·langgraph-agent-patterns
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langgraph-agent-patterns

Implement multi-agent coordination patterns (supervisor-subagent, router, orchestrator-worker, handoffs) for LangGraph applications. Use when users want to (1) implement multi-agent systems, (2) coordinate multiple specialized agents, (3) choose between coordination patterns, (4) set up supervisor-subagent workflows, (5) implement router-based agent selection, (6) create parallel orchestrator-worker patterns, (7) implement agent handoffs, (8) design state schemas for multi-agent systems, or (9) debug multi-agent coordination issues.

22Installs·3Trend·@lubu-labs

Installation

$npx skills add https://github.com/lubu-labs/langchain-agent-skills --skill langgraph-agent-patterns

How to Install langgraph-agent-patterns

Quickly install langgraph-agent-patterns AI skill to your development environment via command line

  1. Open Terminal: Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.)
  2. Run Installation Command: Copy and run this command: npx skills add https://github.com/lubu-labs/langchain-agent-skills --skill langgraph-agent-patterns
  3. Verify Installation: Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code, Cursor, or OpenClaw

Source: lubu-labs/langchain-agent-skills.

SKILL.md

View raw

Implement and configure multi-agent coordination patterns for LangGraph applications.

| Pattern | Best For | When to Use |

| Supervisor | Complex workflows, dynamic routing | Agents need to collaborate, routing is context-dependent | | Router | Simple categorization, independent tasks | One-time routing, deterministic decisions | | Orchestrator-Worker | Parallel execution, high throughput | Independent subtasks, results need aggregation |

Implement multi-agent coordination patterns (supervisor-subagent, router, orchestrator-worker, handoffs) for LangGraph applications. Use when users want to (1) implement multi-agent systems, (2) coordinate multiple specialized agents, (3) choose between coordination patterns, (4) set up supervisor-subagent workflows, (5) implement router-based agent selection, (6) create parallel orchestrator-worker patterns, (7) implement agent handoffs, (8) design state schemas for multi-agent systems, or (9) debug multi-agent coordination issues. Source: lubu-labs/langchain-agent-skills.

Facts (cite-ready)

Stable fields and commands for AI/search citations.

Install command
npx skills add https://github.com/lubu-labs/langchain-agent-skills --skill langgraph-agent-patterns
Category
</>Dev Tools
Verified
First Seen
2026-02-22
Updated
2026-03-10

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Quick answers

What is langgraph-agent-patterns?

Implement multi-agent coordination patterns (supervisor-subagent, router, orchestrator-worker, handoffs) for LangGraph applications. Use when users want to (1) implement multi-agent systems, (2) coordinate multiple specialized agents, (3) choose between coordination patterns, (4) set up supervisor-subagent workflows, (5) implement router-based agent selection, (6) create parallel orchestrator-worker patterns, (7) implement agent handoffs, (8) design state schemas for multi-agent systems, or (9) debug multi-agent coordination issues. Source: lubu-labs/langchain-agent-skills.

How do I install langgraph-agent-patterns?

Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/lubu-labs/langchain-agent-skills --skill langgraph-agent-patterns Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code, Cursor, or OpenClaw

Where is the source repository?

https://github.com/lubu-labs/langchain-agent-skills