### Hono Getting Started

Source: https://hono.dev/

Guides for getting started with Hono on different platforms such as Basic setup, Cloudflare Workers, Cloudflare Pages, Deno, Bun, and Fastly Compute.

```en-US
Getting Started:
  Basic
  Cloudflare Workers
  Cloudflare Pages
  Deno
  Bun
  Fastly Compute
```

--------------------------------

### LangGraph Quick Start Guides

Source: https://context7_llms

Guides to help users get started with LangGraph, including launching a local server, using template applications, and deploying to LangGraph Cloud.

```markdown
## Get Started 🚀

- **LangGraph Server Quickstart**: Launch a local server and interact via REST API and Studio.
- **LangGraph Template Quickstart**: Build with LangGraph Platform using templates.
- **Deploy with LangGraph Cloud Quickstart**: Deploy an app using LangGraph Cloud.
```

--------------------------------

### Project Setup and Dependency Installation

Source: https://context7_llms

Commands to create a new project directory, navigate into it, and install necessary LangChain.js packages for building agents.

```bash
mkdir langgraph-agent
cd langgraph-agent
npm install @langchain/core @langchain/langgraph @langchain/openai @langchain/community
```

--------------------------------

### Edge Config Getting Started

Source: https://vercel.com/docs/functions/runtimes/edge-runtime

Initial steps and guide to get started with Vercel Edge Config.

```nextjs-app
/docs/edge-config/get-started
```

--------------------------------

### Install Dependencies

Source: https://context7_llms

Installs the necessary LangGraph, OpenAI, and Zod packages for the example.

```bash
npm install @langchain/langgraph @langchain/openai @langchain/core zod
```

--------------------------------

### Get Started Section with Call to Action

Source: https://redis.io/

Presents a 'Get started' section with a call to action to contact a Redis expert and try Redis.

```html
<div class=\"styles_section__Qbg1z style_spacing_bottom_lg__eQJkp style_spacing_top_lg__a3UAb styles_section__c6ata\">
  <div class=\"styles_container__ImQx_\" style=\{}\">
    <div class=\"styles_contentContainer__6I_qK\">
      <div class=\"styles_textContainer__VskM7\">
        <h2 class=\"styles_text__Pngqw styles_variant_body__cB6dZ styles_color_inherit__QrOD5 styles_size_3xl__fVITx styles_weight_medium__pp5xw styles_textAlignment_center__mZV95\">Get started</h2>
        <div class=\"styles_content__nBDXs\" color=\"themeFgBody\" config=\"{'p': {'textAlignment': 'center', 'size': 'sm'}}\">
          <p>Speak to a Redis expert and learn more about enterprise-grade Redis today.</p>
        </div>
      </div>
      <div class=\"styles_buttons__kdhlT\">
        <a href=\"/try-free/\" class=\"styles_button__GJyyi styles_button__GJyyi\" variant=\"rounded\" size=\"md\" theme=\"primary\">
          <span>Try Redis</span>
        </a>
        <a href=\"#\" class=\"styles_button__GJyyi\" variant=\"rounded\" size=\"md\" theme=\"secondary\">
          <span>Contact Us</span>
        </a>
      </div>
    </div>
  </div>
</div>
```

--------------------------------

### LangGraph.js Tutorials Overview

Source: https://context7_llms

Introduction to LangGraph.js tutorials, focusing on building language agents and applications. Includes a comprehensive quick start and guides on common workflows and deploying to LangGraph Cloud.

```markdown
# Tutorials

Welcome to the LangGraph.js Tutorials! Learn to build agents and applications.

## Quick Start

- **Quick Start**: Build an agent from scratch.
- **Common Workflows**: Overview of LLM workflows with LangGraph.
- **LangGraph Cloud Quick Start**: Build and deploy an agent to LangGraph Cloud.
```

--------------------------------

### LangGraph Platform Application Setup

Source: https://context7_llms

Guides for setting up your application for deployment to LangGraph Platform, including requirements files and Dockerfile customization.

```javascript
# requirements.txt example
langchain
langgraph

# pyproject.toml example
[tool.poetry.dependencies]
python = "^3.8"
langchain = "*"
langgraph = "*"

# JavaScript setup example (package.json)
{
  "dependencies": {
    "langchain": "*",
    "langgraph": "*"
  }
}
```

--------------------------------

### Install Dependencies

Source: https://context7_llms

Installs the necessary LangGraph, OpenAI, Langchain core, Zod, and UUID packages for the example.

```bash
npm install @langchain/langgraph @langchain/openai @langchain/core zod uuid
```

--------------------------------

### Install LangGraph Dependencies

Source: https://context7_llms

Installs the necessary LangGraph, OpenAI, and core Langchain packages for the guide.

```bash
npm install @langchain/langgraph @langchain/openai @langchain/core
```

--------------------------------

### Redis Documentation and Getting Started

Source: https://redis.io/

Provides access to official Redis documentation, command references, and quick start guides for new users.

```APIDOC
Docs:
  - Description: Access the official Redis documentation.
  - Type: External Link (https://redis.io/docs/latest/)

Commands:
  - Description: Reference for all Redis commands.
  - Type: External Link (https://redis.io/docs/latest/commands/)

Quick starts:
  - Description: Guides to help you get started with Redis quickly.
  - Type: External Link (https://redis.io/docs/get-started/)
```

--------------------------------

### Install LangGraph Dependencies

Source: https://context7_llms

Installs the necessary LangGraph, OpenAI, and Core Langchain packages using npm. This is a prerequisite for running the LangGraph examples.

```bash
npm install @langchain/langgraph @langchain/openai @langchain/core
```

--------------------------------

### Install Dependencies

Source: https://context7_llms

Installs the necessary LangGraph, OpenAI, and Zod libraries for the project. Ensure you have Node.js and npm installed.

```bash
npm install @langchain/langgraph @langchain/openai @langchain/core zod
```

--------------------------------

### Package Installation

Source: https://context7_llms

Installs the necessary LangGraph.js and related packages using npm.

```bash
npm install @langchain/langgraph @langchain/anthropic @langchain/core
```

--------------------------------

### Install Dependencies

Source: https://context7_llms

Installs the necessary LangChain packages for building agents and using LangGraph.

```bash
yarn add langchain @langchain/anthropic @langchain/langgraph @langchain/core
```

--------------------------------

### LangGraphJS Setup and Graph Compilation

Source: https://context7_llms

Shows the installation of necessary LangGraphJS packages and the compilation of a `StateGraph`. This includes defining nodes and adding edges, with a note on specifying `ends` for nodes using `Command` for validation.

```typescript
import { StateGraph } from "@langchain/langgraph";

// NOTE: there are no edges between nodes A, B and C!
const graph = new StateGraph(StateAnnotation)
  .addNode("nodeA", nodeA, {
    ends: ["nodeB", "nodeC"],
  })
  .addNode("nodeB", nodeB)
  .addNode("nodeC", nodeC)
  .addEdge("__start__", "nodeA")
  .compile();
```

--------------------------------

### Install Packages

Source: https://context7_llms

Installs the necessary LangGraphJS, OpenAI, and core LangChain packages using yarn.

```bash
yarn add @langchain/langgraph @langchain/openai @langchain/core
```

--------------------------------

### Install LangGraph Packages

Source: https://context7_llms

Installs the necessary LangChain and LangGraph packages using npm.

```bash
npm install @langchain/langgraph @langchain/core @langchain/openai
```

--------------------------------

### Install LangGraphJS Packages

Source: https://context7_llms

Installs the necessary LangGraphJS, OpenAI, and core LangChain packages using yarn.

```bash
yarn add @langchain/langgraph @langchain/openai @langchain/core
```

--------------------------------

### Install LangGraphJS Packages

Source: https://context7_llms

Command to install the required LangGraphJS and Langchain Core packages using yarn.

```bash
yarn add @langchain/langgraph @langchain/core
```

--------------------------------

### LangGraph Cloud SaaS Deployment Guide

Source: https://context7_llms

Provides instructions and information on deploying LangGraph APIs using the Cloud SaaS offering. Covers prerequisites, deployment, revisions, asynchronous deployment, and architecture.

```markdown
# Cloud SaaS

!!! info "Prerequisites"
    - [LangGraph Platform](./langgraph_platform.md)
    - [LangGraph Server](./langgraph_server.md)

## Overview

LangGraph's Cloud SaaS is a managed service that provides a scalable and secure environment for deploying LangGraph APIs. It is designed to work seamlessly with your LangGraph API regardless of how it is defined, what tools it uses, or any dependencies. Cloud SaaS provides a simple way to deploy and manage your LangGraph API in the cloud.

## Deployment

A **deployment** is an instance of a LangGraph API. A single deployment can have many [revisions](#revision). When a deployment is created, all the necessary infrastructure (e.g. database, containers, secrets store) are automatically provisioned. See the [architecture diagram](#architecture) below for more details.

See the [how-to guide](/langgraphjs/cloud/deployment/cloud.md#create-new-deployment) for creating a new deployment.

## Revision

A revision is an iteration of a [deployment](#deployment). When a new deployment is created, an initial revision is automatically created. To deploy new code changes or update environment variable configurations for a deployment, a new revision must be created. When a revision is created, a new container image is built automatically.

See the [how-to guide](/langgraphjs/cloud/deployment/cloud.md#create-new-revision) for creating a new revision.

## Asynchronous Deployment

Infrastructure for [deployments](#deployment) and [revisions](#revision) are provisioned and deployed asynchronously. They are not deployed immediately after submission. Currently, deployment can take up to several minutes.

## Architecture

!!! warning "Subject to Change"
The Cloud SaaS deployment architecture may change in the future.

A high-level diagram of a Cloud SaaS deployment.

![diagram](img/langgraph_cloud_architecture.png)

## Related

- [Deployment Options](./deployment_options.md)
```

--------------------------------

### LangGraphJS Setup: Install Dependencies

Source: https://context7_llms

Installs the necessary LangGraphJS and related packages using yarn.

```bash
yarn add @langchain/langgraph @langchain/openai @langchain/core
```

--------------------------------

### Get State Output Example

Source: https://context7_llms

Example output showing the next steps in the graph execution after an interrupt.

```output
[
  'tools'
]
```

--------------------------------

### Setup Environment Variables

Source: https://github.com/langchain-ai/memory-agent

Instructions for setting up the environment by copying an example .env file and defining required API keys for services like Anthropic or OpenAI.

```bash
cp .env.example .env
# Add API keys to .env file:
# ANTHROPIC_API_KEY=your-api-key
# OPENAI_API_KEY=your-api-key
```

--------------------------------

### Initial Agent Output Example

Source: https://context7_llms

Example output from the agent after the initial user prompt, showing the 'askHuman' tool being invoked to ask for the user's location.

```output
================================ human Message (1) =================================
Use the search tool to ask the user where they are, then look up the weather there
--- ASKING HUMAN ---
================================ ai Message (1) =================================
[
  {
    type: 'text',
    text: "Certainly! I'll use the askHuman tool to ask the user about their location, and then use the search tool to look up the weather for that location. Let's start by asking the user where they are."
  },
  {
    type: 'tool_use',
    id: 'toolu_015UDVFoXcMV7KjRqPY78Umk',
    name: 'askHuman',
    input: {
      input: 'Where are you currently located? Please provide a city and country or region.'
    }
  }
]
```

--------------------------------

### OpenAI API Key Setup

Source: https://github.com/langchain-ai/memory-agent-js

Instructions for setting up your OpenAI API key. This involves signing up on the OpenAI platform and adding your key to a .env file for use in your project.

```bash
OPENAI_API_KEY=your-api-key
```

--------------------------------

### Express.js Hello World Example

Source: https://expressjs.com/

A basic example demonstrating how to set up a simple Express.js server that listens on a specified port and responds with 'Hello World!' to requests on the root path. This snippet requires Node.js and the Express package to be installed.

```javascript
const express = require('express')
const app = express()
const port = 3000

app.get('/', (req, res) => {
  res.send('Hello World!')
})

app.listen(port, () => {
  console.log(`Example app listening on port ${port}`)
})
```

--------------------------------

### Getting Started: Install Dependencies

Source: https://github.com/langchain-ai/new-langgraph-project

Instructions for installing project dependencies, including the LangGraph CLI. The LangGraph CLI is essential for running and managing LangGraph applications.

```markdown
Install dependencies, along with the [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli/), which will be used to run
```

--------------------------------

### Environment Variable Setup

Source: https://github.com/langchain-ai/memory-agent-js

This snippet shows how to copy the example environment file to create your own configuration file. It's a common practice for managing sensitive information like API keys.

```bash
cp .env.example .env
```

--------------------------------

### Tool Execution Output Example

Source: https://context7_llms

Example output showing the tool call arguments and the final response after tool execution.

```output
What's the weather like in SF currently?
-----
[
  {
    name: 'search',
    args: { query: 'current weather in San Francisco' },
    type: 'tool_call',
    id: 'call_ZtmtDOyEXDCnXDgowlit5dSd'
  }
]
-----

Cold, with a low of 13 ℃
-----

The current weather in San Francisco is cold, with a low of 13°C.
-----
```

--------------------------------

### Project Setup and Environment Variables

Source: https://context7_llms

Installs necessary LangChain packages and configures environment variables for LangSmith tracing and project naming. This setup is crucial for developing and monitoring LangGraph applications.

```bash
npm install @langchain/langgraph @langchain/openai @langchain/core uuid zod
```

```typescript
//process.env.OPENAI_API_KEY = "sk_...";

// Optional, add tracing in LangSmith
//process.env.LANGCHAIN_API_KEY = "ls__...";
process.env.LANGCHAIN_CALLBACKS_BACKGROUND = "true";
process.env.LANGCHAIN_TRACING_V2 = "true";
process.env.LANGCHAIN_PROJECT = "Time Travel: LangGraphJS";
```

--------------------------------

### LangGraph Server Overview

Source: https://context7_llms

Provides an overview of LangGraph Server, its prerequisites, and its core concepts like assistants, persistence, and task queues.

```markdown
# LangGraph Server

!!! info "Prerequisites"
    - [LangGraph Platform](./langgraph_platform.md)
    - [LangGraph Glossary](low_level.md)

## Overview

LangGraph Server offers an API for creating and managing agent-based applications. It is built on the concept of [assistants](assistants.md), which are agents configured for specific tasks, and includes built-in [persistence](persistence.md#memory-store) and a **task queue**. This versatile API supports a wide range of agentic application use cases, from background processing to real-time interactions.
```

--------------------------------

### LangChain Documentation Links

Source: https://www.langchain.com/contact-sales

Links to LangChain documentation for Python and JavaScript, covering core concepts and getting started guides.

```APIDOC
LangChain Python Documentation:
  URL: https://python.langchain.com/docs/introduction/
  Description: Introduction and documentation for the LangChain Python library.

LangChain JavaScript Documentation:
  URL: https://js.langchain.com/docs/introduction/
  Description: Introduction and documentation for the LangChain JavaScript library.
```

--------------------------------

### LangGraph.js Web Environment Example

Source: https://context7_llms

Demonstrates how to use LangGraph.js in a web environment by importing from '@langchain/langgraph/web'. This example defines a graph state, a node function, builds a state graph, compiles it, and invokes the runnable to get a final state.

```typescript
import {
  END,
  START,
  StateGraph,
  Annotation,
} from "@langchain/langgraph/web";
import { BaseMessage, HumanMessage } from "@langchain/core/messages";

const GraphState = Annotation.Root({
  messages: Annotation<BaseMessage[]>({
    reducer: (x, y) => x.concat(y),
  }),
});

const nodeFn = async (_state: typeof GraphState.State) => {
  return { messages: [new HumanMessage("Hello from the browser!")] };
};

// Define a new graph
const workflow = new StateGraph(GraphState)
  .addNode("node", nodeFn)
  .addEdge(START, "node")
  .addEdge("node", END);

const app = workflow.compile({});

// Use the Runnable
const finalState = await app.invoke(
  { messages: [] },
);

console.log(finalState.messages[finalState.messages.length - 1].content);
```

```output
Hello from the browser!
```

--------------------------------

### Setup OpenAI API Key

Source: https://github.com/langchain-ai/retrieval-agent-template-js

Instructions for setting up the OpenAI API key by signing up and adding the key to the .env file.

```bash
OPENAI_API_KEY=your-api-key
```

--------------------------------

### LangChain.js Functions Quickstart

Source: https://vercel.com/docs/functions/runtimes/edge-runtime

Provides a starting point for using LangChain.js Functions, covering essential setup and basic usage for various frameworks like Next.js.

```javascript
import { run } from '@langchain/core/runnables';
import { ChatOpenAI } from '@langchain/openai';

const model = new ChatOpenAI({ temperature: 0.8 });

async function main() {
    const response = await run(model, 'What is LangChain?');
    console.log(response);
}

main();
```

--------------------------------

### LangGraph Template Applications

Source: https://context7_llms

Provides links to open-source reference applications for LangGraph, available in Python and JavaScript/TypeScript. These templates help users get started quickly with common agentic workflows.

```Python
https://github.com/langchain-ai/new-langgraph-project
https://github.com/langchain-ai/react-agent
https://github.com/langchain-ai/memory-agent
https://github.com/langchain-ai/retrieval-agent-template
https://github.com/langchain-ai/data-enrichment
```

```JavaScript
https://github.com/langchain-ai/new-langgraphjs-project
https://github.com/langchain-ai/react-agent-js
https://github.com/langchain-ai/memory-agent-js
https://github.com/langchain-ai/retrieval-agent-template-js
https://github.com/langchain-ai/data-enrichment-js
```

--------------------------------

### OpenAI API Key Setup

Source: https://github.com/langchain-ai/data-enrichment-js

Instructions for setting up your OpenAI API key in the .env file for use with the application.

```bash
OPENAI_API_KEY=your-api-key
```

--------------------------------

### How-to Guides Overview

Source: https://js.langchain.com/docs/concepts/runnables/

Guides on specific functionalities and advanced configurations in LangChain.js.

```javascript
// How-to guides:
// How to add memory to chatbots: /docs/how_to/chatbots_memory
// How to use example selectors: /docs/how_to/example_selectors
// Installation: /docs/how_to/installation
// How to stream responses from an LLM: /docs/how_to/streaming_llm
// How to stream chat model responses: /docs/how_to/chat_streaming
// How to embed text data: /docs/how_to/embed_text
// How to use few shot examples in chat models: /docs/how_to/few_shot_examples_chat
// How to cache model responses: /docs/how_to/llm_caching
// How to cache chat model responses: /docs/how_to/chat_model_caching
// Richer outputs: /docs/how_to/custom_llm
// How to use few shot examples: /docs/how_to/few_shot_examples
// How to use output parsers to parse an LLM response into structured format: /docs/how_to/output_parser_structured
// How to return structured data from a model: /docs/how_to/structured_output
// How to add ad-hoc tool calling capability to LLMs and Chat Models: /docs/how_to/tools_prompting
// Richer outputs: /docs/how_to/custom_chat
// How to do per-user retrieval: /docs/how_to/qa_per_user
// How to track token usage: /docs/how_to/chat_token_usage_tracking
// How to track token usage: /docs/how_to/llm_token_usage_tracking
// How to pass through arguments from one step to the next: /docs/how_to/passthrough
// How to compose prompts together: /docs/how_to/prompts_composition
// How to use legacy LangChain Agents (AgentExecutor): /docs/how_to/agent_executor
// How to add values to a chain's state: /docs/how_to/assign
// How to attach runtime configuration to a Runnable: /docs/how_to/binding
```

--------------------------------

### Chat Model Start and Stream Events Example

Source: https://js.langchain.com/docs/how_to/streaming

Illustrates the structure of the initial 'on_chat_model_start' event and subsequent 'on_chat_model_stream' events. This helps in understanding the data payload, including input, model details, and message chunks.

```javascript
events.slice(0, 3);
```

--------------------------------

### OpenAI API Key Setup

Source: https://github.com/langchain-ai/retrieval-agent-template-js

Instructions for setting up the OpenAI API key. This involves signing up for an API key and adding it to your .env file.

```shell
OPENAI_API_KEY=your-api-key
```

--------------------------------

### Replay Output Example

Source: https://context7_llms

Example output from replaying a past state, showing the agent's responses and tool calls.

```output
Cold, with a low of 13 ℃
-----

The current weather in San Francisco is cold, with a low of 13°C.
-----
```

--------------------------------

### Cloudflare Workers Testing with Miniflare

Source: https://developers.cloudflare.com/workers/runtime-apis/nodejs/asynclocalstorage/

Guides for using Miniflare to test Cloudflare Workers, including getting started, writing tests, and core features like scheduled events, WebSockets, compatibility dates, and modules.

```APIDOC
Testing:
  Miniflare:
    Overview: /workers/testing/miniflare/
    Get Started: /workers/testing/miniflare/get-started/
    Writing tests: /workers/testing/miniflare/writing-tests/
    Core:
      ⏰ Scheduled Events: /workers/testing/miniflare/core/scheduled/
      ✉️ WebSockets: /workers/testing/miniflare/core/web-sockets/
      📅 Compatibility Dates: /workers/testing/miniflare/core/compatibility/
      📚 Modules: /workers/testing/miniflare/core/modules/
```

--------------------------------

### Setup Cohere API Key

Source: https://github.com/langchain-ai/retrieval-agent-template-js

Instructions for setting up the Cohere API key by signing up and adding the key to the .env file.

```bash
COHERE_API_KEY=your-api-key
```

--------------------------------

### Using Few-Shot Examples in Chat Models

Source: https://js.langchain.com/docs/how_to/streaming

Explains how to leverage few-shot examples with Langchain chat models. Providing a few examples of desired input-output pairs can significantly improve the model's ability to follow instructions and generate accurate responses. This guide covers formatting and passing these examples.

```python
from langchain_openai import ChatOpenAI\nfrom langchain_core.messages import HumanMessage, SystemMessage, AIMessage\n\n# Initialize the chat model\nchat = ChatOpenAI()\n\n# Define few-shot examples\nexamples = [\n    HumanMessage(content="Translate 'I love programming' to French."),\n    AIMessage(content="J'adore la programmation."),\n    HumanMessage(content="Translate 'Hello world' to Spanish."),\n    AIMessage(content="Hola mundo."),\n]\n\n# User's actual query\user_query = "Translate 'Thank you very much' to German."\n\n# Combine system message, examples, and user query\messages = [\n    SystemMessage(\n        "You are a helpful assistant that translates English to other languages."\n    )\n] + examples + [HumanMessage(content=user_query)]\n\n# Invoke the chat model with the combined messages\response = chat.invoke(messages)\n\nprint(f"User Query: {user_query}")\nprint(f"Translation: {response.content}")
```

--------------------------------

### Using Reference Examples for Extraction

Source: https://js.langchain.com/docs/how_to/streaming

Explains how to use reference examples to guide the extraction process, improving the accuracy and reliability of information extraction tasks.

```python
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int

parser = PydanticOutputParser(pydantic_object=Person)

prompt = PromptTemplate(
    template="Extract person information.\n{format_instructions}\nInput: {text}",
    input_variables=["text"],
    partial_variables={"format_instructions": parser.get_format_instructions()}
)

llm = OpenAI(temperature=0)
chain = LLMChain(llm=llm, prompt=prompt)

# The prompt includes format_instructions which guides the LLM to output in the desired format,
# effectively using the Pydantic model as a reference example.
```

--------------------------------

### ReAct Agent Creation and Customization

Source: https://context7_llms

Guides on creating ReAct agents, adding memory, system prompts, human-in-the-loop functionality, and returning structured output.

```python
import { AgentExecutor } from "langchain_core/agents"
import { ChatPromptTemplate } from "@langchain/core/prompts"
import { ChatOpenAI } from "@langchain/openai"

const model = new ChatOpenAI({ temperature: 0 })
const prompt = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant."],
  ["human", "{input}"],
  ["ai", "{agent_scratchpad}"],
])

const agent = new AgentExecutor({
  agent: { name: "react-agent" }, // Placeholder for actual agent definition
  tools: [], // Placeholder for actual tools
  llm: model,
  prompt: prompt,
})

// Example usage:
// const result = await agent.invoke({ input: "Hello, world!" })
// console.log(result)
```

--------------------------------

### Install LangGraph Studio and Configure Environment

Source: https://github.com/langchain-ai/react-agent

This snippet shows the initial steps to set up LangGraph Studio, including creating a .env file and copying an example configuration. It's a common starting point for projects using this tool.

```shell
cp .env.example .env
```

--------------------------------

### Install Dependencies

Source: https://context7_llms

Installs the necessary LangChain and OpenAI packages using npm. This command ensures that the project has the required libraries for building LangGraph applications with OpenAI integration.

```bash
npm install @langchain/langgraph @langchain/openai @langchain/core zod
```

--------------------------------

### Google Tag Manager Integration (JavaScript)

Source: https://docs.smith.langchain.com/

This script integrates Google Tag Manager (GTM) into the webpage. It initializes the `dataLayer` array and asynchronously loads the GTM script from Google's servers, ensuring proper tracking and analytics setup.

```javascript
window.dataLayer=window.dataLayer||[] !function(e,t,a,n){e[n]=e[n]||[],e[n].push({"gtm.start":(new Date).getTime(),event:"gtm.js"});var g=t.getElementsByTagName(a)[0],m=t.createElement(a);m.async=!0,m.src="https://www.googletagmanager.com/gtm.js?id=GTM-WRT5MXDT",g.parentNode.insertBefore(m,g)}(window,document,"script","dataLayer")
```