{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"stagehand","slug":"stagehand","name":"Stagehand","type":"framework","url":"https://github.com/browserbase/stagehand","page_url":"https://unfragile.ai/stagehand","categories":["automation","data-pipelines"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"stagehand__cap_0","uri":"capability://automation.workflow.natural.language.semantic.action.execution.with.vision.dom.fusion","name":"natural language semantic action execution with vision-dom fusion","description":"Executes browser actions from natural language commands by fusing vision-based element detection with DOM parsing. The act() primitive accepts plain English instructions like 'click the login button' and internally routes through a hybrid handler architecture that combines screenshot analysis with DOM traversal, enabling the LLM to ground language in both visual and structural context. Uses a handler-based dispatch system that abstracts away selector brittleness by reasoning about element semantics rather than CSS paths.","intents":["I want to automate web interactions without writing brittle CSS selectors","I need to click elements that change layout or styling between page loads","I want to describe actions in plain English and have them execute reliably"],"best_for":["Teams building web automation that tolerates minor latency for robustness","Developers migrating from Playwright/Selenium who want less selector maintenance","Non-technical stakeholders defining automation workflows in natural language"],"limitations":["Vision-based detection adds 500ms-2s per action due to screenshot capture and LLM inference","Requires active browser session with rendering capability — cannot work on headless-only environments without visual output","LLM reasoning can fail on ambiguous UI (e.g., multiple identical buttons) without additional context","No built-in retry logic for transient failures — requires wrapping in application-level error handling"],"requires":["Node.js 18+","Playwright 1.40+ (underlying browser control)","LLM API key (OpenAI, Anthropic, or compatible provider)","Active browser instance (local or Browserbase cloud)"],"input_types":["natural language string (e.g., 'click the submit button')","optional context object with page state"],"output_types":["void (action executed)","error object if action fails"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stagehand__cap_1","uri":"capability://data.processing.analysis.structured.data.extraction.with.schema.driven.llm.parsing","name":"structured data extraction with schema-driven llm parsing","description":"Extracts typed data from web pages by combining screenshot capture with DOM analysis, then passing both to an LLM with a schema constraint. The extract() primitive accepts a TypeScript type or JSON schema and returns validated structured data matching that schema. Internally, it builds a context window containing the visual page state and DOM tree, instructs the LLM to locate and parse the requested data, and validates output against the schema before returning.","intents":["I need to scrape product prices, titles, and ratings from an e-commerce page as structured JSON","I want to extract form field values and validate they match expected types","I need to pull data from pages with dynamic layouts that change between loads"],"best_for":["Data engineers building web scraping pipelines that need schema validation","Teams extracting data from sites with frequently changing HTML structure","Developers who want type-safe extraction without writing CSS selectors"],"limitations":["Schema validation adds latency — extraction is not real-time suitable for high-frequency polling","LLM hallucination can produce data matching schema but not present on page — requires post-extraction validation","Large pages with many similar elements may confuse the LLM's localization (e.g., which product row to extract from)","No streaming output — entire extraction must complete before returning, blocking on LLM latency"],"requires":["Node.js 18+","Playwright 1.40+","LLM API key with vision capability (GPT-4V, Claude 3.5+, or equivalent)","TypeScript or JSON schema definition for output type"],"input_types":["TypeScript type or JSON schema","optional selector hints or element context"],"output_types":["typed object matching schema","array of typed objects","validation error if schema mismatch"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stagehand__cap_10","uri":"capability://automation.workflow.evaluation.and.benchmarking.system.for.automation.quality","name":"evaluation and benchmarking system for automation quality","description":"Provides a built-in evaluation framework for measuring automation success rates, latency, and cost across different models and configurations. The evaluation system defines test categories (e.g., e-commerce, form filling, data extraction) and runs automation workflows against benchmark sites, collecting metrics on success rate, steps taken, LLM calls, and execution time. Results are aggregated and compared across model/configuration combinations to guide optimization.","intents":["I want to measure how well my automation performs across different LLM models","I need to benchmark automation latency and cost before deploying to production","I want to track automation quality improvements as I refine my workflows"],"best_for":["Teams evaluating LLM models for automation suitability","Developers optimizing automation performance before production deployment","Organizations tracking automation quality metrics over time"],"limitations":["Evaluations are time-consuming (hours to days for full benchmark suite) due to LLM inference latency","Benchmark sites may change, invalidating historical comparisons","Evaluation results are specific to benchmark sites — may not generalize to production pages","No automatic anomaly detection — requires manual review of evaluation results","Evaluation infrastructure requires significant compute resources (multiple concurrent browser sessions)"],"requires":["Node.js 18+","Playwright 1.40+","LLM API keys for models being evaluated","Benchmark site access (public or internal)"],"input_types":["evaluation configuration (models, categories, sites)","automation workflows to evaluate"],"output_types":["evaluation results object with success rate, latency, cost metrics","comparison report across models/configurations"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stagehand__cap_11","uri":"capability://automation.workflow.cli.tool.for.interactive.browser.automation.and.debugging","name":"cli tool for interactive browser automation and debugging","description":"Provides a command-line interface (browse CLI) for interactive browser automation and debugging. The CLI launches a browser session, accepts natural language commands, and executes them via Stagehand's core primitives. It includes a daemon architecture for session persistence, network capture for debugging, and real-time feedback on action execution. Developers can use the CLI to explore pages, test automation logic, and debug failures interactively.","intents":["I want to test automation commands interactively before adding them to my workflow","I need to debug why an automation action failed by seeing what the browser saw","I want to explore a page structure and discover elements without writing code"],"best_for":["Developers prototyping and debugging automation workflows","QA teams exploring page structure and testing automation logic","Non-technical users testing automation without writing code"],"limitations":["CLI is interactive and blocking — not suitable for automated testing pipelines","Network capture adds overhead and storage requirements — not suitable for long-running sessions","Daemon session management requires manual cleanup if process crashes","No built-in session sharing — each CLI instance has its own browser session","Limited to single-threaded command execution — cannot parallelize multiple commands"],"requires":["Node.js 18+","Playwright 1.40+","LLM API key","Terminal with TTY support"],"input_types":["natural language commands (e.g., 'click the login button')","optional flags (--model, --headless, --debug)"],"output_types":["action result with visual feedback","network capture logs (optional)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stagehand__cap_12","uri":"capability://automation.workflow.http.api.server.for.remote.automation.execution","name":"http api server for remote automation execution","description":"Exposes Stagehand capabilities via HTTP API, enabling remote automation execution from any HTTP client. The server implements REST endpoints for act(), extract(), observe(), and agent operations, with OpenAPI specification for SDK generation. Multi-region routing supports load balancing across Browserbase instances. Developers can deploy the server and call it from any language/framework, decoupling automation logic from client code.","intents":["I want to call Stagehand automation from a different service or language","I need to deploy automation as a microservice accessible via HTTP","I want to load-balance automation across multiple Browserbase regions"],"best_for":["Teams with polyglot architectures needing language-agnostic automation","Organizations deploying automation as a managed service","Teams using Browserbase multi-region infrastructure"],"limitations":["HTTP round-trip latency (50-200ms) adds overhead compared to in-process calls","Server requires persistent deployment — not suitable for serverless/ephemeral environments","No built-in authentication — requires external auth layer (API keys, OAuth)","Session state is server-side — clients cannot directly inspect browser state","Streaming responses not supported — all results must be buffered before returning"],"requires":["Node.js 18+","Deployment infrastructure (Docker, Kubernetes, etc.)","LLM API keys for server instances","Optional: Browserbase account for cloud execution"],"input_types":["HTTP POST/GET requests with JSON payload","OpenAPI-compatible request format"],"output_types":["JSON response with action result","HTTP error codes for failures"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stagehand__cap_13","uri":"capability://automation.workflow.error.handling.and.sdk.error.classification.system","name":"error handling and sdk error classification system","description":"Implements a structured error handling system that classifies automation failures into semantic categories (e.g., element not found, navigation timeout, LLM error) with detailed error messages and recovery suggestions. SDK errors are typed and include context (page state, action attempted, LLM response) to aid debugging. The error system integrates with logging and observability to track failure patterns.","intents":["I want to understand why my automation failed with specific error details","I need to handle different failure types differently (retry vs abort)","I want to track failure patterns to identify systemic issues"],"best_for":["Teams building production automation that needs robust error handling","Developers debugging automation failures with detailed error context","Organizations tracking automation reliability metrics"],"limitations":["Error classification is heuristic-based — some failures may be misclassified","Error context can be large (full page state, LLM response) — requires careful logging to avoid storage bloat","No automatic error recovery — developers must implement retry logic","Error messages are English-only — not suitable for non-English environments"],"requires":["Node.js 18+","Logging infrastructure (optional, for error tracking)"],"input_types":["automation operation that fails"],"output_types":["typed error object with category, message, context, recovery suggestions"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stagehand__cap_14","uri":"capability://automation.workflow.logging.metrics.and.observability.integration","name":"logging, metrics, and observability integration","description":"Integrates structured logging and metrics collection throughout Stagehand's execution, tracking action execution, LLM calls, cache hits/misses, and performance metrics. Logs are emitted at configurable levels (debug, info, warn, error) and can be routed to external observability systems (DataDog, New Relic, etc.). Metrics include latency per operation, token usage, cost, and success rates, enabling performance monitoring and cost optimization.","intents":["I want to monitor automation performance and identify bottlenecks","I need to track LLM token usage and costs for cost optimization","I want to integrate Stagehand metrics with my observability platform"],"best_for":["Teams running production automation that need performance visibility","Organizations tracking automation costs and optimizing for efficiency","Developers debugging performance issues in complex workflows"],"limitations":["Logging overhead can impact performance — verbose logging may slow automation by 5-10%","Metrics collection requires external backend — no built-in metrics storage","Log volume can be large for long-running workflows — requires log aggregation/rotation","No automatic alerting — requires external monitoring to detect anomalies","Metrics are point-in-time — no built-in time-series analysis"],"requires":["Node.js 18+","Optional: observability platform (DataDog, New Relic, etc.)","Optional: log aggregation system (ELK, Splunk, etc.)"],"input_types":["log level configuration","optional observability backend configuration"],"output_types":["structured logs with context","metrics (latency, tokens, cost, success rate)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stagehand__cap_2","uri":"capability://automation.workflow.element.discovery.and.observation.via.dom.vision.synthesis","name":"element discovery and observation via dom + vision synthesis","description":"Discovers and describes interactive elements on a page by synthesizing DOM structure with visual analysis. The observe() primitive returns a list of observable elements with their semantic properties (role, label, visibility, interactivity) by parsing the DOM tree and cross-referencing with screenshot analysis. This enables developers to query 'what buttons are visible?' or 'find all input fields' without writing selectors, using the LLM to understand element semantics.","intents":["I need to find all clickable elements on a page without knowing their selectors","I want to discover form fields and their labels dynamically","I need to check if an element is visible or interactive before acting on it"],"best_for":["Developers building exploratory automation (e.g., testing unknown pages)","Teams writing adaptive workflows that adjust based on available UI elements","QA engineers discovering page structure for test case generation"],"limitations":["Returns all observable elements, which can be hundreds on complex pages — requires filtering/pagination","Element descriptions are LLM-generated and may be imprecise for unusual UI patterns","No real-time updates — observe() captures a snapshot; dynamic elements added after call are not visible","Adds 1-2s latency per call due to screenshot + DOM parsing + LLM analysis"],"requires":["Node.js 18+","Playwright 1.40+","LLM API key with vision capability","Active browser session with rendered page"],"input_types":["optional filter criteria (e.g., 'buttons', 'inputs', 'links')"],"output_types":["array of element objects with properties: role, label, locator, visibility, interactivity"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stagehand__cap_3","uri":"capability://planning.reasoning.multi.step.agent.orchestration.with.tool.based.reasoning","name":"multi-step agent orchestration with tool-based reasoning","description":"Orchestrates multi-step browser automation workflows by decomposing high-level goals into sequences of act/extract/observe calls. The agent() system uses an LLM with access to a tool registry (DOM tools, Hybrid tools, or Computer Use Agent tools) to reason about task decomposition, decide which tool to call next, and track progress toward the goal. Internally, it maintains agent context (variables, execution history, page state), handles tool invocation via a handler dispatch system, and implements self-healing through caching and cache invalidation when page state changes.","intents":["I want to automate a multi-step workflow like 'log in, search for product, add to cart, checkout' without writing step-by-step code","I need an agent that can adapt its strategy if a page layout changes mid-workflow","I want to track agent reasoning and debug why a workflow failed"],"best_for":["Teams building complex automation workflows with variable page states","Developers who want LLM-driven task decomposition without manual step orchestration","QA teams automating end-to-end user journeys with recovery from transient failures"],"limitations":["Agent reasoning adds 2-5s overhead per step due to LLM decision-making — not suitable for latency-critical automation","Tool hallucination: agent may call tools with invalid parameters or in wrong order — requires goal validation","No built-in long-term memory — agent context resets between sessions unless explicitly persisted","Complex workflows may exceed LLM context window, requiring manual chunking or state management","Self-healing via cache invalidation is heuristic-based and may miss subtle page state changes"],"requires":["Node.js 18+","Playwright 1.40+","LLM API key (GPT-4, Claude 3.5+, or equivalent with function calling)","Tool definitions (built-in or custom MCP tools)"],"input_types":["goal string (e.g., 'log in and search for shoes')","optional agent configuration (model, tool mode, max steps)","optional variables object for context"],"output_types":["agent result object with success status, final state, execution trace","error with failure reason and last successful step"],"categories":["planning-reasoning","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stagehand__cap_4","uri":"capability://automation.workflow.deterministic.action.caching.with.self.healing.replay","name":"deterministic action caching with self-healing replay","description":"Caches the results of act() and extract() calls with deterministic replay and self-healing capabilities. The ActCache system stores action outcomes (e.g., 'clicking button X navigated to page Y') and replays them on subsequent runs if the preconditions (page state, element presence) are met. If preconditions change, the cache is invalidated and the action is re-executed. This enables workflows to skip expensive LLM calls for repeated actions while automatically adapting to page changes.","intents":["I want to speed up repeated automation runs by caching action results","I need automation to recover from transient failures by replaying cached successful steps","I want to avoid re-running expensive LLM inference for actions that haven't changed"],"best_for":["Teams running repeated automation workflows (e.g., daily data scraping)","Developers building deterministic workflows that should be fast on subsequent runs","QA teams that need to replay test steps with minimal LLM calls"],"limitations":["Cache invalidation is heuristic-based (DOM change detection) and may miss subtle state changes, leading to stale replays","Cache storage requires persistent backend (file system, database) — no in-memory-only option for distributed systems","Cache keys are based on action semantics, not exact LLM prompts — different phrasings of same action may not hit cache","Cached results are page-specific — cache from one site cannot be reused on another","Large workflows accumulate large caches — no automatic pruning or TTL management"],"requires":["Node.js 18+","CacheStorage backend (file system, Redis, or custom implementation)","Deterministic page structure (pages must be reproducible for cache hits)"],"input_types":["act() or extract() calls with caching enabled"],"output_types":["cached result if preconditions met","fresh result if cache invalidated","cache metadata (hit/miss, age)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stagehand__cap_5","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.model.selection.and.fallback","name":"multi-provider llm abstraction with model selection and fallback","description":"Abstracts LLM provider differences (OpenAI, Anthropic, Ollama, custom) behind a unified client interface, enabling model selection, provider fallback, and cost optimization. The LLM Client Architecture supports configuring primary and fallback models, routing requests based on capability requirements (vision, function calling), and handling provider-specific response formats. Developers specify model preferences via configuration, and Stagehand automatically selects the appropriate provider and handles API differences.","intents":["I want to use different LLM providers (OpenAI, Anthropic, local Ollama) without changing my automation code","I need to fall back to a cheaper model if my primary provider is rate-limited","I want to optimize costs by using vision-capable models only when necessary"],"best_for":["Teams using multiple LLM providers and wanting unified abstraction","Developers building cost-sensitive automation that needs provider flexibility","Organizations with on-premise LLM requirements (Ollama, vLLM)"],"limitations":["Model capability differences (e.g., vision quality, function calling format) may cause behavior variance across providers","Fallback logic is sequential — if primary provider fails, fallback adds latency before retry","Custom provider integration requires implementing provider-specific API client","No automatic model selection based on task complexity — developers must manually choose models"],"requires":["Node.js 18+","API keys for selected providers (OpenAI, Anthropic, etc.)","Model configuration in environment or constructor options"],"input_types":["model name string (e.g., 'gpt-4-vision', 'claude-3-5-sonnet')","optional provider configuration object"],"output_types":["LLM response in normalized format","provider metadata (model used, tokens consumed)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stagehand__cap_6","uri":"capability://automation.workflow.hybrid.tool.mode.selection.dom.hybrid.computer.use.agent","name":"hybrid tool mode selection (dom, hybrid, computer use agent)","description":"Allows developers to choose between three tool execution modes for agent actions: DOM-only (fast, selector-based), Hybrid (balanced, vision + DOM), or Computer Use Agent (slow, pure vision). The agent system routes tool calls through the selected mode, trading off speed vs robustness. DOM mode uses Playwright locators directly; Hybrid mode uses vision + DOM fusion; CUA mode delegates to a vision-based agent provider (Anthropic Computer Use, etc.). Developers configure mode per agent or per action.","intents":["I want fast automation for well-structured pages using DOM selectors","I need robust automation for pages with dynamic layouts using vision + DOM","I want to use pure vision-based automation for complex UI patterns that defy DOM analysis"],"best_for":["Teams with mixed page types (some well-structured, some dynamic) needing per-action mode selection","Developers optimizing for latency on predictable pages and robustness on unpredictable ones","Organizations evaluating different automation approaches without rewriting code"],"limitations":["DOM mode fails on pages with dynamic selectors or shadow DOM — requires fallback to Hybrid/CUA","Hybrid mode adds vision latency (500ms-2s) compared to DOM-only","CUA mode is slowest (2-5s per action) and most expensive (vision API calls)","Mode switching mid-workflow requires explicit configuration — no automatic mode selection based on page type","Tool implementations differ between modes — same action may behave differently in DOM vs Hybrid vs CUA"],"requires":["Node.js 18+","Playwright 1.40+ (for DOM mode)","LLM API key with vision (for Hybrid mode)","Computer Use Agent provider (for CUA mode, e.g., Anthropic)"],"input_types":["mode string: 'dom' | 'hybrid' | 'cua'","optional per-action mode override"],"output_types":["action result (same across modes)","mode metadata (which mode was used)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stagehand__cap_7","uri":"capability://tool.use.integration.custom.tool.integration.via.mcp.model.context.protocol","name":"custom tool integration via mcp (model context protocol)","description":"Enables developers to extend agent capabilities with custom tools via the Model Context Protocol (MCP). Custom tools are registered in the agent's tool registry and invoked by the LLM during reasoning. MCP provides a standardized interface for tool definition (schema, parameters, execution logic) and allows tools to be implemented in any language and run in separate processes. Stagehand's agent system handles tool invocation, parameter validation, and result marshaling.","intents":["I want to add domain-specific tools (e.g., database queries, API calls) to my automation agent","I need to integrate external services (payment APIs, CRM systems) into my automation workflow","I want to extend Stagehand with custom logic without modifying the core framework"],"best_for":["Teams building complex automation workflows that require external service integration","Developers extending Stagehand with domain-specific tools","Organizations with existing tool ecosystems (Claude tools, etc.) wanting to reuse them"],"limitations":["MCP tool invocation adds latency (network round-trip to tool process) — not suitable for high-frequency calls","Tool schema must be precise — LLM may misuse tools if schema is ambiguous","No built-in tool result validation — custom tools must validate their own outputs","Tool process lifecycle management is developer's responsibility — no automatic restart on failure","Debugging tool execution requires access to tool process logs — harder than debugging in-process code"],"requires":["Node.js 18+","MCP server implementation (can be in any language)","Tool schema definition (JSON Schema format)"],"input_types":["MCP tool definition with name, description, input schema","tool implementation (function or external process)"],"output_types":["tool result (any JSON-serializable type)","tool error if execution fails"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stagehand__cap_8","uri":"capability://automation.workflow.browser.session.management.with.local.and.cloud.execution","name":"browser session management with local and cloud execution","description":"Manages browser sessions with support for both local execution (via Playwright) and cloud execution (via Browserbase). The V3 class initializes a browser connection through a CDP (Chrome DevTools Protocol) abstraction layer that works with local browsers or Browserbase cloud instances. Developers specify execution environment via configuration, and Stagehand handles connection setup, session lifecycle, and cleanup. Cloud execution enables headless automation without local browser installation.","intents":["I want to run automation locally for development and in the cloud for production","I need to automate without installing browsers on my server","I want to use Browserbase's managed browser infrastructure for reliability"],"best_for":["Teams with hybrid local/cloud automation needs","Developers deploying automation to serverless or containerized environments","Organizations using Browserbase for managed browser infrastructure"],"limitations":["Local execution requires browser installation and system resources — not suitable for resource-constrained environments","Cloud execution adds network latency (100-500ms per operation) compared to local","Session state is not automatically persisted across restarts — requires explicit state management","Browserbase cloud execution requires API key and incurs costs","CDP connection is synchronous — no built-in support for concurrent browser sessions in single process"],"requires":["Node.js 18+","Playwright 1.40+","For local: Chrome/Chromium browser installed","For cloud: Browserbase API key and account"],"input_types":["execution environment config: 'local' | 'browserbase'","optional browser launch options (headless, viewport, etc.)"],"output_types":["browser session object with page/frame management"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stagehand__cap_9","uri":"capability://automation.workflow.page.and.frame.context.management.with.v3context","name":"page and frame context management with v3context","description":"Manages page and frame context through the V3Context abstraction, which tracks the current page, active frame, and navigation state. The context system enables multi-frame automation (iframes, shadow DOM) by maintaining a frame stack and routing actions to the correct frame. V3Context also tracks page state changes (navigation, DOM mutations) and invalidates caches when state changes, enabling self-healing automation.","intents":["I need to automate interactions within iframes without manually switching contexts","I want to track page navigation and adapt my automation to new pages","I need to handle multi-frame pages (e.g., embedded widgets) transparently"],"best_for":["Teams automating complex pages with iframes or shadow DOM","Developers building multi-page workflows that need automatic context switching","QA teams testing embedded widgets and cross-frame interactions"],"limitations":["Frame detection is heuristic-based — may miss deeply nested or dynamically created frames","Shadow DOM traversal requires special handling — not all shadow DOM elements are discoverable","Context switching adds latency (100-200ms per frame switch) due to CDP communication","No automatic frame selection — developer must specify target frame for actions","Cross-frame communication is limited — cannot directly access data across frame boundaries"],"requires":["Node.js 18+","Playwright 1.40+","Active browser session with rendered page"],"input_types":["frame selector or frame object","optional context configuration"],"output_types":["V3Context object with current page/frame state"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stagehand__headline","uri":"capability://automation.workflow.ai.powered.browser.automation.framework","name":"ai-powered browser automation framework","description":"Stagehand is an AI-driven framework that allows users to automate browser actions using natural language commands, making web interactions more intuitive and accessible for developers.","intents":["best AI browser automation framework","AI browser automation for web scraping","browser automation tools for developers","natural language browser automation solutions","automate web actions with AI"],"best_for":["developers seeking intuitive automation tools"],"limitations":[],"requires":[],"input_types":["natural language commands"],"output_types":["automated browser actions"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","Playwright 1.40+ (underlying browser control)","LLM API key (OpenAI, Anthropic, or compatible provider)","Active browser instance (local or Browserbase cloud)","Playwright 1.40+","LLM API key with vision capability (GPT-4V, Claude 3.5+, or equivalent)","TypeScript or JSON schema definition for output type","LLM API keys for models being evaluated","Benchmark site access (public or internal)","LLM API key"],"failure_modes":["Vision-based detection adds 500ms-2s per action due to screenshot capture and LLM inference","Requires active browser session with rendering capability — cannot work on headless-only environments without visual output","LLM reasoning can fail on ambiguous UI (e.g., multiple identical buttons) without additional context","No built-in retry logic for transient failures — requires wrapping in application-level error handling","Schema validation adds latency — extraction is not real-time suitable for high-frequency polling","LLM hallucination can produce data matching schema but not present on page — requires post-extraction validation","Large pages with many similar elements may confuse the LLM's localization (e.g., which product row to extract from)","No streaming output — entire extraction must complete before returning, blocking on LLM latency","Evaluations are time-consuming (hours to days for full benchmark suite) due to LLM inference latency","Benchmark sites may change, invalidating historical comparisons","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.296Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=stagehand","compare_url":"https://unfragile.ai/compare?artifact=stagehand"}},"signature":"XrFi0lbHo9bWSpPr5KKNBlfHpeh28gqto5L0IbND1OMA+WdDXQ9ZuYi/Vza9ipFdazil8CzapFI8t4Hd8ygSCw==","signedAt":"2026-06-22T01:17:09.554Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/stagehand","artifact":"https://unfragile.ai/stagehand","verify":"https://unfragile.ai/api/v1/verify?slug=stagehand","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"}}