ScreenMind
Open-source macOS app that captures your screen, analyzes it locally with Gemma 4, and builds a searchable, chat-able AI memory.
ScreenMind is the most credible free, fully open-source Recall alternative on macOS right now. MIT licensing beats screenpipe's source-available terms, and a single Gemma 4 family handling vision, audio, and reasoning replaces the usual OCR + Whisper + external LLM stack. The privacy story is concrete rather than a slogan: no cloud calls after the model download, no telemetry, Fernet encryption with the OS keyring, and a sensitive-data filter that redacts credit cards, SSNs, API keys, and passwords before anything is stored. If you're privacy-obsessed and comfortable with a GitHub clone plus an in-app model download, the payoff is a searchable screen memory that never phones home. If you
Verified 12h ago · liveness 76/100 · cite: rightaichoice.com/tools/screenmind
- Privacy-first macOS users who want Recall-style screen memory without telemetry or plaintext storage
- Developers building automations on screen history via the Agent platform or MCP Server
- Researchers and knowledge workers who need a searchable, on-device log of their day
- Meeting-heavy professionals who want local Zoom/Teams/Meet transcription instead of a subscription
- Windows or Linux primary users — ScreenMind is positioned as a macOS app despite cross-platform autostart code
- Teams needing shared dashboards, centralized screen history, or admin controls
- Anyone requiring cloud sync or multi-device access to their screen memory
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Skip ScreenMind if your work machine is Windows or Linux, or if you need shared team dashboards and centralized admin controls rather than a per-user local memory.
You pay your own hardware and storage costs — a 12B Gemma 4 model plus an ever-growing screenshot index will consume disk and VRAM that no license fee covers.
ScreenMind is $0 under the MIT license — no paid tier, no usage caps, no seat minimums. Against Recall, which ships with Windows and carries no separate fee but stores data in plaintext, ScreenMind costs you hardware and setup time instead of money. Against screenpipe, the closest open-source peer, the practical difference is licensing terms: MIT here versus source-available there. Subscription screen-memory and meeting-transcription tools charge recurring seat fees that ScreenMind replaces
In short
ScreenMind — Open-source macOS app that captures your screen, analyzes it locally with Gemma 4, and builds a searchable, chat-able AI memory. Best for Privacy-first macOS users who want Recall-style screen memory without telemetry or plaintext storage, Developers building automations on screen history via the Agent platform or MCP Server, Researchers and knowledge workers who need a searchable, on-device log of their day. Free to use.
What people actually say about ScreenMind — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
21 mentions across 5 sources (Hacker News, YouTube, Product Hunt, Bluesky, GitHub) · researched Jul 26, 2026.
Average across the 5 sources that answered — each source counts once, not each post.
- +100% local – no cloud dependency or telemetry at all.
- +Free and open-source (MIT license) – no subscription or hidden fees.
- +Uses Gemma 4 with vision, audio, and reasoning on-device.
- +Smart content-change capture avoids pointless screenshots.
- +Three analysis modes (Accurate, Balanced, Fast) suit different hardware.
- −Linux Wayland capture is completely broken for many users.
- −Frequently misidentifies applications as VSCode or Programming.
- −Only captures main screen on multi-monitor setups.
- −No Windows multi-monitor support confirmed.
- −Limited community and slow support response times.
Viability Score
How well maintained and how widely used is ScreenMind? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Smart capture using pHash content-change detection instead of a fixed timer
- Gemma 4 vision analysis: app detection, activity categorization, mood, scene description, spatial layout
- Hybrid search combining MiniLM semantic embeddings with FTS5 keyword matching
- Conversational RAG chat over screen history with follow-up context
- Voice memos via Ctrl+Shift+V, transcribed by Gemma 4's native audio encoder (no Whisper)
- Auto-detected Zoom, Teams, and Google Meet transcription with structured summaries
- Three analysis modes: Accurate (~76s), Balanced (~40s), Fast (~12s)
- Per-app pHash cache with 3-tier caching and app-aware staleness
- Chat-first GPU priority that cancels in-flight analysis in under 1 second
- Auto-pause for games, video editors, and 3D software
- Sensitive data filter redacts credit cards, SSNs, API keys, and passwords before storage
- AES encryption at rest (Fernet + OS keyring) with dashboard PIN lock
- Incognito mode: one-click pause, nothing recorded
- Model Hub with in-app downloads: Gemma 4 E2B, E4B, 12B in Q4_0, Q8_0, BF16
- Agent platform for Markdown or Python automation agents
About ScreenMind
ScreenMind is an MIT-licensed macOS app that captures your screen, runs every frame through Gemma 4 locally, and builds a searchable AI memory you can chat with on your own machine. The repo (github.com/ayushh0110/ScreenMind) sits at roughly 216 GitHub stars with 16 forks and 90 commits. Microsoft showed demand for screen-aware AI with Recall, but Recall shipped with plaintext storage and telemetry; ScreenMind's answer is zero cloud calls after the initial model download, no telemetry, and AES encryption at rest using Fernet plus the OS keyring. Capture is content-aware rather than timer-based: pHash deduplication and app-aware staleness in a three-tier cache mean the tool fires when your screen actually changes, and it auto-pauses for games, video editors, and 3D software. Each frame goes through Gemma 4 for app detection, activity categorization, mood, scene description, and spatial layout. You pick from three analysis modes — Accurate (~76s), Balanced (~40s), or Fast (~12s). Retrieval is hybrid: MiniLM semantic embeddings alongside FTS5 keyword search, so "the Figma link Alex pasted" works as well as an exact string match. Conversational RAG chat supports follow-ups, gets GPU priority over background analysis, and cancels in-flight frame analysis in under a second. Beyond the core, ScreenMind bundles voice memos (hold Ctrl+Shift+V, transcribed by Gemma 4's native audio encoder rather than Whisper), auto-detected Zoom/Teams/Meet transcription with structured summaries, an analytics dashboard with hourly heatmaps, and Day Rewind timelapse playback. The in-app Model Hub downloads Gemma 4 E2B, E4B, or 12B in Q4_0, Q8_0, or BF16 without a terminal. An Agent platform runs Markdown or Python agents, and an MCP Server exposes screen history to Claude Desktop, Cursor, and VS Code. It reads as the open-source counterweight to screenpipe — MIT versus source-available licensing, one model family instead of an OCR + Whisper + external LLM stack.
Behind the Verdict
What ScreenMind gets right is the part most Recall-alikes get wrong: it treats capture as an inference-budget problem rather than a recording problem. pHash content-change detection plus per-app staleness means communication apps refresh faster than IDEs, and games, video editors, and 3D software are auto-paused entirely. Fewer frames analyzed means the local GPU stays usable, and the Chat-First GPU Priority — chat cancels in-flight analysis in under 1s — means the machine stays responsive when you actually want to ask something. The parallel pipeline that runs dev-context detection and embedding generation concurrently saves roughly 2-3s per frame. The privacy engineering is more than positioning. AES encryption at rest (Fernet + OS keyring), a sensitive-data filter that redacts credit cards, SSNs, API keys, and passwords before storage, a dashboard PIN lock, and one-click incognito mode that records nothing. Compare that to Recall's plaintext store, which is exactly what triggered the backlash ScreenMind is answering. Zero cloud calls after the initial model download is the claim the whole product rests on, and it's structurally verifiable — it's on GitHub under MIT. The breadth is surprising for a 90-commit project. Voice memos via Ctrl+Shift+V transcribed by Gemma 4's native audio encoder (no Whisper dependency), auto-detected Zoom/Teams/Meet transcription with structured summaries, an analytics dashboard with hourly heatmaps, Day Rewind timelapse playback, an in-app Model Hub that pulls Gemma 4 E2B/E4B/12B in Q4_0/Q8_0/BF16 without a terminal, a Markdown/Python Agent platform, and an MCP Server that exposes screen history to Claude Desktop, Cursor, and VS Code. The MCP route is the smart integration play — instead of building connectors, it plugs your screen memory into tools you already use. Where it falls short: macOS-first despite autostart code paths for Windows Registry and Linux XDG; no team dashboards, shared history, admin controls, or cloud sync; setup is a GitHub clone plus in-app model download rather than a one-click installer, so non-technical users will bounce. Support is the repo itself — 216 stars, 16 forks, 90 commits, no SLA and no paid tier behind it. Hardware requirements scale with the model variant you pick, and the three analysis modes (Accurate ~76s, Balanced ~40s, Fast ~12s) make the throughput-versus-depth tradeoff explicit rather than hiding it. Watch the issue tracker and commit cadence before standardizing a team on it.
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Real-world workflow fit
Concrete scenarios for the personas ScreenMind actually fits — and what changes day-one when you adopt it.
You install ScreenMind from the GitHub repo, download Gemma 4 E4B through the in-app Model Hub, and leave it capturing in Balanced mode. Three days later you remember a config flag you saw in a terminal but never copied.
Outcome: Hybrid search (MiniLM embeddings + FTS5) surfaces the frame by meaning rather than exact string, and the MCP Server lets you pull that context directly into Cursor or VS Code instead of switching apps.
ScreenMind auto-detects a Zoom call, records and transcribes it with Gemma 4's native audio encoder, and generates a structured summary. No meeting bot joins the call and nothing leaves the machine.
Outcome: You get a local transcript and summary without a per-seat transcription subscription, and the analytics dashboard hourly heatmap shows where your week actually went.
You switch on incognito mode for sensitive sessions and rely on the sensitive-data filter to redact credit cards, SSNs, API keys, and passwords before storage, with AES encryption at rest via Fernet and the OS keyring.
Outcome: A searchable, PIN-locked archive of your research day that never leaves your laptop — no telemetry, no cloud calls after the initial model download.
Use Cases
- Recall a code snippet you saw in a tutorial but never bookmarked.
- Search screen history for an exact error message that appeared days ago.
- Chat with your screen memory to retrieve meeting notes or project ideas.
- Auto-detect and transcribe Zoom, Teams, or Meet calls locally with structured summaries.
- Build a custom Markdown or Python agent that uses screen history as decision context.
- Keep a private, encrypted archive of everything displayed on your monitor.
- Dictate voice memos with Ctrl+Shift+V and have them transcribed on-device alongside a screenshot.
Models Under the Hood
as of 2026-09-22
Limitations
- ScreenMind is an early open-source GitHub project (roughly 216 stars, 16 forks, 90 commits) whose documentation and support live primarily in the repository, so you're relying on the maintainer and community rather than a support contract.
- It is positioned as a macOS app even though autostart code paths exist for Windows Registry and Linux XDG.
- Everything runs locally through Gemma 4, so throughput is bounded by your hardware and by the analysis mode you choose — Accurate runs around 76s per frame, Balanced around 40s, Fast around 12s.
- There is no shared team dashboard, no centralized screen history, no admin control plane, and no cloud sync or multi-device access.
- Setup is a GitHub clone plus an in-app model download rather than a one-click installer, which non-technical users will find rough.
- Integration coverage beyond the MCP Server path is not documented in the material available for this refresh.
as of 2026-09-29
Verification history
We have re-verified ScreenMind 12 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 12 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published ScreenMind tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free (open source, MIT)
$0
Ideal for
Individual macOS users who want a private screen memory without a subscription — especially developers and researchers comfortable installing from GitHub.
What this tier adds
Starting tier: the entire product at $0 under MIT, with no usage caps, no seat minimums, and all features — chat, search, meetings, analytics, Day Rewind, Agent platform, and MCP Server — included. You cover only your own hardware and storage.
Where the pricing makes sense
The company stage and team size where ScreenMind's pricing actually pencils out — and where peers do it cheaper.
ScreenMind is $0 under the MIT license — no paid tier, no usage caps, no seat minimums. Against Recall, which ships with Windows and carries no separate fee but stores data in plaintext, ScreenMind costs you hardware and setup time instead of money. Against screenpipe, the closest open-source peer, the practical difference is licensing terms: MIT here versus source-available there. Subscription screen-memory and meeting-transcription tools charge recurring seat fees that ScreenMind replaces
Setup time & first value
How long it actually takes to get something useful out of ScreenMind — broken out by persona, not the marketing-page minute.
Plan on 20-40 minutes for a developer: clone the GitHub repo, install dependencies, launch the app, and download a Gemma 4 variant through the Model Hub (E2B is the lightest; 12B takes longest and needs the most VRAM). Non-technical users should budget an hour or more, since there's no one-click installer. First value arrives as soon as the first frames are analyzed and indexed in your chosen
Switching to or from ScreenMind
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Microsoft Recall: import nothing — you start a fresh local index, but your data is encrypted at rest and never leaves the machine, unlike Recall's plaintext store.
- →From screenpipe: reinstall from the GitHub repo and re-point any pipelines at the MCP Server, which exposes screen history to Claude Desktop, Cursor, and VS Code.
- →From manual screenshots: turn on content-change capture so frames are only stored when the screen actually changes, replacing your fixed-interval screenshot habit.
- →From cloud meeting-transcription subscriptions: rely on auto-detected Zoom/Teams/Meet capture with Gemma 4 audio transcription running locally.
- ↗To screenpipe: export your local index and re-ingest, accepting the move from MIT licensing to source-available terms.
- ↗To Microsoft Recall: only if you move your primary machine to Windows, and accept plaintext storage and telemetry in exchange for OS integration.
- ↗To a cloud meeting-transcription service: if you decide local Gemma 4 transcription throughput is too slow for your call volume.
- ↗To a team-oriented screen-memory product: if you outgrow per-user local memory and need shared dashboards and admin controls.
Integrations
Resources & Guides
- Resourcegithub.com
ScreenMind · ScreenMind
Helpful link from github.com
- Resourcegithub.com
MCP SETUP · ScreenMind
Helpful link from github.com
- Resourcegithub.com
Architecture · ScreenMind
Helpful link from github.com
- Resourcegithub.com
CONTRIBUTING · ScreenMind
Helpful link from github.com
- Documentationgithub.com
Docs · ScreenMind
Full product docs from github.com
Tutorials & Learning
YouTube returned 6 videos for “ScreenMind”, and we withheld 6: 6 could not be judged, because “ScreenMind” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about ScreenMind.
Official links
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Featured Head-to-Head Comparisons
Screenmind vs Sublime Security
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Screenmind vs Polycam
Don't pit them head-to-head — they solve completely different problems. If you need high-fidelity 3D scanning of real-world spaces with floor plans and drone support, Polycam is the clear choice, especially at under $12/mo for Pro. If you want a private, local screen memory that indexes your entire desktop activity and lets you chat with it, ScreenMind is a unique, free, and powerful tool — provided you're on macOS.
Screenmind vs Push Security
If your priority is browser security – blocking AiTM phishing, shadow SaaS, and AI data leaks – Push Security is the clear choice. But if you need private, on-device screen memory for personal productivity and recall, ScreenMind is a groundbreaking free tool. They solve completely different problems; pick based on whether you're securing a workforce or augmenting your own memory.
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