TextGrad: Automatic ''Differentiation'' via Text -- using large language models to backpropagate textual gradients. Published in Nature.
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Updated
Jul 25, 2025 - Python
TextGrad: Automatic ''Differentiation'' via Text -- using large language models to backpropagate textual gradients. Published in Nature.
End-to-end Generative Optimization for AI Agents
A very fast, very minimal prompt optimizer
Claude Code skill that transforms vague prompts into structured, expert-level prompts using 7 research-backed frameworks (CO-STAR, RISEN, RISE, TIDD-EC, RTF, CoT, CoD)
A unified, modular framework for prompt optimization
Collection of Claude Skills for DSPy framework - program language models, optimize prompts, and build RAG pipelines systematically
AI agent that turns your rough ideas into perfect image generation prompts. 7-component formula, 70+ creative techniques, 9 domain modes.
SCOPE: Self-evolving Context Optimization via Prompt Evolution - A framework for automatic prompt optimization
Claude Code for DSPy: Comprehensive CLI to Optimize Your DSPy Code. our AI-Powered DSPy Development Assistant
Turn Claude Code into its own Meta-Harness — a skill that evolves the scaffolding around a fixed model (memory, retrieval, context, prompts) via a native propose→score→Pareto loop. Native reimplementation of Meta-Harness (Lee et al. 2026).
State-of-the-art prompting techniques implementation with DSpy - Manager-style prompts, role personas, meta-prompting, and more
A lightweight implementation of the GEPA (Genetic-Pareto) prompt optimization method for large language models.
Claude Code skill that forces AI to understand before executing. Three disciplines: cognition check, requirement understanding, method search.
MCP server integrating GEPA (Genetic-Evolutionary Prompt Architecture) for automatic prompt optimization with Claude Desktop
Make your agents learn from experience. One protocol for weights, harness and routing.
CLI text optimizer built on GEPA. Uses Agentic Coding CLI's as mutator and observer -- no api keys required
🚀 Lightweight Python library for building production LLM applications with smart context management and automatic token optimization. Save 10-20% on API costs while fitting RAG docs, chat history, and prompts into your token budget.
A framework for pitting LLMs against each other in an evolving library of games ⚔
RHO: Evolving Agents in the Dark — Retrospective Harness Optimization via Self-Preference. Improving LLM agents from unlabeled past trajectories (arXiv:2606.05922).
PromptCraft is a prompt perturbation toolkit from the character, word, and sentence levels for prompt robustness analysis. PyPI Package: pypi.org/project/promptcraft
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