📘 Overview of Pieces
👉 Summary
In a developer's daily workflow, context loss is one of the most costly sources of friction — in both time and mental energy. Finding a code snippet opened last week, remembering a bug diagnosis made before an interruption, retrieving a Slack link shared in a meeting... these micro context losses accumulate and significantly degrade productivity. Pieces was designed specifically to solve this problem with a unique approach: an OS-level long-term memory engine that automatically captures work context without any manual action.
💡 What is Pieces?
Pieces is a desktop AI application for developers that functions as a persistent, contextual second brain. Its LTM-2 (Long-Term Memory) engine silently and continuously captures what you do on your machine — open code, visited tabs, conversations, meetings, snippets — and makes it queryable in natural language. The tool includes a contextual AI copilot (Pieces Copilot), an AI-enriched snippet management system (Pieces Drive), and integrates into the tools you already use through native plugins and the MCP protocol.
🧩 Key features
Pieces groups several key features around memory and context. The Long-Term Memory module (LTM-2) automatically captures work activity at the OS level over a rolling 9-month period, enabling precise time-based queries like "What was I working on yesterday evening on this project?". Pieces Copilot is an AI assistant that has access to your entire captured memory, in addition to the selected LLM's capabilities, enabling genuinely contextualized responses to your work. Pieces Drive is an AI-enriched snippet manager: save, automatically enrich (tags, description, language), transform (refactor, change language), and share code snippets. Native plugins cover VS Code, JetBrains, Chrome, Obsidian, and other tools, avoiding context switching. MCP support exposes Pieces memory to any MCP client (GitHub Copilot, Cursor, Claude, Goose). Finally, privacy is guaranteed by on-device operation by default, with cloud as an optional, user-controlled feature.
🚀 Use cases
Pieces adapts to several concrete situations developers face daily. For technical research, Pieces automatically memorizes every page, link, and excerpt consulted without needing to create a bookmark. For post-interruption recovery, a developer can query Pieces to find exactly where they left off after a meeting or weekend. For snippet management, developers quickly save and retrieve useful code snippets from any tool. For code review and debugging, session context is preserved and queryable. For collaboration, snippets can be shared with enrichments via custom links or GitHub Gists.
🤝 Benefits
Pieces' primary advantage is drastically reducing the cognitive load associated with context management — a problem all developers know but that few tools truly address. By automatically capturing all work context, Pieces frees up mental energy for what truly matters: code, thinking, and problem solving. The on-device-by-default approach addresses legitimate privacy concerns for teams working on sensitive projects. MCP compatibility transforms the memory accumulated in Pieces into a shareable resource for all the LLMs in a developer's ecosystem, multiplying its value.
💰 Pricing
Pieces offers two plans. The Free plan is permanent and includes the AI copilot, Pieces Drive, access to local memory history, and unlimited conversations. The Pro plan at $18.99/month unlocks access to premium LLMs including Claude Sonnet 4, Claude Opus 4, Gemini 2.5, and early access to new models as they launch. The desktop application is available for Windows, macOS, and Linux, as well as via a browser extension.
📌 Conclusion
Pieces occupies a very specific but extremely valuable niche in the AI tools ecosystem for developers: persistent, contextual long-term memory at the operating system level. Its on-device-by-default approach and ability to integrate via MCP across all of a developer's LLMs and tools make it a unique solution. The free plan is generous and sufficient for most use cases. An essential reference for any developer who wants to stop losing context and build a truly persistent working memory.
