Agent Prompt Optimizer by Kayba

What it does

Agent Prompt Optimizer turns your agents’ mistakes into better prompts autonomously. Instead of manual prompt engineering, it watches where agents fail, extracts reusable insights, and updates prompts so they stop repeating errors. Watch your agents get better with every run. Drop it into existing agents or frameworks (e.g. LangChain) with just a few lines of code. Fully open source - try it on your agent right now and tell us what it learns.

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  Dall-E 2 Prompt Optimizer2022 · mostrecommendedbooks.com · ▲138

  Helps you write better prompts

• RA
  RL Agent that can auto-optimize your LLM prompts2024 · nomadic-ml.github.io · ▲14

  Hey everyone! Along with my team, I've developed a reinforcement learning system that automatically optimizes LLM prompts, complete with a visualization feature to track both prompt structure and learning progress over time. Take a look here: https://nomadic-ml.github.io/nomadic/cookbooks/Nomadic_Promp... Check out our website too:https://www.nomadicml.com/ In terms of how this visualization works: The RL Prompt Optimizer employs a reinforcement learning framework to iteratively improve prompts used for language model evaluations. At each episode, the…

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  Nowadays, a common AI tech stack has hundreds of different prompts running across different LLMs. Three key problems: - Choices, picking from 100s of LLMs the best LLM for that 1 prompt is gonna be challenging, you're probably not picking the most optimized LLM for a prompt you wrote. - Scaling/Upgrading, similar to choices but you want to keep consistency of your output even when models depreciate or configurations change. - Prompt management is scary, if something works, you'll never want to touch it but you should be able to without fear of everything breaking. So we launched Prompt…

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  Prompt Optimizer2025 · mindpal.space · ▲17

  Free AI Prompt Optimizer - Improve Your AI Prompts Instantly. Supports ChatGPT, Claude, and all major AI models.

• RA
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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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