New LangGraph Project

This template demonstrates a simple application implemented using LangGraph, designed for showing how to get started with LangGraph Studio.

The core logic defined in src/agent/graph.py, showcases a straightforward application that responds with a fixed string and the configuration provided.

This template provides a basic foundation for a LangGraph application that can be extended to create more complex agents.

Getting Started

1. Install the LangGraph CLI.

pip install --upgrade "langgraph-cli[inmem]"

2. Create a .env file.

cp .env.example .env

3. If desired, add your LangSmith API key in your .env file.

LANGSMITH_API_KEY=lsv2...

4. Install dependencies

cd path/to/your/app
pip install -e .

5. Customize the code as needed.
6. Start the LangGraph Server.

langgraph dev

How to customize

1. Define a configuration: Create a configuration.py file and define a configuration schema. For example, in a chatbot application you may want to define a dynamic system prompt or LLM to use. For more information on configurations 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.

You can also quickly extend this template by:

• Adding custom tools or functions to enhance the chatbot's capabilities.
• Implementing additional logic for handling specific types of user queries or tasks.
• Integrating external APIs or databases to provide more dynamic responses.

Development

While iterating on your graph, 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 will be appended to 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.