# How to integrate Graphhopper MCP with LangChain

```json
{
  "title": "How to integrate Graphhopper MCP with LangChain",
  "toolkit": "Graphhopper",
  "toolkit_slug": "graphhopper",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/graphhopper/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/graphhopper/framework/langchain.md",
  "updated_at": "2026-05-06T08:14:45.265Z"
}
```

## Introduction

This guide walks you through connecting Graphhopper to LangChain using the Composio tool router. By the end, you'll have a working Graphhopper agent that can optimize delivery routes for multiple trucks, find all areas reachable within 15 minutes, convert a list of addresses to coordinates through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Graphhopper account through Composio's Graphhopper MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Graphhopper with

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

## TL;DR

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

The Graphhopper MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Graphhopper account. It provides structured and secure access to powerful routing, geocoding, and optimization services, so your agent can perform actions like planning routes, solving vehicle routing problems, geocoding addresses, and generating travel time isochrones on your behalf.
- Advanced route planning and optimization: Calculate complex routes for cars, bikes, or trucks using advanced parameters, waypoints, and custom profiles, all without manual map work.
- Batch distance and time calculations: Let your agent generate distance or time matrices for multiple origins and destinations, streamlining logistics and route optimization tasks.
- Geocoding and reverse geocoding: Convert between street addresses and GPS coordinates, or look up locations by latitude/longitude, making address management and mapping effortless.
- Isochrone map generation: Automatically create isochrone polygons to visualize areas reachable within a specific travel time or distance from any point—perfect for delivery zones, emergency planning, or site selection.
- Vehicle routing problem (VRP) solving: Offload complex fleet and logistics challenges to your agent, letting it assign deliveries, optimize routes, and minimize travel distances or costs using Graphhopper's VRP tools.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `GRAPHHOPPER_CLUSTER_POST` | Capacity Clustering | Tool to solve capacity clustering problem. Use when assigning a set of customers to clusters to minimize total distance synchronously. |
| `GRAPHHOPPER_GEOCODE_GET` | GraphHopper Geocoding | Tool to perform forward or reverse geocoding. Use when converting between textual addresses and latitude/longitude coordinates. |
| `GRAPHHOPPER_ISOCHRONE_GET` | Get Isochrone | Tool to compute isochrone polygons for a given point. Use when you need to determine areas reachable within time or distance constraints. |
| `GRAPHHOPPER_MATRIX_POST` | Calculate Matrix | Tool to calculate distance, time, or weight matrices via POST. Use when you have multiple origins/destinations or a symmetric point set and need a single batch request. |
| `GRAPHHOPPER_PROFILES_GET` | Get Custom Profiles | Tool to retrieve a list of all user-defined routing profiles. Use when you need to list custom profiles. |
| `GRAPHHOPPER_ROUTE_POST` | POST Route | Tool to calculate complex routes via POST /route. Use when you need advanced route planning with custom parameters. |
| `GRAPHHOPPER_UPLOAD_GPX_FILE` | Upload GPX File | Tool to upload a GPX file to a public file hosting endpoint. Returns a public URL which can be used where a 's3key' is required. |
| `GRAPHHOPPER_VRP_POST` | GraphHopper VRP POST | Tool to initiate VRP optimization. Use when you need to solve vehicle routing problems synchronously. |

## Supported Triggers

None listed.

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

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

url = session.mcp.url
```

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

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

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

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

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

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

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

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## Frequently Asked Questions

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

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

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

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

---
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