Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
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What the file says
# 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
```
## Core Concepts
### 1. LangGraph Agents
LangGraph is the standard for building agents in 2026. It provides:
**Key Features:**
…
The line at the top of this page pins the label git:20260516.4d9ab13, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
GET https://markdownregistry.com/api/v1/artifacts/art_f7xbsm2nechls7v7
GET https://markdownregistry.com/api/v1/resolve?ref=nicholyx/ai-skills/langchain-architecture
GET https://markdownregistry.com/api/v1/blob/d0f20d0a3baa59a121ead565dee5d0fb98e50ed44d9fb550a521a798676e14e4
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