Python Quickstart

Python support is currently in Alpha. The core features are functional, but the API may change in future releases.

This guide will help you create your first AI-powered application using Genkit for Python. You’ll learn how to initialize Genkit, make your first generation request, and work with flows.

Prerequisites

Python 3.10 or later
pip or uv package manager
A Google AI API key (get one at Google AI Studio)

Step 1: Install Genkit

Install Genkit with the Google AI plugin:

pip install "genkit[google-genai]"

Step 2: Install Genkit CLI

The CLI is the same across all languages:

npm install -g genkit-cli

Step 3: Set Your API Key

Set your Google AI API key as an environment variable:

export GOOGLE_GENAI_API_KEY="your-api-key-here"

Create a .env file in your project root:

GOOGLE_GENAI_API_KEY=your-api-key-here

Step 4: Create Your First Application

Create a file named app.py:

app.py

from genkit import Genkit
from genkit.plugins.google_genai import GoogleGenAI, gemini_2_0_flash

ai = Genkit(
    plugins=[GoogleGenAI()],
    model=gemini_2_0_flash,
)

response = await ai.generate(prompt="Tell me a joke")
print(response.text)

Genkit for Python uses async/await. You’ll need to run this in an async context.

Step 5: Run Your Application

Since Genkit uses async functions, you need to run it properly:

app.py

import asyncio
from genkit import Genkit
from genkit.plugins.google_genai import GoogleGenAI, gemini_2_0_flash

async def main():
    ai = Genkit(
        plugins=[GoogleGenAI()],
        model=gemini_2_0_flash,
    )

    response = await ai.generate(prompt="Tell me a joke")
    print(response.text)

if __name__ == "__main__":
    asyncio.run(main())

Run with the Genkit CLI for tracing:

genkit start -- python app.py

Or run directly:

python app.py

Access the Developer UI

When running with genkit start, open http://localhost:4000 to access the Developer UI.

Create a Flow

Flows are the primary abstraction in Genkit for encapsulating AI logic:

from genkit import Genkit
from genkit.plugins.google_genai import GoogleGenAI, gemini_2_0_flash

ai = Genkit(
    plugins=[GoogleGenAI()],
    model=gemini_2_0_flash,
)

@ai.flow()
async def tell_joke(topic: str) -> str:
    response = await ai.generate(
        prompt=f"Tell me a joke about {topic}"
    )
    return response.text

# Run the flow
joke = await tell_joke("programming")
print(joke)

Generate Structured Output

Genkit can generate type-safe structured data using Pydantic models:

from pydantic import BaseModel
from genkit import Genkit
from genkit.plugins.google_genai import GoogleGenAI, gemini_2_0_flash

class Recipe(BaseModel):
    title: str
    ingredients: list[str]
    steps: list[str]

ai = Genkit(
    plugins=[GoogleGenAI()],
    model=gemini_2_0_flash,
)

response = await ai.generate(
    prompt="Create a recipe for chocolate chip cookies",
    output_schema=Recipe
)

recipe = response.output
print(f"Title: {recipe.title}")
print(f"Ingredients: {recipe.ingredients}")

Define Tools

Tools allow AI models to call your Python functions:

from genkit import Genkit
from genkit.plugins.google_genai import GoogleGenAI, gemini_2_0_flash
from pydantic import BaseModel

class WeatherInput(BaseModel):
    location: str

ai = Genkit(
    plugins=[GoogleGenAI()],
    model=gemini_2_0_flash,
)

@ai.tool()
async def get_weather(input: WeatherInput) -> str:
    # In a real app, call a weather API
    return f"The weather in {input.location} is sunny and 72°F"

response = await ai.generate(
    prompt="What's the weather in San Francisco?",
    tools=[get_weather]
)
print(response.text)

Create an HTTP Server

Deploy your flows as HTTP endpoints using the built-in flow server:

from genkit import Genkit
from genkit.plugins.google_genai import GoogleGenAI, gemini_2_0_flash
from genkit.servers.asgi import create_flows_asgi_app

ai = Genkit(
    plugins=[GoogleGenAI()],
    model=gemini_2_0_flash,
)

@ai.flow()
async def tell_joke(topic: str) -> str:
    response = await ai.generate(
        prompt=f"Tell me a joke about {topic}"
    )
    return response.text

# Create ASGI app that exposes all flows
app = create_flows_asgi_app(registry=ai.registry)

# Run with: uvicorn app:app --reload

Run the server:

uvicorn app:app --reload

Test your endpoint:

curl -X POST http://localhost:8000/tell_joke \
  -H "Content-Type: application/json" \
  -d '{"data": "programming"}'

Use with Flask

You can also integrate Genkit with Flask:

from flask import Flask
from genkit import Genkit
from genkit.plugins.google_genai import GoogleGenAI, gemini_2_0_flash
from genkit.plugins.flask import genkit_flask_handler

app = Flask(__name__)

ai = Genkit(
    plugins=[GoogleGenAI()],
    model=gemini_2_0_flash,
)

@app.route('/joke', methods=['POST'])
@genkit_flask_handler(ai)
@ai.flow()
async def joke(topic: str) -> str:
    response = await ai.generate(
        prompt=f"Tell me a joke about {topic}"
    )
    return response.text

if __name__ == '__main__':
    app.run()

Try Other Model Providers

Genkit supports multiple AI providers:

from genkit import Genkit
from genkit.plugins.google_genai import GoogleGenAI
from genkit.plugins.anthropic import Anthropic
from genkit.plugins.ollama import Ollama

ai = Genkit(
    plugins=[
        GoogleGenAI(),
        Anthropic(),
        Ollama(server_address="http://localhost:11434"),
    ],
)

# Use Claude
response = await ai.generate(
    model="anthropic/claude-3-5-sonnet-20241022",
    prompt="Hello, Claude!"
)

# Use local Ollama models
response = await ai.generate(
    model="ollama/llama2",
    prompt="Hello, Llama!"
)

Development with uv

For faster dependency management, use uv:

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create a new project
uv init my-genkit-app
cd my-genkit-app

# Install dependencies
uv pip install "genkit[google-genai]"

# Run with uv
uv run python app.py

Learn More

Python API Reference

Explore the complete Python API

Structured Output

Generate type-safe responses with Pydantic

Flask Integration

Deploy flows with Flask

Deployment

Deploy your Python applications

Overview

Getting Started

Core Concepts

Guides

Model Providers

Deployment

Developer Tools

Python Quickstart

Prerequisites

Step 1: Install Genkit

Step 2: Install Genkit CLI

Step 3: Set Your API Key

Step 4: Create Your First Application

Step 5: Run Your Application

Access the Developer UI

Create a Flow

Generate Structured Output

Define Tools

Create an HTTP Server

Use with Flask

Try Other Model Providers

Development with uv

Learn More

Python API Reference

Structured Output

Flask Integration

Deployment

Build docs developers (and LLMs) love

Overview

Getting Started

Core Concepts

Guides

Model Providers

Deployment

Developer Tools

​Prerequisites

​Step 1: Install Genkit

​Step 2: Install Genkit CLI

​Step 3: Set Your API Key

​Step 4: Create Your First Application

​Step 5: Run Your Application

​Access the Developer UI

​Create a Flow

​Generate Structured Output

​Define Tools

​Create an HTTP Server

​Use with Flask

​Try Other Model Providers

​Development with uv

​Learn More

Python API Reference

Structured Output

Flask Integration

Deployment

Build docs developers (and LLMs) love

Prerequisites

Step 1: Install Genkit

Step 2: Install Genkit CLI

Step 3: Set Your API Key

Step 4: Create Your First Application

Step 5: Run Your Application

Access the Developer UI

Create a Flow

Generate Structured Output

Define Tools

Create an HTTP Server

Use with Flask

Try Other Model Providers

Development with uv

Learn More