{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-scrapegraphai","slug":"scrapegraphai","name":"ScrapeGraphAI","type":"repo","url":"https://github.com/ScrapeGraphAI/Scrapegraph-ai","page_url":"https://unfragile.ai/scrapegraphai","categories":["data-pipelines"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-scrapegraphai__cap_0","uri":"capability://automation.workflow.natural.language.to.dag.scraping.pipeline.compilation","name":"natural language to dag scraping pipeline compilation","description":"Converts natural language extraction requirements into directed acyclic graphs (DAGs) of processing nodes without requiring CSS selectors or XPath expressions. The system parses user intent, constructs a node execution plan, and orchestrates LLM calls across a pipeline where each node reads from and writes to a shared state dictionary, enabling declarative scraping workflows that adapt to page structure changes automatically.","intents":["I want to extract data from a website by describing what I need in plain English instead of writing selectors","I need a scraper that adapts when website HTML structure changes without code modifications","I want to build complex multi-step extraction workflows without learning CSS/XPath syntax"],"best_for":["Non-technical business users building scraping workflows","Data engineers prototyping extraction pipelines rapidly","Teams maintaining scrapers across sites with frequent layout changes"],"limitations":["LLM-based extraction adds latency (typically 2-10 seconds per page depending on model and page complexity)","Accuracy depends on LLM quality and prompt engineering; may require refinement for complex nested structures","No built-in caching of compiled DAGs — recompiles on each execution unless manually cached","Limited to sequential node execution in BaseGraph; parallel execution requires custom graph implementation"],"requires":["Python 3.8+","API key for at least one LLM provider (OpenAI, Anthropic, Google, Ollama, etc.)","Web browser backend (Playwright, Selenium, BrowserBase, or Scrape.do account)"],"input_types":["natural language description (string)","URL (string)","HTML/PDF/CSV/JSON/XML/Markdown document (file or string)"],"output_types":["structured JSON","Python dictionaries","typed dataclasses"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scrapegraphai__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.backend.abstraction.with.unified.interface","name":"multi-provider llm backend abstraction with unified interface","description":"Provides a unified abstraction layer supporting 20+ LLM providers (OpenAI, Anthropic, Google, AWS Bedrock, Ollama, Nvidia, etc.) through a common interface, enabling users to swap providers without changing scraping logic. The system handles provider-specific API differences, token counting, model selection, and fallback strategies through a pluggable model registry that maps provider names to concrete LLM implementations.","intents":["I want to switch between OpenAI, Anthropic, and local Ollama models without rewriting my scraper","I need to use the cheapest available LLM provider for cost optimization","I want to run scraping pipelines on-premise using Ollama while keeping the same code"],"best_for":["Teams evaluating multiple LLM providers for cost/latency tradeoffs","Organizations with on-premise LLM requirements (Ollama, local models)","Developers building multi-tenant scraping platforms with provider flexibility"],"limitations":["Provider-specific features (vision, function calling) may not be uniformly exposed across all backends","Token counting varies by provider; some providers lack accurate token estimation","Rate limiting and quota management are provider-specific and not abstracted","Fallback chains require manual configuration; no automatic provider failover"],"requires":["Python 3.8+","API credentials for chosen provider(s) (API keys, endpoints, etc.)","For Ollama: local Ollama server running on accessible network"],"input_types":["provider name (string: 'openai', 'anthropic', 'ollama', etc.)","model identifier (string: 'gpt-4', 'claude-3-sonnet', etc.)","LLM configuration dict (temperature, max_tokens, etc.)"],"output_types":["LLM response (string)","structured output (JSON if schema provided)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scrapegraphai__cap_10","uri":"capability://image.visual.multi.modal.content.processing.with.image.and.audio.handling","name":"multi-modal content processing with image and audio handling","description":"Processes multi-modal content including images and audio through specialized nodes (ImageToTextNode, TextToSpeechNode) that convert between modalities. Images are converted to text descriptions via vision LLMs, enabling extraction from visual content. Audio is converted to text via speech-to-text, enabling scraping of audio content. This allows scraping workflows to handle rich media content alongside text.","intents":["I want to extract text from images on a webpage using vision AI","I need to transcribe audio content and extract information from it","I want to process mixed-media pages with text, images, and audio"],"best_for":["Applications processing rich media content (product images, infographics, videos)","Accessibility workflows converting media to text","Multi-modal data extraction from complex pages"],"limitations":["Vision LLM calls add significant latency (2-5 seconds per image) and cost","Image quality and resolution affect OCR/vision accuracy; low-quality images may fail","Audio transcription accuracy depends on audio quality and language; accents and background noise degrade results","No built-in image preprocessing; users must handle image cleaning and optimization"],"requires":["Python 3.8+","Vision LLM provider (OpenAI Vision, Claude Vision, Google Vision, etc.)","For audio: speech-to-text provider (OpenAI Whisper, Google Cloud Speech, etc.)"],"input_types":["image (file path, URL, or bytes)","audio (file path or bytes)","image description prompt (optional string)"],"output_types":["image description (string)","transcribed text (string)","extracted structured data (JSON)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scrapegraphai__cap_11","uri":"capability://data.processing.analysis.schema.based.output.validation.and.transformation","name":"schema-based output validation and transformation","description":"Validates and transforms extracted data against user-defined schemas (JSON Schema, Pydantic models, dataclasses) to ensure output conforms to expected structure and types. The system uses schema_transform utilities to map LLM outputs to typed structures, handle type coercion, and validate constraints. This ensures downstream systems receive data in the expected format with type safety.","intents":["I want to ensure extracted data matches my expected schema before passing to downstream systems","I need to convert extracted data to typed Python objects (dataclasses, Pydantic models)","I want to validate that extracted values meet constraints (min/max, regex patterns, etc.)"],"best_for":["Data pipelines requiring strict schema compliance","Teams using typed Python (Pydantic, dataclasses)","Systems with downstream validation requirements"],"limitations":["Schema validation adds latency; complex schemas with many constraints can slow extraction","LLM outputs may not perfectly match schema; requires fallback or error handling for mismatches","Schema definition is developer responsibility; no automatic schema inference from data","Type coercion may lose information (e.g., string to int conversion); requires careful schema design"],"requires":["Python 3.8+","JSON Schema, Pydantic, or dataclass definitions","schema_transform utilities from ScrapeGraphAI"],"input_types":["extracted data (dict or string)","schema definition (JSON Schema, Pydantic model, or dataclass)"],"output_types":["validated and transformed data (typed object or dict)","validation errors (list of error messages)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scrapegraphai__cap_12","uri":"capability://text.generation.language.prompt.engineering.and.llm.behavior.customization","name":"prompt engineering and llm behavior customization","description":"Enables fine-grained control over LLM behavior through prompt templates, system messages, and configuration parameters (temperature, max_tokens, top_p, etc.). Users can customize extraction logic by modifying prompts without changing code, and the system supports prompt versioning and A/B testing. This allows optimization of extraction accuracy and cost without modifying graph structure.","intents":["I want to customize the extraction prompt to improve accuracy for my specific use case","I need to adjust LLM parameters (temperature, max_tokens) to balance quality and cost","I want to test different prompts and measure their impact on extraction quality"],"best_for":["Teams optimizing extraction accuracy through prompt tuning","Cost-sensitive deployments tuning token usage","Researchers experimenting with different prompting strategies"],"limitations":["Prompt engineering is empirical; no guaranteed way to find optimal prompts","LLM behavior is non-deterministic; same prompt may produce different results","Prompt changes may break downstream logic if output format changes unexpectedly","No built-in A/B testing framework; requires manual experiment setup"],"requires":["Python 3.8+","Understanding of LLM prompting best practices","Access to LLM provider for testing"],"input_types":["prompt template (string with placeholders)","system message (string)","LLM parameters (dict with temperature, max_tokens, etc.)"],"output_types":["customized LLM response (string or structured data)","prompt execution metrics (latency, tokens used, cost)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scrapegraphai__cap_13","uri":"capability://automation.workflow.error.handling.and.fallback.strategies.in.extraction.pipelines","name":"error handling and fallback strategies in extraction pipelines","description":"Provides mechanisms for handling extraction failures through fallback nodes, retry logic, and error recovery strategies. When a node fails (e.g., LLM call times out, page fetch fails), the system can automatically retry with different parameters, fall back to alternative extraction methods, or skip the node and continue with partial results. This improves robustness for large-scale scraping where some failures are inevitable.","intents":["I want my scraper to retry failed requests automatically instead of stopping","I need fallback extraction methods when primary extraction fails","I want to continue processing even if some pages fail, collecting partial results"],"best_for":["Large-scale scraping projects where some failures are expected","Robust production systems requiring high availability","Pipelines processing heterogeneous sources with varying reliability"],"limitations":["Retry logic adds latency; exponential backoff can slow down pipelines significantly","Fallback strategies require manual definition; no automatic fallback discovery","Partial results may be incomplete or inconsistent; downstream systems must handle missing data","Error recovery is node-specific; global error handling policies are difficult to implement"],"requires":["Python 3.8+","Error handling configuration (retry counts, backoff strategy, fallback nodes)"],"input_types":["node configuration with retry/fallback settings (dict)","error context (exception, state, etc.)"],"output_types":["successful result or fallback result (dict or string)","error log (dict with error details and recovery strategy used)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scrapegraphai__cap_2","uri":"capability://automation.workflow.flexible.data.acquisition.with.multiple.browser.backends","name":"flexible data acquisition with multiple browser backends","description":"Abstracts web page fetching across four distinct backends (Playwright, Selenium, BrowserBase, Scrape.do) through a unified FetchNode interface, enabling users to choose between local browser automation, cloud-based rendering, or headless scraping based on target site requirements. The system handles JavaScript execution, dynamic content loading, and anti-bot detection transparently, with automatic fallback between backends if configured.","intents":["I need to scrape JavaScript-heavy sites that require full browser rendering","I want to use cloud-based scraping to avoid IP blocking and bot detection","I need to switch from local Playwright to BrowserBase without changing my scraping logic"],"best_for":["Teams scraping sites with heavy JavaScript rendering requirements","Organizations needing cloud-based scraping to avoid IP bans","Developers building scalable scraping infrastructure with multiple backend options"],"limitations":["Playwright/Selenium require local browser installation and consume significant memory (~100-500MB per instance)","BrowserBase and Scrape.do add API call latency (typically 3-15 seconds) and require paid accounts for production use","Not all backends support identical features (e.g., Scrape.do has limited JavaScript execution control)","Backend selection is static per graph execution; dynamic switching mid-pipeline not supported"],"requires":["Python 3.8+","For Playwright: Playwright browsers installed (chromium, firefox, webkit)","For Selenium: WebDriver binary (chromedriver, geckodriver, etc.)","For BrowserBase: BrowserBase API key and account","For Scrape.do: Scrape.do API key and account"],"input_types":["URL (string)","backend configuration (dict with 'backend' key: 'playwright', 'selenium', 'browserbase', 'scrape_do')","browser options (headless mode, viewport size, user agent, etc.)"],"output_types":["rendered HTML (string)","page content (bytes)","screenshot (image)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scrapegraphai__cap_3","uri":"capability://data.processing.analysis.format.agnostic.document.parsing.and.extraction","name":"format-agnostic document parsing and extraction","description":"Processes multiple document formats (HTML, PDF, CSV, JSON, XML, Markdown) through a unified parsing pipeline that extracts structured content regardless of source format. The system uses format-specific parsers (HTML via BeautifulSoup/lxml, PDF via PyPDF2/pdfplumber, CSV via pandas, etc.) and normalizes output to a common intermediate representation that downstream LLM nodes can process uniformly.","intents":["I want to extract data from both HTML pages and PDF documents using the same scraper logic","I need to parse CSV files and JSON APIs with identical extraction workflows","I want to handle mixed-format sources (HTML + PDF + JSON) in a single pipeline"],"best_for":["Data teams processing heterogeneous document sources","Enterprises with legacy systems producing multiple output formats","Developers building universal data extraction platforms"],"limitations":["PDF parsing quality varies by PDF type (scanned images vs text-based); OCR not built-in","CSV/JSON parsing assumes well-formed input; malformed files require preprocessing","Format detection is explicit (user specifies format); automatic detection not supported","Large documents (>50MB) may cause memory issues; streaming processing not implemented"],"requires":["Python 3.8+","Format-specific libraries: beautifulsoup4 (HTML), pdfplumber or PyPDF2 (PDF), pandas (CSV), built-in json (JSON)"],"input_types":["HTML (string or file path)","PDF (file path or bytes)","CSV (file path or string)","JSON (string or file path)","XML (string or file path)","Markdown (string or file path)"],"output_types":["normalized document representation (dict with 'content', 'metadata', 'format')","structured data (JSON, dataclass instances)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scrapegraphai__cap_4","uri":"capability://automation.workflow.composable.node.system.with.custom.node.creation","name":"composable node system with custom node creation","description":"Provides a BaseNode abstraction that enables developers to create custom processing nodes by implementing a simple interface (execute method that reads from and writes to shared state). Nodes are composable building blocks that can be chained in custom graph topologies, with built-in nodes covering fetch, parse, generate, RAG, search, and conditional logic. The system handles node dependency resolution, state threading, and error propagation automatically.","intents":["I want to create a custom node that applies domain-specific validation or transformation logic","I need to build a scraping graph with conditional branching based on extracted data","I want to extend ScrapeGraphAI with proprietary data enrichment or filtering nodes"],"best_for":["Advanced developers building specialized scraping workflows","Teams with domain-specific processing requirements (validation, enrichment, filtering)","Organizations extending ScrapeGraphAI for internal platforms"],"limitations":["Custom nodes must follow BaseNode interface; no automatic type checking or validation","State dictionary is untyped (dict[str, Any]); no schema enforcement between nodes","Error handling in custom nodes is developer's responsibility; no built-in retry or fallback logic","Node execution is sequential in BaseGraph; parallel execution requires custom graph implementation"],"requires":["Python 3.8+","Understanding of BaseNode interface and state dictionary structure","Knowledge of LLM integration if node uses LLM calls"],"input_types":["state dictionary (dict[str, Any])","node configuration (dict)"],"output_types":["modified state dictionary (dict[str, Any])"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scrapegraphai__cap_5","uri":"capability://memory.knowledge.rag.integrated.extraction.with.vector.storage","name":"rag-integrated extraction with vector storage","description":"Integrates Retrieval-Augmented Generation (RAG) capabilities through a RAGNode that embeds document chunks into vector stores (Chroma, Pinecone, Weaviate, etc.) and retrieves relevant context before LLM-based extraction. This enables semantic search over scraped content, reducing token usage and improving accuracy for large documents by providing only relevant excerpts to the LLM rather than full page content.","intents":["I want to extract specific information from very long documents by retrieving only relevant sections","I need to perform semantic search over scraped content to find contextually relevant data","I want to reduce LLM token usage by providing only relevant document excerpts instead of full pages"],"best_for":["Teams processing large documents (research papers, legal contracts, technical documentation)","Applications requiring semantic search over scraped content","Cost-sensitive deployments where token reduction is critical"],"limitations":["Vector store setup and embedding model selection adds complexity and latency (embedding typically 0.5-2 seconds per document)","Chunking strategy (chunk size, overlap) significantly impacts retrieval quality; requires tuning per use case","Vector stores require persistent storage and management; no built-in cleanup or lifecycle management","Embedding quality depends on model choice; domain-specific embeddings may require fine-tuning"],"requires":["Python 3.8+","Vector store library (chromadb, pinecone-client, weaviate-client, etc.)","Embedding model (OpenAI embeddings, Hugging Face, local models via Ollama)","For cloud vector stores: API credentials (Pinecone, Weaviate, etc.)"],"input_types":["document content (string or list of strings)","query (string)","vector store configuration (dict)"],"output_types":["retrieved context chunks (list of strings)","relevance scores (list of floats)","extracted answer (string or JSON)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scrapegraphai__cap_6","uri":"capability://search.retrieval.web.search.integration.with.context.aware.retrieval","name":"web search integration with context-aware retrieval","description":"Integrates web search capabilities through SearchNode and SearchNodeWithContext that query search engines (Google, Bing, DuckDuckGo via SerpAPI, Tavily, etc.) and retrieve results with optional context enrichment. This enables scraping workflows to augment extracted data with real-time search results, perform fact-checking, or gather supplementary information from multiple sources within a single pipeline.","intents":["I want to enrich scraped data with real-time search results from multiple sources","I need to verify extracted information by searching for corroborating sources","I want to gather data from both direct scraping and search results in one workflow"],"best_for":["Data enrichment pipelines combining scraping with search results","Fact-checking and verification workflows","Applications requiring real-time data augmentation"],"limitations":["Search API calls add latency (typically 1-3 seconds per query) and incur per-query costs","Search result quality and relevance depend on query formulation; requires prompt engineering","Rate limiting and quota management vary by search provider; no built-in throttling","Search results are time-sensitive; caching strategy must account for freshness requirements"],"requires":["Python 3.8+","Search API credentials (SerpAPI, Tavily, or other provider)","Network connectivity for search API calls"],"input_types":["search query (string)","search context (optional dict with additional parameters)","number of results (int)"],"output_types":["search results (list of dicts with title, url, snippet, etc.)","enriched context (string or structured data)"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scrapegraphai__cap_7","uri":"capability://code.generation.editing.code.generation.for.custom.extraction.logic","name":"code generation for custom extraction logic","description":"Generates Python code snippets for custom extraction logic based on natural language descriptions and page structure analysis. The system analyzes HTML/document structure, infers extraction patterns, and generates executable Python code that can be executed directly or used as a starting point for further customization. This bridges the gap between declarative natural language requests and imperative extraction code.","intents":["I want to generate Python code for extracting data from a specific page structure","I need to convert a natural language extraction requirement into executable code","I want to use generated code as a starting point and customize it further"],"best_for":["Developers who prefer code-based extraction over declarative graphs","Teams needing to customize generated extraction logic","Rapid prototyping of extraction patterns"],"limitations":["Generated code quality depends on LLM capability and page structure clarity; may require manual refinement","Generated code is not guaranteed to be syntactically correct or executable without testing","No automatic validation or testing of generated code; developer responsibility to verify","Generated code may not handle edge cases or dynamic content; requires hardening"],"requires":["Python 3.8+","LLM provider with code generation capability (GPT-4, Claude, etc.)"],"input_types":["natural language extraction requirement (string)","page HTML or document content (string)","extraction target schema (optional dict or JSON schema)"],"output_types":["Python code (string)","executable function or class"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scrapegraphai__cap_8","uri":"capability://planning.reasoning.conditional.logic.and.control.flow.in.scraping.pipelines","name":"conditional logic and control flow in scraping pipelines","description":"Implements conditional branching through ConditionalNode that evaluates conditions on extracted data and routes execution to different downstream nodes based on results. This enables dynamic pipeline behavior where extraction logic adapts based on intermediate results, enabling workflows like 'if price > threshold, extract additional details' or 'if element exists, parse it; otherwise, use fallback'.","intents":["I want to extract different data depending on what's found on the page","I need to implement fallback extraction logic if primary extraction fails","I want to conditionally enrich data based on extracted values"],"best_for":["Complex scraping workflows with multiple extraction paths","Adaptive scrapers that adjust behavior based on page content","Pipelines with fallback or error recovery logic"],"limitations":["Conditional logic is evaluated at runtime; no static analysis or optimization","Complex nested conditions can make DAG topology difficult to visualize and debug","No built-in support for loops; iterative logic requires custom graph implementations","Condition evaluation adds latency; complex conditions with LLM calls can be slow"],"requires":["Python 3.8+","Understanding of ConditionalNode interface and state dictionary"],"input_types":["state dictionary (dict[str, Any])","condition expression (string or callable)"],"output_types":["routing decision (string indicating next node)","modified state (dict[str, Any])"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scrapegraphai__cap_9","uri":"capability://automation.workflow.batch.processing.and.multi.source.scraping","name":"batch processing and multi-source scraping","description":"Enables batch processing of multiple URLs or documents through graph iteration patterns that apply the same extraction logic across collections of sources. The system handles batching, parallelization (where supported), and result aggregation, allowing users to scrape hundreds of pages with a single graph definition. Multi-source scraping combines results from different sources (web pages, APIs, documents) into unified output.","intents":["I want to scrape 100+ URLs with the same extraction logic without writing loops","I need to combine data from multiple sources (web pages, APIs, documents) into one result","I want to process batches of documents efficiently with automatic result aggregation"],"best_for":["Large-scale data collection projects (100+ sources)","Batch processing pipelines with uniform extraction logic","Multi-source data consolidation workflows"],"limitations":["Sequential batch processing can be slow for large collections; parallel execution requires custom implementation","Memory usage scales with batch size; very large batches (10k+ items) may cause memory issues","Error handling in batch mode is all-or-nothing; partial failures require custom retry logic","Result aggregation assumes uniform output schema; heterogeneous results require post-processing"],"requires":["Python 3.8+","List of URLs or documents to process","Sufficient memory for batch size (typically 1-10MB per item)"],"input_types":["list of URLs (list[str])","list of documents (list[str] or list[dict])","batch configuration (dict with batch_size, parallel, etc.)"],"output_types":["list of extracted results (list[dict] or list[str])","aggregated results (dict with summary statistics)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API key for at least one LLM provider (OpenAI, Anthropic, Google, Ollama, etc.)","Web browser backend (Playwright, Selenium, BrowserBase, or Scrape.do account)","API credentials for chosen provider(s) (API keys, endpoints, etc.)","For Ollama: local Ollama server running on accessible network","Vision LLM provider (OpenAI Vision, Claude Vision, Google Vision, etc.)","For audio: speech-to-text provider (OpenAI Whisper, Google Cloud Speech, etc.)","JSON Schema, Pydantic, or dataclass definitions","schema_transform utilities from ScrapeGraphAI","Understanding of LLM prompting best practices"],"failure_modes":["LLM-based extraction adds latency (typically 2-10 seconds per page depending on model and page complexity)","Accuracy depends on LLM quality and prompt engineering; may require refinement for complex nested structures","No built-in caching of compiled DAGs — recompiles on each execution unless manually cached","Limited to sequential node execution in BaseGraph; parallel execution requires custom graph implementation","Provider-specific features (vision, function calling) may not be uniformly exposed across all backends","Token counting varies by provider; some providers lack accurate token estimation","Rate limiting and quota management are provider-specific and not abstracted","Fallback chains require manual configuration; no automatic provider failover","Vision LLM calls add significant latency (2-5 seconds per image) and cost","Image quality and resolution affect OCR/vision accuracy; low-quality images may fail","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.049Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=scrapegraphai","compare_url":"https://unfragile.ai/compare?artifact=scrapegraphai"}},"signature":"no7mXHIWiaSaE9yf2QaLZc9+szKCYwXhj+GagYMDLMlAd6uuDkde8Flcy2oFueNkeZJwezVW2FmD4Gv7Oa/UCA==","signedAt":"2026-06-20T17:08:58.133Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/scrapegraphai","artifact":"https://unfragile.ai/scrapegraphai","verify":"https://unfragile.ai/api/v1/verify?slug=scrapegraphai","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"}}