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.