A look at constructing production-grade multi-agent systems, managing state updates, and handling cyclic feedback loops.
Handling Cyclic Workflows
Simple agent chains run in linear steps. Real-world tasks require feedback loops—e.g., an agent writes code, a compiler agent runs it, reports an error, and the first agent rewrites the code. LangGraph (developed by LangChain) models these steps as nodes and edges in a state graph, giving us absolute control over execution.
Code Snippet
// Python example: Basic state definition in LangGraph
from typing import TypedDict, List
from langgraph.graph import StateGraph, END
class AgentState(TypedDict):
messages: List[str]
current_step: int
workflow = StateGraph(AgentState)
# Define nodes
workflow.add_node("agent", call_agent)
workflow.add_node("executor", execute_action)
# Set entry point and edges
workflow.set_entry_point("agent")
workflow.add_conditional_edges("agent", should_continue, {"continue": "executor", "end": END})Frequently Asked Questions
When should we use LangGraph over standard chains?
When your business process contains feedback loops, self-correction nodes, or dynamic routing decision trees.