A practical guide to using Python's asyncio for building faster, more scalable LLM and AI applications.
Read online at asyncio-for-ai.github.io
If you've used the OpenAI or Gemini API, you've probably seen asyncio scattered throughout the documentation. You might've copied async/await without understanding what it does. You might've wondered if it actually matters.
It matters.
asyncio is a library for writing concurrent code in Python. It can help you write faster code, especially if you're doing I/O work. Think making API calls over a network or building data pipelines that read from disk.
Most asyncio tutorials explain how it works. This guide is about why it matters.
This guide is about using asyncio to write faster, more scalable code.
This guide walks through six real-world applications of asyncio:
- LLM Responses — Call LLM APIs concurrently without blocking
- Rate Limiters — Control API request throughput to stay within rate limits
- Data Pipelines — Process large datasets with producer-consumer pipelines
- Request Batchers — Batch multiple requests for efficiency
- Web Crawlers — Efficiently crawl the web and parse web pages
- Tool-Calling Agents — Build agents that execute tools concurrently
Each section follows a challenge-solution format inspired by John Crickett's Coding Challenges.
You're encouraged to attempt each challenge before reading the solution.
Hi! I'm Abdul. I build infrastructure for Gemini fine-tuning and batch inference at Google. I care about making AI development easier, faster, and more accessible.
If you find this guide helpful, check out my blog at abdulsaleh.dev.
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cargo install mdbook
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