Edge

Control flow that defines transitions between nodes.

Overview

Edges determine "what to do next". Nodes perform processing, and edges dictate the next action.

Types of Edges

1. Normal Edges (Fixed Transitions)

Always transition to a specific node:

from langgraph.graph import START, END

# From START to node_a
builder.add_edge(START, "node_a")

# From node_a to node_b
builder.add_edge("node_a", "node_b")

# From node_b to end
builder.add_edge("node_b", END)

2. Conditional Edges (Dynamic Transitions)

Determine the destination based on state:

from typing import Literal

def should_continue(state: State) -> Literal["continue", "end"]:
    if state["iteration"] < state["max_iterations"]:
        return "continue"
    return "end"

# Add conditional edge
builder.add_conditional_edges(
    "agent",
    should_continue,
    {
        "continue": "tools",  # Go to tools if continue
        "end": END            # End if end
    }
)

3. Entry Points

Define the starting point of the graph:

# Simple entry
builder.add_edge(START, "first_node")

# Conditional entry
builder.add_conditional_edges(
    START,
    route_start,
    {
        "path_a": "node_a",
        "path_b": "node_b"
    }
)

Parallel Execution

Nodes with multiple outgoing edges will have all destination nodes execute in parallel in the next step:

# From node_a to multiple nodes
builder.add_edge("node_a", "node_b")
builder.add_edge("node_a", "node_c")

# node_b and node_c execute in parallel

To aggregate results from parallel execution, use a Reducer:

from operator import add

class State(TypedDict):
    results: Annotated[list, add]  # Aggregate results from multiple nodes

Edge Control with Command

Specify the next destination from within a node:

from langgraph.types import Command

def smart_node(state: State) -> Command:
    result = analyze(state["data"])

    if result["confidence"] > 0.8:
        return Command(
            update={"result": result},
            goto="finalize"
        )
    else:
        return Command(
            update={"result": result, "needs_review": True},
            goto="human_review"
        )

Conditional Branching Implementation Patterns

Pattern 1: Tool Call Loop

def should_continue(state: State) -> Literal["continue", "end"]:
    messages = state["messages"]
    last_message = messages[-1]

    # Continue if there are tool calls
    if last_message.tool_calls:
        return "continue"
    return "end"

builder.add_conditional_edges(
    "agent",
    should_continue,
    {
        "continue": "tools",
        "end": END
    }
)

Pattern 2: Routing

def route_query(state: State) -> Literal["search", "calculate", "general"]:
    query = state["query"]

    if "calculate" in query or "+" in query:
        return "calculate"
    elif "search" in query:
        return "search"
    return "general"

builder.add_conditional_edges(
    "router",
    route_query,
    {
        "search": "search_node",
        "calculate": "calculator_node",
        "general": "general_node"
    }
)

Important Principles

Explicit Control Flow: Transitions should be transparent and traceable
Type Safety: Explicitly specify destinations with Literal
Leverage Parallel Execution: Execute independent tasks in parallel

01_core_concepts_node.md - Node implementation
02_graph_architecture_routing.md - Routing patterns
05_advanced_features_map_reduce.md - Parallel processing patterns

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