LangGraph Parallel Execution FAQs

Question 1

Does this skill include timeout management?

Accepted Answer

Yes, it provides implementation patterns for wrapping parallel tasks in asyncio.wait_for to prevent slow workers or API calls from blocking the entire execution.

Question 2

How does this skill handle errors in parallel branches?

Accepted Answer

The skill provides patterns using asyncio.gather with return_exceptions=True, ensuring that a single failing branch does not terminate the entire workflow.

Question 3

How many parallel tasks are recommended?

Accepted Answer

To avoid overwhelming LLM API rate limits and local resources, it is generally recommended to limit concurrency to between 5 and 10 branches.

Question 4

What is the fan-out/fan-in pattern in LangGraph?

Accepted Answer

It is an architectural pattern where a single node triggers multiple independent nodes to run concurrently (fan-out) and then merges their results into a final aggregation node (fan-in).

Question 5

When should I use the LangGraph Send API?

Accepted Answer

The Send API should be used when you need to dynamically route state to multiple instances of a node in parallel, such as when processing a list of items of unknown length.

LangGraph Parallel Execution

Key Features

Use Cases

LangGraph Parallel Execution

Key Features

Use Cases