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

# Simple Gemini LLM

> Learn how to use Google's Gemini models with Cycls to build a streaming AI assistant.

This guide shows you how to integrate the `google-genai` SDK into your Cycls agent to access Gemini models.

You will learn how to:

* Define dependencies for the Docker environment.
* Configure the Gemini API client inside the agent handler.
* Stream responses from Gemini back to the user.

## Prerequisites

* Python 3.9+
* `cycls` package installed
* Google AI Studio API Key
* Docker installed (for local testing)

```bash theme={null}
pip install cycls
```

## Step 1: Create the Agent

Create a new file called `app.py`:

```python theme={null}
import cycls

@cycls.app(pip=["google-genai"], copy=[".env"])
async def app(context):
    import os
    from google import genai

    # Convert messages to Gemini format
    contents = [
        {
            'role': 'model' if m['role'] == 'assistant' else 'user',
            'parts': [{'text': m['content']}]
        }
        for m in context.messages if m['role'] != 'system'
    ]

    # Initialize Gemini client
    client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))

    # Stream the response
    async for chunk in await client.aio.models.generate_content_stream(
        model="gemini-2.5-flash",
        contents=contents,
        config={'system_instruction': "You are a helpful AI assistant."}
    ):
        if chunk.text:
            yield chunk.text

app.local()
```

## Step 2: Configure Environment

Create a `.env` file in the same directory with your API key:

```env theme={null}
GEMINI_API_KEY=your_api_key_here
```

## Step 3: Run the Agent

Execute your agent script:

```bash theme={null}
python app.py
```

The agent will start locally and provide an endpoint for interaction.

## Full Code

Here is the complete `app.py` file:

```python theme={null}
import cycls

@cycls.app(pip=["google-genai"], copy=[".env"])
async def app(context):
    import os
    from google import genai

    # Convert messages to Gemini format
    contents = [
        {
            'role': 'model' if m['role'] == 'assistant' else 'user',
            'parts': [{'text': m['content']}]
        }
        for m in context.messages if m['role'] != 'system'
    ]

    # Initialize Gemini client
    client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))

    # Stream the response
    async for chunk in await client.aio.models.generate_content_stream(
        model="gemini-2.5-flash",
        contents=contents,
        config={'system_instruction': "You are a helpful AI assistant."}
    ):
        if chunk.text:
            yield chunk.text

app.local()
```

## Deploy to Cloud

To deploy to production:

```python theme={null}
import cycls
import os

cycls.api_key = os.getenv("CYCLS_API_KEY")

@cycls.app(pip=["google-genai"], copy=[".env"])
async def app(context):
    from google import genai

    contents = [
        {
            'role': 'model' if m['role'] == 'assistant' else 'user',
            'parts': [{'text': m['content']}]
        }
        for m in context.messages if m['role'] != 'system'
    ]

    client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))

    async for chunk in await client.aio.models.generate_content_stream(
        model="gemini-2.5-flash",
        contents=contents,
        config={'system_instruction': "You are a helpful AI assistant."}
    ):
        if chunk.text:
            yield chunk.text

app.deploy()
```