Async Python Patterns
SkillAI & modelsasync-python-patterns is a skill that gives an AI agent guidance and worked code patterns for writing asynchronous Python. It covers asyncio, concurrent programming, and async/await patterns for building non-blocking, I/O-bound applications, and helps the agent decide when async is appropriate versus sync or multiprocessing.
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Details
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No other account needed.
Have a Python 3.7 or newer environment available, since the examples use asyncio.run.
What your AI can do with it
- Provides async/await patterns and worked examples for asynchronous Python
- Explains the event loop, coroutines, tasks, futures, async context managers, and async
- Shows concurrent execution with asyncio.gather and task creation
- Covers error handling and timeouts in async code
- Includes a decision guide for when to use asyncio, multiprocessing, threads, or plain
- Includes examples for building async APIs and concurrent I/O workloads
Getting started
- Have a Python 3.7 or newer environment available, since the examples use asyncio.run.
- Add the async-python-patterns skill to the agent's available skills.
- Ask the agent to write or review async Python code, such as an async API or concurrent I/O workload, and it will apply the skill's patterns.
What this skill tells your AI
The instructions your AI receives, as published by wshobson/agents in plugins/python-development/skills/async-python-patterns/SKILL.md and read by ahel’s review.
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)
Signals
- GitHub stars
- 40k
- Forks
- 4k
- Last commit
- Sep 2026
Others that do the same job
Questions
- When should this skill be used?
- When building async web APIs, concurrent systems, or I/O-bound applications requiring non-blocking operations, such as concurrent database, file, or network work, scrapers, WebSocket servers, background tasks, and queues.
- Does it help decide whether async is the right choice?
- Yes. It includes a sync vs async decision guide: asyncio for many concurrent network or database calls, multiprocessing or thread pools for CPU-bound work, asyncio.to_thread for mixed workloads, and plain sync for simple scripts.
- Which async frameworks does it cover?
- It mentions FastAPI, aiohttp, and Sanic as frameworks for building async web APIs with high concurrency.
Advanced
- Item type
- skill
- Key
async-python-patterns-wshobson- Source
- github.com/wshobson/agents
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