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async-python-patterns

Implement asyncio concurrency patterns for non-blocking Python applications.

Updated Jul 29, 2026
Search Tags:#fastapi#rate limiting#python#concurrency#asyncio#async await#event loop
One-click install
npx skills add https://github.com/MaiconGambini/opencode-harness-guide --skill async-python-patterns-maicongambini
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: async-python-patterns
Source: https://github.com/MaiconGambini/opencode-harness-guide/tree/main/skills/async-python-patterns
Command: npx skills add https://github.com/MaiconGambini/opencode-harness-guide --skill async-python-patterns-maicongambini

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires aiohttp, pytest-asyncio.

What problem does it solve? Writing concurrent Python code with asyncio is error-prone: developers forget awaits, block the event loop, mishandle cancellation, or lack patterns for rate limiting and backpressure. This Skill provides structured guidance and ready-to-adapt patterns for building correct async applications. ## Core Features & Use Cases - Concurrency Patterns: Covers gather(), task creation, producer-consumer queues, semaphores for rate limiting, and async locks for shared state. - Robustness Guidance: Includes timeout handling, structured error handling with gather(return_exceptions=True), and proper cancellation cleanup. - Real-World Examples: Demonstrates web scraping with aiohttp, concurrent database queries, WebSocket servers, and connection pooling. - Use Case: When building a FastAPI service that must call 20 upstream APIs per request, use the semaphore and gather patterns to run requests concurrently with controlled rate limits and timeouts. ## Quick Start Ask the AI to show how to fetch multiple URLs concurrently in Python with asyncio, including timeouts, rate limiting, and error handling.

Frequently Asked Questions about async-python-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run multiple async tasks concurrently in Python?▼

Use asyncio.gather() to run multiple coroutines concurrently and collect their results in order. For long-running work, create tasks with asyncio.create_task() and await them when needed.

How to limit concurrent requests with asyncio?▼

Use asyncio.Semaphore to cap concurrency. Wrap each operation in 'async with semaphore' so only a fixed number of tasks run at once, which is the standard pattern for rate limiting API calls.

When should I not use asyncio in Python?▼

Avoid asyncio for CPU-bound workloads, since the single-threaded event loop gains nothing there; use multiprocessing instead. It is also unnecessary for simple synchronous scripts or environments that cannot run an event loop.

Why does my async code block the event loop?▼

Blocking happens when synchronous calls like time.sleep() or CPU-heavy work run inside a coroutine. Replace them with asyncio.sleep() or offload blocking work to a thread pool via loop.run_in_executor().

How do I add timeouts to async operations in Python?▼

Wrap the coroutine with asyncio.wait_for(coro, timeout=seconds). It raises asyncio.TimeoutError when the deadline is exceeded, which you can catch to handle slow operations gracefully.

How do I test async Python code with pytest?▼

Use the pytest-asyncio plugin and mark tests with @pytest.mark.asyncio so async test functions run on an event loop. You can then await coroutines directly and assert on results or expected exceptions like TimeoutError.

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