Antigravity for Python Developers: Django, FastAPI & Flask Complete Workflows
Why Python Developers Love Antigravity
Python developers have unique needs. We build everything from data pipelines to web APIs to machine learning systems. We need an AI assistant that understands Python's ecosystem deeply—not just syntax, but the idioms, patterns, and best practices that make Python code truly Pythonic.
Google Antigravity delivers exactly that. With its 1 million token context window and Gemini 3's deep training on Python codebases, it's the most capable AI assistant for Python development in 2025. This guide will show you how to leverage Antigravity for Django, FastAPI, Flask, and beyond.
Setting Up Antigravity for Python Development
Step 1: Install and Configure
Install VS Code and the Antigravity extension
Sign in with your Google account
Install the Python extension for VS Code
Configure your Python interpreter (Cmd+Shift+P → "Python: Select Interpreter")
Step 2: Create a Python-Focused .antigravity File
# Python Development Rules for Antigravity
## Python Version and Style
- Target Python 3.11+ features when appropriate
- Follow PEP 8 style guidelines strictly
- Use type hints for all function signatures
- Prefer f-strings over .format() or % formatting
- Use pathlib for file system operations
- Prefer dataclasses or Pydantic models over plain dicts
## Code Organization
- Keep functions small and focused (max 20 lines ideal)
- Use meaningful variable names (no single letters except loop indices)
- Group imports: stdlib, third-party, local (with blank lines between)
- Add docstrings to all public functions and classes
## Error Handling
- Use specific exception types, never bare except
- Include context in exception messages
- Use logging instead of print statements
- Handle edge cases explicitly
## Testing
- Write pytest-style tests
- Use fixtures for common setup
- Mock external dependencies
- Aim for descriptive test names
Step 3: Install Python MCP Servers
{
"mcpServers": {
"python-lsp": {
"command": "python",
"args": ["-m", "pylsp"],
"description": "Python Language Server for deep code understanding"
},
"postgres": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-postgres"],
"env": {
"DATABASE_URL": "postgresql://user:pass@localhost/db"
}
}
}
}
"Create a new Django 5 project called 'marketplace' with:
Custom user model with email as username
Products app with categories, reviews, and images
Orders app with cart functionality
RESTful API using Django REST Framework
PostgreSQL database configuration
Environment variables with python-dotenv
Docker configuration for development
pytest setup with factory_boy for testing"
Django Model Generation
Antigravity excels at generating complex Django models:
# products/models.py
from django.db import models
from django.utils.text import slugify
from django.core.validators import MinValueValidator
from mptt.models import MPTTModel, TreeForeignKey
from decimal import Decimal
import uuid
class Category(MPTTModel):
"""Product category with nested structure using MPTT."""
name = models.CharField(max_length=100)
slug = models.SlugField(max_length=120, unique=True, blank=True)
description = models.TextField(blank=True)
image = models.ImageField(upload_to='categories/', blank=True)
parent = TreeForeignKey(
'self',
on_delete=models.CASCADE,
null=True,
blank=True,
related_name='children'
)
is_active = models.BooleanField(default=True)
class MPTTMeta:
order_insertion_by = ['name']
def save(self, *args, **kwargs):
if not self.slug:
self.slug = slugify(self.name)
super().save(*args, **kwargs)
class Product(models.Model):
"""E-commerce product with full feature set."""
name = models.CharField(max_length=200)
slug = models.SlugField(max_length=220, unique=True, blank=True)
description = models.TextField()
price = models.DecimalField(
max_digits=10,
decimal_places=2,
validators=[MinValueValidator(Decimal('0.01'))]
)
sale_price = models.DecimalField(
max_digits=10,
decimal_places=2,
null=True,
blank=True
)
sku = models.CharField(max_length=50, unique=True, blank=True)
stock = models.PositiveIntegerField(default=0)
category = models.ForeignKey(
Category,
on_delete=models.SET_NULL,
null=True,
related_name='products'
)
is_active = models.BooleanField(default=True)
created_at = models.DateTimeField(auto_now_add=True)
updated_at = models.DateTimeField(auto_now=True)
@property
def current_price(self) -> Decimal:
"""Return sale price if available, otherwise regular price."""
return self.sale_price or self.price
@property
def is_in_stock(self) -> bool:
"""Check if product is in stock."""
return self.stock > 0
FastAPI Development with Antigravity
FastAPI Project Setup
Agent Mode prompt for a complete FastAPI project:
"Create a FastAPI project for a task management API:
Structure:
app/main.py - FastAPI application
app/api/ - route handlers organized by resource
app/models/ - SQLAlchemy models
app/schemas/ - Pydantic models for request/response
app/services/ - business logic layer
app/core/ - config, security, dependencies
Features:
JWT authentication with refresh tokens
PostgreSQL with async SQLAlchemy
Alembic migrations
Redis for caching
Rate limiting
Request validation with Pydantic
Proper error handling
OpenAPI documentation"
FastAPI Async Endpoints
# app/api/v1/tasks.py
from fastapi import APIRouter, Depends, HTTPException, Query, status
from sqlalchemy.ext.asyncio import AsyncSession
from typing import Annotated
from app.schemas.task import TaskCreate, TaskResponse, TaskListResponse
from app.services.task_service import TaskService
from app.api.deps import get_current_user, get_task_service
router = APIRouter(prefix="/tasks", tags=["tasks"])
@router.get("", response_model=TaskListResponse)
async def list_tasks(
current_user: Annotated[User, Depends(get_current_user)],
task_service: Annotated[TaskService, Depends(get_task_service)],
status: str | None = Query(None),
skip: int = Query(0, ge=0),
limit: int = Query(20, ge=1, le=100),
) -> TaskListResponse:
"""List all tasks for the current user with optional filtering."""
tasks, total = await task_service.get_user_tasks(
user_id=current_user.id,
skip=skip,
limit=limit,
)
return TaskListResponse(items=tasks, total=total, skip=skip, limit=limit)
@router.post("", response_model=TaskResponse, status_code=status.HTTP_201_CREATED)
async def create_task(
task_data: TaskCreate,
current_user: Annotated[User, Depends(get_current_user)],
task_service: Annotated[TaskService, Depends(get_task_service)],
) -> TaskResponse:
"""Create a new task for the current user."""
task = await task_service.create_task(
user_id=current_user.id,
data=task_data,
)
return task
Pydantic Models with Antigravity
# app/schemas/user.py
from pydantic import BaseModel, EmailStr, Field, field_validator, ConfigDict
from datetime import datetime
import re
class UserBase(BaseModel):
"""Base user schema with common fields."""
email: EmailStr
username: str = Field(min_length=3, max_length=50)
full_name: str | None = Field(None, max_length=100)
@field_validator('username')
@classmethod
def username_not_reserved(cls, v: str) -> str:
reserved = {'admin', 'root', 'system', 'api'}
if v.lower() in reserved:
raise ValueError(f"Username '{v}' is reserved")
return v.lower()
class UserCreate(UserBase):
"""Schema for creating a new user."""
password: str = Field(min_length=8, max_length=128)
password_confirm: str
@field_validator('password')
@classmethod
def password_strength(cls, v: str) -> str:
if not re.search(r'\d', v):
raise ValueError('Password must contain at least one number')
return v
class UserResponse(UserBase):
"""Schema for user responses (without password)."""
id: int
is_active: bool
created_at: datetime
model_config = ConfigDict(from_attributes=True)
# tests/services/test_task_service.py
import pytest
from unittest.mock import AsyncMock
from app.services.task_service import TaskService
from app.schemas.task import TaskCreate, TaskUpdate
from app.core.exceptions import TaskNotFoundError, PermissionDeniedError
@pytest.fixture
def task_repository():
"""Mock task repository."""
return AsyncMock()
@pytest.fixture
def task_service(task_repository):
"""Task service with mocked repository."""
return TaskService(repository=task_repository)
class TestCreateTask:
"""Tests for task creation."""
async def test_create_task_with_valid_data(
self,
task_service: TaskService,
task_repository: AsyncMock,
):
"""Should create task with valid data."""
task_data = TaskCreate(title="Test Task", priority="high")
task_repository.create.return_value = expected_task
result = await task_service.create_task(user_id=1, data=task_data)
assert result.title == task_data.title
task_repository.create.assert_called_once()
class TestGetTask:
"""Tests for task retrieval."""
async def test_get_nonexistent_task(
self,
task_service: TaskService,
task_repository: AsyncMock,
):
"""Should raise TaskNotFoundError when task doesn't exist."""
task_repository.get_by_id.return_value = None
with pytest.raises(TaskNotFoundError):
await task_service.get_task(task_id=999, user_id=1)
Python-Specific Antigravity Tips
1. Use Type Hints in Prompts
Include type information for better code generation:
"Create a function that takes a list of User objects and returns a dict mapping user_id (int) to their task count (int)"
2. Reference Python Idioms
"Use a context manager for database connections"
"Implement this as a generator for memory efficiency"
"Use walrus operator where it improves readability"
3. Specify Python Version Features
"Use match statement (Python 3.10+) for this routing logic"
"Use the new type parameter syntax from Python 3.12"
4. Request Documentation
"Include Google-style docstrings with Args, Returns, and Raises sections"
Frequently Asked Questions
Does Antigravity work with virtual environments?
Yes! Configure your VS Code Python interpreter to use your venv, and Antigravity will understand your installed packages.
Can Antigravity generate requirements.txt?
Absolutely. Ask: "Generate a requirements.txt with pinned versions for this project" and it will analyze imports and suggest dependencies.
Can it generate async code?
Yes, Antigravity excels at async Python. It understands asyncio, aiohttp, and async database libraries like asyncpg and async SQLAlchemy.
Does it know popular Python libraries?
Antigravity knows Django, FastAPI, Flask, SQLAlchemy, Pydantic, Celery, pandas, NumPy, pytest, and most popular Python packages.
Conclusion
Antigravity transforms Python development. Whether you're building Django web apps, FastAPI microservices, or Flask APIs, it generates production-ready, Pythonic code that follows best practices.
The key is teaching Antigravity your preferences through .antigravity rules and clear prompts. Once configured, it becomes an incredibly productive pair programming partner.
Python's philosophy is "there should be one obvious way to do it." Antigravity helps you find that way, faster than ever before.
Next Steps
Set up your Python .antigravity rules file using the template above
Install relevant MCP servers for your database and tools
Try generating a small feature in your current project