How to integrate FutureAGI MCP with Autogen

This guide walks you through connecting FutureAGI to AutoGen using the Composio tool router. By the end, you'll have a working FutureAGI agent that can list agent definitions, browse datasets, review evaluation tasks, view test runs, and inspect scenarios through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a FutureAGI account through Composio's FutureAGI MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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FutureAGI is an AI evaluation and observability platform for testing, tracing, simulating, and improving AI systems. Use it to measure model quality, debug failures, manage datasets, and close the loop with annotation workflows.

9 Tools

Introduction

This guide walks you through connecting FutureAGI to AutoGen using the Composio tool router. By the end, you'll have a working FutureAGI agent that can list agent definitions, browse datasets, review evaluation tasks, view test runs, and inspect scenarios through natural language commands.

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

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

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

The FutureAGI MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your FutureAGI account. It provides structured and secure access to your evaluations, datasets, annotation workflows, agent definitions, and simulation resources, so your agent can review evaluation tasks, find datasets, inspect annotation work, browse agents, and explore simulations on your behalf.

  • Evaluation setup discovery: Have your agent find evaluation tasks and custom evaluation configurations for a specific project or task name.
  • Dataset and label review: Let the agent search your datasets and review annotation labels by name, type, project, or dataset.
  • Annotation queue oversight: Direct your agent to list annotation queues by name or status and include item counts when needed.
  • Agent and test inspection: Instruct your agent to find agent definitions and review configured run tests by name, source type, or prompt template without running them.
  • Simulation resource exploration: Have your agent browse personas and scenarios using agent type, simulation mode, agent definition, or search text.

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

Supported Tools

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

List Agent Definitions

List one page of agent definitions in the connected FutureAGI organization, optionally filtering by type, name, assistant ID, or agent ID.

List Annotation Labels

List one page of FutureAGI annotation labels by type, name, project, or dataset, optionally including usage counts.

List Annotation Queues

List one page of FutureAGI annotation queues by status or name, optionally including item counts.

List Custom Eval Configs

List custom evaluation configurations, optionally restricted to a FutureAGI project or evaluation task.

List Datasets

List one page of datasets in the connected FutureAGI organization, optionally searching by name.

List Eval Tasks

List one page of FutureAGI evaluation tasks, optionally filtering by project ID or task name.

List Personas

List one page of built-in and workspace personas for FutureAGI simulations, with type, simulation-mode, and text filters.

List Run Tests

List one page of configured FutureAGI run tests without executing them, optionally filtering by name, source type, or prompt template.

List Scenarios

List one page of FutureAGI simulation scenarios, optionally filtering by agent definition, agent type, or search text.

FRAMEWORKS

How to build FutureAGI MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use FutureAGI MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use FutureAGI MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use FutureAGI MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use FutureAGI MCP with Claude Cowork

Codex logo

Codex

Use FutureAGI MCP with Codex

OpenClaw logo

OpenClaw

Use FutureAGI MCP with OpenClaw

Hermes logo

Hermes

Use FutureAGI MCP with Hermes

CLI logo

CLI

Use FutureAGI MCP with CLI

Google ADK logo

Google ADK

Use FutureAGI MCP with Google ADK

LangChain logo

LangChain

Use FutureAGI MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use FutureAGI MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use FutureAGI MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use FutureAGI MCP with LlamaIndex

CrewAI logo

CrewAI

Use FutureAGI MCP with CrewAI

FAQ

Frequently asked questions

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

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

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