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Asyncio.gather

As explained in AI Agents in Action, Second Edition

A Python asyncio function that concurrently schedules awaitable objects and returns their results together.

asyncio.gather is a Python asyncio pattern for processing independent awaitable tasks concurrently and collecting their results together. It can shorten task-loop runtime compared with sequential processing. section 9.5 section 9.6

Use it when work items do not depend on one another. Keep sequential processing when tasks share ordering or state, and check rate limits before increasing concurrency. section 9.6

A practical decision checklist:

  • Confirm that each item can run independently. section 9.6
  • Compare parallel and sequential completion time rather than assuming a fixed speedup. section 9.5
  • Watch rate limits and preserve explicit tracking of completed, failed, and pending items. section 9.6

Parallel task processing

An exercise copies a retrying task loop, applies the asyncio.gather parallel pattern with batch_size=3, then compares total execution time and completion rates with the sequential version. section 9.5

How AI Agents in Action, Second Edition covers it

How deep it goes
Provides an implementation pattern and comparison exercise, but defers detailed API mechanics and concurrency internals.
Examples
Uses five text-transformation tasks, a batch size of three, and sequential-versus-parallel timing and completion-rate comparisons.
What it assumes
Assumes tasks can be judged independent and introduces rate-limit awareness as a condition for enabling parallel execution.
What you can do after
Apply it via Python asyncio to parallelize independent task-loop items and evaluate speedup against sequential processing.
How it connects
Connects parallel iteration with task loops, retry logic, task independence, and rate limits.

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This explainer was generated by Manning's knowledge graph from the text of AI Agents in Action, Second Edition. The section links point to the parts of the book it draws on.