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Screenpipe

productivity
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Source-available AI screen recorder that captures your entire workday locally on Mac and Windows - search every app, meeting, and website in plain English.

Screenpipe is a source-available, privacy-first screen and audio capture tool that records everything on your Mac or Windows PC locally - every app, browser tab, terminal, and meeting - and makes it all searchable in plain English through local AI models. Founded in 2024 and backed by Y Combinator, Screenpipe has accumulated 21,000+ GitHub stars from developers who want a self-hosted alternative to cloud-based AI memory tools like Rewind and Limitless. The codebase is published on GitHub and publicly auditable, but it ships under the Screenpipe Commercial License rather than an OSI-approved open source license: personal, non-commercial, and evaluation use can be free, while commercial use requires a paid license. The platform uses OCR to extract text from every captured frame, enables natural language search of the full history, and exposes a Pipe plugin system so developers can build custom automations on top of the screen timeline using TypeScript. A native MCP server lets Claude Desktop, Cursor, and other MCP-compatible AI tools read current screen context for more accurate assistance without manual copy-pasting.

#self-hosted
#privacy
#screen-recording
#productivity
#ai-assistant
#automation
Freemium

Free plan available

Update Tool
screenpipe.com
Freemium
Pricing Model
Productivity & Workflow
Category
2024
Since
Free Plan
Access

Key Features

  • Continuous screen and audio recording runs locally on Mac and Windows with no cloud uploads - all captured data stays on the local machine by default
  • OCR pipeline extracts searchable text from every screen frame, making all on-screen content - including terminal output and PDFs - findable by keyword
  • Local AI processing with Ollama integration keeps all data on-device - screen content is not sent to external servers without explicit per-session consent
  • Pipe plugin system lets developers build custom automations on top of the screen capture timeline using a TypeScript SDK with access to OCR and audio data
  • Native MCP server exposes screen memory to Claude Desktop, Cursor, and other MCP-compatible AI tools so they can read current screen context for better assistance
  • Natural language search queries the full history of everything seen, typed, or heard across all apps with results in under 200ms using local embeddings
  • Timeline view shows a scrollable visual history of all screen activity organized by app, website, and time period for retrospective review of any workday

Use Cases

  • Developers using the Screenpipe MCP server to give Claude or Cursor live screen context for more accurate code suggestions without manual copy-pasting
  • Remote workers building a searchable archive of all meetings, documentation, and browser research without cloud storage or manual note-taking during calls
  • Researchers reviewing every web page, paper, and note visited during a research session using the natural language search interface the next day
  • Engineers building custom pipe automations that trigger actions - Slack messages, calendar events, or code snippets - based on patterns in screen capture output

Pros

  • 21,000+ GitHub stars and a publicly readable codebase mean the local-only privacy claims can be checked against the source rather than taken on trust
  • MCP server integration is the only screen memory tool that natively feeds context to Claude, Cursor, and other AI coding tools without manual copy-pasting
  • Free plan captures screen, audio, and meetings on one device and shares context with Claude, Codex, and other AI tools, with no payment method required to start

Cons

  • Source-available under the Screenpipe Commercial License, not OSI open source - the code can be read and audited, but commercial use requires a paid license and the usual rights to fork or redistribute do not apply
  • Continuous screen recording generates large local storage requirements - a full 8-hour workday can consume multiple gigabytes of capture data per day
  • Running local AI models for search and summarization requires a machine with enough RAM to run Ollama models alongside other active applications
  • Windows support is newer and has fewer tested Pipe plugins than Mac - some automations documented in the community are Mac-only at this stage

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