# How to integrate Graphhopper MCP with Pydantic AI

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

## Introduction

This guide walks you through connecting Graphhopper to Pydantic AI 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 Pydantic AI 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)
- [LangChain](https://composio.dev/toolkits/graphhopper/framework/langchain)
- [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:
- How to set up your Composio API key and User ID
- How to create a Composio Tool Router session for Graphhopper
- How to attach an MCP Server to a Pydantic AI agent
- How to stream responses and maintain chat history
- How to build a simple REPL-style chat interface to test your Graphhopper workflows

## What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.
Key features include:
- Type Safety: Built on Pydantic for automatic data validation
- MCP Support: Native support for Model Context Protocol servers
- Streaming: Built-in support for streaming responses
- Async First: Designed for async/await patterns

## 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

Before starting, make sure you have:
- Python 3.9 or higher
- A Composio account with an active API key
- Basic familiarity with Python and async programming

### 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

Install the required libraries.
What's happening:
- composio connects your agent to external SaaS tools like Graphhopper
- pydantic-ai lets you create structured AI agents with tool support
- python-dotenv loads your environment variables securely from a .env file
```bash
pip install composio pydantic-ai python-dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates your agent to Composio's API
- USER_ID associates your session with your account for secure tool access
- OPENAI_API_KEY to access OpenAI LLMs
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key
```

### 4. Import dependencies

What's happening:
- We load environment variables and import required modules
- Composio manages connections to Graphhopper
- MCPServerStreamableHTTP connects to the Graphhopper MCP server endpoint
- Agent from Pydantic AI lets you define and run the AI assistant
```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
```

### 5. 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
```python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Graphhopper
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["graphhopper"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
```

### 6. Initialize the Pydantic AI Agent

What's happening:
- The MCP client connects to the Graphhopper endpoint
- The agent uses GPT-5 to interpret user commands and perform Graphhopper operations
- The instructions field defines the agent's role and behavior
```python
# Attach the MCP server to a Pydantic AI Agent
graphhopper_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[graphhopper_mcp],
    instructions=(
        "You are a Graphhopper assistant. Use Graphhopper tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
```

### 7. Build the chat interface

What's happening:
- The agent reads input from the terminal and streams its response
- Graphhopper API calls happen automatically under the hood
- The model keeps conversation history to maintain context across turns
```python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Graphhopper.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
```

### 8. Run the application

What's happening:
- The asyncio loop launches the agent and keeps it running until you exit
```python
if __name__ == "__main__":
    asyncio.run(main())
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Graphhopper
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["graphhopper"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    graphhopper_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[graphhopper_mcp],
        instructions=(
            "You are a Graphhopper assistant. Use Graphhopper tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Graphhopper.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

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

## Conclusion

You've built a Pydantic AI agent that can interact with Graphhopper through Composio's Tool Router. With this setup, your agent can perform real Graphhopper actions through natural language.
You can extend this further by:
- Adding other toolkits like Gmail, HubSpot, or Salesforce
- Building a web-based chat interface around this agent
- Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Graphhopper for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

## 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)
- [LangChain](https://composio.dev/toolkits/graphhopper/framework/langchain)
- [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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- [Formsite](https://composio.dev/toolkits/formsite) - Formsite lets you build online forms and surveys with drag-and-drop simplicity. Capture, manage, and integrate form responses securely for streamlined workflows.
- [Hyperbrowser](https://composio.dev/toolkits/hyperbrowser) - Hyperbrowser is a next-generation platform for scalable browser automation. It empowers AI agents to interact with web apps, automate workflows, and handle browser sessions at scale.
- [La Growth Machine](https://composio.dev/toolkits/lagrowthmachine) - La Growth Machine automates multi-channel sales outreach and routine tasks for sales teams. Streamline your workflow and focus on closing more deals.
- [Leverly](https://composio.dev/toolkits/leverly) - Leverly is a workflow automation platform that connects and coordinates actions across your apps. It streamlines repetitive processes so your business runs smoother, faster, and with fewer manual steps.
- [Maintainx](https://composio.dev/toolkits/maintainx) - Maintainx is a cloud-based CMMS for centralizing maintenance data, communication, and workflows. It helps organizations streamline maintenance operations and improve team coordination.
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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 Pydantic AI?

Yes, you can. Pydantic AI 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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