CoolPrompt and promptolution
These two frameworks are competitors, as both aim to provide unified and modular solutions for automatic prompt optimization, suggesting they offer overlapping functionalities for similar use cases.
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 promptolution
automl/promptolution
A unified, modular Framework for Prompt Optimization
Supports multiple state-of-the-art prompt optimization algorithms (CAPO, EvoPrompt, OPRO) with a unified LLM backend spanning API-based models, local inference via vLLM/transformers, and cluster deployments. Built-in response caching, parallelized inference, and detailed token tracking enable cost-efficient, reproducible large-scale experiments. Decomposes optimization into modular components—Task, Predictor, LLM, and Optimizer—allowing researchers to customize any stage without rigid abstractions.
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