Search

Credits: 1Also at: /search, /exa/search

Exa-compatible search. Pass contents (text, highlights, pruned_context) to get page Markdown, highlights and a query-pruned context per result, included in the 1 credit; without contents, results carry no page text. Accepts camelCase or snake_case.

Parameters

Example request

curl -s https://api.contextfetch.dev/v1/search \  -H "Authorization: Bearer YOUR_API_KEY" \  -H "Content-Type: application/json" \  -d '{"query":"vector database benchmarks 2026","numResults":3,"contents":{"text":true,"highlights":true,"pruned_context":true}}'

# pip install exa-pyfrom exa_py import Exa exa = Exa(api_key="YOUR_API_KEY", base_url="https://api.contextfetch.dev") res = exa.search(    "vector database benchmarks 2026",    num_results=5,    contents={"text": True, "highlights": True},)for r in res.results:    print(r.url, r.highlights)

// npm i exa-jsimport Exa from "exa-js"; const exa = new Exa("YOUR_API_KEY", "https://api.contextfetch.dev"); const res = await exa.search("vector database benchmarks 2026", {  numResults: 5,  contents: { text: true, highlights: true },});for (const r of res.results) console.log(r.url, r.highlights);

Example response

{
  "requestId": "4f9d2c0e8b5a4b0f9a1e6c2d7b3f8a10",
  "results": [
    {
      "id": "0b6f3c1e-5d2a-5f4b-9c8e-7a1d2e3f4b5c",
      "url": "https://example.com/blog/vector-database-benchmarks",
      "title": "Vector database benchmarks",
      "score": 0.93,
      "publishedDate": "2026-02-11",
      "text": "# Vector database benchmarks\n\nWe measured recall and p99 latency for...",
      "highlights": ["We measured recall and p99 latency for..."],
      "highlightScores": [0.97],
      "prunedContext": "We measured recall and p99 latency for..."
    }
  ],
  "costDollars": {"total": 0.001},
  "searchTimeMs": 412,
  "contextfetch": {"cache": "miss", "timingsMs": {...}, "tokens": {...}, "sources": {...}}
}

Response headers

Errors