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How to integrate Ably MCP with Autogen

Connect Autogen to Ably MCP. List all active channels and their details, get message history from 'support-chat' channel, and more using natural language, with authentication handled for you.

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Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences.

25 Tools

Introduction

This guide walks you through connecting Ably to AutoGen using the Composio tool router. By the end, you'll have a working Ably agent that can publish messages to channels, read channel history and presence, send push notifications, manage push devices, and check app stats through natural language commands.

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

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

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

The Ably MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Ably account. It provides structured and secure access to your channels, messages, presence data, application statistics, and push notifications, so your agent can publish messages, inspect active channels, review channel history, monitor presence, and manage push notifications on your behalf.

  • Channel messaging: Have your agent publish a message to one channel or send batches of messages across multiple channels.
  • Channel activity and history: Let the agent list active channels, retrieve channel details, review message history, and inspect earlier versions of a message.
  • Presence monitoring: Direct your agent to check who is currently present in one or more channels and review past presence activity.
  • Push notification management: Instruct your agent to send individual or batch push notifications, manage channel subscriptions, and register, update, inspect, or unregister receiving devices.
  • Application insights: Have your agent retrieve Ably service time and review application usage statistics.

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

Supported Tools

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

Query Batch Presence

Queries the presence states of multiple Ably channels in a single request, retrieving member presence details for the specified channels.

Query Batch Presence History

Query presence history for multiple Ably channels, with optional start and end times and forwards or backwards ordering.

Delete Channel Subscription

Unsubscribes a device or client from push notifications for a specified channel, or across all channels if no channel is provided.

Get Channel Details

Retrieves status and occupancy metadata for a specific Ably channel, including connection, presence, publisher, and subscriber metrics.

Get Channel History

Retrieves a page of message history for a specified Ably channel, with time range, limit, and direction filters.

Get Channel Presence

Tool to obtain the set of members currently present for a channel.

Get Message Versions

Retrieves a page of historical versions of a specific Ably channel message, including the original and subsequent updates or deletes.

Get Channel Presence History

This tool retrieves the history of presence messages for a specified channel in Ably.

Get Push Device Registration

Retrieves a device's push registration by device ID, including available platform, state, recipient, and metadata fields.

Get Ably Service Time

This tool retrieves the current server time from Ably's service in milliseconds since the epoch.

Get Application Stats

Retrieve a page of Ably application usage statistics by minute, hour, day or month, with optional time range, direction and limit.

List Channels

Tool to enumerate all active channels in the Ably application.

List Push Channels

Lists a page of Ably channels with at least one subscribed device for push notifications.

List Push Channel Subscriptions

Lists a page of push channel subscriptions filtered by channel, client ID, or device ID, with pagination links; at least one filter is required.

List Registered Push Devices

List a page of Ably push device registrations, optionally filtered by device ID or client ID.

Patch Push Device Registration

Partially update an existing Ably push device registration.

Batch Publish Messages

Tool to batch publish messages to multiple channels in parallel.

Publish Message to Channel

Publishes a message to a specified Ably channel with a string or JSON object payload and an optional event name and client ID.

Publish Push Notification

Tool to publish a push notification directly to device(s) via Ably's Push Notifications API.

Batch Publish Push Notifications

Tool to batch publish push notifications directly to specific recipients.

Register Push Device

Register an iOS, Android, or browser device with Ably to receive push notifications.

Request Access Token

Request an Ably access token with optional capabilities, client ID and lifetime.

Unregister All Push Devices

Unregister matching Ably push devices, optionally filtering by device ID or client ID, but not both.

Unregister Push Device

Unregister a single Ably push device by device ID.

Update Push Device Registration

Upsert an Ably push device registration, creating it or replacing an existing registration.

FRAMEWORKS

How to build Ably MCP Agent with another framework

Claude Code logo

Claude Code

Use Ably MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Ably MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Ably MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Ably MCP with Claude Agent SDK

Codex logo

Codex

Use Ably MCP with Codex

OpenClaw logo

OpenClaw

Use Ably MCP with OpenClaw

Hermes logo

Hermes

Use Ably MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Ably MCP with Atomic Agent

Google ADK logo

Google ADK

Use Ably MCP with Google ADK

LangChain logo

LangChain

Use Ably MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Ably MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Ably MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Ably MCP with LlamaIndex

CrewAI logo

CrewAI

Use Ably MCP with CrewAI

Pydantic AI logo

Pydantic AI

Use Ably MCP with Pydantic AI

FAQ

Frequently asked questions

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

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

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