{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-notte","slug":"notte","name":"Notte","type":"framework","url":"https://github.com/nottelabs/notte","page_url":"https://unfragile.ai/notte","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-notte__cap_0","uri":"capability://automation.workflow.browser.automation.via.natural.language.agents","name":"browser-automation-via-natural-language-agents","description":"Enables autonomous browser control through natural language instructions by decomposing user intents into sequential browser actions (click, type, navigate, extract). Uses an agentic loop that interprets high-level goals, perceives page state via DOM/visual analysis, and executes granular browser operations without requiring explicit step-by-step scripting. The framework handles state management across multi-step workflows and recovers from transient failures through retry logic.","intents":["I want to automate a multi-step web workflow (login → search → extract data → export) by describing it in plain English","I need to build a bot that can navigate unfamiliar websites and complete tasks without hardcoded selectors","I want to test web applications by having an agent interact with them like a real user would"],"best_for":["teams building RPA solutions without deep Selenium/Playwright expertise","developers prototyping web automation agents that need to handle dynamic, unstructured websites","non-technical users who want to automate repetitive browser tasks via natural language"],"limitations":["Latency per action cycle likely 1-3 seconds due to LLM inference + browser rendering","May struggle with highly dynamic JavaScript-heavy SPAs that change DOM structure rapidly","No built-in handling for CAPTCHAs, multi-factor authentication, or anti-bot detection","Accuracy depends on LLM's ability to understand page context — complex or poorly-structured HTML may confuse the agent"],"requires":["Node.js 16+ or Python 3.8+","API key for LLM provider (OpenAI, Anthropic, or local model)","Chromium/Chrome browser installed or access to headless browser binary","Network connectivity to target websites"],"input_types":["natural language instruction (string)","URL or page context (string)","optional initial state or constraints (JSON)"],"output_types":["structured extraction results (JSON)","action transcript (array of executed steps)","screenshots or DOM snapshots (optional)","success/failure status with reasoning"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-notte__cap_1","uri":"capability://planning.reasoning.multi.step.task.decomposition.and.execution","name":"multi-step-task-decomposition-and-execution","description":"Breaks down complex, multi-step user goals into atomic browser actions and executes them sequentially with state tracking. The framework maintains context across steps (e.g., remembering extracted data from step 1 for use in step 3), validates action outcomes, and adjusts subsequent steps based on actual page state rather than assumed state. Implements a planning-reasoning loop that re-evaluates the task after each action.","intents":["I want to execute a 10-step workflow (e.g., search → filter → compare → purchase) and have the agent adapt if pages load differently than expected","I need to extract data from multiple pages and correlate it without writing custom glue code","I want the agent to recover gracefully if a step fails (e.g., button not found) and try an alternative approach"],"best_for":["developers building complex RPA workflows with conditional branching","data engineers automating web scraping across multiple sites with varying structures","QA teams automating end-to-end test scenarios with dynamic assertions"],"limitations":["Context window constraints may limit how much state can be carried across very long workflows (50+ steps)","No built-in support for parallel task execution — all steps are sequential","Hallucination risk increases with task complexity; agent may invent actions that don't exist","Debugging multi-step failures is harder than debugging linear scripts because causality is implicit in the agent's reasoning"],"requires":["LLM with sufficient context window (8K+ tokens recommended)","Browser instance with stable connectivity","Task definition in natural language or structured format (likely YAML/JSON)"],"input_types":["natural language task description (string)","structured task plan with substeps (JSON/YAML)","initial context or constraints (JSON)"],"output_types":["execution transcript with step-by-step results (JSON)","extracted data from all steps (JSON)","failure report with recovery attempts (JSON)","visual evidence (screenshots per step, optional)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-notte__cap_2","uri":"capability://image.visual.visual.and.dom.based.page.understanding","name":"visual-and-dom-based-page-understanding","description":"Combines DOM parsing and visual (screenshot-based) analysis to understand page structure and identify interactive elements. The framework likely extracts both semantic information from HTML (buttons, forms, links) and visual context from rendered screenshots, then uses this dual representation to locate elements and understand their purpose. This hybrid approach handles both well-structured semantic HTML and visually-driven layouts where semantic meaning is unclear.","intents":["I want the agent to find and click buttons even if they're dynamically rendered or have non-standard HTML","I need to extract data from tables, lists, or cards that have complex nested structures","I want the agent to understand page context visually (e.g., 'click the red button in the top-right') without relying on CSS selectors"],"best_for":["automation of modern web apps with dynamic, component-based UIs","scraping from websites with poor semantic HTML or heavy JavaScript rendering","scenarios where CSS selectors are fragile or change frequently"],"limitations":["Screenshot analysis adds latency (~500ms-1s per page) and requires GPU or cloud vision API","Visual understanding may fail on pages with overlapping elements, animations, or poor contrast","Requires rendering the page in a real browser (headless Chromium) rather than parsing raw HTML","Large pages with many interactive elements may exceed token limits when describing all elements to the LLM"],"requires":["Headless browser (Chromium/Chrome)","Vision-capable LLM (GPT-4V, Claude 3 Vision, or local vision model)","Sufficient memory for screenshot storage and processing"],"input_types":["rendered page (via browser automation)","DOM tree (HTML)","screenshot (PNG/JPEG)"],"output_types":["element locators (CSS selectors, XPath, or coordinates)","semantic understanding of page structure (JSON)","extracted text and data (JSON)","visual annotations (optional)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-notte__cap_3","uri":"capability://automation.workflow.intelligent.element.targeting.and.interaction","name":"intelligent-element-targeting-and-interaction","description":"Identifies and interacts with page elements (buttons, inputs, links, dropdowns) using a combination of semantic understanding, visual context, and fallback strategies. Rather than relying on brittle CSS selectors, the framework uses natural language descriptions of elements ('the submit button in the top-right'), visual coordinates, or semantic roles to locate and interact with them. Implements retry logic and alternative interaction methods (e.g., keyboard navigation if clicking fails).","intents":["I want to click a button that has no stable ID or class, only visual context","I need to fill a form where field labels and inputs are visually separated or dynamically positioned","I want the agent to handle interactions that fail (element not clickable, covered by overlay) and retry with alternative methods"],"best_for":["automation of websites with unstable or dynamically-generated HTML","scenarios where CSS selectors break frequently due to UI updates","teams that want to avoid maintaining selector-based test suites"],"limitations":["Ambiguity when multiple elements match the same description (e.g., 'the blue button')","Overlay detection and handling may not work for all types of overlays (modals, tooltips, sticky headers)","Keyboard navigation fallback may not work for all interactive elements (custom components)","Coordinate-based targeting breaks if page layout changes (responsive design, zoom levels)"],"requires":["Browser automation library (Playwright, Puppeteer, or Selenium)","Vision-capable LLM for visual element description (optional but recommended)","Page rendering in a consistent viewport size"],"input_types":["natural language element description (string)","visual coordinates (x, y)","semantic role or ARIA attributes (string)","DOM path or XPath (string)"],"output_types":["interaction result (success/failure)","element state after interaction (JSON)","error details if interaction failed (string)","alternative actions attempted (array)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-notte__cap_4","uri":"capability://planning.reasoning.agentic.loop.with.perception.and.action","name":"agentic-loop-with-perception-and-action","description":"Implements a closed-loop agent architecture where the agent perceives page state (via DOM/vision), reasons about the current situation relative to the goal, selects an action, executes it, and then re-perceives to validate the outcome. This loop continues until the goal is achieved or a failure condition is met. The framework manages the agent's internal state (goal, progress, history) and implements stopping conditions to prevent infinite loops.","intents":["I want an agent that can adapt its strategy based on what it observes, rather than following a pre-written script","I need the agent to detect when it's stuck (e.g., in a loop or facing an unexpected page) and take corrective action","I want visibility into the agent's reasoning process and decision-making at each step"],"best_for":["developers building adaptive automation for unpredictable or frequently-changing websites","teams that need to debug agent behavior and understand why it made certain decisions","scenarios where the exact sequence of steps is unknown upfront (e.g., dynamic workflows)"],"limitations":["Perception-action loop adds latency (1-3 seconds per iteration) due to LLM inference","Agent may get stuck in local optima or infinite loops if stopping conditions are poorly defined","Reasoning transparency is limited — the agent's internal thought process is only visible if explicitly logged","Scaling to very long workflows (100+ steps) may exceed LLM context windows and require state summarization"],"requires":["LLM with function-calling or tool-use capability","Browser automation library with fast page state queries","Logging/monitoring infrastructure to track agent decisions (optional but recommended)"],"input_types":["goal statement (natural language string)","initial page state (URL or DOM)","optional constraints or preferences (JSON)"],"output_types":["action transcript (array of perception-action pairs)","final outcome (success/failure with reason)","reasoning trace (optional, if logging enabled)","extracted results (JSON)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-notte__cap_5","uri":"capability://automation.workflow.error.detection.and.recovery.with.retry.strategies","name":"error-detection-and-recovery-with-retry-strategies","description":"Detects when browser actions fail or produce unexpected results (element not found, page didn't load, action timed out) and implements recovery strategies such as retrying with different selectors, waiting for elements to appear, scrolling to reveal hidden elements, or taking alternative action paths. The framework distinguishes between transient failures (retry) and permanent failures (abort or escalate) based on error type and retry count.","intents":["I want the agent to handle flaky websites that occasionally fail to load or respond","I need automatic recovery from transient errors without manual intervention","I want the agent to try alternative approaches when the primary action fails (e.g., use keyboard instead of mouse)"],"best_for":["automation of production websites with variable performance or reliability","scenarios where manual intervention is expensive or impossible (unattended RPA)","teams that want to reduce false negatives in automation (failed runs due to transient issues)"],"limitations":["Over-aggressive retry logic may mask real failures and waste time on impossible tasks","Distinguishing transient from permanent failures requires heuristics that may not work for all error types","Recovery strategies are limited to what the framework implements — custom recovery logic may not be possible","Retry loops add latency and may hit rate limits on target websites"],"requires":["Browser automation library with timeout and error handling","Configurable retry policies (max retries, backoff strategy)","Logging to track retry attempts and recovery success"],"input_types":["action to execute (string or function)","error type (string)","retry configuration (JSON)"],"output_types":["action result (success/failure)","retry history (array of attempts)","final error if all retries exhausted (string)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-notte__cap_6","uri":"capability://data.processing.analysis.structured.data.extraction.from.web.pages","name":"structured-data-extraction-from-web-pages","description":"Extracts structured data (JSON, CSV, or custom schemas) from web pages by parsing DOM elements, tables, lists, and cards into a defined schema. The framework can infer schema from examples, accept explicit schema definitions, or use natural language descriptions of what data to extract. Handles nested structures, pagination, and data validation to ensure extracted data matches the expected schema.","intents":["I want to scrape product listings and extract name, price, rating into a JSON array","I need to extract data from a table with complex headers and merged cells","I want to validate that extracted data matches a schema (e.g., price is a number, date is ISO format)"],"best_for":["data engineers building web scraping pipelines","teams extracting data from multiple websites with varying structures","scenarios where data needs to be validated and normalized before downstream processing"],"limitations":["Schema inference may fail for ambiguous or inconsistent data structures","Nested or deeply-structured data may be difficult to extract without explicit schema","Data validation is limited to type checking and basic constraints — complex business logic validation requires custom code","Pagination handling requires explicit configuration or detection logic"],"requires":["Target page must be rendered in browser (not raw HTML)","Schema definition (JSON Schema, TypeScript interface, or natural language description)","Optional: examples of expected output for schema inference"],"input_types":["rendered page (via browser automation)","schema definition (JSON Schema, TypeScript, or natural language)","extraction instructions (natural language or structured)"],"output_types":["extracted data (JSON, CSV, or custom format)","validation report (success/failure per record)","extraction confidence scores (optional)","raw HTML snippets for failed extractions (optional)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-notte__cap_7","uri":"capability://automation.workflow.multi.browser.and.environment.support","name":"multi-browser-and-environment-support","description":"Abstracts browser implementation details and supports multiple browser engines (Chromium, Firefox, WebKit) and execution environments (local, cloud, headless, headed). The framework provides a unified API for browser operations regardless of the underlying engine, handles environment-specific configurations (proxy, authentication, user agent), and manages browser lifecycle (launch, close, cleanup).","intents":["I want to run the same automation on different browsers to test cross-browser compatibility","I need to execute automation in a cloud environment (e.g., AWS Lambda, Docker) without managing browser infrastructure","I want to test with different user agents or network conditions (throttling, proxy)"],"best_for":["QA teams testing web applications across multiple browsers","teams deploying automation to cloud or containerized environments","developers who want to abstract away browser-specific implementation details"],"limitations":["Browser abstraction may hide engine-specific behaviors or bugs","Cloud execution adds latency and cost compared to local execution","Some advanced browser features (DevTools protocol, extensions) may not be available across all engines","Headless mode may behave differently from headed mode (e.g., rendering, JavaScript execution)"],"requires":["Browser binaries installed or accessible (Chromium, Firefox, WebKit)","Cloud credentials if using cloud execution (AWS, Azure, etc.)","Docker or containerization if deploying to cloud"],"input_types":["browser type (string: 'chromium', 'firefox', 'webkit')","execution environment (string: 'local', 'cloud', 'docker')","environment configuration (JSON: proxy, user agent, etc.)"],"output_types":["browser instance (object)","execution logs (string)","performance metrics (JSON)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-notte__cap_8","uri":"capability://automation.workflow.performance.optimization.and.speed.claims","name":"performance-optimization-and-speed-claims","description":"Implements optimizations to minimize latency and maximize throughput in browser automation, such as parallel action execution, DOM caching, screenshot optimization, and LLM prompt caching. The framework's claim of being 'fastest' likely stems from these optimizations combined with efficient state management and minimal overhead in the perception-action loop. Provides metrics and profiling to identify bottlenecks.","intents":["I want to automate high-volume tasks (100+ workflows per day) and need to minimize per-action latency","I need to understand where time is being spent in my automation (LLM inference, browser rendering, network) to optimize","I want to run multiple automations in parallel without overwhelming system resources"],"best_for":["teams running high-volume RPA workloads where latency directly impacts cost","developers optimizing automation performance for production deployment","scenarios where parallel execution is feasible (independent tasks)"],"limitations":["Performance gains may be marginal if bottleneck is external (slow website, network latency)","Parallel execution introduces complexity in state management and error handling","Caching strategies may cause stale data issues if page state changes frequently","Performance metrics are only useful if the framework provides detailed profiling — generic metrics may hide real bottlenecks"],"requires":["Multi-core CPU for parallel execution","Sufficient memory for caching and parallel browser instances","Optional: profiling/monitoring tools to measure performance"],"input_types":["automation task (string or object)","performance configuration (JSON: parallelism, caching strategy, etc.)"],"output_types":["execution result (success/failure)","performance metrics (JSON: latency, throughput, resource usage)","profiling data (optional)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-notte__cap_9","uri":"capability://automation.workflow.reliability.and.consistency.guarantees","name":"reliability-and-consistency-guarantees","description":"Implements mechanisms to ensure automation runs reliably and produces consistent results across multiple executions, such as idempotency checks, state validation, deterministic action selection, and failure detection. The framework's claim of being 'most reliable' likely stems from these guarantees combined with comprehensive error handling and recovery strategies. Provides observability to detect and diagnose reliability issues.","intents":["I want to run the same automation multiple times and get the same result each time","I need to detect when automation fails silently (e.g., wrong data extracted but no error raised)","I want to ensure that failed automation doesn't leave the system in an inconsistent state"],"best_for":["production RPA deployments where reliability is critical","teams running unattended automation that must handle failures gracefully","scenarios where data consistency is important (financial transactions, inventory updates)"],"limitations":["Idempotency guarantees may not be possible for all operations (e.g., submitting a form that creates a new record)","State validation adds overhead and may slow down automation","Deterministic action selection may be suboptimal in some cases (e.g., multiple valid paths to goal)","Observability requires comprehensive logging which adds latency and storage overhead"],"requires":["Deterministic LLM behavior (temperature=0 or similar)","Comprehensive logging and monitoring infrastructure","State management system to track automation progress"],"input_types":["automation task (string or object)","reliability configuration (JSON: idempotency checks, validation rules, etc.)"],"output_types":["execution result (success/failure)","reliability metrics (JSON: consistency score, failure rate, etc.)","state validation report (JSON)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["Node.js 16+ or Python 3.8+","API key for LLM provider (OpenAI, Anthropic, or local model)","Chromium/Chrome browser installed or access to headless browser binary","Network connectivity to target websites","LLM with sufficient context window (8K+ tokens recommended)","Browser instance with stable connectivity","Task definition in natural language or structured format (likely YAML/JSON)","Headless browser (Chromium/Chrome)","Vision-capable LLM (GPT-4V, Claude 3 Vision, or local vision model)","Sufficient memory for screenshot storage and processing"],"failure_modes":["Latency per action cycle likely 1-3 seconds due to LLM inference + browser rendering","May struggle with highly dynamic JavaScript-heavy SPAs that change DOM structure rapidly","No built-in handling for CAPTCHAs, multi-factor authentication, or anti-bot detection","Accuracy depends on LLM's ability to understand page context — complex or poorly-structured HTML may confuse the agent","Context window constraints may limit how much state can be carried across very long workflows (50+ steps)","No built-in support for parallel task execution — all steps are sequential","Hallucination risk increases with task complexity; agent may invent actions that don't exist","Debugging multi-step failures is harder than debugging linear scripts because causality is implicit in the agent's reasoning","Screenshot analysis adds latency (~500ms-1s per page) and requires GPU or cloud vision API","Visual understanding may fail on pages with overlapping elements, animations, or poor contrast","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"ecosystem":0.39999999999999997,"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":"2026-06-17T09:51:03.579Z","last_scraped_at":"2026-05-03T14:00:23.056Z","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=notte","compare_url":"https://unfragile.ai/compare?artifact=notte"}},"signature":"c/sc1mQibPhzBfecL97ymwmPs347+UBefc3rQJLLVe/R4Z5WkzPHtcKPf2I3exMV+jiUDGt/WwAOMr1WYgvaCw==","signedAt":"2026-06-22T17:25:33.089Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/notte","artifact":"https://unfragile.ai/notte","verify":"https://unfragile.ai/api/v1/verify?slug=notte","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"}}