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Cycles and Conditional Edges in LangGraph: Controlling Workflow Execution

When designing workflows or AI pipelines, sometimes we need to loop through certain steps multiple times before reaching an endpoint. Cycles and conditional edges in LangGraph allow us to control the flow of execution dynamically.

In this article, we’ll explore how to implement looping behavior and conditional branching using LangGraph. We'll modify the graph dynamically based on conditions to create an adaptive workflow.

Understanding Cycles and Conditional Edges

🔁 Cycles in a Graph

A cycle occurs when a node can lead back to a previous node, creating a loop. This allows repeated execution of a process until a condition is met.

🛤 Conditional Edges

Unlike standard edges, conditional edges allow dynamic routing based on input values. Instead of following a fixed path, a function determines the next node based on the current state.

Implementing Cycles and Conditional Transitions

Let’s define a state structure using TypedDict:

from typing import TypedDict from langgraph.graph import END, StateGraph class InputState(TypedDict): string_value: str numeric_value: int
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This state includes:

  • string_value: A string that gets modified each cycle.
  • numeric_value: An integer that determines when to exit the loop.

Defining the State Modifier Node

Each time a node is visited, it updates the state:

def modify_state(input: InputState): input["string_value"] += "a" # Append "a" to the string  input["numeric_value"] += 1 # Increment the number  return input
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This function modifies the input every time it is executed.

Adding Conditional Routing

We define a router function to decide whether to continue looping or end execution:

def router(input: InputState): if input["numeric_value"] < 5: # If the number is less than 5, loop back  return "branch_a" else: # Otherwise, exit the graph  return "__end__"
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Building the Graph with Cycles

Now, let’s construct the state graph:

graph = StateGraph(InputState) # Define nodes graph.add_node("branch_a", modify_state) graph.add_node("branch_b", modify_state) # Define edges graph.add_edge("branch_a", "branch_b") # A → B  # Conditional routing from B graph.add_conditional_edges( "branch_b", router, {"branch_a": "branch_a", "__end__": END} ) graph.set_entry_point("branch_a") # Start from A  runnable = graph.compile()
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How the Graph Works

  1. Execution starts at branch_a.
  2. State is modified, and control moves to branch_b.
  3. The router function checks numeric_value:
    • If less than 5, execution loops back to branch_a.
    • If 5 or more, execution ends.

Visualizing the Graph

To understand the structure, we can visualize it using Mermaid diagrams:

from IPython.display import Image, display from langchain_core.runnables.graph import MermaidDrawMethod display( Image( runnable.get_graph().draw_mermaid_png( draw_method=MermaidDrawMethod.API, ) ) )
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This will generate a flowchart showing the looping behavior.

Cycles

Why Use Cycles and Conditional Edges?

✅ Efficient looping without manually repeating code.

✅ Dynamic decision-making based on input conditions.

✅ Adaptive workflows for AI pipelines and automation.

✅ Prevents infinite loops by setting clear exit conditions.

Conclusion

In this tutorial, we explored how cycles and conditional edges enable dynamic workflow control. This is particularly useful for iterative tasks like data processing, AI feedback loops, and task automation.

Next steps? Try modifying the loop condition or adding more decision points to see how the graph behavior changes! 🚀

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