When should I choose LangChain over LangGraph?

LangChain is best for straightforward agents and RAG pipelines, while LangGraph is recommended for complex workflows requiring custom state machines and human-in-the-loop interactions.

How does it handle conversation memory?

The skill provides patterns for various memory types, including Buffer, Window, Summary, and Vector memory, to manage context windows effectively in long-running chats.

Can I use this for building autonomous agents?

Absolutely. It includes guidance for ReAct agents, Plan-and-Execute patterns, and structured tool calling to help LLMs interact with external APIs and data.

Does this skill support RAG implementation?

Yes, it covers the entire RAG lifecycle including document loading, splitting strategies, embedding selection, and advanced retrieval techniques like MMR or hybrid search.

What is the LangChain Agents skill for Claude Code?

It is a specialized capability that provides Claude with best practices and implementation patterns for building complex LLM applications using the LangChain framework.

LangChain Agents & RAG

Name: LangChain Agents & RAG
Author: eyadsibai

byeyadsibai

Data Science & ML

Architects sophisticated LLM applications using autonomous agents, retrieval-augmented generation (RAG), and complex memory management patterns.

About

This skill equips Claude with specialized knowledge to design and implement production-grade AI applications using the LangChain framework. It provides deep expertise in constructing autonomous agents via the ReAct pattern, optimizing RAG pipelines for high-accuracy document retrieval, and managing conversation state through various memory strategies. By leveraging this skill, developers can efficiently swap LLM providers, integrate vector databases, and implement observability through LangSmith, ensuring best practices are followed throughout the AI development lifecycle.

Key Features

Configuration of conversation memory types from simple buffers to vector-based recall
End-to-end RAG pipeline design including semantic chunking and hybrid retrieval
Standardized integration for vector stores like Chroma, FAISS, and Pinecone
Implementation of ReAct, Plan-and-Execute, and Structured Chat agent patterns
0 GitHub stars
Observability workflows using LangSmith for tracing and evaluating LLM calls

Use Cases

Building autonomous research agents that utilize external tools and reasoning loops
Developing context-aware chatbots with long-term memory and document-based knowledge
Rapidly prototyping multi-provider AI applications with unified interfaces and tool calling

About

Key Features

Configuration of conversation memory types from simple buffers to vector-based recall
End-to-end RAG pipeline design including semantic chunking and hybrid retrieval
Standardized integration for vector stores like Chroma, FAISS, and Pinecone
Implementation of ReAct, Plan-and-Execute, and Structured Chat agent patterns
0 GitHub stars
Observability workflows using LangSmith for tracing and evaluating LLM calls

Use Cases

Building autonomous research agents that utilize external tools and reasoning loops
Developing context-aware chatbots with long-term memory and document-based knowledge
Rapidly prototyping multi-provider AI applications with unified interfaces and tool calling