scraperapi-research-agent

name	scraperapi-research-agent
description	Autonomous web research agent — takes a research question, uses ScraperAPI to discover and scrape relevant sources, uploads content as file artifacts to the Anthropic Files API, then feeds everything to Claude for synthesis into a cited research report. All in one flow. Use when user asks: "research X for me and give me a cited report", "investigate Y online and summarize what you find", "do a deep dive on Z using real web sources", "find information about X across multiple websites and cite your sources", "run the scraperapi research agent on this topic". Produces a structured markdown report with inline citations and a numbered source list. Invoke whenever the user wants multi-source web research that requires scraping real pages, not just answering from memory. Note: Transmits user-supplied queries, URLs, and content to ScraperAPI; uploads scraped content to the Anthropic Files API.
metadata	{"openclaw":{"requires":{"env":["SCRAPERAPI_API_KEY","ANTHROPIC_API_KEY"]},"emoji":"🔬","homepage":"https://docs.scraperapi.com/"}}

ScraperAPI Research Agent

End-to-end autonomous research: ScraperAPI finds and fetches sources → Anthropic Files API ingests them as cited documents → Claude synthesizes a report.

Run it:

# Install dependencies
pip install requests anthropic

# Set env vars
export SCRAPERAPI_API_KEY=your-key
export ANTHROPIC_API_KEY=your-key

# Run
python skills/scraperapi-research-agent/scripts/research_agent.py \
  --question "What are the best practices for rate limiting in web APIs?" \
  --max-sources 5 \
  --output report.md

See scripts/research_agent.py for the full implementation.

---

Planning Checklist

Before starting a research run, establish:

• Question clarity — Is the question specific enough to produce useful search queries? Vague questions like "tell me about AI" produce noise. Better: "What are the tradeoffs between RAG and fine-tuning for domain-specific LLMs?"
• Source count — How many sources are needed? 3–5 is usually sufficient for a factual summary; 8–10 for a comparative analysis. More sources = more ScraperAPI credits.
• Recency — Does the answer depend on recent events? Search queries will use recent date filters.
• Credit budget — Each source costs ~1 credit to scrape (more with JS rendering). 5 sources = ~5–10 credits total.
• Stop condition — Define when to stop. The default stop is --max-sources (5). Do not loop indefinitely.

---

Research Loop

1. PLAN
   ↓ Claude decomposes the question into 2–3 targeted search queries

2. DISCOVER
   ↓ ScraperAPI google/search structured endpoint → list of (url, title, snippet)

3. DEDUPLICATE
   ↓ Filter to top N unique URLs (default: 5), skipping PDFs and low-quality domains

4. FETCH
   ↓ ScraperAPI scrape each URL as markdown (output_format=markdown)
   ↓ Skip pages returning < 200 characters (blocked, error pages)

5. UPLOAD
   ↓ Upload each scraped page to Anthropic Files API as a text/plain artifact
   ↓ Store file_id for each source

6. SYNTHESIZE
   ↓ Claude (claude-opus-4-8, adaptive thinking) reads all document artifacts
   ↓ Returns structured report with inline citations [1], [2]...

7. CLEAN UP
   ↓ Delete uploaded file artifacts from Anthropic
   ↓ Write or print the final report

STOP when: max_sources reached, or all queries exhausted (whichever comes first).

---

Stop Conditions

The agent stops when any of the following is true:

1. --max-sources reached (default: 5) — limits credit spend
2. All search queries exhausted — no more URLs to explore
3. --max-credits exceeded — hard cap on ScraperAPI credit use (optional)

Without stop conditions, a research loop will keep fetching until credits are gone.

---

Key Parameters

Flag	Default	Description
--question	(required)	Research question
--max-sources	5	Max pages to scrape (credit budget)
--output	stdout	Write report to file
--country	us	ScraperAPI country code for geo-targeted results
--model	claude-opus-4-8	Anthropic model for synthesis

---

Output Format

See assets/report_template.md for the report structure.

The report is a markdown document with:

• Title derived from the research question
• Summary — 2–3 sentence executive summary
• Findings — structured sections with inline [N] citations
• Sources — numbered bibliography with URLs and titles

---

Credit Cost Estimate

Sources	Scraping credits	Anthropic tokens	Total estimate
3	~3	~15K in / ~2K out	Low
5	~5	~25K in / ~3K out	Medium
10	~10	~50K in / ~5K out	Higher

Prompt caching applies to the scraped content on repeated runs for the same question.

---

ScraperAPI Endpoints Used

• Google Search — GET https://api.scraperapi.com/structured/google/search — finds source URLs
• Scrape — GET https://api.scraperapi.com/?output_format=markdown — fetches page content

See ScraperAPI docs for rate limits and credit costs.

Anthropic APIs Used

• Files API (beta) — uploads scraped pages as document artifacts
• Messages API — Claude synthesizes the report with citations

Requires ANTHROPIC_API_KEY with access to claude-opus-4-8 and the Files API beta.