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How to see a skill evolve in one offline demo run with Agentdescent

Run the packaged demo to watch an evolution loop learn a rule with no API key.

Short answer: pip install "agentdescent>=0.7.0"; then agentdescent demo; read the printed line that starts with what it learned: to see the rule the loop committed. All steps.

Who it is forA developer evaluating whether AgentDescent fits their prompt or skill work, who wants proof it runs before adding any keys.

The problemReading about parallel self-evolving agents does not show how an edit gets proposed, merged, and committed.

The outcomeA completed offline evolution that prints a held-out reward and the rule it learned.

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. pip install "agentdescent>=0.7.0"
  2. agentdescent demo
  3. Read the printed line that starts with what it learned: to see the rule the loop committed.
  4. Optional: agentdescent doctor to see which CLIs and keys this machine has.

Prompt for your agent

In the AgentDescent repo, run `pip install "agentdescent>=0.7.0"` then `agentdescent demo`. Report the held-out reward and the rule learned from the output. Verify that the output includes a line starting with `what it learned:`.

Caveat. The packaged demo runs offline, but the research comparisons need a clone of the repo and are outside the wheel.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for Birfy/agentdescent and "See a skill evolve in one offline demo run"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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