{
 "schema_version": 1,
 "generated": "2026-10-19",
 "id": "google-research-harness2--try-self-improving-agent-demo-no-keys",
 "repo": "google-research/harness2",
 "repo_url": "https://github.com/google-research/harness2",
 "repo_slug": "google-research-harness2",
 "title": "Watch an agent improve its own setup in 5 minutes, no API key",
 "page_title": "How to watch an agent improve its own setup in 5 minutes, no API key with harness2",
 "summary": "Run the built-in demo to see both recursion modes edit a harness with real Python executions and zero credentials.",
 "who": "Curious developer who saw Harness2 trending and wants proof it runs before investing in setup",
 "problem": "You cannot tell whether a self-improving harness framework actually works without wiring up models and paying for API calls.",
 "outcome": "A runs/demo folder showing parallel and sequential recursion with deterministic editors, produced locally in seconds.",
 "steps": [
  "Create the environment: uv venv --python 3.12 .venv",
  "Activate it: source .venv/bin/activate",
  "Install the package: uv pip install -e '.[render]'",
  "Run the demo: harness2-demo --output runs/demo --k 2",
  "Open the runs/demo folder and read the adopted harnesses and reflection notes."
 ],
 "agent_prompt": "Clone google-research/harness2, set up a Python 3.12 uv environment, install it, and run harness2-demo with --k 2 into runs/demo. Summarize what files were produced and verify the demo ran without any model credentials.",
 "agents": [
  "any"
 ],
 "difficulty": "Beginner",
 "time": "15 minutes",
 "basis": "field",
 "caveat": "The demo uses deterministic editors, not real models, so it shows the mechanics of the loop and says nothing about real-world score gains.",
 "category": "Software & DevOps",
 "tags": [
  "Software & DevOps"
 ],
 "lang": "python",
 "requirements": [
  "Linux, Python 3.12 and uv for installation",
  "No credentials needed for the local demo (harness2-demo)",
  "Model-backed runs need Node.js 20+, the Google Cloud CLI, a Vertex AI project and model IDs in .env, and gcloud application-default login",
  "LAB needs Pandoc and Poppler; WorkBuddy-Bench needs Docker",
  "Model-backed benchmark runs incur API costs"
 ],
 "risk": "dual-use",
 "seen_in_the_wild": true,
 "best": false,
 "score": 0.725,
 "evidence": [
  {
   "platform": "x",
   "url": "https://x.com/i/web/status/2112227371847729175",
   "posted": "2026-10-19"
  }
 ],
 "url": "https://dedupedai.com/use-cases/google-research-harness2/try-self-improving-agent-demo-no-keys/",
 "md": "https://dedupedai.com/use-cases/google-research-harness2/try-self-improving-agent-demo-no-keys/index.md",
 "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 hello@dedupedai.com and we will look at it promptly."
}