Agent Scenario

Build a Research Agent

Combine web scraping, PDF parsing, and data extraction skills to build an autonomous research agent that gathers, processes, and structures information at scale.

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How it works

Discover

Scrape search engines, academic databases, and news sites

Parse

Extract structured data from PDFs, HTML, and documents

Analyze

Run NLP analysis, summarization, and entity extraction

Store

Output clean, structured JSON ready for your pipeline

Example: Academic paper research pipeline

research_agent.py

from claw0x import Client

client = Client(api_key="ck_live_...")

# 1. Search the web for recent papers
results = client.call("web-scraper-pro",
    url="https://arxiv.org/search/?query=LLM+agents")

# 2. Parse each PDF
for paper in results.data["items"][:5]:
    parsed = client.call("pdf-parser",
        url=paper["pdf_url"])

    # 3. Validate author emails
    for author in parsed.data["authors"]:
        valid = client.call("email-validator",
            email=author["email"])
        print(f"{author['name']}: {valid.data['is_valid']}")

Skills for research agents

Production-ready APIs your agent can call right now.

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