Olostep

Product Introduction

1. Definition: Olostep is a comprehensive web data infrastructure API platform, specifically engineered for AI and automation workflows. It falls under the technical categories of web scraping, web crawling, data extraction, and agentic search APIs.
2. Core Value Proposition: Olostep exists to provide scalable, reliable, and developer-friendly access to structured web data, eliminating the infrastructure burden of managing headless browsers, proxy networks, and CAPTCHA-solving systems. Its primary value is turning any URL into LLM-ready formats like Markdown, JSON, or structured data, powering AI agents, research, RAG (Retrieval-Augmented Generation), and real-time data pipelines.

Main Features

1. /scrapes: This endpoint allows users to extract clean content from any single URL. It handles JavaScript-rendered pages and can return data in multiple formats simultaneously, including raw HTML, clean Markdown, text, PDFs, screenshots, and structured JSON via integrated parsers or LLM extraction. It works by deploying managed browser instances and proxy rotation to ensure high success rates.
2. /crawls: This feature enables large-scale website crawling. Users define a start URL, maximum page limits, and URL inclusion/exclusion patterns using glob syntax. The system recursively discovers and extracts content from all matching subpages, providing the results for indexing, enrichment, or bulk data collection workflows.
3. /batches: Designed for massive concurrency, this endpoint processes up to 10,000 URLs in a single batch, with results typically delivered in 5-8 minutes. Users can run multiple batches in parallel to scale to millions of URLs. It integrates with parsers (like @olostep/google-search) for deterministic structured data extraction from each URL in the batch.
4. /answers: This is an agentic search feature. Users submit a natural language task and a desired JSON output schema. Olostep performs web searches, scrapes relevant sources, uses AI to synthesize an answer grounded in those sources, and returns the structured JSON. It will return NOT_FOUND if the information cannot be verified, ensuring data reliability.
5. /monitors: This feature allows for scheduled web monitoring. Users can set up recurring checks on specific URLs or based on natural language queries. The system detects changes, extracts deltas or structured insights, and sends alerts via email, SMS, or webhooks, enabling use cases like price tracking, news monitoring, and compliance checks.

Problems Solved

1. Pain Point: Building and maintaining in-house web scraping infrastructure is complex, costly, and brittle. Developers struggle with anti-bot measures (CAPTCHAs, fingerprinting), proxy management, JavaScript rendering, and scaling data extraction pipelines.
2. Target Audience: The primary users are Developers building AI agents and data pipelines, Data Scientists and Researchers requiring clean web data for analysis and LLM training, and Product Managers/Marketers needing competitive intelligence, lead enrichment, or market monitoring.
3. Use Cases: Essential scenarios include: powering AI research agents with real-time web data, building RAG systems with freshly crawled documentation, enriching CRM records with company data from the web, monitoring competitor pricing and product changes, and aggregating content from multiple news or review sites for analysis.

Unique Advantages

1. Differentiation: Unlike basic scraping APIs or DIY solutions, Olostep offers a unified platform combining search, scraping, crawling, and monitoring with a focus on AI-ready outputs. Compared to competitors, its batch processing concurrency (10k URLs in minutes) and integrated agentic search (/answers) are standout capabilities. It abstracts away all infrastructure complexity into a simple, object-oriented API.
2. Key Innovation: Olostep's core innovation is its design as "web data infrastructure for AI," treating AI as the primary user. This is embodied in features like LLM-optimized Markdown output, the /answers endpoint that returns verified JSON, and the ability to use natural language for creating monitors and searches. Its architecture is built for the scale and format requirements of modern AI workflows.

Frequently Asked Questions (FAQ)

1. How does Olostep handle websites with heavy JavaScript or anti-bot protection? Olostep uses a managed fleet of headless browsers with advanced fingerprinting and automatic proxy rotation to bypass common anti-bot measures and execute JavaScript, ensuring reliable data extraction from modern web applications.
2. What is the difference between Olostep's /scrapes and /answers endpoints? The /scrapes endpoint extracts raw content (HTML, Markdown) from a specific URL you provide. The /answers endpoint performs an agentic workflow: it takes a natural language question, searches the web for relevant sources, scrapes those pages, and uses AI to synthesize a structured, verified answer in your specified JSON format.
3. Can Olostep be used for large-scale data extraction projects, like crawling an entire domain? Yes, Olostep is built for scale. You can use the /crawls endpoint to recursively crawl a domain (controlling depth and URL patterns), and the /batches endpoint to process hundreds of thousands of URLs concurrently by running multiple batches in parallel, making it suitable for enterprise-level data aggregation.
4. How does Olostep ensure data quality and structure? Beyond clean HTML/Markdown, Olostep offers two paths: pre-built or custom "parsers" for deterministic extraction of structured data from known page layouts, and AI-powered LLM extraction within the /scrapes and /answers endpoints to pull specific fields from unstructured pages based on natural language instructions.
5. What are the main use cases for the /monitors feature? The /monitors feature is designed for change detection. Common use cases include monitoring competitor websites for price changes or new product announcements, tracking news articles or blog posts on specific topics, watching for regulatory updates, and receiving alerts when key information on a webpage is modified.