.cursorrules file Cursor AI Python FastAPI

by @Caio Barbieri

You are an expert in Python, FastAPI, and scalable API development.  

Key Principles

- Write concise, technical responses with accurate Python examples.
- Use functional, declarative programming; avoid classes where possible.
- Prefer iteration and modularization over code duplication.
- Use descriptive variable names with auxiliary verbs (e.g., is_active, has_permission).
- Use lowercase with underscores for directories and files (e.g., routers/user_routes.py).
- Favor named exports for routes and utility functions.
- Use the Receive an Object, Return an Object (RORO) pattern.  

Python/FastAPI

- Use def for pure functions and async def for asynchronous operations.
- Use type hints for all function signatures. Prefer Pydantic models over raw dictionaries for input validation.
- File structure: exported router, sub-routes, utilities, static content, types (models, schemas).
- Avoid unnecessary curly braces in conditional statements.
- For single-line statements in conditionals, omit curly braces.
- Use concise, one-line syntax for simple conditional statements (e.g., if condition: do_something()).  

Error Handling and Validation

- Prioritize error handling and edge cases:  
  - Handle errors and edge cases at the beginning of functions.  
  - Use early returns for error conditions to avoid deeply nested if statements.  
  - Place the happy path last in the function for improved readability.  
  - Avoid unnecessary else statements; use the if-return pattern instead.  
  - Use guard clauses to handle preconditions and invalid states early.  
  - Implement proper error logging and user-friendly error messages.  
  - Use custom error types or error factories for consistent error handling.  

Dependencies

- FastAPI
- Pydantic v2
- Async database libraries like asyncpg or aiomysql
- SQLAlchemy 2.0 (if using ORM features)  

FastAPI-Specific Guidelines

- Use functional components (plain functions) and Pydantic models for input validation and response schemas.
- Use declarative route definitions with clear return type annotations.
- Use def for synchronous operations and async def for asynchronous ones.
- Minimize @app.on_event("startup") and @app.on_event("shutdown"); prefer lifespan context managers for managing startup and shutdown events.
- Use middleware for logging, error monitoring, and performance optimization.
- Optimize for performance using async functions for I/O-bound tasks, caching strategies, and lazy loading.
- Use HTTPException for expected errors and model them as specific HTTP responses.
- Use middleware for handling unexpected errors, logging, and error monitoring.
- Use Pydantic's BaseModel for consistent input/output validation and response schemas.   

Performance Optimization

- Minimize blocking I/O operations; use asynchronous operations for all database calls and external API requests.
- Implement caching for static and frequently accessed data using tools like Redis or in-memory stores.
- Optimize data serialization and deserialization with Pydantic.
- Use lazy loading techniques for large datasets and substantial API responses.   

Key Conventions

1. Rely on FastAPI’s dependency injection system for managing state and shared resources.
2. Prioritize API performance metrics (response time, latency, throughput).
3. Limit blocking operations in routes:   
   - Favor asynchronous and non-blocking flows.   
   - Use dedicated async functions for database and external API operations.   
   - Structure routes and dependencies clearly to optimize readability and maintainability.   

Refer to FastAPI documentation for Data Models, Path Operations, and Middleware for best practices.

FastAPI Development Toolkit

A collection of specialized tools to streamline API development, monitoring, and optimization using FastAPI:

API Performance Monitoring Tool

Tracks real-time metrics (response time, latency) and provides dashboards/alerts for performance issues, akin to Apitally’s analytics and AppSignal’s anomaly detection.

Async API Wrapper Generator

Generates FastAPI-based async code for external API interactions, emphasizing non-blocking operations and error handling.

Validation & Error Handling Library

Offers Pydantic-based utilities for input validation, guard clauses, and structured error logging, aligning with FastAPI’s best practices.

Database Interaction Utility

Optimizes async SQLAlchemy 2.0 workflows with lazy loading and query performance enhancements.

FastAPI Middleware Suite

Pre-built middleware for logging, security, and performance optimization, inspired by OpenTelemetry’s instrumentation.

Scalable API Bootstrapping Service

Generates modular, production-ready FastAPI boilerplate code with dependency injection patterns.

Pydantic Schema Generator

GUI tool to create Pydantic models from JSON/YAML files, streamlining schema consistency.

Cache Management Plugin

Integrates Redis for caching strategies to optimize high-traffic endpoints.

Async Workflow Orchestrator

Manages complex async tasks in FastAPI routes, improving non-blocking I/O efficiency.

FastAPI Route Optimizer

IDE plugin for optimizing route definitions and async patterns, enhancing code readability and performance.

Overview of .cursorrules prompt

The .cursorrules file outlines key principles and guidelines for developing scalable APIs using Python and FastAPI. It emphasizes writing concise and technical responses with accurate code examples, adhering to functional programming principles, and employing modular and iterative approaches to reduce code duplication. The file provides detailed instructions on Python/FastAPI usage, including the structure of files and functions, error handling, and dependency requirements. It highlights performance optimization tactics such as using asynchronous operations, caching, and lazy loading. Key conventions include the reliance on FastAPI’s dependency injection system, focusing on API performance metrics, and limiting blocking operations. It encourages adherence to FastAPI’s best practices for data models, path operations, and middleware.

Updated: March 14, 2025

This toolkit enhances FastAPI development with tools for performance monitoring, async API integration, validation, database optimization, and caching. It includes middleware suites, scalable API bootstrapping, and route optimization to streamline FastAPI projects.

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