LangGraph Series - What Is LangGraph? - Explained Simply

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AI Summary

Summary: Lang Chain and Langra Introduction

• Lang Chain Overview:
  • Open-source framework for AI applications.
  • Celebrated one year with a new library, Langra.
• Langra Features:
  • Simplifies agent creation and runtime management.
  • Introduces Cycles for repetitive agent operations.
• Agent Executors in Langra:
  • Agent Executor: Rebuilt version of Lang Chain’s executor.
  • Chat Agent Executor: Manages state as a message list, ideal for chat-based models.
• Building an Agent Executor:
  • Environment setup with necessary packages.
  • API keys for OpenAI, Tavali, and LSmith (for logging).
  • Create Lang Chain agent, define state, nodes, and edges.
  • Compile and run the graph to observe agent decisions.
• Chat Agent Executor:
  • Install Lang Chain and related packages.
  • Set up tools, model, and agent state.
  • Create nodes and edges for agent actions.
  • Compile and run the graph, observe with LSmith.
• Modifying Executors:
  • Humans in the Loop:
    • Add human validation before tool actions.
    • Modify call tool function for user approval.
  • Managing Agent Steps:
    • Filter messages for the model to consider.
    • Customize agent interaction with message history.
  • Force Calling:
    • Ensure a tool is called first in the workflow.
    • Add a new node to initiate tool call.
• Conclusion:
  • Encourages exploring Langra for more applications.
  • Links and resources will be provided for further learning.
  • Invites engagement through likes, subscriptions, and comments.

Final Thoughts:

• The video aims to educate on Langra’s capabilities and how to utilize it for building AI agents.
• Viewers are encouraged to stay updated with AI news and engage with the content provided.