deep-research

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Procedure for answering a question with a thorough, cross-checked, cited web-research report using the Keenable search + fetch tools.

AI & Automation 9,848 stars 1535 forks Updated today Apache-2.0

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# deep-research — cited, cross-checked web research Use this for any question that needs current, verifiable information from the web. The deliverable is a synthesized answer where every load-bearing claim is backed by a source you actually read. ## Tools - `search_web_pages(query, [site], [published_after], [published_before], [mode])` — discover candidate sources. Write the `query` as a natural-language description of the ideal page, not keywords. Use `mode: pro` (default). - `fetch_page_content(url, [max_chars])` — read the full page (markdown). A search snippet is NEVER sufficient evidence — fetch before you cite. ## Procedure 1. **Plan.** Break the question into 3-6 focused sub-queries that together cover it. For contested or high-stakes questions, plan at least two independent angles. 2. **Search.** Run `search_web_pages` per sub-query. Prefer primary sources; use `published_after` for anything time-sensitive. 3. **Read.** `fetch_page_content` on the 2-3 most promising results per sub-query. Quote/cite only what you read, not what a snippet implied. 4. **Cross-check.** Verify every load-bearing claim against ≥2 INDEPENDENT sources (independent = different owners, not mirrors of one another). When sources disagree, surface the disagreement rather than picking silently. 5. **Synthesize.** Write a structured answer. Each non-obvious claim gets an inline citation to the URL you fetched. Separate "well-supported" from "uncertain / single-sour...

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Author
omnigent-ai
Repository
omnigent-ai/omnigent
Created
3 months ago
Last Updated
today
Language
Python
License
Apache-2.0

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