langchain-dev-utils 1.2.14

A practical utility library for LangChain and LangGraph development

🦜️🧰 langchain-dev-utils

🚀 High-efficiency toolkit designed for LangChain and LangGraph developers

📚 English • 中文

This is the English version. For the Chinese version, please visit 中文版本

✨ Why choose langchain-dev-utils?

Tired of writing repetitive code in LangChain development? langchain-dev-utils is the solution you need! This lightweight yet powerful toolkit is designed to enhance the development experience of LangChain and LangGraph, helping you:

• ⚡ Boost development efficiency - Reduce boilerplate code, allowing you to focus on core functionality
• 🧩 Simplify complex workflows - Easily manage multi-model, multi-tool, and multi-agent applications
• 🔧 Enhance code quality - Improve consistency and readability, reducing maintenance costs
• 🎯 Accelerate prototype development - Quickly implement ideas, iterate and validate faster

🎯 Core Features

• 🔌 Unified model management - Specify model providers through strings, easily switch and combine different models
• 💬 Flexible message handling - Support for chain-of-thought concatenation, streaming processing, and message formatting
• 🛠️ Powerful tool calling - Built-in tool call detection, parameter parsing, and human review functionality
• 🤖 Efficient Agent development - Simplify agent creation process, expand more common middleware
• 📊 Flexible state graph composition - Support for serial and parallel composition of multiple StateGraphs

⚡ Quick Start

1. Install langchain-dev-utils:

   pip install -U langchain[openai] langchain-dev-utils
   

2. Start using:

    from langchain.tools import tool
    from langchain_core.messages import HumanMessage
    from langchain_dev_utils.chat_models import register_model_provider, load_chat_model
    from langchain_dev_utils.agents import create_agent
   
    # Register model provider
    register_model_provider("vllm", "openai-compatible", "http://localhost:8000/v1")
   
    @tool
    def get_current_weather(location: str) -> str:
        """Get the current weather for the specified location"""
        return f"25 degrees, {location}"
   
    # Dynamically load model using string
    model = load_chat_model("vllm:qwen3-4b")
    response = model.invoke("Hello")
    print(response)
   
    # Create agent
    agent = create_agent("vllm:qwen3-4b", tools=[get_current_weather])
    response = agent.invoke({"messages": [HumanMessage(content="What's the weather like in New York today?")]})
    print(response)
   

For more features of this library, please visit the full documentation

🛠️ GitHub Repository

Visit the GitHub repository to view the source code and report issues.

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