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Pieces is an AI-powered productivity tool that automatically captures and resurfaces everything you work on — code, documents, conversations, browser tabs, and more — without any manual effort. Built around a Long-Term Memory Engine (LTM-2), Pieces forms persistent memories of your workflow across all the apps you use, giving you a searchable, time-aware record of your entire work history.
At its core, Pieces offers three main pillars: the Long-Term Memory Engine, which continuously captures context from your development and research workflow; the Pieces Timeline, a central workspace for reviewing, summarizing, and interacting with captured memories; and Conversational Search, an intelligent chat interface powered by a choice of leading cloud or local LLM providers. Users can query their own work history in natural language, asking time-based questions such as what they researched last week or what decisions were made in a specific meeting.
Pieces runs on-device by default, meaning all captured data stays on the user's machine. Cloud connectivity is optional and user-controlled, making it suitable for enterprise compliance requirements. Pieces integrates with major developer tools including VS Code, Chrome, Edge, Firefox, Brave, Opera, Sublime Text, Obsidian, and MCP-compatible AI tools such as GitHub Copilot, Cursor, Claude Desktop, and Goose. A command-line interface is also available for terminal-based workflows.
The platform is designed for developers, researchers, writers, and anyone managing complex knowledge work. With over 150,000 developers at top companies using Pieces, the tool is positioned as an invisible second brain built directly into the operating system.
- Automatically capture code snippets, documents, and browser tabs without manual saving
- Retrieve work history using natural language time-based queries across months of activity
- Generate stand-up reports and status updates based on actual captured workflow activity
- Maintain context across research sessions by connecting links, highlights, and keywords automatically
- Recall meeting decisions, discussion participants, and action items from past conversations
- Use long-term memory as context for LLM conversations in Cursor, GitHub Copilot, or Claude
- Search through months of coding activity to find past implementations or debugging decisions
- Capture collaboration context across tools and teammates during joint development sessions
- Access workflow memory directly from the browser during web research without switching apps
- Connect personal context to AI coding assistants via MCP for context-rich code generation
- Preserve deep work flow state by automatically logging ongoing debugging and building sessions
- Generate project timelines and documentation from captured work history
