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How to compare published self-evolution algorithms with a dry run with Agentdescent

Run a dry-run comparison of the ported algorithms without any API key.

Short answer: git clone https://github.com/Birfy/agentdescent && cd agentdescent; then pip install -e; python -m examples.run_demo. All steps.

Who it is forA researcher or engineer choosing which self-evolution method to adopt, who needs to see how the ports behave before paying for model calls.

The problemPublished methods come with different loops and schedulers, which makes fair comparison difficult.

The outcomeA side-by-side run of the example comparison, with each port's behavior visible under the same scheduler.

What you need first

  • Python 3.9 or newer
  • pip to install the agentdescent package from PyPI
  • For model-backed runs: an OpenAI-compatible endpoint, its base URL, and an API key set in environment variables (OPENAI_BASE_URL and OPENAI_API_KEY)
  • For agent-plugin use: Claude Code or Codex installed, and git to clone the repo

Steps

  1. git clone https://github.com/Birfy/agentdescent && cd agentdescent
  2. pip install -e .
  3. python -m examples.run_demo
  4. Pass the --dry-run flag to a port's example script to run it without an API key. (inferred, check the README)

Prompt for your agent

Clone the AgentDescent repo, install it with `pip install -e .`, and run `python -m examples.run_demo`. Expected output: the comparison results for the example ports. Verify that the run completes without an API key set.

Caveat. Dry runs show the mechanics of each port but do not reproduce the benchmark scores reported for them.

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Using the Deduped MCP, get the setup steps for Birfy/agentdescent and "Compare published self-evolution algorithms with a dry run"

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