How to integrate DevCycle MCP with Autogen

This guide walks you through connecting DevCycle to AutoGen using the Composio tool router. By the end, you'll have a working DevCycle agent that can list active flags in production, create checkout rollout feature flag, summarize recent devcycle flag changes through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a DevCycle account through Composio's DevCycle MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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DevCycle is a feature management platform for feature flags, targeting, releases, and monitoring. It helps teams ship safely with controlled rollouts, experiments, and fast rollback.

9 Tools

Introduction

This guide walks you through connecting DevCycle to AutoGen using the Composio tool router. By the end, you'll have a working DevCycle agent that can list active flags in production, create checkout rollout feature flag, summarize recent devcycle flag changes through natural language commands.

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

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

Also integrate DevCycle with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

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

The DevCycle MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your DevCycle account. It provides structured and secure access to your projects, features, environments, audiences, configurations, and variables, so your agent can list projects, inspect feature configurations, search audiences, review environments, and manage variables on your behalf.

  • Project and feature discovery: Have your agent list available DevCycle projects and filter their current features to find the work that matters.
  • Feature configuration review: Let the agent inspect a feature's targeting configurations across every environment or within one selected environment.
  • Environment and audience lookup: Direct your agent to list project environments, search audiences, and check whether each audience is in use.
  • Variable discovery: Instruct your agent to list and filter project variables, including those associated with a specific feature.
  • Variable management: Have your agent create typed variables, update their editable properties, or permanently delete a selected variable.

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

Step by step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A DevCycle account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
2

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.
3

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 DevCycle via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

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 DevCycle connections to use
5

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 DevCycle session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["dev_cycle"]
    )
    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 DevCycle tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

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
7

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 DevCycle assistant agent with MCP tools
    agent = AssistantAgent(
        name="dev_cycle_assistant",
        description="An AI assistant that helps with DevCycle 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 DevCycle tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any DevCycle 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 DevCycle 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 DevCycle 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 DevCycle session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["dev_cycle"]
    )
    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 DevCycle assistant agent with MCP tools
        agent = AssistantAgent(
            name="dev_cycle_assistant",
            description="An AI assistant that helps with DevCycle 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 DevCycle 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 DevCycle 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 DevCycle, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

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

Create Variable

Create a typed variable in a DevCycle project, optionally associating it with a feature.

Delete Variable

Permanently delete exactly one DevCycle variable, selected by key or stable ID, from the specified project.

List Audiences

List and search project audiences, optionally including whether each audience is used.

List Environments

List environments in a DevCycle project without exposing SDK key values.

List Feature Configurations

Inspect a feature's targeting configurations across all environments or one selected environment.

List Features

List and filter current Feature v2 resources in a DevCycle project.

List Projects

List DevCycle projects visible to the connected Management service account, with filtering and controlled pagination.

List Variables

List and filter variables in a DevCycle project, including variables associated with a feature.

Update Variable

Update one or more editable properties of an existing DevCycle variable.

FRAMEWORKS

How to build DevCycle MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use DevCycle MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use DevCycle MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use DevCycle MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use DevCycle MCP with Claude Cowork

Codex logo

Codex

Use DevCycle MCP with Codex

OpenClaw logo

OpenClaw

Use DevCycle MCP with OpenClaw

Hermes logo

Hermes

Use DevCycle MCP with Hermes

CLI logo

CLI

Use DevCycle MCP with CLI

Google ADK logo

Google ADK

Use DevCycle MCP with Google ADK

LangChain logo

LangChain

Use DevCycle MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use DevCycle MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use DevCycle MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use DevCycle MCP with LlamaIndex

CrewAI logo

CrewAI

Use DevCycle MCP with CrewAI

FAQ

Frequently asked questions

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

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

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