Does this skill support semantic routing?

Yes, it includes templates for routing based on semantic similarity, embeddings, or intent detection to route queries to specific specialized nodes.

What is a Quality Gate pattern?

A Quality Gate is a routing pattern that evaluates the output of a previous step and only allows the workflow to proceed if specific quality thresholds are met.

What is LangGraph routing used for?

LangGraph routing is used to dynamically determine the next node in an AI workflow based on the current state, allowing for branching, looping, and decision-making.

How do these patterns prevent infinite loops in AI agents?

The patterns include built-in retry counters and mandatory fallback paths to ensure that workflows eventually reach an END state or manual review.

Can I use this with any LangGraph state definition?

Yes, the routing functions are designed to be generic and can be adapted to any custom StateGraph or WorkflowState you define.

LangGraph Routing Patterns

Name: LangGraph Routing Patterns
Author: yonatangross

byyonatangross

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Data Science & ML

Implements dynamic, state-based routing and retry logic for complex LangGraph AI agent workflows.

About

The LangGraph Routing skill provides production-ready patterns for controlling execution flow within stateful AI applications. It enables developers to implement sophisticated logic such as conditional branching, quality gates, and automated retry loops based on real-time state data. By utilizing these standardized templates, developers can build resilient multi-agent systems that handle errors gracefully, validate outputs before proceeding, and route tasks semantically to the most appropriate processing nodes.

Key Features

Quality gate logic for output validation and multi-step verification
Automated retry loop patterns to handle transient LLM failures
Conditional edge implementation for dynamic workflow branching
29 GitHub stars
Semantic routing templates for intent-based task distribution
Standardized error handling and safe exit (END) state management

Use Cases

Building self-healing AI agents that automatically retry tasks upon failure
Developing multi-stage content pipelines with mandatory quality check gates
Creating complex intent-based routers that direct user queries to specialized sub-graphs

About

Key Features

Quality gate logic for output validation and multi-step verification
Automated retry loop patterns to handle transient LLM failures
Conditional edge implementation for dynamic workflow branching
29 GitHub stars
Semantic routing templates for intent-based task distribution
Standardized error handling and safe exit (END) state management

Use Cases

Building self-healing AI agents that automatically retry tasks upon failure
Developing multi-stage content pipelines with mandatory quality check gates
Creating complex intent-based routers that direct user queries to specialized sub-graphs