Flask — The Python Micro Web Framework

What it is

Flask is a lightweight WSGI web application framework for Python. It provides routing, request handling, templating (Jinja2), and session management with minimal overhead. Flask follows a microframework philosophy: include only what you need, add extensions for everything else.

Flask is the minimalist counterpart to Django. Where Django provides an all-in-one solution with ORM, admin, and auth built in, Flask gives you a foundation and lets you choose your own database layer, authentication system, and project structure.

How it saves time or tokens

Flask gets a web server running in under 10 lines of Python. No project scaffolding, no configuration files, no boilerplate. For prototyping, this speed-to-first-request is unmatched.

Flask is one of the most documented Python frameworks. AI coding assistants produce accurate Flask code reliably because the patterns are simple and well-represented in training data.

Additionally, the project's well-structured documentation and active community mean developers spend less time troubleshooting integration issues. When AI coding assistants generate code for this tool, they can reference established patterns from the documentation, producing correct implementations with fewer iterations and lower token costs.

How to use

1. Install Flask:

pip install flask

1. Create a minimal application:

from flask import Flask, jsonify, request

app = Flask(__name__)

@app.route('/api/hello')
def hello():
    name = request.args.get('name', 'World')
    return jsonify({'message': f'Hello, {name}'})

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

1. Add extensions as needed: Flask-SQLAlchemy for database, Flask-Login for authentication, Flask-CORS for cross-origin requests.

1. Deploy with Gunicorn or uWSGI behind Nginx for production.

Example

from flask import Flask, jsonify, request
from flask_sqlalchemy import SQLAlchemy

app = Flask(__name__)
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///app.db'
db = SQLAlchemy(app)

class Task(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    title = db.Column(db.String(200), nullable=False)
    done = db.Column(db.Boolean, default=False)

@app.route('/api/tasks', methods=['GET'])
def list_tasks():
    tasks = Task.query.all()
    return jsonify([{'id': t.id, 'title': t.title, 'done': t.done} for t in tasks])

Related on TokRepo

• AI Tools for Coding — AI tools that generate Python web applications
• AI Tools for API — API development frameworks and tools

Common pitfalls

• Using Flask's built-in development server in production. Always deploy with Gunicorn or uWSGI. The development server is single-threaded and not designed for production traffic.
• Not using application factories for larger projects. As Flask apps grow, the factory pattern (create_app function) prevents circular imports and enables testing.
• Ignoring Flask-Migrate for database schema changes. Without migration tracking, schema changes require manual SQL or database recreation.
• Failing to review community discussions and changelogs before upgrading. Breaking changes in major versions can disrupt existing workflows. Pin versions in production and test upgrades in staging first.