# Web Search API for AI Agents: Search, Scrape, and Reason

> Build a grounded research workflow with ReplyNodes: search the web, select sources, scrape usable context, and keep citations attached.

- Canonical URL: https://replynodes.com/blog/web-search-api-for-ai-agents/
- Published: 2026-09-15
- Updated: 2026-09-15
- Author: ReplyNodes Team
- Publisher: ReplyNodes (https://replynodes.com/)

Search results are not research context by themselves. A useful agent workflow searches for candidate sources, selects relevant results, retrieves the pages, and keeps source URLs attached to the final answer.

## Search first

```bash
export REPLYNODES_API_KEY='YOUR_REPLYNODES_API_KEY'
curl --get \
  -H "Authorization: Bearer ${REPLYNODES_API_KEY}" \
  --data-urlencode "text=web scraping API for AI agents" \
  --data-urlencode "limit=3" \
  https://api.replynodes.com/v1/web/search
```

The search operation supports declared parameters such as text, engines, language, region, date, site, limit, start, and cursor. Use only parameters in the current API contract.

## Then retrieve source context

Search responses can contain ads and organic records, so do not treat every result as an organic ranking. Inspect each result’s type, title, URL, snippet, domain, and engine. Select sources using the user’s intent and your own domain policy, then pass a selected URL to `webcontext/scrape`.

```bash
curl --get \
  -H "Authorization: Bearer ${REPLYNODES_API_KEY}" \
  --data-urlencode "url=https://example.com/source" \
  https://api.replynodes.com/v1/webcontext/scrape
```

## A verified run

A production query for “web scraping API for AI agents” returned HTTP `200` and structured search records. The response included both advertising and organic results, which is why the workflow labels result type before using a source. Never present a search response as proof that a page ranks in a specific position without controlling for engine, query, date, and result type.

## Keep the chain of evidence

Store the query, selected URL, retrieval request ID, and extracted passage together. Tell the model which text is source content and which instructions come from your application. Cite the source URL in the answer and re-fetch volatile facts when freshness matters.

Start with the [Search guide](https://docs.replynodes.com/docs/guides/search), [Web context guide](https://docs.replynodes.com/docs/guides/web), and [Quickstart](https://docs.replynodes.com/docs/quickstart).