langchain-init

Initialize a new LangChain TypeScript project with optimal configuration for building AI agents.

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Install skill "langchain-init" with this command: npx skills add laurigates/claude-plugins/laurigates-claude-plugins-langchain-init

/langchain:init

Initialize a new LangChain TypeScript project with optimal configuration for building AI agents.

Context

Detect the environment:

  • node --version

  • Node.js version

  • which bun

  • Check if Bun is available

Parameters

Parameter Description Default

project-name

Name of the project directory Required

Execution

  1. Create Project Directory

mkdir -p $1 && cd $1

  1. Initialize Package

If Bun is available:

bun init -y

Otherwise:

npm init -y

  1. Install Dependencies

Core packages:

Package manager: bun or npm

bun add langchain @langchain/core @langchain/langgraph bun add @langchain/openai # Default model provider

Dev dependencies

bun add -d typescript @types/node tsx

  1. Create TypeScript Config

Create tsconfig.json :

{ "compilerOptions": { "target": "ES2022", "module": "NodeNext", "moduleResolution": "NodeNext", "esModuleInterop": true, "strict": true, "skipLibCheck": true, "outDir": "dist", "declaration": true }, "include": ["src/**/*"], "exclude": ["node_modules", "dist"] }

  1. Create Project Structure

mkdir -p src

  1. Create Example Agent

Create src/agent.ts :

import { ChatOpenAI } from "@langchain/openai"; import { createReactAgent } from "@langchain/langgraph/prebuilt"; import { tool } from "@langchain/core/tools"; import { z } from "zod";

// Example tool const greetTool = tool( async ({ name }) => Hello, ${name}!, { name: "greet", description: "Greet someone by name", schema: z.object({ name: z.string().describe("The name to greet"), }), } );

// Create the agent const model = new ChatOpenAI({ model: "gpt-4o", temperature: 0, });

export const agent = createReactAgent({ llm: model, tools: [greetTool], });

// Run if executed directly if (import.meta.url === file://${process.argv[1]}) { const result = await agent.invoke({ messages: [{ role: "user", content: "Say hello to Claude" }], }); console.log(result.messages[result.messages.length - 1].content); }

  1. Create Environment Template

Create .env.example :

OpenAI (default)

OPENAI_API_KEY=sk-...

Optional: Anthropic

ANTHROPIC_API_KEY=sk-ant-...

Optional: LangSmith tracing

LANGCHAIN_TRACING_V2=true

LANGCHAIN_API_KEY=ls__...

LANGCHAIN_PROJECT=my-project

  1. Update package.json Scripts

Add to package.json :

{ "scripts": { "dev": "tsx watch src/agent.ts", "start": "tsx src/agent.ts", "build": "tsc", "typecheck": "tsc --noEmit" } }

  1. Create .gitignore

node_modules/ dist/ .env *.log

Post-Actions

Display success message with next steps:

  • Copy .env.example to .env and add API key

  • Run bun dev or npm run dev to start

  • Check LangChain docs for more examples

Suggest installing additional model providers if needed:

  • @langchain/anthropic for Claude

  • @langchain/google-genai for Gemini

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