How to integrate Rocketlane MCP with Autogen

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Introduction

This guide walks you through connecting Rocketlane to AutoGen using the Composio tool router. By the end, you'll have a working Rocketlane agent that can create a new onboarding project for acme corp, log two hours to client implementation task, archive completed projects from last quarter, get detailed info for company with id 12345 through natural language commands.

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

The Rocketlane MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Rocketlane account. It provides structured and secure access to your onboarding projects, tasks, and customer data, so your agent can perform actions like creating tasks, managing companies, tracking time entries, and handling project organization on your behalf.

  • Project and company management: Easily direct your agent to create new projects or companies, retrieve detailed company info, and keep your workspace organized.
  • Task creation and deletion: Have your agent add new tasks to any project or swiftly delete outdated tasks using their unique identifiers.
  • Time entry tracking: Log time spent on tasks or projects, review details, or delete time entries for accurate billing and reporting.
  • Custom field insights: Retrieve all available custom fields or fetch specific field details to tailor onboarding workflows to your needs.
  • Project archiving and cleanup: Archive completed projects for future reference or permanently delete projects when they're no longer needed, keeping your workspace tidy.

Supported Tools & Triggers

Tools
Archive Project by IDArchives a specific project based on its unique identifier.
Create CompanyCreates a new company (account) in rocketlane.
Create TaskCreates a new task.
Create Time EntryTool to create a new time entry in rocketlane.
Delete ProjectThis tool allows users to permanently delete a project in rocketlane.
Delete Task By IDDelete a specific task using its unique identifier (taskid).
Delete Time Entry by IDDelete a specific time entry using its unique identifier (timeentryid).
Get All FieldsRetrieve all custom fields available in the system.
Get CompanyThis tool retrieves detailed information about a specific company/account in rocketlane by its id.
Get Field By IDRetrieve detailed information about a specific custom field using its unique identifier (fieldid).
Get Project by IDRetrieves detailed information about a specific project using its unique identifier.
Get Task By IdRetrieve extensive information about a specific task using the task's unique identifier (taskid).
Get Template By IDRetrieve detailed information about a specific template using its unique identifier (templateid).
Get Time EntriesTool to retrieve all time entries from rocketlane.
Get Time Entry By IDRetrieve detailed information about a specific time entry using its unique identifier (timeentryid).
Get User By IDRetrieve detailed information about a specific user using their unique identifier (userid).
List CompaniesThis tool retrieves a list of all companies/accounts in rocketlane.
List Company FieldsThis tool retrieves a list of all available company/account fields in rocketlane.
List Company Note FieldsThis tool retrieves a list of all available note fields for companies in rocketlane.
List CurrenciesReturns a predefined list of commonly used currencies since rocketlane api doesn't provide a dedicated currencies endpoint.
List Customer UsersList customer users.
List Project FieldsThis tool retrieves a list of all project fields in rocketlane, including both default and custom fields.
List Project PhasesThis tool retrieves a list of project phases from rocketlane.
List ProjectsThis tool retrieves a list of all projects in the rocketlane instance.
List Task FieldsThis tool retrieves a list of all task fields in rocketlane.
List TemplatesThis tool retrieves a list of all available templates in rocketlane.
List UsersThis tool retrieves all users in the rocketlane instance.
List Vendor UsersList vendor users by filtering users with type 'partner'.
Retrieve Subscription DetailsRetrieves detailed information about the current subscription.
Search User By EmailSearch user by email id.
Update CompanyThis tool updates an existing company/account in rocketlane.
Update Project By IdUpdates an existing project's details using its unique identifier.
Update Time Entry by IDUpdate existing time entry details using its unique identifier (timeentryid).

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

How to build Rocketlane MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Rocketlane MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Rocketlane MCP with Claude Agent SDK

Google ADK

Google ADK

Use Rocketlane MCP with Google ADK

LangChain

LangChain

Use Rocketlane MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Rocketlane MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Rocketlane MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Rocketlane MCP with LlamaIndex

CrewAI

CrewAI

Use Rocketlane MCP with CrewAI

FAQ

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

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

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

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

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