{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-browser-use--browser-use","slug":"browser-use--browser-use","name":"browser-use","type":"agent","url":"https://browser-use.com","page_url":"https://unfragile.ai/browser-use--browser-use","categories":["ai-agents"],"tags":["ai-agents","ai-tools","browser-automation","browser-use","llm","playwright","python"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-browser-use--browser-use__cap_0","uri":"capability://planning.reasoning.llm.driven.autonomous.browser.control.via.chrome.devtools.protocol","name":"llm-driven autonomous browser control via chrome devtools protocol","description":"Translates LLM decisions into browser actions by maintaining a bidirectional bridge between language model outputs and Chrome DevTools Protocol (CDP) commands. The Agent system executes a loop where it captures browser state (DOM, screenshots, page metadata), sends structured context to an LLM provider (OpenAI, Anthropic, Gemini, or local models), parses the LLM's action schema output, and executes actions like click, type, navigate, and extract through CDP. Includes built-in error recovery, loop detection, and behavioral nudges to prevent agent stalling.","intents":["I want to automate a multi-step web task (e.g., fill a form, search, extract data) without writing brittle selectors","I need an AI agent to navigate unfamiliar websites and complete tasks autonomously","I want to test how my LLM performs on real-world browser automation benchmarks"],"best_for":["Teams building autonomous AI agents for web automation","Developers prototyping LLM-powered RPA solutions","Researchers evaluating LLM reasoning on interactive tasks"],"limitations":["Requires Chrome/Chromium browser installation; no Firefox or Safari support","LLM context window limits task complexity — long multi-step workflows may exceed token budgets","Loop detection uses heuristics (repeated actions, unchanged DOM) which can produce false positives on dynamic sites","No built-in persistence for agent state across process restarts — requires external serialization","Performance degrades on JavaScript-heavy sites with frequent DOM mutations due to re-serialization overhead"],"requires":["Python 3.9+","Chrome/Chromium browser (local or remote via CDP)","API key for at least one LLM provider (OpenAI, Anthropic, Google, or local Ollama/LM Studio)","Playwright library (bundled with browser-use)"],"input_types":["Natural language task description (string)","Browser URL (string)","Optional: initial browser state (cookies, localStorage via storage state JSON)"],"output_types":["Structured action trace (list of executed actions with timestamps)","Extracted data (text, structured JSON from page content)","Final browser state (screenshot, DOM snapshot, page metadata)"],"categories":["planning-reasoning","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-browser-use--browser-use__cap_1","uri":"capability://data.processing.analysis.dom.to.text.serialization.with.interactive.element.indexing","name":"dom-to-text serialization with interactive element indexing","description":"Converts raw HTML/CSS/JavaScript DOM trees into LLM-readable markdown and text formats by traversing the DOM, detecting interactive elements (buttons, inputs, links), calculating visibility based on CSS and viewport geometry, and assigning stable numeric indices. The DOM Processing Engine uses a Watchdog pattern to monitor DOM mutations, re-serialize only changed subtrees, and maintain coordinate mappings for accurate click targeting. Outputs include markdown extraction (headings, text content), HTML serialization with element indices, and a browser state summary with page title and URL.","intents":["I need to represent a complex web page as structured text so an LLM can understand what's clickable","I want to map LLM action references (e.g., 'click element 42') back to exact DOM coordinates","I need to detect which page elements are actually visible to the user (not hidden by CSS or overflow)"],"best_for":["Developers building LLM agents that need pixel-accurate interaction","Teams optimizing token usage by compressing page content into markdown","Researchers analyzing how LLMs parse and reason about web UI structure"],"limitations":["Visibility calculation is approximate — CSS transforms, clip-path, and complex stacking contexts may produce false positives/negatives","Re-serialization on every DOM mutation adds ~50-200ms latency per change on large DOMs (10k+ elements)","Shadow DOM and iframes are partially supported but not fully traversed; content inside shadow roots may be invisible to the agent","Dynamic content loaded via JavaScript after initial page load requires explicit wait conditions; no automatic detection of async content","Markdown extraction loses semantic structure (e.g., table layouts become flat text)"],"requires":["Chrome/Chromium with DevTools Protocol enabled","Playwright library for DOM access","JavaScript execution context in the browser"],"input_types":["DOM tree (via Chrome DevTools Protocol)","Viewport dimensions (width, height in pixels)","Optional: CSS computed styles for visibility calculation"],"output_types":["Markdown text (headings, paragraphs, lists with element indices)","HTML string with data-element-id attributes","JSON object with element coordinates, visibility flags, and action types"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-browser-use--browser-use__cap_10","uri":"capability://data.processing.analysis.structured.data.extraction.with.schema.based.validation","name":"structured data extraction with schema-based validation","description":"Extracts structured data from web pages by defining a schema (JSON Schema or Pydantic model) and using the agent to navigate to the relevant page, locate the data, and extract it in the specified format. The extraction action validates the extracted data against the schema and returns structured output (JSON, Python objects). Supports both single-page extraction (extract data from current page) and multi-page extraction (navigate through pages and aggregate results). Includes error handling for schema validation failures and retry logic for incomplete extractions.","intents":["I want to extract product information (name, price, rating) from an e-commerce site in structured JSON format","I need to scrape data from multiple pages and aggregate results into a single dataset","I want to validate extracted data against a schema before returning it to my application"],"best_for":["Teams building data pipelines that extract data from websites","Developers integrating web scraping into data processing workflows","Researchers collecting datasets from web sources"],"limitations":["Schema validation is strict; missing or extra fields cause extraction to fail","Extraction accuracy depends on page layout consistency; changes to page structure may break extraction","No built-in support for complex data types (e.g., nested objects, arrays); requires custom schema definition","Extraction is sequential; no support for parallel extraction from multiple pages","No caching of extracted data; repeated extractions from the same page require re-navigation"],"requires":["Schema definition (JSON Schema or Pydantic model)","Browser session with access to the target website","LLM capable of understanding schema and extracting data"],"input_types":["Schema definition (JSON Schema or Pydantic model)","Page URL or current page content","Optional: extraction instructions (natural language)"],"output_types":["Extracted data (JSON or Python object matching schema)","Validation errors (if schema validation fails)","Extraction confidence score"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-browser-use--browser-use__cap_11","uri":"capability://data.processing.analysis.telemetry.and.usage.tracking.with.cost.estimation","name":"telemetry and usage tracking with cost estimation","description":"Tracks agent execution metrics (actions taken, LLM calls, tokens used, time elapsed) and estimates costs based on LLM provider pricing. Collects telemetry data on agent performance, error rates, and task completion rates. Supports optional cloud sync to aggregate metrics across multiple agent runs and deployments. Provides detailed cost breakdowns per LLM provider and per task. Includes privacy controls to disable telemetry collection if needed.","intents":["I want to understand how much my agents are costing me across different LLM providers","I need to track agent performance metrics and identify bottlenecks","I want to aggregate metrics across multiple agent runs to understand overall system performance"],"best_for":["Teams managing production agents with cost constraints","Developers optimizing agent performance and cost","Organizations tracking AI spending across multiple projects"],"limitations":["Cost estimation is based on published pricing; actual costs may vary due to volume discounts or custom pricing","Telemetry collection adds overhead (~10-50ms per call) and may slow down agent execution","Cloud sync requires network connectivity and may expose usage data to third parties","Privacy controls are opt-in; telemetry is enabled by default","No support for custom pricing models or internal cost allocation"],"requires":["Agent execution loop with action and LLM call tracking","LLM provider pricing data (built-in for major providers)","Optional: cloud sync credentials"],"input_types":["Agent execution metrics (actions, LLM calls, tokens)","LLM provider configuration (model name, pricing)","Optional: custom pricing rules"],"output_types":["Cost estimate (total, per provider, per task)","Performance metrics (actions per minute, success rate)","Telemetry report (JSON or CSV)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-browser-use--browser-use__cap_12","uri":"capability://tool.use.integration.custom.tool.registration.and.action.extensibility","name":"custom tool registration and action extensibility","description":"Enables developers to define custom actions beyond the built-in set (click, type, navigate, extract) by registering custom tool classes that implement a standard interface. Custom tools are integrated into the action execution pipeline and exposed to the LLM as available actions. Supports tool-specific error handling, validation, and documentation. Tools are discovered at runtime and can be dynamically registered or unregistered. Includes examples and templates for common custom tools (screenshot, download, execute JavaScript).","intents":["I want to add a custom action (e.g., screenshot, download file) that my agent can use","I need to integrate domain-specific tools (e.g., API calls, database queries) into my agent","I want to extend browser-use with capabilities specific to my use case"],"best_for":["Developers building specialized agents for domain-specific tasks","Teams integrating browser-use into larger automation systems","Researchers extending browser-use with novel capabilities"],"limitations":["Custom tool registration requires code changes; no dynamic tool discovery from external sources","Tool documentation is manual; no automatic generation from code","Tool error handling is custom; no built-in error recovery strategies for custom tools","Tool execution is sequential; no support for parallel tool execution","No built-in testing framework for custom tools"],"requires":["Python 3.9+","Understanding of browser-use tool interface and action schema","Pydantic models for tool parameter validation"],"input_types":["Tool class definition (implementing standard interface)","Tool parameters (Pydantic model)","Tool documentation (docstring)"],"output_types":["Tool registration confirmation","Tool availability in LLM action schema","Tool execution result"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-browser-use--browser-use__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.integration.with.structured.output.schema.optimization","name":"multi-provider llm integration with structured output schema optimization","description":"Abstracts LLM provider differences (OpenAI, Anthropic Claude, Google Gemini, local Ollama) behind a unified interface that automatically optimizes action schemas per provider's capabilities. Handles provider-specific structured output formats (OpenAI's JSON mode, Anthropic's tool_use, Gemini's function calling), manages token counting and cost tracking, implements exponential backoff retry logic for rate limits and transient failures, and serializes agent state into provider-specific message formats. Supports both cloud-based and local LLM backends with fallback chains.","intents":["I want to swap LLM providers (e.g., OpenAI to Claude) without rewriting agent code","I need to track token usage and costs across different LLM calls","I want to use local LLMs (Ollama, LM Studio) for privacy or cost reasons without changing my agent logic"],"best_for":["Teams evaluating multiple LLM providers for agent performance","Developers building cost-optimized agents with provider fallbacks","Organizations with privacy requirements needing local LLM support"],"limitations":["Schema optimization is provider-specific; some providers (e.g., local Ollama) may not support structured output, falling back to regex parsing which is error-prone","Token counting is approximate for some providers; actual usage may differ by 5-10% due to tokenizer differences","Retry logic uses exponential backoff with fixed max retries (default 3); no adaptive retry strategies for specific error types","Local LLM support requires manual model selection and parameter tuning; no automatic model discovery","Message serialization adds ~100-300ms overhead per LLM call due to schema validation and format conversion"],"requires":["Python 3.9+","API keys for cloud providers (OpenAI, Anthropic, Google) OR local LLM server (Ollama, LM Studio, vLLM)","Network connectivity for cloud providers or local server running on accessible port"],"input_types":["Agent state (conversation history, current browser state, action schema)","LLM provider configuration (model name, API key, temperature, max_tokens)","Optional: custom system prompt"],"output_types":["Structured action object (action type, parameters, reasoning)","Token usage metrics (input tokens, output tokens, cost estimate)","Error trace with retry count and backoff delay"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-browser-use--browser-use__cap_3","uri":"capability://planning.reasoning.loop.detection.and.behavioral.nudges.for.agent.stalling.prevention","name":"loop detection and behavioral nudges for agent stalling prevention","description":"Detects when an agent enters repetitive action cycles (e.g., clicking the same button repeatedly, typing the same text) by comparing recent action history and DOM snapshots. When a loop is detected, the system applies behavioral nudges: suggesting alternative actions, modifying the system prompt to encourage exploration, or triggering a 'judge' evaluation to assess task progress. Uses heuristics like action frequency analysis, DOM change detection, and coordinate repetition to identify stalls. Includes configurable thresholds and nudge strategies.","intents":["I want my agent to recover from getting stuck on a single action without manual intervention","I need to understand why an agent is looping and get suggestions for breaking the cycle","I want to set limits on how many times an agent can repeat the same action before trying something else"],"best_for":["Developers building long-running autonomous agents for production tasks","Teams debugging agent behavior and understanding failure modes","Researchers studying LLM reasoning on complex, multi-step tasks"],"limitations":["Loop detection is heuristic-based and may produce false positives on legitimate repeated actions (e.g., pagination through search results)","Nudge strategies are rule-based and not adaptive; they don't learn from past nudge effectiveness","Judge evaluation requires an additional LLM call, adding latency and cost","No built-in mechanism to distinguish between productive loops (e.g., retrying a failed action) and unproductive ones","Thresholds are global; no per-action or per-task customization without code changes"],"requires":["Agent execution loop with action history tracking","DOM snapshots at each step (for comparison)","Optional: Judge system for progress evaluation"],"input_types":["Action history (list of recent actions with timestamps)","DOM snapshots (before and after each action)","Loop detection configuration (threshold, nudge strategy)"],"output_types":["Loop detection flag (boolean)","Loop type classification (action repetition, coordinate repetition, DOM stasis)","Suggested nudge action (alternative action, prompt modification, judge evaluation)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-browser-use--browser-use__cap_4","uri":"capability://memory.knowledge.message.compaction.and.context.window.optimization","name":"message compaction and context window optimization","description":"Automatically compresses agent conversation history to fit within LLM context windows by summarizing old messages, removing redundant state information, and prioritizing recent actions. Uses a compaction strategy that identifies the most important historical context (e.g., task definition, key decisions) while discarding verbose intermediate steps. Tracks token usage across the conversation and triggers compaction when approaching the LLM's max_tokens limit. Maintains a compact representation of agent state (current page, recent actions, key findings) to preserve context fidelity.","intents":["I want to run long multi-step tasks that exceed my LLM's context window without losing task context","I need to minimize token usage and costs for long-running agents","I want the agent to remember key decisions and findings even after compacting old messages"],"best_for":["Teams running agents on tasks with 50+ steps or complex workflows","Cost-sensitive deployments where token usage is a primary concern","Developers building agents for long-running background tasks"],"limitations":["Compaction is lossy; detailed intermediate steps are discarded, potentially losing context for debugging","Summarization quality depends on the LLM's ability to extract key information; summaries may be inaccurate or incomplete","Compaction adds latency (additional LLM call for summarization) and cost (tokens for summary generation)","No mechanism to preserve task-specific context that may be needed later; compaction is generic","Compact representation may be insufficient for complex tasks with interdependent steps"],"requires":["Agent execution loop with message history tracking","Token counting per LLM provider","LLM capable of summarization (most modern LLMs)"],"input_types":["Message history (list of agent messages and LLM responses)","Token budget (max_tokens for the LLM)","Compaction strategy configuration (threshold, summary length)"],"output_types":["Compacted message history (reduced token count)","Summary of discarded messages (for reference)","Token usage report (before and after compaction)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-browser-use--browser-use__cap_5","uri":"capability://automation.workflow.browser.session.lifecycle.management.with.profile.persistence","name":"browser session lifecycle management with profile persistence","description":"Manages Chrome browser instances through a SessionManager that handles process lifecycle (launch, shutdown, graceful termination), maintains a pool of CDP connections for multi-tab scenarios, and persists browser state (cookies, localStorage, sessionStorage) across sessions via storage state JSON files. Supports browser profile configuration (user data directory, launch arguments, proxy settings) and handles popup/dialog interactions. Implements signal handling for graceful shutdown and cleanup of browser processes on agent termination.","intents":["I want to reuse browser state (login sessions, preferences) across multiple agent runs","I need to run multiple browser tabs/windows in parallel for concurrent tasks","I want to ensure browser processes are properly cleaned up even if my agent crashes"],"best_for":["Teams building production agents that need persistent login state","Developers running concurrent browser automation tasks","Organizations with strict resource cleanup requirements"],"limitations":["Storage state persistence is limited to cookies and localStorage; session-specific state (in-memory JavaScript objects) is lost","Multi-tab support requires manual target/frame management; no automatic tab discovery or switching","Popup and dialog handling is basic; complex modal interactions may require custom logic","Browser profile persistence is Chrome-specific; no cross-browser support","Graceful shutdown timeout is fixed (default 30s); long-running operations may be forcefully terminated"],"requires":["Chrome/Chromium browser installation","Write access to filesystem for storage state JSON and user data directory","Python signal handling support (Unix-like systems or Windows with signal module)"],"input_types":["Browser configuration (launch arguments, profile path, proxy settings)","Storage state JSON (cookies, localStorage from previous session)","Optional: target/frame identifiers for multi-tab scenarios"],"output_types":["BrowserSession object (CDP connection, process handle)","Storage state JSON (for persistence)","Browser metadata (user agent, viewport dimensions)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-browser-use--browser-use__cap_6","uri":"capability://data.processing.analysis.event.driven.dom.monitoring.with.watchdog.pattern","name":"event-driven dom monitoring with watchdog pattern","description":"Monitors DOM mutations in real-time using a Watchdog pattern that listens for browser events (DOMContentLoaded, load, mutation events) and triggers re-serialization only when the DOM changes. Maintains a cache of the last serialized DOM state and compares new snapshots to detect meaningful changes. Supports event filtering to ignore cosmetic changes (e.g., CSS animations) and focus on structural changes (e.g., new elements, attribute changes). Enables efficient state tracking without full-page re-parsing on every step.","intents":["I want to detect when a page has finished loading before taking the next action","I need to know which parts of the page changed after my last action","I want to minimize re-serialization overhead by only updating changed DOM subtrees"],"best_for":["Developers building agents for dynamic, JavaScript-heavy websites","Teams optimizing agent performance on sites with frequent DOM updates","Researchers studying real-time page state tracking"],"limitations":["Event-driven monitoring requires JavaScript execution in the browser; no support for sites that disable JavaScript","Mutation event filtering is heuristic-based; cosmetic vs. structural changes may be misclassified","Event listeners may be removed by page scripts, causing missed updates","No support for detecting changes in iframes or shadow DOM","Event-driven approach adds complexity; debugging event-related issues is harder than synchronous polling"],"requires":["Chrome/Chromium with JavaScript execution enabled","Playwright library for event listener injection","Browser support for MutationObserver API"],"input_types":["DOM mutation events (from browser)","Event filter configuration (ignored selectors, change types)","Previous DOM snapshot (for comparison)"],"output_types":["Event type classification (load, mutation, navigation)","Changed DOM subtree (for re-serialization)","Event metadata (timestamp, affected elements)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-browser-use--browser-use__cap_7","uri":"capability://tool.use.integration.action.execution.pipeline.with.error.recovery.and.retry.logic","name":"action execution pipeline with error recovery and retry logic","description":"Executes LLM-generated actions (click, type, navigate, extract, scroll, wait) through a unified pipeline that validates action schemas, translates them to CDP commands, handles execution errors, and implements exponential backoff retry logic. Supports action-specific error handling (e.g., element not found, stale element reference) with recovery strategies like re-serializing the DOM and retrying. Tracks action execution state and provides detailed error traces for debugging. Includes built-in actions for common tasks (click, type, navigate, extract) and extensibility for custom actions.","intents":["I want my agent to recover from transient errors (e.g., element not found) without failing the entire task","I need detailed logs of what actions were executed and why they succeeded or failed","I want to add custom actions (e.g., screenshot, download file) without modifying the core agent"],"best_for":["Teams building resilient agents for production tasks","Developers debugging agent behavior and action failures","Researchers extending browser-use with custom actions"],"limitations":["Retry logic is generic; no action-specific retry strategies (e.g., wait longer for slow pages before retrying click)","Error recovery is limited to re-serialization and retry; no support for alternative action suggestions","Custom action registration requires code changes; no dynamic action discovery","Action execution is sequential; no support for parallel action execution","Timeout handling is global; no per-action timeout customization"],"requires":["Action schema definition (Pydantic models for validation)","Chrome DevTools Protocol connection","DOM serialization for element lookup"],"input_types":["Action object (action type, parameters, reasoning)","Current browser state (DOM, screenshot, page metadata)","Retry configuration (max retries, backoff strategy)"],"output_types":["Action execution result (success/failure, output data)","Error trace (error type, message, recovery attempts)","Updated browser state (screenshot, DOM after action)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-browser-use--browser-use__cap_8","uri":"capability://planning.reasoning.judge.system.for.task.progress.evaluation.and.trace.analysis","name":"judge system for task progress evaluation and trace analysis","description":"Evaluates agent progress on a task by analyzing the execution trace (sequence of actions, state changes, LLM decisions) and determining if the agent is making meaningful progress toward the goal. The Judge uses an LLM to assess whether recent actions are productive, whether the agent has achieved the task objective, or whether it should try a different approach. Provides structured feedback on task completion status, confidence scores, and suggestions for next steps. Integrates with loop detection to trigger evaluation when the agent may be stuck.","intents":["I want to know if my agent has successfully completed a task without manual inspection","I need to understand why an agent failed and get suggestions for recovery","I want to evaluate agent performance on a benchmark of tasks"],"best_for":["Teams evaluating agent performance on task benchmarks","Developers debugging agent failures and understanding root causes","Researchers studying LLM reasoning on task completion assessment"],"limitations":["Judge evaluation requires an additional LLM call, adding latency and cost","Judge assessment is subjective and depends on the LLM's understanding of the task; may produce false positives/negatives","No built-in mechanism to distinguish between task completion and accidental success","Judge feedback is text-based; no structured task completion metrics","Trace analysis is limited to action history; no access to intermediate LLM reasoning or decision-making process"],"requires":["Execution trace (sequence of actions and state changes)","Task definition (natural language description)","LLM capable of reasoning about task completion"],"input_types":["Execution trace (actions, state changes, LLM decisions)","Task definition (natural language)","Optional: expected output or success criteria"],"output_types":["Task completion status (completed, in-progress, failed)","Confidence score (0-1)","Feedback and suggestions (text)","Structured metrics (actions taken, time elapsed, success indicators)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-browser-use--browser-use__cap_9","uri":"capability://automation.workflow.multi.interface.deployment.python.api.cli.tui.mcp.server","name":"multi-interface deployment (python api, cli, tui, mcp server)","description":"Provides multiple interfaces for running browser-use agents: a Python API for programmatic integration, a command-line interface (CLI) for one-off tasks, a text-based user interface (TUI) using Textual for interactive debugging, and a Model Context Protocol (MCP) server for integration with other AI tools. Each interface abstracts the underlying agent logic while providing interface-specific features (e.g., TUI shows live screenshots and action logs, MCP server exposes agent capabilities as tools). Enables seamless switching between development, testing, and production deployment modes.","intents":["I want to run a quick browser automation task from the command line without writing Python code","I need to debug an agent interactively and see what it's doing in real-time","I want to integrate browser-use into my existing AI tool ecosystem via MCP","I want to embed browser-use into my Python application"],"best_for":["Solo developers prototyping browser automation tasks","Teams integrating browser-use into larger AI systems","Researchers experimenting with different agent configurations","DevOps engineers deploying agents in production"],"limitations":["CLI interface is limited to simple tasks; complex workflows require Python API","TUI requires terminal support and may not work in headless environments","MCP server integration requires compatible MCP clients; not all AI tools support MCP","Python API requires Python 3.9+; no support for other languages without language bindings","Interface-specific features (e.g., TUI screenshots) add overhead and may slow down agent execution"],"requires":["Python 3.9+ (for Python API and CLI)","Terminal emulator with color support (for TUI)","MCP-compatible client (for MCP server integration)"],"input_types":["Task description (natural language string)","Browser URL (string)","Optional: configuration file (JSON or YAML)"],"output_types":["Task result (success/failure, extracted data)","Execution trace (actions, state changes)","Screenshots and logs (for debugging)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Chrome/Chromium browser (local or remote via CDP)","API key for at least one LLM provider (OpenAI, Anthropic, Google, or local Ollama/LM Studio)","Playwright library (bundled with browser-use)","Chrome/Chromium with DevTools Protocol enabled","Playwright library for DOM access","JavaScript execution context in the browser","Schema definition (JSON Schema or Pydantic model)","Browser session with access to the target website","LLM capable of understanding schema and extracting data"],"failure_modes":["Requires Chrome/Chromium browser installation; no Firefox or Safari support","LLM context window limits task complexity — long multi-step workflows may exceed token budgets","Loop detection uses heuristics (repeated actions, unchanged DOM) which can produce false positives on dynamic sites","No built-in persistence for agent state across process restarts — requires external serialization","Performance degrades on JavaScript-heavy sites with frequent DOM mutations due to re-serialization overhead","Visibility calculation is approximate — CSS transforms, clip-path, and complex stacking contexts may produce false positives/negatives","Re-serialization on every DOM mutation adds ~50-200ms latency per change on large DOMs (10k+ elements)","Shadow DOM and iframes are partially supported but not fully traversed; content inside shadow roots may be invisible to the agent","Dynamic content loaded via JavaScript after initial page load requires explicit wait conditions; no automatic detection of async content","Markdown extraction loses semantic structure (e.g., table layouts become flat text)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8939655575155036,"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":"2026-05-03T13:57:06.483Z","last_commit":"2026-05-03T02:02:20Z"},"community":{"stars":91818,"forks":10444,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=browser-use--browser-use","compare_url":"https://unfragile.ai/compare?artifact=browser-use--browser-use"}},"signature":"//Lajx/Jr+zeXljfxdQzgUgGvx4cmsslQzvVN2nxjNgUAnGcYfiVaGbDMsaeCMfi0GGwrAIlrHsbFKpnZEEgBg==","signedAt":"2026-06-22T02:32:24.610Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/browser-use--browser-use","artifact":"https://unfragile.ai/browser-use--browser-use","verify":"https://unfragile.ai/api/v1/verify?slug=browser-use--browser-use","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"}}