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How to evolve a better agent for a measurable goal with KADATH

Run several epochs of competing agents on one goal and keep the leader that scores highest on the locked benchmark.

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

Credentials. Your OpenAI API key is saved in .kadath/config.env, and every organism, Grader, and specialist call draws on that key, so repeated epochs can run up model costs.

Who it is forA developer who has one recurring task, such as a repeatable analysis or coding objective, and has found that a single prompt gives uneven results.

The problemOne prompt or one agent produces inconsistent output, and tuning it by hand is slow and hard to compare.

The outcomeA ranked population of agents with per-epoch scores, and a top agent whose prompt, source, and framework were refined through selection under the same benchmark.

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 in the KADATH folder and enter your OpenAI API key and model ID on first launch.
  2. Choose the new run option and enter the goal, the duration of each epoch, the population size, and the number of epochs.
  3. Read the approval screen, which shows the objective, metric, weighted rubric, required evidence, automatic failures, and known limitations, then approve it if it matches the goal.
  4. Watch the live dashboard for epoch progress, the current leaderboard, and recent high-level activity while the run executes.
  5. After the final epoch, review the leaderboard and the top agent's results, and use the exports the kernel produces for the run.

Prompt for your agent

In the KADATH repository, start the runtime with ./kadath.sh and create a run for the goal <goal>. Report the best fitness and median fitness of the top five for each epoch, and confirm the approved benchmark lists the required evidence and automatic failures before the run starts.

Caveat. KADATH spends a large number of model tokens across every organism, Grader, and specialist call, so cost grows with population size and epoch count.

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Disclaimer

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