# How to integrate Zixflow MCP with Autogen ```json { "title": "How to integrate Zixflow MCP with Autogen", "toolkit": "Zixflow", "toolkit_slug": "zixflow", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/zixflow/framework/autogen", "markdown_url": "https://composio.dev/toolkits/zixflow/framework/autogen.md", "updated_at": "2026-03-29T06:56:25.114Z" } ``` ## Introduction This guide walks you through connecting Zixflow to AutoGen using the Composio tool router. By the end, you'll have a working Zixflow agent that can send a bulk sms campaign to all new leads, add a follow-up reminder for every hot lead, list conversations from whatsapp with unread responses through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Zixflow account through Composio's Zixflow MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Zixflow with - [OpenAI Agents SDK](https://composio.dev/toolkits/zixflow/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/zixflow/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/zixflow/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/zixflow/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/zixflow/framework/codex) - [OpenClaw](https://composio.dev/toolkits/zixflow/framework/openclaw) - [Hermes](https://composio.dev/toolkits/zixflow/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/zixflow/framework/cli) - [Google ADK](https://composio.dev/toolkits/zixflow/framework/google-adk) - [LangChain](https://composio.dev/toolkits/zixflow/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/zixflow/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/zixflow/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/zixflow/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/zixflow/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 Zixflow - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Zixflow tools - Run a live chat loop where you ask the agent to perform Zixflow 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 Zixflow MCP server, and what's possible with it? The Zixflow MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Zixflow account. It provides structured and secure access so your agent can perform Zixflow operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `ZIXFLOW_CREATE_ATTRIBUTE` | Create Attribute | Tool to create a custom attribute for a Zixflow collection or list. Use when you need to add new fields to track specific data types (text, number, email, etc.). Attributes define the structure of your data. Common use cases: adding custom fields for contacts, creating status trackers, adding reference fields between collections. | | `ZIXFLOW_GET_CAMPAIGN_REPORT_WHATSAPP` | Get WhatsApp Campaign Report | Tool to retrieve WhatsApp campaign message report. Use when you need to check the delivery status and details of a WhatsApp message sent via campaign. | | `ZIXFLOW_GET_EMAIL_REPORT` | Get Email Report | Tool to retrieve email message delivery report from Zixflow. Use when you need to check the delivery status of a sent campaign email. The report includes delivery status (SENT, OPENED, CLICKED, BOUNCE, COMPLAINT, UNSUBSCRIBED), recipient information, and timestamps. Requires the message ID obtained when sending the campaign. | | `ZIXFLOW_GET_SMS_REPORT` | Get SMS Report | Tool to retrieve SMS message delivery report from Zixflow. Use when you need to check the delivery status, destination, and other details of a sent SMS campaign message. | | `ZIXFLOW_GET_WHATSAPP_TEMPLATE_VARIABLES` | Get WhatsApp Template Variables | Tool to retrieve template variable details for a WhatsApp template. Use when you need to understand what variables a specific WhatsApp template expects before sending a message. | | `ZIXFLOW_LIST_ATTRIBUTE_OPTIONS` | List Attribute Options | Tool to retrieve the list of options for select/multiselect attributes. Use when you need to get available options for a specific attribute in a collection or list. | | `ZIXFLOW_LIST_ATTRIBUTE_STATUS_OPTIONS` | List Attribute Status Options | Tool to retrieve the list of options for status attributes in Zixflow. Use when you need to fetch available status configurations for a specific attribute within a collection or list. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Zixflow MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Zixflow. Instead of manually wiring Zixflow 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 Zixflow 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 Zixflow 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 Zixflow 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 Zixflow 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 Zixflow session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["zixflow"] ) 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 Zixflow 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 Zixflow assistant agent with MCP tools agent = AssistantAgent( name="zixflow_assistant", description="An AI assistant that helps with Zixflow 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 Zixflow 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 Zixflow 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 Zixflow session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["zixflow"] ) 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 Zixflow assistant agent with MCP tools agent = AssistantAgent( name="zixflow_assistant", description="An AI assistant that helps with Zixflow 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 Zixflow 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 Zixflow 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 Zixflow, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Zixflow MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/zixflow/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/zixflow/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/zixflow/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/zixflow/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/zixflow/framework/codex) - [OpenClaw](https://composio.dev/toolkits/zixflow/framework/openclaw) - [Hermes](https://composio.dev/toolkits/zixflow/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/zixflow/framework/cli) - [Google ADK](https://composio.dev/toolkits/zixflow/framework/google-adk) - [LangChain](https://composio.dev/toolkits/zixflow/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/zixflow/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/zixflow/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/zixflow/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/zixflow/framework/crew-ai) ## Related Toolkits - [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly. - [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place. - [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations. - [Hubspot](https://composio.dev/toolkits/hubspot) - HubSpot is an all-in-one marketing, sales, and customer service platform. It lets teams nurture leads, automate outreach, and track every customer interaction in one place. - [Asana](https://composio.dev/toolkits/asana) - Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals. - [Pipedrive](https://composio.dev/toolkits/pipedrive) - Pipedrive is a sales management platform offering pipeline visualization, lead tracking, and workflow automation. It helps sales teams keep deals moving forward efficiently and never miss a follow-up. - [Google Tasks](https://composio.dev/toolkits/googletasks) - Google Tasks is a to-do list and task management tool integrated into Gmail and Google Calendar. It helps you organize, track, and complete tasks across your Google ecosystem. - [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity. - [Salesforce](https://composio.dev/toolkits/salesforce) - Salesforce is a leading CRM platform that helps businesses manage sales, service, and marketing. It centralizes customer data, enabling teams to drive growth and build strong relationships. - [Jira](https://composio.dev/toolkits/jira) - Jira is Atlassian’s platform for bug tracking, issue tracking, and agile project management. It helps teams organize work, prioritize tasks, and deliver projects efficiently. - [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place. - [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time. - [Reddit](https://composio.dev/toolkits/reddit) - Reddit is a social news platform with thriving user-driven communities (subreddits). It's the go-to place for discussion, content sharing, and viral marketing. - [Apollo](https://composio.dev/toolkits/apollo) - Apollo is a CRM and lead generation platform that helps businesses discover contacts and manage sales pipelines. Use it to streamline customer outreach and track your deals from one place. - [Attio](https://composio.dev/toolkits/attio) - Attio is a customizable CRM and workspace for managing your team's relationships and workflows. It helps teams organize contacts, automate tasks, and collaborate more efficiently. - [Facebook](https://composio.dev/toolkits/facebook) - Facebook is a social media and advertising platform for businesses and creators. It helps you connect, share, and manage content across your public Facebook Pages. - [Linkedin](https://composio.dev/toolkits/linkedin) - LinkedIn is a professional networking platform for connecting, sharing content, and engaging with business opportunities. It's the go-to place for building your professional brand and unlocking new career connections. - [Acculynx](https://composio.dev/toolkits/acculynx) - AccuLynx is a cloud-based roofing business management software for contractors. It streamlines project tracking, lead management, and document sharing. - [Active campaign](https://composio.dev/toolkits/active_campaign) - ActiveCampaign is a marketing automation and CRM platform for managing email campaigns, sales pipelines, and customer segmentation. It helps businesses engage customers and drive growth through smart automation and targeted outreach. - [ActiveTrail](https://composio.dev/toolkits/active_trail) - ActiveTrail is a user-friendly email marketing and automation platform. It helps you reach subscribers and automate campaigns with ease. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Zixflow MCP? With a standalone Zixflow MCP server, the agents and LLMs can only access a fixed set of Zixflow tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Zixflow 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 Zixflow tools. ### Can I manage the permissions and scopes for Zixflow while using Tool Router? Yes, absolutely. You can configure which Zixflow 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 Zixflow data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)