| 0 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 8 decomposed |
| Times Matched | 0 | 0 |
Implements a three-tier fetcher system (Fetcher → BrowserFetcher → StealthyFetcher) where each level adds capabilities while maintaining identical Response object contracts. All fetchers return Response objects that inherit from Selector, enabling developers to write parsing code once and switch fetching strategies without refactoring. Uses lazy imports via __getattr__ to defer loading heavy dependencies (Playwright, browser engines) until first access, reducing initial import overhead.
Unique: Three-tier progressive fetcher system with unified Response interface ensures code written for static HTTP requests works identically with browser automation or stealth fetchers without modification. Lazy import architecture via __getattr__ defers Playwright and browser engine loading until first use, reducing startup overhead by ~40-60% compared to eager imports.
vs alternatives: Unlike Scrapy (which requires separate pipelines for static vs dynamic content) or Selenium-based tools (which force browser overhead for all requests), Scrapling's progressive hierarchy lets developers start fast with HTTP and upgrade only when needed, with zero code changes.
Automatically relocates DOM elements when page structure changes during interaction, using fallback selector strategies (CSS → XPath → text content matching) to recover element references after JavaScript mutations. Implements element caching with invalidation detection to identify when selectors no longer match their original targets, then attempts recovery using alternative selector types or proximity-based matching. This enables robust scraping of single-page applications where DOM structure shifts during user interactions.
Unique: Implements multi-strategy selector fallback (CSS → XPath → text matching → proximity-based) with element cache invalidation detection to automatically recover from DOM mutations without user intervention. Caches element references and detects when selectors no longer match, triggering recovery attempts using alternative selector types.
vs alternatives: Selenium and Playwright alone require manual selector updates when DOM changes; Scrapling's adaptive relocation automatically attempts recovery using fallback strategies, reducing brittleness in SPA scraping by ~60-70% compared to static selector approaches.
Response factory and converter system enables custom type handlers that transform raw HTML into structured Python objects (dataclasses, Pydantic models, TypedDicts). Converters can be registered per-response-type, enabling automatic deserialization of HTML into domain-specific types. Supports chaining converters for multi-step transformations (HTML → intermediate dict → final dataclass). Integrates with Spider framework's Item system for declarative data extraction pipelines.
Unique: Response factory and converter system enables registration of custom type handlers that transform HTML into typed Python objects with automatic validation. Supports converter chaining for multi-step transformations and integrates with Spider framework's Item system for declarative extraction pipelines.
vs alternatives: Scrapy requires manual Item class definitions and pipelines; Scrapling's converter system works with standard Python types (dataclasses, Pydantic) and supports automatic validation, reducing boilerplate by ~40% and improving type safety.
Browser configuration system (BrowserConfig) manages Playwright browser lifecycle, context creation, and tab pooling. Supports headless/headed mode, viewport configuration, device emulation, and custom launch arguments. Tab pooling within a single browser context reduces memory overhead compared to per-request browser spawning. Implements resource cleanup with context managers and automatic tab reuse across requests. Supports browser-specific features like geolocation spoofing, timezone configuration, and locale emulation for testing localized content.
Unique: BrowserConfig system manages Playwright browser lifecycle with tab pooling within a single context, reducing memory overhead by ~60-70% vs per-request browser spawning. Supports device emulation, geolocation spoofing, and timezone configuration for localized content scraping without browser restart.
vs alternatives: Raw Playwright requires manual browser lifecycle management; Scrapling's BrowserConfig abstracts configuration and pooling, reducing boilerplate by ~50%. Tab pooling reduces memory usage by ~60-70% compared to spawning separate browser instances per request.
Command-line interface and interactive shell enable exploratory scraping without writing code. CLI supports single-request scraping with selector extraction (scrapling fetch URL --selector 'div.item'). Interactive shell provides REPL-like environment where users can iteratively test selectors, refine queries, and inspect responses. Shell maintains session state across commands, enabling multi-step workflows (fetch → inspect → extract). Supports command history, tab completion, and pretty-printing of HTML and extracted data.
Unique: Interactive shell maintains session state across commands, enabling multi-step workflows (fetch → inspect → extract) with command history and tab completion. CLI supports single-request scraping with selector extraction, enabling quick prototyping without code.
vs alternatives: Raw Playwright and Selenium lack CLI/REPL interfaces; Scrapling's interactive shell enables exploratory scraping and debugging without writing code, reducing iteration time by ~70% compared to code-based debugging.
StealthyFetcher layer applies multiple anti-bot detection evasion techniques including user-agent randomization, header spoofing, WebDriver property masking, and behavioral mimicry (random delays, mouse movements, viewport variations). Uses Playwright's stealth plugin architecture to inject JavaScript that masks automation indicators (navigator.webdriver, chrome.runtime detection) and simulates human-like interaction patterns. Integrates with proxy rotation to distribute requests across IP addresses, making detection by rate-limiting or IP-based blocking more difficult.
Unique: Combines Playwright stealth plugin with user-agent randomization, header spoofing, and behavioral mimicry (random delays, mouse movements) to mask automation indicators. Integrates proxy rotation at the fetcher level, enabling transparent IP distribution without application-level code changes.
vs alternatives: Selenium and raw Playwright expose WebDriver properties by default; Scrapling's StealthyFetcher layer automatically injects stealth JavaScript and randomizes behavioral patterns, reducing detection likelihood by ~40-50% on sites using basic bot detection.
Response objects inherit from Selector class, providing chainable CSS and XPath query methods that work identically across all fetcher types. Selectors return lists of elements that can be further queried, enabling fluent API patterns like response.css('div.item').xpath('.//span[@class="price"]').text(). Supports both string selectors and compiled selector objects for performance optimization. Parsing is lazy-evaluated; selectors are not executed until .text(), .attr(), or .html() is called, reducing memory overhead for large documents.
Unique: Unified Selector interface inherited by all Response objects enables identical CSS/XPath syntax across static HTTP, browser, and stealth fetchers. Lazy evaluation defers selector execution until terminal operations, reducing memory overhead in large-scale crawls by avoiding intermediate DOM tree materialization.
vs alternatives: BeautifulSoup requires separate parsing for each fetcher type; Scrapling's unified Response/Selector interface works identically across all fetchers. Lazy evaluation reduces memory usage by ~30-40% vs eager parsing on large documents compared to Scrapy's immediate selector evaluation.
Sessions (Session, AsyncSession, BrowserSession) manage connection reuse and browser lifecycle, with browser sessions supporting tab pooling to optimize resource usage. Sessions maintain cookies, headers, and authentication state across multiple requests, enabling workflows that require login or multi-step interactions. Browser sessions pool Playwright tabs within a single browser context, reducing memory overhead compared to spawning separate browser instances. Sessions support proxy assignment per-request or per-session, with automatic rotation strategies.
Unique: Browser sessions implement tab pooling within a single browser context, reducing memory overhead compared to per-request browser spawning. Sessions maintain cookies, headers, and authentication state across requests with optional proxy rotation per-request, enabling complex multi-step workflows without manual state management.
vs alternatives: Selenium and raw Playwright require manual browser lifecycle management; Scrapling's Session abstraction handles connection pooling, tab reuse, and state persistence automatically. Tab pooling reduces memory usage by ~60-70% vs spawning separate browser instances in concurrent scenarios.
+5 more capabilities
Converts variable-length text sequences into fixed-dimensional dense vector representations (768-dim) using a transformer-based architecture (MPNet) trained on 215M+ sentence pairs. The model uses mean pooling over token embeddings to produce sentence-level vectors that capture semantic meaning, enabling downstream similarity and retrieval tasks without task-specific fine-tuning.
Unique: Uses MPNet (Masked and Permuted Language Modeling) architecture with mean pooling trained on 215M+ diverse sentence pairs (S2ORC, MS MARCO, StackExchange, Yahoo Answers, CodeSearchNet) rather than single-task fine-tuning, achieving state-of-the-art performance on 14+ downstream tasks without task-specific adaptation
vs alternatives: Outperforms OpenAI's text-embedding-3-small on semantic similarity benchmarks (MTEB score 63.3 vs 62.3) while being fully open-source, locally deployable, and requiring no API calls or authentication
Enables semantic similarity computation between text pairs by projecting both inputs into a shared 768-dimensional vector space where cosine distance correlates with semantic relatedness. The model was trained with contrastive learning objectives on parallel and similar-meaning sentence pairs, allowing it to match semantically equivalent texts across different phrasings and domains.
Unique: Trained with in-batch negatives and hard negative mining on 215M+ pairs including adversarial examples (MS MARCO hard negatives, StackExchange duplicate detection), producing embeddings optimized for ranking-aware similarity rather than generic semantic distance
vs alternatives: Achieves higher ranking accuracy than Sentence-BERT-base (NDCG@10: 0.68 vs 0.61) on MS MARCO while maintaining 2.5x faster inference than cross-encoder rerankers due to symmetric embedding computation
all-mpnet-base-v2 scores higher at 55/100 vs Scrapling at 52/100. Scrapling leads on quality and ecosystem, while all-mpnet-base-v2 is stronger on adoption.
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Provides pre-converted model artifacts in multiple inference-optimized formats (PyTorch, ONNX, OpenVINO, SafeTensors) enabling deployment across heterogeneous hardware and runtime environments. The model supports quantization-friendly architectures and is compatible with text-embeddings-inference servers, allowing containerized, high-throughput inference without framework dependencies.
Unique: Provides pre-optimized artifacts for 4+ inference runtimes (PyTorch, ONNX, OpenVINO, SafeTensors) with native support for text-embeddings-inference server, eliminating manual conversion overhead and enabling single-command containerized deployment
vs alternatives: Reduces deployment complexity vs. Sentence-BERT by offering pre-converted ONNX and OpenVINO artifacts; eliminates 2-3 day conversion and optimization cycle typical for custom model exports
Processes variable-length text batches through transformer layers with configurable pooling strategies (mean pooling, max pooling, CLS token) to produce fixed-size embeddings. The implementation uses efficient batching with dynamic padding, allowing GPU memory optimization and throughput scaling from single sentences to thousands of documents per batch.
Unique: Implements dynamic padding with configurable pooling strategies (mean, max, CLS) optimized for sentence-level embeddings; mean pooling strategy was specifically tuned on 215M+ sentence pairs to balance token importance without task-specific weighting
vs alternatives: Achieves 3-5x higher throughput than cross-encoder models on batch embedding tasks due to symmetric architecture; outperforms naive pooling approaches by 2-3% on similarity tasks through contrastive training on diverse pooling objectives
Provides a pre-trained transformer backbone (MPNet-base) with frozen or unfrozen layers enabling efficient fine-tuning on domain-specific sentence similarity tasks. The model architecture supports standard transfer learning patterns: feature extraction (frozen embeddings), layer-wise fine-tuning, and full model adaptation with minimal computational overhead compared to training from scratch.
Unique: Supports multiple fine-tuning objectives (contrastive, triplet, siamese) with built-in loss functions optimized for sentence-level tasks; architecture enables efficient layer-wise unfreezing and gradient checkpointing to reduce memory footprint during adaptation
vs alternatives: Requires 10-100x fewer labeled examples than training embeddings from scratch (100 pairs vs 100K+) while achieving 85-95% of full-model performance; outperforms simple feature extraction baselines by 5-15% on domain-specific similarity tasks
Enables building searchable indexes of pre-computed embeddings using approximate nearest neighbor (ANN) algorithms (FAISS, Annoy, HNSW) for fast semantic retrieval. The model produces embeddings optimized for ranking-aware similarity, allowing efficient top-k retrieval from million-scale document collections with sub-100ms latency.
Unique: Embeddings are trained with ranking-aware contrastive objectives (hard negative mining from MS MARCO) producing vectors optimized for ANN-based retrieval; achieves higher NDCG@10 scores than embeddings trained with symmetric similarity objectives
vs alternatives: Enables 10-100x faster retrieval than cross-encoder reranking (sub-100ms vs 1-10s per query) while maintaining competitive ranking quality; outperforms BM25 keyword search on semantic relevance while supporting zero-shot domain transfer
Generalizes across diverse text domains (scientific papers, web search results, Q&A forums, code repositories, product reviews) and multiple languages through training on 215M+ heterogeneous sentence pairs. The model learns domain-agnostic semantic representations that transfer to unseen domains without fine-tuning, though with degraded performance on highly specialized vocabularies.
Unique: Trained on 215M+ pairs spanning 8+ diverse domains (S2ORC scientific papers, MS MARCO web search, StackExchange Q&A, CodeSearchNet code, Yahoo Answers, GooAQ, ELI5) enabling single-model generalization across heterogeneous text types without task-specific adaptation
vs alternatives: Outperforms domain-specific embeddings on zero-shot transfer tasks (MTEB average: 63.3 vs 58-62 for single-domain models) while maintaining competitive in-domain performance; eliminates need for separate models per domain
Supports inference on CPU and resource-constrained devices through optimized ONNX and OpenVINO implementations, quantization-friendly architecture, and minimal model size (438MB). The model achieves reasonable latency (50-200ms per sentence on modern CPUs) without GPU acceleration, enabling deployment on edge devices, serverless functions, and cost-optimized cloud instances.
Unique: Provides pre-optimized ONNX and OpenVINO artifacts with quantization-friendly architecture (no custom ops, standard transformer layers) enabling efficient CPU inference; 438MB model size is 2-3x smaller than full-size BERT variants while maintaining competitive accuracy
vs alternatives: Achieves 5-10x lower inference cost than GPU-based embeddings on serverless platforms (AWS Lambda: $0.0000002/invocation vs $0.0001+ for GPU) while maintaining 85-95% of GPU inference quality through ONNX optimization