New LangGraph Project

Agent Architecture

This repository contains multiple specialized agents organized in the following structure:

src/
- graph: code example to build graph and its component without involve any llm
- llm: practice need llm and interact with llm
- agent: based on langchain agent object
- deep: practice on deep agent framework

Getting Started

1. Install dependencies, along with the LangGraph CLI, which will be used to run the server.

cd path/to/your/app
pip install -e . "langgraph-cli[inmem]"

2. (Optional) Customize the code and project as needed. Create a .env file if you need to use secrets.

cp .env.example .env

If you want to enable LangSmith tracing, add your LangSmith API key to the .env file.

# .env
LANGSMITH_API_KEY=lsv2...

3. Start the LangGraph Server.

langgraph dev

For more information on getting started with LangGraph Server, see here.

How to customize

1. Define runtime context: Modify the Context class in the graph.py file to expose the arguments you want to configure per assistant. For example, in a chatbot application you may want to define a dynamic system prompt or LLM to use. For more information on runtime context in LangGraph, see here.

2. Extend the graph: The core logic of the application is defined in graph.py. You can modify this file to add new nodes, edges, or change the flow of information.

Development

While iterating on your graph in LangGraph Studio, you can edit past state and rerun your app from previous states to debug specific nodes. Local changes will be automatically applied via hot reload.

Follow-up requests extend the same thread. You can create an entirely new thread, clearing previous history, using the + button in the top right.

For more advanced features and examples, refer to the LangGraph documentation. These resources can help you adapt this template for your specific use case and build more sophisticated conversational agents.

LangGraph Studio also integrates with LangSmith for more in-depth tracing and collaboration with teammates, allowing you to analyze and optimize your chatbot's performance.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.