We open-sourced optimize_anything, an API that optimizes any text artifact. You provide a starting artifact (or just describe what you want) and an evaluator and it handles the search.
It extends GEPA (our state of the art prompt optimizer) to code, agent architectures, scheduling policies, and more.
Two key ideas:
(1) diagnostic feedback (stack traces, rendered images, profiler output) is a first-class API concept the LLM proposer reads to make targeted fixes, and
(2) Pareto-efficient search across metrics preserves specialized strengths instead of averaging them away.
Results across 8 domains show optimize_anything can create:
- learned agent skills pushing Claude Code to near-perfect accuracy simultaneously making it 47% faster,
- cloud scheduling algorithms cutting costs 40%,
- an evolved ARC-AGI agent going from 32.5% → 89.5%,
- CUDA kernels beating baselines,
- circle packing outperforming AlphaEvolve's solution,
- and blackbox solvers matching and outperforming Optuna.
We open-sourced optimize_anything, an API that optimizes any text artifact. You provide a starting artifact (or just describe what you want) and an evaluator and it handles the search.
import gepa.optimize_anything as oa
result = oa.optimize_anything( seed_candidate="<your artifact>", evaluator=evaluate, # returns score + diagnostics )
It extends GEPA (our state of the art prompt optimizer) to code, agent architectures, scheduling policies, and more.
Two key ideas: (1) diagnostic feedback (stack traces, rendered images, profiler output) is a first-class API concept the LLM proposer reads to make targeted fixes, and (2) Pareto-efficient search across metrics preserves specialized strengths instead of averaging them away.
Results across 8 domains show optimize_anything can create: - learned agent skills pushing Claude Code to near-perfect accuracy simultaneously making it 47% faster, - cloud scheduling algorithms cutting costs 40%, - an evolved ARC-AGI agent going from 32.5% → 89.5%, - CUDA kernels beating baselines, - circle packing outperforming AlphaEvolve's solution, - and blackbox solvers matching and outperforming Optuna.