darwinloop 0.1.0

Self-improvement engine for AI agents — evolve any agent autonomously using the Darwin Gödel Machine algorithm.

darwinloop

darwinloop — self-improvement engine for AI agents

Point darwinloop at any Python agent, define what "good" looks like with benchmark tasks, and darwinloop will autonomously improve the code — iteration by iteration — using an LLM, without you writing a single patch.

Based on the Darwin Gödel Machine (Zhang et al., ICLR 2026).

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Why darwinloop?

• Measurable gains. The football-score-agent router went from 51% → 80% accuracy in 5 iterations — 16 more teams recognised, pronoun follow-ups fixed, competition-vs-team routing corrected. Zero manual patches.
• Fully auditable. Every change is recorded as a unified diff. Every generation is preserved in an immutable JSON archive. Roll back anytime.
• Works on any Python agent. uAgents, LangChain, LangGraph, raw Python — if it's a .py file, darwinloop can evolve it.

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Quickstart

pip install darwinloop

from darwinloop import DarwinLoop, BenchmarkTask

tasks = [
    BenchmarkTask(id="t1", name="live_scores",
                  input="live scores now", expected="live"),
    BenchmarkTask(id="t2", name="vs_competition",
                  input="Arsenal vs Chelsea result", expected="competition"),
]

dl = DarwinLoop(target="my_agent/router.py", tasks=tasks, model="asi1")
result = dl.run(iterations=5)

print(f"Score: {result.base_score:.2f} → {result.best_score:.2f} (+{result.score_delta:.2f})")
result.apply()            # write best version back to router.py
result.save_report()      # save darwinloop_report.md

Expected output:

darwinloop  — self-improvement engine for AI agents

Evaluating base agent (router.py)…
  ✓ agent_0000: score=0.51 (5/10 tasks passed)

── ITERATION 1/5 ──────────────────────────────────────────
  Selected parents: ['agent_0000']
  Proposal: Add pronoun (they/them/their) follow-up handling using ctx.last_team
    Evaluating… score=0.60
  agent_0000 → agent_0001  Score: 0.51 → 0.60 (+0.09)

[… 4 more iterations …]

Evolution complete!  Score: 0.51 → 0.80  (+0.29  best: agent_0004  gen 4)

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How it works

Your agent code
      │
      ▼
┌─────────────┐
│  Benchmark  │  Run tasks in isolated sandbox → score (0.0–1.0)
└──────┬──────┘
       │ failures
       ▼
┌─────────────┐
│  Diagnose   │  LLM analyses code + failures → improvement proposal
└──────┬──────┘
       │ proposal
       ▼
┌─────────────┐
│   Improve   │  LLM uses editor tools (str_replace) to apply change
└──────┬──────┘
       │ new code
       ▼
┌─────────────┐
│  Re-score   │  Run benchmarks again on new code
└──────┬──────┘
       │ score > old?
      YES → keep it (add to archive)
       NO → discard it (archive still records it for open-ended exploration)
       │
       └── repeat N iterations

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LLM Support

Provider	Model	Set env var
ASI:One (default)	asi1	ASI1_API_KEY
Anthropic	claude-3-5-sonnet-20241022	ANTHROPIC_API_KEY
OpenAI	gpt-4o	OPENAI_API_KEY
Mock (free)	—	--dry-run

Get an ASI:One API key at asi1.ai — it's the Fetch.ai ecosystem LLM.

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Benchmark Packs

Pre-built domain packs so you don't need to write tasks from scratch:

from darwinloop import DarwinLoop
from darwinloop.packs import RoutingPack, CommercePack, SupportPack

# Routing agent (intent classification)
dl = DarwinLoop(target="agent/router.py",
                pack=RoutingPack(intents=["live", "team", "competition", "fixtures"]))

# Commerce agent (product search, cart, checkout)
dl = DarwinLoop(target="agent/shop.py", pack=CommercePack())

# Customer support agent
dl = DarwinLoop(target="agent/support.py", pack=SupportPack())

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CLI Reference

# Evolve a specific file
darwinloop evolve agent/router.py --iterations 5 --model asi1

# Dry run (free, no API key needed)
darwinloop evolve agent/ --dry-run --auto

# Use a built-in benchmark pack
darwinloop evolve agent/router.py --pack routing --iterations 5

# Load benchmarks from a file
darwinloop evolve agent/router.py --tasks benchmarks.py --iterations 10

# Auto-generate benchmarks from agent code
darwinloop scaffold agent/router.py --output benchmarks.py

# View a previous run report
darwinloop report darwinloop_output/

# Diff two generations
darwinloop diff darwinloop_output/ --from agent_0000 --to agent_0004

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Real Example: Football Agent

The examples/football/ directory contains the real football-score-agent router and its benchmark tasks.

darwinloop evolve examples/football/football_router.py \
    --tasks examples/football/benchmarks.py \
    --iterations 5 --model asi1

DGM-discovered improvements in 5 iterations:

#	Improvement	Score impact
1	Pronoun follow-up (they/their/them → last team)	+0.09
2	+16 clubs (Juventus, Atletico, Napoli, Dortmund…)	+0.08
3	Competition-signal priority (vs, result, score)	+0.07
4	Fixture regex expansion (next game, upcoming game)	+0.05

Total: 0.51 → 0.80 (+0.29)

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Safety

darwinloop is designed to be the most trustworthy self-improvement library available.

Guarantee	Implementation
AST validation before execution	sandbox/validator.py blocks eval, exec, shell=True
Subprocess isolation	All agent code runs in a child process, never in the darwinloop process
Hard timeouts	Sandbox default 30s, configurable via sandbox_timeout
No network in sandbox	Network imports trigger warnings; calls fail at runtime
Immutable archive	AgentEntry records are never modified after creation
Diff transparency	Every change recorded as unified diff
Revert anytime	All generations preserved; load archive and roll back
Dry run mode	MockLLMClient tests full pipeline at zero cost
Score regression protection	New code kept only if score strictly improves
Human checkpoints	In non---auto mode, prompts before each iteration

See SECURITY.md for full details.

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Contributing

See CONTRIBUTING.md. PRs welcome.

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License

Apache 2.0 — see LICENSE.