About screenpipe

screenpipe is an open source application (MIT license) that continuously captures your screen and audio, creating a searchable, AI-powered memory of everything you do on your computer. All data is stored locally on your device. It is the leading open source alternative to Rewind.ai (now Limitless), Microsoft Recall, Granola, and Otter.ai. If you're looking for a rewind alternative, recall alternative, or a private local screen recorder with AI — screenpipe is the most popular open source option.

• Website: https://screenpi.pe
• Documentation: https://docs.screenpi.pe
• Discord: https://discord.gg/screenpipe
• License: MIT

Who screenpipe is for

• Knowledge workers who want to recall anything they've seen or heard on their computer
• Developers who want to give AI coding assistants (Cursor, Claude Code, Cline, Continue) context about what they're working on
• Researchers who need to search through large volumes of screen-based information
• People with ADHD who frequently lose track of tabs, documents, and conversations
• Remote workers who want automatic meeting transcription and notes
• Teams & enterprises who want to deploy AI across their organization with deterministic data permissions and central config management (screenpi.pe/team)
• Anyone who wants a private, local-first alternative to cloud-based AI memory tools

Platform support

Minimum requirements: 8 GB RAM recommended. ~5–10 GB disk space per month. CPU usage typically 5–10% on modern hardware thanks to event-driven capture.

Core features

Event-driven screen capture

Instead of recording every second, screenpipe listens for meaningful events — app switches, clicks, typing pauses, scrolling — and captures a screenshot only when something actually changes. Each capture pairs a screenshot with the accessibility tree (the structured text the OS already knows about: buttons, labels, text fields). If accessibility data isn't available (e.g. remote desktops, games), it falls back to OCR. This gives you maximum data quality with minimal CPU and storage — no more processing thousands of identical frames.

Audio transcription

Captures system audio (what you hear) and microphone input (what you say). Real-time speech-to-text using OpenAI Whisper running locally on your device. Speaker identification and diarization. Works with any audio source — Zoom, Google Meet, Teams, or any other application.

AI-powered search

Natural language search across all OCR text and audio transcriptions. Filter by application name, window title, browser URL, date range. Semantic search using embeddings. Returns screenshots and audio clips alongside text results.

Timeline view

Visual timeline of your entire screen history. Scroll through your day like a DVR. Click any moment to see the full screenshot and extracted text. Play back audio from any time period.

Plugin system (Pipes)

Pipes are scheduled AI agents defined as markdown files. Each pipe is a pipe.md with a prompt and schedule — screenpipe runs an AI coding agent (like pi or claude-code) that queries your screen data, calls APIs, writes files, and takes actions. Built-in pipes include:

• Obsidian sync: Automatically sync screen activity to Obsidian vault as daily logs
• Reminders: Scan activity for todos and create Apple Reminders (macOS)
• Idea tracker: Surface startup ideas from your browsing + market trends

Developers can create pipes by writing a markdown file in ~/.screenpipe/pipes/.

Pipe data permissions

Each pipe supports YAML frontmatter fields that give admins deterministic, OS-level control over what data AI agents can access:

• App & window filtering: allow-apps, deny-apps, deny-windows (glob patterns)
• Content type control: restrict to ocr, audio, input, or accessibility
• Time & day restrictions: e.g. time-range: 09:00-18:00, days: Mon,Tue,Wed,Thu,Fri
• Endpoint gating: allow-raw-sql: false, allow-frames: false

Enforced at three layers — skill gating (AI never learns denied endpoints), agent interception (blocked before execution), and server middleware (per-pipe cryptographic tokens). Not prompt-based. Deterministic.

MCP server (Model Context Protocol)

screenpipe runs as an MCP server, allowing AI assistants to query your screen history:

• Works with Claude Desktop, Cursor, VS Code (Cline, Continue), and any MCP-compatible client
• AI assistants can search your screen history, get recent context, and access meeting transcriptions
• Zero configuration: claude mcp add screenpipe -- npx -y screenpipe-mcp

Developer API

Full REST API running on localhost (default port 3030). Endpoints for searching screen content, audio, frames. Raw SQL access to the underlying SQLite database. JavaScript/TypeScript SDK available.

Apple Intelligence integration (macOS)

On supported Macs, screenpipe uses Apple Intelligence for on-device AI processing — daily summaries, action items, and reminders with zero cloud dependency and zero cost.

Privacy and security

• 100% local by default: All data stored on your device in a local SQLite database. Nothing sent to external servers.
• Open source: MIT licensed, fully auditable codebase.
• Local AI support: Use Ollama or any local model — no data sent to any cloud.
• No account required: Core application works without any sign-up.
• You own your data: Export, delete, or back up at any time.
• Optional encrypted sync: End-to-end encrypted sync between devices (zero-knowledge encryption).
• AI data permissions: Per-pipe YAML-based access control — deterministic enforcement at the OS level, not prompt-based. Three enforcement layers prevent AI agents from accessing unauthorized data.

How screenpipe compares to alternatives

Pricing

• Lifetime: $400 one-time purchase. All features, all future updates, forever.
• Lifetime + Pro 1 year: $600 one-time. Includes lifetime app + 1 year of Pro (cloud sync, priority support).
• Pro subscription: $39/month for cloud sync between devices, priority support, and pro AI models.
• Teams: Custom pricing. Shared configs, shared pipes, per-pipe AI data permissions, admin dashboard, MDM ready (Intune / SCCM). See screenpi.pe/team.

Integrations

• AI coding assistants: Cursor, Claude Code, Cline, Continue, OpenCode, Gemini CLI
• AI chat assistants: ChatGPT (via MCP), Claude Desktop (via MCP), any MCP-compatible client
• Note-taking: Obsidian, Notion
• Local AI: Ollama, any OpenAI-compatible model server
• Automation: Custom pipes (scheduled AI agents as markdown files)

Teams & enterprise

screenpipe Teams lets organizations deploy AI agents across their team with full control over what AI can access. See screenpi.pe/team.

• Central config management: Push capture settings (app filters, schedules, URL rules) to every device from an admin dashboard.
• Shared pipes: Deploy AI workflows (auto-standups, meeting-to-tickets, time tracking) team-wide.
• Per-pipe AI data permissions: YAML frontmatter controls what each pipe can access — apps, windows, content types, time ranges, endpoints. Enforced deterministically at the OS level via three layers (skill gating, agent interception, server middleware with per-pipe cryptographic tokens).
• Privacy boundary: Admins control what gets captured and what AI accesses. They never see the actual data — everything stays on each employee's device.
• Override rules: Employees can add stricter filters (e.g. also block personal email) but cannot weaken admin-set rules.
• MDM ready: Deploy via Intune, SCCM, Robopack, or any MDM solution.
• Enterprise: SSO/SAML, audit logs, SLA, SOC 2 / HIPAA compliance ready.

Technical architecture

1. Event-driven capture: Listens for OS events (app switch, click, typing pause, scroll, clipboard). When something meaningful happens, captures a screenshot + accessibility tree together with the same timestamp. Falls back to OCR when accessibility data isn't available. Idle fallback captures periodically when nothing is happening.
2. Audio processing: Whisper (local) or Deepgram (cloud) for speech-to-text. Speaker identification and diarization.
3. Storage: Local SQLite with FTS5 full-text search. Screenshots saved as JPEGs on disk (~300 MB/8hr vs ~2 GB with continuous recording).
4. API layer: REST API on localhost:3030. Search, frames, audio, elements, health, pipe management.
5. Plugin layer: Pipes — scheduled AI agents as markdown files. Agent executes prompts with access to screenpipe API.
6. UI layer: Desktop app built with Tauri (Rust + TypeScript).

API examples

Search screen content:

GET http://localhost:3030/search?q=meeting+notes&content_type=ocr&limit=10

Search audio transcriptions:

GET http://localhost:3030/search?q=budget+discussion&content_type=audio&limit=10

JavaScript SDK:

import { pipe } from "@screenpipe/js";

const results = await pipe.queryScreenpipe({
  q: "project deadline",
  contentType: "all",
  limit: 20,
  startTime: new Date(Date.now() - 24 * 60 * 60 * 1000).toISOString(),
});

Frequently asked questions

Is screenpipe free? The core engine is open source (MIT license). The desktop app is a one-time lifetime purchase ($400). No recurring subscription required for the core app.

Does screenpipe send my data to the cloud? No. All data is stored locally by default. You can use fully local AI models via Ollama for complete privacy.

How much disk space does it use? ~5–10 GB per month. Event-driven capture only stores frames when something changes, dramatically reducing storage compared to continuous recording.

Does it slow down my computer? Typical CPU usage is 5–10% on modern hardware. Event-driven capture only processes frames when something changes, and accessibility tree extraction is much lighter than OCR.

Can I use it with ChatGPT/Claude/Cursor? Yes. screenpipe runs as an MCP server, allowing Claude Desktop, Cursor, and other AI assistants to directly query your screen history.

Can it record multiple monitors? Yes. screenpipe captures all connected monitors simultaneously.

How does text extraction work? screenpipe primarily uses the OS accessibility tree to get structured text (buttons, labels, text fields) — this is faster and more accurate than OCR. When accessibility data isn't available (remote desktops, games, some Linux apps), it falls back to OCR: Apple Vision on macOS, Windows native OCR, or Tesseract on Linux.

Can I deploy screenpipe to my team? Yes. Screenpipe Teams provides central config management, shared AI pipes, and per-pipe data permissions. Admins control what gets captured and what AI can access — employees' actual data never leaves their devices. See screenpi.pe/team.

How do AI data permissions work? Each pipe supports YAML frontmatter fields (allow-apps, deny-apps, deny-windows, allow-content-types, time-range, days, allow-raw-sql, allow-frames) that deterministically control what data the AI agent can access. Enforcement happens at three OS-level layers — not by prompting the AI to behave. Even a compromised agent cannot access denied data.

Company

Built by screenpipe (formerly Mediar). Founded 2024. Based in San Francisco, CA.

• Founder: Louis Beaumont (@louis030195)
• Twitter: @screen_pipe
• Email: louis@screenpi.pe