{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-volcengine--minecontext","slug":"volcengine--minecontext","name":"MineContext","type":"repo","url":"https://github.com/volcengine/MineContext","page_url":"https://unfragile.ai/volcengine--minecontext","categories":["research-search"],"tags":["agent","context-engineering","electron","embedding-models","javascript","memory","proactive-ai","python","python3","rag","react","typescript","vector-database","vision-language-model"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-volcengine--minecontext__cap_0","uri":"capability://image.visual.continuous.screenshot.capture.with.interval.scheduling","name":"continuous-screenshot-capture-with-interval-scheduling","description":"Captures full-screen screenshots at configurable 5-second intervals via Electron's native screen capture APIs, storing raw image files to disk and queuing them for asynchronous VLM processing. The system uses a dedicated screenshot monitor thread that respects display state (active/idle) and integrates with the context capture pipeline to timestamp and batch screenshots for efficient processing without blocking the UI.","intents":["I want to passively record what the user is doing on their screen without manual intervention","I need to capture visual context at regular intervals to build a temporal activity timeline","I want to process screenshots asynchronously so capture doesn't impact application responsiveness"],"best_for":["developers building local-first activity tracking systems","teams implementing privacy-preserving productivity analytics","builders creating context-aware AI assistants that need visual grounding"],"limitations":["5-second capture interval creates ~17,280 screenshots per 24-hour period, requiring significant disk I/O and storage (~50-100GB daily at 1080p)","No built-in multi-monitor support — captures primary display only","Screenshot processing queue can back up if VLM inference is slower than capture rate, requiring manual queue management","Electron screen capture has platform-specific limitations on macOS with privacy controls and Linux with Wayland"],"requires":["Electron 13+ with native screen capture module","Python 3.8+ backend for async queue processing","Minimum 100GB free disk space for 24-hour retention","VLM service (local or remote) with <5s inference latency per screenshot"],"input_types":["system display buffer (native Electron screen capture)","configuration parameters (interval, resolution, format)"],"output_types":["PNG/JPEG image files on disk","screenshot metadata (timestamp, resolution, file path) in SQLite","queued tasks for VLM processing pipeline"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-volcengine--minecontext__cap_1","uri":"capability://image.visual.vision.language.model.based.screenshot.analysis","name":"vision-language-model-based-screenshot-analysis","description":"Processes captured screenshots through configurable VLM services (local or remote) to extract semantic descriptions of visual content, including detected activities, UI elements, text content, and contextual information. The system maintains a pluggable VLM client architecture supporting multiple providers (Doubao, OpenAI Vision, local models via Ollama) with fallback chains and caching of VLM responses to avoid redundant inference on duplicate frames.","intents":["I need to understand what's happening in screenshots without manual review","I want to extract semantic meaning from visual context to enable intelligent search and retrieval","I need to support multiple VLM providers with automatic fallback if one service is unavailable"],"best_for":["teams building activity intelligence systems that need visual understanding","developers creating privacy-first alternatives to cloud-based screen recording","builders implementing semantic search over visual activity logs"],"limitations":["VLM inference latency (2-10s per screenshot depending on model) creates processing bottlenecks when capture rate exceeds inference throughput","VLM hallucination and inconsistency across similar frames can produce noisy embeddings and unreliable search results","No built-in deduplication — duplicate or near-duplicate screenshots are processed independently, wasting compute","Local VLM models (e.g., LLaVA) have significantly lower accuracy than cloud models, impacting downstream context quality"],"requires":["VLM service endpoint (local via Ollama or remote API)","API credentials for remote VLM providers (Doubao, OpenAI, etc.)","Python 3.8+ with async HTTP client (aiohttp)","Minimum 8GB VRAM for local VLM inference, 16GB+ recommended"],"input_types":["PNG/JPEG screenshot images (1080p or higher)","VLM provider configuration (endpoint, model name, API key)","optional: custom prompts for VLM analysis"],"output_types":["structured VLM response (text description of screenshot content)","extracted metadata (detected activities, UI elements, text)","embeddings for vector storage (via embedding model pipeline)","cached VLM responses in SQLite for deduplication"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-volcengine--minecontext__cap_10","uri":"capability://tool.use.integration.electron.based.desktop.ui.with.react.state.management","name":"electron-based-desktop-ui-with-react-state-management","description":"Provides a cross-platform desktop UI built with Electron and React, managing application state through a centralized store (Redux or similar) with async middleware for backend API calls. The UI includes dashboard components for viewing summaries/todos/tips, search interface for context retrieval, settings panel for configuration, and real-time notifications for proactive content delivery. Electron main process handles window management, system tray integration, and native OS interactions.","intents":["I want a native desktop application that works on Windows, macOS, and Linux","I need a responsive UI that displays activity summaries, todos, and tips","I want system tray integration and notifications for proactive content delivery"],"best_for":["developers building cross-platform desktop applications","teams implementing native UI for local-first AI systems","builders creating productivity dashboards with real-time updates"],"limitations":["Electron applications have high memory overhead (300-500MB per instance) due to bundled Chromium","React state management adds complexity for real-time updates from background processes","IPC (Inter-Process Communication) between Electron main and renderer processes adds latency (~10-50ms per message)","Platform-specific UI issues require testing on Windows, macOS, and Linux","Packaging and distribution complexity increases with code signing and auto-update requirements"],"requires":["Node.js 14+ and npm/yarn","Electron 13+","React 17+","State management library (Redux, Zustand, etc.)","Electron Builder for packaging"],"input_types":["backend API responses (summaries, todos, tips, search results)","user interactions (clicks, form submissions, search queries)","system events (window focus, tray clicks)"],"output_types":["rendered UI components","API requests to backend","system notifications","file dialogs for document upload"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-volcengine--minecontext__cap_11","uri":"capability://tool.use.integration.rest.api.backend.with.fastapi.and.async.processing","name":"rest-api-backend-with-fastapi-and-async-processing","description":"Provides a REST API backend built with FastAPI and Python, exposing endpoints for context operations (capture, search, retrieval), consumption management (summaries, todos, tips), and configuration. The backend uses async/await for non-blocking I/O, integrates with background task queues (Celery, RQ) for long-running operations, and maintains SQLite and vector database connections. API is served on localhost:1733 by default with CORS enabled for Electron frontend.","intents":["I need a backend API for context capture, storage, and retrieval","I want async processing for long-running operations like VLM inference","I need REST endpoints for frontend integration without tight coupling"],"best_for":["developers building local-first AI systems with REST APIs","teams implementing async processing pipelines","builders creating decoupled frontend-backend architectures"],"limitations":["FastAPI async is single-threaded — CPU-bound operations (embedding, VLM inference) block the event loop if not offloaded","Background task queues add operational complexity — requires separate worker processes and message broker","REST API overhead (~50-100ms per request) adds latency compared to direct function calls","No built-in API authentication — localhost-only deployment assumes trusted environment","Scaling beyond single machine requires database replication and load balancing"],"requires":["Python 3.8+","FastAPI 0.95+","Uvicorn ASGI server","Background task queue (Celery, RQ, or APScheduler)","SQLite and vector database clients"],"input_types":["HTTP requests (JSON payloads)","query parameters (search, filters)","file uploads (documents)"],"output_types":["JSON responses (context, summaries, todos, tips)","file downloads (documents, exports)","HTTP status codes and error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-volcengine--minecontext__cap_12","uri":"capability://data.processing.analysis.context.type.abstraction.with.unified.schema","name":"context-type-abstraction-with-unified-schema","description":"Defines a unified context schema supporting multiple context types (screenshots, documents, activities, todos, tips, summaries) with common metadata (timestamp, source, type, embeddings) and type-specific fields. The system maintains context type definitions in code and database schema, enabling polymorphic queries that treat different context types uniformly while preserving type-specific information. Context merging logic combines related items (e.g., multiple screenshots of same activity) into higher-level abstractions.","intents":["I want to store different types of context (screenshots, documents, activities) in a unified system","I need to query across context types without separate queries for each type","I want to merge related context items into higher-level abstractions"],"best_for":["developers building multimodal context systems","teams implementing unified context search across multiple sources","builders creating context-aware AI with heterogeneous data"],"limitations":["Unified schema requires careful design to accommodate all context types without bloat","Polymorphic queries are more complex than type-specific queries","Context merging logic is heuristic-based and can produce incorrect merges","Type-specific fields create schema flexibility challenges in relational databases","No built-in versioning — context updates create new records rather than versions"],"requires":["SQLite with flexible schema design (JSON columns or separate tables)","Context type definitions (Python dataclasses or Pydantic models)","Database migration tools for schema evolution"],"input_types":["context items of various types (screenshots, documents, activities)","context metadata (timestamp, source, type)","type-specific fields"],"output_types":["unified context objects with common and type-specific fields","merged context abstractions (activity clusters, document summaries)","polymorphic query results across context types"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-volcengine--minecontext__cap_13","uri":"capability://data.processing.analysis.activity.monitoring.and.temporal.indexing","name":"activity-monitoring-and-temporal-indexing","description":"Tracks user activity by analyzing captured context (screenshots, documents, interactions) and extracting activity records with temporal boundaries (start time, end time, duration). The system maintains a temporal index enabling efficient queries by time range, activity type, and duration. Activity records include metadata (application/document name, activity description, confidence score) and references to source context items.","intents":["I want to understand what activities the user was engaged in and when","I need to query activities by time range and type efficiently","I want to track activity duration and patterns over time"],"best_for":["developers building activity tracking systems","teams implementing productivity analytics","builders creating temporal activity analysis"],"limitations":["Activity extraction from screenshots is heuristic-based and produces false positives/negatives","Temporal boundaries are inferred from screenshot timestamps and can be imprecise","No built-in activity classification — requires manual definition or ML-based classification","Activity merging (combining related screenshots into single activity) is lossy — fine-grained details are lost","Confidence scores are not calibrated — users cannot rely on them for filtering"],"requires":["Context database with screenshots and descriptions","Activity extraction logic (heuristic or ML-based)","Temporal index on SQLite (indexed timestamp columns)","Activity type definitions"],"input_types":["screenshot context with timestamps","VLM descriptions of activities","activity type definitions"],"output_types":["activity records (start time, end time, type, description)","temporal indices for efficient range queries","activity metadata (confidence, source references)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-volcengine--minecontext__cap_2","uri":"capability://memory.knowledge.dual.database.context.storage.with.vector.search","name":"dual-database-context-storage-with-vector-search","description":"Stores captured context in a dual-database architecture: SQLite for structured metadata (timestamps, activity types, document references) and ChromaDB/Qdrant for vector embeddings enabling semantic similarity search. The system maintains a unified schema across both stores with automatic synchronization, allowing queries to combine structured filters (date range, activity type) with semantic search (find similar activities) in a single operation.","intents":["I need to store massive amounts of context (screenshots, documents, activities) efficiently with fast retrieval","I want to search for past context using semantic similarity, not just keyword matching","I need to combine structured queries (time range, type) with semantic search in a single operation"],"best_for":["developers building context-aware AI systems with local-first storage requirements","teams implementing privacy-preserving activity analytics without cloud dependencies","builders creating semantic search over personal activity logs and documents"],"limitations":["Dual-database synchronization adds ~50-100ms latency per write operation and requires manual consistency management","Vector database (ChromaDB/Qdrant) requires separate process/container, adding deployment complexity","SQLite has poor concurrent write performance — multiple processes writing simultaneously will cause lock contention","No built-in sharding or partitioning — single-machine storage limits practical capacity to ~1-2 years of continuous capture","Vector search quality depends entirely on embedding model quality; poor embeddings produce irrelevant results"],"requires":["Python 3.8+ with SQLite3 driver","ChromaDB 0.3+ or Qdrant 1.0+ for vector storage","Embedding model service (local or remote) for generating vectors","Minimum 50GB disk space for SQLite + vector database (scales with retention period)"],"input_types":["structured context objects (timestamp, activity type, source, metadata)","text descriptions from VLM analysis","document content and metadata","embedding vectors (768-1536 dimensions depending on model)"],"output_types":["SQLite query results (structured metadata with timestamps)","vector similarity search results (ranked by cosine distance)","combined query results (structured + semantic)","context objects hydrated from both databases"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-volcengine--minecontext__cap_3","uri":"capability://data.processing.analysis.embedding.model.based.context.vectorization","name":"embedding-model-based-context-vectorization","description":"Converts text descriptions from VLM analysis and document content into high-dimensional embeddings (768-1536 dimensions) using configurable embedding models (local or remote). The system maintains an embedding client with provider abstraction, supporting multiple backends (Doubao embeddings, OpenAI embeddings, local models via Ollama) with batch processing for efficiency and caching to avoid recomputing embeddings for identical text.","intents":["I need to convert text context into vectors for semantic similarity search","I want to support multiple embedding model providers with automatic fallback","I need efficient batch embedding processing to handle thousands of context items"],"best_for":["developers building semantic search over activity logs and documents","teams implementing vector-based context retrieval without cloud dependencies","builders creating similarity-based context recommendations"],"limitations":["Embedding quality varies significantly across models — cheaper/local models produce lower-quality vectors with worse semantic clustering","Batch embedding API calls have rate limits (typically 100-1000 requests/minute), creating bottlenecks during bulk processing","Embedding vectors are model-specific — switching embedding models requires recomputing all historical vectors","Local embedding models (e.g., sentence-transformers) require 4-8GB VRAM and have 10-50x slower inference than cloud APIs","No built-in deduplication — identical text is embedded multiple times if not explicitly cached"],"requires":["Embedding model service (local via Ollama or remote API)","API credentials for remote embedding providers","Python 3.8+ with async HTTP client","Minimum 4GB VRAM for local embedding models, 8GB+ recommended"],"input_types":["text descriptions (from VLM analysis or documents)","embedding model configuration (provider, model name, dimensions)","batch of text items for efficient processing"],"output_types":["embedding vectors (768-1536 dimensions, float32)","cached embeddings in SQLite for deduplication","batch embedding results with metadata"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-volcengine--minecontext__cap_4","uri":"capability://text.generation.language.proactive.activity.summarization.with.scheduled.generation","name":"proactive-activity-summarization-with-scheduled-generation","description":"Automatically generates daily activity summaries by querying the context database for all activities within a 24-hour window, processing them through an LLM with a structured prompt template, and storing the summary as a consumable artifact. The system uses a scheduler (APScheduler or similar) to trigger summary generation at configurable times (default 08:00), with fallback to manual regeneration and debug mode for prompt refinement.","intents":["I want to automatically generate daily summaries of user activity without manual intervention","I need to understand what happened over the past 24 hours in a concise, human-readable format","I want to customize summary generation prompts and regenerate summaries with different parameters"],"best_for":["developers building personal productivity dashboards","teams implementing automated activity reporting","builders creating AI-powered daily standup generation"],"limitations":["Summary quality depends on context quality — noisy or incomplete context produces poor summaries","LLM inference latency (5-30s depending on model) means summary generation blocks other operations if not async","No built-in deduplication of activities — duplicate screenshots of the same activity inflate summary length","Scheduled generation can miss activities if system is offline at trigger time; no catch-up mechanism","Prompt engineering required to achieve good summaries — default prompts may not match user expectations"],"requires":["LLM service endpoint (local or remote)","Context database with activity records and timestamps","Scheduler (APScheduler 3.8+) for periodic generation","Python 3.8+ with async task queue (Celery, RQ, or similar)"],"input_types":["activity records from context database (24-hour window)","summary prompt template (customizable)","LLM configuration (model, temperature, max tokens)"],"output_types":["text summary (500-2000 words)","structured summary metadata (generation time, activity count, confidence)","stored summary artifact in database"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-volcengine--minecontext__cap_5","uri":"capability://text.generation.language.intelligent.todo.extraction.from.context","name":"intelligent-todo-extraction-from-context","description":"Analyzes captured context (screenshots, documents, activity descriptions) to automatically extract actionable todos and tasks using LLM-based extraction with structured prompts. The system runs extraction at configurable intervals (default 1800s/30 minutes), deduplicates extracted todos against existing items, and stores them with metadata (source context, extraction confidence, priority) for consumption in the UI.","intents":["I want to automatically capture tasks and todos from my activity without manual entry","I need to extract actionable items from screenshots and documents","I want to avoid duplicate todos while capturing all relevant tasks"],"best_for":["developers building task management systems that learn from user activity","teams implementing automated todo capture from work context","builders creating AI-powered task extraction from screenshots"],"limitations":["LLM hallucination produces false-positive todos that don't match actual user intent","Deduplication is fuzzy and imperfect — similar but distinct todos may be merged or duplicates may be missed","Extraction quality depends on context quality — noisy screenshots or incomplete descriptions produce poor todos","No user feedback loop — extracted todos can't be marked as incorrect to improve future extraction","Interval-based extraction (30 minutes) can miss time-sensitive tasks if extraction runs between task creation and interval trigger"],"requires":["LLM service endpoint for extraction","Context database with recent activities and descriptions","Scheduler for periodic extraction (APScheduler 3.8+)","Deduplication logic (fuzzy matching, embedding similarity, or manual rules)"],"input_types":["recent context items (activities, screenshots, documents)","todo extraction prompt template","existing todos for deduplication"],"output_types":["extracted todo items (text, priority, source context)","deduplication metadata (matched existing todo, confidence)","stored todos in database with source references"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-volcengine--minecontext__cap_6","uri":"capability://text.generation.language.smart.tips.generation.with.contextual.relevance","name":"smart-tips-generation-with-contextual-relevance","description":"Generates contextually relevant tips and suggestions by analyzing recent activity patterns and context, using LLM-based generation with prompt templates that reference detected activities and user behavior. The system runs tip generation at configurable intervals (default 3600s/1 hour), filters tips for relevance using embedding similarity, and stores them with metadata for proactive delivery to the user.","intents":["I want to receive intelligent suggestions based on my current activity and patterns","I need to generate tips that are contextually relevant to what the user is doing","I want to avoid irrelevant or repetitive tips through intelligent filtering"],"best_for":["developers building proactive AI assistants","teams implementing activity-aware suggestion systems","builders creating personalized productivity tips"],"limitations":["Tip relevance is subjective — LLM-generated tips may not match user expectations or preferences","Filtering for relevance requires embedding similarity, which depends on embedding model quality","No user feedback mechanism — tips can't be marked as irrelevant to improve future generation","Interval-based generation (1 hour) may produce stale tips if activity patterns change rapidly","Tip generation quality degrades with sparse or noisy context"],"requires":["LLM service endpoint for tip generation","Embedding model for relevance filtering","Context database with recent activities","Scheduler for periodic generation"],"input_types":["recent activity patterns and context","tip generation prompt template","user preferences or tip categories (optional)"],"output_types":["generated tips (text, category, relevance score)","filtered tips based on relevance threshold","stored tips with metadata and timestamps"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-volcengine--minecontext__cap_7","uri":"capability://search.retrieval.semantic.context.retrieval.with.hybrid.search","name":"semantic-context-retrieval-with-hybrid-search","description":"Enables retrieval of relevant past context using hybrid search combining vector similarity (semantic search) with structured filters (time range, activity type, source). The system queries the vector database for semantically similar context items and applies SQLite filters to narrow results, returning ranked results with relevance scores. This supports both programmatic API access and UI-based search interfaces.","intents":["I want to find past context similar to what I'm working on now","I need to search for activities by semantic meaning, not just keywords","I want to combine time-based and categorical filters with semantic search"],"best_for":["developers building context-aware AI assistants with memory","teams implementing semantic search over activity logs","builders creating intelligent context retrieval for creative work"],"limitations":["Search quality depends on embedding model quality — poor embeddings produce irrelevant results","Hybrid search requires two database queries (vector + SQL), adding ~100-200ms latency","Vector similarity is not transitive — similar items may not form coherent clusters","No ranking across vector and structured results — combining scores requires manual weighting","Large result sets (>1000 items) require pagination, complicating UX"],"requires":["Vector database (ChromaDB/Qdrant) with indexed embeddings","SQLite database with structured metadata","Embedding model for query vectorization","Python 3.8+ with async database clients"],"input_types":["search query (text)","optional: structured filters (date range, activity type, source)","optional: similarity threshold or result limit"],"output_types":["ranked context items (text, metadata, relevance score)","source references (screenshot path, document, timestamp)","structured metadata for each result"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-volcengine--minecontext__cap_8","uri":"capability://data.processing.analysis.multimodal.document.ingestion.and.processing","name":"multimodal-document-ingestion-and-processing","description":"Accepts user-uploaded documents (PDF, DOCX, images, etc.) and processes them through a unified pipeline: file type detection, content extraction (text via OCR or parsing), VLM analysis for visual content, embedding generation, and storage in dual databases. The system maintains a document vault with metadata (upload time, file type, source) and integrates documents into the context search and retrieval system.","intents":["I want to upload documents and have them automatically indexed for semantic search","I need to extract text and visual content from PDFs and images","I want documents to be searchable alongside activity context"],"best_for":["developers building document management systems with semantic search","teams implementing multimodal document processing","builders creating unified search over documents and activity"],"limitations":["Document processing pipeline is sequential — large documents block other operations if not async","OCR quality varies significantly across document types and image quality","VLM analysis of document images can be slow (5-30s per page) for large documents","No built-in document versioning — updated documents create duplicates rather than versions","Supported file types are limited — unsupported formats are rejected without graceful fallback"],"requires":["File upload API endpoint (FastAPI/Flask)","OCR service (Tesseract, local or cloud-based)","VLM service for visual content analysis","Document parsing libraries (PyPDF2, python-docx, etc.)","Async task queue for background processing"],"input_types":["document files (PDF, DOCX, PNG, JPG, etc.)","document metadata (title, tags, source)"],"output_types":["extracted text content","VLM analysis of visual content","embeddings for semantic search","document metadata in SQLite","document vault entries with file references"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-volcengine--minecontext__cap_9","uri":"capability://tool.use.integration.configurable.llm.provider.abstraction.with.fallback.chains","name":"configurable-llm-provider-abstraction-with-fallback-chains","description":"Provides a pluggable LLM client architecture supporting multiple providers (OpenAI, Anthropic, local Ollama, custom endpoints) with automatic fallback chains and provider-specific configuration. The system maintains a provider registry, handles API authentication, manages rate limiting, and implements retry logic with exponential backoff. Configuration is stored in YAML/JSON files with UI-based settings management.","intents":["I want to support multiple LLM providers without hardcoding provider logic","I need automatic fallback if my primary LLM provider is unavailable","I want to configure LLM providers through UI settings without code changes"],"best_for":["developers building LLM-powered applications with provider flexibility","teams implementing cost optimization through provider selection","builders creating resilient AI systems with fallback chains"],"limitations":["Provider abstraction adds ~50-100ms latency per LLM call due to routing and configuration lookup","Fallback chains require multiple API calls on provider failure, increasing latency and cost","Different providers have different model capabilities (context length, function calling, vision) — abstraction cannot hide these differences","Rate limiting is per-provider — fallback chains don't aggregate rate limits across providers","Configuration management requires manual provider setup and credential management"],"requires":["Python 3.8+ with async HTTP client","API credentials for each configured provider","Configuration files (YAML/JSON) for provider settings","Provider-specific SDKs (openai, anthropic, etc.)"],"input_types":["provider configuration (name, endpoint, API key, model)","fallback chain definition (ordered list of providers)","LLM request (prompt, temperature, max tokens, etc.)"],"output_types":["LLM response from primary or fallback provider","provider metadata (which provider was used, latency)","error details if all providers fail"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Electron 13+ with native screen capture module","Python 3.8+ backend for async queue processing","Minimum 100GB free disk space for 24-hour retention","VLM service (local or remote) with <5s inference latency per screenshot","VLM service endpoint (local via Ollama or remote API)","API credentials for remote VLM providers (Doubao, OpenAI, etc.)","Python 3.8+ with async HTTP client (aiohttp)","Minimum 8GB VRAM for local VLM inference, 16GB+ recommended","Node.js 14+ and npm/yarn","Electron 13+"],"failure_modes":["5-second capture interval creates ~17,280 screenshots per 24-hour period, requiring significant disk I/O and storage (~50-100GB daily at 1080p)","No built-in multi-monitor support — captures primary display only","Screenshot processing queue can back up if VLM inference is slower than capture rate, requiring manual queue management","Electron screen capture has platform-specific limitations on macOS with privacy controls and Linux with Wayland","VLM inference latency (2-10s per screenshot depending on model) creates processing bottlenecks when capture rate exceeds inference throughput","VLM hallucination and inconsistency across similar frames can produce noisy embeddings and unreliable search results","No built-in deduplication — duplicate or near-duplicate screenshots are processed independently, wasting compute","Local VLM models (e.g., LLaVA) have significantly lower accuracy than cloud models, impacting downstream context quality","Electron applications have high memory overhead (300-500MB per instance) due to bundled Chromium","React state management adds complexity for real-time updates from background processes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5857292641377865,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:58:32.037Z","last_commit":"2026-03-12T03:49:07Z"},"community":{"stars":5302,"forks":395,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=volcengine--minecontext","compare_url":"https://unfragile.ai/compare?artifact=volcengine--minecontext"}},"signature":"TCUKFFUTfDY157P9At5iEj1Qj3ujHdbT+HZfBBe7CzqoPazqIyg5I6M86MbKN0KohYgludT1+H818offsFSkBQ==","signedAt":"2026-06-21T23:35:45.216Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/volcengine--minecontext","artifact":"https://unfragile.ai/volcengine--minecontext","verify":"https://unfragile.ai/api/v1/verify?slug=volcengine--minecontext","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"}}