7 Async Patterns for Running Agents Concurrently in Python

The article outlines seven distinct asyncio‑based patterns for executing AI agents concurrently in Python, ranging from simple `asyncio.gather` for parallel launches to more controlled approaches using `asyncio.Semaphore` and `asyncio.BoundedSemaphore` for rate‑limiting. It details when each pattern is optimal—e.g., using a semaphore when external API quotas must be respected, or `asyncio.TaskGroup` for structured concurrency that automatically cancels children on failure. The discussion also covers the trade‑off between event‑loop overhead and the simplicity of thread‑pool executors, and it demonstrates how to integrate these patterns into production pipelines that orchestrate LLM inference and retrieval services.

⚡ Key Takeaways

• The article enumerates seven patterns: `asyncio.gather`, `asyncio.create_task`, `asyncio.TaskGroup`, `asyncio.Semaphore`, `asyncio.BoundedSemaphore`, `asyncio.wait`, and a custom context manager for cancellation.
• It recommends `asyncio.Semaphore` to enforce a maximum of 10 concurrent API calls when interacting with a rate‑limited LLM endpoint.
• Using `asyncio.TaskGroup` eliminates the need for manual cancellation logic, reducing latency spikes during batch processing of 50 agents.
• The patterns are implemented directly in Python’s standard `asyncio` library, requiring no external

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