Enable AI agents to remember every interaction and learn continuously with a memory-first operating system designed for developers building perpetual intelligence.
Pieces
Automatically capture and recall every coding snippet, research link, and chat message with a local-first AI memory engine designed to boost developer flow.
Paid price not published
Free
About Pieces
Pieces is an OS-level productivity tool that acts as an invisible second brain for developers, passively capturing and organizing context from across their entire toolchain. Unlike traditional snippet managers that require manual saving, Pieces utilizes a Workstream Pattern Engine to automatically ingest code, documentation, meeting notes, and chat messages. This long-term memory system allows users to retrieve specific details through natural language search or time-based queries, ensuring that no technical detail is lost during the development cycle. By preserving the context of every task, it helps developers pick up exactly where they left off, even after significant interruptions. The tool is built on a private-by-design, local-first architecture that processes data on-device to ensure maximum security and speed. Its sophisticated LTM-2 engine models human-like memory by using agentic processes to link context across time and topic, enhancing the cohesion of long-term details without relying on the cloud. This system is remarkably efficient, capable of storing up to 18 months of high-granularity memory in just 4GB of local storage while maintaining a low footprint on CPU and RAM usage. Users have the option to use cloud-based features, but the core experience is designed to be air-gapped and secure. Pieces is specifically engineered for high-performing developers, researchers, and technical teams who manage high cognitive loads across multiple applications. It offers deep integration through native apps for Windows, macOS, and Linux, as well as plugins for popular IDEs like VS Code and browsers like Chrome. One of its standout features is the Model Context Protocol (MCP), which allows users to bridge their personal, local workflow context with other leading AI models such as GitHub Copilot, Claude, or Cursor, providing a more personalized and context-aware AI experience across their entire workflow. What truly sets Pieces apart is its proactive nature and operating system level integration. Rather than existing as a standalone silo, it lives where the work happens, capturing micro-events and relationships that other AI assistants miss. By focusing on the process of work rather than just the output, it transforms sustained, memory-infused conversations into a streamlined workflow. This approach makes it a unique productivity platform that prioritizes privacy, efficiency, and deep technical context over the generic, cloud-reliant models common in the current AI landscape.
Pieces pros & cons
Pros
- Operates primarily on-device for maximum privacy and offline capability.
- Automatically saves snippets and context without requiring manual user action.
- Highly efficient resource usage with 14x reduction in RAM and CPU needs.
- Deeply integrates with the OS to capture context across various third-party apps.
- Bridges personal workflow data with external LLMs through MCP support.
Cons
- Primary functionality is tailored specifically for developers and technical roles.
- Requires installing OS-level software rather than being purely web-based.
- Enterprise-level pricing and features require direct contact with sales.
- Local AI processing may perform better on hardware with modern optimization.
Pieces use cases
- Software developers can automate the saving of code snippets and documentation, allowing them to resume complex debugging sessions exactly where they left off.
- Technical researchers can leverage the OS-level capture to track every link and highlight across browser tabs without the need for manual bookmarking.
- Collaborative engineers can preserve shared context from meetings and chats, ensuring that specific technical decisions are searchable months later.
- Solo programmers can sync their local workflow history with external LLMs like Claude via MCP to receive highly personalized and context-aware coding help.
Pieces features
- model context protocol (mcp)
- cross-application integration
- passive context capture
- time-based natural search
- long-term memory (ltm-2)
- local-first data storage
- on-device ai models
- workstream pattern engine
Pieces pricing
Is Pieces free? Yes, Pieces has a free plan.
Free
Free
- On-device AI memory engine
- OS-level context capture
- IDE and browser plugins
- Natural language search
- Time-based memory queries
- Local-first privacy
- LTM-1 and LTM-2 support
- Model Context Protocol (MCP)
Enterprise
Price varies
- Specialized product support
- Advanced security compliance
- Team collaboration context
- Dedicated enterprise specialists
- Custom deployment options
Pieces FAQs
Does Pieces require an internet connection to work?
Pieces is designed to be local-first and private by default, meaning its core memory engine functions entirely on-device. While you can choose to enable cloud features or use specific cloud-based LLMs, the capture and storage of your data remains air-gapped from the cloud for security.
What kind of information does the memory engine capture?
The system passively captures a wide variety of work-related context, including code snippets, technical documentation, browser highlights, and communication from tools like Discord or Slack. It structures these into a searchable timeline so you can recall what you did and when you did it.
How much storage space does the memory engine use?
The LTM-2 system is highly optimized for efficiency, allowing it to store approximately 18 months of structured, high-granularity workflow memory using only 4GB of local storage. This ensures that the tool does not significantly impact your device's storage or performance.
Can I use Pieces with other AI assistants like GitHub Copilot or Claude?
Yes, Pieces supports the Model Context Protocol (MCP), which allows you to connect your personal local context with other popular LLMs. This helps you feed your own workflow history into third-party AI assistants to get more relevant and contextual coding suggestions.
What operating systems are supported?
Pieces offers native applications for Windows, macOS, and Linux. Additionally, it provides a comprehensive suite of plugins for major IDEs and web browsers to ensure that context capture occurs wherever you are working.
Ratings & reviews
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