langchain-framework

To build LLM agents with LangChain, use this framework to implement tool-calling patterns and multi-step orchestration logic. It provides structured guidance for defining tools, integrating models, and managing agent loops for complex workflows.

Use LangGraph for agent orchestration when your workflow requires stateful graph workflows or human-in-the-loop approval. LangChain is sufficient for simpler chains, while LangGraph handles complex, multi-turn conversations with state management.

The best way to structure stateful graph workflows for LLM agents is by using LangGraph. This framework facilitates state machine patterns to manage multi-step orchestration, track conversation state, and handle complex conditional logic.

Yes, this framework provides clear selection guidance to choose between LCEL, LangGraph, and Deep Agents. It helps determine the right approach based on your task complexity, ensuring best practices for orchestration and tool-calling.

Manage agent loops and state for customer support agents by utilizing LangGraph state machines. This approach handles multi-turn conversations, maintains state across interactions, and supports human-in-the-loop approval steps.