How to integrate Adafruit IO MCP with Autogen

This guide walks you through connecting Adafruit IO to AutoGen using the Composio tool router. By the end, you'll have a working Adafruit IO agent that can summarize temperature feed anomalies today, publish humidity reading to greenhouse feed, list recent door sensor events through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Adafruit IO account through Composio's Adafruit IO MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Adafruit IO is Adafruit's cloud platform for connected device and IoT project data. It helps you store, visualize, and act on sensor feeds without running your own backend.

21 Tools

Introduction

This guide walks you through connecting Adafruit IO to AutoGen using the Composio tool router. By the end, you'll have a working Adafruit IO agent that can summarize temperature feed anomalies today, publish humidity reading to greenhouse feed, list recent door sensor events through natural language commands.

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

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

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

The Adafruit IO MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Adafruit IO account. It provides structured and secure access to your feeds, device data, groups, dashboards, and account status, so your agent can publish readings, review data, organize feeds, manage dashboards, and monitor usage on your behalf.

  • Feed creation and management: Have your agent create, review, update, or delete feeds used to store readings from your connected devices.
  • Device data publishing and review: Let the agent publish timestamped values, inspect recent feed data, and retrieve, correct, or remove specific readings.
  • Feed organization with groups: Direct your agent to create and update groups, add or remove feeds, and review how your device feeds are organized.
  • Dashboard management: Instruct your agent to create dashboards for feed data, review existing dashboards and their blocks, update dashboard details, or remove dashboards.
  • Account and usage monitoring: Have your agent check your Adafruit IO account identity, plan limits, and current data write usage before publishing more readings.

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

Supported Tools

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

Add Feeds to Group

Sequentially add 1-10 existing feeds to one group using one documented provider request per feed.

Create Dashboard

Create a dashboard for visualizing and controlling feed data.

Create Feed

Create a feed, optionally within an existing group.

Create Group

Create a group for organizing feeds.

Delete Dashboard

Permanently delete one dashboard by exact key.

Delete Data Point

Permanently delete one feed data point by ID.

Delete Data Points

Permanently delete 1-30 feed data points sequentially through Adafruit IO's single-data-point DELETE endpoint, bounded by the Free-plan limit of 30 data mutations per minute.

Delete Feed

Permanently delete one feed and its retained data by exact feed key.

Delete Group

Permanently delete one group by exact key.

Get Account Status

Return compact identity, plan limits, and current data-write throttle usage for the connected Adafruit IO account.

Get Dashboards

List dashboards, or retrieve one dashboard and its blocks by key.

Get Data Point

Retrieve one feed data point by its exact ID.

Get Feeds

List feeds, or retrieve one feed by key with optional data summary details.

Get Groups

List groups with feed summaries, or retrieve one group by key.

List Feed Data

Return one newest-first page of feed data with an opaque cursor for the next older page.

Publish Feed Data

Publish one or multiple timestamped values to a feed, using the batch endpoint only when multiple points are supplied.

Remove Feeds from Group

Sequentially remove 1-10 feeds from one group using one documented provider request per feed.

Update Dashboard

Update an existing dashboard by exact dashboard key.

Update Data Point

Replace the value and optional metadata of one existing feed data point.

Update Feed

Update an existing feed by exact feed key.

Update Group

Update an existing group by exact group key.

FRAMEWORKS

How to build Adafruit IO MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Adafruit IO MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Adafruit IO MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Adafruit IO MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Adafruit IO MCP with Claude Cowork

Codex logo

Codex

Use Adafruit IO MCP with Codex

OpenClaw logo

OpenClaw

Use Adafruit IO MCP with OpenClaw

Hermes logo

Hermes

Use Adafruit IO MCP with Hermes

CLI logo

CLI

Use Adafruit IO MCP with CLI

Google ADK logo

Google ADK

Use Adafruit IO MCP with Google ADK

LangChain logo

LangChain

Use Adafruit IO MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Adafruit IO MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Adafruit IO MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Adafruit IO MCP with LlamaIndex

CrewAI logo

CrewAI

Use Adafruit IO MCP with CrewAI

FAQ

Frequently asked questions

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

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

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