async-python-patterns
wshobson/agents
Master asyncio, concurrent programming, and async/await patterns for high-performance Python applications.
What is async-python-patterns?
Comprehensive guidance for implementing asynchronous Python applications using asyncio and async/await for building high-performance, non-blocking systems. Use this skill when building async web APIs, concurrent I/O operations, web scrapers, real-time applications, or microservices that require non-blocking concurrent execution.
- Understand event loops, coroutines, tasks, and futures in asyncio
- Implement concurrent execution patterns with gather() and task creation
- Handle errors, timeouts, and cancellation in async code
- Apply async context managers and iterators for resource management
- Optimize I/O-bound workloads with non-blocking operations
- Test async code using pytest-asyncio and proper testing patterns
How to install async-python-patterns
npx skills add https://github.com/wshobson/agents --skill async-python-patterns- Python 3.7 or later
- Basic understanding of Python functions and control flow
- Familiarity with synchronous Python programming
How to use async-python-patterns
- 1.Review the Sync vs Async Decision Guide to determine if async is appropriate for your use case
- 2.Study the Core Concepts section to understand event loops, coroutines, tasks, and futures
- 3.Start with Pattern 1 (Basic Async/Await) to write your first async function
- 4.Use Pattern 2 (gather()) for concurrent execution of multiple async operations
- 5.Apply Pattern 3 (Task Creation) for managing long-running background tasks
- 6.Implement Pattern 4 (Error Handling) to safely handle failures in concurrent operations
- 7.Add Pattern 5 (Timeout Handling) to prevent operations from hanging indefinitely
- 8.Avoid common pitfalls: always await async functions, use asyncio.sleep() instead of time.sleep(), handle cancellation properly, and keep code fully async or fully sync within a call path
Use cases
- Building async web APIs with FastAPI, aiohttp, or Sanic
- Implementing concurrent database and network requests
- Creating web scrapers with concurrent request handling
- Developing real-time applications like WebSocket servers and chat systems
- Processing multiple independent tasks simultaneously without blocking
- Backend developers building high-concurrency APIs
- Python developers optimizing I/O-bound applications
- Microservice architects implementing async communication
- Web scraper developers handling multiple concurrent requests
- Real-time application developers using WebSockets or event-driven systems
async-python-patterns FAQ
Use async for I/O-bound operations with many concurrent network/database calls, web APIs with high concurrency, or real-time applications. Use sync for simple scripts with few connections or CPU-bound work. Avoid mixing sync and async in the same call path.
gather() runs multiple coroutines concurrently and waits for all to complete, returning results in order. create_task() schedules a coroutine on the event loop and returns immediately, allowing you to do other work before awaiting the result.
Use try/except blocks around await statements, or pass return_exceptions=True to gather() to collect exceptions alongside successful results. Filter results to separate successes from failures.
The function returns a coroutine object without executing. The coroutine will never run unless you await it or pass it to asyncio.create_task(). This is a common source of bugs.
Always use async-compatible operations like asyncio.sleep() instead of time.sleep(), and offload CPU-bound work using asyncio.to_thread(). Never call blocking synchronous functions directly in async code.
Full instructions (SKILL.md)
Source of truth, from wshobson/agents.
name: async-python-patterns description: Master Python asyncio, concurrent programming, and async/await patterns for high-performance applications. Use when building async APIs, concurrent systems, or I/O-bound applications requiring non-blocking operations.
Async Python Patterns
Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems.
When to Use This Skill
- Building async web APIs (FastAPI, aiohttp, Sanic)
- Implementing concurrent I/O operations (database, file, network)
- Creating web scrapers with concurrent requests
- Developing real-time applications (WebSocket servers, chat systems)
- Processing multiple independent tasks simultaneously
- Building microservices with async communication
- Optimizing I/O-bound workloads
- Implementing async background tasks and queues
Sync vs Async Decision Guide
Before adopting async, consider whether it's the right choice for your use case.
| Use Case | Recommended Approach |
|---|---|
| Many concurrent network/DB calls | asyncio |
| CPU-bound computation | multiprocessing or thread pool |
| Mixed I/O + CPU | Offload CPU work with asyncio.to_thread() |
| Simple scripts, few connections | Sync (simpler, easier to debug) |
| Web APIs with high concurrency | Async frameworks (FastAPI, aiohttp) |
Key Rule: Stay fully sync or fully async within a call path. Mixing creates hidden blocking and complexity.
Core Concepts
1. Event Loop
The event loop is the heart of asyncio, managing and scheduling asynchronous tasks.
Key characteristics:
- Single-threaded cooperative multitasking
- Schedules coroutines for execution
- Handles I/O operations without blocking
- Manages callbacks and futures
2. Coroutines
Functions defined with async def that can be paused and resumed.
Syntax:
async def my_coroutine():
result = await some_async_operation()
return result
3. Tasks
Scheduled coroutines that run concurrently on the event loop.
4. Futures
Low-level objects representing eventual results of async operations.
5. Async Context Managers
Resources that support async with for proper cleanup.
6. Async Iterators
Objects that support async for for iterating over async data sources.
Quick Start
import asyncio
async def main():
print("Hello")
await asyncio.sleep(1)
print("World")
# Python 3.7+
asyncio.run(main())
Fundamental Patterns
Pattern 1: Basic Async/Await
import asyncio
async def fetch_data(url: str) -> dict:
"""Fetch data from URL asynchronously."""
await asyncio.sleep(1) # Simulate I/O
return {"url": url, "data": "result"}
async def main():
result = await fetch_data("https://api.example.com")
print(result)
asyncio.run(main())
Pattern 2: Concurrent Execution with gather()
import asyncio
from typing import List
async def fetch_user(user_id: int) -> dict:
"""Fetch user data."""
await asyncio.sleep(0.5)
return {"id": user_id, "name": f"User {user_id}"}
async def fetch_all_users(user_ids: List[int]) -> List[dict]:
"""Fetch multiple users concurrently."""
tasks = [fetch_user(uid) for uid in user_ids]
results = await asyncio.gather(*tasks)
return results
async def main():
user_ids = [1, 2, 3, 4, 5]
users = await fetch_all_users(user_ids)
print(f"Fetched {len(users)} users")
asyncio.run(main())
Pattern 3: Task Creation and Management
import asyncio
async def background_task(name: str, delay: int):
"""Long-running background task."""
print(f"{name} started")
await asyncio.sleep(delay)
print(f"{name} completed")
return f"Result from {name}"
async def main():
# Create tasks
task1 = asyncio.create_task(background_task("Task 1", 2))
task2 = asyncio.create_task(background_task("Task 2", 1))
# Do other work
print("Main: doing other work")
await asyncio.sleep(0.5)
# Wait for tasks
result1 = await task1
result2 = await task2
print(f"Results: {result1}, {result2}")
asyncio.run(main())
Pattern 4: Error Handling in Async Code
import asyncio
from typing import List, Optional
async def risky_operation(item_id: int) -> dict:
"""Operation that might fail."""
await asyncio.sleep(0.1)
if item_id % 3 == 0:
raise ValueError(f"Item {item_id} failed")
return {"id": item_id, "status": "success"}
async def safe_operation(item_id: int) -> Optional[dict]:
"""Wrapper with error handling."""
try:
return await risky_operation(item_id)
except ValueError as e:
print(f"Error: {e}")
return None
async def process_items(item_ids: List[int]):
"""Process multiple items with error handling."""
tasks = [safe_operation(iid) for iid in item_ids]
results = await asyncio.gather(*tasks, return_exceptions=True)
# Filter out failures
successful = [r for r in results if r is not None and not isinstance(r, Exception)]
failed = [r for r in results if isinstance(r, Exception)]
print(f"Success: {len(successful)}, Failed: {len(failed)}")
return successful
asyncio.run(process_items([1, 2, 3, 4, 5, 6]))
Pattern 5: Timeout Handling
import asyncio
async def slow_operation(delay: int) -> str:
"""Operation that takes time."""
await asyncio.sleep(delay)
return f"Completed after {delay}s"
async def with_timeout():
"""Execute operation with timeout."""
try:
result = await asyncio.wait_for(slow_operation(5), timeout=2.0)
print(result)
except asyncio.TimeoutError:
print("Operation timed out")
asyncio.run(with_timeout())
Detailed worked examples and patterns
Detailed sections (starting with ## Advanced Patterns) live in references/details.md. Read that file when the navigation summary above is insufficient.
Common Pitfalls
1. Forgetting await
# Wrong - returns coroutine object, doesn't execute
result = async_function()
# Correct
result = await async_function()
2. Blocking the Event Loop
# Wrong - blocks event loop
import time
async def bad():
time.sleep(1) # Blocks!
# Correct
async def good():
await asyncio.sleep(1) # Non-blocking
3. Not Handling Cancellation
async def cancelable_task():
"""Task that handles cancellation."""
try:
while True:
await asyncio.sleep(1)
print("Working...")
except asyncio.CancelledError:
print("Task cancelled, cleaning up...")
# Perform cleanup
raise # Re-raise to propagate cancellation
4. Mixing Sync and Async Code
# Wrong - can't call async from sync directly
def sync_function():
result = await async_function() # SyntaxError!
# Correct
def sync_function():
result = asyncio.run(async_function())
Testing Async Code
import asyncio
import pytest
# Using pytest-asyncio
@pytest.mark.asyncio
async def test_async_function():
"""Test async function."""
result = await fetch_data("https://api.example.com")
assert result is not None
@pytest.mark.asyncio
async def test_with_timeout():
"""Test with timeout."""
with pytest.raises(asyncio.TimeoutError):
await asyncio.wait_for(slow_operation(5), timeout=1.0)
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