Can this skill help with RAG implementation?

Yes, it includes specific patterns for document loading, text splitting, vector store integration, and building efficient retrieval-augmented generation pipelines.

How does it handle conversation state?

The skill provides implementations for various memory types like ConversationBuffer, Summary, and VectorStoreMemory to manage context window limits and historical relevance.

What is the LangChain Architecture skill?

It is a specialized capability for Claude Code that provides patterns and best practices for building complex applications using the LangChain framework, including agents and memory systems.

Does it support different AI agent types?

It covers multiple agent architectures including ReAct, OpenAI Functions, and Structured Chat to handle different levels of task autonomy.

Is this suitable for production LLM apps?

Absolutely. It includes production checklists covering error handling, token monitoring, rate limiting, and observability through the LangChain callback system.

LangChain App Architect

Name: LangChain App Architect
Author: HermeticOrmus

byHermeticOrmus

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データサイエンスとML

Builds sophisticated LLM applications using the LangChain framework with advanced agent, memory, and tool integration patterns.

概要

LangChain Architecture is a specialized Claude Code skill designed to streamline the development of production-grade LLM applications. It provides expert guidance on implementing autonomous AI agents, complex multi-step workflows, and various memory management strategies to maintain conversational state. Whether you are building RAG pipelines, integrating external APIs as tools, or optimizing performance through caching and streaming, this skill ensures your LangChain implementations follow industry best practices for modularity, scalability, and observability.

主な機能

Comprehensive memory management including Buffer, Summary, and VectorStore strategies
2 GitHub stars
Robust RAG pipeline implementation for document processing and semantic retrieval
Custom callback systems for production-grade logging, monitoring, and debugging
Autonomous agent design using ReAct, OpenAI Functions, and Structured Chat patterns
Modular chain construction for sequential, routed, and transform workflows

ユースケース

Creating complex multi-step LLM workflows for data extraction and transformation
Implementing context-aware Q&A systems using Retrieval-Augmented Generation
Building autonomous AI agents capable of using external tools and specialized APIs

概要

主な機能

Comprehensive memory management including Buffer, Summary, and VectorStore strategies
2 GitHub stars
Robust RAG pipeline implementation for document processing and semantic retrieval
Custom callback systems for production-grade logging, monitoring, and debugging
Autonomous agent design using ReAct, OpenAI Functions, and Structured Chat patterns
Modular chain construction for sequential, routed, and transform workflows

ユースケース

Creating complex multi-step LLM workflows for data extraction and transformation
Implementing context-aware Q&A systems using Retrieval-Augmented Generation
Building autonomous AI agents capable of using external tools and specialized APIs