How to integrate Lumify MCP with Autogen

This guide walks you through connecting Lumify to AutoGen using the Composio tool router. By the end, you'll have a working Lumify agent that can list sports events, analyze betting signals, check event scores, search players, and plan credit costs through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Lumify account through Composio's Lumify MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Lumify is a sports intelligence API for schedules, live scores, odds, statistics, and betting insights. Use it to power sports apps, analytics workflows, and explainable betting tools with credit-metered access.

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Introduction

This guide walks you through connecting Lumify to AutoGen using the Composio tool router. By the end, you'll have a working Lumify agent that can list sports events, analyze betting signals, check event scores, search players, and plan credit costs through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Lumify account through Composio's Lumify MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Lumify with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

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 Lumify
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Lumify tools
  • Run a live chat loop where you ask the agent to perform Lumify 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 Lumify MCP server, and what's possible with it?

The Lumify MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Lumify account. It provides structured and secure access to sports schedules, live scores, odds, statistics, and explainable betting intelligence, so your agent can find events, check scores, analyze betting information, research players and teams, and monitor credit usage on your behalf.

  • Sports and event discovery: Have your agent browse available sports, leagues, and seasons, then find scheduled, live, or past events using clear filters.
  • Live scores and event details: Let the agent check an event's current score and status or retrieve full details for multiple events at once.
  • Odds and betting analysis: Direct your agent to review current and historical odds, public betting splits, deterministic statistics, and explainable intelligence for a selected event.
  • Player and team research: Instruct your agent to search for players or teams and review a player's schedule and results by date or event status.
  • Credit planning and monitoring: Have your agent check your account tier, remaining credits, limits, and trial state, or estimate the credit cost of planned Lumify requests before running them.

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 Lumify 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 Lumify 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 Lumify 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 Lumify session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["lumify"]
    )
    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 Lumify 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 Lumify assistant agent with MCP tools
    agent = AssistantAgent(
        name="lumify_assistant",
        description="An AI assistant that helps with Lumify 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 Lumify 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 Lumify 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 Lumify 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 Lumify 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 Lumify session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["lumify"]
    )
    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 Lumify assistant agent with MCP tools
        agent = AssistantAgent(
            name="lumify_assistant",
            description="An AI assistant that helps with Lumify 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 Lumify 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 Lumify 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 Lumify, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

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

Get Credit Usage

Get the Lumify account tier, usage, limits, trial state, and remaining credits.

Get Event Analysis

Fetch selected intelligence, odds, odds history, public splits, and/or deterministic stats for one event.

Get Events

Fetch full details for 1-25 event IDs in one API request, optionally including odds or intelligence.

Get Event Score

Get a compact current score and status snapshot for one Lumify event.

List Events

Find scheduled, live, or historical Lumify events with structured filters and one-page cursor pagination.

List Player Events

List one Lumify player's schedule and results with date and status filters, returning one cursor-controlled page.

List Sports or Seasons

List Lumify sports and leagues or seasons.

Plan Credit Costs

Discover Lumify estimator tool identifiers or estimate credit ranges for up to 50 planned calls without executing them.

Search Players

Search Lumify players by sport, partial name, country, activity, or tennis ranking and return one cursor-controlled page.

Search Teams

Search teams by sport, league, conference, division, country, active status, or partial team name and return one cursor-controlled page.

FRAMEWORKS

How to build Lumify MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Lumify MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Lumify MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Lumify MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Lumify MCP with Claude Cowork

Codex logo

Codex

Use Lumify MCP with Codex

OpenClaw logo

OpenClaw

Use Lumify MCP with OpenClaw

Hermes logo

Hermes

Use Lumify MCP with Hermes

CLI logo

CLI

Use Lumify MCP with CLI

Google ADK logo

Google ADK

Use Lumify MCP with Google ADK

LangChain logo

LangChain

Use Lumify MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Lumify MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Lumify MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Lumify MCP with LlamaIndex

CrewAI logo

CrewAI

Use Lumify MCP with CrewAI

FAQ

Frequently asked questions

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

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

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