{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-bytedance--ui-tars-desktop","slug":"bytedance--ui-tars-desktop","name":"UI-TARS-desktop","type":"agent","url":"https://agent-tars.com","page_url":"https://unfragile.ai/bytedance--ui-tars-desktop","categories":["ai-agents"],"tags":["agent","agent-tars","browser-use","computer-use","cowork","gui-agent","gui-operator","mcp","mcp-server","multimodal","tars","ui-tars","vision","vlm"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-bytedance--ui-tars-desktop__cap_0","uri":"capability://automation.workflow.multimodal.gui.automation.via.vision.language.model.screenshot.analysis","name":"multimodal gui automation via vision-language model screenshot analysis","description":"Enables autonomous desktop/web UI interaction by capturing screenshots, analyzing them with vision-language models (VLM), and executing click/type/scroll actions based on visual understanding. The system uses a closed-loop action cycle: screenshot → VLM analysis → action generation → execution, with support for both local VLM providers (Doubao-1.5-UI-TARS) and remote OpenAI-compatible endpoints. The GUIAgent SDK abstracts operator implementations for different platforms (local desktop via Electron, remote via VNC).","intents":["I need an AI agent to automate repetitive UI workflows across web and desktop applications without writing brittle selectors","I want to test my application UI by having an AI agent interact with it visually like a human would","I need to migrate legacy desktop applications by having an AI agent perform GUI operations that can't be accessed via APIs"],"best_for":["teams automating cross-platform UI testing and RPA workflows","developers building GUI automation agents without access to application source code","enterprises migrating from traditional RPA tools to AI-native automation"],"limitations":["VLM inference latency (typically 2-5 seconds per action cycle) makes real-time interaction slower than native automation","Accuracy depends on VLM quality and screenshot clarity; complex UI layouts with overlapping elements may cause action hallucination","No built-in OCR fallback for text-heavy interfaces; relies entirely on VLM visual understanding","Remote VNC-based automation adds network latency and requires VNC server setup on target machines"],"requires":["OpenAI API key or compatible VLM endpoint (Claude, Gemini, or local Doubao-1.5-UI-TARS)","Electron runtime for local desktop automation or VNC server for remote automation","Python 3.9+ or Node.js 18+ depending on deployment target","System permissions for screenshot capture and input simulation (macOS/Windows/Linux)"],"input_types":["natural language task descriptions","screenshot images (PNG/JPEG)","UI state context and previous action history"],"output_types":["structured action commands (click, type, scroll, wait)","action execution results with success/failure status","screenshot evidence of completed actions"],"categories":["automation-workflow","image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--ui-tars-desktop__cap_1","uri":"capability://planning.reasoning.composable.multi.plugin.agent.orchestration.with.tool.routing","name":"composable multi-plugin agent orchestration with tool routing","description":"Provides a plugin-based agent architecture (ComposableAgent) that dynamically routes tasks to specialized sub-agents: GUI automation, code execution, web browsing, and MCP tool integration. Each plugin implements a standardized interface and receives context from a central orchestrator, enabling agents to delegate work (e.g., 'execute this Python code' → CodeAgent, 'click the login button' → GUIAgent). The system uses a T5 format streaming parser to handle tool calls and agent responses in a structured, resumable manner.","intents":["I want to build an agent that can handle complex multi-step tasks requiring different capabilities (UI automation, code execution, web search) without hardcoding task routing logic","I need to extend an agent with custom tools and capabilities without modifying the core agent loop","I want to compose agents that can delegate subtasks to specialized sub-agents and aggregate their results"],"best_for":["teams building complex AI agents with heterogeneous tool requirements","developers creating extensible agent frameworks where plugins can be added/removed at runtime","organizations needing to isolate different agent capabilities (e.g., code execution in sandbox, GUI automation on local machine)"],"limitations":["Plugin communication overhead adds ~50-100ms per delegation; not suitable for latency-critical real-time applications","Requires explicit plugin registration and interface compliance; incompatible plugins will cause runtime failures","No built-in plugin versioning or backward compatibility; breaking changes in plugin interfaces require coordinated updates","Debugging multi-plugin execution flows is complex due to distributed state across plugin instances"],"requires":["TypeScript 4.8+ for type-safe plugin interface definitions","Node.js 18+ for async plugin execution and event streaming","Understanding of the T5 format for tool call serialization","Each plugin requires its own runtime environment (e.g., Python sandbox for CodeAgent, browser for GUIAgent)"],"input_types":["natural language task descriptions","structured tool call specifications","plugin configuration objects"],"output_types":["aggregated results from multiple plugins","structured tool call responses","event streams with plugin execution traces"],"categories":["planning-reasoning","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--ui-tars-desktop__cap_10","uri":"capability://search.retrieval.semantic.search.system.with.web.search.integration.and.result.ranking","name":"semantic search system with web search integration and result ranking","description":"Integrates semantic search capabilities that enable agents to query the web, process results, and extract relevant information. The system supports multiple search backends (Google, Bing, custom search engines) and ranks results using semantic similarity and relevance scoring. Search results are formatted for agent consumption with metadata (URL, snippet, ranking score). The search integration is exposed as a tool that agents can invoke as part of their workflows.","intents":["I want my agent to search the web for information and use results to complete tasks","I need to extract relevant information from search results and process it programmatically","I want to integrate web search into agent workflows without writing custom search logic"],"best_for":["agents that need to gather information from the web as part of their workflows","developers building research and information-gathering agents","teams automating web-based information extraction tasks"],"limitations":["Search API rate limits and costs; high-frequency searches may exceed quotas","Search result quality depends on query formulation; poor queries return irrelevant results","Web content changes frequently; cached results may become stale","Search results may contain outdated or incorrect information; no built-in fact verification"],"requires":["Search API key (Google Custom Search, Bing Search, etc.)","Network connectivity for search queries","Result parsing and ranking logic"],"input_types":["search queries (natural language or structured)","search parameters (language, region, result count, etc.)"],"output_types":["ranked search results with URLs and snippets","result metadata (ranking score, relevance, source)","extracted information from results"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--ui-tars-desktop__cap_11","uri":"capability://automation.workflow.agent.hook.system.with.lifecycle.callbacks.and.custom.event.handling","name":"agent hook system with lifecycle callbacks and custom event handling","description":"Provides a hook-based extension system where developers can register callbacks at key agent lifecycle points (before/after tool calls, on errors, on completion). Hooks receive full context (agent state, tool call details, results) and can modify behavior (e.g., logging, metrics collection, custom error handling). The system supports both synchronous and asynchronous hooks, with error handling to prevent hook failures from breaking agent execution.","intents":["I want to add custom logging and monitoring to agent execution without modifying core agent code","I need to implement custom error handling or retry logic for specific tool calls","I want to collect metrics and telemetry from agent execution"],"best_for":["teams building observability and monitoring for agents","developers extending agent behavior with custom logic","organizations collecting metrics and analytics from agent execution"],"limitations":["Hook execution adds latency; complex hooks can slow down agent execution","Hook errors can cause agent failures if not properly handled; requires defensive coding","Hook ordering is not guaranteed; hooks may execute in unpredictable order","Debugging hook interactions is complex due to distributed callback logic"],"requires":["Hook registration API","Understanding of agent lifecycle and state","Async/await support for asynchronous hooks"],"input_types":["hook registration with callback functions","hook context (agent state, tool calls, results)"],"output_types":["hook execution results","modified agent state or behavior","metrics and telemetry data"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--ui-tars-desktop__cap_12","uri":"capability://text.generation.language.llm.processing.pipeline.with.streaming.response.handling.and.token.management","name":"llm processing pipeline with streaming response handling and token management","description":"Implements a processing pipeline that sends agent context and tool calls to LLMs with streaming response handling. The pipeline manages token counting, context window management, and response parsing. It supports streaming responses where tokens are processed incrementally, enabling real-time UI updates and early stopping. The pipeline handles different LLM response formats (OpenAI, Anthropic, etc.) and normalizes them into a unified agent response format.","intents":["I want to stream LLM responses in real-time for better UX and faster feedback","I need to manage token usage and stay within context window limits","I want to handle different LLM providers without changing my agent code"],"best_for":["teams building interactive agents with real-time response streaming","developers optimizing token usage and costs","organizations using multiple LLM providers"],"limitations":["Streaming adds complexity to response parsing; partial responses may be incomplete","Token counting is approximate; actual token usage may differ from estimates","Context window management requires careful prompt engineering; exceeding limits causes failures","Streaming latency depends on LLM response time; slow models may cause UI delays"],"requires":["LLM API with streaming support","Token counting library (tiktoken, etc.)","Response parsing logic for different LLM formats"],"input_types":["agent context and messages","tool call specifications","LLM configuration (model, temperature, etc.)"],"output_types":["streamed LLM responses","parsed tool calls and actions","token usage statistics"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--ui-tars-desktop__cap_13","uri":"capability://automation.workflow.agent.runner.with.loop.execution.error.recovery.and.max.step.limits","name":"agent runner with loop execution, error recovery, and max-step limits","description":"Implements the core agent execution loop that repeatedly calls the LLM, executes tool calls, and processes results until completion or max-step limit. The runner handles errors gracefully with retry logic and fallback strategies. It maintains execution state (current step, tool calls, results) and can pause/resume execution. The runner enforces safety limits (max steps, timeout) to prevent infinite loops and resource exhaustion.","intents":["I want to run agents with automatic error recovery and safety limits","I need to pause and resume agent execution for long-running tasks","I want to prevent agents from running indefinitely or consuming excessive resources"],"best_for":["teams running production agents with reliability requirements","developers building agents that need safety limits and error recovery","organizations running multi-tenant agent services"],"limitations":["Max-step limits may cause agents to fail on complex tasks that require many steps","Error recovery adds latency; retry logic may cause 10-30 second delays","Pause/resume requires explicit checkpointing; not all agent states are resumable","Timeout enforcement depends on OS-level process management; may not work reliably in all environments"],"requires":["Agent configuration with max-step and timeout limits","Error handling and retry logic","State management for pause/resume"],"input_types":["agent configuration","initial task description","resume state (for resuming execution)"],"output_types":["execution results and final output","execution trace with all steps","error messages and recovery actions"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--ui-tars-desktop__cap_2","uri":"capability://automation.workflow.browser.automation.with.intelligent.element.interaction.and.search.integration","name":"browser automation with intelligent element interaction and search integration","description":"Provides browser control capabilities through Playwright/Puppeteer integration with semantic element understanding. The system can navigate URLs, interact with form elements, extract content, and perform searches using integrated search infrastructure. It supports both direct element selection (via CSS/XPath) and semantic interaction (via VLM-based element identification). The browser automation layer integrates with the search system to handle web queries and result processing within agent workflows.","intents":["I need an agent to automate web form filling, data extraction, and multi-page navigation workflows","I want to perform web searches and process results programmatically as part of an agent task","I need to test web applications by automating user interactions like clicking, typing, and scrolling"],"best_for":["developers building web scraping and data extraction agents","teams automating web-based business processes (form submission, account management)","QA engineers automating cross-browser testing workflows"],"limitations":["Requires browser binary (Chromium/Firefox) which adds ~500MB to deployment size","JavaScript-heavy SPAs may require explicit wait conditions; implicit waits can cause timeouts","Cannot interact with content inside iframes or shadow DOM without explicit traversal logic","Search integration depends on external search provider availability; no fallback for search failures"],"requires":["Playwright 1.40+ or Puppeteer 21+","Chromium/Firefox/WebKit browser binary","Node.js 18+","Network connectivity for web navigation and search queries"],"input_types":["URLs and navigation targets","element selectors (CSS/XPath) or semantic descriptions","form data and interaction commands","search queries"],"output_types":["extracted page content (HTML/text)","screenshots of page state","search results with ranking and metadata","interaction success/failure status"],"categories":["automation-workflow","search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--ui-tars-desktop__cap_3","uri":"capability://code.generation.editing.code.execution.in.isolated.sandbox.with.output.capture.and.error.handling","name":"code execution in isolated sandbox with output capture and error handling","description":"The CodeAgent plugin executes arbitrary code (Python, JavaScript, etc.) in isolated sandbox environments with resource limits, capturing stdout/stderr and return values. The system uses containerized or process-level isolation to prevent malicious code from accessing the host system. Execution results are streamed back to the agent with full error context, allowing the agent to handle failures and retry with modified code. Integration with the agent loop enables iterative code refinement based on execution feedback.","intents":["I want an agent to write and execute code to solve problems, with the ability to see execution results and fix errors","I need to safely execute untrusted code in an agent workflow without risking the host system","I want to enable agents to perform data analysis, mathematical computations, and file operations programmatically"],"best_for":["teams building code-generation agents that need to validate generated code","developers creating AI-powered development assistants that write and test code","organizations running multi-tenant agent services with untrusted code execution"],"limitations":["Sandbox overhead adds 500ms-2s per execution; not suitable for real-time code evaluation","Resource limits (CPU, memory, disk) may cause legitimate long-running computations to timeout","No persistent state between executions; each code run starts with a clean environment","File system access is restricted to sandbox directories; cannot access host files without explicit mounting","Network access from sandbox may be disabled for security; external API calls require explicit allowlisting"],"requires":["Docker or similar containerization for process isolation","Python 3.9+ and/or Node.js 18+ depending on supported languages","Resource limits configuration (CPU, memory, timeout)","File system mount points for sandbox input/output"],"input_types":["code strings (Python, JavaScript, etc.)","execution parameters and environment variables","input files and data"],"output_types":["stdout/stderr output","return values and execution results","error messages with stack traces","execution metadata (duration, resource usage)"],"categories":["code-generation-editing","automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--ui-tars-desktop__cap_4","uri":"capability://tool.use.integration.model.context.protocol.mcp.client.with.multi.provider.tool.integration","name":"model context protocol (mcp) client with multi-provider tool integration","description":"Implements an MCP client that discovers, registers, and invokes tools from MCP servers (local and remote). The system maintains a tool registry with schema information, handles tool call serialization/deserialization, and manages MCP server lifecycle (startup, shutdown, reconnection). The MCP agent plugin routes tool calls from the main agent to appropriate MCP servers, with support for multiple concurrent MCP server connections. Transport layer supports stdio, HTTP, and WebSocket protocols for MCP communication.","intents":["I want to integrate external tools and services into my agent via the MCP standard without writing custom integrations","I need to connect my agent to multiple MCP servers (e.g., filesystem tools, database tools, API tools) simultaneously","I want to extend my agent with third-party tools while maintaining a clean, standardized interface"],"best_for":["teams building extensible agents that need to integrate with diverse external tools","developers creating MCP servers that want to expose tools to AI agents","organizations standardizing on MCP for agent-tool communication"],"limitations":["MCP server discovery and startup adds 1-3 seconds of initialization overhead","Tool schema validation is strict; incompatible tool definitions will cause registration failures","No built-in tool caching; each tool call requires round-trip to MCP server","Transport layer latency (especially HTTP/WebSocket) can add 100-500ms per tool call","MCP server crashes or network failures require manual reconnection; no automatic recovery"],"requires":["MCP server implementations (local or remote)","Node.js 18+ for MCP client runtime","Network connectivity for remote MCP servers","Tool schema definitions in MCP format"],"input_types":["MCP server connection configurations","tool call requests with parameters","tool schema specifications"],"output_types":["tool execution results","tool schema definitions","MCP server status and metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--ui-tars-desktop__cap_5","uri":"capability://automation.workflow.agent.event.streaming.with.structured.t5.format.parsing.and.resumable.execution","name":"agent event streaming with structured t5 format parsing and resumable execution","description":"Implements a streaming event architecture where agent execution produces a continuous stream of structured events (tool calls, responses, state changes) in T5 format. The T5 format uses delimited markers to structure tool calls and responses, enabling partial parsing and resumable execution. Events are streamed to clients in real-time, allowing UI updates and external monitoring. The streaming parser handles incomplete messages and can resume parsing from arbitrary points, supporting long-running agent sessions with network interruptions.","intents":["I want to monitor agent execution in real-time with detailed event traces for debugging and transparency","I need to build a UI that updates as the agent executes, showing tool calls, results, and reasoning","I want to resume agent execution after network failures or crashes without losing progress"],"best_for":["teams building agent monitoring and observability dashboards","developers creating interactive agent UIs with real-time updates","organizations running long-running agent sessions that need fault tolerance"],"limitations":["T5 format parsing adds ~10-20ms overhead per event; high-frequency events may cause bottlenecks","Event storage and persistence require external infrastructure (database, message queue); no built-in persistence","Resumable execution requires explicit checkpointing; not all agent states are resumable","Event ordering guarantees depend on transport layer; out-of-order events can cause state inconsistencies"],"requires":["T5 format parser implementation (provided by framework)","Event streaming transport (WebSocket, Server-Sent Events, or message queue)","Optional: event storage backend for persistence and replay"],"input_types":["agent execution events","tool call requests and responses","state change notifications"],"output_types":["structured event stream in T5 format","real-time event notifications","execution traces and logs"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--ui-tars-desktop__cap_6","uri":"capability://automation.workflow.agent.session.lifecycle.management.with.rest.api.and.persistence","name":"agent session lifecycle management with rest api and persistence","description":"Manages agent session creation, execution, and state persistence through a REST API (AgentSession). Each session maintains execution history, tool call logs, and agent state, with support for querying and resuming sessions. Sessions are persisted to a backend store (database or file system), enabling long-lived agent workflows that survive server restarts. The session API provides endpoints for creating sessions, submitting queries, streaming results, and retrieving execution history.","intents":["I want to create long-running agent sessions that persist across server restarts and network interruptions","I need to query agent execution history and retrieve results from past sessions","I want to build a multi-user agent service where each user has isolated sessions with their own execution context"],"best_for":["teams building production agent services with persistence requirements","developers creating agent APIs that need to support long-lived workflows","organizations running multi-tenant agent platforms with session isolation"],"limitations":["Session persistence adds database latency (10-50ms per operation); high-frequency updates may cause bottlenecks","Session state can grow large for long-running agents; no built-in cleanup or archival","Concurrent session access requires locking or optimistic concurrency control; conflicts may cause execution failures","Session recovery from crashes may require manual intervention if state is partially corrupted"],"requires":["Persistent storage backend (PostgreSQL, MongoDB, SQLite, etc.)","REST API server (Express, FastAPI, etc.)","Session serialization format (JSON, Protocol Buffers, etc.)"],"input_types":["session creation requests with configuration","query strings and parameters","session IDs for retrieval"],"output_types":["session metadata and status","execution history and logs","query results and agent responses"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--ui-tars-desktop__cap_7","uri":"capability://automation.workflow.web.ui.configuration.system.with.dynamic.routing.and.workspace.management","name":"web ui configuration system with dynamic routing and workspace management","description":"Provides a web-based UI for agent configuration, execution, and monitoring through a React-based frontend with dynamic routing. The UI supports creating and managing agent sessions, configuring tool integrations, viewing execution traces, and accessing workspace resources (files, code, etc.). The configuration system allows runtime modification of agent settings without restarting the server. Workspace navigation enables browsing and managing files created by agents during execution.","intents":["I want a user-friendly interface to configure and run agents without writing code","I need to monitor agent execution with real-time updates and detailed execution traces","I want to manage agent workspaces and access files created by agents"],"best_for":["non-technical users who want to configure and run agents via UI","teams building internal agent tools with web interfaces","developers debugging agent execution with visual traces and logs"],"limitations":["Web UI adds ~200-500ms latency for each interaction due to network round-trips","Complex agent configurations may be difficult to express through UI forms; advanced users may prefer CLI/API","Real-time updates depend on WebSocket or Server-Sent Events; fallback to polling adds latency","Workspace file management is limited to text files; binary files require download/upload"],"requires":["React 18+ for UI framework","Node.js 18+ for backend server","WebSocket or Server-Sent Events support for real-time updates","File system access for workspace management"],"input_types":["agent configuration forms","query strings and parameters","file uploads and workspace operations"],"output_types":["rendered HTML UI","JSON configuration objects","file downloads and workspace contents"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--ui-tars-desktop__cap_8","uri":"capability://automation.workflow.electron.desktop.application.with.local.gui.automation.and.remote.vnc.support","name":"electron desktop application with local gui automation and remote vnc support","description":"Packages the UI-TARS agent stack as a native Electron desktop application with dual automation modes: local (direct screenshot/input on the same machine) and remote (VNC-based control of remote machines). The application manages system permissions for screenshot and input simulation, handles VNC connection lifecycle, and provides a native UI for agent configuration and execution. The Electron main process bridges between the renderer (React UI) and native system APIs for screenshot capture and input simulation.","intents":["I want to run GUI automation agents on my local machine without setting up a server","I need to automate GUI tasks on remote machines via VNC without exposing the agent to the internet","I want a native desktop application for agent configuration and monitoring with system integration"],"best_for":["individual developers and small teams automating local GUI tasks","enterprises automating remote desktop workflows via VNC","users who prefer native desktop applications over web interfaces"],"limitations":["Local automation requires system permissions (accessibility, screenshot) which vary by OS and may require user approval","Remote VNC automation adds network latency and requires VNC server setup on target machines","Electron application size is ~200-300MB; not suitable for resource-constrained environments","Cross-platform support requires testing on macOS, Windows, and Linux; some features may be OS-specific"],"requires":["Electron 25+","Node.js 18+","System permissions for screenshot and input simulation (varies by OS)","VNC server on remote machines (for remote automation mode)"],"input_types":["agent configuration through UI","VNC connection parameters","task descriptions and automation scripts"],"output_types":["screenshots and execution traces","automation results and logs","system notifications and alerts"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--ui-tars-desktop__cap_9","uri":"capability://tool.use.integration.vlm.provider.abstraction.with.multi.model.support.and.fallback.routing","name":"vlm provider abstraction with multi-model support and fallback routing","description":"Abstracts vision-language model providers (OpenAI, Claude, Gemini, local Doubao-1.5-UI-TARS) behind a unified interface, enabling agents to switch between models without code changes. The system handles provider-specific API differences (request/response formats, authentication), manages API quotas and rate limits, and supports fallback routing when a provider is unavailable. Configuration allows specifying primary and fallback models, with automatic failover on errors.","intents":["I want to use different VLM providers for GUI automation without changing my agent code","I need to handle VLM provider outages by automatically falling back to alternative models","I want to optimize costs by using cheaper models for simple tasks and premium models for complex ones"],"best_for":["teams using multiple VLM providers and wanting to switch between them","developers building resilient agents that need fallback models","organizations optimizing VLM costs with provider-specific pricing strategies"],"limitations":["Model output format differences may cause inconsistencies; fallback models may produce different action formats","API quota management requires external tracking; no built-in quota enforcement","Fallback routing adds latency when primary provider fails; retry logic may cause 10-30 second delays","Model-specific optimizations (e.g., prompt engineering for Doubao) may not transfer to other providers"],"requires":["API keys for at least one VLM provider (OpenAI, Anthropic, Google, etc.)","Provider-specific SDKs or HTTP clients","Configuration for primary and fallback models"],"input_types":["screenshots and visual context","task descriptions and prompts","provider configuration"],"output_types":["structured action commands","model responses and reasoning","provider metadata and usage statistics"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key or compatible VLM endpoint (Claude, Gemini, or local Doubao-1.5-UI-TARS)","Electron runtime for local desktop automation or VNC server for remote automation","Python 3.9+ or Node.js 18+ depending on deployment target","System permissions for screenshot capture and input simulation (macOS/Windows/Linux)","TypeScript 4.8+ for type-safe plugin interface definitions","Node.js 18+ for async plugin execution and event streaming","Understanding of the T5 format for tool call serialization","Each plugin requires its own runtime environment (e.g., Python sandbox for CodeAgent, browser for GUIAgent)","Search API key (Google Custom Search, Bing Search, etc.)","Network connectivity for search queries"],"failure_modes":["VLM inference latency (typically 2-5 seconds per action cycle) makes real-time interaction slower than native automation","Accuracy depends on VLM quality and screenshot clarity; complex UI layouts with overlapping elements may cause action hallucination","No built-in OCR fallback for text-heavy interfaces; relies entirely on VLM visual understanding","Remote VNC-based automation adds network latency and requires VNC server setup on target machines","Plugin communication overhead adds ~50-100ms per delegation; not suitable for latency-critical real-time applications","Requires explicit plugin registration and interface compliance; incompatible plugins will cause runtime failures","No built-in plugin versioning or backward compatibility; breaking changes in plugin interfaces require coordinated updates","Debugging multi-plugin execution flows is complex due to distributed state across plugin instances","Search API rate limits and costs; high-frequency searches may exceed quotas","Search result quality depends on query formulation; poor queries return irrelevant results","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7723095374061575,"quality":0.35,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:56:56.344Z","last_commit":"2026-04-29T07:27:48Z"},"community":{"stars":29597,"forks":2909,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=bytedance--ui-tars-desktop","compare_url":"https://unfragile.ai/compare?artifact=bytedance--ui-tars-desktop"}},"signature":"bI3gTnNXn/ToEXkPiWIbaPdQH/3sZp3M2fLI79A9ViADsKYKE//Qbo46heZWxAvt0xl7eU+A1UCGjq4pxNJoDw==","signedAt":"2026-06-22T02:58:49.252Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/bytedance--ui-tars-desktop","artifact":"https://unfragile.ai/bytedance--ui-tars-desktop","verify":"https://unfragile.ai/api/v1/verify?slug=bytedance--ui-tars-desktop","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"}}