Which architecture patterns does it recommend?

It considers various LangGraph-specific patterns including parallelization, intent-based routing, subgraphs, and orchestrator-worker models.

Does this skill modify my code automatically?

No, this skill focuses on analysis and proposal generation. The suggested changes are typically implemented and evaluated by the companion arch-tune command.

What metrics does it analyze?

It measures baseline performance across several dimensions, specifically mean and standard deviation for latency, API cost, and response accuracy.

What is the LangGraph Architecture Analysis skill?

It is a specialized tool for Claude Code that evaluates the structure and performance of LangGraph applications to suggest data-driven optimizations.

LangGraph Architecture Analysis

Name: LangGraph Architecture Analysis
Author: hiroshi75

byhiroshi75

データサイエンスとML

Analyzes LangGraph agent architectures to identify performance bottlenecks and generate data-driven improvement strategies.

概要

LangGraph Architecture Analysis provides a comprehensive diagnostic framework for AI agents built with LangGraph. It measures baseline metrics like latency, cost, and accuracy, maps out current node-edge structures, and identifies specific architectural bottlenecks. By referencing established LangGraph patterns such as parallelization and routing, it generates multiple improvement proposals that can be automatically implemented and evaluated by companion tools, making it essential for developers looking to optimize complex agentic workflows.

主な機能

Automated baseline performance measurement including latency, cost, and accuracy
Generation of 3-5 diverse architectural improvement proposals for parallel exploration
Detailed analysis reports and standardized improvement documentation
0 GitHub stars
Deep graph structure mapping for StateGraph and MessageGraph definitions
Identification of sequential bottlenecks and high-cost LLM nodes

ユースケース

Reducing latency in complex multi-step AI agent workflows
Improving agent accuracy by identifying and restructuring weak graph nodes
Lowering operational costs by optimizing model selection and routing logic

概要

主な機能

Automated baseline performance measurement including latency, cost, and accuracy
Generation of 3-5 diverse architectural improvement proposals for parallel exploration
Detailed analysis reports and standardized improvement documentation
0 GitHub stars
Deep graph structure mapping for StateGraph and MessageGraph definitions
Identification of sequential bottlenecks and high-cost LLM nodes

ユースケース

Reducing latency in complex multi-step AI agent workflows
Improving agent accuracy by identifying and restructuring weak graph nodes
Lowering operational costs by optimizing model selection and routing logic