentation, or anyone who regularly picks up a project after weeks away. The time-based querying is genuinely useful when you're returning to something cold.
It also fits developers running several AI tools who want a shared memory layer underneath them — the MCP server lets other assistants pull your Pieces context, which turns it from a standalone tool into infrastructure.
It's the wrong pick for: anyone uncomfortable with ambient capture, developers who work in one or two tools with little context switching, people looking for code generation (this doesn't write code), and anyone who needs it to work out of the box without calibration — figuring out what to capture and how to query it well takes some onboarding.
| Tool | What it remembers | Capture method | Local option | Entry price |
|---|---|---|---|---|
| Pieces | Everything, 9 months | Passive, OS-level | ✅ Yes | Free / $18.99 |
| Cursor | Your codebase | Repository index | ❌ | Free / $20 |
| Obsidian | What you write | Manual notes | ✅ Local-first | Free / paid sync |
| Mem | What you save | Manual + AI organize | ❌ | Paid |
| GitHub Copilot | Open files | Active context | ❌ | $10 |
Pricing checked August 2026 from each provider's pages. Competitor prices are approximate and change frequently.
Pieces vs Cursor: Not competitors — Cursor indexes your codebase to write code; Pieces captures your whole workflow to recall context. Cursor knows your repo, Pieces knows what you were doing in the browser two weeks ago. Running both is common, and Pieces' MCP server can feed context to tools like Cursor.
Pieces vs Obsidian: Obsidian is a local-first note system where you write what you want to remember, giving you full control and zero ambient capture. Pieces captures automatically, which is more complete and less deliberate. For intentional knowledge management, Obsidian; for recall of things you never wrote down, Pieces.
Pieces vs Mem: Mem organizes what you deliberately save with AI assistance. Pieces captures passively at the OS level. Mem is a smarter notes app; Pieces is a recording of your work. The gap between them is exactly the privacy trade.
Pieces vs Copilot: GitHub Copilot writes code from your open files. Pieces remembers your work across everything. Different jobs entirely, and they pair well — most Pieces users run a code assistant alongside it.
| Plan | Price | What you get |
|---|---|---|
| Free | $0 | Copilot, Pieces Drive, full local memory and chat history, local models, 9-month LTM |
| Pro | $18.99/mo | Premium cloud models (Claude Sonnet and Opus, Gemini), early access to new models |
| Teams | Contact sales | Shared team context, multi-LLM including Ollama, priority support |
| Enterprise | Custom | Admin controls, deployment options |
Pricing checked August 2026 against pieces.app. The free tier is permanent, not a trial, and includes the full nine-month long-term memory with local model support. Pro adds premium cloud models on top.
The free tier deserves real credit here, because it isn't the usual crippled version. You get the copilot, the snippet manager, the complete nine-month memory, and local model support — the actual product, permanently, for nothing. What you don't get is premium cloud models.
That makes the upgrade question refreshingly simple. If local models handle your queries well enough — and for "what was I working on Tuesday" they generally do — the free tier is complete. If you want Claude Opus reasoning over your captured context, that's what the $18.99 buys.
For a specialized productivity tool, $18.99 is priced fairly against the $20 that's become the default for AI subscriptions. But it's genuinely optional in a way most tools' paid tiers aren't, and that's worth noting when so much of the category treats "free" as a demo.
Returning to a cold project: A developer picks up work abandoned six weeks ago and asks what they were doing and why, getting an answer from captured context rather than reconstructing it from commits.
Finding a snippet you didn't save: Something you wrote last month, never bookmarked, and can't quite remember the shape of — retrieved by describing it in plain language.
Meeting reorientation: Asking what you were working on before a meeting derailed the afternoon, and getting your last open files, tabs, and threads back.
Feeding context to other AI tools: Using the MCP server so Cursor, Claude, or another assistant can pull your Pieces memory as context, rather than re-explaining your project every session.
Research recall: Querying weeks of accumulated links, highlights, and documentation across a long research effort without having maintained notes.
Pieces solves a real problem that most tools ignore, and it solves it in the only way that actually works — by capturing what you didn't know to save. The nine-month LTM-2 window with plain-language time-based querying is genuinely useful when returning to cold work or hunting for something you half-remember, and the MCP server makes it infrastructure for whatever other AI tools you run rather than another silo. The free tier being the complete product with local models is unusually generous.
The reservations are two. First, the privacy trade is inherent and unavoidable: Pieces works because it records your desktop, and local processing mitigates that without eliminating it. That's a personal call, and if it bothers you, no amount of on-device processing will fix the feeling. Second, LTM-2 is relatively new technology, and calibrating what gets captured and how to query it well takes some onboarding — this isn't a tool that delivers value in the first ten minutes.
For developers who lose real time to context switching and are comfortable with passive capture, recommend, starting free. For anyone uneasy about ambient recording, that instinct is worth respecting.
Note: Pieces does not currently have an active affiliate program with AIVario. We earn no commission, and this rating carries no commercial incentive.
Best for: Developers working across many tools, anyone returning to projects after weeks away, people running multiple AI tools who want a shared memory layer, privacy-conscious users who want local processing Not ideal for: Anyone uncomfortable with ambient desktop capture, developers who work in one or two tools, people looking for code generation, users who need instant value without calibration Bottom line: The most complete answer to developer context loss, and it works by recording everything. Free tier is the real product; the privacy question is yours to answer before you install it.
Yes, and the free tier is substantial. It includes the Pieces Copilot, Pieces Drive snippet manager, full local memory and chat history, and local model support — permanently, not as a trial. Pro at $18.99/month adds premium cloud models including Claude Sonnet and Opus and Gemini, plus early access to new models. Teams and Enterprise are quote-based. If you're comfortable with local models, you may never need to pay.
The second-generation Long-Term Memory engine, and the reason Pieces exists. It runs at the OS level, passively capturing what you do across every application — code you open, tabs you visit, conversations, meetings, documents — over a rolling nine-month window. You then query it in natural language with time references: what was I working on before this meeting, what did that project summary conclude three months ago. No manual bookmarking or tagging.
Effectively, yes — and you should decide how you feel about that before installing it. LTM-2 captures context at the OS level across all your apps and websites. That's precisely what makes it useful and precisely what makes some developers uncomfortable. Pieces does run locally with on-device processing and local model support, which is a genuine mitigation, but the honest framing is that you're trading ambient capture for recall. If that trade bothers you, it's the wrong tool.
A snippet manager stores what you deliberately save. Pieces captures what you didn't think to save — which is where the time actually goes. The friction it targets is micro context loss: finding a snippet from last week, remembering a bug diagnosis you made before an interruption, retrieving a link someone dropped in Slack. Pieces Drive handles traditional snippet management too, but the long-term memory is the differentiator.
No, and it isn't trying to. Pieces is a horizontal memory layer, not a code-completion or agentic-coding tool. It doesn't write your features or refactor your files. Many developers run it alongside Copilot or Cursor, using those for code generation and Pieces for recall across the whole workflow. Its MCP server means other AI tools can query your Pieces memory as context, which is arguably the most useful way to use it.
The 2026 additions push it from passive recall toward active assistance. Agentic LTM does multi-turn reasoning across your memory rather than single lookups. Reflection Mode adds self-correcting reasoning in real time. Google Calendar integration makes calendar events first-class context in summaries. And Meeting Prep generates structured pre-reads with scheduled prep time — the first Pieces summary that takes action rather than just reporting.
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