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Basic Information

screenpipe is an open source developer platform and AI app store that captures a user’s desktop context 24/7 and enables developers to build, sandbox, publish, and monetize desktop AI apps. The project records screen and microphone activity locally and indexes that history into an API so applications can use rich, continuous context. It provides a desktop app and a command line installer, cross-platform support for macOS, Linux and Windows, and a plugin system called "pipe" that runs Next.js apps inside a sandboxed Rust environment. Developers can create pipes, register and publish them to the store, and integrate monetization. The README emphasizes privacy and local-first processing, lists resource figures for recording, and points to templates and community resources for building native desktop integrations.

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App Details

Features
The README highlights a set of concrete capabilities: continuous 24/7 local screen and mic recording with reported resource figures (about 10% CPU, 4 GB RAM and a recording size listed as 15 gb/m), indexing of recorded history into an API, a plugin system named pipe for building sandboxed Next.js desktop apps inside Rust, a CLI installer and desktop app, native Apple and Windows OCR, templates for Tauri and Electron integrations, a pipe store for publishing and monetizing plugins, developer tooling provided via an @screenpipe/dev package, community projects and bounties, and integrations and companion projects announced in the project news.
Use Cases
screenpipe helps developers create context-aware, desktop-native AI applications by providing a local, continuous source of user context and a full developer workflow for building and distributing apps. The indexed desktop history lets models act with richer, long-term context rather than isolated prompts. The sandboxed pipe environment and templates reduce friction for packaging Next.js apps as desktop experiences. Publishing and pipe store monetization let developers distribute and earn from plugins. Native OCR and cross-platform support broaden use cases. The local-first design is positioned to preserve privacy while enabling powerful automations, and the project’s community, bounties, and companion tools accelerate adoption and extension by developers.

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