Which LangChain agent types are supported?

The skill covers various agent architectures including ReAct, OpenAI Functions, Structured Chat, and Conversational agents.

Can this skill help with RAG implementation?

Yes, it includes patterns for the entire RAG lifecycle, including document loading, text splitting, embedding generation, and vector store retrieval.

Does it provide guidance on memory management?

Yes, it offers best practices for different memory types like ConversationBufferMemory, ConversationSummaryMemory, and VectorStoreMemory to handle long-term context.

What is the LangChain Architecture skill?

It is a specialized capability for Claude Code that provides expert guidance and code patterns for building LLM applications using the LangChain framework.

LangChain Application Architecture

Name: LangChain Application Architecture
Author: carloscroc

bycarloscroc

Data Science & ML

Architects and implements sophisticated LLM applications using LangChain for agents, memory management, and complex workflows.

About

This skill empowers Claude with expert-level knowledge of the LangChain framework to build production-grade LLM applications. It covers essential architectural patterns including autonomous agent design, multi-step sequential chains, and sophisticated memory management strategies. Whether you are implementing Retrieval-Augmented Generation (RAG) pipelines, integrating external APIs as tools, or optimizing token usage through efficient document processing, this skill provides the implementation patterns and best practices needed to create scalable, modular AI systems.

Key Features

Autonomous agent implementation using ReAct and OpenAI function calling
0 GitHub stars
Modular chain composition for complex, multi-step LLM reasoning
Comprehensive RAG pipelines with document loaders and vector stores
Performance monitoring and debugging via custom callback systems
Advanced memory management including summary and vector-based context

Use Cases

Creating complex conversational interfaces with persistent context
Building autonomous AI agents capable of using tools and APIs
Developing RAG systems for querying private document repositories

About

Key Features

Autonomous agent implementation using ReAct and OpenAI function calling
0 GitHub stars
Modular chain composition for complex, multi-step LLM reasoning
Comprehensive RAG pipelines with document loaders and vector stores
Performance monitoring and debugging via custom callback systems
Advanced memory management including summary and vector-based context

Use Cases

Creating complex conversational interfaces with persistent context
Building autonomous AI agents capable of using tools and APIs
Developing RAG systems for querying private document repositories