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 — 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:
- 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 andOptuna.
pip install gepa | Detailed Blog with runnable code for all 8 case studies | Website
