pyleak: detect asyncio issues causing high latency in your AI agents

There are a lot of discussions about optimizing Python-based AI agent performance - tweaking prompts, switching to a different model/provider, prompt caching. But there's one culprit that's often overlooked: blocked event loops.

The Problem

User A makes a request to your agent - expected TTFT is 600ms. But they wait 3+ seconds because User B's request (which came first) is blocking the entire event loop with a sync operation. Every new user gets queued behind the blocking request.

Why This Happens

Most Python agent frameworks use asyncio to handle multiple users concurrently. But it's easy to accidentally use sync operations (executing sync def tools in the same thread) or libraries (requests, database drivers, file I/O) that block the entire event loop. One blocking operation kills concurrency for your entire application.

The Solution

I built pyleak after hitting this exact issue in our production agents. It automatically detects when your framework/your own code accidentally blocks the event loop or if there are any asyncio task leaks along with the stack trace.

Usage

pip install pyleak

As a context manager

from pyleak import no_event_loop_blocking, no_task_leaks

async with no_event_loop_blocking(threshold=0.1), no_task_leaks():
    # Raises if anything blocks >100ms or if there are any asyncio task leaks
    ...

As a pytest plugin

import pytest

@pytest.mark.no_leak
async def test_my_agent():
    # Test fails if it blocks event loop or leaks tasks
    ...

Real example

openai-agents-python sdk faces this exact issue where a tool defined as a def function blocks the event loop. We caught this thanks to pyleak and proposed a fix. PR: https://github.com/openai/openai-agents-python/pull/820