Development of AI Multi-Agent Systems Using Python and LangGraph
Description
Development of AI multi-agent systems using Python and LangGraph enables the creation of advanced solutions where specialized agents collaborate to solve complex tasks. LangGraph, as part of the LangChain ecosystem, provides a robust framework for building multi-agent workflows powered by large language models (LLMs). By defining states, nodes, and transitions, developers can efficiently manage interactions between agents, ensuring modularity and adaptability. This architecture supports dynamic, scalable, and intelligent systems capable of responding to real-time changes and user requirements, making it an ideal choice for modern AI applications.
Requirements
- Python environment: Python 3.9+ with dedicated virtual environment
Preparation instructions
To participate in this workshop, you'll need to prepare your development environment and API access in advance.
IDE Installation (recommendations)
- Cursor IDE (recommended): https://www.cursor.com/
- Windsurf IDE: https://windsurf.com/editor
- VS Code: https://code.visualstudio.com/ + AI extension (GitHub Copilot/Codeium)
Python Setup
Create and activate virtual environment:
python -m venv wsc-workshop source wsc-workshop/bin/activate # macOS/Linux wsc-workshop\Scripts\activate # Windows
Install packages
pip install langchain langgraph langchain-openai langchain-community tavily-python python-dotenv
API Keys
- OpenAI: Sign up at https://platform.openai.com/signup → API keys → Create new secret key
- Tavily: Sign up at https://tavily.com/ → Dashboard at https://app.tavily.com/home → Create API key
Environment Variables
Create .env file:
OPENAI_API_KEY=your_openai_api_key_here TAVILY_API_KEY=your_tavily_api_key_here
Speaker
