About screenpipe
screenpipe is a source-available application that continuously records a computer's screen and audio to build a searchable, AI-powered memory of everything done on it. It is built for knowledge workers who want to recall what they saw or heard, developers who want to give AI coding assistants context, and teams that want to find work worth automating.
screenpipe listens for meaningful events, such as app switches, clicks and typing pauses, and captures a screenshot only when something changes, pairing it with the accessibility tree or OCR text. Audio is transcribed locally in real time with Whisper, or with Deepgram in the cloud. The data stays on the device by default and feeds natural-language search and AI agents.
screenpipe offers a visual timeline to scroll through a day's screen history, natural-language search across screen text and audio transcripts, and workflow mapping to surface tasks worth automating. Its Pipes system runs scheduled AI agents defined as markdown files, such as meeting summaries or daily recaps, with YAML frontmatter giving admins deterministic, OS-level control over what data each pipe can access.
screenpipe installs as a desktop app for macOS, Windows or Linux, or as a CLI via npx, and connects to coding agents such as Claude through an MCP server. 8 GB of system RAM is recommended. It is released under the Screenpipe Commercial License: personal, non-commercial use is permitted, while commercial use requires a paid license.
Use cases
- Giving AI coding assistants context about what a developer is working on
- Automatically summarizing a meeting that just ended
- Helping people with ADHD keep track of tabs, documents and conversations
Key features
- Captures screen and audio continuously and stores it locally by default
- Uses event-driven capture, screenshotting only on meaningful changes
- Transcribes audio in real time locally with Whisper
- Searches screen text and audio transcripts with natural language
- Shows a visual timeline of a user's entire screen history
- Runs scheduled AI agents (Pipes) defined as markdown files
- Maps workflows to surface work worth automating
- Gives admins deterministic, OS-level control over what data each pipe can access
Pros and cons
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