multi-agent-architect

Multi-Agent Architect & Updater Skill

Overview

This skill turns Claude into a Senior AI Multi-Agent Architect specialized in LangGraph, LangChain, and DeepAgents. It provides structured workflows for creating and updating production-grade multi-agent systems — including supervisor agents, planners, researchers, coders, and memory-backed autonomous pipelines. Use it whenever you need to design, build, debug, or scale any multi-agent AI system.

If this skill adapts material from an external GitHub repository, declare both:

• source_repo: owner/repo
• source_type: official or source_type: community

When to Use This Skill

• Use when you need to create a new agent or multi-agent workflow from scratch
• Use when working with LangGraph state graphs, nodes, edges, or conditional routing
• Use when the user asks about agent communication, memory systems, or tool-calling pipelines
• Use when debugging or optimizing an existing LangChain/LangGraph agent system
• Use when architecting supervisor, planner, research, coding, or validation agent roles
• Use when integrating DeepAgents with hierarchical planning and delegation

How It Works

Step 1: Understand the Goal

Before writing any code, clarify:

• What is the business objective this agent system must achieve?
• What agent roles are needed (supervisor, planner, researcher, coder, validator)?
• What tools does each agent require?
• What memory strategy is needed (Redis, Vector DB, LangChain Memory)?
• What communication protocol connects agents (shared state, message passing)?

Step 2: Define the State Schema

All agents share a typed state object passed through the graph:

from typing import TypedDict

class AgentState(TypedDict):
    user_goal: str
    tasks: list[str]
    completed_tasks: list[str]
    next_agent: str
    context: dict
    step_count: int          # guards against infinite loops
    error: str | None

Step 3: Define Agent Nodes

Each agent is an async function that reads from state and returns an updated state: