What is the LangGraph Architect skill?

It is a specialized extension for Claude Code that provides deep expertise, templates, and architectural patterns for building graph-based AI agents using the LangGraph framework.

Can I use it for Human-in-the-Loop workflows?

Yes, it includes specific patterns and sub-agent configurations for building approval flows, manual overrides, and human-in-the-loop execution controls.

Which graph patterns does this skill support?

It supports all major agentic patterns including Prompt Chaining, Parallelization, Routing, Orchestrator-Worker, Evaluator-Optimizer, and Subgraphs.

How does it handle agent memory and persistence?

The skill provides implementation guides for short-term persistence using checkpointers and long-term memory management using the LangGraph Store API.

LangGraph Architect

Name: LangGraph Architect
Author: hiroshi75

byhiroshi75

Ciencia de Datos y ML

Architects and implements sophisticated graph-based AI agents and multi-agent workflows using LangGraph.

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LangGraph Architect is a comprehensive skill designed to guide developers through the entire lifecycle of building complex, reliable AI systems using LangGraph. It provides curated architectural patterns—such as Parallelization, Orchestrator-Worker, and Evaluator-Optimizer—alongside implementation templates for state management, persistence, and tool integration. By offering specialized sub-agents for functional modules and clear project structure guidelines, it enables the creation of scalable, autonomous agentic workflows that surpass simple linear chains.

Características Principales

Professional implementation of StateGraph, MessageGraph, and custom agent workflows
Optimized project structuring and sub-agent orchestration for parallel development
Integrated Human-in-the-Loop patterns for approval flows and manual overrides
Advanced memory management with checkpointers, persistence, and long-term storage
Curated architecture patterns including Routing, Parallelization, and Subgraphs
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Casos de Uso

Implementing an autonomous chatbot with memory persistence and human approval steps
Designing an Orchestrator-Worker system to handle complex, multi-step research tasks
Building a multi-agent RAG system with complex retrieval and reranking logic

Acerca de

Características Principales

Professional implementation of StateGraph, MessageGraph, and custom agent workflows
Optimized project structuring and sub-agent orchestration for parallel development
Integrated Human-in-the-Loop patterns for approval flows and manual overrides
Advanced memory management with checkpointers, persistence, and long-term storage
Curated architecture patterns including Routing, Parallelization, and Subgraphs
0 GitHub stars

Casos de Uso

Implementing an autonomous chatbot with memory persistence and human approval steps
Designing an Orchestrator-Worker system to handle complex, multi-step research tasks
Building a multi-agent RAG system with complex retrieval and reranking logic