Querit API

Querit is a web retrieval system built for LLM consumption. Two endpoints, both POST, both JSON, both authenticated the same way:

• https://api.querit.ai/v1/search - ranked web results with snippets and optional page text
• https://api.querit.ai/v1/contents - crawl 1-10 URLs and return their content

This skill is for building and debugging an integration - code that will run in the user's own application. To simply retrieve something for the current task, use the Querit MCP server's querit_search and querit_contents tools if they are connected, rather than writing a script here.

Authentication

Authorization: Bearer <QUERIT_API_KEY>
Content-Type: application/json

Keys are created on the Querit platform's API Key management page. Read the key from the QUERIT_API_KEY environment variable - never hardcode it, never log it, never write it to a file that could be committed. When a user pastes a key into the conversation, use it for the call at hand and suggest they export it instead:

export QUERIT_API_KEY="..."   # or the agent's own scoped config

If the user has no key, point them at https://www.querit.ai to sign up, and at https://www.querit.ai/en/playground to try queries before writing code.

Workflow

1. Verify the key and the endpoint first

scripts/querit_smoke.py uses only the Python standard library:

python3 scripts/querit_smoke.py --search "what does salesforce do"
python3 scripts/querit_smoke.py --contents https://example.com
python3 scripts/querit_smoke.py --search "openclaw skills" --need-content --count 5

It reads QUERIT_API_KEY, prints HTTP status, latency, result count, a normalized preview of each hit, and per-URL crawl status, and never prints the key. --raw dumps the untouched JSON. Prove the credential works before touching the user's codebase, so a bad key does not get debugged through their application stack.

2. Pick the endpoint

The last two rows are the decision that matters. needContent: true returns sentence[] only for results whose text was already available, and the field is simply absent otherwise. If every top result must have text, search then fetch, because /v1/contents reports per-URL success or failure explicitly.

3. Choose parameters

A request with every filter branch populated:

{
  "query": "quantum computing breakthroughs",
  "count": 10,
  "needContent": true,
  "filters": {
    "languages": { "include": ["english"] },
    "geo": { "countries": { "include": ["united states"] } },
    "timeRange": { "date": "d7" },
    "sites": { "include": ["arxiv.org"] }
  }
}

Languages and countries are lowercase full names, not ISO codes. timeRange.date is a relative window (d7, w2, m3, y1) or an absolute 2026-08-01to2026-08-10. references/search-api.md has the full value sets, the plan caps, and when each filter is worth applying; references/contents-api.md does the same for format, crawlTimeout, and extrasMeta.

4. Write the integration

references/python-integration.md has the SDK call, a REST client with retry and a QPS limiter, batch chunking for contents, and result normalization for RAG. For other languages, build against the request and response tables in references/search-api.md and references/contents-api.md.

Four field-level details that break naive parsers, whatever the language:

• results.result is nested one level deeper than most search APIs. Iterating results yields dict keys, not hits.
• sentence is absent, not empty, when a result has no text. Every field in a result is optional.
• snippet can contain HTML fragments. Sanitize before rendering or sending to a model.
• On /v1/contents, results and statuses are joined by id, not by position, and partial batches return 200.

5. Handle failures

references/troubleshooting.md covers 400, 401, 403, 429, timeouts, and the several ways a 200 carries nothing useful. The two that surprise people: 403 is usually per-endpoint subscription rather than a bad key, and 429 needs a client-side limiter rather than more retries.

References

• references/search-api.md - /v1/search: request and response contract, accepted filter values, when to apply each.
• references/contents-api.md - /v1/contents: contract, the results/statuses pairing, choosing format and crawlTimeout.
• references/troubleshooting.md - error codes, empty responses, and isolating the failing layer.
• references/python-integration.md - SDK usage, a REST client with retry and rate limiting, batch concurrency, normalizing results.

Official docs: https://www.querit.ai/en/docs/overview/about