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How to run a web research goal with evolved browsing agents with KADATH

Let an evolved population search the web through local SearXNG and rank answers by a locked benchmark.

Authorization notice. KADATH is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Who it is forA researcher or analyst with a question that needs current public sources, who wants several agents to compete on source quality.

The problemA single research agent may miss sources or overstate findings, and there is no fixed way to compare how well different research approaches did.

The outcomeA ranked set of research agents whose answers are graded on evidence from public web sources, with the top agent's sources and workers recorded in its frozen evidence.

What you need first

  • Docker, because KADATH prepares Docker images and runs organisms in containers
  • An OpenAI API key and an OpenAI model ID, entered on first launch
  • A terminal where you can run ./kadath.sh
  • A measurable goal with a clear outcome that can be checked with the available environment

Steps

  1. Run ./kadath.sh and enter your OpenAI API key and model ID on first launch.
  2. Enter the research goal, and in the approval screen confirm that web search and direct HTTP retrieval are enabled for the run.
  3. Approve the benchmark only if the required evidence asks for named public sources and the anti-fraud rules cover unsupported claims.
  4. Monitor the live dashboard for worker activity and crash count while agents search and retrieve pages.
  5. Open the top agent's frozen evidence and check the cited sources against the approved required evidence list.

Prompt for your agent

In the KADATH repository, start the runtime with ./kadath.sh and create a run for the research goal <goal> with web search enabled. Report the top agent's fitness and list the public sources it cites, and verify that each cited source appears in its frozen evidence.

Caveat. Browser access is only available when the Architect approves it for the run, so the research tools available to agents depend on the approved benchmark and runtime inventory.

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Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked "Seen in the wild", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email [email protected] and we will look at it promptly. Full disclaimer page