How to integrate Clientary MCP with Autogen

This guide walks you through connecting Clientary to AutoGen using the Composio tool router. By the end, you'll have a working Clientary agent that can create new invoice for a client, list all active projects this month, send payment reminder to overdue clients through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Clientary account through Composio's Clientary MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Clientary

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Introduction

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

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

Also integrate Clientary with

ChatGPT OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenCode Cursor VS Code OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI LlamaIndex CrewAI

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

The Clientary MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Clientary account. It provides structured and secure access so your agent can perform Clientary operations on your behalf.

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:

Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step08 STEPS

Prerequisites

You will need:

A Composio API key
An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
A Clientary 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 Clientary 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 Clientary 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 Clientary session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["clientary"]
    )
    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 Clientary 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 Clientary assistant agent with MCP tools
    agent = AssistantAgent(
        name="clientary_assistant",
        description="An AI assistant that helps with Clientary 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 Clientary 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 Clientary 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 Clientary 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 Clientary 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 Clientary session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["clientary"]
    )
    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 Clientary assistant agent with MCP tools
        agent = AssistantAgent(
            name="clientary_assistant",
            description="An AI assistant that helps with Clientary 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 Clientary 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 Clientary 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 Clientary, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

TOOLS

Supported Tools

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

Create Client

Tool to create a new client record in Clientary.

Create Contact

Tool to create a new contact within a specified client.

Create Expense

Tool to create a new expense record in Clientary to track expenditures within your account.

Create Lead

Tool to create a new lead record in Clientary.

Create Project

Tool to create a new project in Clientary with name and rate.

Create Task

Tool to create a new task in Clientary.

Delete Client

Tool to remove a client and all associated projects, invoices, estimates, and contacts.

Delete Lead

Tool to permanently delete a lead and all associated Estimates and Contacts.

Delete Payment

Tool to remove an existing payment from an invoice.

Delete Payment Profile

Tool to remove a specific payment profile from a client's account.

Delete Recurring Schedule

Tool to remove a recurring schedule by its identifier.

Get Client

Tool to fetch details for a specific client using its ID.

Get Contact

Tool to retrieve a single contact by its ID.

Get Estimate

Tool to retrieve details for a single estimate by ID.

Get Expense

Tool to retrieve details for a single expense record in Clientary.

Get Hour Entry

Tool to obtain details about a specific time entry in Clientary.

Get Invoice

Tool to retrieve detailed information for a specific invoice by ID.

Get Lead

Tool to retrieve a single lead by its ID.

Get Project

Tool to retrieve a single project by its identifier.

Get Staff

Tool to retrieve a single staff member by their ID.

Get Task

Tool to retrieve a specific task by its ID.

List Client Contacts

Tool to retrieve all contacts for a specific client with pagination support.

List Client Expenses

Tool to retrieve all expenses for a specific client within an optional date range.

List Client Invoices

Tool to retrieve all invoices for a specific client with pagination support (30 results per page).

List Client Projects

Tool to retrieve all projects associated with a specific client with pagination support (10 results per page).

List Clients

Tool to retrieve all clients with pagination support (10 results per page).

List Expenses

Tool to retrieve expenses by date range (defaults to current fiscal year).

List Leads

Tool to retrieve all leads with pagination support.

List Payments

Tool to retrieve all payments with pagination support (30 results per page).

List Project Estimates

Tool to retrieve estimates scoped to a particular project with pagination support (30 results per page).

List Project Expenses

Tool to retrieve all expenses for a specific project within an optional date range.

List Project Hours

Tool to retrieve all time tracking entries logged against a specific project.

List Project Invoices

Tool to retrieve all invoices linked to a specific project with pagination support (30 results per page).

List Projects

Tool to retrieve all projects with pagination support (10 results per page).

List Staff

Tool to retrieve all staff members for an account.

List Tasks

Tool to retrieve all tasks with pagination support (50 results per page).

Send Invoice Message

Tool to send an invoice message to recipients via email.

Update Client

Tool to update an existing client record in Clientary with partial or complete field modifications.

Update Expense

Tool to update an existing expense record in Clientary with partial or complete field modifications.

Update Hour Entry

Tool to modify an existing time entry in Clientary with partial or complete field updates.

Update Project

Tool to update an existing project in Clientary with partial or complete field modifications.

Update Task

Tool to update an existing task in Clientary with partial or complete field modifications.

FRAMEWORKS

How to build Clientary MCP Agent with another framework

ChatGPT

Use Clientary MCP with ChatGPT

OpenAI Agents SDK

Use Clientary MCP with OpenAI Agents SDK

Claude Agent SDK

Use Clientary MCP with Claude Agent SDK

Claude Code

Use Clientary MCP with Claude Code

Claude Cowork

Use Clientary MCP with Claude Cowork

Codex

Use Clientary MCP with Codex

Cursor

Use Clientary MCP with Cursor

OpenClaw

Use Clientary MCP with OpenClaw

Hermes

Use Clientary MCP with Hermes

CLI

Use Clientary MCP with CLI

Google ADK

Use Clientary MCP with Google ADK

LangChain

Use Clientary MCP with LangChain

Vercel AI SDK

Use Clientary MCP with Vercel AI SDK

Mastra AI

Use Clientary MCP with Mastra AI

LlamaIndex

Use Clientary MCP with LlamaIndex

CrewAI

Use Clientary MCP with CrewAI

OpenCode

Use Clientary MCP with OpenCode

VS Code

Use Clientary MCP with VS Code

MORE TOOLKITS

Explore Other Toolkits

Toolkit marketplace

Google Sheets

Oauth2

Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly.

Notion

Oauth2Api Key

Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place.

Airtable

Oauth2Api Key

Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations.

Hubspot

Oauth2Api Key

HubSpot is an all-in-one marketing, sales, and customer service platform. It lets teams nurture leads, automate outreach, and track every customer interaction in one place.

FAQ

Frequently asked questions

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

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

Start with Clientary.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Clientary tool your agent needs.Free to start.

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Customer stories

Teams ship real agents on Composio

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Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Clientary MCP with Autogen

Connect Clientary without auth hassles

Introduction

Also integrate Clientary with

TL;DR

What is AutoGen?

What is the Clientary MCP server, and what's possible with it?

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

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies and create Tool Router session

Configure MCP parameters for Autogen

Create the model client and agent

Run the interactive chat loop

Complete Code

Conclusion

Supported Tools

How to build Clientary MCP Agent with another framework

ChatGPT

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

Cursor

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

OpenCode

VS Code

Explore Other Toolkits

Google Sheets

Notion

Airtable

Hubspot

Frequently asked questions

What are the differences in Tool Router MCP and Clientary MCP?+

Can I use Tool Router MCP with Autogen?+

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

How safe is my data with Composio Tool Router?+

Start with Clientary.It takes 30 seconds.

Teams ship real agents on Composio