How to integrate Flowiseai MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Flowiseai to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Flowiseai agent that can list all chatflows available in my account, clone an existing chatflow for testing, delete chat messages from a specific chatflow, export a chatflow configuration to json through natural language commands.

This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Flowiseai account through Composio's Flowiseai 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 necessary dependencies
  • Initialize Composio and create a Tool Router session for Flowiseai
  • Configure an AI agent that can use Flowiseai as a tool
  • Run a live chat session where you can ask the agent to perform Flowiseai operations

What is open-ai-agents-sdk?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.

Key features include:

  • Hosted MCP Tools: Connect to external services through hosted MCP endpoints
  • SQLite Sessions: Persist conversation history across interactions
  • Simple API: Clean interface with Agent, Runner, and tool configuration
  • Streaming Support: Real-time response streaming for interactive applications

What is the Flowiseai MCP server, and what's possible with it?

The Flowiseai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Flowiseai account. It provides structured and secure access to your FlowiseAI workspace, so your agent can manage chatflows, automate workflow creation, clone or delete existing flows, and handle tool configurations on your behalf.

  • Automated chatflow creation and management: Instantly create new chatflows, fetch details of existing ones, or update and organize your LLM workflows programmatically.
  • Seamless cloning and exporting of chatflows: Duplicate any chatflow with a single request or export them for backup, sharing, or versioning across projects.
  • Easy clean-up and deletion: Direct your agent to delete chatflows, remove outdated tools, or erase chat messages to keep your workspace tidy and relevant.
  • Tool and workflow introspection: Retrieve detailed metadata for specific tools or chatflows so your agent can make informed decisions about which components to use or modify.
  • Effortless import and migration: Import chatflows from exported JSON files, making it simple to migrate or restore entire AI workflows with minimal manual effort.

Supported Tools & Triggers

Tools
Clone ChatflowTool to clone an existing chatflow.
Create ChatflowTool to create a new chatflow in FlowiseAI.
Delete ChatflowTool to delete a chatflow by its ID.
Delete Chat MessagesTool to delete chat messages for a specific chatflow.
Delete Tool By IDTool to delete a specific FlowiseAI tool by ID.
Export ChatflowTool to export a chatflow by its ID.
Get All ChatflowsTool to retrieve all chatflows.
Get Single ChatflowTool to retrieve a chatflow by its ID.
Get Tool By IDTool to retrieve a specific FlowiseAI tool by its ID.
Import ChatflowTool to import a chatflow from an exported JSON file.
List All ToolsTool to retrieve a list of all tools.
List Chat MessagesTool to list chat messages of a chatflow.
Update Chatflow DetailsTool to update details of an existing chatflow.
Update Tool By IDTool to update a specific tool by its ID.

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

Before starting, make sure you have:
  • Composio API Key and OpenAI API Key
  • Primary know-how of OpenAI Agents SDK
  • A live Flowiseai project
  • Some knowledge of Python or Typescript

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

Install dependencies

pip install composio_openai_agents openai-agents python-dotenv

Install the Composio SDK and the OpenAI Agents SDK.

Set up environment variables

bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com

Create a .env file and add your OpenAI and Composio API keys.

Import dependencies

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
What's happening:
  • You're importing all necessary libraries.
  • The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Flowiseai.

Set up the Composio instance

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
What's happening:
  • load_dotenv() loads your .env file so OPENAI_API_KEY and COMPOSIO_API_KEY are available as environment variables.
  • Creating a Composio instance using the API Key and OpenAIAgentsProvider class.

Create a Tool Router session

# Create a Flowiseai Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["flowiseai"]
)

mcp_url = session.mcp.url

What is happening:

  • You give the Tool Router the user id and the toolkits you want available. Here, it is only flowiseai.
  • The router checks the user's Flowiseai connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Flowiseai.
  • This approach keeps things lightweight and lets the agent request Flowiseai tools only when needed during the conversation.

Configure the agent

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Flowiseai. "
        "Help users perform Flowiseai operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
What's happening:
  • We're creating an Agent instance with a name, model (gpt-5), and clear instructions about its purpose.
  • The agent's instructions tell it that it can access Flowiseai and help with queries, inserts, updates, authentication, and fetching database information.
  • The tools array includes a HostedMCPTool that connects to the MCP server URL we created earlier.
  • The headers dict includes the Composio API key for secure authentication with the MCP server.
  • require_approval: 'never' means the agent can execute Flowiseai operations without asking for permission each time, making interactions smoother.

Start chat loop and handle conversation

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
What's happening:
  • The program prints a session URL that you visit to authorize Flowiseai.
  • After authorization, the chat begins.
  • Each message you type is processed by the agent using Runner.run().
  • The responses are printed to the console, and conversations are saved locally using SQLite.
  • Typing exit, quit, or q cleanly ends the chat.

Complete Code

Here's the complete code to get you started with Flowiseai and open-ai-agents-sdk:

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["flowiseai"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Flowiseai. "
        "Help users perform Flowiseai operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())

Conclusion

This was a starter code for integrating Flowiseai MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Flowiseai.

Key features:

  • Hosted MCP tool integration through Composio's Tool Router
  • SQLite session persistence for conversation history
  • Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

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Mastra AI

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CrewAI

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FAQ

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

With a standalone Flowiseai MCP server, the agents and LLMs can only access a fixed set of Flowiseai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Flowiseai and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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 Flowiseai tools.

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

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

Used by agents from

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HubSpot
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Context
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Letta
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Agent.ai
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Entelligence
Rolai

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