Pieces

What is Pieces?

Pieces is a desktop AI coding companion built around a Long-Term Memory Engine that runs on your own machine. Instead of living inside a single editor, it works as a background layer that continuously captures the context of your workday — the code you were reading in your IDE, the pages open in your browser, the commands in your terminal, and the chats you had with other assistants. That captured timeline is what makes its Copilot different: you can ask “what was that config snippet I looked at this morning?” or “summarize what I changed in the auth flow yesterday” and get an answer grounded in your actual activity rather than a blank-slate chat. It also ships Pieces Drive, a snippet manager that auto-tags, enriches, and semantically searches the code fragments you save.

Because capture and the default models run locally through a runtime called PiecesOS, your context stays on your device and core features keep working offline. The Free tier is genuinely usable — local models, unlimited snippets, and up to nine months of memory with no credit card. The Pro plan at $18.99 per month (or $169.99 per year) adds unlimited use of premium cloud models including Claude, GPT, Gemini, and Grok, each selectable in fast, balanced, or extra-thinking modes. Pieces is positioned as a complement to your editor, not a replacement: through its MCP server it can hand your on-device context to assistants like Claude and Cursor, so those tools do the heavy lifting on code while Pieces supplies the memory.

Who is it for?

Pieces suits developers who lose time reconstructing context — switching between an IDE, browser research, terminal, and AI chats — and want a private, on-device layer that remembers it all so their assistant does not start from scratch. It is most valuable to people who already use a primary coding agent and want to feed it richer, longer-lived context rather than to those looking for a single tool to autonomously write and ship code; for that job, Claude Code or Cursor remain the stronger choice and Pieces sits alongside them.

• Context-switching developers who juggle many tabs, tools, and tasks and want their AI to recall earlier work instead of being re-briefed each time.
• Privacy-conscious and offline engineers who prefer on-device capture and local models, or who work in restricted environments where cloud tools are limited.
• Snippet-heavy developers who collect reusable code and want auto-tagging, enrichment, and fast semantic search through Pieces Drive.
• Teams building institutional memory who want captured context and knowledge to persist and, on paid plans, sync and be shared across the group.