An open-source, local-first memory layer for searchable screen and audio history
Advisor score
Documented editorial evaluation
The decision
screenpipe earns a shortlist for technical, privacy-conscious users willing to own configuration and auditing. It is a poor fit for unmanaged corporate deployment or anyone unwilling to map exactly what is captured, retained, searchable, and sent outward.
Best for
Technical users wanting local activity recall; Open-source and local-model workflows.
Choose something else if
Unmanaged workplace surveillance; Devices containing unfilterable sensitive data
Evidence
Editorial research pending · 8.0/10
Pricing checked
From $400 · 2026-08-24
Free access is available, with limits.
The project is open source, but packaged desktop access and associated services have their own commercial terms; verify the current license boundary.
screenpipe offers unusually controllable local capture, search, MCP, and automation, but continuous recording creates serious consent, exclusion, storage, security, and review obligations.
screenpipe earns a shortlist for technical, privacy-conscious users willing to own configuration and auditing. It is a poor fit for unmanaged corporate deployment or anyone unwilling to map exactly what is captured, retained, searchable, and sent outward.
Use a separate device profile for one approved week. Exclude password managers, banking, personal messages, and sensitive client apps before capture. Measure recall precision, missed context, storage growth, CPU impact, redaction misses, deletion, pause reliability, model data paths, and whether MCP or pipes expose more context than intended.
Personal Recommendation
screenpipe earns a shortlist for technical, privacy-conscious users willing to own configuration and auditing. It is a poor fit for unmanaged corporate deployment or anyone unwilling to map exactly what is captured, retained, searchable, and sent outward.
Try the recommendation
Local-first and open-source architecture
Overall Score
Editorial Review Framework
Overall Score
8.0
Ease of Use
8.2
AI Quality
8.0
Features
8.4
Speed
8.0
Integrations
8.2
Value for Money
7.8
Customer Support
7.8
Learning Curve
7.8
Recommended Because…
Local-first and open-source architecture
Scores use a 0-10 editorial scale: 9–10 exceptional, 8–8.9 strong, 7–7.9 capable with material tradeoffs, 6–6.9 limited fit, and below 6 not recommended. Scores publish only when a documented evaluation supports them.
Reusable trial worksheet
Turn this review’s buyer test into evidence. Your entries autosave only in this browser and are never added to shared shortlist links.
Confirm the tool meets every must-have workflow and stakeholder requirement.
Review starting point: Technical users wanting local activity recall; Open-source and local-model workflows; Builders using REST or MCP
Run the same representative work you would use in production; do not score a polished demo.
Review starting point: Complete three to five representative tasks with known acceptable outcomes and compare them with your current process.
Calculate the effective cost per accepted result, including usage, review, corrections, and required add-ons.
Review starting point: Official comparison documentation listed a $400 lifetime desktop price when reviewed. Local or cloud model costs, transcription, storage/sync, and team or enterprise arrangements can change total cost. Verify the checkout and exact included services before purchase. Reviewed August 24, 2026.
Define an acceptance threshold, test known answers and edge cases, and record every correction.
Review starting point: Editorial quality signals: features 4.2/5; AI quality 4.0/5. Validate these signals in your own work.
Verify what data enters the product, who can access it, how long it is retained, and whether it trains models.
Review starting point: Use approved low-risk data first. Check roles, consent, deletion, subprocessors, model-training settings, and the contract—not only the marketing page.
Test the real handoffs, permissions, failure states, and export path your team depends on.
Review starting point: Claude, ChatGPT, Codex, Ollama, Obsidian, Notion, n8n
Record training, governance, reliability, accessibility, ownership, and change-management risks before rollout.
Review starting point: Continuous recording creates high privacy risk; Optional cloud paths alter the data boundary; Technical setup and maintenance are substantial
Loading saved worksheet… · private to this device or your optional account
Evaluation
Research-based
Price posture
From $400/month
Reviewed
2026-08-24
No authentic product screenshot is published for this review. DiscoverAI does not use generated interface images as product evidence.
Editorial freshness
Pricing and material product claims were checked August 24, 2026.
Community evidence
Structured, editor-moderated experience—not star ratings. This complements our independent review and never changes its score.
No approved community evidence yet. Be the first verified user to contribute.
The code is open source, while the packaged desktop product and optional services have commercial terms. Verify the current license and checkout before adoption.
Local capture and storage are the default, but cloud models, transcription, sync, archive, connections, or LAN access change the data path.
It can capture screen content, accessibility text, OCR, input-related events where available, and audio transcripts, subject to configured filters and permissions.
Yes. It documents MCP and API connections for compatible assistants, but those connections can expose sensitive history and should be tightly scoped.
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screenpipe offers unusually controllable local capture, search, MCP, and automation, but continuous recording creates serious consent, exclusion, storage, security, and review obligations.
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