single_turn

README

ADK Single-turn Agent as Sub-agent Sample

Overview

This sample demonstrates how a "single_turn" mode agent can act as an autonomous sub-agent to an LLM agent, utilizing schemas and tools without ever interacting with the user.

Note: This is the recommended mechanism to replace the older AgentTool pattern. Unlike AgentTool, using a single_turn sub-agent preserves the sub-agent's internal interactions (like tool calls) in the session history.

Single-turn agents are designed to execute their function fully in one prompt-response cycle. In this sample:

1. phone_recommender: A single-turn agent that receives structured input (UserPreferences), uses a mocked tool (check_phone_price), and returns structured output (PhoneRecommendation).
2. root_agent: The main agent that interacts with the user, translates their natural language request into the structured UserPreferences, and delegates to phone_recommender.

Sample Inputs

• I need a phone mostly for gaming. I have about $1000 to spend.

• What is a good cheap phone from Google for basic tasks?

• I love photography but prefer smaller phones. My budget is $600.

Graph

graph TD
    root_agent --> phone_recommender
    phone_recommender -.->|uses| check_phone_price[check_phone_price tool]

How To

1. Define a sub-agent with Mode: llmagent.ModeSingleTurn, InputSchema, OutputSchema:

   phoneRecommender, err := llmagent.New(llmagent.Config{
       Name:         "phone_recommender",
       Model:        model,
       Mode:         llmagent.ModeSingleTurn,
       InputSchema:  userPreferencesSchema,
       OutputSchema: phoneRecommendationSchema,
       Tools:        []tool.Tool{checkPhonePriceTool},
       // ...
   })
   

2. Assign it to a parent agent:

   rootAgent, err := llmagent.New(llmagent.Config{
       SubAgents: []agent.Agent{phoneRecommender},
       // ...
   })
   

Run

Set your API key and run the sample with the console launcher:

export GOOGLE_API_KEY=...
go run ./examples/multiagent/single_turn console

Documentation

Overview

Package main demonstrates how a "single_turn" mode agent can act as an autonomous sub-agent to an LLM agent, utilizing schemas and tools without ever interacting with the user.

The phone_recommender sub-agent receives structured input (UserPreferences), uses a mocked tool (check_phone_price), and returns structured output (PhoneRecommendation). The root_agent translates the user's natural language request into the structured input and delegates to the sub-agent.