# How to integrate Honeybadger MCP with Autogen ```json { "title": "How to integrate Honeybadger MCP with Autogen", "toolkit": "Honeybadger", "toolkit_slug": "honeybadger", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/honeybadger/framework/autogen", "markdown_url": "https://composio.dev/toolkits/honeybadger/framework/autogen.md", "updated_at": "2026-05-12T10:15:01.607Z" } ``` ## Introduction This guide walks you through connecting Honeybadger to AutoGen using the Composio tool router. By the end, you'll have a working Honeybadger agent that can report a new deployment to honeybadger, upload javascript source maps after release, send a custom error event for diagnostics through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Honeybadger account through Composio's Honeybadger MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Honeybadger with - [OpenAI Agents SDK](https://composio.dev/toolkits/honeybadger/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/honeybadger/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/honeybadger/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/honeybadger/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/honeybadger/framework/codex) - [OpenClaw](https://composio.dev/toolkits/honeybadger/framework/openclaw) - [Hermes](https://composio.dev/toolkits/honeybadger/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/honeybadger/framework/cli) - [Google ADK](https://composio.dev/toolkits/honeybadger/framework/google-adk) - [LangChain](https://composio.dev/toolkits/honeybadger/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/honeybadger/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/honeybadger/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/honeybadger/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/honeybadger/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 Honeybadger - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Honeybadger tools - Run a live chat loop where you ask the agent to perform Honeybadger 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 Honeybadger MCP server, and what's possible with it? The Honeybadger MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Honeybadger account. It provides structured and secure access to your error monitoring and deployment data, so your agent can perform actions like reporting exceptions, tracking deployments, sending custom events, and managing source maps on your behalf. - Error and exception reporting: Instantly notify Honeybadger of new exceptions or critical errors by sending detailed diagnostic data, including stack traces and context information, for fast troubleshooting. - Automated deployment tracking: Let your agent report new deployments to Honeybadger after every release, so you always have up-to-date context for error tracking and performance monitoring. - Scheduled task monitoring: Use the agent to report check-ins (pings) for scheduled jobs, ensuring your background tasks are running reliably and on time. - Custom telemetry and event logging: Send structured NDJSON events to Honeybadger Insights, allowing you to capture and analyze application-specific metrics and events. - Source map and file uploads: Upload JavaScript source maps and supporting files to Honeybadger for improved error de-minification and debugging of production errors. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `HONEYBADGER_REPORT_CHECK_IN` | Report Check-In | Reports a check-in (ping) to Honeybadger for uptime monitoring. Check-ins are used to monitor scheduled tasks, cron jobs, and background processes. By pinging this endpoint regularly, you signal that your task is running on schedule. If Honeybadger doesn't receive a ping within the expected timeframe, it will alert you that the task may have failed or stopped running. Use this action at the end of successful task executions to notify Honeybadger the task completed as expected. | | `HONEYBADGER_REPORT_CHECK_IN_WITH_PAYLOAD` | Report Check-In With Payload | Report a check-in with additional payload data to Honeybadger. Use when monitoring scheduled tasks or cron jobs and need to send metrics, status, or metadata (up to 20KB). | | `HONEYBADGER_REPORT_DEPLOYMENT` | Report Deployment | Report a new deployment to Honeybadger for deployment tracking and error correlation. Use this tool after deploying code to notify Honeybadger, which allows you to: - Track deployment history on your project's Deployments page - Correlate errors with specific deployments - Automatically resolve errors when deploying to an environment All deployment fields are optional, but providing environment and revision is recommended for better tracking. | | `HONEYBADGER_REPORT_EVENT` | Report Event | Send custom events to Honeybadger Insights for tracking, monitoring, and analytics. Use this action to record any structured event data such as: - User activity and behavioral events (logins, page views, feature usage) - Application errors and exceptions with context - Performance metrics and timing data - Custom business events and audit trails - System health and operational metrics Events are sent as newline-delimited JSON (NDJSON) and can include any custom fields. The API returns tracking IDs for each successfully recorded event. | | `HONEYBADGER_REPORT_EXCEPTION` | Report Exception | Tool to report an exception notice to Honeybadger. Use when sending error details (stack trace, context) for diagnostics. | | `HONEYBADGER_UPLOAD_FILE_TO_S3` | Upload File to S3 | Tool to upload a local file to a managed S3 bucket. Use when preparing files for source-map uploads. | | `HONEYBADGER_UPLOAD_SOURCE_MAP` | Upload Source Map | Upload JavaScript source maps to Honeybadger for error stack trace de-minification. Use this tool after deploying minified JavaScript assets to enable Honeybadger to display un-minified, readable stack traces when errors occur. Source maps allow Honeybadger to map minified code back to your original source code with proper file names, function names, and line numbers. The tool uploads: (1) the minified JS file, (2) its corresponding .map file, and optionally (3) additional source files referenced by the map, all associated with the production URL. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Honeybadger MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Honeybadger. Instead of manually wiring Honeybadger 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 Honeybadger 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 Honeybadger 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 Honeybadger 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 Honeybadger 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 Honeybadger session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["honeybadger"] ) 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 Honeybadger 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 Honeybadger assistant agent with MCP tools agent = AssistantAgent( name="honeybadger_assistant", description="An AI assistant that helps with Honeybadger 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 Honeybadger 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 Honeybadger 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 Honeybadger session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["honeybadger"] ) 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 Honeybadger assistant agent with MCP tools agent = AssistantAgent( name="honeybadger_assistant", description="An AI assistant that helps with Honeybadger 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 Honeybadger 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 Honeybadger 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 Honeybadger, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Honeybadger MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/honeybadger/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/honeybadger/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/honeybadger/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/honeybadger/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/honeybadger/framework/codex) - [OpenClaw](https://composio.dev/toolkits/honeybadger/framework/openclaw) - [Hermes](https://composio.dev/toolkits/honeybadger/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/honeybadger/framework/cli) - [Google ADK](https://composio.dev/toolkits/honeybadger/framework/google-adk) - [LangChain](https://composio.dev/toolkits/honeybadger/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/honeybadger/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/honeybadger/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/honeybadger/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/honeybadger/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. It lets developers build modern apps without managing infrastructure. - [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms. - [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place. - [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences. - [Abuselpdb](https://composio.dev/toolkits/abuselpdb) - Abuselpdb is a central database for reporting and checking IPs linked to malicious online activity. Use it to quickly identify and report suspicious or abusive IP addresses. - [Alchemy](https://composio.dev/toolkits/alchemy) - Alchemy is a blockchain development platform offering APIs and tools for Ethereum apps. It simplifies building and scaling Web3 projects with robust infrastructure. - [Algolia](https://composio.dev/toolkits/algolia) - Algolia is a hosted search API that powers lightning-fast, relevant search experiences for web and mobile apps. It helps developers deliver instant, typo-tolerant, and scalable search without complex infrastructure. - [Anchor browser](https://composio.dev/toolkits/anchor_browser) - Anchor browser is a developer platform for AI-powered web automation. It transforms complex browser actions into easy API endpoints for streamlined web interaction. - [Apiflash](https://composio.dev/toolkits/apiflash) - Apiflash is a website screenshot API for programmatically capturing web pages. It delivers high-quality screenshots on demand for automation, monitoring, or reporting. - [Apiverve](https://composio.dev/toolkits/apiverve) - Apiverve delivers a suite of powerful APIs that simplify integration for developers. It's designed for reliability and scalability so you can build faster, smarter applications without the integration headache. - [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence. - [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place. - [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines. - [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure. - [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. 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. - [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Honeybadger MCP? With a standalone Honeybadger MCP server, the agents and LLMs can only access a fixed set of Honeybadger tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Honeybadger 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 Honeybadger tools. ### Can I manage the permissions and scopes for Honeybadger while using Tool Router? Yes, absolutely. You can configure which Honeybadger 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 Honeybadger 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)