Screenpipe

Quick Verdict

Screenpipe is for people who watch a large amount of information go past every day and cannot get it back afterwards — knowledge workers, developers, consultants. It captures screen and audio in the background, transcribes locally into searchable text, and exposes that personal memory over MCP to assistants like Claude Code and Cursor, so you can ask “what was the price on that quote page last Wednesday” or “what did the client actually say about the delivery date.”

Two things separate it from ordinary screen recorders. First, capture is event-driven — it screenshots on app switches, clicks, and typing pauses instead of recording continuously, which keeps storage and CPU cost far lower. Second, data stays local by default, with per-app, per-window, and per-URL exclusions plus redaction of sensitive fields. The hard part of this category was never the technology; it is whether you accept having your screen recorded at all. Settle that question before installing.

Best For

• Developers and technical consultants: Recover debugging steps, commands, errors, and docs, then hand the context to an AI assistant
• Consultants and sales: Heavy client communication where you need to recall commitments, quotes, and objections precisely
• Researchers and content workers: Dozens of pages a day that should become a searchable archive
• Heavy MCP users: Already running agents and want to add “your own history” as context
• Not ideal for: anyone handling regulated data, employees on devices with a no-recording policy, or shared computers — the compliance risk outweighs the benefit

Key Features

• Event-driven screen capture: Screenshots at app switches, clicks, and typing pauses rather than continuous recording
• Local transcription: Whisper on-device, with cloud options such as Deepgram; meetings, calls, and video become searchable text
• Natural-language search: Ask in a sentence instead of recalling exact keywords
• Timeline view: Replay a whole day of activity, useful for status reports and retros
• MCP server: Exposes local memory as tools to Claude Desktop, Cursor, and other MCP clients
• Pipes automation: Scheduled agents defined in Markdown, with built-ins for meeting notes, daily summaries, and standup updates
• Privacy controls: Exclude apps, windows, and URLs; automatically redact sensitive fields
• Integrations: Connects with Linear, Slack, Notion, GitHub, Obsidian, and HubSpot
• Cross-platform: macOS (Apple Silicon and Intel), Windows 10/11, and Linux

Use Cases

• Recovering lost information: A price, an error message, or a page you saw once
• Automatic meeting notes: A Pipe transcribes and summarizes on a schedule, pushing to Notion or Slack
• Weekly reports and time review: Summarize what actually happened instead of reconstructing from memory
• Feeding context to assistants: Let a model in Cursor query your debugging session from three days ago
• Client follow-up: Retrieve call content to verify what you committed to

Pricing

Screenpipe uses a source-available Screenpipe Commercial License: free for personal, non-commercial use, with a paid license required for commercial use. Published tiers run roughly $25/month Standard, $50/seat/month Pro, and $150/seat/month Enterprise — confirm current terms on the official pricing page.

Note that this is not OSI open source. The code is auditable but not freely usable commercially, so the assumption “we self-host, therefore it’s free” does not hold for companies; clarify licensing before procurement. Cloud transcription such as Deepgram adds API cost, while sticking to local Whisper has zero marginal cost but consumes local compute.

Pros

• Event-driven capture is far cheaper than continuous recording, so running it all day is viable
• Data stays local by default, with per-app, per-window, and per-URL controls
• MCP support gives existing AI assistants access to personal history
• Pipes define agents in Markdown, keeping the extension barrier low
• Full desktop platform coverage and a mature integration set

Cons

• Source-available but not open source; commercial use requires a paid license
• Disk usage grows continuously and needs an active retention policy
• Local transcription accuracy is limited for non-English speech and jargon
• Clear compliance risk on corporate or shared devices
• Requires reliable international network access for account and cloud model features

Alternatives

FAQ

Is Screenpipe open source?

Not exactly. It is source-available: the code can be inspected, but the Screenpipe Commercial License makes it free only for personal, non-commercial use. Commercial use requires a paid license.

Does my screen data go to the cloud?

It stays local by default. If you enable cloud transcription or let a cloud model analyze content, that data leaves your machine — decide that tradeoff deliberately.

Will it slow down my computer?

Event-driven capture keeps steady-state usage modest, but local Whisper transcription is CPU- and GPU-heavy, so laptops running it all day will run hot and lose battery life.

Can it record other people in meetings?

Technically yes, but recording others often requires notice or consent depending on jurisdiction, and corporate meetings may be governed by internal policy. Confirm compliance first.

How good is non-English support?

The interface and search work, but transcription and on-screen text recognition depend on the model you choose; small local models struggle with accents and domain jargon, so verify anything important.

How does it work with Claude Code and Cursor?

It ships an MCP server that plugs directly into Claude Code or Cursor, letting the assistant query your local history while answering.

Bottom Line

Screenpipe addresses a genuinely neglected gap: AI assistants know a lot about the world and almost nothing about you. Continuous capture plus local storage plus MCP exposure turns personal history into usable agent context, and the engineering — event-driven capture, Markdown-defined Pipes, cross-platform support — is more solid than most projects in this category.

The barrier is a decision, not a technical one: are you comfortable being recorded, does your employer allow it, and will you pay for a commercial license? If all three answers are yes, it gives your AI workflow context nothing else can. If any answer is no, do not force it — start with something narrower like claude-mem, where the risk matches the reward.