How to integrate Affinity MCP with Autogen

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Introduction

This guide walks you through connecting Affinity to AutoGen using the Composio tool router. By the end, you'll have a working Affinity agent that can list all companies added this week, show all opportunities in active pipeline, get recent contacts linked to acme corp, summarize all lists where john doe appears through natural language commands.

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

The Affinity MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Affinity account. It provides structured and secure access to your deal, company, and contact data, so your agent can analyze lists, fetch opportunity details, extract company insights, and organize people or organizations on your behalf.

  • Company data extraction and analysis: Let your agent retrieve detailed company profiles, summarize list entries, and pull custom field data for deeper insights and reporting.
  • Opportunity pipeline management: Automatically fetch and review all ongoing opportunities, track changes, and surface high-priority deals for follow-up.
  • List and view organization: Ask your agent to access entries across lists or saved views, aggregating metadata and field values for efficient CRM workflows.
  • Contact and relationship intelligence: Effortlessly browse, analyze, and summarize person records, including their list memberships, activity, and custom fields.
  • Automated CRM reporting: Generate summaries, export data, and monitor changes across companies, people, and opportunities to keep your pipeline up-to-date and actionable.

Supported Tools & Triggers

Tools
Get a company s list entriesSummarize company data across all lists, including list-specific fields and metadata like creation date and author.
Get a company s listsReturns metadata for all the lists on which the given company appears.
Get all companiesAffinity api allows paginated access to company info and custom fields.
Get all list entries on a listAccess and export essential data and metadata for companies, persons, or opportunities from a list, specifying data via `fieldids` or `fieldtypes`.
Get all list entries on a saved viewUse the endpoint to access rows in a saved view with specific filters and selected fields from a web app.
Get all opportunitiesPagination through opportunities in affinity yields basic info but excludes field data.
Get all personsThe affinity api offers paginated access to person data using `fieldids` or `fieldtypes`.
Get a person s list entriesSummary: browse rows for a person in all lists, showing field data and entry metadata like creation time and author.
Get a person s listsReturns metadata for all the lists on which the given person appears.
Get a single companyRetrieve basic company info and specific field data by using `fieldids` or `fieldtypes` parameters.
Get a single opportunityGet basic details about an opportunity without field data via provided endpoints.
Get a single personUse get `/v2/persons/fields` with `fieldids` or `fieldtypes` for detailed data; basic info by default.
Get current userReturns metadata about the current user.
Get metadata on all listsReturns metadata on lists.
Get metadata on a single listReturns metadata on a single list.
Get metadata on a single list s fieldsReturns metadata on the fields available on a single list.
Get metadata on a single saved viewReturns metadata on a single saved view.
Get metadata on company fieldsReturns metadata on non-list-specific company fields.
Get metadata on person fieldsReturns metadata on non-list-specific person fields.
Get metadata on saved viewsReturns metadata on the saved views on a list.

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 Affinity 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 Affinity 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 Affinity 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 Affinity session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["affinity"]
    )
    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 Affinity 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 Affinity assistant agent with MCP tools
    agent = AssistantAgent(
        name="affinity_assistant",
        description="An AI assistant that helps with Affinity 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 Affinity 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 Affinity 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 Affinity 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 Affinity and AutoGen:

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

How to build Affinity MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Affinity MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Affinity MCP with Claude Agent SDK

Google ADK

Google ADK

Use Affinity MCP with Google ADK

LangChain

LangChain

Use Affinity MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Affinity MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Affinity MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Affinity MCP with LlamaIndex

CrewAI

CrewAI

Use Affinity MCP with CrewAI

FAQ

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

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

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

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

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