Optimize_anything: A Universal API for Optimizing Any Text Parameter
We built optimize_anything, an API that optimizes any artifact representable as text — code, prompts, agent architectures, configs, even SVGs. It extends GEPA (our prompt optimizer, discussed here previously: https://arxiv.org/abs/2507.19457) far beyond prompts. The API is deliberately minimal. You provide what to optimize and how to measure it: import gepa.optimize_anything as oa def evaluate(candidate: str) -> tuple[float, dict]: result = run_my_system(candidate) return result.score, {"error": result.stderr, "runtime": f"{result.time_ms}ms"} result =…
Optimize_anything: A Universal API for Optimizing Any Text Parameter launched on February 20, 2026 with 8 votes, #1,230 of 16,627 launches that month and more than 75% of that year's launches.
What it does
GEPA
Today we are introducing optimize_anything , a declarative API that optimizes any artifact representable as text (e.g., code, prompts, agent architectures, vector graphics, configurations). It extends GEPA (Genetic-Pareto, our state-of-the-art LLM prompt optimizer) far beyond prompts. You declare what to optimize and how to measure it; the system handles the search. Testing it across several domains , we find optimize_anything consistently matches or outperforms domain-specific tools, including some purpose-built for each task. With one API, you can: The key insight is that a surprisingly wide range of problems can be formulated as optimizing a text artifact: speeding up a CUDA kernel,…from gepa-ai.github.io
In the maker’s words, at launch
We built optimize_anything, an API that optimizes any artifact representable as text — code, prompts, agent architectures, configs, even SVGs. It extends GEPA (our prompt optimizer, discussed here previously: https://arxiv.org/abs/2507.19457) far beyond prompts. The API is deliberately minimal.…
Does a similar job
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Hi HN, we are the founders of Relari (https://www.relari.ai). We launched our LLM evaluation stack on HN a few months ago (https://news.ycombinator.com/item?id=39641105), which is now used in production by AI teams at companies like Vanta and PwC. We have since expanded to directly optimizing parts of an LLM pipeline using a data-driven approach. In particular, we see a lot of potential in the Auto Prompt Optimization—which could be an attractive alternative to fine-tuning in many cases—to use data to align LLMs for domain-specific tasks. Here’s a demo video:…
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