Pieces

Pieces is an OS-level AI companion that creates a persistent, long-term memory of your entire workstream—from code snippets and documentation to chats and research. It eliminates the friction of context switching and manual organization, ensuring developers and technical teams can instantly recall what they did, in which application, and when. Pieces helps you build faster and smarter by ensuring your AI tools always have the critical context they need.

Key Features

🧠 LTM-2: Long-Term Memory Engine

The proprietary Long-Term Memory Engine (LTM-2) automatically forms secure, time-based memories of code, documents, and communications right within your workflow, without requiring manual input. Every saved item or snippet stays linked to the original context, allowing you to use time-based queries to find exactly what you need, even after nine months.

🛠️ Seamless Cross-Tool Context Capture (Plugins)

Pieces is designed to work where you are, minimizing context switching. Through dedicated plugins for tools like VS Code, Chrome, and various operating systems (Windows, Linux, macOS), Pieces captures and preserves your flow whether you are researching, debugging, or collaborating, ensuring a unified memory across your entire digital environment.

🔒 Private by Design, Local by Default

Security and user control are paramount. Pieces runs on-device, processing data locally and offline whenever possible, making it fast, secure, and air-gapped from the cloud. The platform gives you end-to-end control over your memories, allowing you to enable, disable, or delete data for maximum privacy and security.

🤝 Contextual LLM Integration (MCP)

Pieces ensures your AI tools know what you know. The Model Context Protocol (MCP) server connects your private, long-term memory directly with leading LLMs, including GitHub Copilot, Claude, and Gemini. This provides real-time, personalized context to your favorite language models, moving beyond generalized public data.

💡 Automated Code Enrichment and Transformation

Capture code snippets effortlessly from your IDE, images, files, or websites. Pieces automatically enriches these snippets by tracking collaborators, detecting sensitive information, and improving them for readability or performance, or transforming them to a different programming language when needed.

Use Cases

Streamlining Deep Work and Debugging When you encounter a complex bug or need to reference a specific solution implemented months ago, you don't have to rely on fragmented chat logs or buried commit messages. Pieces captures the full context—the code, the relevant documentation you read, and the chat where a solution was discussed—allowing you to use natural language search to instantly surface the exact memory and pick up precisely where you left off.

Effortless Research and Documentation During technical research, Pieces quietly captures every important link, highlight, and keyword you encounter, eliminating the need for constant bookmarking or note-taking. This capability means you can focus entirely on absorbing the information, knowing that a fully indexed, searchable memory of your research session is being built automatically in the background.

Empowering Personalized AI Interactions Utilize the Pieces Copilot to ask questions about your specific, private projects. Instead of receiving generic answers, the Copilot leverages your LTM-2 memory—your historical code, documentation, and specific project details—to provide highly accurate, context-aware assistance tailored to your unique workflow and knowledge base.

Why Choose Pieces?

Pieces is not simply a repository; it is a dedicated workflow assistant that fundamentally changes how you interact with context and AI.

• Beyond Autocomplete: Unlike tools focused solely on code completion within the IDE, Pieces acts as an AI connector that works across your entire workflow. It provides persistent memory and context awareness across all the tools you use, maximizing efficiency regardless of your current application.
• True Data Sovereignty: Pieces processes data locally on-device. This robust privacy posture—where nothing is sent to the cloud unless explicitly permitted—is crucial for individual developers and high-security corporate deployments, ensuring sensitive context remains secure within your environment.
• Deeper LLM Reasoning: By providing personal context via the MCP, Pieces ensures that advanced LLMs like Claude 4 Sonnet and Opus can perform next-level reasoning based on your actual history and projects, leading to more relevant and actionable AI assistance than models trained only on public data.

Conclusion

By combining OS-level memory capture with privacy-focused AI, Pieces provides the persistent context engine necessary for modern, high-velocity development. Stop losing crucial details to context switching and start building with the full power of your past work.