What is Galapagos?¶
Galapagos is an open-source platform for LLM-driven evolutionary search on scientific-discovery and optimization tasks. It expresses every published method as the same six-component loop, scores them all with the same verifiable evaluators, and ranks the results on one leaderboard — so the numbers are finally comparable.
Install
pip install open-galapagos — see Installation
Ships today
13 runnable scaffolds, 1,274 task cards (1,269 runnable).
A method is
a choice of what fills six slots — Population, SelectionPolicy, PromptBuilder, Proposer, Evaluator, Memory — over one Genome.
A task is
a seed program plus a deterministic scorer. It owns its own Docker image and is scored inside it by default.
A run produces
the best solution, plus an ETIF trajectory — every candidate and evolutionary behavior in one causal record.
What qualifies as a discovery task?¶
Every research-grade Galapagos task must pass the same three-part admission gate:
| Requirement | Meaning |
|---|---|
| Unsolved | No known reproducible solution has achieved the documented perfect score under the task's fixed protocol and resource budget. |
| Open-ended | The task rewards ongoing research or optimization through continuous or finely graded feedback, with multiple meaningful ways to improve. |
| Verifiable | A trusted, deterministic evaluator independently recomputes the score from candidate output using pinned fixtures, seeds, dependencies, budgets, and tolerances. |
All three are required. Saturated benchmarks, pass/fail-only exercises, subjective-only judges, and candidate-reported scores are not research tasks. Read the complete task eligibility policy.
from galapagos import GalapagosModel, GalapagosConfig, GalapagosScaffold, GalapagosTask
model = GalapagosModel.from_card(name="openai/gpt-5.5", host="openrouter")
config = GalapagosConfig.from_config(scaffold_name="openevolve")
scaffold = GalapagosScaffold.from_card(name="openevolve", config=config, model=model)
task = GalapagosTask.from_card(name="circle_packing")
result = scaffold.run(task=task)
print(result.best_score)
The loop¶
Every method in Galapagos — a plain mutate-and-select loop, an adaptive bandit, an autonomous CLI agent — is this one loop. Only the implementation filling each slot changes.
Iterate until general.max_iterations; a finite-frontier method may finish earlier when no state
remains expandable.
What it gives you¶
| One loop, many methods | A method is a card naming six components. Porting one means writing the slots that differ, not a new harness. → Components |
| One schema for tasks | A task declares its seed, scorer, and requirements, and normally owns its container image. Runnable, environment-compatible task/scaffold pairs use the same task evaluator. → Task environments |
| Agents are not a special case | claude_code hands the whole loop to a Claude Code CLI session, while algotune_agent exposes a command-driven optimization workspace. Both still map onto the same component slots. → Scaffolds |
| Every run tells its story | A finished run exports to ETIF — a causal timeline of candidates, evaluations, failures, migrations, crossovers, adaptations, agents, and tools. → Trajectories |
| Cards are the protocol | Task and scaffold YAML cards load locally and mirror to the Hub; model cards are local, while discovery bundles use the Hub API/browser flow. → Cards |
| Visible review state | Discovery and leaderboard rows expose pending, accepted, or rejected. Acceptance currently promotes a row; automatic evaluator replay is not implemented. → The Hub |
The two kinds of method¶
| Tier | Who drives the search | Scaffolds |
|---|---|---|
| search | The scaffold does. A frozen LLM is just the variation operator; the scaffold owns selection and prompting. | openevolve, adaevolve, evox, gepa, ale_agent, algotune_agent, meta_harness, best_of_n, best_of_n_attempts, topk, beam_search |
| agent | The agent does. An autonomous CLI agent edits the solution and runs the evaluator itself; the framework scores its checkpoints. | claude_code |
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Your first evolutionary search, in five lines.
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Run Galapagos or choose the correct task, scaffold, and upstream-review submission path.
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An explosion of methods, no fair way to compare them. The case for one leaderboard.
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The loop, the six components, the Genome, cards, trajectories.
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The production Hub, the discovery pipeline, and the leaderboard.
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A runnable, review-visible method port mapped onto all six Galapagos components.