LangGraphChatBot is a production-ready, modular framework for building and deploying AI agents using LangGraph and Streamlit. It demonstrates a clean separation of concerns, robust state management, and a dynamic user interface for real-world AI applications.
Unlike monolithic scripts, this project follows a strict enterprise-grade structure:
src/ui/: Decoupled presentation layer using Streamlit.src/graphs/: Specialized graph builders for different use cases.src/nodes/: Discrete logic components for easy testing and scaling.src/llms/: Abstracted LLM configurations (Groq, etc.).
The chatbot features a polished, interactive dashboard that allows users to:
- Configure model parameters (Temperature, Token limits).
- Switch between different autonomous use cases.
- Visualize agent thought-processes in real-time.
The system uses a factory pattern to build and compile graphs on the fly based on user requirements, demonstrating high flexibility in agentic orchestration.
- Orchestration: LangGraph
- Frontend: Streamlit
- LLM Integration: Groq (Llama 3 / Mixtral)
- State Management: Stateful TypedDicts
- Dependency Management: Modern Python (pip/requirements)
LangGraphChatBot/
├── app.py # Entry point for Streamlit
├── src/
│ ├── langgraphagenticai/
│ │ ├── graph/ # Graph assembly logic
│ │ ├── nodes/ # Individual node functions
│ │ ├── llms/ # Model wrappers
│ │ ├── ui/ # UI components & display logic
│ │ └── state/ # State definitions
├── requirements.txt # Project dependencies
└── README.md # Professional documentation
- Python 3.9+
- Groq API Key
pip install -r requirements.txtstreamlit run app.pyCreated as part of the LangGraph Learning Series - Tier 1 Showcase.