# How to integrate Flowiseai MCP with Autogen ```json { "title": "How to integrate Flowiseai MCP with Autogen", "toolkit": "Flowiseai", "toolkit_slug": "flowiseai", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/flowiseai/framework/autogen", "markdown_url": "https://composio.dev/toolkits/flowiseai/framework/autogen.md", "updated_at": "2026-05-12T10:11:53.393Z" } ``` ## Introduction This guide walks you through connecting Flowiseai to AutoGen using the Composio tool router. By the end, you'll have a working Flowiseai agent that can list all chatflows available in your account, clone an existing chatflow for testing, delete chat messages from a specific chatflow through natural language commands. This guide will help you understand how to give your AutoGen 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. ## Also integrate Flowiseai with - [OpenAI Agents SDK](https://composio.dev/toolkits/flowiseai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/flowiseai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/flowiseai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/flowiseai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/flowiseai/framework/codex) - [OpenClaw](https://composio.dev/toolkits/flowiseai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/flowiseai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/flowiseai/framework/cli) - [Google ADK](https://composio.dev/toolkits/flowiseai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/flowiseai/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/flowiseai/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/flowiseai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/flowiseai/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/flowiseai/framework/crew-ai) ## 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 Flowiseai - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Flowiseai tools - Run a live chat loop where you ask the agent to perform Flowiseai 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 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 | Tool slug | Name | Description | |---|---|---| | `FLOWISEAI_CLONE_CHATFLOW` | Clone Chatflow | Tool to clone an existing chatflow. Use when you need to duplicate a chatflow by its ID. | | `FLOWISEAI_CREATE_CHATFLOW` | Create Chatflow | Creates a new chatflow in FlowiseAI. Chatflows are visual workflows that define AI agent behavior using nodes and edges. Use this to programmatically create single-agent (CHATFLOW) or multi-agent (MULTIAGENT) flows. Only 'name' is required; type defaults to 'CHATFLOW' and flowData defaults to an empty flow structure. | | `FLOWISEAI_CREATE_DOCUMENT_STORE` | Create Document Store | Creates a new document store in FlowiseAI. Document stores are used to manage embeddings and vector data for AI applications. Use this to programmatically create storage for documents that will be embedded and searched. | | `FLOWISEAI_CREATE_LEAD` | Create Lead | Tool to create a new lead in a chatflow. Use when you need to capture lead information from a chat session for follow-up or CRM integration. | | `FLOWISEAI_CREATE_TOOL` | Create Tool | Tool to create a new FlowiseAI tool. Use when you need to create a custom tool with specific name, description, and color. Optionally provide icon URL, JSON schema, or JavaScript function code. | | `FLOWISEAI_CREATE_VARIABLE` | Create Variable | Creates a new variable in FlowiseAI. Variables are used to store configuration values, API keys, and other data that can be referenced across chatflows. Use this to programmatically create string or number variables with optional values. | | `FLOWISEAI_DELETE_CHATFLOW` | Delete Chatflow | Tool to delete a chatflow by its ID. Use after confirming the chatflow ID is correct. | | `FLOWISEAI_DELETE_CHAT_MESSAGES` | Delete Chat Messages | Tool to delete chat messages for a specific chatflow. Use when you need to remove messages based on optional filters. Use after confirming the chatflow ID. | | `FLOWISEAI_DELETE_DOCUMENT_STORE` | Delete Document Store | Tool to delete a specific document store by its ID. Use when you need to permanently remove a document store. This action is destructive and cannot be undone. | | `FLOWISEAI_DELETE_TOOL_BY_ID` | Delete Tool By ID | Permanently deletes a FlowiseAI tool by its unique ID. This action is destructive and cannot be undone. Use FLOWISEAI_LIST_ALL_TOOLS first to verify the correct tool ID before deletion. | | `FLOWISEAI_DELETE_VARIABLE` | Delete Variable | Tool to delete a variable by its unique ID. Use when you need to permanently remove a variable from FlowiseAI. | | `FLOWISEAI_EDIT_DOCUMENT_STORE_FILE_CHUNK` | Edit Document Store File Chunk | Tool to update a specific chunk in a FlowiseAI document store. Use when you need to modify the content or metadata of an existing chunk. At least one of pageContent or metadata must be provided. | | `FLOWISEAI_GET_ALL_CHATFLOWS` | Get All Chatflows | Retrieves all chatflows from the authenticated FlowiseAI account. Use this to list available chatflows, get their IDs for subsequent operations (like update, delete, export), or check chatflow deployment status. Returns an empty list if no chatflows exist. | | `FLOWISEAI_GET_ALL_CHAT_MESSAGE_FEEDBACK` | Get All Chat Message Feedback | Tool to list all chat message feedbacks for a chatflow. Use when you need to view feedback given on messages in a specific chatflow. | | `FLOWISEAI_GET_ALL_LEADS_FOR_CHATFLOW` | Get All Leads for Chatflow | Tool to retrieve all leads for a specific chatflow. Use when you need to see lead information collected from a chatflow's interactions. | | `FLOWISEAI_GET_ALL_UPSERT_HISTORY` | Get All Upsert History | Tool to retrieve all upsert history records for a specific chatflow. Use when you need to view the history of upsert operations. | | `FLOWISEAI_GET_ALL_VARIABLES` | Get All Variables | Tool to retrieve a list of all variables. Use when you need to list all variables available in the FlowiseAI workspace. Returns an empty list if no variables exist. | | `FLOWISEAI_GET_DOCUMENT_STORE_BY_ID` | Get Document Store By ID | Tool to retrieve a document store by its ID. Use when you have a document store ID and need its full details including configuration and status. | | `FLOWISEAI_GET_DOCUMENT_STORE_FILE_CHUNKS` | Get Document Store File Chunks | Tool to get chunks from a specific document loader. Use when you need to retrieve chunked content from a document in a FlowiseAI document store. | | `FLOWISEAI_GET_SINGLE_CHATFLOW` | Get Single Chatflow | Tool to retrieve a chatflow by its ID. Use when you have a chatflow ID and need its full details. | | `FLOWISEAI_GET_TOOL_BY_ID` | Get Tool By ID | Tool to retrieve a specific FlowiseAI tool by its ID. Use when you need detailed metadata of a tool before interacting with it. | | `FLOWISEAI_LIST_ALL_TOOLS` | List All Tools | Tool to retrieve a list of all tools. Use when you need to list every tool available after authentication. | | `FLOWISEAI_LIST_ASSISTANTS` | List Assistants | Tool to retrieve a list of all assistants. Use when you need to list every assistant available in the authenticated FlowiseAI account. | | `FLOWISEAI_LIST_CHAT_MESSAGES` | List Chat Messages | Tool to list chat messages of a chatflow. Use after selecting a chatflow when you need to view its messages. | | `FLOWISEAI_PING_SERVER` | Ping Server | Tool to ping the FlowiseAI server to verify it is running and accessible. Use this to perform a health check before executing other operations or to diagnose connectivity issues. | | `FLOWISEAI_UPDATE_CHATFLOW_DETAILS` | Update Chatflow Details | Tool to update details of an existing chatflow. Use when you have confirmed the chatflow ID and want to modify its fields. | | `FLOWISEAI_UPDATE_DOCUMENT_STORE` | Update Document Store | Tool to update a specific document store. Use when you need to modify properties of an existing document store. | | `FLOWISEAI_UPDATE_TOOL_BY_ID` | Update Tool By ID | Updates a FlowiseAI tool's properties by its ID. Use this to modify tool name, description, color, icon, JSON schema, or JavaScript function code. Requires the tool's UUID from LIST_ALL_TOOLS or GET_TOOL_BY_ID. At least one field must be provided for update. | | `FLOWISEAI_UPDATE_VARIABLE` | Update Variable | Tool to update a variable by its ID. Use when you need to modify a variable's name, value, or type. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Flowiseai MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Flowiseai. Instead of manually wiring Flowiseai APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 1. Prerequisites You will need: - A Composio API key - An OpenAI API key (used by Autogen's OpenAIChatCompletionClient) - A Flowiseai account you can connect to Composio - Some basic familiarity with Autogen and Python async ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies Install Composio, Autogen extensions, and dotenv. What's happening: - composio connects your agent to Flowiseai via MCP - autogen-agentchat provides the AssistantAgent class - autogen-ext-openai provides the OpenAI model client - autogen-ext-tools provides MCP workbench support ```bash pip install composio python-dotenv pip install autogen-agentchat autogen-ext-openai autogen-ext-tools ``` ### 3. Set up environment variables 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 Flowiseai connections to use ```bash COMPOSIO_API_KEY=your-composio-api-key OPENAI_API_KEY=your-openai-api-key USER_ID=your-user-identifier@example.com ``` ### 4. Import dependencies and create Tool Router session What's happening: - load_dotenv() reads your .env file - Composio(api_key=...) initializes the SDK - create(...) creates a Tool Router session that exposes Flowiseai tools - session.mcp.url is the MCP endpoint that Autogen will connect to ```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 Flowiseai session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["flowiseai"] ) url = session.mcp.url ``` ### 5. Configure MCP parameters for Autogen 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 ```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")} ) ``` ### 6. Create the model client and agent What's happening: - OpenAIChatCompletionClient wraps the OpenAI model for Autogen - McpWorkbench connects the agent to the MCP tools - AssistantAgent is configured with the Flowiseai tools from the workbench ```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 Flowiseai assistant agent with MCP tools agent = AssistantAgent( name="flowiseai_assistant", description="An AI assistant that helps with Flowiseai operations.", model_client=model_client, workbench=workbench, model_client_stream=True, max_tool_iterations=10 ) ``` ### 7. Run the interactive chat loop What's happening: - The script prompts you in a loop with You: - Autogen passes your input to the model, which decides which Flowiseai 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 ```python print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Flowiseai 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") ``` ## Complete Code ```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 Flowiseai session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["flowiseai"] ) 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 Flowiseai assistant agent with MCP tools agent = AssistantAgent( name="flowiseai_assistant", description="An AI assistant that helps with Flowiseai 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 Flowiseai 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 Flowiseai 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 Flowiseai, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Flowiseai MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/flowiseai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/flowiseai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/flowiseai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/flowiseai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/flowiseai/framework/codex) - [OpenClaw](https://composio.dev/toolkits/flowiseai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/flowiseai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/flowiseai/framework/cli) - [Google ADK](https://composio.dev/toolkits/flowiseai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/flowiseai/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/flowiseai/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/flowiseai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/flowiseai/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/flowiseai/framework/crew-ai) ## Related Toolkits - [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed. - [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service. - [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows. - [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow. - [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows. - [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application. - [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities. - [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization. - [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications. - [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations. - [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key. - [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient. - [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions. - [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences. - [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement. - [Cody](https://composio.dev/toolkits/cody) - Cody is an AI assistant built for businesses, trained on your company's knowledge and data. It delivers instant answers and insights, tailored for your team. - [Context7 MCP](https://composio.dev/toolkits/context7_mcp) - Context7 MCP delivers live, version-specific code docs and examples right from the source. It helps developers and AI agents instantly retrieve authoritative programming info—no more out-of-date docs. - [Customgpt](https://composio.dev/toolkits/customgpt) - CustomGPT.ai lets you build and deploy chatbots tailored to your own data and business needs. Get precise and context-aware AI conversations without writing code. - [Datarobot](https://composio.dev/toolkits/datarobot) - Datarobot is a machine learning platform that automates model development, deployment, and monitoring. It empowers organizations to quickly gain predictive insights from large datasets. - [Deepgram](https://composio.dev/toolkits/deepgram) - Deepgram is an AI-powered speech recognition platform for accurate audio transcription and understanding. It enables fast, scalable speech-to-text with advanced audio intelligence features. ## Frequently Asked Questions ### 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 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 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. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)