promptulate and PromptAgent
These are complements: Promptulate provides the general LLM automation framework for building agent applications, while PromptAgent offers a specialized prompt optimization technique that could be integrated into Promptulate workflows to improve prompt quality during agent development.
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 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.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work