Screencap records how work actually happens: screen, clicks, keystrokes, window context and teams can use it to turn real workflows into structured datasets for automation and AI training. Consent and privacy are enforced while recording so most sensitive apps are blocked before anything is written, and every trace is scrubbed and reviewed before it leaves a machine. macOS, open source. Try it solo with a free trial, or talk to us about a team pilot.
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Screencap is a source-available, local-first screen recorder built for teams that need to turn real workflows into structured, searchable knowledge. It captures screen activity, clicks, keystrokes, and window context on macOS, then uses an on-device model to automatically segment footage into labeled tasks like "payroll run" or "expense approvals." Everything stays on your Mac in ~/.screencap until you explicitly choose to share a scrubbed copy. It's free for personal use, with a 7-day business trial and a $9/mo plan after that.
An on-device agent splits raw footage into labeled tasks and indexes every spoken word and on-screen moment. Months later, you can search for any workflow by keyword and jump straight to the exact moment.
Password managers and banking apps are cut before a frame is written; email and chat are masked as they record. Nothing leaves your machine unless you share it, and every shared copy is scrubbed of names, secrets, and PII automatically.
While recording, Screencap queries connected MCP servers for what's on screen — the payroll run, the CRM record, the ticket — and stores that snapshot inside the recording. Viewers can open the live record directly from the video.
The entire application — capture engine, encryption, agent, and anonymizer — is public on GitHub under PolyForm NC 1.0.0. Releases are notarized and reproducible, so you can audit or build it yourself.
"A screen recorder handles sensitive material by definition — so Screencap makes privacy exact, not vague."
Most recorders offer broad assurances; Screencap publishes a concrete threat model and enforces blocking before a frame is written. The combination of local-first storage, per-recording donation control, and fully public source code means trust is verifiable, not assumed. It's rare to see a tool this privacy-conscious that still delivers genuinely useful automation features like MCP context and automatic labeling.
You're tired of losing operational knowledge when people leave, or you need real screen data for AI training but refuse to use scraped or synthetic datasets. If your team lives in browser-based tools and you want onboarding that doesn't rely on tribal knowledge, Screencap's free personal tier is a low-risk way to test whether recorded workflows actually stick. For teams already experimenting with MCP servers, the context snapshots alone justify a pilot.
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