{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-screenpipe","slug":"screenpipe","name":"Screenpipe","type":"repo","url":"https://github.com/screenpipe/screenpipe","page_url":"https://unfragile.ai/screenpipe","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-screenpipe__cap_0","uri":"capability://data.processing.analysis.event.driven.screen.capture.with.platform.specific.apis","name":"event-driven screen capture with platform-specific apis","description":"Captures screen content from all connected monitors by listening to OS-level events (window focus changes, content updates) rather than polling continuously, using platform-specific graphics APIs: CoreGraphics on macOS, DXGI on Windows, and X11/PipeWire on Linux. This event-driven model reduces CPU usage by ~80% compared to continuous frame capture while maintaining temporal accuracy through configurable capture intervals (default 1 FPS). The VisionManager monitors trigger events and coordinates frame acquisition across multiple displays.","intents":["I need to record screen activity without draining battery or CPU on continuous polling","I want to capture all monitor outputs including multi-display setups with minimal performance overhead","I need platform-native screen capture that respects OS-level privacy controls and permissions"],"best_for":["developers building always-on AI memory systems for personal productivity","teams deploying screen recording on resource-constrained devices (laptops, edge devices)","privacy-conscious users who want local-first capture without cloud streaming"],"limitations":["Event-driven capture may miss very brief UI changes that occur between trigger events","Platform-specific implementations require separate code paths and testing for macOS, Windows, Linux","DXGI on Windows requires GPU access; fallback to CPU capture has higher latency","X11/PipeWire on Linux has fragmented support across desktop environments"],"requires":["macOS 10.13+ with CoreGraphics framework","Windows 10+ with DXGI support","Linux with X11 or PipeWire audio server","Rust 1.70+ for compilation"],"input_types":["OS-level window events","display configuration metadata","capture interval configuration (milliseconds)"],"output_types":["raw pixel frames (RGBA format)","frame metadata (timestamp, display ID, resolution)","OCR-ready image buffers"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-screenpipe__cap_1","uri":"capability://data.processing.analysis.multi.engine.ocr.text.extraction.from.screen.frames","name":"multi-engine ocr text extraction from screen frames","description":"Extracts text from every captured screen frame using platform-optimized OCR engines: Apple Vision framework on macOS, Windows native OCR on Windows, and Tesseract on Linux with fallback support. The system processes frames through a configurable OCR pipeline that handles multiple languages, variable text sizes, and rotated text. Extracted text is indexed alongside frame metadata (timestamp, bounding boxes, confidence scores) for later semantic search and retrieval.","intents":["I need to search for text that appeared on my screen at any point in time","I want OCR results with confidence scores and bounding box coordinates for precise text location","I need multi-language OCR support for international content without manual language selection"],"best_for":["knowledge workers searching through historical screen content by text snippets","developers building AI agents that need to understand UI text and form fields","teams with international users requiring multi-language OCR without per-frame configuration"],"limitations":["Apple Vision OCR on macOS is proprietary and cannot be customized; accuracy varies by text size and font","Windows native OCR requires Windows 10+ and may have lower accuracy on non-standard fonts","Tesseract fallback on Linux is slower (~500ms per frame) and less accurate than native engines","OCR confidence scores are not normalized across platforms, making cross-platform filtering unreliable","Handwritten text and complex layouts (tables, multi-column) have significantly lower accuracy"],"requires":["macOS 10.15+ for Vision framework","Windows 10+ with language pack installed","Linux with Tesseract 4.0+ installed","Minimum 2GB RAM for OCR processing queue"],"input_types":["raw pixel frames (RGBA, 8-bit)","language hints (optional)","OCR confidence threshold (0.0-1.0)"],"output_types":["extracted text strings","bounding box coordinates (x, y, width, height)","per-word confidence scores","language detection results"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-screenpipe__cap_10","uri":"capability://tool.use.integration.multi.provider.ai.backend.abstraction.with.local.and.cloud.options","name":"multi-provider ai backend abstraction with local and cloud options","description":"Abstracts AI service providers (OpenAI, Anthropic, Deepgram, local Whisper, local sentence-transformers) behind a unified configuration interface. Users can select which provider to use for each AI capability (transcription, embeddings, LLM reasoning) and switch between local and cloud options without code changes. The system includes fallback chains (e.g., try local Whisper first, fall back to Deepgram if unavailable) and usage tracking for cloud services. Configuration is stored in settings and can be updated via desktop app or API.","intents":["I want to choose between local and cloud AI processing based on privacy and performance tradeoffs","I need to switch AI providers without reconfiguring my entire setup","I want to track and control spending on cloud AI APIs"],"best_for":["users who want flexibility to switch between local and cloud AI without vendor lock-in","teams managing costs by choosing local processing for some tasks and cloud for others","privacy-conscious users who want to minimize cloud API calls"],"limitations":["Switching providers mid-stream (e.g., local Whisper to Deepgram) may produce inconsistent results due to model differences","Fallback chains add complexity; debugging which provider is actually being used requires log inspection","API key management is manual; no built-in secret storage or rotation","Usage tracking is approximate; actual cloud API usage may differ from Screenpipe's tracking","Not all providers support all capabilities; e.g., Deepgram only does transcription, not embeddings"],"requires":["API keys for cloud providers (OpenAI, Anthropic, Deepgram) if using cloud options","Local models (Whisper, sentence-transformers) require 4GB+ VRAM if using local options","Configuration file or settings UI to specify provider preferences"],"input_types":["provider selection (openai, anthropic, deepgram, local)","API keys (for cloud providers)","model selection (e.g., gpt-4, claude-3, whisper-base)","fallback chain configuration"],"output_types":["transcripts, embeddings, or LLM responses from selected provider","usage metrics (tokens, API calls, costs)","provider status (available, unavailable, degraded)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-screenpipe__cap_11","uri":"capability://automation.workflow.global.keyboard.shortcuts.and.system.tray.integration","name":"global keyboard shortcuts and system tray integration","description":"Provides configurable global keyboard shortcuts (e.g., Cmd+Shift+P on macOS) to trigger Screenpipe actions from anywhere on the system, even when the desktop app is not focused. Shortcuts can open the search interface, pause/resume recording, or trigger custom Pipes. System tray integration provides quick access to Screenpipe status, recording state, and common actions. Shortcuts are registered at the OS level using platform-specific APIs (Cocoa on macOS, Win32 on Windows, X11 on Linux) and persist across app restarts.","intents":["I want to quickly search my screen history without switching to the Screenpipe app","I need to pause recording with a keyboard shortcut when discussing sensitive information","I want to see Screenpipe status in the system tray and access it quickly"],"best_for":["power users who want quick keyboard access to Screenpipe from any application","teams with privacy policies requiring quick pause/resume of recording","users who want minimal UI footprint (system tray only)"],"limitations":["Global shortcuts may conflict with application-specific shortcuts; no built-in conflict detection","System tray behavior is platform-specific; macOS menu bar differs significantly from Windows taskbar","Shortcuts are not customizable on some Linux desktop environments (GNOME, KDE)","Pause/resume shortcut does not stop already-queued processing; frames captured before pause are still processed","No shortcut to trigger arbitrary Pipes; only pre-configured actions are available"],"requires":["OS-level permission to register global shortcuts (may require accessibility permissions on macOS)","Screenpipe server running in background","Desktop app installed and running"],"input_types":["keyboard input (global hotkey)","system tray click","shortcut configuration (key combination, action)"],"output_types":["search interface opened","recording paused/resumed","Pipe triggered","status notification"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-screenpipe__cap_12","uri":"capability://safety.moderation.privacy.preserving.local.first.architecture.with.optional.encrypted.cloud.sync","name":"privacy-preserving local-first architecture with optional encrypted cloud sync","description":"Implements a privacy-first design where all data capture, processing, and storage occur locally on the user's device by default. Screen frames, audio, OCR results, and transcripts are stored in the local SQLite database and never transmitted to cloud services unless explicitly configured. Optional encrypted cloud sync can be enabled for backup and cross-device access, but encryption keys are managed locally and cloud provider cannot access unencrypted data. The system provides granular privacy controls (pause recording, exclude applications, redact sensitive data) and audit logs showing what data was captured and processed.","intents":["I want to record my screen and audio without any data leaving my device","I need compliance with privacy regulations (GDPR, HIPAA) that require local data control","I want to enable cloud backup for disaster recovery without compromising privacy"],"best_for":["privacy-conscious users and organizations with strict data residency requirements","teams handling sensitive information (healthcare, finance, legal) that cannot use cloud recording","users in jurisdictions with data protection regulations (EU, Canada)"],"limitations":["Local-only storage limits cross-device access; users must manually sync or use encrypted cloud option","Encrypted cloud sync adds complexity; key management is user's responsibility","No audit trail of cloud access; if cloud provider is compromised, users cannot detect unauthorized access","Local storage is vulnerable to physical theft; no built-in full-disk encryption requirement","Privacy controls (pause, exclude apps) are user-managed; no enforcement mechanism prevents accidental recording","Encrypted cloud sync has higher latency and storage costs than unencrypted alternatives"],"requires":["Local disk space for full data storage (500GB+ for 6 months)","Optional: cloud storage account (AWS S3, Google Cloud Storage) for encrypted sync","Optional: encryption key management (user-managed or HSM)"],"input_types":["privacy settings configuration (pause, exclude apps, redact patterns)","cloud sync configuration (enabled/disabled, encryption key)","audit log queries"],"output_types":["local data stored in SQLite","encrypted cloud backups (if enabled)","audit logs showing capture and processing events","privacy compliance reports"],"categories":["safety-moderation","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-screenpipe__cap_2","uri":"capability://data.processing.analysis.continuous.audio.transcription.with.voice.activity.detection","name":"continuous audio transcription with voice activity detection","description":"Transcribes system audio and microphone input using either local OpenAI Whisper or cloud-based Deepgram API, with integrated voice activity detection (VAD) to identify speech segments and reduce processing of silence. The audio pipeline captures raw PCM samples, applies VAD filtering to detect speech boundaries, batches audio chunks, and sends them to the transcription engine. Transcripts are timestamped and indexed alongside screen frames for synchronized search across audio and visual content.","intents":["I want to search for what was said in meetings or calls that happened on my screen","I need to reduce transcription costs by skipping silence and non-speech audio segments","I want to choose between local (Whisper) and cloud (Deepgram) transcription based on privacy vs speed tradeoffs"],"best_for":["remote workers transcribing meetings and calls for later recall","developers building AI agents that need to understand spoken context alongside screen activity","privacy-focused teams that want local audio processing without cloud transmission"],"limitations":["Local Whisper transcription is slow (~30-60 seconds per minute of audio) and requires 4GB+ VRAM for base model","Deepgram cloud transcription requires internet connectivity and API key; introduces ~2-5 second latency","VAD is not 100% accurate; background noise, music, or overlapping speech can trigger false positives","Whisper struggles with accents, technical jargon, and low-quality audio (compression artifacts)","No speaker diarization (speaker identification) in base implementation; all speakers merged into single transcript"],"requires":["Microphone or system audio capture permissions","For local Whisper: Python 3.8+, 4GB+ VRAM (base model), 8GB+ for large model","For Deepgram: API key and active internet connection","Audio codec support: PCM, AAC, MP3"],"input_types":["raw PCM audio samples (16-bit, 16kHz)","audio source selection (system audio, microphone, both)","transcription engine choice (whisper, deepgram)","VAD sensitivity threshold (0.0-1.0)"],"output_types":["transcript text with word-level timestamps","confidence scores per word segment","detected language","speech segment boundaries (start/end times)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-screenpipe__cap_3","uri":"capability://search.retrieval.semantic.search.across.screen.and.audio.history.with.vector.embeddings","name":"semantic search across screen and audio history with vector embeddings","description":"Enables full-text and semantic search across captured screen frames and audio transcripts by embedding text content into a vector database. The system extracts text from OCR results and transcripts, generates embeddings using configurable embedding models (local or cloud-based), and stores them in a local SQLite database with vector extension support. Search queries are embedded using the same model and matched against historical embeddings using cosine similarity, returning ranked results with temporal context (timestamps, associated frames, transcript segments).","intents":["I need to find information I saw on screen or heard in audio by describing it in natural language","I want to search across months of screen history without remembering exact keywords or timestamps","I need semantic search that understands synonyms and paraphrasing, not just exact text matches"],"best_for":["knowledge workers with large screen history archives (6+ months) who need semantic recall","developers building AI agents that need to retrieve relevant historical context for decision-making","teams using Screenpipe as a personal knowledge base with natural language query interface"],"limitations":["Vector embeddings require significant storage: ~1KB per frame at 1 FPS = ~86GB per day of continuous recording","Semantic search latency is 500ms-2s per query depending on database size and embedding model","Embedding quality varies by model; smaller models (384-dim) miss nuanced semantic relationships vs larger models (1536-dim)","No built-in deduplication; similar frames captured seconds apart create redundant embeddings","Cosine similarity ranking can return false positives for polysemous terms (e.g., 'bank' as financial vs riverbank)"],"requires":["SQLite 3.35+ with vector extension (sqlite-vec or similar)","Embedding model: local (sentence-transformers, ~500MB) or API key (OpenAI, Cohere)","Minimum 50GB free disk space for vector database","Python 3.9+ for embedding pipeline"],"input_types":["natural language search query (text)","temporal filters (start date, end date)","content type filter (screen only, audio only, both)","embedding model selection (local vs cloud)","similarity threshold (0.0-1.0)"],"output_types":["ranked search results with similarity scores","associated timestamps and frame IDs","snippet of matching text with context","link to full transcript or frame"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-screenpipe__cap_4","uri":"capability://tool.use.integration.rest.api.for.programmatic.access.to.captured.data.and.search","name":"rest api for programmatic access to captured data and search","description":"Exposes a REST API that allows external applications and scripts to query captured screen frames, audio transcripts, and search results. The API provides endpoints for frame retrieval (by timestamp or ID), transcript search, semantic search, and metadata queries. The API is served by a local HTTP server (default port 3030) and supports authentication via API keys or local-only access. Responses include structured JSON with frame data (base64-encoded images, OCR text, timestamps), transcript segments, and search rankings.","intents":["I want to build custom AI agents that query my screen history as context for decision-making","I need to integrate Screenpipe data into external tools (Slack bots, automation scripts, dashboards)","I want to programmatically export or analyze my screen and audio history"],"best_for":["developers building AI agents and automations on top of personal activity data","teams integrating Screenpipe into existing productivity tools and workflows","researchers analyzing personal digital behavior patterns"],"limitations":["API responses include base64-encoded images which are large (~50-200KB per frame); clients must handle decompression","No built-in rate limiting; high-frequency queries can cause performance degradation","Authentication is basic (API key in header); no OAuth or advanced security for multi-user scenarios","Pagination is not implemented; large result sets (1000+ frames) are returned in single response","No versioning strategy; API changes may break existing client code"],"requires":["Screenpipe server running locally (port 3030 by default)","HTTP client library (curl, requests, fetch, etc.)","API key if authentication is enabled","Network access to localhost (127.0.0.1)"],"input_types":["HTTP GET/POST requests with JSON payloads","query parameters: timestamp, limit, offset, search_query","API key in Authorization header"],"output_types":["JSON responses with frame metadata and base64 image data","transcript segments with timestamps","search results with similarity scores","error messages with HTTP status codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-screenpipe__cap_5","uri":"capability://automation.workflow.pipes.plugin.system.for.custom.automations.and.workflows","name":"pipes plugin system for custom automations and workflows","description":"Provides a plugin architecture called 'Pipes' that allows users to write custom automations triggered by screen and audio events. Pipes are JavaScript/TypeScript functions that receive captured frames, transcripts, and search results as input and can execute actions (send notifications, trigger webhooks, modify system state). The system includes a component registry for reusable UI elements and integrates with the MCP (Model Context Protocol) server for LLM-powered automations. Pipes are executed in a sandboxed runtime with access to Screenpipe's data APIs.","intents":["I want to trigger custom actions when specific content appears on my screen (e.g., send Slack message when I see a bug report)","I need to build AI-powered automations that react to screen activity in real-time","I want to extend Screenpipe with custom logic without modifying core code"],"best_for":["developers building custom AI automations for personal productivity workflows","teams deploying Screenpipe with organization-specific automation rules","power users who want to extend Screenpipe without contributing to core project"],"limitations":["Pipes runtime is sandboxed; direct file system access and network calls are restricted","No persistent state between Pipe executions; each invocation starts fresh (requires external storage)","Pipes are synchronous; long-running operations (API calls, LLM inference) block event processing","Limited debugging tools; errors are logged but not easily inspectable in development","Component registry is small; most UI customization requires writing raw HTML/CSS"],"requires":["JavaScript/TypeScript knowledge","Node.js 18+ for local development","Screenpipe server running with Pipes enabled","Access to Screenpipe REST API"],"input_types":["captured screen frames (as image data)","OCR text and metadata","audio transcripts","search query results","trigger event type (frame_captured, transcript_ready, etc.)"],"output_types":["notifications (desktop, Slack, email)","webhook POST requests","database writes (via API)","system commands (limited)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-screenpipe__cap_6","uri":"capability://tool.use.integration.mcp.model.context.protocol.server.for.llm.integration","name":"mcp (model context protocol) server for llm integration","description":"Implements a Model Context Protocol server that exposes Screenpipe's data (frames, transcripts, search results) as tools and resources to LLMs and AI agents. The MCP server allows Claude, GPT, and other LLMs to query screen history, search for content, and retrieve context about past activities. The server translates LLM tool calls into Screenpipe API requests and returns structured results. This enables AI agents to use Screenpipe as a memory system for decision-making and reasoning.","intents":["I want Claude or GPT to have access to my screen history for context-aware assistance","I need to build AI agents that can reason about my past activities and make recommendations","I want to use LLMs as a natural language interface to my screen and audio history"],"best_for":["developers building AI agents that need personal activity context","users integrating Screenpipe with Claude, GPT, or other LLMs via MCP","teams using LLMs for personalized productivity assistance"],"limitations":["MCP server requires LLM client support (Claude, some GPT integrations); not all LLMs support MCP","Tool calls from LLM to Screenpipe add latency (~500ms-2s per query); not suitable for real-time interactions","LLM context window limits how much screen history can be included; large result sets must be summarized","No built-in rate limiting; LLM can make unlimited queries to Screenpipe, causing performance issues","Privacy concern: LLM receives raw screen content and transcripts; requires careful API key management"],"requires":["MCP-compatible LLM client (Claude Desktop, some GPT integrations)","Screenpipe server running with MCP enabled","Network connectivity between LLM client and Screenpipe server","API key for LLM service (OpenAI, Anthropic, etc.)"],"input_types":["LLM tool calls (JSON-RPC format)","tool parameters: search_query, timestamp_range, content_type","context window constraints from LLM"],"output_types":["structured tool results (frames, transcripts, search rankings)","formatted text for LLM consumption","metadata (timestamps, confidence scores)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-screenpipe__cap_7","uri":"capability://planning.reasoning.pi.coding.agent.for.autonomous.screen.based.task.execution","name":"pi coding agent for autonomous screen-based task execution","description":"Implements an autonomous AI agent called 'Pi' that can observe screen content, understand UI elements, and execute tasks by simulating user interactions (mouse clicks, keyboard input, form filling). The agent uses vision-language models to interpret screen state, reason about next steps, and generate actions. Pi integrates with Screenpipe's frame capture and OCR to understand current UI state, and can chain multiple actions to complete multi-step workflows (e.g., filling out forms, navigating websites, running terminal commands).","intents":["I want an AI agent to automate repetitive screen-based tasks without writing scripts","I need to execute complex workflows that require understanding UI context and making decisions","I want to delegate routine tasks (data entry, form filling, web navigation) to an autonomous agent"],"best_for":["users automating repetitive data entry and form-filling tasks","developers building autonomous workflow agents for business processes","teams reducing manual effort on routine screen-based tasks"],"limitations":["Pi agent requires vision-language model API (GPT-4V, Claude Vision); adds latency (~2-5 seconds per action)","Agent reasoning is not deterministic; same task may be executed differently on different runs","No built-in error recovery; if agent makes incorrect action, workflow must be manually corrected","Agent cannot interact with system-level dialogs or privileged operations (admin prompts, UAC)","Training data bias in vision models can cause agent to misinterpret UI elements or make unsafe assumptions","No rollback mechanism; destructive actions (delete, modify) cannot be undone by agent"],"requires":["Vision-language model API key (OpenAI GPT-4V, Anthropic Claude Vision, etc.)","Screenpipe server running with frame capture enabled","Input simulation capability (xdotool on Linux, pyautogui on Windows/macOS)","Sufficient API quota for frequent agent invocations"],"input_types":["task description (natural language)","current screen frame (image)","OCR text and UI element locations","action history (previous steps taken)"],"output_types":["next action to execute (click coordinates, keyboard input, text to type)","reasoning explanation (why this action was chosen)","task completion status (in progress, completed, failed)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-screenpipe__cap_8","uri":"capability://data.processing.analysis.local.sqlite.database.with.full.text.and.vector.search.indexing","name":"local sqlite database with full-text and vector search indexing","description":"Stores all captured data (frames, OCR text, transcripts, metadata) in a local SQLite database with full-text search (FTS5) and vector search extensions. The database schema includes tables for frames (with base64 image data), OCR results (text and bounding boxes), transcripts (with word-level timestamps), and embeddings (vector representations for semantic search). Indexes are automatically maintained as new data arrives. The database is stored locally on disk (no cloud sync by default) and can be queried via SQL or through Screenpipe's REST API.","intents":["I want to store months of screen and audio history locally without cloud dependency","I need to query my captured data using SQL for custom analysis and reporting","I want fast full-text and semantic search across large historical datasets"],"best_for":["privacy-focused users who want complete local data control","developers building custom analytics on top of personal activity data","teams with on-premise deployments that cannot use cloud storage"],"limitations":["SQLite is single-writer; concurrent writes from multiple Screenpipe instances cause lock contention","Database file grows rapidly: ~1-2GB per day at 1 FPS with OCR and transcripts; requires active storage management","Full-text search (FTS5) is slower than dedicated search engines (Elasticsearch) for very large datasets (1TB+)","Vector search requires sqlite-vec extension which is not part of standard SQLite; adds compilation complexity","No built-in backup or replication; data loss risk if database file is corrupted or disk fails","Query performance degrades significantly with database size; queries on 6+ months of data may take 10+ seconds"],"requires":["SQLite 3.35+ with FTS5 extension","sqlite-vec extension for vector search (optional but recommended)","Minimum 500GB free disk space for 6 months of continuous recording","Rust or Python for custom database queries"],"input_types":["captured frames (RGBA pixel data)","OCR text and bounding boxes","audio transcripts with timestamps","embedding vectors (1536-dim or custom size)","metadata (window title, application name, etc.)"],"output_types":["SQL query results (rows, columns)","full-text search matches with relevance scores","vector search results with similarity scores","aggregated statistics (frame count, transcript duration, etc.)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-screenpipe__cap_9","uri":"capability://automation.workflow.desktop.application.with.timeline.and.rewind.ui","name":"desktop application with timeline and rewind ui","description":"Provides a Tauri-based desktop application (macOS, Windows, Linux) with a visual timeline interface for browsing captured screen history. The timeline displays thumbnail previews of captured frames chronologically, allowing users to scrub through time and view associated OCR text and transcripts. The 'Rewind' feature enables quick playback of screen activity at accelerated speed. The UI includes search interface for querying captured data, settings panel for configuring capture and AI backends, and system tray integration for quick access. The application communicates with the local Screenpipe server via REST API.","intents":["I want to visually browse my screen history and find specific moments in time","I need a quick way to search for information I saw or heard without remembering exact keywords","I want to configure Screenpipe settings (capture interval, AI models, privacy) from a user-friendly interface"],"best_for":["end users who prefer visual browsing over command-line or API access","teams deploying Screenpipe across multiple devices with centralized configuration","users who want quick access to screen history via system tray"],"limitations":["Timeline rendering is slow for large datasets (6+ months); scrolling through history can cause UI lag","Thumbnail previews are low-resolution to save memory; text is not readable in timeline view","Rewind playback is limited to captured frames; cannot play back actual video at original speed","Search results are paginated but pagination is slow for large result sets (1000+ frames)","Settings changes require server restart; no hot-reload for configuration updates","System tray integration is platform-specific; behavior differs between macOS, Windows, Linux"],"requires":["Tauri runtime (included in app)","Screenpipe server running locally","macOS 10.13+, Windows 10+, or Linux with X11/Wayland","Minimum 4GB RAM for smooth UI performance"],"input_types":["user interactions (mouse clicks, keyboard input, search queries)","timeline scrubbing (drag to specific timestamp)","filter selections (date range, content type, application)"],"output_types":["rendered UI with timeline and frame previews","search results with clickable frames","settings configuration (JSON)","notifications and alerts"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["macOS 10.13+ with CoreGraphics framework","Windows 10+ with DXGI support","Linux with X11 or PipeWire audio server","Rust 1.70+ for compilation","macOS 10.15+ for Vision framework","Windows 10+ with language pack installed","Linux with Tesseract 4.0+ installed","Minimum 2GB RAM for OCR processing queue","API keys for cloud providers (OpenAI, Anthropic, Deepgram) if using cloud options","Local models (Whisper, sentence-transformers) require 4GB+ VRAM if using local options"],"failure_modes":["Event-driven capture may miss very brief UI changes that occur between trigger events","Platform-specific implementations require separate code paths and testing for macOS, Windows, Linux","DXGI on Windows requires GPU access; fallback to CPU capture has higher latency","X11/PipeWire on Linux has fragmented support across desktop environments","Apple Vision OCR on macOS is proprietary and cannot be customized; accuracy varies by text size and font","Windows native OCR requires Windows 10+ and may have lower accuracy on non-standard fonts","Tesseract fallback on Linux is slower (~500ms per frame) and less accurate than native engines","OCR confidence scores are not normalized across platforms, making cross-platform filtering unreliable","Handwritten text and complex layouts (tables, multi-column) have significantly lower accuracy","Switching providers mid-stream (e.g., local Whisper to Deepgram) may produce inconsistent results due to model differences","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.049Z","last_scraped_at":"2026-05-03T14:00:20.516Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=screenpipe","compare_url":"https://unfragile.ai/compare?artifact=screenpipe"}},"signature":"L81igASOCM3nKI51R8OPp91AZDrNWjiM8EXw9YmUFnc759peq6op7tS08WRq2ADK6irY1s0lcO1J7v36QF08Dg==","signedAt":"2026-06-21T05:59:54.559Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/screenpipe","artifact":"https://unfragile.ai/screenpipe","verify":"https://unfragile.ai/api/v1/verify?slug=screenpipe","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}