Agent skill · software engineering · wshobson
langchain-architecture
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.
Why this skill is useful
Adds executable scripts and complex workflows for building autonomous AI agents using LangChain and LangGraph, which are not covered in standard documentation.
What it needs
Requires langchain, langgraph installed locally. About 3k tokens when loaded. Last updated 2026-08-05. 38,570 stars on the source repository.
What this skill does
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) Core Concepts 1. LangGraph Agents LangGraph is the standard for building agents in 2026. It provides: Key Features: StateGraph: Explicit state management with typed state Durable Execution: Agents persist through failures Human-in-the-Loop: Inspect and modify state at any point Memory: Short-term and long-term memory across sessions Checkpointing: Save and resume agent state Agent Patterns: ReAct: Reasoning + Acting with createreactagent Plan-and-Execute: Separate planning and execution nodes Multi-Agent: Supervisor routing between specialized agents Tool-Calling: Structured tool invocation with Pydantic schemas 2. State Management LangGraph uses TypedDict for explicit state: 3. Memory Systems Modern memory implementations: ConversationBufferMemory: Stores all messages (short conversations) ConversationSummaryMemory: Summarizes older messages (long conversations) ConversationTokenBufferMemory: Token-based windowing VectorStoreRetrieverMemory: Semantic similarity retrieval LangGraph Checkpointers: Persistent state across sessions 4. Document Processing Loading, transforming, and storing documents: Components: Document Loaders: Load from various sources Text Splitters: Chunk documents intelligently Vector Stores: Store and retrieve embeddings Retrievers: Fetch relevant documents 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills wshobson/langchain-architecture