Overview

LangGraph Routing Skill enables designing conditional edges and state-driven transitions within LangGraph workflows. It supports multi-path branches, router nodes, and robust fallback routes to ensure predictable agent behavior.

How This Skill Works

The skill defines routing patterns such as conditional edges via add_conditional_edges, dedicated router nodes for decisions, and state-based routing that reads state values to select the next node. It also supports optional LLM-based routing to determine the next node when decisions are ambiguous. Configuration options control routing function definitions, path mappings, defaults, cycle detection, and max iteration limits.

When to Use It

• Designing a LangGraph workflow with multiple outcomes
• Implementing state-driven transitions in plan-and-execute-agent processes
• Handling fallback and error routing paths in complex workflows
• Creating dynamic routing functions for multi-path branches
• Enforcing cycle limits and avoiding infinite loops

Quick Start

1. Step 1: Define routing functions and the state schema for your LangGraph workflow
2. Step 2: Add conditional edges and router nodes to represent decision points
3. Step 3: Configure default/fallback routes and enable cycle- and iteration-limit safeguards, then test with representative scenarios

Best Practices

• Document clear routing logic with edge names and conditions
• Handle all possible states to prevent dead-ends
• Implement explicit fallback paths for failures
• Avoid infinite cycles with cycle detection and limits
• Use descriptive edge names to improve maintainability

Example Use Cases

• Routing a customer-support flow based on issue type and priority
• Order processing with routing depending on stock, region, and delivery method
• Error handling that retries or escalates via router nodes
• LLM-assisted routing to decide next node in ambiguous user intents
• Cycle-detection configuration to prevent infinite loops in long-running workflows

Frequently Asked Questions