{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-trycua--cua","slug":"trycua--cua","name":"cua","type":"agent","url":"https://cua.ai","page_url":"https://unfragile.ai/trycua--cua","categories":["ai-agents","model-training"],"tags":["agent","ai-agent","apple","computer-use","computer-use-agent","containerization","cua","desktop-automation","hacktoberfest","lume","macos","manus","operator","swift","virtualization","virtualization-framework","windows","windows-sandbox"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-trycua--cua__cap_0","uri":"capability://planning.reasoning.vision.language.model.driven.screenshot.interpretation.and.action.reasoning","name":"vision-language model-driven screenshot interpretation and action reasoning","description":"Captures desktop screenshots and feeds them to 100+ integrated vision-language models (Claude, GPT-4V, Gemini, local models via adapters) to reason about UI state and determine appropriate next actions. Uses a unified message format (Responses API) across heterogeneous model providers, enabling the agent to understand visual context and generate structured action commands without brittle selector-based logic.","intents":["I want my agent to understand what's on screen and decide what to click or type next","I need to support multiple VLM providers without rewriting agent logic","I want to use local open-source models instead of cloud APIs for privacy"],"best_for":["Teams building autonomous desktop automation agents","Researchers evaluating VLM performance on UI understanding tasks","Enterprises requiring multi-model flexibility for cost/latency optimization"],"limitations":["VLM inference latency varies by provider (cloud APIs 1-5s, local models 5-30s depending on hardware)","Screenshot resolution and color depth impact token consumption and reasoning quality","No built-in hallucination detection — agent may attempt invalid actions if model misinterprets UI"],"requires":["Python 3.9+ or Node.js 18+","API keys for at least one VLM provider (OpenAI, Anthropic, Google, etc.) OR local model setup (Ollama, vLLM)","Execution environment (Docker, Lume VM, Windows Sandbox, or host OS)"],"input_types":["PNG/JPEG screenshots (variable resolution)","Task descriptions (natural language strings)","Agent state (previous actions, error messages)"],"output_types":["Structured action commands (click, type, scroll, key press)","Reasoning traces (model's explanation of action choice)","Confidence scores (if model provides)"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-trycua--cua__cap_1","uri":"capability://automation.workflow.multi.os.sandboxed.execution.environment.provisioning.and.lifecycle.management","name":"multi-os sandboxed execution environment provisioning and lifecycle management","description":"Provisions isolated execution environments across macOS (via Lume VMs), Linux (Docker), Windows (Windows Sandbox), and host OS, with unified provider abstraction. Handles VM/container lifecycle (creation, snapshot management, cleanup), resource allocation, and OS-specific action handlers (keyboard/mouse events, clipboard, file system access) through a pluggable provider architecture that abstracts platform differences.","intents":["I want to run agents on different operating systems without rewriting environment code","I need to isolate agent execution to prevent system damage or data leakage","I want to snapshot and restore environments for reproducible testing"],"best_for":["Teams running agents across heterogeneous infrastructure (macOS dev machines, Linux servers, Windows enterprise)","Researchers benchmarking agent behavior across OS platforms","Security-conscious organizations requiring sandboxed automation"],"limitations":["Lume provider (macOS) requires Apple Silicon or Intel Mac with virtualization support; adds 30-60s VM boot overhead","Docker provider requires container runtime and may have UI rendering limitations for some applications","Windows Sandbox provider limited to Windows 10/11 Pro/Enterprise; no persistent state between runs without custom setup","Host provider offers no isolation — agent actions affect live system"],"requires":["Python 3.9+ or Node.js 18+","For Lume: macOS 12+, Apple Silicon or Intel with VT-x/AMD-V","For Docker: Docker Engine 20.10+","For Windows Sandbox: Windows 10/11 Pro/Enterprise with Hyper-V enabled","For Host: Direct OS access (no additional requirements)"],"input_types":["Provider configuration (provider type, resource limits, image specs)","Action commands (click, type, scroll, file operations)"],"output_types":["Environment handle/connection object","Screenshots (PNG/JPEG from environment)","Execution logs and error traces"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-trycua--cua__cap_10","uri":"capability://automation.workflow.lume.vm.management.with.snapshot.and.restore.capabilities.for.macos","name":"lume vm management with snapshot and restore capabilities for macos","description":"Provides Lume provider for provisioning and managing macOS virtual machines with native support for snapshot creation, restoration, and cleanup. Handles VM lifecycle (boot, shutdown, resource allocation) with optimized startup times. Integrates with image registry for VM image management and caching. Supports both Apple Silicon and Intel Macs. Enables deterministic testing through snapshot-based environment reset between agent runs.","intents":["I want to run agents on macOS VMs with fast startup and cleanup","I need to snapshot environments for reproducible testing","I want to manage VM resources (CPU, memory, disk) for cost optimization"],"best_for":["Teams testing agents on macOS applications","Researchers requiring deterministic macOS environments","Organizations running agents at scale on macOS infrastructure"],"limitations":["Lume provider requires macOS host with virtualization support (Apple Silicon or Intel with VT-x)","VM boot time adds 30-60s overhead per agent run","Snapshot/restore operations require disk space for VM images","Limited to macOS host — cannot run on Linux or Windows servers"],"requires":["macOS 12+ host with Apple Silicon or Intel processor with VT-x","Lume provider installed and configured","Sufficient disk space for VM images (10GB+ per image)"],"input_types":["VM configuration (CPU, memory, disk)","Image specification (macOS version, pre-installed software)","Snapshot/restore commands"],"output_types":["VM handle/connection object","Snapshot IDs","VM status and resource metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-trycua--cua__cap_11","uri":"capability://automation.workflow.cli.and.gradio.web.ui.for.agent.execution.and.monitoring","name":"cli and gradio web ui for agent execution and monitoring","description":"Provides command-line interface (CLI) for quick-start agent execution, configuration, and testing without writing code. Includes Gradio-based web UI for interactive agent control, real-time monitoring, and trajectory visualization. CLI supports task specification, model selection, environment configuration, and result export. Web UI enables non-technical users to run agents and view execution traces with HUD visualization.","intents":["I want to run agents from the command line without writing code","I need a web interface for non-technical users to control agents","I want to monitor agent execution in real-time with visual feedback"],"best_for":["Developers prototyping agents quickly","Non-technical users running pre-configured agents","Teams demonstrating agent capabilities to stakeholders"],"limitations":["CLI has limited customization — complex agent logic requires SDK usage","Web UI may have latency issues for real-time monitoring with high-resolution screenshots","No built-in authentication — requires external security layer for multi-user deployments","Gradio UI is not suitable for production dashboards (limited styling, no advanced features)"],"requires":["Python 3.9+ (for CLI and web UI)","Gradio library (for web UI)","Web browser (for web UI access)"],"input_types":["Task description (CLI argument or web form)","Model selection (CLI flag or web dropdown)","Environment configuration (CLI config file or web form)"],"output_types":["Execution results (CLI output or web display)","Trajectories (downloadable or viewable in web UI)","HUD visualization (web UI overlay)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-trycua--cua__cap_12","uri":"capability://automation.workflow.docker.provider.for.linux.based.agent.execution.with.container.isolation","name":"docker provider for linux-based agent execution with container isolation","description":"Implements Docker provider for running agents in containerized Linux environments with full isolation. Handles container lifecycle (creation, cleanup), image management, and volume mounting for persistent storage. Supports custom Dockerfiles for environment customization. Provides X11/Wayland display server integration for GUI application interaction. Enables reproducible agent execution across different host systems.","intents":["I want to run agents in isolated containers for security and reproducibility","I need to deploy agents on Linux servers without VM overhead","I want to customize the agent execution environment with Docker"],"best_for":["Teams deploying agents on Linux infrastructure","Developers requiring container-based isolation","Organizations standardizing on Docker for deployment"],"limitations":["GUI rendering in containers may have performance issues (X11 forwarding overhead)","Some applications may not work in containerized environments (e.g., applications requiring kernel modules)","Container startup time adds 5-10s overhead per agent run","Volume mounting may have permission issues on some host systems"],"requires":["Docker Engine 20.10+","X11 or Wayland display server (for GUI applications)","Sufficient disk space for container images"],"input_types":["Container configuration (image, environment variables, volumes)","Dockerfile (for custom environments)","Display server configuration"],"output_types":["Container handle/connection object","Screenshots (from container display)","Execution logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-trycua--cua__cap_13","uri":"capability://automation.workflow.windows.sandbox.and.host.provider.for.windows.based.agent.execution","name":"windows sandbox and host provider for windows-based agent execution","description":"Implements Windows Sandbox provider for isolated agent execution on Windows 10/11 Pro/Enterprise, and host provider for direct OS execution. Windows Sandbox provider creates ephemeral sandboxed environments with automatic cleanup. Host provider enables direct agent execution on live Windows system without isolation. Both providers support native Windows input simulation (SendInput API) and clipboard operations. Handles Windows-specific action execution (window management, registry access).","intents":["I want to run agents on Windows with isolation to prevent system damage","I need to test agents on live Windows systems without VM overhead","I want to support Windows-specific applications and workflows"],"best_for":["Teams testing agents on Windows applications","Developers requiring Windows-specific automation","Organizations with Windows-heavy infrastructure"],"limitations":["Windows Sandbox provider requires Windows 10/11 Pro/Enterprise (not Home edition)","Windows Sandbox has no persistent state between runs — requires custom setup for stateful testing","Host provider offers no isolation — agent actions affect live system","Some applications may not work in Windows Sandbox (e.g., applications requiring specific drivers)"],"requires":["Windows 10/11 Pro/Enterprise (for Windows Sandbox provider)","Hyper-V enabled (for Windows Sandbox)","User-level permissions (no admin required for Windows Sandbox)"],"input_types":["Sandbox configuration (image, environment variables)","Action commands (Windows-specific input simulation)"],"output_types":["Sandbox handle/connection object","Screenshots (from sandbox or host)","Execution logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-trycua--cua__cap_14","uri":"capability://automation.workflow.telemetry.and.logging.system.with.structured.error.tracking","name":"telemetry and logging system with structured error tracking","description":"Implements comprehensive telemetry and logging infrastructure capturing agent execution metrics (latency, token usage, action success rate), errors, and performance data. Supports structured logging with contextual information (task ID, agent ID, timestamp). Integrates with external monitoring systems (e.g., Datadog, CloudWatch) for centralized observability. Provides error categorization and automatic error recovery suggestions. Enables debugging through detailed execution logs with configurable verbosity levels.","intents":["I want to monitor agent performance and identify bottlenecks","I need to track errors and debug agent failures","I want to integrate agent telemetry with existing monitoring infrastructure"],"best_for":["Teams running agents in production with observability requirements","Developers debugging complex agent failures","Organizations requiring compliance and audit logging"],"limitations":["Telemetry collection adds overhead (logging, metric aggregation)","Structured logging requires careful log level configuration to avoid log spam","External monitoring integration requires additional setup and credentials","Error recovery suggestions are heuristic-based and may not apply to all failure modes"],"requires":["Python 3.9+ or Node.js 18+","Logging configuration (log level, output format)","Optional: external monitoring service credentials (Datadog, CloudWatch, etc.)"],"input_types":["Agent execution events (actions, errors, metrics)","Logging configuration (level, format, output destination)"],"output_types":["Structured logs (JSON, text)","Metrics (latency, token usage, success rate)","Error reports with recovery suggestions"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-trycua--cua__cap_2","uri":"capability://planning.reasoning.agentic.loop.orchestration.with.custom.agent.loop.extensibility","name":"agentic loop orchestration with custom agent loop extensibility","description":"Implements the core agent loop (screenshot → LLM reasoning → action execution → repeat) via the ComputerAgent class, with pluggable callback system and custom loop support. Developers can override loop behavior at multiple extension points: custom agent loops (modify reasoning/action selection), custom tools (add domain-specific actions), and callback hooks (inject monitoring/logging). Supports both synchronous and asynchronous execution patterns.","intents":["I want to run a standard agent loop without writing boilerplate","I need to inject custom logic (e.g., tool validation, action filtering) into the agent loop","I want to implement a specialized loop variant (e.g., hierarchical planning, multi-agent coordination)"],"best_for":["Developers building production agents with standard loop requirements","Researchers experimenting with novel agent loop architectures","Teams requiring domain-specific action extensions (e.g., API calls, database operations)"],"limitations":["Extension points require understanding of internal loop structure and message formats","No built-in multi-agent coordination — custom loops must implement agent-to-agent communication","Callback system is synchronous; async callbacks may block loop execution","Loop state is not automatically persisted — requires external storage for resumable agents"],"requires":["Python 3.9+ (for Python SDK) or Node.js 18+ (for TypeScript SDK)","Understanding of ComputerAgent API and callback signatures","Execution environment (Docker, Lume, Windows Sandbox, or host)"],"input_types":["Task description (string)","Custom loop class (subclass of base loop)","Custom tool definitions (callable functions or tool classes)","Callback functions (for hooks)"],"output_types":["Agent execution trace (screenshots, actions, reasoning)","Final task result (success/failure)","Callback-injected data (custom monitoring outputs)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-trycua--cua__cap_3","uri":"capability://automation.workflow.cross.platform.os.level.action.execution.with.semantic.understanding","name":"cross-platform os-level action execution with semantic understanding","description":"Translates high-level action commands (click, type, scroll, key press, file operations) into OS-specific low-level operations through platform-specific handlers. Uses semantic understanding of UI coordinates and element positions to map VLM-generated actions to actual screen locations. Handles clipboard operations, file system access, and keyboard/mouse event generation with platform-specific APIs (macOS native events, Linux X11/Wayland, Windows input simulation).","intents":["I want the agent to click on UI elements identified by the VLM without brittle coordinate mapping","I need reliable keyboard and mouse input across different operating systems","I want to support file operations and clipboard interactions in agent workflows"],"best_for":["Developers building agents that interact with diverse applications","Teams requiring reliable cross-platform action execution","Researchers studying UI interaction patterns across OS platforms"],"limitations":["Coordinate mapping accuracy depends on screenshot resolution and VLM understanding of UI layout","Some applications may not respond to simulated input (e.g., games with anti-cheat, high-security applications)","Clipboard operations may fail if application has clipboard restrictions","File system access limited by sandbox permissions (Docker, Windows Sandbox restrict host filesystem access)"],"requires":["Execution environment with appropriate OS-level permissions","For macOS: Accessibility permissions for Lume VM","For Linux: X11 or Wayland display server","For Windows: User-level permissions (no admin required for Windows Sandbox)"],"input_types":["Action command objects (click, type, scroll, key_press, file_op)","Coordinates (x, y pixel positions)","Text strings (for typing)","File paths (for file operations)"],"output_types":["Execution status (success/failure)","Error messages (if action failed)","Clipboard contents (if clipboard operation)","File operation results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-trycua--cua__cap_4","uri":"capability://data.processing.analysis.trajectory.recording.and.agent.execution.tracing.with.hud.visualization","name":"trajectory recording and agent execution tracing with hud visualization","description":"Records complete agent execution traces (screenshots, actions, reasoning, timestamps) into structured trajectory files for post-execution analysis and debugging. Integrates with HUD (Heads-Up Display) system to visualize agent actions overlaid on screenshots in real-time or post-hoc. Supports trajectory export in multiple formats for benchmarking and evaluation workflows. Enables deterministic replay of agent trajectories for debugging and reproducibility testing.","intents":["I want to see exactly what my agent did and why it made each decision","I need to debug agent failures by replaying execution with visualization","I want to export agent trajectories for benchmarking and evaluation"],"best_for":["Developers debugging agent behavior and failures","Researchers evaluating agent performance on benchmarks (OSWorld, etc.)","Teams conducting post-mortem analysis of agent execution"],"limitations":["Trajectory files can be large (100MB+ for long-running agents with high-resolution screenshots)","HUD visualization requires compatible display/rendering environment","Replay functionality may not be 100% deterministic if environment state changes between runs","No built-in trajectory compression — requires external tools for storage optimization"],"requires":["Python 3.9+ or Node.js 18+","Storage for trajectory files (local disk or cloud storage)","For HUD visualization: compatible rendering environment (browser, desktop app)"],"input_types":["Agent execution (screenshots, actions, reasoning from agent loop)","Trajectory configuration (format, verbosity level)"],"output_types":["Trajectory files (JSON, structured format)","HUD visualization (HTML/interactive format)","Replay data (for deterministic re-execution)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-trycua--cua__cap_5","uri":"capability://tool.use.integration.multi.provider.vlm.integration.with.native.and.composed.model.support","name":"multi-provider vlm integration with native and composed model support","description":"Provides unified SDK interface to 100+ vision-language models across multiple providers (OpenAI, Anthropic, Google, local models via Ollama/vLLM). Supports native computer-use models (Claude with native tool use) and composed models (standard VLMs with grounding adapters that convert visual understanding to action commands). Implements provider-specific authentication, rate limiting, and error handling with fallback mechanisms. Local model adapters enable on-premise deployment without cloud API dependencies.","intents":["I want to use different VLM providers without changing agent code","I need to run agents locally without sending screenshots to cloud APIs","I want to optimize for cost by switching between expensive and cheap models"],"best_for":["Teams requiring multi-model flexibility for cost/latency optimization","Enterprises with data privacy requirements preventing cloud API usage","Researchers comparing VLM performance on agent tasks"],"limitations":["Native computer-use models (Claude) provide better action generation than composed models with adapters","Local model inference requires significant GPU memory (24GB+ for 7B models, 40GB+ for 13B models)","Composed models with grounding adapters add latency (additional inference pass for action generation)","Provider API rate limits may throttle agent execution in high-throughput scenarios"],"requires":["Python 3.9+ or Node.js 18+","API keys for cloud providers (OpenAI, Anthropic, Google) OR local model setup (Ollama, vLLM, llama.cpp)","For local models: GPU with sufficient VRAM (24GB+ recommended)"],"input_types":["Provider configuration (provider type, model name, API key)","Screenshots (PNG/JPEG)","Task descriptions (natural language)"],"output_types":["Action commands (structured format)","Reasoning traces (model explanation)","Token usage metrics (for cost tracking)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-trycua--cua__cap_6","uri":"capability://automation.workflow.budget.and.cost.management.with.token.tracking.and.rate.limiting","name":"budget and cost management with token tracking and rate limiting","description":"Tracks API token consumption and costs across VLM provider calls, with configurable budget limits and rate limiting. Implements cost estimation before execution and actual cost tracking post-execution. Supports per-agent, per-task, and global budget constraints with automatic throttling or termination when limits are exceeded. Integrates with provider-specific pricing models (OpenAI, Anthropic, Google) for accurate cost calculation.","intents":["I want to prevent runaway agent costs from expensive VLM calls","I need to track and optimize agent execution costs across multiple runs","I want to implement per-user or per-task budget constraints"],"best_for":["Teams running agents at scale with cost-sensitive workloads","Enterprises requiring cost tracking and chargeback mechanisms","Researchers optimizing agent efficiency and cost-per-task metrics"],"limitations":["Cost estimation is approximate until actual API calls complete","Rate limiting may cause agent execution delays in high-throughput scenarios","Budget tracking requires real-time API cost data; pricing changes may cause inaccuracies","No built-in cost optimization (e.g., model selection based on cost/performance tradeoff)"],"requires":["Python 3.9+ or Node.js 18+","API keys with billing enabled for VLM providers","Budget configuration (limits, thresholds)"],"input_types":["Budget configuration (max tokens, max cost, rate limits)","Agent execution parameters (model, task complexity)"],"output_types":["Cost estimates (pre-execution)","Actual costs (post-execution)","Budget status (remaining budget, utilization %)","Rate limit status (requests/minute, tokens/minute)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-trycua--cua__cap_7","uri":"capability://data.processing.analysis.benchmarking.and.evaluation.framework.with.osworld.integration","name":"benchmarking and evaluation framework with osworld integration","description":"Provides evaluation infrastructure for agent performance assessment using standardized benchmarks (OSWorld, etc.). Implements evaluation workflows that execute agents on benchmark tasks, collect trajectories, and compute metrics (success rate, cost per task, steps to completion). Integrates with OSWorld benchmark suite for comparative evaluation. Supports custom evaluation metrics and task definitions. Generates evaluation reports with detailed performance breakdowns.","intents":["I want to evaluate my agent's performance on standardized benchmarks","I need to compare agent performance across different models or configurations","I want to measure agent efficiency (cost, steps, time per task)"],"best_for":["Researchers publishing agent performance results","Teams comparing agent implementations or model choices","Enterprises validating agent readiness for production deployment"],"limitations":["OSWorld benchmark requires specific environment setup (may not be compatible with all execution environments)","Evaluation is time-consuming (hours to days for full benchmark suite)","Metrics are task-dependent; not all metrics apply to all task types","No built-in statistical significance testing — requires external tools for rigorous comparison"],"requires":["Python 3.9+","OSWorld benchmark data and environment setup","Execution environment (Docker, Lume, Windows Sandbox)","Storage for evaluation results and trajectories"],"input_types":["Benchmark task definitions (OSWorld format)","Agent configuration (model, loop parameters)","Evaluation metrics (custom or predefined)"],"output_types":["Evaluation results (success rate, cost, steps)","Detailed trajectories (for post-hoc analysis)","Evaluation reports (HTML, JSON)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-trycua--cua__cap_8","uri":"capability://code.generation.editing.python.and.typescript.sdk.with.unified.api.across.languages","name":"python and typescript sdk with unified api across languages","description":"Provides parallel SDKs in Python (cua-agent, cua-computer) and TypeScript (cua-agent, cua-computer) with unified API design enabling developers to write agent code in their preferred language. Both SDKs expose ComputerAgent, Computer, and execution environment classes with identical method signatures and behavior. Supports both synchronous and asynchronous execution patterns. Includes CLI tools for quick-start and testing.","intents":["I want to build agents in Python or TypeScript without learning different APIs","I need to integrate agents into existing Python or Node.js applications","I want to use async/await patterns for non-blocking agent execution"],"best_for":["Teams with mixed Python/TypeScript codebases","Developers preferring their language of choice","Projects requiring async execution patterns"],"limitations":["TypeScript SDK may lag Python SDK in feature updates","Some advanced features (custom loops, callbacks) may have different APIs between languages","Async patterns in TypeScript add complexity vs. synchronous Python code","Type safety in TypeScript requires more verbose type annotations"],"requires":["Python 3.9+ (for Python SDK) or Node.js 18+ (for TypeScript SDK)","Package manager (pip for Python, npm/yarn for TypeScript)"],"input_types":["Agent configuration (model, environment, task)","Custom code (agent loops, tools, callbacks)"],"output_types":["Agent execution results","Trajectories and traces","Error messages and logs"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-trycua--cua__cap_9","uri":"capability://tool.use.integration.mcp.model.context.protocol.server.integration.for.tool.extension","name":"mcp (model context protocol) server integration for tool extension","description":"Implements MCP server support enabling agents to call external tools and services through standardized MCP protocol. Allows developers to expose custom tools, APIs, and services as MCP resources that agents can discover and invoke. Supports both built-in tools (file operations, web search) and custom tools via MCP server registration. Handles tool discovery, invocation, and result integration into agent reasoning loop.","intents":["I want my agent to call external APIs and services (e.g., web search, database queries)","I need to expose custom business logic as tools the agent can use","I want to use standardized MCP protocol for tool integration"],"best_for":["Teams building agents with external service dependencies","Developers extending agents with domain-specific tools","Organizations standardizing on MCP for tool integration"],"limitations":["MCP server setup requires additional infrastructure and maintenance","Tool invocation adds latency (network calls, MCP protocol overhead)","No built-in tool selection optimization — agent may call irrelevant tools","Tool result integration into agent reasoning depends on VLM understanding of tool outputs"],"requires":["Python 3.9+ or Node.js 18+","MCP server implementation (custom or third-party)","Network connectivity between agent and MCP server"],"input_types":["Tool definitions (MCP schema)","Tool invocation requests (from agent)","Tool results (from MCP server)"],"output_types":["Tool discovery results (available tools)","Tool invocation results (tool output)","Integration into agent reasoning (action commands)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+ or Node.js 18+","API keys for at least one VLM provider (OpenAI, Anthropic, Google, etc.) OR local model setup (Ollama, vLLM)","Execution environment (Docker, Lume VM, Windows Sandbox, or host OS)","For Lume: macOS 12+, Apple Silicon or Intel with VT-x/AMD-V","For Docker: Docker Engine 20.10+","For Windows Sandbox: Windows 10/11 Pro/Enterprise with Hyper-V enabled","For Host: Direct OS access (no additional requirements)","macOS 12+ host with Apple Silicon or Intel processor with VT-x","Lume provider installed and configured","Sufficient disk space for VM images (10GB+ per image)"],"failure_modes":["VLM inference latency varies by provider (cloud APIs 1-5s, local models 5-30s depending on hardware)","Screenshot resolution and color depth impact token consumption and reasoning quality","No built-in hallucination detection — agent may attempt invalid actions if model misinterprets UI","Lume provider (macOS) requires Apple Silicon or Intel Mac with virtualization support; adds 30-60s VM boot overhead","Docker provider requires container runtime and may have UI rendering limitations for some applications","Windows Sandbox provider limited to Windows 10/11 Pro/Enterprise; no persistent state between runs without custom setup","Host provider offers no isolation — agent actions affect live system","Lume provider requires macOS host with virtualization support (Apple Silicon or Intel with VT-x)","VM boot time adds 30-60s overhead per agent run","Snapshot/restore operations require disk space for VM images","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6906256144640807,"quality":0.5,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:57:04.027Z","last_commit":"2026-05-03T04:38:41Z"},"community":{"stars":15547,"forks":958,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=trycua--cua","compare_url":"https://unfragile.ai/compare?artifact=trycua--cua"}},"signature":"e4jqEExuYdAD52KlgQWE6JvwpEs0QuzqzxY/du3WkBrKbX5Kikp39XBXA/JToQ+fZ8VxUoMdQ9OcUcC6+2jZBw==","signedAt":"2026-06-22T05:20:23.533Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/trycua--cua","artifact":"https://unfragile.ai/trycua--cua","verify":"https://unfragile.ai/api/v1/verify?slug=trycua--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"}}