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langgraph-agent-patterns

Implement multi-agent coordination patterns for LangGraph applications.

1|Updated Dec 22, 2025
Search Tags:#workflow automation#multi-agent#coordination#langgraph#ai systems#agent patterns
One-click install
npx skills add https://github.com/dhar174/langgraph_system_generator --skill langgraph-agent-patterns-dhar174
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langgraph-agent-patterns
Source: https://github.com/dhar174/langgraph_system_generator/tree/main/.github/skills/langgraph-agent-patterns
Command: npx skills add https://github.com/dhar174/langgraph_system_generator --skill langgraph-agent-patterns-dhar174

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langgraph, langchain-core, langchain-openai, langchain-anthropic, langchain-community, pydantic, tenacity, transformers, scikit-learn, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit and guidance for implementing complex multi-agent coordination patterns within LangGraph applications, enabling the creation of advanced AI systems.

Core Features & Use Cases

  • Pattern Implementation: Offers ready-to-use guides and code for Supervisor, Router, Orchestrator-Worker, and Handoffs patterns.
  • State Management: Provides examples and best practices for designing state schemas tailored to each pattern.
  • Use Case: Develop a customer support system where a router directs queries to specialized supervisor teams (e.g., sales, technical support), each managing its own set of agents.

Quick Start

Implement the supervisor-subagent pattern by running the generate_supervisor_graph.py script with your desired team members.

Frequently Asked Questions about langgraph-agent-patterns

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

FAQPage Schema
How do I implement a supervisor-subagent pattern for multi-agent coordination in LangGraph?▼

The supervisor-subagent pattern in LangGraph assigns a central supervisor agent to route tasks to specialized subagents. You can generate this multi-agent coordination structure by running the generate_supervisor_graph.py script with your desired team members.

What multi-agent coordination patterns are available for building complex AI systems?▼

Available multi-agent coordination patterns include supervisor-subagent, router, orchestrator-worker, and handoffs. These patterns support dynamic routing, parallel execution, sequential workflows, and state schema design for complex AI systems.

How do I design state schemas for dynamic routing and parallel execution in LangGraph?▼

State schema design for LangGraph involves defining data structures tailored to each coordination pattern. This skill provides examples and best practices for configuring state schemas that enable dynamic routing and parallel execution across multiple agents.

Can I use LangChain models with LangGraph for orchestrator-worker workflows?▼

Yes, orchestrator-worker workflows in LangGraph support integration with LangChain models. The implementation depends on langchain-core, langchain-openai, and langchain-anthropic, allowing you to configure agents that dynamically distribute tasks to worker agents.

When should I use a router pattern versus an orchestrator-worker pattern for agent workflows?▼

Use the router pattern to direct queries to specialized supervisor teams, such as routing customer support questions to sales or technical support. Use the orchestrator-worker pattern when an orchestrator needs to dynamically decompose tasks and manage parallel execution across workers.

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