> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cascadeflow.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Google ADK

> Plugin-based harness integration for Google Agent Development Kit with budget enforcement and metrics tracking.

cascadeflow integrates with Google's Agent Development Kit (ADK) through the `BasePlugin` system. Call `enable()` to get a plugin that plugs into `Runner(plugins=[...])`, keeping runtime measurement and enforcement close to the ADK execution flow instead of pushing it out to a separate proxy layer.

## Install

```bash theme={null}
pip install "cascadeflow[google-adk]"
```

Requires Python 3.10+.

## Quick Start

```python theme={null}
import asyncio
from google.adk.agents import Agent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai.types import Content, Part

import cascadeflow
from cascadeflow.integrations.google_adk import GoogleADKHarnessConfig, enable

cascadeflow.init(mode="observe")

# Enable harness plugin
config = GoogleADKHarnessConfig(
    fail_open=True,
    enable_budget_gate=True,
)
plugin = enable(config=config)

# Create ADK agent
agent = Agent(
    name="research_agent",
    model="gemini-2.5-flash",
    instruction="You are a helpful research assistant.",
)

# Run with plugin
session_service = InMemorySessionService()
runner = Runner(agent=agent, plugins=[plugin])

async def main():
    with cascadeflow.run(budget=0.50) as session:
        user_content = Content(parts=[Part(text="Explain cascadeflow")])
        async for event in runner.run_async(
            session_id="test",
            user_id="user-1",
            new_message=user_content,
        ):
            pass  # Process streaming events

        print(session.summary())

asyncio.run(main())
```

## Configuration

```python theme={null}
config = GoogleADKHarnessConfig(
    fail_open=True,          # Continue on harness errors
    enable_budget_gate=True, # Enforce budget caps
)
```

## Supported Gemini Models

| Model            | Input \$/1M | Output \$/1M | Energy Coeff |
| ---------------- | ----------- | ------------ | ------------ |
| gemini-2.5-flash | \$0.15      | \$0.60       | 0.30         |
| gemini-2.5-pro   | \$1.25      | \$10.00      | 1.20         |
| gemini-2.0-flash | \$0.10      | \$0.40       | 0.25         |
| gemini-1.5-flash | \$0.075     | \$0.30       | 0.20         |
| gemini-1.5-pro   | \$1.25      | \$5.00       | 1.00         |

## Budget Enforcement

When budget is exceeded in `enforce` mode, the plugin returns an `LlmResponse` with `error_code="BUDGET_EXCEEDED"`. The ADK runner handles this as a graceful stop.

## Why This Integration Matters

* ADK runners can stay framework-native while gaining runtime governance
* Budget control and traces apply at the actual execution boundary
* The integration keeps the in-process latency advantage intact

## Limitations

* Tool gating is not applied (intentional design choice — ADK manages tool execution internally)
* Model switching depends on ADK's model configuration

<Tip>
  **Example on GitHub:** [integrations/google\_adk\_harness.py](https://github.com/lemony-ai/cascadeflow/blob/main/examples/integrations/google_adk_harness.py)
</Tip>