Use Case

Search Data API for AI Agents and LLM Tools

AI agents work better when search context arrives in a stable format. Raw pages force each workflow to normalize titles, links, snippets, and result modules before the agent can reason over them.

OrbitScraper returns structured search data and optional markdown output so agent pipelines can move directly into retrieval, summarization, ranking, or execution logic.

Where this fits

  • Research agents that gather live search context
  • Copilots that enrich internal answers with current results
  • Automation tools that route search data into downstream actions
  • LLM workflows that need prompt-ready markdown summaries

Why teams use it

Agents already handle orchestration. They should not also own result-page normalization. A stable API response keeps prompts smaller, tool outputs more predictable, and downstream reasoning easier to test.

Output formats

  • structured organic results for retrieval pipelines
  • local and SERP module data for agent reasoning
  • markdown summaries for prompt-ready consumption
  • consistent fields for tool calling and orchestration
  • engine metadata for routing and traceability

Example workflow

A research copilot receives a topic, requests search results through the API with `markdown=true`, stores the structured JSON for tracing, and feeds the markdown summary into the agent prompt for grounded output.

Visual Reference

Example agent and copilot workflow surfaces built on structured search data.

Research agent dashboard

Research Agent

Collect live search context before drafting summaries or action plans.

Copilot workflow panel

Copilot Extension

Inject normalized search data into internal copilots without extra parsing steps.

Lead intelligence workflow

Lead Intelligence

Support prospecting agents with company and result-page context.

Prompt-ready output example

Prompt-Ready Output

Return markdown summaries directly into LLM prompts or agent memory.

Practical implementation notes

  • Store the structured JSON for auditability and downstream analytics
  • Use the markdown field for prompt input, summaries, or agent memory
  • Keep engine metadata so agent decisions stay traceable
  • Use the same request shape for dashboards, copilots, and automation tools