# How to integrate Convex MCP with Autogen ```json { "title": "How to integrate Convex MCP with Autogen", "toolkit": "Convex", "toolkit_slug": "convex", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/convex/framework/autogen", "markdown_url": "https://composio.dev/toolkits/convex/framework/autogen.md", "updated_at": "2026-06-18T09:21:44.444Z" } ``` ## Introduction This guide walks you through connecting Convex to AutoGen using the Composio tool router. By the end, you'll have a working Convex agent that can list records from convex tasks table, run convex query for active users, inspect convex deployment function logs through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Convex account through Composio's Convex MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Convex with - [OpenAI Agents SDK](https://composio.dev/toolkits/convex/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/convex/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/convex/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/convex/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/convex/framework/codex) - [OpenClaw](https://composio.dev/toolkits/convex/framework/openclaw) - [Hermes](https://composio.dev/toolkits/convex/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/convex/framework/cli) - [Google ADK](https://composio.dev/toolkits/convex/framework/google-adk) - [LangChain](https://composio.dev/toolkits/convex/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/convex/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/convex/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/convex/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/convex/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 Convex - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Convex tools - Run a live chat loop where you ask the agent to perform Convex 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 Convex MCP server, and what's possible with it? The Convex MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Convex account. It provides structured and secure access so your agent can perform Convex operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `CONVEX_CREATE_DEPLOY_KEY` | Create deploy key | Tool to create a deploy key for use with the Convex CLI. Use when you need to generate credentials for CLI-based development or deployment workflows. The generated key provides administrative access to the specified deployment. | | `CONVEX_CREATE_DEPLOYMENT` | Create Deployment | Tool to create a new deployment for a Convex project. Use when you need to create a development, production, or custom deployment. Specify the deployment type and optional configuration like class, reference, and region. | | `CONVEX_CREATE_PROJECT` | Create Project | Tool to create a new project on a Convex team, optionally provisioning a dev or prod deployment. Use when you need to initialize a new Convex project in a team. | | `CONVEX_DELETE_CUSTOM_DOMAIN` | Delete Custom Domain | Tool to remove a custom domain from a Convex deployment. Use when you need to delete a previously configured custom domain. | | `CONVEX_DELETE_DEPLOYMENT` | Delete Deployment | Tool to delete a Convex deployment. Use when you need to permanently remove a deployment and all its data. WARNING: This action will delete all data and files in the deployment and cannot be undone. | | `CONVEX_DELETE_PROJECT` | Delete project | Deletes a Convex project and all its deployments permanently. Use when you need to permanently remove a project and all associated data. This operation cannot be undone. | | `CONVEX_EXECUTE_QUERY_BATCH` | Execute Query Batch | Tool to execute multiple Convex query functions in a single batch request. Use when you need to fetch data from multiple queries efficiently in one API call. | | `CONVEX_GET_DEPLOYMENT` | Get Deployment Details | Tool to retrieve details about a Convex cloud deployment. Use when you need to get information about a specific deployment including its configuration, region, creation time, and status. | | `CONVEX_GET_PROJECT_BY_ID` | Get Project by ID | Tool to retrieve detailed information about a specific Convex project by its ID. Use when you need to fetch project metadata including name, slug, team association, and creation time. | | `CONVEX_GET_PROJECT_BY_SLUG` | Get Project by Slug | Tool to retrieve a Convex project by its slug within a team. Use when you need to fetch project details using human-readable identifiers instead of numeric IDs. | | `CONVEX_GET_QUERY_TIMESTAMP` | Get Query Timestamp | Tool to get the latest timestamp for queries from Convex deployment. Use when you need to retrieve the current query timestamp from the Convex API. | | `CONVEX_GET_TOKEN_DETAILS` | Get token details | Tool to retrieve token details for the authenticated token. Returns the team ID for team tokens or project ID for project tokens. Especially useful after receiving a token from an OAuth flow to identify which team or project it belongs to. | | `CONVEX_LIST_DEPLOY_KEYS` | List Deploy Keys | Tool to list all deploy keys for a specified Convex deployment. Use when you need to view all authentication tokens that can be used to deploy to this deployment. | | `CONVEX_LIST_DEPLOYMENT_CLASSES` | List deployment classes | Tool to list available deployment classes for a Convex team. Use when you need to check which deployment classes are available for a specific team. | | `CONVEX_LIST_DEPLOYMENT_REGIONS` | List deployment regions | Tool to list available deployment regions for a Convex team. Use when you need to check which regions are available for deploying a team's backend. | | `CONVEX_LIST_DEPLOYMENTS` | List Deployments | Tool to list all deployments for a Convex project. Use when you need to see all deployments (production, preview, or local) for a specific project. | | `CONVEX_LIST_LOG_STREAMS` | List Log Streams | Tool to list all existing log stream configurations in a deployment. Use when you need to view configured log streaming destinations like Datadog, Webhook, Axiom, or Sentry. | | `CONVEX_LIST_PROJECTS` | List Projects | Tool to list all projects for a specific Convex team. Use when you need to retrieve all projects associated with a team by team ID. | | `CONVEX_UPDATE_DEPLOYMENT` | Update Deployment | Tool to update properties of an existing Convex deployment. Use when you need to modify deployment settings such as dashboard edit confirmation or deployment reference. Only the fields provided in the request are modified. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Convex MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Convex. Instead of manually wiring Convex 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 Convex 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 Convex 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 Convex 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 Convex 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 Convex session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["convex"] ) 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 Convex 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 Convex assistant agent with MCP tools agent = AssistantAgent( name="convex_assistant", description="An AI assistant that helps with Convex 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 Convex 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 Convex 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 Convex session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["convex"] ) 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 Convex assistant agent with MCP tools agent = AssistantAgent( name="convex_assistant", description="An AI assistant that helps with Convex 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 Convex 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 Convex 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 Convex, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Convex MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/convex/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/convex/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/convex/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/convex/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/convex/framework/codex) - [OpenClaw](https://composio.dev/toolkits/convex/framework/openclaw) - [Hermes](https://composio.dev/toolkits/convex/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/convex/framework/cli) - [Google ADK](https://composio.dev/toolkits/convex/framework/google-adk) - [LangChain](https://composio.dev/toolkits/convex/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/convex/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/convex/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/convex/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/convex/framework/crew-ai) ## Related Toolkits - [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. 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It makes it easy for teams to organize data and automate workflows without writing code. - [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows. - [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights. - [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews. - [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Convex MCP? With a standalone Convex MCP server, the agents and LLMs can only access a fixed set of Convex tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Convex 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 Convex tools. ### Can I manage the permissions and scopes for Convex while using Tool Router? Yes, absolutely. You can configure which Convex 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 Convex data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) ยท [Composio docs](https://docs.composio.dev/llms.txt)