一款提示词优化器,助力于编写高质量的提示词
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Updated
Jan 1, 2026 - TypeScript
一款提示词优化器,助力于编写高质量的提示词
TextGrad: Automatic ''Differentiation'' via Text -- using large language models to backpropagate textual gradients. Published in Nature.
End-to-end Generative Optimization for AI Agents
Unified Go interface for Language Model (LLM) providers. Simplifies LLM integration with flexible prompt management and common task functions.
Awesome-LLM-Prompt-Optimization: a curated list of advanced prompt optimization and tuning methods in Large Language Models
A very fast, very minimal prompt optimizer
A CLI tool for logging and analyzing Claude Code and Cursor ai-driven coding session.
Evolve Agent Development Framework.
A unified, modular Framework for Prompt Optimization
SCOPE: Self-evolving Context Optimization via Prompt Evolution - A framework for automatic prompt optimization
A lightweight implementation of the GEPA (Genetic-Pareto) prompt optimization method for large language models.
State-of-the-art prompting techniques implementation with DSpy - Manager-style prompts, role personas, meta-prompting, and more
MCP server integrating GEPA (Genetic-Evolutionary Prompt Architecture) for automatic prompt optimization with Claude Desktop
Claude Code for DSPy: Comprehensive CLI to Optimize Your DSPy Code. our AI-Powered DSPy Development Assistant
A framework for pitting LLMs against each other in an evolving library of games ⚔
🚀 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.
PromptCraft is a prompt perturbation toolkit from the character, word, and sentence levels for prompt robustness analysis. PyPI Package: pypi.org/project/promptcraft
Advanced MCP server providing cutting-edge prompt optimization tools with research-backed strategies
Enterprise-grade prompt engineering toolkit: Distilled best practices, production-ready meta-prompts, and a professional AI agent that transforms simple requirements into battle-tested prompts.
We introduce CAPO, a novel prompt optimization algorithm that integrates racing and multi-objective optimization for cost-efficiency and leverages few-shot examples and task descriptions, outperforming SOTA discrete prompt optimization methods.
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