# 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`).
1. `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

```mermaid
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="single_turn"`, `input_schema`, `output_schema`:

   ```python
   phone_recommender = Agent(
       name="phone_recommender",
       mode="single_turn",
       input_schema=UserPreferences,
       output_schema=PhoneRecommendation,
       tools=[check_phone_price],
       ...
   )
   ```

1. Assign it to a parent agent:

   ```python
   root_agent = Agent(
       sub_agents=[phone_recommender],
       ...
   )
   ```

## Related Guides

- [LlmAgent Single-Turn Mode](../../../../docs/guides/agents/llm_agent/single_turn.md) - Guide explaining the behavior and configuration of single-turn agents.