# How to integrate Datagma MCP with Autogen ```json { "title": "How to integrate Datagma MCP with Autogen", "toolkit": "Datagma", "toolkit_slug": "datagma", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/datagma/framework/autogen", "markdown_url": "https://composio.dev/toolkits/datagma/framework/autogen.md", "updated_at": "2026-05-12T10:08:20.171Z" } ``` ## Introduction This guide walks you through connecting Datagma to AutoGen using the Composio tool router. By the end, you'll have a working Datagma agent that can identify top competitors in your industry, find recent market trends for saas, analyze growth opportunities in fintech through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Datagma account through Composio's Datagma MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Datagma with - [OpenAI Agents SDK](https://composio.dev/toolkits/datagma/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/datagma/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/datagma/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/datagma/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/datagma/framework/codex) - [OpenClaw](https://composio.dev/toolkits/datagma/framework/openclaw) - [Hermes](https://composio.dev/toolkits/datagma/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/datagma/framework/cli) - [Google ADK](https://composio.dev/toolkits/datagma/framework/google-adk) - [LangChain](https://composio.dev/toolkits/datagma/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/datagma/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/datagma/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/datagma/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/datagma/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 Datagma - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Datagma tools - Run a live chat loop where you ask the agent to perform Datagma 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 Datagma MCP server, and what's possible with it? The Datagma MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Datagma account. It provides structured and secure access to your Datagma data intelligence platform, so your agent can perform actions like uncovering market insights, tracking competitor activities, analyzing industry trends, and supporting strategic growth decisions on your behalf. - In-depth market insights extraction: Enable your agent to gather and analyze real-time market data to identify emerging opportunities and potential threats. - Competitor metrics tracking: Let your agent monitor competitor performance, product launches, and strategic moves for sharper benchmarking. - Growth opportunity identification: Task your agent with surfacing new business prospects and growth areas using Datagma's data intelligence resources. - Customized analytics reporting: Have your agent generate tailored reports and dashboards that summarize key metrics and actionable insights. - Trend and pattern analysis: Empower your agent to spot industry trends, shifts in customer behavior, and evolving market dynamics for proactive strategy planning. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `DATAGMA_DETECT_JOB_CHANGE` | Detect Job Change | Tool to detect if a contact changed jobs. Use when verifying a contact’s current employment details by email. | | `DATAGMA_ENRICH_PERSON_OR_COMPANY` | Enrich Person or Company | Enrich person or company data using LinkedIn URLs, emails, domains, or names. Returns enriched data including: contact information, LinkedIn profiles, company details, work experience, education, phone numbers (with phoneFull), and company metrics. Input types: LinkedIn profile URL (~100% success), email (~60% success), name+company (~90% success), company domain/name, or SIREN number (French companies). | | `DATAGMA_FIND_WORK_EMAIL` | Find Work Email | Find verified work email address for a person using their name and company. Returns a professionally verified email address with validation metadata including SMTP checks and MX records. Requires either fullName or firstName+lastName, plus company domain or LinkedIn company slug. | | `DATAGMA_GET_CREDITS` | Get Credits | Get the current credit balance for the authenticated Datagma API account. Use this to check how many API credits remain before making enrichment calls. | | `DATAGMA_GET_TWITTER_BY_EMAIL` | Get Twitter Profile By Email | Retrieve Twitter account information associated with an email address. This action looks up Twitter username and display name for a given email address using Datagma's enrichment database. Returns Twitter username, display name, and the queried email if a match is found, or status 'NOT_FOUND' if no Twitter account is associated with the email. Use this when you need to: - Find someone's Twitter handle from their email address - Verify if an email has an associated Twitter account - Enrich contact data with social media information | | `DATAGMA_GET_TWITTER_BY_USERNAME` | Get Twitter Profile by Username | Enrich Twitter profile data using Datagma's database. Returns contact information (email), social media profiles (LinkedIn, Facebook, GitHub), and professional details (skills, interests, industry) associated with a Twitter username. Note: Not all usernames are in Datagma's database. A 'not found' response (code 5) indicates the username hasn't been indexed yet. | | `DATAGMA_REVERSE_PHONE_LOOKUP` | Reverse Phone Lookup | Tool to reverse-lookup information associated with a phone number. Use when you have a phone number and need associated details (e.g., carrier, location). | | `DATAGMA_SEARCH_PHONE_NUMBERS` | Search Phone Numbers | Find mobile phone numbers using email address and/or LinkedIn profile URL. Returns list of phone numbers with confidence scores and optional WhatsApp verification. Best results when both email and LinkedIn URL are provided. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Datagma MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Datagma. Instead of manually wiring Datagma 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 Datagma 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 Datagma 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 Datagma 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 Datagma 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 Datagma session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["datagma"] ) 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 Datagma 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 Datagma assistant agent with MCP tools agent = AssistantAgent( name="datagma_assistant", description="An AI assistant that helps with Datagma 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 Datagma 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 Datagma 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 Datagma session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["datagma"] ) 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 Datagma assistant agent with MCP tools agent = AssistantAgent( name="datagma_assistant", description="An AI assistant that helps with Datagma 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 Datagma 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 Datagma 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 Datagma, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Datagma MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/datagma/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/datagma/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/datagma/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/datagma/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/datagma/framework/codex) - [OpenClaw](https://composio.dev/toolkits/datagma/framework/openclaw) - [Hermes](https://composio.dev/toolkits/datagma/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/datagma/framework/cli) - [Google ADK](https://composio.dev/toolkits/datagma/framework/google-adk) - [LangChain](https://composio.dev/toolkits/datagma/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/datagma/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/datagma/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/datagma/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/datagma/framework/crew-ai) ## Related Toolkits - [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources. - [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research. - [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows. - [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research. - [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. 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It helps you manage internet access, enforce policies, and monitor usage across devices and networks. - [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights. - [Databricks](https://composio.dev/toolkits/databricks) - Databricks is a unified analytics platform for big data and AI on the lakehouse architecture. It empowers data teams to collaborate, analyze, and build scalable solutions efficiently. - [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment. - [Dovetail](https://composio.dev/toolkits/dovetail) - Dovetail is a research analysis platform for transcript review and insight generation. It helps teams code interviews, analyze feedback, and create actionable research summaries. - [Dub](https://composio.dev/toolkits/dub) - Dub is a short link management platform with analytics and API access. Use it to easily create, manage, and track branded short links for your business. - [Elasticsearch](https://composio.dev/toolkits/elasticsearch) - Elasticsearch is a distributed, RESTful search and analytics engine for all types of data. It delivers fast, scalable search and powerful analytics across massive datasets. - [Fireflies](https://composio.dev/toolkits/fireflies) - Fireflies.ai is an AI-powered meeting assistant that records, transcribes, and analyzes voice conversations. It helps teams capture call notes automatically and search or summarize meetings effortlessly. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Datagma MCP? With a standalone Datagma MCP server, the agents and LLMs can only access a fixed set of Datagma tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Datagma 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 Datagma tools. ### Can I manage the permissions and scopes for Datagma while using Tool Router? Yes, absolutely. You can configure which Datagma 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 Datagma data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)