# How to integrate Wachete MCP with LangChain

```json
{
  "title": "How to integrate Wachete MCP with LangChain",
  "toolkit": "Wachete",
  "toolkit_slug": "wachete",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/wachete/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/wachete/framework/langchain.md",
  "updated_at": "2026-05-12T10:29:55.603Z"
}
```

## Introduction

This guide walks you through connecting Wachete to LangChain using the Composio tool router. By the end, you'll have a working Wachete agent that can monitor a webpage for price changes, list all your active web watchers, delete a watcher monitoring an old url through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Wachete account through Composio's Wachete MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Wachete with

- [OpenAI Agents SDK](https://composio.dev/toolkits/wachete/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/wachete/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/wachete/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/wachete/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/wachete/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/wachete/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/wachete/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/wachete/framework/cli)
- [Google ADK](https://composio.dev/toolkits/wachete/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/wachete/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/wachete/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/wachete/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/wachete/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Connect your Wachete project to Composio
- Create a Tool Router MCP session for Wachete
- Initialize an MCP client and retrieve Wachete tools
- Build a LangChain agent that can interact with Wachete
- Set up an interactive chat interface for testing

## What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.
Key features include:
- Agent Framework: Build agents that can use tools and make decisions
- MCP Integration: Connect to external services through Model Context Protocol adapters
- Memory Management: Maintain conversation history across interactions
- Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

## What is the Wachete MCP server, and what's possible with it?

The Wachete MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, and more directly to your Wachete account. It provides structured and secure access to your web monitoring setup, so your agent can create watchers, monitor webpages for changes, manage your folders, and keep you notified about updates—all automatically.
- Automated webpage monitoring: Let your agent create new watchers to track changes on any web page or specific elements, so you never miss an update.
- Watcher management and cleanup: Effortlessly remove obsolete monitors by deleting watchers when you no longer need to track certain content.
- Folder structure navigation: Retrieve and explore the content of your Wachete folders, listing all subfolders and active watchers for better organization.
- Real-time change notifications: Instantly pull notifications about detected changes across all your monitored pages, keeping you up to date at a glance.
- Comprehensive watcher overview: Ask your agent to list all configured watchers, making it easy to review, audit, or adjust your monitoring strategy as your needs evolve.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `WACHETE_CREATE_UPDATE_FOLDER` | Create or update folder | Create a new folder or update an existing folder in Wachete. Folders help organize watchers into hierarchical structures. Omit the id parameter to create a new folder, or provide an id to update an existing one. |
| `WACHETE_CREATE_WATCHER` | Create Watcher | Create or update a Wachete watcher to monitor web page changes. Watchers check pages at specified intervals and send alerts when changes are detected. Use SinglePage mode for monitoring a single page, or Portal mode to crawl and monitor multiple linked pages. |
| `WACHETE_DELETE_FOLDER` | Delete folder | Permanently deletes a folder along with all nested subfolders and watchers (monitoring tasks). This is a destructive operation that cannot be undone. Use when you need to remove an entire folder structure. All subfolders and monitoring tasks within the folder will be permanently deleted. Obtain the folder ID from the Get Folder Content action before calling. Example: "Delete the folder with ID 576b3f7e-e126-4e92-9b95-f72a8d187a18" |
| `WACHETE_DELETE_WATCHER` | Delete watcher | Deletes a website monitoring watcher (task) by its unique ID. This operation is idempotent - deleting a non-existent or already-deleted watcher will succeed without error. Use when you need to permanently remove a monitoring task. Obtain the watcher ID from List Watchers or Create Watcher actions before calling. Example: "Delete the watcher with ID 974b65b5-6ccb-4996-812c-5a678c2455e8" |
| `WACHETE_GET_CRAWLER_PAGES` | Get crawler pages | Retrieves all pages monitored by a crawler watcher (portal monitor). Use this to get detailed information about each page being tracked including URLs, last check timestamps, content changes, and error states. Only works with portal-type watchers that monitor multiple pages. |
| `WACHETE_GET_DATA_HISTORY` | Get Data History | Retrieve history for a wachet (monitor). Returns timestamped snapshots of monitored content showing when changes occurred. Supports time range filtering and optional diff with previous value. Use continuationToken for pagination when retrieving large histories. |
| `WACHETE_GET_FOLDER_CONTENT` | Get folder content | Retrieves the contents of a Wachete folder, including subfolders and watcher tasks. Use this tool to: - List all subfolders and tasks in the root folder (omit parentId) - List contents of a specific folder (provide parentId) - Navigate the folder hierarchy using the path breadcrumb - Check task statuses and last check data Returns subfolders, tasks with their monitoring details, folder path, and pagination token. |
| `WACHETE_GET_WATCHER` | Get watcher by ID | Retrieve complete watcher (monitor) definition by ID. Use this to get detailed configuration and current status of a specific monitoring task including URL, XPath selector, alerts, notification endpoints, and latest check results. |
| `WACHETE_LIST_NOTIFICATIONS` | List notifications | Retrieves notifications from Wachete watchers. Returns notifications for all watchers or filtered by specific watcher ID and/or time range. Useful for checking recent changes detected by your web page monitors. |
| `WACHETE_LIST_WATCHERS` | List watchers | List all monitoring watchers (tasks) configured in your Wachete account. Optionally filter by search query. Returns up to 500 watchers with details including name, URL, monitoring settings, and notification configuration. |
| `WACHETE_MOVE_ITEMS_TO_FOLDER` | Move Items to Folder | Move tasks (watchers) and folders to a specified destination folder. Use this to organize your monitoring structure by relocating items within the folder hierarchy. Provide at least one of folderIds or taskIds to move items. Set folderId to null to move items to root level. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Wachete MCP server is an implementation of the Model Context Protocol that connects your AI agent to Wachete. It provides structured and secure access so your agent can perform Wachete operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

No description provided.

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider.
- Keep the API key safe.
Composio API Key
- Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates your requests to Composio's API
- COMPOSIO_USER_ID identifies the user for session management
- OPENAI_API_KEY enables access to OpenAI's language models
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

No description provided.
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
```

### 5. Initialize Composio client

What's happening:
- We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
- Creating a Composio instance that will manage our connection to Wachete tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
```

```typescript
const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to Wachete tools
- The create method takes the user ID and specifies which toolkits should be available
- The returned session.mcp.url is the MCP server URL that your agent will use
- This approach allows the agent to dynamically load and use Wachete tools as needed
```python
# Create Tool Router session for Wachete
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['wachete']
)

url = session.mcp.url
```

```typescript
const session = await composio.create(
    userId as string,
    {
        toolkits: ['wachete']
    }
);

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "wachete-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
```

```typescript
const client = new MultiServerMCPClient({
    "wachete-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Wachete related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Wachete related question or task to the agent.\n");

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Complete Code

```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['wachete']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "wachete-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Wachete related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['wachete']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "wachete-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Wachete related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Conclusion

You've successfully built a LangChain agent that can interact with Wachete through Composio's Tool Router.
Key features of this implementation:
- Dynamic tool loading through Composio's Tool Router
- Conversation history maintenance for context-aware responses
- Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

## How to build Wachete MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/wachete/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/wachete/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/wachete/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/wachete/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/wachete/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/wachete/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/wachete/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/wachete/framework/cli)
- [Google ADK](https://composio.dev/toolkits/wachete/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/wachete/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/wachete/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/wachete/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/wachete/framework/crew-ai)

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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Wachete MCP?

With a standalone Wachete MCP server, the agents and LLMs can only access a fixed set of Wachete tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Wachete and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Wachete tools.

### Can I manage the permissions and scopes for Wachete while using Tool Router?

Yes, absolutely. You can configure which Wachete scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

### How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Wachete data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)