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Guide · Agent skill

How to use the LangChain & LangGraph Architecture agent skill

Updated · written with AI from the project's own documentation and checked against our catalogue data

The langchain-architecture skill is a set of reference guidance that teaches an AI agent how to design LLM applications with LangChain 1.x and LangGraph. Once installed, Claude can follow its patterns to write code for agents, state management, memory, document retrieval and tracing. This guide explains what the skill does, how to install it, how to ask for it, and what to check before you rely on the code it produces.

LangChain & LangGraph Architecturewshobson/agents · MIT

Quick start

  1. 1Download and unzip. Download the skill ZIP from this page and unzip it so you have a langchain-architecture folder containing SKILL.md and references/details.md.
  2. 2Place the skill folder. Move the folder to ~/.claude/skills/ for personal use or to .claude/skills/ inside a project. Or upload it under Settings → Capabilities → Skills in the Claude apps.
  3. 3Prepare your Python environment. Install langchain, langchain-core, langgraph and a provider package such as langchain-anthropic. Have your Anthropic API key ready as an environment variable.
  4. 4Ask for what you need. Describe your LangChain or LangGraph goal in plain language, naming the feature such as a ReAct agent, memory or RAG retrieval.
  5. 5Review the generated code. Check tools, memory storage, API key handling and any placeholder logic before running anything.
  6. 6Test and harden. Run the tests the agent writes, replace in-memory storage with persistent storage if needed, and add tracing with LangSmith if you have an account.

What is an agent skill and what does this one do?

An agent skill is a folder containing a SKILL.md file with instructions, plus optional reference files that the agent reads when it needs more detail. The skill changes how the agent approaches a task rather than adding a program you run yourself.

This skill covers LangGraph agent patterns such as ReAct and multi-agent supervisors, state with TypedDict, memory options, document loaders, text splitters, vector stores and retrievers, and LangSmith tracing. It also includes a Quick Start ReAct agent with search and calculator tools, example tests, and Redis LLM caching. The skill itself does not execute anything.

Install the langchain-architecture skill

Download the ZIP for this skill from our page and unzip it. For a personal setup, place the unzipped folder in ~/.claude/skills/. For a single project, place it in the project's .claude/skills/ folder.

You can also upload the skill in the Claude apps. Open Settings, go to Capabilities, then Skills, and upload the skill there.

  • Personal install: ~/.claude/skills/langchain-architecture
  • Project install: .claude/skills/langchain-architecture inside your project
  • Claude apps: Settings → Capabilities → Skills

How do you trigger the skill with a natural request?

You do not need a special command. Describe a LangChain or LangGraph task in plain language and the agent should pick up the skill when it matches the description: building LangChain applications, implementing AI agents, or creating complex LLM workflows.

Name the libraries and the goal. The more specific the request, the better the guidance fits.

  • Build a LangGraph agent that remembers a user's conversation across sessions.
  • Create a RAG pipeline with LangGraph that retrieves documents from a vector store before answering.
  • Write a ReAct agent with a search tool and a calculator tool using LangChain 1.x.
  • Set up LangSmith tracing for my LangGraph workflow.

What do you need before you start?

The generated code expects a Python environment with the langchain, langchain-core and langgraph packages, plus a provider integration such as langchain-anthropic or langchain-openai. The Quick Start example uses an Anthropic API key.

Some optional parts need extra accounts or services: a Redis server for caching, Pinecone or Voyage AI accounts for vector storage and embeddings, and a LangSmith account for tracing.

  • Python environment with langchain, langchain-core and langgraph
  • langchain-anthropic or another provider package such as langchain-openai
  • Anthropic API key for the example code
  • Optional: Redis server, Pinecone or Voyage AI accounts, LangSmith account
pip install langchain langchain-core langgraph langchain-anthropic

Review the example agent before running it

The Quick Start agent defines a search_database tool and a calculate tool, then creates a ReAct agent with an in-memory checkpointer. The calculate tool evaluates math expressions with Python's ast module rather than eval. The search_database tool is a placeholder that returns a fixed string, so you must connect it to your real data.

Memory is stored with MemorySaver, which is in-memory, so it resets when the process ends. The thread_id in the config keeps separate conversations apart. Treat the code as a starting structure and add your own storage, error handling and tests before production use.

  • Replace the placeholder search_database body with your real data source.
  • Swap MemorySaver for a persistent checkpointer if memory must survive restarts.
  • Use a different thread_id for each user or conversation.

What about safety, scripts and network access?

According to the listing, this skill runs no scripts and makes no network calls on its own. It provides guidance that the agent uses to write code. Any code the agent writes, including the example that calls Claude and optional services like Redis or Pinecone, will make network calls when you run it.

Review generated code for API key handling, data access and cost before running it against real systems. Keep keys in environment variables, as the Pinecone example does with os.environ.

  • The skill itself: no scripts, no network use.
  • Generated code: may call LLM APIs and external services when you run it.
  • Keep API keys out of source files.

What people use it for

  • Prototyping a ReAct agent with search and calculator tools in LangChain 1.x.
  • Adding conversation memory that persists per thread_id using LangGraph checkpointers.
  • Building a RAG workflow in LangGraph with a Pinecone vector store and Voyage AI embeddings.
  • Adding Redis-backed LLM caching to reduce repeated calls during development.
  • Creating a supervisor pattern where one agent routes work to specialised agents.

Frequently asked questions

Is the LangChain architecture skill free to use?

The listing states the skill is licensed under MIT. The libraries it references, such as LangChain and LangGraph, are separate projects with their own terms, and API providers like Anthropic charge for model use. Check each provider's terms for your use.

What do I need to install to use this skill?

You need a Python environment with langchain, langchain-core and langgraph, plus a provider package such as langchain-anthropic. The example code also needs an Anthropic API key. Redis, Pinecone, Voyage AI and LangSmith are optional.

Where do I put the skill folder?

Place it in ~/.claude/skills/ for personal use or in .claude/skills/ inside a project. You can also upload it in the Claude apps under Settings → Capabilities → Skills.

Does the skill run code on my computer?

No. The listing says the skill runs no scripts and uses no network. It gives the agent guidance, and any code the agent writes will only run when you execute it.

Can I use this with OpenAI or other models instead of Claude?

The skill mentions other provider integrations such as langchain-openai, so the same LangChain patterns can apply with those packages. The example code is written for ChatAnthropic, so you would swap the model class and key.

Why does my agent forget things between runs?

The Quick Start uses MemorySaver, which keeps state in memory only, so it resets when the process stops. Use a persistent checkpointer and reuse the same thread_id for the same conversation.

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Our verdict

This skill suits Python developers who want a structured starting point for LangChain 1.x and LangGraph agents, retrieval and memory. It is not suited to non-programmers wanting a finished app, or to projects that do not use LangChain or LangGraph. Review and harden any generated code before using it in production.

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