Overview
Dive into building your first AI agent with LangGraph in this step-by-step tutorial covering agent frameworks, graph-based workflows, and a practical budget coach example using Plaid API integration and Streamlit.
Syllabus
-
- Introduction to AI Agents
-- Definition and fundamentals of AI agents
-- Overview of agent frameworks
- Introduction to LangGraph
-- What is LangGraph?
-- Key features and advantages
-- Setting up the environment
- Working with Graph-Based Workflows
-- Understanding graph-based workflows
-- Modeling workflows in LangGraph
- Building an AI Agent Framework
-- Designing the agent architecture
-- Defining tasks and goals
- Practical Example: Budget Coach Agent
-- Overview of the budget coach use case
-- Planning the agent's functionality
- Integrating the Plaid API
-- Introduction to Plaid API
-- Setting up and configuring the Plaid API
-- Retrieving financial data from the Plaid API
- Implementing the Agent in LangGraph
-- Developing the agent's logic in LangGraph
-- Handling workflows and data integration
- User Interface Design with Streamlit
-- Introduction to Streamlit
-- Building the user interface for the budget coach agent
- Testing and Debugging
-- Techniques for testing the agent
-- Debugging common issues
- Deployment and Maintenance
-- Deploying the AI agent
-- Maintaining and updating the agent
- Conclusion and Next Steps
-- Recap of the course content
-- Suggested further learning paths in AI and LangGraph
Taught by
Tags
On-Demand
