How to integrate Retently MCP with Autogen

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Introduction

This guide walks you through connecting Retently to AutoGen using the Composio tool router. By the end, you'll have a working Retently agent that can list all customer feedback from last week, add 'urgent' tag to negative feedback, get latest nps score for my account, create new customer with email and company through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Retently account through Composio's Retently 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 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 Retently
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Retently tools
  • Run a live chat loop where you ask the agent to perform Retently 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 Retently MCP server, and what's possible with it?

The Retently MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Retently account. It provides structured and secure access to your customer feedback and survey data, so your agent can perform actions like analyzing feedback, managing customers, organizing survey results, and tagging feedback—completely on your behalf.

  • Automated customer management: Effortlessly create, update, or delete customers in your Retently workspace, ensuring your CRM data stays up to date.
  • Feedback analysis and retrieval: Retrieve recent feedback, pull detailed feedback entries, or get a list of all customer responses for easy sentiment tracking and reporting.
  • Survey and campaign insights: Instantly fetch all your Retently campaigns or get the latest NPS score to stay on top of your customer satisfaction metrics.
  • Feedback organization with tags and topics: Let your agent categorize and organize feedback by adding tags or topics, so you can quickly identify trends and areas for improvement.
  • Advanced customer lookup: Quickly get detailed information about any customer by their unique ID, perfect for personalizing follow-ups or resolving support issues.

Supported Tools & Triggers

Tools
Add Feedback TagsAdd tags to feedback items by providing feedback ids and corresponding tags.
Add Feedback TopicsAdd topics to feedback items by providing feedback ids and corresponding topics.
Create or Update CustomersTool to create new customers or update existing ones by providing their details, including email, name, company, tags, and properties.
Delete CustomersDelete customers from the workspace by providing their unique ids.
Get CampaignsTool to retrieve a list of campaigns associated with the account.
Get Customer By IDTool to retrieve detailed information about a specific customer by their unique id.
Get CustomersRetrieve a list of customers with optional parameters for pagination, sorting, and filtering by email or date range.
Get FeedbackTool to retrieve feedback received from customers.
Get Feedback by IDTool to retrieve detailed information about specific feedback by its unique id.
Get Latest ScoreTool to retrieve the latest nps score for the account.
Get OutboxRetrieve the outbox of surveys that are scheduled to be sent.
Get ReportsTool to retrieve reports related to nps surveys, including scores and trends.
Get TemplatesTool to retrieve a list of survey templates available in the account.
Send Transactional SurveyTool to send a transactional survey to customers.
Unsubscribe CustomersUnsubscribe customers from receiving surveys by providing their email addresses.

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

You will need:

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

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 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 Retently via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

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 Retently connections to use

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 Retently session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["retently"]
    )
    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 Retently tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to

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

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 Retently assistant agent with MCP tools
    agent = AssistantAgent(
        name="retently_assistant",
        description="An AI assistant that helps with Retently 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 Retently tools from the workbench

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Retently 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 Retently 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 Retently 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 Retently session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["retently"]
    )
    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 Retently assistant agent with MCP tools
        agent = AssistantAgent(
            name="retently_assistant",
            description="An AI assistant that helps with Retently 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 Retently 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 Retently 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 Retently, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

How to build Retently MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Retently MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Retently MCP with Claude Agent SDK

Google ADK

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Use Retently MCP with Google ADK

LangChain

LangChain

Use Retently MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Retently MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Retently MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Retently MCP with LlamaIndex

CrewAI

CrewAI

Use Retently MCP with CrewAI

Claude Code

Claude Code

Use Retently MCP with Claude Code

FAQ

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

With a standalone Retently MCP server, the agents and LLMs can only access a fixed set of Retently tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Retently and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with Autogen?

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 Retently tools.

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

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

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