CoolPrompt and PromptAgent
These are competitors offering different automatic prompt optimization strategies—CoolPrompt uses iterative refinement with language model feedback, while PromptAgent uses strategic planning with expert-level optimization—targeting the same use case of automating prompt engineering.
About CoolPrompt
CTLab-ITMO/CoolPrompt
Automatic Prompt Optimization Framework
Implements multiple optimization algorithms (HyPE, ReflectivePrompt, DistillPrompt) that iteratively refine prompts through LLM-based feedback and evaluation metrics. LLM-agnostic architecture supports any Langchain-compatible model while generating synthetic evaluation data when datasets are unavailable, and automatically detects task types for scenarios without explicit specifications.
About PromptAgent
maitrix-org/PromptAgent
This is the official repo for "PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization". PromptAgent is a novel automatic prompt optimization method that autonomously crafts prompts equivalent in quality to those handcrafted by experts, i.e., expert-level prompts.
Employs Monte Carlo Tree Search (MCTS) to strategically sample model errors and iteratively refine prompts through reward simulation, unifying prompt sampling and evaluation in a single principled framework. Supports diverse model backends including OpenAI APIs, PaLM, Hugging Face text generation models, and vLLM for local inference, with YAML-based configuration for flexible experimentation. Integrates with BIG-bench tasks and the LLM Reasoners library, enabling optimization across reasoning and knowledge-intensive domains.
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