Agent skill · software engineering · sickn33
multi-agent-architect
Design and optimize production-grade multi-agent systems with LangGraph, LangChain, and DeepAgents for complex AI workflows.
Why this skill is useful
Adds executable scripts and structured workflows for designing and optimizing multi-agent systems that are not commonly found in public documentation.
What it needs
Requires langchain, langgraph installed locally. About 6k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
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: sourcerepo: owner/repo sourcetype: official or sourcetype: 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: Step 3: Define Agent Nodes Each agent is an async function that reads from state and returns an updated state: Step 4: Build the LangGraph Wire nodes together with edges and conditional routing: Step 5: Add Memory Step 6: Run the Graph Step 7: Expose via FastAPI (optional) --- Updating an Existing Agent When the user wants to update or debug an existing agent, structure the response as: --- …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/multi-agent-architect