How to integrate Svix MCP with Autogen

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Introduction

This guide walks you through connecting Svix to AutoGen using the Composio tool router. By the end, you'll have a working Svix agent that can list all webhook endpoints for app x, create a new webhook endpoint for payments, update application rate limit to 1000/min, get delivery attempts for message id 12345 through natural language commands.

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

The Svix MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Svix account. It provides structured and secure access to your webhooks infrastructure, so your agent can perform actions like managing applications, configuring endpoints, sending webhooks, and monitoring delivery attempts on your behalf.

  • Application management and automation: Ask your agent to create, update, list, or delete Svix applications, making it easy to manage webhook-enabled projects programmatically.
  • Endpoint configuration: Have your agent register, retrieve, or remove webhook endpoints for your applications, ensuring your events get delivered to the right places.
  • Webhook delivery tracking: Let your agent fetch detailed information about message delivery attempts, helping you monitor reliability and debug failed webhooks with ease.
  • Comprehensive application insights: Retrieve metadata and details for any Svix application, so your agent can surface key info or audit your webhook ecosystem.
  • Automated cleanup and maintenance: Direct your agent to delete outdated applications or endpoints, streamlining your webhook management and reducing clutter.

Supported Tools & Triggers

Tools
Create ApplicationTool to create a new svix application.
Delete Svix ApplicationTool to delete an application by its id.
Get ApplicationTool to retrieve details of a specific svix application by its id.
List ApplicationsTool to list all applications.
Update Svix ApplicationTool to update an existing svix application by id.
Get Attempt DetailsTool to retrieve details of a specific message attempt.
List Message AttemptsTool to list all delivery attempts for a specific message.
Create EndpointTool to create a new svix webhook endpoint.
Delete EndpointTool to delete an endpoint.
Get EndpointTool to retrieve details of a specific endpoint.
List EndpointsTool to list all endpoints for a specific application.
Patch EndpointTool to partially update an endpoint’s configuration.
Patch Endpoint HeadersTool to partially update headers for a specific endpoint.
Recover Failed WebhooksTool to recover messages that failed to send to an endpoint.
Replay Missing WebhooksTool to replay missing webhooks for a specific endpoint.
Get Endpoint SecretTool to retrieve the secret for a specific endpoint.
Rotate Endpoint SecretTool to rotate the signing secret key for an endpoint.
Send Example MessageTool to send a test message for a specific event type to an endpoint.
Get Endpoint StatsTool to retrieve basic statistics for a specific endpoint.
Get Endpoint TransformationTool to retrieve transformation settings for a specific endpoint.
Set Endpoint TransformationTool to set or update transformation settings for an endpoint.
Update EndpointTool to update an existing endpoint.
Update Endpoint HeadersTool to completely replace headers for a specific endpoint.
Create Event TypeTool to create a new event type or unarchive an existing one.
Delete Event TypeTool to delete an event type.
Get Event TypeTool to retrieve details of a specific event type by its id.
List Event TypesTool to retrieve a list of all event types.
Update Event TypeTool to update an existing event type by id.
Create IntegrationTool to create a new integration for a specific application.
Delete IntegrationTool to delete an integration.
Get IntegrationTool to retrieve details of a specific integration.
List IntegrationsTool to list all integrations for a specific application.
Update IntegrationTool to update an existing integration by id.
Create MessageTool to create a new message for a specific application in svix.
Get MessageTool to retrieve details of a specific message by its id.
List MessagesTool to list all messages for a specific application.
Create SourceTool to create a source for message ingestion.

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

How to build Svix MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Svix MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Svix MCP with Claude Agent SDK

Google ADK

Google ADK

Use Svix MCP with Google ADK

LangChain

LangChain

Use Svix MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Svix MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Svix MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Svix MCP with LlamaIndex

CrewAI

CrewAI

Use Svix MCP with CrewAI

FAQ

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

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

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

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

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