{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-d4vinci--scrapling","slug":"d4vinci--scrapling","name":"Scrapling","type":"framework","url":"https://scrapling.readthedocs.io/en/latest/","page_url":"https://unfragile.ai/d4vinci--scrapling","categories":["data-pipelines"],"tags":["ai","ai-scraping","automation","crawler","crawling","crawling-python","data","data-extraction","mcp","mcp-server","playwright","python","scraping","selectors","stealth","web-scraper","web-scraping","web-scraping-python","webscraping","xpath"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-d4vinci--scrapling__cap_0","uri":"capability://automation.workflow.progressive.http.to.browser.fetcher.hierarchy.with.unified.response.interface","name":"progressive http-to-browser fetcher hierarchy with unified response interface","description":"Implements a three-tier fetcher system (Fetcher for static HTTP, dynamic browser fetcher for JavaScript-heavy sites, StealthyFetcher for anti-bot detection) where all tiers return the same Response object inheriting from Selector. This allows developers to start with fast HTTP requests and transparently upgrade to browser automation without changing parsing code. Uses lazy imports via __getattr__ to defer loading heavy dependencies (Playwright, browser engines) until first access, minimizing initial memory footprint and import latency.","intents":["Start with fast static HTTP scraping and upgrade to browser automation only when JavaScript rendering is required","Write scraping code once that works identically across HTTP, browser, and stealth fetchers","Minimize memory overhead by deferring Playwright and browser engine imports until needed","Transparently handle sites that require JavaScript execution without rewriting parsing logic"],"best_for":["Teams building adaptive scrapers that need to handle both static and dynamic content","Developers wanting to optimize performance by starting simple and escalating complexity","Projects requiring code reuse across different fetching strategies"],"limitations":["Browser-based fetchers have higher latency (~2-5s per request) compared to HTTP fetchers (~100-500ms)","Lazy imports add minimal overhead on first access but require careful dependency management","Unified Response interface may abstract away fetcher-specific optimizations or capabilities"],"requires":["Python 3.10+","Playwright for browser automation (optional, lazy-loaded)","httpx or requests library for HTTP fetching"],"input_types":["URL string","HTTP method (GET, POST, etc.)","Headers dict","Request body (optional)"],"output_types":["Response object (inherits from Selector)","Parsed HTML/DOM tree","CSS/XPath query results"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-d4vinci--scrapling__cap_1","uri":"capability://data.processing.analysis.adaptive.element.relocation.and.dynamic.selector.resolution","name":"adaptive element relocation and dynamic selector resolution","description":"Implements intelligent selector resolution that automatically relocates elements when DOM structure changes between requests, using tree-sitter AST parsing or similar structural analysis to maintain selector validity across page mutations. When a CSS or XPath selector fails, the system analyzes the current DOM and attempts to find the target element using fallback strategies (attribute matching, structural similarity, text content matching). This enables robust scraping of pages with dynamic or inconsistent HTML structures without manual selector maintenance.","intents":["Scrape pages where DOM structure changes between requests or page loads","Maintain selector validity across minor HTML structure variations without manual updates","Automatically recover from broken selectors by finding semantically equivalent elements","Handle sites that use dynamic class names or IDs generated at runtime"],"best_for":["Developers scraping sites with frequently changing HTML structures","Teams maintaining long-lived scrapers that need to survive minor DOM mutations","Projects targeting sites with dynamically generated class names or IDs"],"limitations":["Fallback resolution adds ~50-200ms latency per failed selector","Structural similarity matching may incorrectly identify elements in highly dynamic pages","Requires sufficient DOM context to disambiguate elements (fails on minimal/identical structures)"],"requires":["Python 3.10+","BeautifulSoup4 or lxml for DOM parsing","Original selector definition (CSS or XPath)"],"input_types":["CSS selector string","XPath expression","HTML/DOM tree"],"output_types":["Element reference","Relocated selector string","Fallback match confidence score"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-d4vinci--scrapling__cap_10","uri":"capability://data.processing.analysis.custom.type.handlers.and.response.transformation.middleware","name":"custom type handlers and response transformation middleware","description":"Provides extensible middleware system for transforming requests and responses through custom handlers. Developers can register custom type handlers that convert Response objects to domain-specific types (e.g., JSON, CSV, custom dataclasses) or apply transformations (e.g., text cleaning, data validation). Middleware is applied in a pipeline: request → fetcher → response → handlers → output. Handlers can be conditional (applied only to certain URLs or response types) and composable (chained together). The system supports both synchronous and asynchronous handlers for integration with async crawlers.","intents":["Transform raw HTML responses into structured data types (JSON, CSV, dataclasses)","Apply custom data cleaning or validation logic to extracted data","Conditionally apply handlers based on URL patterns or response content","Chain multiple handlers together for complex transformations","Integrate custom business logic into the scraping pipeline"],"best_for":["Teams with custom data transformation requirements","Developers building domain-specific scrapers with specialized output formats","Projects requiring data validation or cleaning before storage","Crawlers that need to apply different transformations to different content types"],"limitations":["Custom handlers add latency (~10-100ms per handler) depending on transformation complexity","Handler ordering matters—incorrect ordering can cause unexpected transformations","Async handlers require careful error handling to avoid blocking the crawl","No built-in handler composition utilities (requires manual chaining)"],"requires":["Python 3.10+","Custom handler class or function","Response object from fetcher"],"input_types":["Response object","Request object (for conditional handlers)","Handler configuration (dict)"],"output_types":["Transformed data (JSON, CSV, dataclass, custom type)","Validation errors (if validation handler)","Cleaned/normalized data"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-d4vinci--scrapling__cap_11","uri":"capability://automation.workflow.cli.and.interactive.shell.for.exploratory.scraping","name":"cli and interactive shell for exploratory scraping","description":"Provides command-line interface (CLI) and interactive REPL shell for testing scrapers without writing code. The CLI supports common operations (fetch URL, parse HTML, extract data) with flags for fetcher selection, proxy configuration, and wait strategies. The interactive shell allows developers to iteratively test selectors, refine extraction logic, and debug issues in real-time. Shell sessions maintain state (current URL, parsed HTML, session cookies) across commands, enabling rapid iteration. Output can be formatted as JSON, CSV, or pretty-printed for easy inspection.","intents":["Test scraping logic without writing code using CLI commands","Iteratively refine CSS/XPath selectors in an interactive shell","Debug scraping issues by inspecting HTML and testing selectors in real-time","Quickly fetch and parse a URL to verify content before building a full scraper","Export extracted data in multiple formats (JSON, CSV) for inspection"],"best_for":["Developers prototyping scrapers and testing selectors","Non-technical users exploring web data without coding","Teams debugging scraping issues in production","Quick one-off data extraction tasks"],"limitations":["CLI is limited to simple operations (no complex logic or loops)","Interactive shell requires manual command entry (slower than scripted crawls)","State management in shell is in-memory only (no persistence between sessions)","Output formatting options are limited compared to programmatic API"],"requires":["Python 3.10+","Scrapling installed and in PATH","Terminal/shell access"],"input_types":["URL (string)","CSS/XPath selector (string)","CLI flags (--fetcher, --proxy, --wait-strategy, etc.)","Shell commands (fetch, parse, select, extract)"],"output_types":["HTML content (pretty-printed or raw)","Extracted data (JSON, CSV, text)","Selector test results","Error messages and debugging info"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-d4vinci--scrapling__cap_12","uri":"capability://automation.workflow.resource.management.and.performance.optimization.with.lazy.loading","name":"resource management and performance optimization with lazy loading","description":"Implements lazy loading of heavy dependencies (Playwright, browser engines, proxy libraries) through __getattr__ dynamic imports, reducing initial import time and memory footprint. The system provides resource pooling for browser instances and HTTP connections, automatic cleanup of unused resources, and memory-efficient DOM parsing using streaming where possible. Configuration options allow tuning of pool sizes, timeouts, and resource limits. Monitoring hooks expose resource usage metrics (active connections, browser tabs, memory) for performance analysis and optimization.","intents":["Minimize initial import time and memory overhead by lazy-loading heavy dependencies","Efficiently manage browser instances and HTTP connections through pooling","Monitor resource usage (memory, connections, tabs) for performance optimization","Automatically clean up unused resources to prevent memory leaks","Tune resource limits based on available system resources"],"best_for":["Teams running Scrapling in resource-constrained environments (serverless, containers)","Developers optimizing scraper performance and memory usage","Projects requiring monitoring of resource consumption","Large-scale crawlers that need efficient resource management"],"limitations":["Lazy imports add minimal overhead on first access (~10-50ms)","Resource pooling requires careful tuning to avoid resource exhaustion","Streaming DOM parsing may not work with all HTML parsers (BeautifulSoup vs lxml)","Memory monitoring adds ~1-5% overhead depending on monitoring frequency"],"requires":["Python 3.10+","Optional: psutil for resource monitoring","System resources (RAM, CPU, file descriptors)"],"input_types":["Resource configuration dict (pool sizes, timeouts, limits)","Monitoring configuration (metrics to track, reporting frequency)"],"output_types":["Resource usage metrics (dict or JSON)","Performance warnings (resource exhaustion alerts)","Optimization recommendations"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-d4vinci--scrapling__cap_2","uri":"capability://automation.workflow.stealth.browser.automation.with.anti.detection.evasion","name":"stealth browser automation with anti-detection evasion","description":"Provides StealthyFetcher class that configures Playwright with anti-bot detection evasion techniques including: disabling headless mode indicators, spoofing user agents and device properties, managing WebDriver detection flags, implementing realistic mouse/keyboard behavior patterns, and rotating proxy/IP addresses. The system integrates with proxy rotation middleware to distribute requests across multiple IPs, and configures browser launch parameters to minimize detection signatures. All evasion techniques are composable and can be selectively enabled based on target site requirements.","intents":["Scrape sites with aggressive bot detection (Cloudflare, DataDome, Imperva) without getting blocked","Rotate through proxy pools to distribute requests and avoid IP-based rate limiting","Configure realistic browser behavior (user agent, device properties, timing) to evade detection","Manage browser fingerprinting by spoofing WebDriver and automation detection flags"],"best_for":["Teams scraping heavily-protected sites with advanced bot detection","Projects requiring IP rotation and distributed request patterns","Developers building large-scale crawlers that need to evade rate limiting"],"limitations":["Stealth techniques add 30-50% latency overhead compared to standard browser fetching","Proxy rotation requires external proxy service (residential or datacenter proxies)","Detection evasion is an arms race—techniques may become ineffective as detection systems evolve","Some sites explicitly block Playwright/Chromium even with evasion enabled"],"requires":["Python 3.10+","Playwright with Chromium browser","Proxy list (optional but recommended)","Valid proxy credentials if using authenticated proxies"],"input_types":["URL string","Proxy configuration (host, port, credentials)","Browser configuration dict","User agent string"],"output_types":["Response object with rendered HTML","Browser session with stealth configuration applied","Proxy rotation state"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-d4vinci--scrapling__cap_3","uri":"capability://data.processing.analysis.unified.html.parsing.with.css.and.xpath.selector.support","name":"unified html parsing with css and xpath selector support","description":"Implements Selector class that wraps BeautifulSoup4/lxml and provides unified API for both CSS and XPath selectors, returning Response objects that themselves inherit from Selector for chainable query syntax. Supports advanced selector features including pseudo-selectors, attribute matching, text content filtering, and relative selectors. The Response object maintains context about the source (HTTP, browser, stealth) and allows seamless chaining of selectors (e.g., response.css('div.item').xpath('.//span[@class=\"price\"]').text()).","intents":["Parse HTML using either CSS or XPath selectors interchangeably within the same code","Chain multiple selectors together for nested element extraction","Extract text, attributes, and structured data from HTML in a fluent API style","Work with the same parsing interface regardless of which fetcher (HTTP, browser, stealth) was used"],"best_for":["Developers comfortable with both CSS and XPath who want flexibility","Teams extracting complex nested structures from HTML","Projects requiring consistent parsing API across different data sources"],"limitations":["XPath queries are slower than CSS selectors on large DOMs (~2-5x slower)","Pseudo-selectors like :nth-child() have limited support in XPath","Chainable API adds minimal overhead but may be less efficient than single compiled selector","No built-in support for JavaScript evaluation within selectors (use browser fetcher instead)"],"requires":["Python 3.10+","BeautifulSoup4 or lxml","HTML/DOM tree from fetcher"],"input_types":["CSS selector string (e.g., 'div.item > span.price')","XPath expression (e.g., '//div[@class=\"item\"]//span[@class=\"price\"]')","HTML string or parsed tree"],"output_types":["Element reference","List of elements","Text content string","Attribute value","Response object (for chaining)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-d4vinci--scrapling__cap_4","uri":"capability://automation.workflow.session.based.connection.and.browser.tab.pooling.with.state.management","name":"session-based connection and browser tab pooling with state management","description":"Provides Session and AsyncSession classes that manage connection pooling for HTTP requests and browser tab pooling for Playwright-based fetchers. HTTP sessions reuse TCP connections to reduce latency and overhead. Browser sessions maintain a pool of tabs (configurable size) that are recycled across requests, avoiding the overhead of launching new browser instances. Sessions also manage cookies, headers, and authentication state across multiple requests, with optional persistence to disk. The architecture supports concurrent request handling through async/await patterns.","intents":["Reuse HTTP connections across multiple requests to reduce latency and resource usage","Maintain browser tab pools to avoid launching new browser instances for each request","Preserve cookies and authentication state across multiple requests in a session","Handle concurrent requests efficiently using async/await patterns","Persist session state (cookies, auth tokens) to disk for resuming crawls"],"best_for":["Teams running large-scale crawls requiring efficient resource usage","Developers building authenticated scrapers that need to maintain login state","Projects with concurrent request patterns (async crawlers)","Long-running crawlers that need to resume from checkpoints"],"limitations":["Browser tab pooling adds ~100-300ms overhead for tab acquisition and cleanup","Session state persistence requires external storage (file, database) for distributed crawls","Concurrent requests may hit rate limits faster than sequential requests","Tab pool size must be tuned based on target site's concurrency tolerance"],"requires":["Python 3.10+","httpx for HTTP sessions (optional, lazy-loaded)","Playwright for browser sessions (optional, lazy-loaded)","Async runtime (asyncio) for concurrent operations"],"input_types":["Session configuration dict (pool size, timeout, headers)","Cookie dict or jar","Authentication credentials","Proxy configuration"],"output_types":["Session object (HTTP or browser)","Response object","Serialized session state (JSON/pickle)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-d4vinci--scrapling__cap_5","uri":"capability://automation.workflow.spider.framework.for.declarative.crawl.patterns.with.request.response.lifecycle.hooks","name":"spider framework for declarative crawl patterns with request/response lifecycle hooks","description":"Provides Spider base class that enables declarative crawl patterns through method overrides (start_requests, parse, parse_item) and lifecycle hooks (on_request_start, on_response_received, on_error). Spiders define crawl logic by overriding parse() to extract data and yield new requests, creating a declarative crawl graph. The framework handles request queuing, deduplication, and response routing automatically. Spiders integrate with sessions for connection pooling and support custom middleware for request/response transformation. The architecture follows Scrapy's proven Spider pattern but with Scrapling's unified Response interface.","intents":["Define crawl patterns declaratively by overriding parse() and yielding requests/items","Implement crawl logic with automatic request queuing and deduplication","Hook into request/response lifecycle for logging, error handling, or custom processing","Build multi-page crawls that follow links and extract data from each page","Reuse crawl logic across different target sites by subclassing Spider"],"best_for":["Teams building multi-page crawlers with complex navigation patterns","Developers familiar with Scrapy who want similar patterns with Scrapling's flexibility","Projects requiring declarative crawl definitions for maintainability","Crawlers that need lifecycle hooks for monitoring, error recovery, or custom processing"],"limitations":["Spider framework adds abstraction overhead (~50-100ms per request) compared to raw fetcher calls","Request deduplication requires in-memory set or external storage for distributed crawls","Lifecycle hooks are synchronous—async hooks require custom middleware implementation","No built-in distributed crawling support (requires external task queue like Celery)"],"requires":["Python 3.10+","Spider subclass definition","Fetcher or Session instance","Optional: custom middleware classes"],"input_types":["Spider class definition","Start URLs (list or generator)","Request objects (URL, method, headers, body)","Response objects"],"output_types":["Extracted items (dict or custom class)","New Request objects (for following links)","Crawl statistics and logs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-d4vinci--scrapling__cap_6","uri":"capability://automation.workflow.proxy.management.and.rotation.with.fallback.strategies","name":"proxy management and rotation with fallback strategies","description":"Implements proxy rotation middleware that distributes requests across a configured proxy pool (residential, datacenter, or custom proxies) with automatic fallback when proxies fail. Supports proxy authentication (username/password), per-request proxy selection, and rotation strategies (round-robin, random, weighted). Failed proxies are temporarily blacklisted and retried after a cooldown period. The system integrates with both HTTP fetchers (via httpx proxy config) and browser fetchers (via Playwright proxy settings). Proxy state is tracked across requests and can be persisted for resuming crawls.","intents":["Distribute requests across multiple proxies to avoid IP-based rate limiting","Automatically rotate proxies to spread load and evade detection","Handle proxy failures gracefully with fallback and retry logic","Use authenticated proxies (username/password) transparently","Track proxy performance and blacklist failing proxies temporarily"],"best_for":["Teams running large-scale crawls requiring IP rotation","Developers scraping sites with strict rate limiting","Projects using residential or datacenter proxy services","Crawlers that need to distribute load across multiple IPs"],"limitations":["Proxy rotation adds ~200-500ms latency per request (proxy connection overhead)","Proxy service costs scale with request volume (residential proxies ~$5-50 per GB)","Fallback strategies may cause request duplication if not carefully configured","Proxy blacklisting requires tuning cooldown period to avoid over-blacklisting"],"requires":["Python 3.10+","Proxy list (file or API endpoint)","Proxy service credentials (if authenticated)","httpx or Playwright configured with proxy support"],"input_types":["Proxy list (list of 'host:port' strings or dict with auth)","Rotation strategy (round-robin, random, weighted)","Fallback configuration (retry count, cooldown period)","Request object"],"output_types":["Proxy URL (with auth if applicable)","Proxy rotation state","Blacklist status","Response object"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-d4vinci--scrapling__cap_7","uri":"capability://automation.workflow.wait.strategies.and.page.load.detection.for.dynamic.content","name":"wait strategies and page load detection for dynamic content","description":"Provides configurable wait strategies for browser-based fetchers to handle dynamic content loading: wait for specific elements (CSS/XPath), wait for network idle, wait for JavaScript execution completion, or custom wait conditions. The system detects when a page has finished loading by monitoring network activity, DOM mutations, and JavaScript execution state. Wait strategies are composable (e.g., wait for element AND network idle) and can be applied per-request or per-session. Timeout handling ensures requests don't hang indefinitely on slow or broken pages.","intents":["Wait for dynamically loaded content (AJAX, lazy loading) before extracting data","Detect when a page has finished rendering before proceeding with parsing","Handle slow-loading pages with configurable timeouts","Wait for specific elements to appear before extracting data","Compose multiple wait conditions for complex page load scenarios"],"best_for":["Developers scraping single-page applications (SPAs) with dynamic content","Teams handling sites with lazy-loaded images or infinite scroll","Projects requiring reliable page load detection","Crawlers targeting pages with slow or unreliable JavaScript execution"],"limitations":["Wait strategies add 1-10s latency per request depending on page complexity","Network idle detection may be unreliable on pages with continuous background requests","Custom wait conditions require JavaScript knowledge to implement reliably","Timeout handling may miss content that loads after timeout period"],"requires":["Python 3.10+","Playwright browser fetcher","Page URL or HTML content","Optional: CSS/XPath selectors for element-based waits"],"input_types":["Wait strategy enum (NETWORK_IDLE, LOAD, DOMCONTENTLOADED, ELEMENT, CUSTOM)","CSS/XPath selector (for ELEMENT strategy)","Custom wait function (async callable)","Timeout duration (milliseconds)"],"output_types":["Response object with fully rendered HTML","Page load timing metrics","Wait strategy result (success/timeout)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-d4vinci--scrapling__cap_8","uri":"capability://tool.use.integration.mcp.server.integration.for.ai.native.scraping.workflows","name":"mcp server integration for ai-native scraping workflows","description":"Exposes Scrapling as a Model Context Protocol (MCP) server, allowing AI agents and LLMs to invoke scraping operations through a standardized tool interface. The MCP server wraps Scrapling's fetchers, spiders, and selectors as callable tools with schema-based function signatures. AI agents can compose scraping workflows by chaining tool calls (e.g., fetch URL → parse HTML → extract data → follow links). The server handles tool invocation, error handling, and response serialization transparently. Integration with Claude, ChatGPT, or custom LLM agents enables natural language scraping instructions to be translated into Scrapling operations.","intents":["Enable AI agents to invoke scraping operations through natural language instructions","Compose complex scraping workflows by chaining MCP tool calls","Expose Scrapling capabilities to LLMs without custom API development","Allow AI agents to dynamically select fetchers (HTTP, browser, stealth) based on page requirements","Integrate Scrapling into AI-powered data extraction pipelines"],"best_for":["Teams building AI agents that need web scraping capabilities","Developers integrating Scrapling with Claude, ChatGPT, or custom LLMs","Projects requiring natural language interfaces to scraping workflows","AI-powered data extraction and research tools"],"limitations":["MCP server adds network latency (~100-500ms per tool call) compared to direct library usage","LLM agents may make inefficient scraping decisions (e.g., using browser fetcher when HTTP would suffice)","Tool schema complexity may confuse LLMs, requiring careful prompt engineering","No built-in rate limiting or cost control for AI-driven scraping (can lead to excessive requests)"],"requires":["Python 3.10+","MCP server implementation (provided in server.json)","LLM client with MCP support (Claude, custom implementation)","Network connectivity between LLM and MCP server"],"input_types":["Tool name (string)","Tool arguments (JSON schema)","Natural language instructions (from LLM)"],"output_types":["Tool result (JSON)","Extracted data","Error messages","Scraping metadata (timing, fetcher used, etc.)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-d4vinci--scrapling__cap_9","uri":"capability://automation.workflow.concurrent.crawling.with.request.queuing.and.deduplication","name":"concurrent crawling with request queuing and deduplication","description":"Implements async-first architecture using Python asyncio for concurrent request handling, with built-in request queuing (FIFO or priority-based) and automatic URL deduplication using bloom filters or in-memory sets. The engine manages concurrent request limits (configurable per-domain or global) to respect rate limits and avoid overwhelming target servers. Failed requests are automatically retried with exponential backoff. The system tracks crawl statistics (requests sent, responses received, errors, deduplication hits) for monitoring and debugging. Distributed crawling is supported through external task queues (Celery, RQ) for multi-process/multi-machine scaling.","intents":["Run concurrent requests to multiple URLs without blocking","Automatically deduplicate URLs to avoid re-scraping the same page","Respect rate limits by controlling concurrent request count per domain","Retry failed requests with exponential backoff","Monitor crawl progress with real-time statistics","Scale crawls across multiple processes or machines"],"best_for":["Teams running large-scale crawls requiring concurrent request handling","Developers building distributed crawlers across multiple machines","Projects with strict rate limiting requirements","Crawlers that need to monitor progress and handle failures gracefully"],"limitations":["Concurrent requests may hit rate limits faster than sequential requests (requires tuning)","In-memory deduplication requires ~1 byte per unique URL (scales to millions of URLs)","Bloom filters reduce memory usage but introduce false positive deduplication","Distributed crawling requires external task queue (adds complexity and latency)","Exponential backoff may cause crawl delays on flaky networks"],"requires":["Python 3.10+","asyncio runtime","Optional: Celery or RQ for distributed crawling","Optional: Redis for distributed deduplication and task queue"],"input_types":["Request queue (list of URLs or Request objects)","Concurrency limit (int)","Rate limit configuration (requests per second per domain)","Retry configuration (max retries, backoff strategy)"],"output_types":["Response objects (streamed as available)","Crawl statistics (requests, responses, errors, deduplication hits)","Failed request list (for manual retry)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-d4vinci--scrapling__headline","uri":"capability://data.processing.analysis.adaptive.web.scraping.framework","name":"adaptive web scraping framework","description":"Scrapling is an adaptive web scraping framework for Python that efficiently handles everything from single HTTP requests to full-scale concurrent crawls, providing advanced features like browser automation and anti-bot stealth capabilities.","intents":["best web scraping framework","web scraping framework for Python","adaptive web scraping solutions","top tools for web data extraction","automated web scraping frameworks"],"best_for":["developers needing flexible scraping solutions","projects requiring stealthy web scraping"],"limitations":[],"requires":["Python 3.10+"],"input_types":["HTML","URLs"],"output_types":["data in various formats"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","Playwright for browser automation (optional, lazy-loaded)","httpx or requests library for HTTP fetching","BeautifulSoup4 or lxml for DOM parsing","Original selector definition (CSS or XPath)","Custom handler class or function","Response object from fetcher","Scrapling installed and in PATH","Terminal/shell access","Optional: psutil for resource monitoring"],"failure_modes":["Browser-based fetchers have higher latency (~2-5s per request) compared to HTTP fetchers (~100-500ms)","Lazy imports add minimal overhead on first access but require careful dependency management","Unified Response interface may abstract away fetcher-specific optimizations or capabilities","Fallback resolution adds ~50-200ms latency per failed selector","Structural similarity matching may incorrectly identify elements in highly dynamic pages","Requires sufficient DOM context to disambiguate elements (fails on minimal/identical structures)","Custom handlers add latency (~10-100ms per handler) depending on transformation complexity","Handler ordering matters—incorrect ordering can cause unexpected transformations","Async handlers require careful error handling to avoid blocking the crawl","No built-in handler composition utilities (requires manual chaining)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8067998737688082,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:56:56.344Z","last_commit":"2026-05-02T16:58:57Z"},"community":{"stars":42382,"forks":3848,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=d4vinci--scrapling","compare_url":"https://unfragile.ai/compare?artifact=d4vinci--scrapling"}},"signature":"5Ih9H++HUw6gFaKZk4y/7mhryQ7MIWkxap2/vyZEoJgMyecc5TQ8mRZW5A3yUSznC9kS6rP6i+BxucTLyeIRAQ==","signedAt":"2026-06-19T18:48:55.464Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/d4vinci--scrapling","artifact":"https://unfragile.ai/d4vinci--scrapling","verify":"https://unfragile.ai/api/v1/verify?slug=d4vinci--scrapling","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"}}