How does the skill handle workflow completion?

It includes state tracking patterns that record completed agents, preventing infinite loops and ensuring the supervisor correctly routes to the END node when tasks are finished.

What is the LangGraph supervisor-worker pattern?

It is an architectural design where a central 'supervisor' node evaluates the workflow state and decides which specialized 'worker' node should execute next, ensuring efficient task delegation.

Does this skill support LLM-driven routing?

Yes, it includes 2026 best practices for using LLMs with structured output (like Pydantic) to make reliable, validated routing decisions instead of simple string parsing.

Can I implement priority-based tasking?

Absolutely. The skill provides specific code patterns for priority-based routing, allowing critical agents like security validators to run before implementation agents.

LangGraph Supervisor Pattern

Name: LangGraph Supervisor Pattern
Author: yonatangross

byyonatangross

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数据科学与机器学习

Coordinates complex multi-agent workflows using a central supervisor to route tasks among specialized LangGraph workers.

关于

This skill provides production-ready implementation patterns for building hierarchical multi-agent systems within the LangGraph ecosystem. It enables developers to design central coordinator agents that dynamically delegate tasks to specialized workers based on state, priority, or LLM-driven logic. By providing templates for round-robin routing, priority-based dispatch, and modern structured-output supervisors, it simplifies the orchestration of complex AI tasks that require a team of specialized sub-agents while maintaining robust state management.

主要功能

Production-grade LangGraph workflow templates
Dynamic worker routing with round-robin and priority support
Comprehensive state tracking for agent progress and completion
Advanced error handling and failure recovery patterns
LLM-based supervisor implementation using structured output validation
29 GitHub stars

使用场景

Building complex RAG systems requiring coordination between multiple data retrieval agents
Orchestrating multi-stage security audits with specialized scanning agents
Managing content pipelines across research, drafting, and validation phases

关于

主要功能

Production-grade LangGraph workflow templates
Dynamic worker routing with round-robin and priority support
Comprehensive state tracking for agent progress and completion
Advanced error handling and failure recovery patterns
LLM-based supervisor implementation using structured output validation
29 GitHub stars

使用场景

Building complex RAG systems requiring coordination between multiple data retrieval agents
Orchestrating multi-stage security audits with specialized scanning agents
Managing content pipelines across research, drafting, and validation phases