Langchain Architecture

This one's for building serious LLM applications with LangChain 1.x and LangGraph, not just chatbot demos. It covers the modern agent patterns like ReAct and plan-execute, shows you how to implement proper state management with TypedDict, and handles the tricky bits like memory persistence across sessions and tool integration with Pydantic schemas. The examples are production-focused, covering RAG pipelines, multi-agent systems, and document processing workflows. If you're moving beyond simple LLM calls and need to build agents that actually maintain state, use tools reliably, and handle complex workflows, this covers the current best practices for 2026.

skills.sh installs: 13.6K
GitHub stars: 42.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add wshobson/agents --skill langchain-architecture --agent claude-code

Installs into .claude/skills of the current project.

Files

LangChain & LangGraph Architecture

Master modern LangChain 1.x and LangGraph for building sophisticated LLM applications with agents, state management, memory, and tool integration.

When to Use This Skill

Building autonomous AI agents with tool access
Implementing complex multi-step LLM workflows
Managing conversation memory and state
Integrating LLMs with external data sources and APIs
Creating modular, reusable LLM application components
Implementing document processing pipelines
Building production-grade LLM applications

Package Structure (LangChain 1.x)

langchain (1.2.x)         # High-level orchestration
langchain-core (1.2.x)    # Core abstractions (messages, prompts, tools)
langchain-community       # Third-party integrations
langgraph                 # Agent orchestration and state management
langchain-openai          # OpenAI integrations
langchain-anthropic       # Anthropic/Claude integrations
langchain-voyageai        # Voyage AI embeddings
langchain-pinecone        # Pinecone vector store