webclaw

POST /v1/search

Web search your agent can act on.

Run a query, get structured results, and scrape every page in the same call.

Give your agent live web results it can reason over and act on, not a wall of HTML. Send a query, get back ranked titles, URLs, and snippets, and flip on scrape to pull clean, LLM-ready content from each result in one request.

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What you get

Everything in one call.

Structured results

Every match comes back as title, URL, snippet, and ranking position, ready to feed straight into your model.

Scrape in one call

Pass scrape and each result returns clean, LLM-ready content alongside its metadata, no second round trip.

Topic targeting

Pick general, news, or finance to shift ranking and source selection toward what your agent actually needs.

Tunable result count

Ask for anything from one result to a hundred so you only pull the depth your workflow can use.

How it works

From URL to output in four steps.

01

Send a query

POST a query string with an optional num and topic to point the search at the right scope.

02

We fetch results

Webclaw runs the search and ranks the matches into a clean, structured list.

03

Scrape on demand

Set scrape and each result is fetched and reduced to LLM-ready content, JS rendering and bot protection handled for you.

04

Results returned

You get a JSON array of titles, URLs, snippets, and positions your agent can act on immediately.

API

One request, structured back.

Search: retrieval augmented generation

  1. What is RAG? - Retrieval-Augmented Generation AI Explained - AWS RAG is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data s

  2. Retrieval-augmented generation - Wikipedia Retrieval-augmented generation (RAG) is a technique that enables large language models (LLMs) to retrieve and incorporate new information.

  3. What Is Retrieval-Augmented Generation aka RAG - NVIDIA Blog Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched ...

  4. What is Retrieval-Augmented Generation (RAG)? - Google Cloud Retrieval-augmented generation (RAG) combines LLMs with external knowledge bases to improve their outputs. Learn more with Google Cloud.

FAQ

Frequently asked questions.

Book a call

It is an endpoint that takes a plain query and returns ranked web results as structured JSON, with title, URL, snippet, and position for each match. Unlike a browser search page, the output is built for an LLM to read directly, and you can ask Webclaw to scrape each result into clean content in the same call.

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API docs