{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-cua","slug":"cua","name":"Cua","type":"mcp","url":"https://github.com/trycua/cua/tree/main/libs/mcp-server","page_url":"https://unfragile.ai/cua","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-cua__cap_0","uri":"capability://tool.use.integration.mcp.protocol.bridging.for.computer.use.agent.execution","name":"mcp protocol bridging for computer-use agent execution","description":"Exposes the Cua ComputerAgent framework as an MCP (Model Context Protocol) server, enabling Claude Desktop and other MCP clients to invoke computer-use capabilities through standardized tool calling. The MCP server translates incoming tool calls into ComputerAgent method invocations, manages screenshot capture and action execution state, and returns structured responses back through the MCP protocol, eliminating the need for direct SDK integration.","intents":["Run computer-use agents directly from Claude Desktop without writing custom integration code","Expose computer-use capabilities to any MCP-compatible client application","Standardize how LLM clients invoke desktop automation and visual reasoning workflows","Enable non-technical users to trigger agent workflows through Claude's native interface"],"best_for":["Teams using Claude Desktop who want agent capabilities without SDK overhead","MCP ecosystem developers building agent-aware applications","Organizations standardizing on MCP for LLM tool integration"],"limitations":["MCP protocol overhead adds ~50-100ms per round-trip vs direct SDK calls","Requires MCP client implementation — not compatible with REST-only integrations","State management across MCP sessions requires explicit session tracking; no built-in persistence","Limited to tools exposed via MCP schema — custom agent loops require SDK-level modification"],"requires":["Claude Desktop 0.1.0+ or compatible MCP client","Python 3.9+ runtime for MCP server process","Cua framework installed (Python SDK)","Valid API credentials for underlying LLM provider (OpenAI, Anthropic, etc.)"],"input_types":["MCP tool call JSON with task description","Screenshot binary data (PNG/JPEG)","Action parameters (click coordinates, text input, scroll deltas)"],"output_types":["MCP tool result JSON with execution status","Screenshot binary data from agent execution","Structured action logs with reasoning traces"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cua__cap_1","uri":"capability://planning.reasoning.vision.language.model.agnostic.agent.loop.orchestration","name":"vision-language model agnostic agent loop orchestration","description":"Implements a unified agent loop that abstracts 100+ vision-language models (Claude, GPT-4V, Gemini, open-source models via Ollama) behind a single ComputerAgent interface. The loop captures screenshots, formats them with task context using the Responses API message format, sends them to the selected VLM, parses structured action responses, and executes OS-level operations. Model selection is decoupled from agent logic through a provider architecture, enabling runtime model switching without code changes.","intents":["Build agents that work with any VLM without rewriting agent logic","Compare agent performance across different models (Claude vs GPT-4V vs open-source)","Migrate between model providers without refactoring agent code","Use local models (Ollama) for privacy-sensitive tasks while maintaining the same agent interface"],"best_for":["Researchers benchmarking agent performance across model families","Teams wanting model flexibility without architectural lock-in","Organizations with privacy requirements needing local model fallbacks","Developers building multi-model agent systems"],"limitations":["Model-specific capabilities (e.g., native tool calling in Claude) are normalized to a common interface, losing some optimization benefits","Response parsing assumes structured action format — models with inconsistent output require custom adapters","Latency varies significantly across models (Claude ~2-5s, local Ollama ~10-30s per step); no built-in latency optimization","Token counting and cost tracking require per-model configuration; no unified cost abstraction"],"requires":["Python 3.9+ with Cua SDK installed","API credentials for at least one supported provider (OpenAI, Anthropic, Google, etc.)","For local models: Ollama 0.1.0+ running locally or accessible via network","Vision-capable model (GPT-4V, Claude 3 Sonnet+, Gemini 2.0, etc.)"],"input_types":["Task description (string)","Screenshot (PNG/JPEG binary)","Agent configuration (model name, temperature, max tokens)"],"output_types":["Structured action (click, type, scroll, wait with parameters)","Reasoning trace (model's explanation of action choice)","Execution status (success, error, retry)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cua__cap_10","uri":"capability://tool.use.integration.http.api.and.websocket.server.for.remote.agent.execution","name":"http api and websocket server for remote agent execution","description":"Exposes agent execution capabilities via HTTP REST API and WebSocket connections, enabling remote clients to trigger agent runs and stream results in real-time. The server is built on FastAPI and handles authentication, request validation, and response serialization. Clients can submit tasks, poll for status, retrieve trajectories, and stream screenshots/actions via WebSocket. The server supports multiple concurrent agent executions with per-request isolation. OS-specific handlers are abstracted, allowing the server to run on any platform and target any execution environment.","intents":["Build web UIs and dashboards for agent execution and monitoring","Integrate agents into existing backend systems via REST API","Stream real-time agent execution to web clients via WebSocket","Enable remote agent execution from non-Python clients (JavaScript, Go, etc.)"],"best_for":["Teams building web-based agent interfaces","Organizations integrating agents into existing backend systems","Developers building multi-client agent orchestration systems","Remote execution scenarios where agent runs on a different machine"],"limitations":["HTTP API adds ~100-200ms latency per request due to serialization and network overhead","WebSocket streaming of large screenshots (1-2MB per screenshot) can saturate bandwidth on slow connections","Server is single-threaded by default; concurrent requests require async handling or process pooling","Authentication is basic (API key); no built-in OAuth or advanced auth mechanisms","State management across requests requires external session store; no built-in persistence"],"requires":["Python 3.9+ with Cua SDK","FastAPI and uvicorn for server runtime","Network connectivity between client and server","Optional: TLS certificates for HTTPS/WSS"],"input_types":["HTTP POST/GET requests with JSON payload (task, model, environment)","WebSocket connection for streaming results"],"output_types":["HTTP JSON responses (status, task ID, results)","WebSocket messages (screenshots, actions, status updates)","Trajectory files (JSON, video)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cua__cap_11","uri":"capability://planning.reasoning.responses.api.message.format.compatibility.for.structured.reasoning","name":"responses api message format compatibility for structured reasoning","description":"Implements the Anthropic Responses API message format for structured agent reasoning and action specification. This format enables models to return structured actions (click, type, scroll) with explicit reasoning, reducing parsing ambiguity and improving reliability. The framework automatically converts model responses in this format into executable actions, handling validation and error recovery. Support for Responses API is built into the agent loop, with fallback to text parsing for models that don't support structured output.","intents":["Improve agent reliability by using structured action output instead of text parsing","Enable models to provide explicit reasoning for each action","Reduce hallucination and parsing errors in action generation","Support models with native structured output capabilities (Claude, GPT-4 with function calling)"],"best_for":["Teams prioritizing agent reliability and interpretability","Models with native structured output support (Claude 3+, GPT-4 with tools)","Workflows requiring explicit reasoning traces","Debugging and analysis scenarios"],"limitations":["Responses API format is model-specific; not all models support it (requires fallback to text parsing)","Structured output may be more verbose than text, increasing token usage by ~10-20%","Validation of structured output adds ~50-100ms per response","Limited to predefined action types; custom actions require schema extension","Some models may not strictly adhere to schema, requiring lenient parsing"],"requires":["Model with Responses API support (Claude 3 Sonnet+, GPT-4 with tools, etc.)","Python 3.9+ with Cua SDK"],"input_types":["Model response in Responses API format (JSON with action and reasoning)","Action schema definition"],"output_types":["Validated action specification (type, parameters)","Reasoning trace (model's explanation)","Validation errors if schema violated"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cua__cap_12","uri":"capability://automation.workflow.telemetry.and.observability.with.structured.logging","name":"telemetry and observability with structured logging","description":"Provides comprehensive telemetry and observability through structured logging, metrics collection, and integration with observability platforms. The system logs all agent loop steps (screenshot, reasoning, action, result) with timestamps, model outputs, and error details. Metrics include latency per step, token usage, cost, and success rates. Logs are structured (JSON) for easy parsing and can be exported to external systems (CloudWatch, Datadog, Prometheus). The telemetry system is pluggable, allowing custom exporters to be registered.","intents":["Monitor agent performance and health in production","Debug agent failures with detailed execution logs","Measure and optimize agent latency and cost","Integrate agent telemetry with existing observability infrastructure"],"best_for":["Production deployments requiring monitoring and alerting","Teams optimizing agent performance","Organizations with existing observability infrastructure","Debugging complex multi-step agent workflows"],"limitations":["Telemetry collection adds ~5-10% overhead per execution","Large-scale deployments may generate significant log volume (GBs per day); requires log aggregation","Custom exporters require implementation; no built-in exporters for all platforms","Sensitive data (screenshots, user input) may be logged; requires explicit sanitization","Log retention and cost management are operator's responsibility"],"requires":["Python 3.9+ with Cua SDK","Optional: External observability platform (CloudWatch, Datadog, etc.)","Log storage and aggregation infrastructure"],"input_types":["Agent execution events (screenshot, action, error)","Telemetry configuration (log level, exporters, sampling)"],"output_types":["Structured logs (JSON with timestamp, level, context)","Metrics (latency, tokens, cost, success rate)","Exported telemetry to external systems"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cua__cap_2","uri":"capability://automation.workflow.multi.environment.execution.with.provider.abstraction","name":"multi-environment execution with provider abstraction","description":"Abstracts execution environments (Docker containers, Lume macOS VMs, Windows Sandbox, host OS) behind a unified provider interface, allowing agents to target different execution contexts without code changes. The provider architecture handles environment-specific screenshot capture (X11/Wayland on Linux, native APIs on macOS/Windows), action execution (xdotool, native APIs), and resource lifecycle management. Agents specify target environment at runtime; the framework routes screenshot and action calls to the appropriate provider implementation.","intents":["Run the same agent code against Windows, macOS, and Linux without platform-specific branching","Execute agents in isolated Docker containers for security and reproducibility","Use Lume-managed macOS VMs for testing macOS-specific workflows at scale","Switch execution environments (host → Docker → VM) without changing agent code"],"best_for":["Teams testing cross-platform automation workflows","Security-conscious organizations requiring sandboxed execution","QA teams running agents against multiple OS versions","Researchers benchmarking agent behavior across environments"],"limitations":["Docker provider adds ~500ms-2s overhead per screenshot due to container communication overhead","Lume VM provider requires macOS host and cloud credentials; not suitable for local-only deployments","Windows Sandbox provider only available on Windows Pro/Enterprise; limited to single-session execution","Host provider has no isolation — malicious agents can access full system; not recommended for untrusted code","Cross-environment state synchronization requires explicit handling; no automatic session migration"],"requires":["Python 3.9+ with Cua SDK","For Docker: Docker daemon running, image with X11/display server configured","For Lume: macOS host, Lume API credentials, cloud account","For Windows Sandbox: Windows Pro/Enterprise, Sandbox feature enabled","For host: Direct OS access with appropriate permissions (sudo for some operations)"],"input_types":["Provider name (string: 'docker', 'lume', 'windows-sandbox', 'host')","Environment configuration (container image, VM specs, sandbox settings)","Task and screenshot/action requests"],"output_types":["Screenshot binary from target environment","Action execution status and logs","Environment-specific metadata (container ID, VM instance ID)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cua__cap_3","uri":"capability://image.visual.screenshot.capture.with.semantic.object.mapping.som","name":"screenshot capture with semantic object mapping (som)","description":"Captures screenshots from the target environment and optionally augments them with semantic object mapping (SOM) — overlaying bounding boxes and labels for interactive UI elements (buttons, inputs, links). The SOM system uses vision models to identify clickable regions and assigns them numeric IDs, enabling agents to reference UI elements by semantic identity rather than pixel coordinates. This reduces hallucination and improves action accuracy, especially for complex interfaces. SOM generation is optional and configurable per agent run.","intents":["Improve agent accuracy by providing semantic labels for UI elements instead of raw pixels","Enable agents to reference UI elements by semantic ID rather than guessing coordinates","Reduce hallucination when agents interact with unfamiliar interfaces","Debug agent behavior by visualizing which UI elements the agent identified"],"best_for":["Agents operating on complex, dynamic UIs (web apps, desktop software)","Teams prioritizing accuracy over speed","Researchers studying agent perception and grounding","Debugging and visualization workflows"],"limitations":["SOM generation adds ~1-3s per screenshot due to additional vision model inference","SOM accuracy depends on vision model quality; may miss or mislabel elements in cluttered interfaces","SOM IDs are ephemeral — they change between screenshots, requiring agents to re-identify elements","Not suitable for real-time, latency-sensitive applications due to overhead","SOM works best for standard UI elements; custom or heavily styled components may not be detected"],"requires":["Vision-capable LLM for SOM generation (Claude 3+, GPT-4V, etc.)","Screenshot capture capability from target environment","Optional: SOM configuration (which element types to label, confidence thresholds)"],"input_types":["Screenshot binary (PNG/JPEG)","SOM configuration (enable/disable, element types, confidence threshold)"],"output_types":["Screenshot binary with SOM overlays (bounding boxes, numeric IDs)","Structured SOM data (element ID, type, bounding box, label)","Raw screenshot without SOM if disabled"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cua__cap_4","uri":"capability://automation.workflow.action.execution.with.os.specific.handlers","name":"action execution with os-specific handlers","description":"Translates high-level action specifications (click, type, scroll, wait) into OS-specific commands executed on the target environment. The framework implements native handlers for Linux (xdotool, X11/Wayland), macOS (native APIs), and Windows (pyautogui, native APIs), abstracting platform differences. Actions are queued, executed sequentially, and validated; failures trigger retry logic or error reporting. The action execution layer is decoupled from agent reasoning, allowing custom action handlers to be plugged in.","intents":["Execute agent-generated actions reliably across Windows, macOS, and Linux","Handle platform-specific quirks (Wayland vs X11, native vs emulated input) transparently","Implement custom action types (e.g., keyboard shortcuts, multi-touch gestures) without modifying core agent","Validate and log all executed actions for debugging and auditing"],"best_for":["Cross-platform automation requiring consistent behavior","Teams needing detailed action logs for compliance or debugging","Custom automation workflows requiring non-standard actions","Environments where input reliability is critical"],"limitations":["Platform-specific handlers have different reliability profiles — Windows Sandbox may have input lag, Wayland support is experimental","Action execution is sequential; parallel actions not supported","No built-in retry logic for transient failures (e.g., window focus issues); requires custom error handling","Some actions (e.g., native file dialogs) may not be automatable on all platforms","Action timing is not guaranteed — rapid action sequences may exceed OS input queue capacity"],"requires":["Python 3.9+ with Cua SDK","Platform-specific tools: xdotool (Linux), native APIs (macOS/Windows)","Display server access (X11/Wayland on Linux, native on macOS/Windows)","Appropriate OS permissions (may require sudo on Linux for some operations)"],"input_types":["Action specification (type: 'click'|'type'|'scroll'|'wait', parameters)","Target coordinates (x, y for click/scroll)","Text input (for type action)","Duration (for wait action)"],"output_types":["Action execution status (success, failure, timeout)","Error details if action failed","Execution timestamp and duration","Post-action screenshot (optional)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cua__cap_5","uri":"capability://planning.reasoning.agent.loop.customization.and.extension.points","name":"agent loop customization and extension points","description":"Provides extension points for customizing the agent loop without modifying core framework code. Developers can implement custom agent loops by subclassing the base loop, overriding specific methods (e.g., screenshot capture, action parsing, reasoning), and registering callbacks at key points (pre/post screenshot, pre/post action, loop completion). The callback system enables monitoring, logging, cost tracking, and conditional loop termination. Custom tools can be registered and made available to agents through a tool registry.","intents":["Implement custom agent reasoning logic (e.g., multi-step planning, hierarchical decomposition)","Add monitoring and observability (cost tracking, latency measurement, trajectory recording)","Integrate custom tools and APIs into the agent loop","Implement domain-specific optimizations (e.g., caching, early termination)"],"best_for":["Teams building specialized agents for specific domains","Researchers experimenting with novel agent architectures","Organizations needing detailed observability and cost tracking","Advanced users implementing multi-agent systems"],"limitations":["Custom loop implementations must handle all state management — no automatic persistence","Callback system is synchronous; long-running callbacks block the agent loop","Limited documentation on extension points — requires reading framework source code","Custom loops may not be compatible with future framework versions","Tool registration is global; no namespace isolation for multi-agent scenarios"],"requires":["Python 3.9+ with Cua SDK","Understanding of Cua agent loop architecture","Familiarity with Python async/await if implementing async extensions"],"input_types":["Custom loop class (subclass of base loop)","Callback functions (pre/post screenshot, pre/post action, etc.)","Tool definitions (name, description, parameters, handler function)"],"output_types":["Modified agent behavior (custom reasoning, actions, termination)","Callback outputs (logs, metrics, state updates)","Tool execution results"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cua__cap_6","uri":"capability://automation.workflow.budget.and.cost.management.with.per.model.tracking","name":"budget and cost management with per-model tracking","description":"Tracks API costs and token usage across agent executions, with per-model cost calculation based on input/output token counts and model-specific pricing. The system maintains a budget limit and can terminate agents when budget is exceeded. Cost tracking is integrated into the agent loop via callbacks, enabling real-time cost monitoring and reporting. Supports multiple cost backends (OpenAI, Anthropic, custom) and generates cost reports by model, task, and time period.","intents":["Monitor and control API spending across agent executions","Implement budget-aware agent termination to prevent runaway costs","Compare cost efficiency across different models and agent strategies","Generate cost reports for billing and optimization analysis"],"best_for":["Teams running agents at scale with cost constraints","Organizations needing detailed cost attribution and reporting","Researchers comparing cost-efficiency across models","Production deployments requiring budget controls"],"limitations":["Cost tracking is approximate — actual API charges may differ due to rounding, minimum charges, or batch discounts","Budget enforcement is client-side; no guarantee against overages if multiple agents run concurrently","Pricing data must be manually updated when models change pricing; no automatic price sync","Cost tracking adds ~10-50ms per loop step due to token counting overhead","No support for reserved capacity or volume discounts"],"requires":["Python 3.9+ with Cua SDK","Model pricing configuration (can be auto-loaded for major providers)","Optional: Budget limit specification"],"input_types":["Model name and pricing data","Input/output token counts from agent execution","Budget limit (optional)"],"output_types":["Cost per execution (input cost, output cost, total)","Cumulative cost tracking","Budget status (remaining, percentage used)","Cost reports (by model, task, time period)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cua__cap_7","uri":"capability://automation.workflow.trajectory.recording.and.replay.for.debugging.and.evaluation","name":"trajectory recording and replay for debugging and evaluation","description":"Records complete agent execution trajectories (screenshots, actions, reasoning, errors) to disk or cloud storage, enabling post-execution analysis, debugging, and evaluation. Trajectories include timestamps, model outputs, action results, and environment state at each step. The system supports trajectory replay — re-executing recorded actions against a fresh environment to validate reproducibility or test modifications. Trajectories can be exported in standard formats (JSON, video) for sharing and analysis.","intents":["Debug agent failures by reviewing complete execution history with screenshots and reasoning","Evaluate agent performance across multiple runs and compare strategies","Test agent robustness by replaying trajectories against modified environments","Create datasets for training and fine-tuning agent models"],"best_for":["Researchers analyzing agent behavior and failure modes","Teams debugging complex multi-step agent workflows","QA teams validating agent consistency across runs","Organizations building agent training datasets"],"limitations":["Trajectory recording adds ~5-10% overhead per execution due to I/O and serialization","Large trajectories (100+ steps with screenshots) consume significant storage (100MB-1GB per trajectory)","Replay is not guaranteed to be deterministic — environment changes or timing issues may cause divergence","Trajectory format is framework-specific; limited interoperability with other tools","Privacy concerns with recording screenshots of sensitive data; requires explicit consent and sanitization"],"requires":["Python 3.9+ with Cua SDK","Storage backend (local filesystem, S3, GCS, etc.)","Optional: Video encoding tools for trajectory video export"],"input_types":["Agent execution (screenshots, actions, reasoning, errors)","Trajectory metadata (task, model, environment, timestamp)"],"output_types":["Trajectory file (JSON with embedded screenshots or references)","Trajectory video (MP4/WebM with annotations)","Trajectory statistics (step count, duration, cost, success rate)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cua__cap_8","uri":"capability://automation.workflow.benchmark.evaluation.against.osworld.and.custom.test.suites","name":"benchmark evaluation against osworld and custom test suites","description":"Integrates with OSWorld benchmark suite and supports custom evaluation workflows for measuring agent performance. The evaluation system runs agents against predefined tasks, collects trajectories, and computes metrics (success rate, step efficiency, cost per task). Results are compared against baseline models and can be visualized in dashboards. The framework supports both automated evaluation (batch runs) and interactive evaluation (human-in-the-loop validation). Custom evaluation metrics can be implemented and registered.","intents":["Benchmark agent performance against standard OSWorld tasks","Compare performance across different models, configurations, and strategies","Measure progress during agent development and optimization","Validate agent behavior against custom domain-specific test suites"],"best_for":["Researchers publishing agent performance results","Teams optimizing agent configurations","Organizations validating agent readiness for production","Developers building custom evaluation frameworks"],"limitations":["OSWorld evaluation requires specific environment setup (Linux VMs, specific software versions); not portable to all platforms","Evaluation is time-consuming — full OSWorld suite may take hours to days to complete","Custom metrics require manual implementation; no standard metric library","Results are sensitive to environment state and timing; reproducibility requires careful setup","Evaluation data can be large (GBs for full benchmark runs); requires significant storage"],"requires":["Python 3.9+ with Cua SDK","For OSWorld: Linux environment, specific software packages (see OSWorld docs)","Execution environment (Docker, VMs, or host)","Sufficient compute resources for batch evaluation"],"input_types":["Task specification (OSWorld task ID or custom task definition)","Agent configuration (model, environment, parameters)","Evaluation metrics (standard or custom)"],"output_types":["Task success/failure status","Metrics (success rate, steps, cost, duration)","Trajectories (for analysis)","Evaluation reports (summary, per-task breakdown, comparisons)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cua__cap_9","uri":"capability://automation.workflow.lume.vm.orchestration.for.macos.testing.at.scale","name":"lume vm orchestration for macos testing at scale","description":"Manages macOS virtual machines via the Lume platform, enabling agents to run against macOS environments without requiring physical hardware. The system handles VM provisioning, lifecycle management (start, stop, snapshot), and image caching. Agents can target specific macOS versions and software configurations by selecting pre-built VM images. The Lume provider abstracts VM communication details, presenting a uniform interface to the agent loop. Supports concurrent VM execution for parallel testing.","intents":["Test agents against macOS without owning physical Macs","Run agents against multiple macOS versions in parallel","Automate macOS-specific workflows (Xcode, Safari, native apps) at scale","Reduce infrastructure costs by using cloud VMs instead of physical hardware"],"best_for":["Teams testing cross-platform agents without macOS hardware","QA teams validating macOS compatibility","Researchers benchmarking agents on macOS","Organizations with macOS-heavy workflows (iOS development, design tools)"],"limitations":["Lume VM provider requires cloud account and API credentials; not suitable for offline or air-gapped deployments","VM startup time is ~30-60s; not suitable for real-time, latency-sensitive workflows","VM snapshots are immutable; state changes don't persist across runs unless explicitly saved","Concurrent VM execution is limited by cloud quota; scaling requires account upgrades","Network latency between agent and VM adds ~100-200ms per screenshot/action vs local execution"],"requires":["Python 3.9+ with Cua SDK","Lume API credentials and cloud account","Network connectivity to Lume cloud","Sufficient cloud quota for desired concurrency"],"input_types":["VM image specification (macOS version, software, configuration)","Agent task and configuration","Concurrency level (number of parallel VMs)"],"output_types":["VM instance ID and connection details","Screenshots and action results from VM","VM lifecycle events (started, stopped, snapshot created)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["Claude Desktop 0.1.0+ or compatible MCP client","Python 3.9+ runtime for MCP server process","Cua framework installed (Python SDK)","Valid API credentials for underlying LLM provider (OpenAI, Anthropic, etc.)","Python 3.9+ with Cua SDK installed","API credentials for at least one supported provider (OpenAI, Anthropic, Google, etc.)","For local models: Ollama 0.1.0+ running locally or accessible via network","Vision-capable model (GPT-4V, Claude 3 Sonnet+, Gemini 2.0, etc.)","Python 3.9+ with Cua SDK","FastAPI and uvicorn for server runtime"],"failure_modes":["MCP protocol overhead adds ~50-100ms per round-trip vs direct SDK calls","Requires MCP client implementation — not compatible with REST-only integrations","State management across MCP sessions requires explicit session tracking; no built-in persistence","Limited to tools exposed via MCP schema — custom agent loops require SDK-level modification","Model-specific capabilities (e.g., native tool calling in Claude) are normalized to a common interface, losing some optimization benefits","Response parsing assumes structured action format — models with inconsistent output require custom adapters","Latency varies significantly across models (Claude ~2-5s, local Ollama ~10-30s per step); no built-in latency optimization","Token counting and cost tracking require per-model configuration; no unified cost abstraction","HTTP API adds ~100-200ms latency per request due to serialization and network overhead","WebSocket streaming of large screenshots (1-2MB per screenshot) can saturate bandwidth on slow connections","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":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.037Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=cua","compare_url":"https://unfragile.ai/compare?artifact=cua"}},"signature":"ovOLW5oW7nSms9fTpTkjKTrajHDHqmRwfaXAX6UbsqGnzhsbcyt6Ocm1mfwsekYGF33E2vW1EXzlx9b2xysECQ==","signedAt":"2026-06-20T05:12:00.619Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cua","artifact":"https://unfragile.ai/cua","verify":"https://unfragile.ai/api/v1/verify?slug=cua","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"}}