langgraph-routing

name	langgraph-routing
description	LangGraph conditional routing patterns. Use when implementing dynamic routing based on state, creating branching workflows, or building retry loops with conditional edges.
context	fork
agent	workflow-architect
version	1.0.0
author	SkillForge
user-invocable	false

LangGraph Conditional Routing

Route workflow execution dynamically based on state.

When to Use

• Dynamic branching based on results
• Retry loops with backoff
• Quality gates with pass/fail paths
• Multi-path workflows

Basic Conditional Edge

from langgraph.graph import StateGraph, END

def route_based_on_quality(state: WorkflowState) -> str:
    """Decide next step based on quality score."""
    if state["quality_score"] >= 0.8:
        return "publish"
    elif state["retry_count"] < 3:
        return "retry"
    else:
        return "manual_review"

workflow.add_conditional_edges(
    "quality_check",
    route_based_on_quality,
    {
        "publish": "publish_node",
        "retry": "generator",
        "manual_review": "review_queue"
    }
)

Quality Gate Pattern

def route_after_quality_gate(state: AnalysisState) -> str:
    """Route based on quality gate result."""
    if state["quality_passed"]:
        return "compress_findings"
    elif state["retry_count"] < 2:
        return "supervisor"  # Retry
    else:
        return END  # Return partial results

workflow.add_conditional_edges(
    "quality_gate",
    route_after_quality_gate,
    {
        "compress_findings": "compress_findings",
        "supervisor": "supervisor",
        END: END
    }
)

Retry Loop Pattern

def llm_call_with_retry(state):
    """Retry failed LLM calls."""
    try:
        result = call_llm(state["input"])
        state["output"] = result
        state["retry_count"] = 0
        return state
    except Exception as e:
        state["retry_count"] += 1
        state["error"] = str(e)
        return state

def should_retry(state) -> str:
    if state.get("output"):
        return "success"
    elif state["retry_count"] < 3:
        return "retry"
    else:
        return "failed"

workflow.add_conditional_edges(
    "llm_call",
    should_retry,
    {
        "success": "next_step",
        "retry": "llm_call",  # Loop back
        "failed": "error_handler"
    }
)

Routing Patterns

Sequential:    A → B → C              (simple edges)
Branching:     A → (B or C)           (conditional edges)
Looping:       A → B → A              (retry logic)
Convergence:   (A or B) → C           (multiple inputs)
Diamond:       A → (B, C) → D         (parallel then merge)

State-Based Router

def dynamic_router(state: WorkflowState) -> str:
    """Route based on multiple state conditions."""
    if state.get("error"):
        return "error_handler"
    if not state.get("validated"):
        return "validator"
    if state["confidence"] < 0.5:
        return "enhance"
    return "finalize"

Key Decisions

Decision	Recommendation
Max retries	2-3 for LLM calls
Fallback	Always have END fallback
Routing function	Keep pure (no side effects)
Edge mapping	Explicit mapping for clarity

Common Mistakes

• No END fallback (workflow hangs)
• Infinite loops (no max retry)
• Side effects in router (hard to debug)
• Missing edge mappings (runtime error)

Related Skills

• langgraph-state - State design for routing
• langgraph-supervisor - Supervisor routing pattern
• agent-loops - ReAct loop patterns

Capability Details

conditional-routing

Keywords: conditional, branch, decision, if-else Solves:

• Route based on conditions
• Implement branching logic
• Create decision nodes

semantic-routing

Keywords: semantic, embedding, similarity, intent Solves:

• Route by semantic similarity
• Intent-based routing
• Embedding-based decisions

router-template

Keywords: template, router, semantic, implementation Solves:

• Semantic router template
• Production router code
• Copy-paste implementation