Router

A central agent inspects each request and routes it to the most appropriate specialist agent based on content and context.

Overview

Routers keep front-line interactions lightweight: they triage requests, choose which specialist should respond, and optionally provide fallbacks when no match is found. Common use cases include customer support triage and language translation hubs.

Demo Scenario: Language Help Desk

Four agents collaborate using gllm-pipeline with conditional routing:

• Router agent – classifies the language query

• Spanish expert – handles Spanish translation and explanations

• Japanese expert – handles Japanese translation and explanations

• General handler – provides fallback for unsupported languages

The pipeline uses the switch() step to route queries based on the router's classification, automatically directing each query to the appropriate specialist.

Diagram

Implementation Steps

1. Create router and specialist agents

   from glaip_sdk import Agent
   
   router_agent = Agent(
       name="router_agent",
       instruction="Classify query: spanish | japanese | other",
       model="openai/gpt-5-mini"
   )
   
   spanish_agent = Agent(
       name="spanish_agent",
       instruction="Spanish language expert...",
       model="openai/gpt-5-mini"
   )
   
   japanese_agent = Agent(
       name="japanese_agent",
       instruction="Japanese language expert...",
       model="openai/gpt-5-mini"
   )
   
   general_agent = Agent(
       name="general_agent",
       instruction="Fallback handler...",
       model="openai/gpt-5-mini"
   )

2. Build pipeline with switch for conditional routing

   from gllm_pipeline.steps import step, switch
   
   route_switch = switch(
       condition=lambda s: s["route_label"].strip().lower(),
       branches={
           "spanish": spanish_step,
           "japanese": japanese_step,
           "other": general_step
       },
       default=general_step,
   )
   
   pipeline = route_step | route_switch
   pipeline.state_type = State

3. Process requests

   for query in queries:
       result = await pipeline.invoke(State(user_query=query, ...))
       print(result['final_answer'])

Full implementation: See router/main.py for complete code with State definition and step configuration.

AgentComponent: See the Agent as Component guide for details on the .to_component() pattern.

How to Run

From the gl-aip/examples/multi-agent-system-patterns directory in the GL SDK Cookbook:

uv run router/main.py

Set the usual environment variables in .env:

OPENAI_API_KEY=your-openai-key-here

Output

--- Processing Query: How do you say 'hello' in German? ---
Answer: Lo siento — solo puedo ayudar con consultas en español o japonés. No puedo proporcionar
traducciones al alemán.

Si te sirve:
- En español: "hola" (pronunciación aproximada: OH-la).
- En japonés: "こんにちは" (konnichiwa).

¿Quieres más saludos o ayuda con pronunciación en español o japonés?

Demo completed

Notes

• This example uses gllm-pipeline with the switch() step for conditional routing.

• The router agent outputs a classification label that determines which branch to execute.

• Add more language specialists by adding new agents and branches to the switch.

• The default parameter in switch() provides a fallback when no branch matches.

• To install gllm-pipeline: uv add gllm-pipeline-binary==0.4.13 (compatible with aip_agents and langgraph <0.3.x)

Related Documentation

• Agents guide — Configure nested agents, memory, and streaming renderers.

• Automation & scripting — Log routing results or wire the router into scheduled jobs.

• Security & privacy — Apply memory and PII controls when routing sensitive requests.