Hands on with LangGraph Agent Workflows - Build a LangChain Coding Agent with Custom Tools
AI Summary
Summary: Building a Lang Chain Application with Lang Graph
- Introduction
- Demonstrated building a Lang chain application using Lang graph.
- Lang graph allows for customization of agents.
- Basics of Interaction with OpenAI
- Discussed manual interaction with OpenAI.
- Explained appending messages and using tools.
- Showed how to set up a conversation and interact with OpenAI’s API.
- Using Lang Chain
- Simplified the process using Lang chain.
- Defined custom tools for addition and multiplication.
- Created an agent to execute tasks without manual requests.
- Lang Graph for Customization
- Lang graph provides more control over agent behavior.
- Defined agent states, node actions, and graph structure.
- Created a graph to specify interactions between nodes.
- Example: Development Team Agent
- Set up a development team agent with multiple roles (context retrieval, code writing, reviewing).
- Used Lang graph to specify the workflow and interactions.
- Demonstrated the ability to loop back for revisions and use state to pass information.
- Conclusion
- Lang graph offers more sophisticated control over agents.
- Allows for efficient token usage by avoiding passing large amounts of data between agents.
- Plans to explore more complex models with Lang graph in future videos.