This template provides a starting point for deploying LangGraph agents as interactive chatbots using Streamlit. It's designed for developers looking to quickly prototype and deploy LLM-powered applications with a user-friendly interface.

How It Works

The template utilizes Streamlit for the frontend interface and LangGraph for orchestrating the LLM agent's logic. The core functionality resides in main.py, where the load_chain function is the primary integration point for custom LangGraph agents. Users can modify this function to incorporate their specific LLM chains and adjust input/output handling as needed.

Quick Start & Requirements

• Install dependencies: pip install -r requirements.txt
• Run locally: streamlit run main.py
• Prerequisites: OPENAI_API_KEY set as a secret environment variable in Streamlit.
• Recommended: LangSmith for debugging and performance monitoring.

Highlighted Details

• Streamlit deployment guide included.
• Integration with LangSmith for tracing and monitoring LLM applications.
• Focus on LangGraph agent deployment.

Maintenance & Community

No specific information on contributors, sponsorships, or community channels is provided in the README.

Licensing & Compatibility

The license is not specified in the README.

Limitations & Caveats

The template is a basic starting point and requires significant modification to integrate custom LangGraph agents. The README does not specify the underlying LangGraph version or compatibility with other LLM frameworks.