promptulate and promptolution
These are **competitors**: both provide frameworks for automating prompt engineering workflows, but Promptulate emphasizes agent development with a broader LLM automation scope while Promptolution focuses specifically on unified prompt optimization as its core function.
About promptulate
Undertone0809/promptulate
🚀Lightweight Large language model automation and Autonomous Language Agents development framework. Build your LLM Agent Application in a pythonic way!
Leverages litellm for unified model abstraction, supporting 25+ providers (OpenAI, Anthropic, Gemini, local Ollama, etc.) through a single `pne.chat()` interface. Provides specialized agent types (WebAgent, ToolAgent, CodeAgent) with atomized planners, lifecycle hooks for custom code injection, and converts Python functions directly into tools without wrapper boilerplate. Integrates LangChain tools, includes prompt caching, streaming/async support, and Streamlit components for rapid prototyping.
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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