AI memory for your screen

AI finally knows what you're doing

screenpipe turns your computer into a personal AI that knows everything you've done. record. search. automate. all local, all private, all yours

┌─────────────────────────────────────────┐
│  screen + audio → local storage → ai   │
└─────────────────────────────────────────┘

• remember everything - never forget what you saw, heard, or did
• search with ai - find anything using natural language
• 100% local - your data never leaves your machine
• open source - inspect, modify, own

download the desktop app — one-time purchase, all features, auto-updates

or run the CLI:

npx screenpipe@latest record

• 5-10% cpu usage
• 0.5-3gb ram
• ~20gb storage/month
• works offline

docs · discord · x · youtube · reddit

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

• 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
• Anyone who wants a private, local-first alternative to cloud-based AI memory tools

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.

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.

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.

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.

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.

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/.

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

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.

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.

• 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).

• 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.

• 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)

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).

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(),
});

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.

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

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