Pieces AI

Pieces AI is an AI-powered memory and productivity platform designed to capture, organize, retrieve, and reuse context from a person’s digital work. Developed by Mesh Intelligent Technologies, Pieces has evolved from an AI-powered developer productivity and code-snippet tool into a broader AI memory layer for modern work. Its current platform is designed to remember what users have worked on and make that context available when they need to resume a task, understand a previous decision, prepare a summary, or provide context to another AI assistant.

At the center of Pieces is its Long-Term Memory (LTM) system. PiecesOS runs in the background on a user’s computer and captures workflow context from supported applications and activities. The current LTM-2.7 engine can capture information such as code copied to the clipboard, screens viewed, and audio from enabled sources. Captured information is processed and stored locally on the user’s device by default.

Pieces organizes this information through its Timeline, which provides a chronological view of work activity and captured memories. Users can search their history, revisit previous work, and understand how a particular task or decision developed over time. This is particularly useful when a user needs to recover information from something they researched or worked on days or weeks earlier.

The platform also provides Conversational Search, an AI chat interface that answers questions using information captured by Long-Term Memory. Users can ask questions about previous work and restrict searches by application, time range, or modality. Local files and folders can also be included as context. Conversations are saved to the Pieces Timeline so they can be revisited later.

Pieces goes beyond simple search by providing Agentic Summaries and one-click productivity artifacts. Users can generate standup updates, day recaps, morning briefs, meeting preparation, custom summaries, and other work-related outputs from their captured context. This can reduce the need to reconstruct information manually from multiple applications.

A major differentiator is Pieces’ cross-tool memory architecture. Pieces MCP allows external AI assistants to access relevant long-term memory from the user’s workflow. PiecesOS acts as an MCP host, allowing tools such as Cursor, VS Code, Claude, ChatGPT, GitHub Copilot, Codex, Windsurf, JetBrains tools, and other MCP-compatible clients to use Pieces memory.

This means a developer can use an AI assistant they already prefer while allowing that assistant to retrieve relevant context from previous work. For example, a user could ask an MCP-connected coding assistant about a decision made during a previous conversation or use earlier research to inform a current coding task.

Pieces supports a wide range of applications and information sources. Its current website highlights Gmail, Outlook, Chrome, Arc, Safari, Slack, Microsoft Teams, Google Chat, Discord, Google Meet, Zoom, Google Docs, Notion, Figma, Claude, ChatGPT, GitHub Copilot, Codex, Perplexity, Cursor, VS Code, JetBrains, and Xcode among the environments from which workflow context can be captured or connected.

The platform is therefore no longer limited to software developers. Pieces’ documentation describes it as suitable for people across different fields and specifically mentions front-end developers, data scientists, DevOps engineers, students, researchers, writers, and collaborators. Users can apply its memory capabilities to coding, research, writing, planning, learning, and collaboration.

For developers, Pieces AI still provides a strong collection of developer productivity features. It integrates with IDEs and development environments and can preserve coding context across sessions. Its web extension can capture and manage code snippets, while PiecesOS and the desktop application provide a persistent context layer around development work.

Pieces also supports multiple AI model families. Current documentation lists Claude, Gemini, and ChatGPT model families, each available in Fast, Balanced, and Extra Thinking modes. Real-time web search is powered by Perplexity. The platform can also work with local models in applicable workflows, giving users additional flexibility between cloud and local AI processing.

Privacy is a significant part of the platform’s design. Pieces states that captured memories are stored on-device by default. PiecesOS performs capture, indexing, storage, and sensitive-data filtering locally, while cloud AI features send only the scoped context needed for a particular request. Pieces states that it is SOC 2 Type II certified and does not use user data to train its models.

Users also have controls over what Pieces captures. The current platform allows users to pause memory capture and disable capture from specific applications and websites. Users can delete information based on time, application, or capture method. Enterprise customers receive additional controls such as organization-wide capture policies and bring-your-own-key options.

Pieces is available across macOS, Windows, and Linux, with PiecesOS providing the underlying local service. The desktop application, PiecesOS, integrations, MCP server, and developer tooling work together as a connected ecosystem rather than as isolated applications.

The platform also provides Pieces Enterprise for organizations that need centralized administration, governance, and control over how AI memory is used across teams. Enterprise capabilities extend the local-first architecture with organizational controls and security features.

Pieces’ product direction has increasingly moved toward helping both people and AI agents remember context. The company’s current positioning is that AI systems are more useful when they understand what a person has already decided, tried, researched, and worked on. This makes Pieces less like a conventional chatbot and more like a persistent contextual memory layer that can work alongside multiple AI assistants.

For users who frequently switch between applications, lose track of research trails, revisit old decisions, or use multiple AI assistants, Pieces can provide a centralized memory of their digital workflow. Its combination of on-device capture, long-term memory, conversational retrieval, summaries, MCP connectivity, and cross-application context makes it particularly useful for knowledge workers and technical teams.

Pricing

Pieces currently offers Pieces Pro for individuals and paid product capabilities for teams and organizations. The official documentation states that Pro provides access to Pieces’ cloud-hosted AI models, Long-Term Memory, and paid product features. Current model families include Claude, Gemini, and ChatGPT.

The current Pieces website advertises a 7-day free trial and directs users to the checkout flow for the current price. Pieces notes that pricing, billing intervals, discounts, taxes, and any trial terms can vary by account, making the checkout page the source of truth for the amount charged.

Because Pieces has changed its pricing structure over time, older fixed prices found in previous Pieces listings should not be treated as current.

Pricing Model

Subscription

Pricing Note

Pieces currently offers a 7-day free trial for its paid experience. The exact current Pro price and billing interval should be taken from the Pieces checkout for the user’s account because Pieces states that price, interval, taxes, discounts, and trial availability can vary by account.

Review

Pieces has developed into a distinctive productivity tool because its primary value is not simply generating AI responses but remembering the user’s work context. The combination of Long-Term Memory, Timeline, Conversational Search, and contextual summaries can help users recover information that would otherwise be scattered across browser tabs, chats, documents, meetings, and development tools.

Its strongest feature is the breadth of context it can connect. Pieces can work across communication, research, documentation, development, and AI applications, allowing users to search their previous activity rather than relying on memory or manually organized notes.

The MCP integration is another major strength. Instead of requiring users to abandon their preferred AI assistant, Pieces can provide its memory layer to MCP-compatible tools such as Cursor, Claude, VS Code, ChatGPT, GitHub Copilot, and Codex. This makes Pieces complementary to other AI tools rather than necessarily replacing them.

Privacy is also a significant advantage. Pieces uses a local-first architecture for captured memories and provides controls over capture, deletion, and application-level exclusions. Its SOC 2 Type II certification and enterprise controls make the platform more attractive for organizations handling sensitive workflow information.

The main limitation is that the platform’s value depends heavily on users being comfortable with continuous contextual capture. Although Pieces provides controls for pausing and excluding applications, organizations and individuals should understand what information is being captured before enabling the full memory experience.

Another consideration is pricing transparency. The current official documentation does not provide one universal public price for every account; Pieces directs users to its checkout because prices and billing terms can vary. This makes current pricing less straightforward to compare with products that publish fixed monthly rates.

Pieces AI is particularly compelling for developers, researchers, students, writers, and knowledge workers who frequently move between applications and need to recover previous context. Its current direction toward becoming a shared memory layer for both people and AI agents gives it a differentiated position in the AI productivity market.