# How to integrate Globalping MCP with LangChain

```json
{
  "title": "How to integrate Globalping MCP with LangChain",
  "toolkit": "Globalping",
  "toolkit_slug": "globalping",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/globalping/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/globalping/framework/langchain.md",
  "updated_at": "2026-05-12T10:13:17.394Z"
}
```

## Introduction

This guide walks you through connecting Globalping to LangChain using the Composio tool router. By the end, you'll have a working Globalping agent that can ping google.com from paris and tokyo, run traceroute to your server from brazil, list all active probes in india through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Globalping account through Composio's Globalping MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Globalping with

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

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Connect your Globalping project to Composio
- Create a Tool Router MCP session for Globalping
- Initialize an MCP client and retrieve Globalping tools
- Build a LangChain agent that can interact with Globalping
- 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 Globalping MCP server, and what's possible with it?

The Globalping MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Globalping account. It provides structured and secure access to global network testing infrastructure, so your agent can perform actions like launching pings, running traceroutes, resolving DNS, and monitoring network health from locations worldwide.
- On-demand network measurements: Have your agent initiate pings, traceroutes, or DNS lookups from specific global probes to test connectivity or diagnose issues.
- Fetch real-time test results: Retrieve detailed per-probe measurement results, including latency, hops, and DNS answers, for network troubleshooting and analysis.
- Probe discovery and selection: List and filter all available online probes by location or network, enabling targeted testing from anywhere in the world.
- Automated health checks: Validate Globalping API health and availability to ensure reliable automation before running critical network tests.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `GLOBALPING_CREATE_MEASUREMENT` | Create Measurement | Tool to create and trigger a network measurement. Use when you need connectivity checks from specific geographic or network locations. |
| `GLOBALPING_GET_LIMITS` | Get Rate Limits | Tool to retrieve current rate limits and credits. Use this to check remaining API quota before making measurements. Returns rate limits based on IP address for unauthenticated requests or user account for authenticated requests. |
| `GLOBALPING_GET_MEASUREMENT` | Get Measurement Results | Retrieve measurement results by ID. Use this after creating a measurement to get the status and detailed results from all participating probes. Poll this endpoint until status is 'finished'. |
| `GLOBALPING_HEALTH_CHECK` | GlobalPing Health Check | Tool to check API health status. Use when verifying API availability. |
| `GLOBALPING_LIST_PROBES` | List Probes | Retrieve all currently online Globalping probes worldwide. Returns probe metadata including location (continent, country, city), network information (ASN, ISP name), and tags (datacenter-network, eyeball-network, cloud provider tags). Use max_results parameter to limit response size since the API typically returns ~3500 active probes. |

## Supported Triggers

None listed.

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

The Globalping MCP server is an implementation of the Model Context Protocol that connects your AI agent to Globalping. It provides structured and secure access so your agent can perform Globalping 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 Globalping 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 Globalping 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 Globalping tools as needed
```python
# Create Tool Router session for Globalping
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['globalping']
)

url = session.mcp.url
```

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

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

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

No description provided.
```python
client = MultiServerMCPClient({
    "globalping-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({
    "globalping-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 Globalping 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 Globalping 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=['globalping']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "globalping-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 Globalping 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: ['globalping']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "globalping-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 Globalping 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 Globalping 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 Globalping MCP Agent with another framework

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

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- [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data.

## Frequently Asked Questions

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

With a standalone Globalping MCP server, the agents and LLMs can only access a fixed set of Globalping tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Globalping 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 Globalping tools.

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

Yes, absolutely. You can configure which Globalping 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 Globalping data and credentials are handled as safely as possible.

---
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