How to integrate Ably MCP with CrewAI

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Introduction

This guide walks you through connecting Ably to CrewAI using the Composio tool router. By the end, you'll have a working Ably agent that can list all active channels and their details, get message history from 'support-chat' channel, show presence history for 'live-event' channel, retrieve daily usage stats for my app through natural language commands.

This guide will help you understand how to give your CrewAI 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.

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Ably connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Ably
  • Build a conversational loop where your agent can execute Ably operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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 real-time messaging infrastructure, so your agent can manage channels, monitor presence, analyze usage, and handle messaging workflows for your applications.

  • Channel management and creation: Seamlessly create, initialize, or retrieve real-time messaging channels so your agent can orchestrate chat, data sync, and collaboration features on demand.
  • Presence tracking and analytics: Ask your agent to query current presence states or review historical presence data across multiple channels, gaining insights into user activity and engagement patterns.
  • Message history and audit: Retrieve detailed message histories from any channel, enabling your agent to audit communication, recover missed messages, or analyze message flows for debugging and compliance.
  • Push notification subscription management: Let your agent list, manage, or unsubscribe devices from push notification channels, ensuring targeted and controlled delivery of real-time alerts to clients.
  • Application statistics and monitoring: Have your agent fetch in-depth usage metrics—like message counts, channel activity, and API request stats—so you can monitor health, optimize performance, and manage resources with confidence.

Supported Tools & Triggers

Tools
Query Batch PresenceThis tool enables querying the presence states of multiple channels in a single api request.
Query Batch Presence HistoryThis tool enables querying presence history for multiple channels in a single api request.
Create Ably ChannelCreates a new channel or retrieves an existing channel in ably.
Delete Channel SubscriptionThis tool allows you to unsubscribe devices or clients from push notifications for specific channels.
Get Channel DetailsThis tool retrieves metadata and details for a specific channel in ably.
Get Channel HistoryThis tool retrieves the message history for a specified ably channel.
Get Channel Presence HistoryThis tool retrieves the history of presence messages for a specified channel in ably.
Get Ably Service TimeThis tool retrieves the current server time from ably's service in milliseconds since the epoch.
Get Application StatsThis tool retrieves your application's usage statistics from ably.
List Push Channel SubscriptionsThis tool retrieves a list of all push notification channel subscriptions.
Publish Message to ChannelThis tool will allow users to publish a message to a specified ably channel using a post request.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Ably connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

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.

Install dependencies

bash
pip install composio crewai crewai-tools python-dotenv
What's happening:
  • composio connects your agent to Ably via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools includes MCP helpers
  • python-dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Ably MCP URL

Create a Composio Tool Router session for Ably

python
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:
  • You create a Ably only session through Composio
  • Composio returns an MCP HTTP URL that exposes Ably tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Ably Assistant",
    goal="Help users interact with Ably through natural language commands",
    backstory=(
        "You are an expert assistant with access to Ably tools. "
        "You can perform various Ably operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Ably MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Ably operations.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Ably related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_agent],
        tasks=[task],
        verbose=False,
    )

    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's happening:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_ably_agent.py

Complete Code

Here's the complete code to get you started with Ably and CrewAI:

python
# file: crewai_ably_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

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 LLM
    llm = LLM(
        model="gpt-5-mini",
        api_key=os.getenv("OPENAI_API_KEY"),
    )

    # Create Ably assistant agent
    toolkit_agent = Agent(
        role="Ably Assistant",
        goal="Help users interact with Ably through natural language commands",
        backstory=(
            "You are an expert assistant with access to Ably tools. "
            "You can perform various Ably operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Ably operations.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Ably related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Ably through Composio's Tool Router. The agent can perform Ably operations through natural language commands. Next steps:
  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations

How to build Ably MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Ably MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Ably MCP with Claude Agent SDK

Google ADK

Google ADK

Use Ably MCP with Google ADK

LangChain

LangChain

Use Ably MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Ably MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Ably MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Ably MCP with LlamaIndex

FAQ

What are the differences in Tool Router MCP and Ably MCP?

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.

Can I use Tool Router MCP with CrewAI?

Yes, you can. CrewAI 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.

Can I manage the permissions and scopes for Ably while using Tool Router?

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.

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 Ably data and credentials are handled as safely as possible.

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