{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-ufo","slug":"ufo","name":"UFO","type":"agent","url":"https://github.com/microsoft/UFO","page_url":"https://unfragile.ai/ufo","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-ufo__cap_0","uri":"capability://automation.workflow.ui.focused.desktop.task.automation.via.visual.perception.and.llm.reasoning","name":"ui-focused desktop task automation via visual perception and llm reasoning","description":"UFO² captures Windows desktop screenshots, annotates UI controls with bounding boxes and accessibility metadata, and uses LLM reasoning to decompose natural language tasks into sequences of UI interactions (clicks, text input, keyboard commands). The Host Agent orchestrates high-level task planning while App Agents execute granular actions within specific applications, maintaining state machines to track progress and handle failures across multi-step workflows.","intents":["Automate repetitive Windows desktop workflows without writing scripts or macros","Execute complex multi-application tasks by reasoning about UI state and control hierarchy","Build agents that can navigate unfamiliar applications by visual understanding alone"],"best_for":["Enterprise automation teams automating legacy Windows applications","RPA developers replacing rule-based bots with LLM-driven visual agents","Organizations needing cross-application workflow automation without API access"],"limitations":["Windows-only (no macOS or Linux desktop support in UFO²)","Requires LLM API calls for every decision point, adding latency (typically 2-5 seconds per action)","Screenshot-based perception vulnerable to UI changes, overlapping windows, or dynamic content rendering","No built-in OCR for handwritten or image-embedded text in UI controls"],"requires":["Windows 10/11 with Python 3.9+","API key for supported LLM provider (OpenAI, Anthropic, Azure OpenAI, or local Ollama)","Administrator privileges for UI automation and COM object access"],"input_types":["natural language task description","desktop screenshots (captured automatically)","UI control metadata from Windows Accessibility API"],"output_types":["sequence of UI actions (click, type, keyboard shortcut)","execution logs with reasoning traces","structured task completion status"],"categories":["automation-workflow","planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ufo__cap_1","uri":"capability://image.visual.multi.modal.screenshot.annotation.and.ui.control.extraction","name":"multi-modal screenshot annotation and ui control extraction","description":"UFO² captures full desktop screenshots and overlays bounding boxes with unique IDs for every interactive UI control (buttons, text fields, dropdowns, etc.) extracted via Windows Accessibility API (UIA) and COM object inspection. Annotations include control type, label, state, and accessibility properties, creating a structured representation of the UI that LLMs can reason about without OCR. The system handles dynamic UI updates by re-capturing and re-annotating on each agent round.","intents":["Provide LLMs with precise, machine-readable UI state instead of raw pixel data","Enable agents to reference specific controls by stable IDs across multiple interaction rounds","Extract accessibility metadata to understand control hierarchy and semantic relationships"],"best_for":["Teams building vision-language models for desktop automation","Developers needing structured UI representations for agent training or evaluation","Accessibility-focused automation requiring semantic control understanding"],"limitations":["Accessibility API coverage varies by application; legacy or custom-drawn UIs may have incomplete metadata","Annotation overhead adds 500ms-2s per screenshot depending on UI complexity","Bounding box coordinates become stale if UI re-renders between capture and action execution","Cannot annotate content inside embedded web views or PDF viewers without additional OCR"],"requires":["Windows 10/11 with UI Automation (UIA) framework enabled","Python 3.9+ with pywinauto or similar Windows automation library","Administrator privileges to access COM objects and UI element properties"],"input_types":["desktop screenshot (PNG/BMP)","Windows window handles and process IDs"],"output_types":["annotated screenshot with bounding boxes and control IDs","structured control metadata (JSON/dict with type, label, state, bounds)","UI hierarchy tree representation"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ufo__cap_10","uri":"capability://tool.use.integration.llm.provider.abstraction.with.multi.provider.support.and.structured.output","name":"llm provider abstraction with multi-provider support and structured output","description":"UFO² abstracts LLM interactions behind a provider-agnostic interface supporting OpenAI, Anthropic, Azure OpenAI, and local Ollama models. The system handles provider-specific details (API authentication, request formatting, response parsing) transparently. For structured outputs, UFO² uses JSON schema validation and function calling APIs (where available) to ensure agents produce well-formed action specifications. Supports custom model integration via a plugin interface.","intents":["Switch between LLM providers without modifying agent code","Ensure agent outputs conform to expected action schemas","Integrate custom or fine-tuned models into the automation framework"],"best_for":["Teams evaluating multiple LLM providers for cost/performance tradeoffs","Organizations with on-premises LLM deployments (Ollama, vLLM)","Builders creating multi-model agents that route requests to different providers"],"limitations":["Provider-specific features (vision, function calling) may not be available across all providers","Structured output validation adds latency (typically 100-500ms per validation)","Model behavior varies significantly across providers; prompt tuning may be needed per provider","Custom model integration requires implementing the provider interface (non-trivial effort)"],"requires":["Python 3.9+ with UFO² LLM module","API keys for selected LLM providers (OpenAI, Anthropic, Azure, etc.)","JSON schema definitions for structured outputs"],"input_types":["prompt (text or multi-modal)","structured output schema (JSON schema)"],"output_types":["LLM response (text)","parsed structured output (JSON)","token usage metrics"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ufo__cap_11","uri":"capability://automation.workflow.configuration.driven.agent.and.deployment.customization","name":"configuration-driven agent and deployment customization","description":"UFO² uses YAML/JSON configuration files to define agent behavior, LLM settings, tool definitions, and deployment modes without code changes. Configuration includes agent type (Host/App), LLM provider and model, prompt templates, tool definitions, knowledge base paths, and deployment mode (local, service, or Galaxy). The system loads configurations at startup and applies them consistently across all agent instances, enabling rapid experimentation and deployment variations.","intents":["Customize agent behavior (model, temperature, tools) without code changes","Define different configurations for development, testing, and production","Enable non-technical users to adjust agent settings via configuration files"],"best_for":["Teams iterating on agent behavior through configuration tuning","Organizations deploying agents across multiple environments with different settings","Builders creating reusable agent templates with configuration-driven customization"],"limitations":["Configuration validation is limited; invalid settings may only be caught at runtime","Complex configurations can become unwieldy; no built-in configuration composition or inheritance","Configuration changes require agent restart; no hot-reload support","No built-in configuration versioning or rollback mechanisms"],"requires":["Python 3.9+ with UFO² configuration module","YAML or JSON configuration files","Understanding of UFO² configuration schema"],"input_types":["configuration file (YAML/JSON)"],"output_types":["parsed configuration object","validation errors (if any)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ufo__cap_12","uri":"capability://automation.workflow.galaxy.web.ui.for.multi.device.task.monitoring.and.control","name":"galaxy web ui for multi-device task monitoring and control","description":"UFO³ Galaxy Framework includes a web-based UI for monitoring and controlling multi-device automation. The UI displays registered devices, running tasks, execution traces, and device health metrics. Users can submit new tasks, view real-time execution progress (including screenshots from remote devices), inspect action history, and manage device lifecycle (register, deregister, restart). The UI communicates with the Galaxy controller via REST APIs or WebSockets for real-time updates.","intents":["Monitor multi-device automation execution in real-time","Submit and track tasks across a device fleet","Debug automation failures by inspecting execution traces and screenshots from remote devices"],"best_for":["Teams managing large device fleets (10+ devices) needing centralized visibility","Organizations requiring audit trails and execution monitoring for compliance","Operators debugging multi-device automation failures"],"limitations":["Web UI adds operational overhead (requires separate deployment and maintenance)","Real-time updates (WebSocket streaming) can consume significant bandwidth for large fleets","UI responsiveness degrades with 100+ concurrent tasks; requires pagination/filtering","No built-in role-based access control; requires external authentication layer"],"requires":["Galaxy controller deployed with REST/WebSocket APIs","Web server (Node.js, Python Flask/FastAPI, etc.)","Browser with WebSocket support"],"input_types":["task submission (form or API)","device management commands (register, deregister)"],"output_types":["device status dashboard","task execution trace (real-time)","device health metrics"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ufo__cap_13","uri":"capability://planning.reasoning.state.machine.based.agent.lifecycle.and.error.recovery","name":"state machine-based agent lifecycle and error recovery","description":"UFO² agents implement explicit state machines defining valid state transitions (e.g., Idle → Planning → Executing → Observing → Idle). Each agent round transitions through states, with state-specific logic for handling errors, retries, and recovery. If an action fails, the agent can retry within the same Round, escalate to the Host Agent, or transition to an error recovery state. State machines enable deterministic behavior, clear error handling, and recovery strategies without ad-hoc exception handling.","intents":["Implement deterministic agent behavior with clear state transitions","Handle failures gracefully with state-specific recovery strategies","Enable debugging by inspecting agent state at each round"],"best_for":["Teams building reliable automation requiring predictable error handling","Builders creating custom agents with complex state logic","Organizations needing to audit agent behavior and state transitions"],"limitations":["State machine complexity grows with number of states and transitions; can become hard to reason about","State transitions add latency (typically 10-50ms per transition)","No built-in visualization of state machines; debugging requires reading code or logs","State machine design is application-specific; reusing state machines across agents is difficult"],"requires":["Python 3.9+ with UFO² agent module","Understanding of state machine design patterns"],"input_types":["current state","event (action result, error, timeout)"],"output_types":["next state","recovery action (if applicable)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ufo__cap_2","uri":"capability://planning.reasoning.host.agent.and.app.agent.hierarchical.task.decomposition","name":"host agent and app agent hierarchical task decomposition","description":"UFO² implements a two-tier agent hierarchy where the Host Agent receives natural language tasks, decomposes them into sub-tasks, and delegates execution to specialized App Agents running within specific application contexts. Each App Agent maintains its own state machine, action history, and application-specific knowledge, communicating results back to the Host Agent. The Host Agent orchestrates task flow, handles inter-application dependencies, and decides when to switch between App Agents or retry failed sub-tasks.","intents":["Break down complex multi-application workflows into manageable sub-tasks for specialized agents","Maintain application-specific context and state without losing high-level task awareness","Enable fault tolerance by isolating failures to individual App Agents while preserving overall task progress"],"best_for":["Teams automating workflows spanning 3+ applications (e.g., email → spreadsheet → document editor)","Organizations needing clear separation between task planning and application-level execution","Builders creating reusable agent templates for specific applications (Outlook, Excel, Word, etc.)"],"limitations":["Host Agent decisions add latency (1-2 LLM calls per task decomposition round)","No built-in mechanism for agents to negotiate or resolve conflicting sub-task requirements","Context switching between App Agents can lose transient state (clipboard, focus, temporary variables)","Requires explicit task decomposition logic; unstructured tasks may decompose poorly"],"requires":["Python 3.9+ with UFO² framework installed","LLM API access for both Host and App Agent reasoning","Target applications installed and accessible on Windows desktop"],"input_types":["natural language task description","optional task constraints or success criteria"],"output_types":["task decomposition tree (sub-tasks with dependencies)","execution trace showing Host Agent decisions and App Agent actions","final task completion status with error logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ufo__cap_3","uri":"capability://automation.workflow.session.and.round.based.execution.lifecycle.management","name":"session and round-based execution lifecycle management","description":"UFO² organizes execution into Sessions (long-lived contexts for a task) and Rounds (individual agent decision cycles). Each Round captures the current UI state (screenshot + annotations), executes one or more actions, observes results, and feeds observations back to the agent for the next Round. Sessions maintain action history, context windows, and error recovery state across multiple Rounds, enabling agents to learn from previous attempts and adapt strategies.","intents":["Track execution progress across multi-step workflows with clear state checkpoints","Enable agents to learn from action outcomes and adjust strategy in subsequent rounds","Provide structured logging and debugging for automation failures"],"best_for":["Teams needing detailed execution traces for compliance or debugging","Builders implementing feedback loops where agents learn from action outcomes","Organizations requiring session persistence (pause/resume automation across system reboots)"],"limitations":["Context window grows with each Round; long sessions may exceed LLM token limits (requires context pruning)","Round latency compounds (2-5s per round × number of rounds = minutes for complex tasks)","No built-in session persistence to disk; sessions lost if process crashes","Action history can become noisy, making it hard for agents to identify relevant prior context"],"requires":["Python 3.9+ with UFO² session management module","Sufficient LLM context window (8K+ tokens recommended for multi-round sessions)"],"input_types":["task description","initial UI state (screenshot)"],"output_types":["session metadata (ID, start time, status)","round-by-round execution trace (action, observation, reasoning)","final session summary with success/failure status"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ufo__cap_4","uri":"capability://automation.workflow.hybrid.action.execution.combining.llm.decisions.with.deterministic.com.automation","name":"hybrid action execution combining llm decisions with deterministic com automation","description":"UFO² supports two action types: LLM-reasoned actions (click, type, keyboard shortcuts decided by the agent) and deterministic COM automation actions (direct method calls to application objects via Windows COM interfaces). The system intelligently routes actions based on precision requirements—using COM for exact operations (e.g., setting cell values in Excel) and LLM reasoning for exploratory tasks (e.g., finding a button in an unfamiliar UI). Hybrid execution reduces LLM latency for well-defined operations while maintaining flexibility for novel scenarios.","intents":["Execute precise, deterministic operations (e.g., Excel cell manipulation) without LLM latency","Combine visual reasoning with programmatic control for maximum reliability","Reduce API costs by using COM automation for repetitive, rule-based actions"],"best_for":["Teams automating Microsoft Office applications (Excel, Word, Outlook) with mixed precision/exploration needs","Organizations seeking to reduce LLM API costs by offloading deterministic operations","Builders creating application-specific agents that leverage COM object models"],"limitations":["COM automation only available for applications exposing COM interfaces (not all Windows apps)","Requires deep knowledge of target application's COM object model and method signatures","COM method calls can fail silently or with cryptic error codes; error handling is application-specific","Mixing LLM and COM actions complicates debugging (unclear which layer caused failures)"],"requires":["Windows 10/11 with COM support enabled","Target application must expose COM interfaces (e.g., Microsoft Office, some enterprise apps)","Python 3.9+ with pywin32 or similar COM binding library"],"input_types":["action specification (LLM-reasoned or COM method call)","application context (window handle, COM object reference)"],"output_types":["action execution result (success/failure)","return value from COM method (if applicable)","screenshot of result state"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ufo__cap_5","uri":"capability://tool.use.integration.mcp.model.context.protocol.tool.integration.and.custom.server.creation","name":"mcp (model context protocol) tool integration and custom server creation","description":"UFO² integrates with the Model Context Protocol (MCP) to expose external tools and services as callable functions within agent reasoning. Agents can invoke MCP servers (local or remote) to access capabilities like web search, file operations, database queries, or custom business logic. The system provides a framework for creating custom MCP servers that wrap application-specific operations, enabling agents to extend their capabilities beyond UI automation.","intents":["Extend agent capabilities with external tools (web search, APIs, databases) without modifying core agent logic","Expose custom business logic as MCP servers for reuse across multiple agents","Integrate third-party services (Slack, Jira, Salesforce) into automation workflows"],"best_for":["Teams building agents that need access to external data sources or APIs","Organizations with custom business logic that should be exposed as agent tools","Builders creating reusable tool libraries for specific domains (finance, HR, sales)"],"limitations":["MCP server latency adds to overall agent decision time (typically 1-3s per tool call)","Tool discovery and schema validation requires upfront MCP server setup and documentation","No built-in error recovery for failed tool calls; agents must handle tool failures in reasoning","MCP servers require separate deployment and lifecycle management (not bundled with UFO²)"],"requires":["Python 3.9+ with MCP client library","MCP server(s) deployed and accessible (local or remote)","Tool schema definitions in JSON or similar format"],"input_types":["tool name and parameters (structured as JSON)","agent reasoning context"],"output_types":["tool execution result (JSON or structured data)","error message if tool call fails"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ufo__cap_6","uri":"capability://automation.workflow.multi.device.task.orchestration.via.galaxy.framework.ufo","name":"multi-device task orchestration via galaxy framework (ufo³)","description":"UFO³ Galaxy Framework extends UFO² to orchestrate tasks across multiple Windows devices. A Constellation Agent receives high-level tasks, decomposes them into device-specific sub-tasks, and distributes execution to UFO² agents running on remote devices via the Agent Interaction Protocol (AIP). The system manages device registration, task routing, result aggregation, and failure recovery across heterogeneous device fleets, enabling workflows that span multiple machines (e.g., data collection on Device A, processing on Device B, reporting on Device C).","intents":["Automate workflows spanning multiple physical or virtual Windows machines","Distribute task execution across a device fleet to parallelize work","Manage device lifecycle and task routing in a centralized Galaxy control plane"],"best_for":["Enterprise teams automating workflows across distributed Windows infrastructure","Organizations with device fleets (RPA centers, testing labs) needing centralized orchestration","Teams building multi-device automation platforms with Galaxy as the control layer"],"limitations":["Network latency between Galaxy controller and remote devices adds 1-5s per task dispatch","Requires network connectivity and firewall rules allowing device-to-controller communication","No built-in device failover; if a device goes offline, tasks assigned to it fail","Task decomposition across devices is non-trivial; poor decomposition leads to inefficient parallelization"],"requires":["Galaxy controller deployed (Python 3.9+ with UFO³ framework)","UFO² agents running on each target device with network access to controller","Agent Interaction Protocol (AIP) server/client implementation","Network connectivity between controller and all devices"],"input_types":["high-level task description","device constraints or preferences (e.g., 'run on Device A only')"],"output_types":["task decomposition across devices","per-device execution traces","aggregated task completion status"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ufo__cap_7","uri":"capability://tool.use.integration.agent.interaction.protocol.aip.for.device.to.controller.communication","name":"agent interaction protocol (aip) for device-to-controller communication","description":"UFO³ implements the Agent Interaction Protocol (AIP), a structured communication protocol enabling UFO² agents on remote devices to register with the Galaxy controller, receive task assignments, report execution status, and stream results back. AIP defines message formats for task requests, action execution, observation reporting, and error handling, abstracting away network transport details. The protocol supports both synchronous (request-response) and asynchronous (streaming) communication patterns.","intents":["Enable reliable communication between Galaxy controller and remote UFO² agents","Stream execution traces and observations from devices back to the controller in real-time","Handle device registration, heartbeats, and failure detection in a distributed system"],"best_for":["Teams building distributed automation platforms on top of UFO³","Developers extending Galaxy with custom communication patterns or transport layers","Organizations needing to monitor and debug multi-device automation in real-time"],"limitations":["AIP message serialization adds overhead (typically 10-50ms per message)","No built-in encryption; requires TLS/mTLS for secure communication in production","Protocol versioning can cause compatibility issues if controller and devices run different UFO³ versions","Streaming large execution traces (screenshots, logs) can saturate network bandwidth"],"requires":["Python 3.9+ with UFO³ AIP implementation","Network connectivity between controller and devices","Shared protocol schema definitions (JSON or protobuf)"],"input_types":["task request (JSON with task description and device constraints)","action execution results from remote agents"],"output_types":["task assignment confirmation","execution status updates","result aggregation from multiple devices"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ufo__cap_8","uri":"capability://memory.knowledge.rag.based.knowledge.infrastructure.with.vector.database.integration","name":"rag-based knowledge infrastructure with vector database integration","description":"UFO² integrates a Retrieval-Augmented Generation (RAG) system that stores domain knowledge (application documentation, automation patterns, troubleshooting guides) in a vector database. When agents encounter novel situations, they query the knowledge base to retrieve relevant context, which is injected into the LLM prompt. The system supports multiple vector database backends (Chroma, Weaviate, Pinecone) and provides tools for creating, updating, and managing knowledge documents.","intents":["Provide agents with domain-specific knowledge without fine-tuning the underlying LLM","Enable agents to handle novel application scenarios by retrieving relevant documentation","Build reusable knowledge bases for specific domains (finance, HR, IT operations)"],"best_for":["Teams automating domain-specific applications (e.g., enterprise software, internal tools)","Organizations with extensive documentation that should inform agent behavior","Builders creating reusable agent templates with embedded domain knowledge"],"limitations":["RAG retrieval adds latency (typically 500ms-2s per query depending on vector DB size)","Knowledge base quality directly impacts agent performance; poor documentation leads to poor retrievals","Vector embeddings can miss relevant context if query and document use different terminology","Maintaining knowledge base currency requires ongoing updates as applications evolve"],"requires":["Python 3.9+ with UFO² RAG module","Vector database (Chroma, Weaviate, Pinecone, or compatible)","Embedding model (OpenAI, Hugging Face, or local)","Knowledge documents in text or markdown format"],"input_types":["knowledge documents (text, markdown, PDF)","query text from agent reasoning"],"output_types":["retrieved document chunks (ranked by relevance)","augmented prompt with retrieved context"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ufo__cap_9","uri":"capability://memory.knowledge.prompt.construction.and.multi.modal.context.management","name":"prompt construction and multi-modal context management","description":"UFO² implements a sophisticated prompt construction system that assembles multi-modal context (screenshots, UI annotations, action history, retrieved knowledge, task description) into structured prompts for LLM reasoning. The system manages prompt components (system instructions, task context, observation history, tool definitions) and applies strategies like context pruning, summarization, and priority-based truncation to fit within LLM token limits. Supports multi-modal prompts combining text, images, and structured data.","intents":["Construct effective prompts that ground agent reasoning in current UI state and task context","Manage token budget across multiple context sources (screenshots, history, knowledge)","Enable agents to reason about multi-modal information (text + images + structured data)"],"best_for":["Teams fine-tuning agent behavior through prompt engineering","Builders creating domain-specific prompt templates for specialized agents","Organizations needing to optimize LLM token usage for cost control"],"limitations":["Prompt quality is highly dependent on manual engineering; no automatic optimization","Context pruning strategies can lose important information if thresholds are misconfigured","Multi-modal prompts require LLM support for vision (not all models support images)","Prompt construction overhead adds 100-500ms per agent round"],"requires":["Python 3.9+ with UFO² prompt module","LLM supporting the desired modalities (text, vision, etc.)","Prompt template definitions (Jinja2 or similar)"],"input_types":["task description","screenshot (PNG/BMP)","UI annotations (JSON)","action history (list of prior actions)","retrieved knowledge (text chunks)"],"output_types":["assembled prompt (text or multi-modal)","token count estimate","context pruning decisions"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Windows 10/11 with Python 3.9+","API key for supported LLM provider (OpenAI, Anthropic, Azure OpenAI, or local Ollama)","Administrator privileges for UI automation and COM object access","Windows 10/11 with UI Automation (UIA) framework enabled","Python 3.9+ with pywinauto or similar Windows automation library","Administrator privileges to access COM objects and UI element properties","Python 3.9+ with UFO² LLM module","API keys for selected LLM providers (OpenAI, Anthropic, Azure, etc.)","JSON schema definitions for structured outputs","Python 3.9+ with UFO² configuration module"],"failure_modes":["Windows-only (no macOS or Linux desktop support in UFO²)","Requires LLM API calls for every decision point, adding latency (typically 2-5 seconds per action)","Screenshot-based perception vulnerable to UI changes, overlapping windows, or dynamic content rendering","No built-in OCR for handwritten or image-embedded text in UI controls","Accessibility API coverage varies by application; legacy or custom-drawn UIs may have incomplete metadata","Annotation overhead adds 500ms-2s per screenshot depending on UI complexity","Bounding box coordinates become stale if UI re-renders between capture and action execution","Cannot annotate content inside embedded web views or PDF viewers without additional OCR","Provider-specific features (vision, function calling) may not be available across all providers","Structured output validation adds latency (typically 100-500ms per validation)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.050Z","last_scraped_at":"2026-05-03T14:00:10.321Z","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=ufo","compare_url":"https://unfragile.ai/compare?artifact=ufo"}},"signature":"rj/M99Alowehhj9G8bY+IHm/dhsdqkYhg7J5ZbkvHuBB0dLqbcrsjHFjVfqtSsk2ACErOUzx/BbTqrpW91jPBA==","signedAt":"2026-06-22T13:20:00.312Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ufo","artifact":"https://unfragile.ai/ufo","verify":"https://unfragile.ai/api/v1/verify?slug=ufo","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"}}