Fresh search context for AI agents and LLM grounding

OpenSERP is useful when an AI workflow needs live web search context rather than a static corpus. Search results, snippets, ranks, and extracted content combine into a context set with source URLs you can trace back.

  • Use search results as fresh evidence for prompts and agents.
  • Extract source pages when snippets are not enough.
  • Keep source URLs and ranks visible for attribution and review.
OpenSERP Cloud Read the docs
Search, then extract
GET /v1/google/search?text=latest+go+release&limit=5
Response
{
  "results": [
    { "rank": 1, "url": "https://go.dev/doc/devel/release",
      "title": "Release History", "snippet": "Go 1.x was released on..." }
  ]
}
// then GET /v1/extract?url=https://go.dev/doc/devel/release&format=markdown
// → markdown your agent can cite back to the source URL

Why search matters for grounding

Search APIs help agents discover recent pages, compare sources, and keep answers tied to URLs that a reviewer can inspect.

Where OpenSERP fits

OpenSERP is a SERP-first option: it exposes search engine results and extraction primitives instead of hiding ranking signals behind an opaque answer API.

Frequently asked questions

How is this different from an answer API?

Answer APIs return a synthesized response. OpenSERP returns the underlying search results — ranks, URLs, snippets — so your agent chooses sources and keeps provenance for review.

Can I keep citations traceable?

Yes. Every result carries its source URL, rank, and engine, so answers built from OpenSERP context can link back to the exact pages used.

Start free, keep the Cloud path open.

Use the self-hosted API today, then move hosted workflows to OpenSERP Cloud when operations become the bottleneck.

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