screenpipe

screenpipe — detailed introduction

screenpipe is an open-source platform and desktop runtime that captures and indexes a user's desktop activity (screen + microphone) locally and exposes that indexed context to developer-built AI apps. The project markets itself as an "AI app store powered by 24/7 desktop history," enabling developers to build desktop-native AI experiences that use a rich, personal context stream.

Key features

• 24/7 local recording: continuously records screen and mic data locally, keeping user data on-device while creating a searchable timeline of desktop history.
• Local-first & privacy oriented: designed to run 100% locally (developers can build apps that access local indexed context), minimizing reliance on cloud for raw context capture.
• Developer tools & plugin system: provides a "pipe" plugin system so devs can scaffold, build, register and publish apps (pipes) using familiar web stacks (Next.js, etc.). Developers can monetize via the built-in store.
• Desktop-native runtime: apps run in a sandboxed environment inside the Rust-based runtime; templates and community templates for Tauri/Electron are available.
• Native OCR & indexing: includes native OCR on macOS/Windows and indexes captured content into an API that apps can query for context-aware behavior.
• Performance targets: advertised lightweight footprint (example numbers in docs: ~10% CPU, ~4 GB RAM, ~15 GB/month storage for recording—used as an implementation guideline for continuous capture).

Typical use cases

• Context-aware automations and agents that need fine-grained personal context (e.g., automatic note generation, task extraction from active windows, intelligent clipboard/history assistants).
• Desktop-native AI apps that integrate with local files, windows, and user activity to provide more useful, timely responses than cloud-only agents.
• Developers building and monetizing small desktop AI utilities—text/ocr pipelines, timeline agents, automated workflows—via the screenpipe store.

How it works (high-level)

1. The desktop app captures screen frames and microphone input continuously and stores them locally.
2. Captured data is processed (OCR, metadata extraction) and indexed into a local API/index.
3. Developers build "pipes" (plugins/apps) that query the local index to provide context-rich AI experiences.
4. Pipes can be published to the screenpipe store where users can install and (optionally) pay for them.

Community & ecosystem

• Open-source repo with documentation, contribution guidelines, and bounties.
• Templates and community projects for integrating screenpipe with Tauri/Electron.
• Active community channels (Discord, Twitter/X, YouTube) and periodic hackathons/prizes to drive ecosystem growth.

Notes & risks

• Because screenpipe captures continuous screen and mic data, user consent, permissions, and careful privacy/security practices are essential. The project emphasizes local-first operation to mitigate cloud exposure, but developers and users should still inspect settings and data flows.

Summary

screenpipe provides a practical platform for building desktop-native AI applications that leverage continuous local context. Its combination of 24/7 local capture, indexing, plugin/store model and developer tooling aims to enable highly contextualized AI apps that run on users' machines.