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How to integrate Graphhopper MCP with Autogen

Connect Autogen to Graphhopper MCP. Optimize delivery routes for multiple trucks, find all areas reachable within 15 minutes, and more using natural language, with authentication handled for you.

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GraphHopper is an enterprise-grade Directions API for routing, optimization, and geocoding across multiple vehicle types. It enables fast, reliable route planning and logistics automation for businesses.

13 Tools

Introduction

This guide walks you through connecting Graphhopper to AutoGen using the Composio tool router. By the end, you'll have a working Graphhopper agent that can plan routes, calculate distance matrices, solve vehicle routing problems, geocode addresses, and compute isochrones through natural language commands.

This guide will help you understand how to give your AutoGen 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

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TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Graphhopper
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Graphhopper tools
  • Run a live chat loop where you ask the agent to perform Graphhopper operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Graphhopper account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Graphhopper via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Graphhopper connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Graphhopper session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["graphhopper"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Graphhopper tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Graphhopper assistant agent with MCP tools
    agent = AssistantAgent(
        name="graphhopper_assistant",
        description="An AI assistant that helps with Graphhopper operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Graphhopper tools from the workbench
8

Run the interactive chat loop

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

# Conversation loop
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")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Graphhopper tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Graphhopper and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Graphhopper session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["graphhopper"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Graphhopper assistant agent with MCP tools
        agent = AssistantAgent(
            name="graphhopper_assistant",
            description="An AI assistant that helps with Graphhopper operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

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

        # Conversation loop
        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")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

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

Conclusion

You now have an Autogen assistant wired into Graphhopper through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Graphhopper, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every Graphhopper action and event your agent gets out of the box.

Capacity Clustering

Solve capacity-constrained clustering problems by assigning customers to clusters while minimizing total travel distance.

GraphHopper Geocoding

Tool to perform forward or reverse geocoding.

Get Cluster Solution

Retrieve the solution of an asynchronous clustering job by job ID.

Get Matrix

Tool to compute a distance and/or time matrix using GET request with query parameters.

Get Matrix Solution

Retrieve the status and result of an asynchronous matrix computation job by job ID.

GET Route

Tool to calculate the best path connecting two or more points using simple GET request.

Get Isochrone

Tool to compute isochrone polygons for a given point.

Calculate Matrix

Tool to calculate distance, time, or weight matrices via POST.

Get Custom Profiles

List custom routing profiles for your GraphHopper account using your own API key.

POST Route

Tool to calculate complex routes via POST /route.

Submit Matrix Job

Submit a matrix computation job for asynchronous processing and receive a job ID to retrieve results later.

Map Match GPX Track

Map-match GPX track points to the road network using your own GraphHopper API key.

GraphHopper VRP POST

Submit a vehicle routing optimization request and return its job status and any available solution.

FRAMEWORKS

How to build Graphhopper MCP Agent with another framework

Claude Code logo

Claude Code

Use Graphhopper MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Graphhopper MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Graphhopper MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Graphhopper MCP with Claude Agent SDK

Grok Bot logo

Grok Bot

Use Graphhopper MCP with Grok Bot

Codex logo

Codex

Use Graphhopper MCP with Codex

OpenClaw logo

OpenClaw

Use Graphhopper MCP with OpenClaw

Hermes logo

Hermes

Use Graphhopper MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Graphhopper MCP with Atomic Agent

Google ADK logo

Google ADK

Use Graphhopper MCP with Google ADK

LangChain logo

LangChain

Use Graphhopper MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Graphhopper MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Graphhopper MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Graphhopper MCP with LlamaIndex

CrewAI logo

CrewAI

Use Graphhopper MCP with CrewAI

Pydantic AI logo

Pydantic AI

Use Graphhopper MCP with Pydantic AI

FAQ

Frequently asked questions

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.

Yes, you can. Autogen 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.

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.

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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