/agent by Firecrawl

Product Introduction

1. Definition: Firecrawl /agent is an AI-powered web scraping API endpoint designed for complex data extraction tasks. It falls into the technical category of autonomous web agents or intelligent data extraction APIs. Unlike traditional scrapers requiring specific URLs and selectors, /agent interprets natural language prompts to navigate websites, interact with dynamic content, and gather structured data.
2. Core Value Proposition: /agent exists to automate the extraction of specific, often hard-to-reach data points or entire datasets from the modern web at scale. Its primary value lies in eliminating the need for manual URL discovery, complex navigation logic, and constant maintenance against site changes, making sophisticated web data extraction accessible via a simple API call. Keywords: AI web scraping, autonomous data extraction, natural language data extraction, structured web data API, scale web scraping.

Main Features

1. Natural Language Prompt Processing: Users describe the desired data in plain English (e.g., "Get all YC W24 companies with founders" or "Get all Nike Air Jordan listings with prices"). The /agent endpoint's AI interprets this prompt, determines the necessary search queries, website navigation paths, and data extraction logic. It leverages large language models (LLMs) and proprietary navigation algorithms to understand intent and execute the task.
2. Autonomous Website Navigation & Interaction: /agent dynamically interacts with websites like a human user. It handles complex elements essential for modern web scraping: JavaScript rendering, pagination, infinite scroll, login walls (if credentials provided via API), pop-ups, and multi-step navigation flows. This allows it to access data on Single Page Applications (SPAs) and other dynamic sites where traditional static scrapers fail. Keywords: dynamic website scraping, JavaScript rendering scraping, handle pagination API, interactive web agent.
3. Structured Data Output (Schema-Driven): Users can define an expected output schema using Pydantic models (Python) or JSON Schema. /agent extracts the relevant data points identified during its navigation and returns them in this highly structured format (JSON). This ensures consistency, eliminates post-processing, and integrates seamlessly into data pipelines. It handles both single datapoints and large-scale datasets. Keywords: structured JSON output, schema-based extraction, Pydantic web scraping, dataset creation API.

Problems Solved

1. Pain Point: Manually discovering URLs, writing and maintaining complex, site-specific scraping scripts (especially for JavaScript-heavy or frequently changing sites), and handling navigation logic is time-consuming, brittle, and requires significant technical expertise. /agent automates this entire workflow.
2. Target Audience: Data Scientists needing specific datasets; Growth/Marketing Teams performing lead generation or competitive intelligence; E-commerce Analysts tracking product listings/prices; Researchers aggregating data from publications; Developers building data-driven applications without deep scraping expertise; Product Managers curating datasets for AI training.
3. Use Cases: Extracting company data (founders, funding) from directories like Crunchbase; Scraping product listings (title, price, SKU) from e-commerce sites like Nike.com; Aggregating news headlines and comments from forums like Hacker News; Building datasets of academic papers from arXiv; Monitoring real estate listings meeting specific criteria; Gathering market data (stock prices, market caps) from financial sites.

Unique Advantages

1. Differentiation: Unlike traditional scraping tools (e.g., BeautifulSoup, Scrapy) requiring explicit URL lists and selector definitions, or simpler extraction APIs needing pre-crawled URLs, /agent starts with a goal and handles discovery and navigation autonomously. It surpasses basic "extract text from this URL" services by performing multi-step tasks. Competitors often require more manual setup or lack the deep navigation intelligence.
2. Key Innovation: The core innovation is the integration of advanced LLMs for prompt understanding and goal decomposition with a robust, autonomous web navigation engine capable of executing complex interactions on real websites. This combination of semantic understanding and dynamic browser-level interaction allows /agent to tackle extraction tasks previously requiring custom, fragile scripts or manual effort. Keywords: LLM-powered web agent, autonomous web navigation API, goal-driven data extraction.

Frequently Asked Questions (FAQ)

1. How much does Firecrawl /agent cost? During its Research Preview, /agent offers 5 free daily runs. Beyond that, pricing is dynamic and credit-based, tied to the complexity of the query (e.g., number of sites navigated, depth of interaction, data volume). Simpler queries use fewer credits; complex ones use more. Users can monitor credit usage in real-time and set maxCredits parameters to control costs.
2. Can Firecrawl /agent extract data from websites requiring login or with heavy JavaScript? Yes, /agent is specifically designed to handle modern, complex websites. It executes JavaScript fully, rendering pages like a real browser. For sites behind login walls, users can provide authentication credentials (cookies, session tokens) via the API to enable access to protected data.
3. Do I need to provide specific URLs to use the /agent API? No, providing specific URLs is optional. The core value of /agent is its ability to start with a natural language prompt (e.g., "Get top 3 Hacker News stories today") and autonomously find the relevant sources and pages. You can provide a starting URL if you know the specific site, but discovery is handled automatically.
4. What output formats does Firecrawl /agent support? /agent primarily returns structured data in JSON format. Users can define the exact structure of this JSON output using a schema (like Pydantic models in Python or JSON Schema), ensuring the extracted data matches their application's needs precisely. Raw HTML or plain text extraction is not the primary focus of the /agent endpoint.
5. How is Firecrawl /agent different from the /extract endpoint? The /extract endpoint requires a specific URL and focuses on extracting content (text, markdown, structured data via LLM) from that single page. /agent is goal-oriented: you describe what data you want (not where it is), and it handles searching, navigating across potentially multiple pages or sites, interacting with elements, and then extracting the specific data points, returning them in your defined schema. /extract is for a known page; /agent finds and extracts data based on intent.