# How to integrate Honeybadger MCP with CrewAI ```json { "title": "How to integrate Honeybadger MCP with CrewAI", "toolkit": "Honeybadger", "toolkit_slug": "honeybadger", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/honeybadger/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/honeybadger/framework/crew-ai.md", "updated_at": "2026-05-12T10:15:01.607Z" } ``` ## Introduction This guide walks you through connecting Honeybadger to CrewAI 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 CrewAI 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) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Honeybadger connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Honeybadger - Build a conversational loop where your agent can execute Honeybadger operations ## What is CrewAI? CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews. Key features include: - Agent Roles: Define specialized agents with specific goals and backstories - Task Management: Create tasks with clear descriptions and expected outputs - Crew Orchestration: Combine agents and tasks into collaborative workflows - MCP Integration: Connect to external tools through Model Context Protocol ## 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 agent to Honeybadger. It provides structured and secure access so your agent can perform Honeybadger operations on your behalf through a secure, permission-based interface. 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 Before starting, make sure you have: - Python 3.9 or higher - A Composio account and API key - A Honeybadger connection authorized in Composio - An OpenAI API key for the CrewAI LLM - Basic familiarity with Python ### 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 **What's happening:** - composio connects your agent to Honeybadger via MCP - crewai provides Agent, Task, Crew, and LLM primitives - crewai-tools[mcp] includes MCP helpers - python-dotenv loads environment variables from .env ```bash pip install composio crewai crewai-tools[mcp] python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates with Composio - USER_ID scopes the session to your account - OPENAI_API_KEY lets CrewAI use your chosen OpenAI model ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key_here ``` ### 4. Import dependencies **What's happening:** - CrewAI classes define agents and tasks, and run the workflow - MCPServerHTTP connects the agent to an MCP endpoint - Composio will give you a short lived Honeybadger MCP URL ```python import os from composio import Composio from crewai import Agent, Task, Crew from crewai_tools import MCPServerAdapter import dotenv dotenv.load_dotenv() COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set") ``` ### 5. Create a Composio Tool Router session for Honeybadger **What's happening:** - You create a Honeybadger only session through Composio - Composio returns an MCP HTTP URL that exposes Honeybadger tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["honeybadger"]) url = session.mcp.url ``` ### 6. Initialize the MCP Server **What's Happening:** - Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication. - MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format. - Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools. - Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops. - Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries. ```python server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users search the internet effectively", backstory="You are a helpful assistant with access to search tools.", tools=tools, verbose=False, max_iter=10, ) ``` ### 7. Create a CLI Chatloop and define the Crew **What's Happening:** - Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable. - Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean. - Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses. - Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context. - Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response. - Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity. ```python print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Complete Code ```python from crewai import Agent, Task, Crew, LLM from crewai_tools import MCPServerAdapter from composio import Composio from dotenv import load_dotenv import os load_dotenv() GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not GOOGLE_API_KEY: raise ValueError("GOOGLE_API_KEY is not set in the environment.") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set in the environment.") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set in the environment.") # Initialize Composio and create a session composio = Composio(api_key=COMPOSIO_API_KEY) session = composio.create( user_id=COMPOSIO_USER_ID, toolkits=["honeybadger"], ) url = session.mcp.url # Configure LLM llm = LLM( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY"), ) server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users with internet searches", backstory="You are an expert assistant with access to Composio Search tools.", tools=tools, llm=llm, verbose=False, max_iter=10, ) print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Conclusion You now have a CrewAI agent connected to Honeybadger through Composio's Tool Router. The agent can perform Honeybadger operations through natural language commands. Next steps: - Add role-specific instructions to customize agent behavior - Plug in more toolkits for multi-app workflows - Chain tasks for complex multi-step operations ## 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) ## 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 CrewAI? Yes, you can. CrewAI 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)