What Screenpipe Does
Screenpipe represents a genuinely novel category of developer tool — a continuous personal context system that gives AI agents access to everything you have seen, heard, and done on your computer. Built in Rust with a Tauri desktop app, it captures screen activity and audio using event-driven architecture that only records when something changes.
Capture Engine and Plugins
The technical implementation is impressive. Instead of recording every frame, Screenpipe listens for meaningful OS events and captures only when something actually changes on screen. Text extraction primarily uses the OS accessibility tree for structured data like buttons, labels, and text fields, falling back to OCR only when needed. This approach is both faster and more accurate than pure OCR solutions; storage and CPU use depend on capture and retention settings, and Screenpipe's pricing page lists about 30 GB per month with configurable retention.
The plugin system called Pipes turns raw screen and audio data into actionable outputs. Pipes available in Screenpipe's pipe store cover meeting notes, CRM updates, time tracking, and expense tracking. Pipes are defined as simple markdown prompt files that run on schedules, querying the Screenpipe API to process your captured data.
AI Integration and Search
MCP server integration means Claude Desktop, Cursor, and other MCP-compatible AI assistants can directly query your screen history. Ask your AI what you discussed in a meeting two hours ago, what error message appeared on screen this morning, or what website you visited yesterday — and get answers with timestamps and source references.
The search functionality transforms your captured data into a queryable knowledge base. Natural language search covers captured screen text and audio transcripts, and the timeline view opens the full screenshot and extracted text for that exact moment, with audio playback for the same period. For developers who frequently need to recall terminal commands, error messages, or discussion context, this persistent memory eliminates the frustration of lost information.
Privacy and Audio Capabilities
Privacy architecture keeps captured history local by default, in a SQLite database at ~/.screenpipe. The source code is public under the Screenpipe Commercial License, which makes it source-available, not OSI open source; versions released under MIT before the June 2026 license change remain MIT. Optional cloud AI, cloud transcription, sync, connectors, and connected AI clients can send selected context off-device, and product analytics and crash reporting are on by default with an opt-out. App exclusion controls let you prevent recording specific applications, and data deletion is available at any time.
Audio capabilities include speaker identification, transcription of meetings and calls, and PII redaction for sensitive conversations. Together, screen OCR and audio transcription create a combined screen and audio record of your workday.
Platform Support and Pricing
Cross-platform support covers macOS, Windows, and Linux with multi-monitor capture. The REST API at localhost:3030 enables custom integrations beyond the built-in pipes and MCP server. The JavaScript SDK provides convenient access for developers building custom applications on top of Screenpipe data.
The official desktop app offers a free plan and paid subscriptions: Basic at $25/month or $250/year for full personal history, Business at $50 per seat per month or $500 per seat per year for cross-device sync and team workspaces, and custom Enterprise pricing; current terms are listed on the official Screenpipe pricing page. Existing lifetime licenses remain valid, and new lifetime purchases are no longer sold. Commercial use of the source code requires a separate paid license.
The Bottom Line
Screenpipe fills a unique niche at the intersection of personal knowledge management and AI agent infrastructure. By providing persistent context about your actual work, it enables AI assistants to give answers grounded in your real experience rather than generic knowledge. The combination of local-by-default storage, event-driven capture, and extensible architecture makes it compelling for developers who want AI that truly understands their workflow.
