multi_agent_system

Standardize Supervisor-Worker multi-agent systems with LangGraph patterns.

Updated Feb 24, 2026

Search Tags:#routing #orchestration #multi-agent systems #langgraph #agent architecture #supervisor-worker

Ready to run

npx skills add https://github.com/ulf1/mas-inquiry --skill multi-agent-system

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: multi_agent_system
Source: https://github.com/ulf1/mas-inquiry/tree/main/.agent/skills/multi_agent_system
Command: npx skills add https://github.com/ulf1/mas-inquiry --skill multi-agent-system

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a standardized framework for developing multi-agent systems (MAS) using LangGraph, specifically the Supervisor-Worker pattern, simplifying the creation of complex agentic workflows.

Core Features & Use Cases

Supervisor-Worker Architecture: Implement robust routing and delegation between specialized agents.
LangGraph Integration: Leverage LangGraph for state management, node orchestration, and graph assembly.
Single-File Agent Pattern: Encapsulate agent logic, schema, and prompts within a single file for maintainability.
Use Case: Develop a customer support system where a supervisor agent routes user queries to specialized worker agents for handling billing, technical issues, or product information.

Quick Start

Use the multi_agent_system skill to create a supervisor agent that routes user queries to a sport log extraction worker.

Frequently Asked Questions about multi_agent_system

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a Supervisor-Worker multi-agent system with LangGraph?▼

To build a Supervisor-Worker multi-agent system with LangGraph, this Skill standardizes agent implementation, routing, and graph assembly. It encapsulates agent logic and schemas within a single file to enable structured agentic workflows and efficient task delegation.

What is the Supervisor-Worker pattern in multi-agent systems?▼

The Supervisor-Worker pattern in multi-agent systems is an architecture where a supervisor agent routes user queries and delegates tasks to specialized worker agents. This pattern simplifies complex workflows by separating routing logic from specialized task execution.

Do I need LangChain and Pydantic to orchestrate agents using LangGraph?▼

Yes, you need LangChain and Pydantic to orchestrate agents using LangGraph. These dependencies are required alongside LangGraph for defining agent schemas, managing state, constructing the orchestration graph, and ensuring structured task delegation.

How do I route user queries to specialized agents in a multi-agent architecture?▼

You route user queries to specialized agents in a multi-agent architecture by implementing a supervisor node using LangGraph. The supervisor evaluates the input and dynamically delegates the query to the appropriate worker agent for processing.

What is the best way to structure agent logic for complex agentic workflows?▼

The best way to structure agent logic for complex agentic workflows is using a single-file agent pattern. This approach encapsulates the agent's logic, Pydantic schema, and prompts within one file, ensuring maintainability and standardized routing across the system.

Can I use this Supervisor-Worker pattern for a customer support system?▼

Yes, you can use this Supervisor-Worker pattern for a customer support system. The supervisor agent can route user queries to specialized worker agents handling specific domains like billing, technical issues, or product information for efficient resolution.