{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-47526486","slug":"robust-llm-extractor-for-websites-in-typescript","name":"Robust LLM extractor for websites in TypeScript","type":"repo","url":"https://github.com/lightfeed/extractor","page_url":"https://unfragile.ai/robust-llm-extractor-for-websites-in-typescript","categories":["data-pipelines"],"tags":["hackernews","show-hn"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-47526486__cap_0","uri":"capability://data.processing.analysis.llm.powered.structured.data.extraction.from.html","name":"llm-powered structured data extraction from html","description":"Extracts structured data from website HTML by leveraging LLM reasoning to understand semantic content and convert unstructured markup into typed JSON schemas. Uses prompt engineering and schema validation to guide LLM output toward consistent, machine-readable formats without requiring manual parsing rules or CSS selectors.","intents":["Extract product listings, prices, and metadata from e-commerce sites without writing CSS selectors","Convert unstructured website content into structured JSON matching a predefined schema","Build web scrapers that adapt to HTML layout changes without code modifications","Populate databases from websites by defining target data schemas instead of parsing rules"],"best_for":["developers building web scraping tools who want to avoid brittle CSS selector maintenance","teams extracting data from multiple websites with varying HTML structures","rapid prototyping of data extraction pipelines without writing custom parsers"],"limitations":["LLM inference latency adds 1-5 seconds per page extraction depending on model and content size","Requires API calls to external LLM providers (OpenAI, Anthropic, etc.), incurring per-request costs","LLM hallucination risk — may invent data fields not present in HTML if schema is ambiguous","No built-in handling of JavaScript-rendered content; requires pre-rendered HTML or separate browser automation","Context window limits may truncate large HTML documents, requiring chunking strategies"],"requires":["TypeScript 4.5+","Node.js 16+","API key for at least one LLM provider (OpenAI, Anthropic, or compatible)","HTML content as string input (from fetch, cheerio, or browser automation)"],"input_types":["HTML string","JSON schema definition","LLM provider configuration"],"output_types":["JSON object matching provided schema","Validation errors if extraction fails schema constraints"],"categories":["data-processing-analysis","web-scraping"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47526486__cap_1","uri":"capability://data.processing.analysis.schema.based.output.validation.and.type.coercion","name":"schema-based output validation and type coercion","description":"Validates LLM-extracted data against a provided JSON schema and automatically coerces types (string to number, date parsing, enum matching) to ensure output conforms to expected structure. Implements schema validation logic that catches hallucinations or malformed LLM responses before returning to user code.","intents":["Ensure extracted data matches expected types before inserting into database","Automatically convert string prices to numbers or date strings to ISO format","Catch LLM errors early by validating against schema constraints","Enforce required fields and reject partial extractions"],"best_for":["production data pipelines requiring data quality guarantees","teams building ETL workflows where schema compliance is critical","developers who want fail-fast validation before downstream processing"],"limitations":["Schema validation adds latency proportional to schema complexity","Type coercion heuristics may fail on ambiguous formats (e.g., '01/02/2024' could be MM/DD or DD/MM)","Does not handle deeply nested or recursive schema structures efficiently","No custom validation rule support — limited to JSON Schema standard constraints"],"requires":["JSON Schema definition provided by user","TypeScript 4.5+ for type inference from schema"],"input_types":["JSON object from LLM extraction","JSON Schema definition"],"output_types":["Validated and coerced JSON object","Validation error report with field-level details"],"categories":["data-processing-analysis","validation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47526486__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.layer","name":"multi-provider llm abstraction layer","description":"Abstracts differences between LLM providers (OpenAI, Anthropic, Ollama, etc.) behind a unified interface, allowing users to swap providers or use multiple models without changing extraction logic. Handles provider-specific API differences, token counting, and model-specific prompt formatting transparently.","intents":["Switch between OpenAI GPT-4 and Anthropic Claude without rewriting extraction code","Use local Ollama models to avoid cloud API costs while maintaining same extraction logic","Compare extraction quality across multiple models by running same extraction on different providers","Implement fallback logic (e.g., try GPT-4, fall back to Claude on rate limit)"],"best_for":["teams evaluating multiple LLM providers for cost/quality tradeoffs","developers building multi-model extraction systems","organizations with on-premise LLM requirements (Ollama, local models)"],"limitations":["Abstraction layer adds ~50-100ms overhead per request due to adapter translation","Not all provider features are exposed — advanced features (vision, function calling) may require provider-specific code","Token counting estimates vary by provider; actual costs may differ from estimates","Prompt formatting differences between models may produce inconsistent extraction quality"],"requires":["API keys for at least one supported LLM provider","Provider-specific SDK or HTTP client (handled by framework)"],"input_types":["Provider configuration object","Model name string","Extraction prompt and HTML content"],"output_types":["Unified response object with extracted data","Provider-agnostic error messages"],"categories":["tool-use-integration","llm-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47526486__cap_3","uri":"capability://automation.workflow.batch.extraction.with.concurrency.control","name":"batch extraction with concurrency control","description":"Processes multiple URLs or HTML documents in parallel with configurable concurrency limits, managing rate limits and API quota to avoid throttling. Implements queue-based batching with retry logic, allowing extraction of hundreds of pages without manual rate-limit handling or request throttling.","intents":["Extract data from 100+ product pages without hitting API rate limits","Process multiple websites concurrently while respecting per-provider quota","Implement exponential backoff and retry logic for failed extractions automatically","Monitor extraction progress and handle partial failures gracefully"],"best_for":["large-scale web scraping projects extracting data from hundreds of pages","teams building data collection pipelines with strict rate-limit budgets","developers who need automatic retry and backoff without manual implementation"],"limitations":["Concurrency limits must be tuned per provider to avoid rate limiting; no automatic detection","Memory usage scales with batch size — large batches may exhaust heap on resource-constrained environments","No built-in persistence of extraction state — failed batches require manual restart or external state tracking","Retry logic is generic; provider-specific error codes may not be handled optimally"],"requires":["Array of URLs or HTML documents","Concurrency limit configuration (typically 5-20 depending on provider)","Optional: retry policy configuration"],"input_types":["Array of HTML strings or URLs","Extraction schema","Concurrency configuration object"],"output_types":["Array of extracted JSON objects","Error report with per-item failure reasons"],"categories":["automation-workflow","batch-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47526486__cap_4","uri":"capability://text.generation.language.prompt.engineering.and.context.optimization","name":"prompt engineering and context optimization","description":"Automatically constructs and optimizes prompts for LLM extraction by injecting schema definitions, examples, and HTML context in a structured format. Implements prompt templates that guide the LLM toward consistent extraction behavior and reduce hallucination through few-shot examples and explicit instructions.","intents":["Generate extraction prompts automatically from schema definitions without manual prompt writing","Inject few-shot examples to improve extraction accuracy for specific website patterns","Optimize prompt length to stay within token limits while preserving extraction quality","Customize extraction behavior (strict vs. lenient, required fields, default values) via prompt configuration"],"best_for":["developers without prompt engineering expertise who want good extraction quality out-of-the-box","teams tuning extraction accuracy for specific website types","rapid prototyping where manual prompt optimization is too slow"],"limitations":["Automatic prompt generation may not match hand-crafted prompts optimized for specific domains","Few-shot example injection increases token usage and latency proportionally to example count","No A/B testing framework for comparing prompt variants — requires manual experimentation","Prompt optimization is heuristic-based; no guarantee of optimal token efficiency"],"requires":["JSON schema definition","Optional: few-shot examples in JSON format","HTML content to extract from"],"input_types":["Schema definition","Example extractions (optional)","HTML content","Prompt configuration object"],"output_types":["Optimized prompt string","Token count estimate"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47526486__cap_5","uri":"capability://automation.workflow.error.recovery.and.fallback.strategies","name":"error recovery and fallback strategies","description":"Implements intelligent fallback mechanisms when extraction fails, including retry with different models, simplified schema extraction, or manual review workflows. Detects extraction failures (schema validation errors, LLM refusals, timeouts) and applies recovery strategies without user intervention.","intents":["Automatically retry failed extractions with a different model if primary extraction fails","Fall back to partial extraction (extract available fields only) when full schema extraction fails","Flag ambiguous or low-confidence extractions for manual review instead of returning potentially incorrect data","Implement graceful degradation for rate-limited or temporarily unavailable providers"],"best_for":["production extraction pipelines requiring high reliability and minimal manual intervention","teams with quality requirements that demand human review of uncertain extractions","large-scale scraping where some failures are inevitable and must be handled gracefully"],"limitations":["Fallback strategies increase total extraction cost (multiple model calls, manual review overhead)","No built-in integration with human review systems — requires custom implementation for manual workflows","Fallback effectiveness depends on strategy configuration; poor configuration may mask real errors","Confidence scoring is heuristic-based and may not correlate with actual extraction accuracy"],"requires":["Fallback strategy configuration (retry models, partial extraction rules, review thresholds)","Optional: integration with manual review system or webhook for flagged items"],"input_types":["Extraction request","Fallback strategy configuration","Optional: manual review endpoint"],"output_types":["Extracted data with confidence score","Fallback indicator (which strategy was used)","Manual review flag if applicable"],"categories":["automation-workflow","error-handling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47526486__cap_6","uri":"capability://data.processing.analysis.html.preprocessing.and.content.normalization","name":"html preprocessing and content normalization","description":"Cleans and normalizes HTML before LLM extraction by removing noise (scripts, styles, ads, tracking), extracting main content, and normalizing whitespace and encoding. Uses heuristics or DOM analysis to identify and preserve semantically important content while reducing token usage and improving extraction accuracy.","intents":["Remove boilerplate HTML (navigation, ads, tracking) to reduce token usage and improve extraction focus","Extract main article content from news sites without manual DOM selection","Normalize HTML encoding and whitespace to prevent LLM confusion from malformed markup","Reduce HTML size by 50-80% before sending to LLM, lowering API costs"],"best_for":["large-scale scraping where token costs are significant","extraction from noisy websites with heavy advertising or tracking","teams building extraction pipelines that need consistent preprocessing"],"limitations":["Content extraction heuristics may remove important data on non-standard website layouts","No semantic understanding of content importance — may remove relevant sidebars or related content","Preprocessing adds 100-500ms latency per page depending on HTML size","Requires tuning of heuristics per website type; one-size-fits-all approach may fail on custom layouts"],"requires":["HTML string input","Optional: preprocessing configuration (content extraction rules, whitelist/blacklist selectors)"],"input_types":["HTML string","Preprocessing configuration object"],"output_types":["Cleaned HTML string","Token count reduction estimate"],"categories":["data-processing-analysis","text-preprocessing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47526486__cap_7","uri":"capability://memory.knowledge.extraction.result.caching.and.deduplication","name":"extraction result caching and deduplication","description":"Caches extraction results by URL or content hash to avoid redundant LLM calls for identical or previously-extracted content. Implements configurable cache backends (in-memory, Redis, file-based) and deduplication logic to detect when the same content has been extracted before.","intents":["Avoid re-extracting the same product page if it's already in cache","Detect duplicate content across different URLs and reuse cached extractions","Reduce API costs by caching results from expensive model calls","Build incremental extraction pipelines that skip already-processed content"],"best_for":["recurring extraction jobs that process overlapping content sets","cost-sensitive scraping where API fees are a major concern","teams building incremental data collection pipelines"],"limitations":["Cache invalidation is manual — no automatic detection of content updates","In-memory caching is limited by available RAM; large-scale scraping requires external cache backend","Content hashing adds latency; hash collisions could cause incorrect cache hits","No built-in cache warming or preloading — cache effectiveness depends on access patterns"],"requires":["Cache backend configuration (in-memory, Redis, or file path)","Optional: cache TTL and eviction policy"],"input_types":["URL or content hash","Extraction request"],"output_types":["Cached extraction result if available, or new extraction result","Cache hit/miss indicator"],"categories":["memory-knowledge","caching"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47526486__cap_8","uri":"capability://automation.workflow.extraction.quality.metrics.and.observability","name":"extraction quality metrics and observability","description":"Tracks extraction quality metrics (success rate, schema compliance, confidence scores, latency) and provides observability into extraction pipeline behavior. Emits structured logs and metrics that integrate with monitoring systems to detect extraction degradation or anomalies.","intents":["Monitor extraction success rate and detect when a website's HTML structure changes","Track extraction latency and cost per item to optimize provider selection","Alert when extraction quality drops below threshold (e.g., schema compliance < 95%)","Debug extraction failures by analyzing logs and metrics for specific URLs or patterns"],"best_for":["production extraction pipelines requiring operational visibility","teams monitoring extraction quality across multiple websites","developers troubleshooting extraction failures and performance issues"],"limitations":["Metrics collection adds overhead (~10-20ms per extraction) that impacts latency","No built-in alerting — requires integration with external monitoring systems (Datadog, Prometheus, etc.)","Metrics are aggregated; per-item debugging requires detailed logs which increase storage costs","Quality metrics are heuristic-based and may not capture all failure modes"],"requires":["Monitoring system integration (Datadog, Prometheus, CloudWatch, or custom HTTP endpoint)","Optional: alerting configuration"],"input_types":["Extraction request and result","Monitoring configuration"],"output_types":["Structured metrics (success rate, latency, cost)","Detailed logs with extraction context"],"categories":["automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47526486__cap_9","uri":"capability://data.processing.analysis.website.specific.extraction.templates.and.adapters","name":"website-specific extraction templates and adapters","description":"Provides pre-built extraction templates and adapters for common websites (e-commerce, news, social media) that optimize prompts, schemas, and preprocessing for known website patterns. Allows users to select a template instead of defining extraction logic from scratch, with customization options for site-specific variations.","intents":["Extract product data from Amazon, eBay, or Shopify without writing custom extraction logic","Parse news articles from major news sites with consistent schema","Extract social media profiles or posts with minimal configuration","Quickly prototype extraction for new websites by adapting existing templates"],"best_for":["developers building extraction pipelines for common website types","non-technical users who want extraction without writing code","rapid prototyping where time-to-extraction is critical"],"limitations":["Templates are generic and may not handle site-specific variations or layout changes","Customizing templates requires understanding the underlying extraction logic","Template maintenance burden — popular sites frequently change HTML structure, requiring template updates","Limited to pre-built templates; custom websites still require manual extraction logic"],"requires":["Website type or template name","Optional: customization configuration for site-specific variations"],"input_types":["Website type identifier","HTML content","Optional: customization parameters"],"output_types":["Extracted data matching template schema","Template metadata (last updated, success rate)"],"categories":["data-processing-analysis","templates"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"low","permissions":["TypeScript 4.5+","Node.js 16+","API key for at least one LLM provider (OpenAI, Anthropic, or compatible)","HTML content as string input (from fetch, cheerio, or browser automation)","JSON Schema definition provided by user","TypeScript 4.5+ for type inference from schema","API keys for at least one supported LLM provider","Provider-specific SDK or HTTP client (handled by framework)","Array of URLs or HTML documents","Concurrency limit configuration (typically 5-20 depending on provider)"],"failure_modes":["LLM inference latency adds 1-5 seconds per page extraction depending on model and content size","Requires API calls to external LLM providers (OpenAI, Anthropic, etc.), incurring per-request costs","LLM hallucination risk — may invent data fields not present in HTML if schema is ambiguous","No built-in handling of JavaScript-rendered content; requires pre-rendered HTML or separate browser automation","Context window limits may truncate large HTML documents, requiring chunking strategies","Schema validation adds latency proportional to schema complexity","Type coercion heuristics may fail on ambiguous formats (e.g., '01/02/2024' could be MM/DD or DD/MM)","Does not handle deeply nested or recursive schema structures efficiently","No custom validation rule support — limited to JSON Schema standard constraints","Abstraction layer adds ~50-100ms overhead per request due to adapter translation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.58,"quality":0.3,"ecosystem":0.46,"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":"2026-06-17T09:51:04.692Z","last_scraped_at":"2026-05-04T08:09:56.918Z","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=robust-llm-extractor-for-websites-in-typescript","compare_url":"https://unfragile.ai/compare?artifact=robust-llm-extractor-for-websites-in-typescript"}},"signature":"cnz+stZEcFzF/iFmnC1XvXlmZm87yKVQNkLfFvMQ9fsWAxuFvF6yeTQ5H5I/+OctbtJKckoSddP1SBrvCNm5BQ==","signedAt":"2026-06-20T03:05:02.043Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/robust-llm-extractor-for-websites-in-typescript","artifact":"https://unfragile.ai/robust-llm-extractor-for-websites-in-typescript","verify":"https://unfragile.ai/api/v1/verify?slug=robust-llm-extractor-for-websites-in-typescript","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"}}