screenpipe

24/7 screen and audio capture with local AI — honestly reviewed. What you actually get, what it costs, and whether it’s worth the setup.

TL;DR

• What it is: Open-source app that records your screen and audio continuously, stores everything locally, and lets you search it with AI or trigger automated agents based on your activity [README][homepage].
• Who it’s for: Knowledge workers, developers using AI coding tools, remote workers who want meeting transcription, and anyone who’s ever lost a tab or forgotten what was said — who also cares enough about privacy to run local infrastructure [README].
• Cost: $400 one-time lifetime license, or $600 for lifetime plus one year of pro features (cloud AI, cloud sync, cloud transcription). No monthly subscription for the core app [homepage].
• Key strength: Fully local, fully offline capable. Your recordings never leave your machine unless you explicitly turn on cloud features. MIT-licensed, open source, auditable [README][homepage].
• Key weakness: 17,262 GitHub stars and growing, but this is still primarily a developer tool dressed up as a consumer product. Non-technical founders will face a real setup curve, especially around MCP integration. Storage appetite (~20GB/month at default settings) requires deliberate disk management [README][homepage].

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What is screenpipe

screenpipe runs in the background on your computer and records everything: your screen, your audio, text extracted via accessibility APIs (with OCR as fallback), and audio transcriptions processed locally. All of it goes into a local SQLite database. Then you can search it with natural language, replay it on a timeline, or wire it up to AI agents that trigger based on what you’re doing.

The pitch in the README is blunt: “screen + audio → local storage → ai.” The website headline is softer — “Your AI finally knows what you’re doing” — but the mechanics are the same [README][homepage].

The comparison the project leans into hardest is against Rewind AI (now rebranded as Limitless) and Microsoft Recall. Both do roughly the same thing — passive screen capture with AI search — but with meaningful differences. Recall only runs on Copilot+ PCs with Windows 11 and specialized hardware. Rewind/Limitless is subscription SaaS. screenpipe runs on macOS, Windows, and Linux, costs a flat fee, and the code is on GitHub [README][homepage].

The project sits at 17,262 GitHub stars. The license in the metadata flags as NOASSERTION, but the README and LLM reference section both state MIT explicitly [README][merged profile]. The distinction matters: MIT means you can inspect, fork, and modify without commercial restrictions.

What makes it different from a simple screen recorder is the automation layer. “Pipes” are essentially background agents that trigger based on your activity — auto-transcribe a Zoom call, push meeting summaries to Slack, track time spent on tasks, update a CRM when you work on a deal. The pipe store lists use cases across meetings, sales, dev tools, analytics, and focus tools [homepage]. There’s also MCP (Model Context Protocol) integration: you can connect screenpipe to Claude via claude mcp add screenpipe -- npx -y screenpipe-mcp and then ask things like “what did I see in the last 5 minutes?” or “summarize today’s conversations” [README].

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Why people choose it over Limitless, Recall, and Otter

The honest answer from the data available is: privacy, price structure, and platform flexibility.

Versus Limitless (formerly Rewind). Limitless is the closest functional competitor. It’s subscription-based SaaS — your data passes through their servers for AI processing unless you stay fully offline, which limits what it can do. screenpipe’s entire value proposition is the opposite: local first, cloud optional, one-time price. The AlternativeTo Rewind alternatives page lists screenpipe directly in the “alternatives” category [1]. The price math is real: Limitless at even $10/month runs $120/year, $600 over five years. screenpipe’s $400 lifetime license breaks even in three and a half years, after which you’re paying nothing.

Versus Microsoft Recall. Recall is free but locked to Copilot+ PCs running Windows 11 with the specific NPU hardware. It’s also screen-only — no audio transcription. screenpipe captures both, runs on any OS, and doesn’t require you to buy new hardware [homepage]. For anyone on macOS, Linux, or an older Windows machine, Recall isn’t even an option.

Versus Otter.ai and Granola. These are meeting-specific transcription tools. They’re good at what they do, but they only run when you remember to start them, and they’re subscription SaaS. screenpipe records everything passively — you don’t have to think about it. The trade-off is the continuous capture model: Otter and Granola don’t need you to think about disk space or CPU overhead; screenpipe does [homepage][README].

Versus building your own. A nontrivial number of developers in this space roll their own screen-capture + SQLite + local LLM setup. screenpipe packages that into something installable with one CLI command and a desktop app, with the pipe store handling the automation layer. It’s the “don’t build this yourself” option for people who understand why you’d want it but don’t want to maintain it [README].

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Features: what it actually does

Core capture:

• Continuous screen recording with accessibility API text extraction (OCR fallback) [README][homepage]
• Local audio transcription (offline, no cloud processing required) [README]
• Speaker identification [homepage]
• PII auto-removal before data is sent to any AI — card numbers, phone numbers, emails, passwords are redacted [homepage]
• Timeline scrub — drag back through your day visually [homepage]
• ⌘+Ctrl+K global search shortcut [homepage]

AI interaction:

• Natural language search across everything you’ve seen and heard [homepage]
• AI chat with video playback and source references [homepage]
• MCP server mode: plug into Claude Desktop, Cursor, Claude Code, and ask questions about your recent screen activity [README]
• Works with local LLMs (the docs reference Ollama integration) [README]

Pipes / automation:

• Pipes are background agents triggered by your activity [README]
• Pipe store with pre-built automations for meetings, sales, dev tools, support, communication, analytics [homepage]
• Examples: auto-transcribe Zoom calls, push meeting summaries to Slack, meeting cost tracker, CRM updates when you work on a deal [homepage]
• REST API for building custom integrations [merged profile]
• npm plugin system with SQLite access [merged profile]

Storage and privacy:

• ~20GB/month at 8h/day default usage [homepage]
• Ultra-compressed: ~63MB per hour of data (72 screenshots, 60 min audio at 6MB, 8,500 words extracted text at 1.5MB) [homepage]
• Works offline [homepage]
• Per-app exclusions — you can tell it to never record your password manager or banking apps [homepage]
• Delete anything at any time [homepage]

Team / enterprise:

• Team deployment with central config management at screenpi.pe/team [README]
• SSO feature listed in canonical features [merged profile]

Hardware requirements:

• 5–10% CPU usage
• 0.5–3GB RAM
• ~20GB storage per month [README]

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Pricing: the actual math

screenpipe has one of the cleaner pricing structures in this space: one-time purchase, no subscription.

• $400 lifetime license — full app, all features, auto-updates [homepage]
• $600 lifetime + one year pro — adds cloud AI, cloud sync, and cloud transcription for a year [homepage]
• CLI (npx screenpipe@latest) — free, open source, no license required for self-built usage [README]

For comparison, the subscription alternatives:

• Limitless (Rewind): pricing not publicly listed as of this review, but historically ~$10–20/month = $120–240/year
• Otter.ai: $8.33–$20/month = $100–240/year
• Granola: ~$10–18/month = $120–216/year
• Microsoft Recall: free (but requires Copilot+ PC hardware, which adds $200–400 to the hardware premium)

If you’re currently paying $15/month for a meeting transcription tool, screenpipe pays for itself in 27 months. If you’re paying for multiple tools that screenpipe would replace (transcription + screen search + activity logging), the math accelerates.

The honest caveat: the $400 price is for people who want the desktop app with auto-updates and support. The core is open source and free to compile and run. If you’re technical enough to run npx screenpipe@latest record and build your own pipes, your cost is storage and compute.

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Deployment reality check

Installation paths:

1. Desktop app (easiest) — download the .dmg, .exe, or Linux package from the onboarding page. This is the primary path and handles setup, permissions, and updates automatically [homepage][README].

2. CLI — npx screenpipe@latest record. Runs the recorder without the GUI. Useful for servers or headless setups [README].

3. MCP integration — claude mcp add screenpipe -- npx -y screenpipe-mcp. Connects your screenpipe instance to Claude so you can query it conversationally [README].

What can go sideways:

The setup for the desktop app is genuinely simple on macOS — it’s a standard .dmg install. The friction starts when you want to do more than basic capture and search:

• MCP integration requires comfort with the CLI and understanding what an MCP server is. Non-technical founders will need to follow docs carefully or get help.
• Pipe configuration — browsing the pipe store and enabling automations is visual, but debugging why a pipe isn’t firing requires more technical comfort.
• Storage management — 20GB/month sounds manageable until you realize that’s 240GB/year. You need either a disk management strategy (the app lets you set retention limits) or enough disk to absorb it.
• Local LLM integration (via Ollama) is listed as a capability but requires a separate Ollama install and configuration. screenpipe doesn’t ship an LLM [README][homepage].
• Permissions — on macOS, the app needs screen recording and microphone permissions. System-level permissions prompts on first launch are expected; some users find this friction surprising.

The homepage notes that it works on macOS (Intel and Apple Silicon), Windows, and Linux [README]. No special hardware required beyond a machine that can handle 5–10% persistent CPU overhead.

Realistic time estimate for a technical user: 15–30 minutes to a working setup including MCP. For a non-technical founder using only the desktop app: 30–60 minutes including the permission setup and storage calculator review. For the full pipes-and-MCP stack: an afternoon.

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Pros and Cons

Pros

• Genuinely local-first. Not “local-first with asterisks.” Your data stays on your machine unless you opt into cloud features. The code is MIT-licensed and auditable [README][homepage].
• One-time pricing. No recurring bill that grows when you use it more. $400 once vs. $15/month forever is a meaningful difference over 3+ years [homepage].
• Cross-platform. macOS, Windows, Linux. No Copilot+ PC tax, no Apple hardware requirement [README].
• MCP integration is real. Asking Claude “what did I see in the last hour?” is a genuinely useful workflow for developers and knowledge workers [README].
• Pipes store = automation without code (mostly). Pre-built automations for common workflows mean you don’t have to build the integration from scratch [homepage].
• PII auto-redaction before AI processing is a thoughtful privacy feature — your passwords and card numbers don’t end up in a prompt [homepage].
• 17,262 GitHub stars signals enough community traction that the project isn’t going away tomorrow [merged profile].
• REST API + SQLite means your data is accessible programmatically and not locked in a proprietary format [merged profile].

Cons

• Primarily a developer tool. The MCP integration, pipe customization, and CLI path all assume technical comfort. The desktop app is accessible, but the full value requires more than clicking through a GUI.
• Storage appetite is real. ~20GB/month means ~240GB/year at default settings. You need disk space and a retention policy [homepage].
• Continuous capture has privacy implications for shared computers. If you share a machine or work in open offices with sensitive screens, always-on recording raises questions you need to answer before deploying.
• Local LLM setup not included. If you want fully offline AI (not just offline capture), you’re configuring Ollama separately [README].
• License metadata inconsistency. The merged profile flags license as NOASSERTION while the README says MIT. Worth confirming directly from the GitHub repo before making a commercial or compliance decision [merged profile][README].
• The enterprise/team tier lacks public pricing. The screenpi.pe/team page exists but pricing for centrally-managed team deployment isn’t listed publicly [README].
• No third-party benchmark reviews available at time of writing — the comparative evaluation data for this tool (speed, accuracy of transcription, OCR quality across languages) is thin in publicly available sources.

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Who should use this / who shouldn’t

Use screenpipe if:

• You regularly lose track of things you’ve seen — links, decisions, what was said in a meeting — and want to fix that at the infrastructure level, not by remembering to take better notes.
• You’re a developer using Claude, Cursor, or Claude Code and want your AI assistant to have context about what you’re actually working on right now.
• You’re paying $10–20/month for Otter.ai, Granola, or a Rewind alternative and the subscription is starting to feel like renting something you should own.
• You care enough about privacy that running local infrastructure feels worth the setup cost.
• You’re comfortable with a desktop app install and occasional CLI work.

Skip it and try Otter.ai or Granola if:

• You only want meeting transcription and summaries, not continuous screen capture.
• You’re non-technical and the phrase “MCP integration” means nothing to you — the pipe automations and AI search will be underused.
• You’re on a Windows machine without admin rights to install applications and grant screen recording permissions.

Skip it and use Microsoft Recall if:

• You just bought a Copilot+ PC and Recall is already baked in. It’s free and handles screen search adequately for basic use.
• You don’t need audio transcription or cross-platform support.

Wait before committing if:

• You’re evaluating this for team deployment. The team tier exists but pricing and deployment specifics aren’t transparent enough for a buying decision without a conversation with the company.
• Storage constraints are tight. 20GB/month is non-negotiable — the compression is already aggressive.

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Alternatives worth considering

• Limitless (formerly Rewind) — the closest functional alternative. Cloud SaaS, subscription pricing, more polished onboarding for non-technical users [1].
• Microsoft Recall — free, built into Windows 11 Copilot+ PCs. Screen-only (no audio), hardware-gated, no cross-platform support [homepage comparison].
• OpenRecall — AGPL-3.0 licensed, free, open source screen capture with search. Lighter than screenpipe, fewer automations, smaller community [1].
• Granola — meeting-specific AI notes, cleaner UX for non-technical users, subscription SaaS, no continuous capture [homepage comparison].
• Otter.ai — industry standard for meeting transcription. Subscription, cloud-based, no screen capture [homepage comparison].
• Windrecorder — Windows-only, GPL-2.0, free, open source. Simpler than screenpipe, fewer features, no MCP or pipe system [1].
• Pieces — developer-focused memory tool, captures code snippets and context rather than full screen. Less overlap than it appears [homepage comparison].

For a non-technical founder who wants meeting notes and basic recall: Granola or Otter.ai are lower friction. For someone who understands what they’re getting into and wants the local-first, no-subscription, full-capture approach: screenpipe is the most capable open-source option in this space [1][README].

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Bottom line

screenpipe is a well-built, genuinely local-first tool that does what it says: records everything, stores it locally, makes it searchable. The one-time $400 price is honest — no per-seat, no per-month, no “you’ve used too much storage this month” surprises. The MCP integration with Claude is the most interesting thing about it for developers in 2026, turning your screen history into live context for AI assistants. The trade-offs are real: it requires technical comfort to get full value from, it has storage overhead that needs managing, and the pipe ecosystem is powerful but not plug-and-play for non-technical users.

If you’re currently paying for multiple subscription tools to accomplish what screenpipe does in one package — and you’re comfortable running local infrastructure — the math favors screenpipe within two to three years. If you want to hand it to a non-technical operations person and walk away, it’s not there yet.

If the setup is the blocker, upready.dev deploys and configures tools like this for founders who want the benefit without the afternoon of command-line work.

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Sources

1. AlternativeTo — Rewind AI Alternatives (listing screenpipe as alternative, 22 likes, “Freemium Open Source”). https://alternativeto.net/software/rewind-ai/

Primary sources:

• GitHub repository and README: https://github.com/screenpipe/screenpipe (17,262 stars, MIT license per README)
• Official website and homepage: https://screenpi.pe
• Screenpipe pricing and FAQ: https://screenpi.pe (FAQ section)
• Screenpipe documentation: https://docs.screenpi.pe
• Screenpipe team page: https://screenpi.pe/team