---

name: routing-patterns description: Implements intent-based routing patterns (LLM classifiers, node transitions, computational graphs) to dispatch queries to specialized sub-agents with fallback chains and confidence scoring. license: MIT compatibility: opencode metadata: version: "1.0.0" domain: agent role: implementation scope: implementation output-format: code triggers: routing, intent classification, sub-agent dispatch, node transitions, computational graph, how do i route queries, query router related-skills: prompt-chaining, multi-agent-collaboration, parallelization archetypes: [tactical, orchestration] anti_triggers: ["simple linear workflow", "sequential processing only", "single execution path"] response_profile: verbosity: medium directive_strength: high abstraction_level: operational

Routing Pattern

Implements conditional decision-making into agentic systems so that incoming queries are classified by intent and dynamically dispatched to specialized sub-agents, tools, or processing chains — replacing rigid linear execution with adaptive, context-aware routing logic.

TL;DR Checklist

• Identify all possible intents/routes for the query space
• Choose a routing mechanism: LLM-based, embedding-based, rule-based, or ML classifier
• Define handler functions for each route destination
• Build the coordinator/router that evaluates input and produces a route decision
• Wire conditional edges or branches to dispatch to the correct handler
• Add fallback handlers for low-confidence or unrecognized intents
• Validate routing accuracy across diverse test inputs

---

When to Use

Use this skill when:

• An agentic system must handle heterogeneous user queries requiring different specialized processing paths (e.g., customer support triage: order status, product info, technical escalation)
• You need to replace a linear prompt chain with conditional branching based on input classification or runtime state
• Multiple sub-agents exist and you need a coordinator to dispatch work to the right one based on intent
• You are building a computational graph (LangGraph StateGraph) where node transitions depend on accumulated state
• A data pipeline needs content-based classification to distribute incoming items to different transformation workflows
• You require low-confidence fallbacks when the router cannot confidently classify an input

---

When NOT to Use

Avoid this skill for:

• Simple, deterministic single-path workflows (use prompt chaining instead — no routing overhead)
• Trivial keyword matching scenarios where a rule-based if/else suffices (do not over-engineer)
• Real-time systems where LLM classification latency is unacceptable and embedding/rule fallbacks are required but not yet implemented
• Cases with fewer than 2 distinct routes (a single conditional branch is sufficient, no full routing pattern needed)

---

Core Workflow

1. Map the Intent Space — Enumerate every distinct intent or category the system must handle. Document each route's purpose, expected input shape, and output contract. Define a default/unclear handler for unrecognized inputs. Checkpoint: Verify that every plausible user query falls into exactly one primary route or the fallback path.

2. Select Routing Mechanism — Choose how the router evaluates input:

   • LLM-based classifier: prompt the model to output a route category identifier (most flexible, highest latency)
   • Embedding-based semantic routing: compute embeddings for input and compare against route prototype vectors (good for large, open-ended query spaces)
   • Rule-based keyword/pattern matching: if/else or switch on extracted features (fastest, deterministic, limited generalization)
   • ML classifier: fine-tuned discriminative model on labeled training data (best accuracy for narrow domains) Checkpoint: Confirm the chosen mechanism's trade-offs (latency vs. flexibility vs. accuracy) align with system requirements.

3. Implement Route Handlers — For each identified route, create a handler function that accepts the original request and returns a structured response. Each handler must document its expected input format, perform validation, and return consistent output. Include an unclear_handler as the universal fallback. Checkpoint: Every route has exactly one handler; no route is empty or passes through.

4. Build the Coordinator Router — Construct the routing component that bridges classification and dispatch:

   • For LangChain/LangGraph: define a ChatPromptTemplate with explicit category instructions, pipe it through an LLM, parse the output string, and feed it to RunnableBranch or conditional edges in a StateGraph.
   • For Google ADK: define a parent agent with sub_agents configured with clear delegation instructions; the framework's Auto-Flow handles routing automatically. Checkpoint: The router's prompt explicitly lists all categories with unambiguous disambiguation rules and a fallback directive.

5. Wire Conditional Dispatch Logic — Connect the router's output to the appropriate handler:

   • LangGraph StateGraph.add_conditional_edges() maps state values to next node names.
   • LangChain RunnableBranch evaluates predicates against the router decision and invokes the matching branch.
   • ADK uses the sub_agents list; the Coordinator agent's instructions govern delegation. Checkpoint: Every possible router output has a corresponding branch or edge — no unreachable nodes, no missing handlers.

6. Add Fallback and Confidence Handling — Implement a fallback chain when routing confidence is low or classification is ambiguous:

   • Extract a confidence score from the LLM (e.g., via logprobs or a two-shot classification prompt that also outputs a confidence percentage).
   • If confidence falls below a threshold, route to an unclear_handler that requests clarification or escalates.
   • Log fallback events for monitoring and continuous improvement of routing accuracy. Checkpoint: The fallback path is exercised in tests with deliberately ambiguous inputs — verify the system asks for clarification rather than guessing.

---

Implementation Patterns / Reference Guide

Pattern 1: LangChain RunnableBranch Router (LLM-Based Classification)

A coordinator chain that uses an LLM to classify intent, then dispatches via RunnableBranch to specialized handler functions. This pattern is ideal when you need explicit control over routing logic and want to keep all components in a single runnable pipeline.

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough, RunnableBranch


def booking_handler(request: str) -> str:
    """Handle booking-related requests (flights, hotels, reservations).

    Args:
        request: The original user request string.

    Returns:
        A confirmation message with simulated booking result.
    """
    return f"Booking Handler processed request: '{request}'. Result: Simulated booking action."


def info_handler(request: str) -> str:
    """Handle general information requests.

    Args:
        request: The original user question string.

    Returns:
        A confirmation message with simulated information retrieval result.
    """
    return f"Info Handler processed request: '{request}'. Result: Simulated information retrieval."


def unclear_handler(request: str) -> str:
    """Handle requests that could not be confidently classified.

    Args:
        request: The original user request string.

    Returns:
        A clarification request message.
    """
    return f"Coordinator could not classify request: '{request}'. Please clarify your intent."


def build_coordinator_router(llm) -> object:
    """Build a LangChain coordinator router with LLM-based intent classification.

    Args:
        llm: A LangChain-compatible chat model instance.

    Returns:
        A composed LangChain runnable that classifies and dispatches requests.
    """
    # Define the routing prompt with explicit category rules
    coordinator_prompt = ChatPromptTemplate.from_messages([
        (
            "system",
            (
                "Analyze the user's request and determine which specialist handler processes it.\n"
                "- If related to booking flights or hotels, output 'booker'.\n"
                "- For all general information questions, output 'info'.\n"
                "- If unclear or not fitting either category, output 'unclear'."
            ),
        ),
        ("user", "{request}"),
    ])

    # Router chain: prompt -> LLM -> parse decision string
    router_chain = coordinator_prompt | llm | StrOutputParser()

    # Define branch predicates and handlers
    branches = {
        "booker": RunnablePassthrough.assign(
            output=lambda x: booking_handler(x["request"]["request"])
        ),
        "info": RunnablePassthrough.assign(
            output=lambda x: info_handler(x["request"]["request"])
        ),
        "unclear": RunnablePassthrough.assign(
            output=lambda x: unclear_handler(x["request"]["request"])
        ),
    }

    # RunnableBranch evaluates in order; last branch is the default
    delegation_branch = RunnableBranch(
        (lambda x: x["decision"].strip() == "booker", branches["booker"]),
        (lambda x: x["decision"].strip() == "info", branches["info"]),
        branches["unclear"],  # Default fallback
    )

    # Compose: parallelize decision + passthrough request, then delegate
    coordinator = {
        "decision": router_chain,
        "request": RunnablePassthrough(),
    } | delegation_branch | (lambda x: x["output"])

    return coordinator

How it works: The {decision, request} dictionary feeds into RunnableBranch, which evaluates lambda predicates against the LLM's classification output. Each matching branch invokes its handler with the original request and extracts the final output.

---

Pattern 2: LangGraph StateGraph with Conditional Node Transitions

LangGraph's state-based graph architecture is ideal for complex routing where decisions depend on accumulated state across multiple steps. Use add_conditional_edges to define dynamic node transitions.

from typing import TypedDict, Annotated
import operator

from langgraph.graph import StateGraph, END


class RouterState(TypedDict):
    """State schema for a LangGraph routing workflow."""
    request: str
    intent: str  # Route decision from classifier
    response: str  # Final output from handler
    confidence: float  # Classification confidence score (0.0–1.0)


# Handler functions — each processes the request for its route category
def booking_node(state: RouterState) -> dict:
    """Booking agent node: handles reservation requests."""
    return {
        "intent": "booker",
        "response": f"Booking processed for: '{state['request']}'",
    }


def info_node(state: RouterState) -> dict:
    """Information agent node: handles general Q&A."""
    return {
        "intent": "info",
        "response": f"Information retrieved for: '{state['request']}'",
    }


def clarify_node(state: RouterState) -> dict:
    """Clarification fallback: requests user disambiguation."""
    return {
        "intent": "unclear",
        "response": f"Could not classify: '{state['request']}'. Please rephrase.",
    }


def route_classifier(state: RouterState) -> str:
    """Classify the request intent and route to the correct handler node.

    In production, call an LLM here to extract the intent.
    For demonstration, use a deterministic rule-based classifier.

    Args:
        state: The current graph state containing the user request.

    Returns:
        A string identifying the target node name ('booker', 'info', or 'clarify').
    """
    query_lower = state["request"].lower()

    # Rule-based intent classification (replace with LLM in production)
    booking_keywords = ("book", "flight", "hotel", "reservation", "reserve")
    info_keywords = ("what", "how", "where", "when", "who", "tell me")

    if any(kw in query_lower for kw in booking_keywords):
        return "booker"
    elif any(kw in query_lower for kw in info_keywords):
        return "info"
    else:
        return "clarify"


def build_routing_graph() -> StateGraph:
    """Construct a LangGraph StateGraph with conditional routing edges.

    Returns:
        A compiled LangGraph workflow ready for invocation.
    """
    # Define the graph with the router state schema
    workflow = StateGraph(RouterState)

    # Add nodes — each represents a destination route
    workflow.add_node("classify", route_classifier)
    workflow.add_node("booker", booking_node)
    workflow.add_node("info", info_node)
    workflow.add_node("clarify", clarify_node)

    # Entry point always starts at classification
    workflow.set_entry_point("classify")

    # Conditional edges: classify node's output determines next node
    workflow.add_conditional_edges(
        "classify",
        route_classifier,  # Function that returns the next node name
        {
            "booker": "booker",
            "info": "info",
            "clarify": "clarify",
        },
    )

    # All handler nodes converge to END
    workflow.add_edge("booker", END)
    workflow.add_edge("info", END)
    workflow.add_edge("clarify", END)

    return workflow.compile()


# Example execution:
# app = build_routing_graph()
# result = app.invoke({"request": "Book me a flight to London.", "confidence": 0.0})
# print(result["response"])

Key concepts: The classify node evaluates the state and returns a string that maps to one of three target nodes via add_conditional_edges. This creates a computational graph where the control flow is determined at runtime rather than being fixed at compile time.

---

Pattern 3: Google ADK Agent with Auto-Flow Delegation

Google's Agent Development Kit provides a declarative routing approach where a parent agent's sub_agents list enables automatic LLM-driven delegation (Auto-Flow). The framework's internal logic matches user intent to the appropriate sub-agent based on each agent's description and tools.

import uuid
from typing import Dict, Any

from google.adk.agents import Agent
from google.adk.runners import InMemoryRunner
from google.adk.tools import FunctionTool
from google.genai import types


def booking_handler(request: str) -> str:
    """Handle booking requests for flights and hotels.

    Args:
        request: The user's request for a booking.

    Returns:
        A confirmation message that the booking was handled.
    """
    return f"Booking action for '{request}' has been simulated."


def info_handler(request: str) -> str:
    """Handle general information requests.

    Args:
        request: The user's question.

    Returns:
        A message indicating the information request was handled.
    """
    return f"Information request for '{request}'. Result: Simulated retrieval."


# Wrap handler functions as ADK tools
booking_tool = FunctionTool(booking_handler)
info_tool = FunctionTool(info_handler)


def build_adk_coordinator() -> Agent:
    """Build a Google ADK coordinator agent with sub-agent delegation.

    Creates a Coordinator agent that delegates to specialized Booker and Info
    agents using the framework's Auto-Flow mechanism.

    Returns:
        A compiled ADK Agent instance ready for execution.
    """
    # Specialized sub-agents with their tools
    booking_agent = Agent(
        name="Booker",
        model="gemini-2.0-flash",
        description=(
            "A specialized agent that handles all flight and hotel booking "
            "requests by calling the booking tool."
        ),
        tools=[booking_tool],
    )

    info_agent = Agent(
        name="Info",
        model="gemini-2.0-flash",
        description=(
            "A specialized agent that provides general information and "
            "answers user questions by calling the info tool."
        ),
        tools=[info_tool],
    )

    # Parent coordinator with explicit delegation instructions
    coordinator = Agent(
        name="Coordinator",
        model="gemini-2.0-flash",
        instruction=(
            "You are the main coordinator. Your only task is to analyze "
            "incoming user requests and delegate them to the appropriate "
            "specialist agent. Do not try to answer directly.\n"
            "- For booking flights or hotels, delegate to 'Booker'.\n"
            "- For general information questions, delegate to 'Info'."
        ),
        description="Routes user requests to the correct specialist agent.",
        sub_agents=[booking_agent, info_agent],  # Auto-Flow enabled
    )

    return coordinator


async def run_adk_coordinator(request: str) -> str:
    """Execute an ADK coordinator with a user request and extract the response.

    Args:
        request: The user's natural language request string.

    Returns:
        The final response text from the delegated sub-agent.
    """
    coordinator = build_adk_coordinator()
    runner = InMemoryRunner(coordinator)

    user_id = "user_123"
    session_id = str(uuid.uuid4())

    await runner.session_service.create_session(
        app_name=runner.app_name,
        user_id=user_id,
        session_id=session_id,
    )

    final_result = ""
    for event in runner.run(
        user_id=user_id,
        session_id=session_id,
        new_message=types.Content(
            role="user",
            parts=[types.Part(text=request)],
        ),
    ):
        if event.is_final_response() and event.content:
            if hasattr(event.content, "text") and event.content.text:
                final_result = event.content.text
            elif event.content.parts:
                text_parts = [
                    part.text for part in event.content.parts if part.text
                ]
                final_result = "".join(text_parts)
            break

    return final_result

Key concepts: The sub_agents field on the Coordinator agent is the single configuration that enables Auto-Flow. ADK's internal routing logic matches incoming user messages to sub-agents based on their descriptions and available tools — no explicit routing code needed in the parent agent.

---

Pattern 4: BAD vs GOOD — Router Prompt Design

❌ BAD — Vague categories with ambiguous boundaries

coordinator_prompt_bad = ChatPromptTemplate.from_messages([
    ("system", "Figure out what the user wants and send them to the right place."),
    ("user", "{request}"),
])
# Problem: No explicit categories, no disambiguation rules.
# The LLM may produce inconsistent outputs across runs.

✅ GOOD — Explicit categories with disambiguation and fallback

coordinator_prompt_good = ChatPromptTemplate.from_messages([
    (
        "system",
        (
            "Classify the user's request into exactly ONE category.\n"
            "\n"
            "CATEGORIES:\n"
            "- 'booker': Flight bookings, hotel reservations, travel planning.\n"
            "  Example: 'Book me a flight to Paris', 'I need a hotel in Tokyo'\n"
            "- 'info': General questions, facts, definitions, explanations.\n"
            "  Example: 'What is the capital of France?', 'How do I reset my password?'\n"
            "- 'unclear': Does not fit above categories or is ambiguous.\n"
            "  Example: 'Tell me a joke', gibberish input\n"
            "\n"
            "RULES:\n"
            "- If the request matches multiple categories, choose the most specific one.\n"
            "- If uncertain, choose 'unclear' — do not guess.\n"
            "- Output ONLY the category name, nothing else.\n"
        ),
    ),
    ("user", "{request}"),
])
# Each category has clear examples and boundary rules.
# Fallback ('unclear') is explicit with a tie-breaking rule.

---

Pattern 5: Confidence-Based Routing with Threshold Fallback

When using LLM classifiers, guard against low-confidence classifications by extracting confidence scores and routing uncertain requests to a clarification handler.

from dataclasses import dataclass


@dataclass
class RouteDecision:
    """Structured output from an intent classifier."""
    category: str
    confidence: float  # 0.0 to 1.0
    raw_output: str


def classify_with_confidence(
    llm,
    request: str,
    confidence_threshold: float = 0.75,
) -> RouteDecision:
    """Classify a request and return the category with a confidence score.

    Uses a two-shot prompt that asks the LLM to also estimate its
    classification confidence as a percentage.

    Args:
        llm: A LangChain-compatible chat model.
        request: The user request string to classify.
        confidence_threshold: Minimum confidence required to accept the routing decision.

    Returns:
        A RouteDecision with category, confidence (0.0–1.0), and raw output.
    """
    prompt = ChatPromptTemplate.from_messages([
        (
            "system",
            (
                "Classify this request into one of three categories: 'booker', "
                "'info', or 'unclear'. Also estimate your confidence as a number "
                "from 0 to 100. Output in JSON format only.\n"
                "Format: {{\"category\": \"<category>\", \"confidence\": <0-100>}}"
            ),
        ),
        ("user", "{request}"),
    ])

    chain = prompt | llm | StrOutputParser()
    raw_output = chain.invoke({"request": request})

    # Parse JSON output to extract category and confidence
    import json

    try:
        parsed = json.loads(raw_output)
        confidence = min(max(parsed["confidence"] / 100.0, 0.0), 1.0)
        category = parsed["category"].strip()
    except (json.JSONDecodeError, KeyError):
        # Fallback: low confidence on parse failure
        confidence = 0.0
        category = "unclear"

    return RouteDecision(
        category=category if confidence >= confidence_threshold else "unclear",
        confidence=confidence,
        raw_output=raw_output,
    )


def apply_fallback(decision: RouteDecision) -> str:
    """Apply fallback logic for low-confidence or unclear classifications.

    Args:
        decision: The classification result from classify_with_confidence.

    Returns:
        A clarification request message if confidence is below threshold.
    """
    if decision.category == "unclear":
        return (
            f"I'm not confident I understood your request correctly "
            f"(confidence: {decision.confidence:.0%}). Could you rephrase?"
        )

    # Confident classification — proceed with normal routing
    handler_map = {
        "booker": lambda: f"Booking processed for '{decision.raw_output}'",
        "info": lambda: f"Information retrieved for '{decision.raw_output}'",
    }

    handler = handler_map.get(decision.category)
    return handler() if handler else "unclear_handler fallback"

---

Constraints

MUST DO

1. Always define a fallback handler — Every routing system must have an unclear_handler (or equivalent) for low-confidence or unrecognized inputs. Never let an unclassified request fall through without handling it explicitly. Follow the Fail Fast law from code-philosophy: invalid states halt with a clarification request, not a silent default.

2. Document every route's input/output contract — Each handler function must specify its expected input shape and return type. Use Python type hints and docstrings as required by the Atomic Predictability principle (pure, predictable functions).

3. Ensure exhaustive branch coverage — Every possible router output string must map to exactly one branch or edge in the dispatch logic. Verify with static analysis: count branches against category count — they must match.

4. Provide explicit disambiguation rules in the classifier prompt — When categories could overlap (e.g., "book a restaurant" vs. "restaurant review"), include tie-breaking rules in the system prompt so the LLM produces deterministic output.

5. Log routing decisions and fallback events — Every classification (category, confidence, raw output) should be logged for monitoring. Fallback events are especially important: they indicate gaps in your route definitions or prompt quality.

6. Reference code-philosophy laws — All routing code must follow the 5 Laws of Elegant Defense:

   • Early Exit: Guard clauses handle edge cases at the top of handlers
   • Parse Don't Validate: Parse data at boundaries, trust internal state
   • Atomic Predictability: Classification functions should be pure and deterministic where possible
   • Fail Fast: Invalid classifications halt with a clarification request immediately
   • Intentional Naming: Route names read like English verbs ("booker", "info", "escalate")

7. Test routing with adversarial inputs — Include test cases for ambiguous, multi-intent, and out-of-scope queries to verify that the fallback chain activates correctly.

MUST NOT DO

1. Never skip the fallback handler — An empty or missing unclear_handler is a critical bug. The system must always return something meaningful, never crash on unclassifiable input.

2. Do not embed routing logic inside handlers — Handlers should only execute their domain task. Routing (classification + dispatch) is a separate concern handled by the coordinator/router component.

3. Never use bare string matching without normalization — Always .strip() and normalize case before comparing router outputs. Inconsistent whitespace causes silent misrouting bugs.

4. Do not hardcode routing categories as magic strings scattered across the codebase — Define category constants or an Enum (e.g., RouteCategory.BOOKER) to prevent typos that create unreachable branches.

5. Never route based solely on a single keyword without context — A request containing "book" might mean "book a flight" (routable) or "book a research paper" (unclear). Use multi-signal classification, not single-keyword heuristics.

6. Do not add new routes without updating the fallback and test suite — Every new route must have: an updated classifier prompt with category examples, a handler implementation, and at least two test cases (one positive, one ambiguous).

---

Output Template

When applying this skill, produce output in the following structure:

1. Intent Space Map — Table of all routes with purpose, keywords, and handler function name.
2. Routing Mechanism Choice — State which mechanism was selected (LLM-based / embedding-based / rule-based / ML classifier) and justify the choice based on latency vs. flexibility requirements.
3. Router Prompt Template — The complete ChatPromptTemplate (or equivalent) with all categories, examples, and disambiguation rules.
4. Handler Implementations — Typed Python functions for each route destination with docstrings and type hints.
5. Dispatch Wiring — The LangChain RunnableBranch, LangGraph add_conditional_edges, or ADK sub-agent configuration connecting the router to handlers.
6. Fallback Configuration — Confidence threshold, fallback handler behavior, and logging strategy.
7. Test Cases — At least 3 test inputs demonstrating correct routing across all categories plus one ambiguous input exercising the fallback path.

---

Related Skills

Skill	Purpose
prompt-chaining	Sequential workflows that precede or follow routed decisions — combine when routing feeds into multi-step chains
multi-agent-collaboration	Coordination between routed sub-agents after dispatch — handles inter-agent communication once routing completes
parallelization	Routes queries to parallel sub-workers for independent processing — use when multiple routes can execute concurrently

📖 skill(local cache): customize-opencode, code-philosophy