# How to integrate Zoho bigin MCP with Autogen ```json { "title": "How to integrate Zoho bigin MCP with Autogen", "toolkit": "Zoho bigin", "toolkit_slug": "zoho_bigin", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/zoho_bigin/framework/autogen", "markdown_url": "https://composio.dev/toolkits/zoho_bigin/framework/autogen.md", "updated_at": "2026-05-06T08:34:46.611Z" } ``` ## Introduction This guide walks you through connecting Zoho bigin to AutoGen using the Composio tool router. By the end, you'll have a working Zoho bigin agent that can add new contact to sales pipeline, list all open deals this week, tag recent leads as 'hot prospects' through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Zoho bigin account through Composio's Zoho bigin MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Zoho bigin with - [ChatGPT](https://composio.dev/toolkits/zoho_bigin/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/zoho_bigin/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/zoho_bigin/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/zoho_bigin/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/zoho_bigin/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/zoho_bigin/framework/codex) - [OpenClaw](https://composio.dev/toolkits/zoho_bigin/framework/openclaw) - [Hermes](https://composio.dev/toolkits/zoho_bigin/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/zoho_bigin/framework/cli) - [Google ADK](https://composio.dev/toolkits/zoho_bigin/framework/google-adk) - [LangChain](https://composio.dev/toolkits/zoho_bigin/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/zoho_bigin/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/zoho_bigin/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/zoho_bigin/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/zoho_bigin/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 Zoho bigin - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Zoho bigin tools - Run a live chat loop where you ask the agent to perform Zoho bigin 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 Zoho bigin MCP server, and what's possible with it? The Zoho bigin MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Zoho bigin account. It provides structured and secure access to your CRM pipeline data, so your agent can manage contacts, track deals, organize records, handle attachments, and streamline your small business workflows—all on your behalf. - Automated record management: Add, update, or delete records in any Zoho bigin module to keep your CRM data accurate and up to date. - Tagging and categorization: Create new tags or apply them to records, making it easy to segment contacts, deals, or companies for better organization. - Attachment handling: Retrieve, download, or delete attachments associated with your records, letting your agent manage files and documents with ease. - Module and data discovery: List available modules and fetch records with sorting, filtering, and pagination—perfect for quickly surfacing the data you need. - Deleted records auditing: Access and review recently deleted records for auditing or restoration, helping you maintain data integrity and recover lost information. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `ZOHO_BIGIN_ADD_RECORDS` | Add Records | Tool to add new records to a module. use when you need to programmatically insert one or more records into bigin after confirming module name and field api names. provide required fields per module (e.g., contacts requires last name). | | `ZOHO_BIGIN_ADD_TAGS_TO_RECORDS` | Add Tags to Records | Tool to add tags to a specific record in a module. use when you need to categorize or label a record after creation or update. | | `ZOHO_BIGIN_CREATE_TAGS` | Create Tags | Tool to create tags for a module. use when you need to add new tags to a specific module in zoho bigin. | | `ZOHO_BIGIN_DELETE_ATTACHMENT` | Delete Attachment | Tool to delete an attachment from a record. use when you need to remove a file after confirming its record id and attachment id. | | `ZOHO_BIGIN_DELETE_RECORDS` | Delete Records | Tool to delete records from a module. use when removing one or multiple records after confirming their ids. | | `ZOHO_BIGIN_DOWNLOAD_ATTACHMENT` | Download Attachment | Tool to download an attachment from a record. use when you need the binary content of a specific attachment after confirming the record and attachment ids. | | `ZOHO_BIGIN_GET_ATTACHMENTS` | Get Attachments | Tool to retrieve attachments for a record. use when you need a paginated list of attachments for a given module record. | | `ZOHO_BIGIN_GET_DELETED_RECORDS` | Get Deleted Records | Tool to get a list of deleted records in a module. use when auditing or restoring recently deleted data (recycle within 60 days, permanent within 120 days). | | `ZOHO_BIGIN_GET_MODULES` | Get Modules | Tool to retrieve a list of all modules. use when you need to discover which modules are available in bigin. | | `ZOHO_BIGIN_GET_RECORDS` | Get Records | Tool to retrieve records from a bigin module. use when listing or querying module data with specific fields, sorting, filtering, and pagination. | | `ZOHO_BIGIN_UPDATE_RECORDS` | Update Records | Tool to update existing records in a module. use when you need to modify one or multiple records after confirming their ids and field api names. supports up to 100 records per call; specify an optional trigger to control workflow execution. | | `ZOHO_BIGIN_UPLOAD_ATTACHMENT` | Upload Attachment | Tool to upload an attachment to a record. use when you need to attach a file or specify a public url for upload to a bigin record. ensure module api name and record id are correct before calling. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Zoho bigin MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Zoho bigin. Instead of manually wiring Zoho bigin 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 Zoho bigin 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 Zoho bigin 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 Zoho bigin 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 Zoho bigin 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 Zoho bigin session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["zoho_bigin"] ) 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 Zoho bigin 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 Zoho bigin assistant agent with MCP tools agent = AssistantAgent( name="zoho_bigin_assistant", description="An AI assistant that helps with Zoho bigin 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 Zoho bigin 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 Zoho bigin 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 Zoho bigin session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["zoho_bigin"] ) 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 Zoho bigin assistant agent with MCP tools agent = AssistantAgent( name="zoho_bigin_assistant", description="An AI assistant that helps with Zoho bigin 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 Zoho bigin 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 Zoho bigin 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 Zoho bigin, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Zoho bigin MCP Agent with another framework - [ChatGPT](https://composio.dev/toolkits/zoho_bigin/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/zoho_bigin/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/zoho_bigin/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/zoho_bigin/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/zoho_bigin/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/zoho_bigin/framework/codex) - [OpenClaw](https://composio.dev/toolkits/zoho_bigin/framework/openclaw) - [Hermes](https://composio.dev/toolkits/zoho_bigin/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/zoho_bigin/framework/cli) - [Google ADK](https://composio.dev/toolkits/zoho_bigin/framework/google-adk) - [LangChain](https://composio.dev/toolkits/zoho_bigin/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/zoho_bigin/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/zoho_bigin/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/zoho_bigin/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/zoho_bigin/framework/crew-ai) ## Related Toolkits - [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. - [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. - [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. - [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. - [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. - [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time. - [Affinity](https://composio.dev/toolkits/affinity) - Affinity is a relationship intelligence CRM that helps private capital investors find, manage, and close more deals. It streamlines deal flow and surfaces key connections to help you win opportunities. - [Agencyzoom](https://composio.dev/toolkits/agencyzoom) - AgencyZoom is a sales and performance platform built for P&C insurance agencies. It helps agents boost sales, retain clients, and analyze producer results in one place. - [Bettercontact](https://composio.dev/toolkits/bettercontact) - Bettercontact is a smart contact enrichment tool for finding emails and phone numbers. It helps boost lead generation with automated, waterfall search across multiple sources. - [Blackbaud](https://composio.dev/toolkits/blackbaud) - Blackbaud provides cloud-based software for nonprofits, schools, and healthcare institutions. It streamlines fundraising, donor management, and mission-driven operations. - [Brilliant directories](https://composio.dev/toolkits/brilliant_directories) - Brilliant Directories is an all-in-one platform for building and managing online membership communities and business directories. It streamlines listings, member management, and engagement tools into a single, easy interface. - [Capsule crm](https://composio.dev/toolkits/capsule_crm) - Capsule CRM is a user-friendly CRM platform for managing contacts and sales pipelines. It helps businesses organize relationships and streamline their sales process efficiently. - [Centralstationcrm](https://composio.dev/toolkits/centralstationcrm) - CentralStationCRM is an easy-to-use CRM software focused on collaboration and long-term customer relationships. It helps teams manage contacts, deals, and communications all in one place. - [Clientary](https://composio.dev/toolkits/clientary) - Clientary is a platform for managing clients, invoices, projects, proposals, and more. It streamlines client work and saves you serious admin time. - [Close](https://composio.dev/toolkits/close) - Close is a CRM platform built for sales teams, combining calling, email automation, and predictive dialers. It streamlines sales workflows and boosts productivity with all-in-one communication tools. - [Dropcontact](https://composio.dev/toolkits/dropcontact) - Dropcontact is a B2B email finder and data enrichment service for professionals. It delivers verified email addresses and enriches contact info with up-to-date data. - [Dynamics365](https://composio.dev/toolkits/dynamics365) - Dynamics 365 is Microsoft's platform combining CRM, ERP, and productivity apps. It streamlines sales, marketing, service, and operations in one place. - [Espocrm](https://composio.dev/toolkits/espocrm) - EspoCRM is an open-source web application for managing customer relationships. It helps businesses organize contacts, track leads, and streamline their sales process. - [Fireberry](https://composio.dev/toolkits/fireberry) - Fireberry is a CRM platform that streamlines customer and sales management. It helps businesses organize contacts, automate sales, and integrate with other business tools. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Zoho bigin MCP? With a standalone Zoho bigin MCP server, the agents and LLMs can only access a fixed set of Zoho bigin tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Zoho bigin 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 Zoho bigin tools. ### Can I manage the permissions and scopes for Zoho bigin while using Tool Router? Yes, absolutely. You can configure which Zoho bigin 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 Zoho bigin data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)