How to integrate Synthflow ai MCP with Autogen

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Introduction

This guide walks you through connecting Synthflow ai to AutoGen using the Composio tool router. By the end, you'll have a working Synthflow ai agent that can create a new ai assistant for customer support, list all current voice assistants in your account, fetch details for team 'sales outreach' through natural language commands.

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

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

Also integrate Synthflow ai with

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

The Synthflow ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Synthflow ai account. It provides structured and secure access to your voice automation tools, so your agent can perform actions like managing voice assistants, handling teams, retrieving phone numbers, and automating call center operations on your behalf.

  • AI assistant management: Create, list, update, or delete AI-powered voice assistants to tailor customer interactions and automate call flows as needed.
  • Team creation and configuration: Set up new teams, modify existing ones, or remove teams to optimize your call center's routing and operational structure.
  • Knowledge base integration: Retrieve and manage knowledge base details to ensure your assistants have accurate, up-to-date information for conversations.
  • Phone number administration: Fetch and organize phone numbers linked to your workspace, making it easy to assign or reassign numbers for inbound and outbound campaigns.
  • Comprehensive assistant and team insights: Access detailed metadata and configuration for both assistants and teams, streamlining oversight and decision-making for your AI-powered operations.

Supported Tools & Triggers

Tools
Add document to knowledge base sourceTool to add a document to a knowledge base source.
Attach Actions to AgentTool to attach one or more actions to an agent.
Attach contact to memory storeTool to attach a contact to a memory store.
Attach knowledge base to agentTool to attach a knowledge base to an agent.
Attach memory store to agentTool to attach a memory store to an agent.
Create ActionTool to create a new action in Synthflow AI.
Create AssistantTool to create a new assistant.
Create a contactTool to create a new contact in Synthflow AI.
Create knowledge baseTool to create a new knowledge base and return its ID.
Create memory storeTool to create a new memory store.
Create Phone BookTool to create a new phone book.
Create phone book entryTool to create a phone book entry.
Create Simulation CaseTool to create a new simulation case.
Create a simulation scenarioTool to create a new simulation scenario.
Create a new simulation suiteTool to create a new simulation suite attached to a specific agent.
Create a new teamTool to create a new team.
Delete an actionTool to delete an existing action.
Delete an assistantTool to delete an existing AI assistant.
Delete a chat sessionTool to delete a chat session.
Delete a contactTool to delete an existing contact.
Delete knowledge baseTool to delete an existing knowledge base.
Delete a knowledge base sourceTool to delete a source from a knowledge base.
Delete a memory storeTool to delete a memory store.
Delete a phone bookTool to delete an existing phone book.
Delete a phone book entryTool to delete a phone book entry.
Delete a simulation caseTool to delete a simulation case by ID.
Delete a simulation scenarioTool to delete an existing simulation scenario.
Delete a simulation suiteTool to delete a simulation suite by ID.
Delete a subaccountTool to delete an existing subaccount.
Delete a teamTool to delete an existing team.
Detach actions from assistantTool to detach one or more actions from an AI assistant.
Detach knowledge baseTool to detach a knowledge base from an AI assistant.
Detach contact from memory storeTool to detach a contact from a memory store.
Detach memory store from agentTool to detach a memory store from an agent.
Execute simulation suiteTool to execute all test cases in a simulation suite.
Export analytics dataTool to export analytics data for calls within a specified date range.
Get action metadataTool to retrieve metadata about a specific action by its ID.
Get AI assistant detailsTool to retrieve details of a specific AI assistant.
Get phone call detailsTool to retrieve the transcript and detailed metadata for a specific phone call.
Get contact detailsTool to retrieve details of a specific contact by its ID.
Get knowledge baseTool to retrieve details of a specific knowledge base by its ID.
Get memory storeTool to retrieve details of a specific memory store by its ID.
Get memory store contact dataTool to retrieve memory data for a specific contact in a memory store.
Get phone numbersTool to retrieve a list of phone numbers associated with a workspace.
Get simulation detailsTool to retrieve details of a specific simulation by ID.
Get Simulation CaseTool to retrieve a simulation case by ID.
Get simulation scenarioTool to retrieve a simulation scenario by ID.
Get simulation suite by IDTool to retrieve a simulation suite by ID.
Get subaccount detailsTool to retrieve detailed metadata about a specific subaccount by ID.
Get team detailsTool to retrieve details of a specific team by its ID.
Initialize ActionTool to initialize a custom action with specified variables.
List actionsTool to list all actions in the workspace.
List AI assistantsTool to list all AI assistants associated with the account.
List call historyTool to retrieve call history (call logs) with filtering to check outcomes/statuses after placing calls.
List chatsTool to retrieve a list of chats, optionally filtered by agent ID.
List contactsTool to retrieve a list of contacts with optional search filtering.
List memory storesTool to list memory stores with optional filtering by title.
List Phone BooksTool to list all phone books in your workspace.
List Simulation CasesTool to list simulation cases with pagination and optional filtering by name or type.
List simulation cases by agentTool to list all simulation cases created for a specific agent.
List simulationsTool to list simulations with pagination and optional filters.
List simulation scenariosTool to list simulation scenarios with pagination and optional filtering.
List simulation sessionsTool to list simulation sessions with pagination and optional filters.
List simulation suitesTool to list simulation suites with pagination and optional filtering.
List subaccountsTool to list all subaccounts associated with the authenticated account.
List teamsTool to list assistant teams.
List voicesTool to list all text-to-speech voices in a workspace.
List webhook logsTool to retrieve paginated webhook logs with filtering and search capability.
Make a voice callTool to initiate a real-time voice call via the AI agent.
Start SimulationTool to start a new simulation using a simulation case.
Update ActionTool to update an existing action in Synthflow AI.
Update AssistantTool to update an existing assistant’s settings.
Update a contactTool to update an existing contact in Synthflow AI.
Update knowledge baseTool to update an existing knowledge base's name or usage conditions.
Update memory storeTool to update an existing memory store's title and description.
Update Simulation CaseTool to update an existing simulation case.
Update a simulation scenarioTool to update an existing simulation scenario.
Update an existing simulation suiteTool to update an existing simulation suite.
Update an existing teamTool to update an existing team.

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

Prerequisites

You will need:

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

How to build Synthflow ai MCP Agent with another framework

FAQ

What are the differences in Tool Router MCP and Synthflow ai MCP?

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

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

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

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