Does this skill include timeout management?

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.

How does this skill handle errors in parallel branches?

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

How many parallel tasks are recommended?

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

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

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).

When should I use the LangGraph Send API?

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

Name: LangGraph Parallel Execution
Author: yonatangross

byyonatangross

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API 开发

Implements high-performance parallel execution patterns for LangGraph workflows using fan-out, fan-in, and map-reduce strategies.

关于

This skill provides standardized patterns for running independent nodes concurrently within LangGraph to optimize performance and reduce latency. It covers essential architectural patterns like fan-out/fan-in for task distribution, the Send API for dynamic routing, and map-reduce for large-scale data processing. With built-in guidance on error isolation and branch-specific timeouts, it enables developers to build resilient multi-agent systems that handle failures gracefully without blocking the entire workflow, making it ideal for complex AI orchestration.

主要功能

Resilient error isolation and per-branch timeout handling
Standardized map-reduce implementation for data processing
Concurrent multi-agent analysis templates
29 GitHub stars
Dynamic routing using the LangGraph Send API
Fan-out/fan-in workflow patterns for task distribution

使用场景

Reducing latency by running multiple independent LLM agents concurrently
Processing large batches of data through a structured map-reduce pipeline
Distributing sub-tasks to specialized workers and aggregating results centrally

关于

主要功能

Resilient error isolation and per-branch timeout handling
Standardized map-reduce implementation for data processing
Concurrent multi-agent analysis templates
29 GitHub stars
Dynamic routing using the LangGraph Send API
Fan-out/fan-in workflow patterns for task distribution

使用场景

Reducing latency by running multiple independent LLM agents concurrently
Processing large batches of data through a structured map-reduce pipeline
Distributing sub-tasks to specialized workers and aggregating results centrally