How to integrate Honeybadger MCP with LangChain

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Introduction

This guide walks you through connecting Honeybadger to LangChain 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, report scheduled task check-in for uptime through natural language commands.

This guide will help you understand how to give your LangChain 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.

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Honeybadger project to Composio
  • Create a Tool Router MCP session for Honeybadger
  • Initialize an MCP client and retrieve Honeybadger tools
  • Build a LangChain agent that can interact with Honeybadger
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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 & Triggers

Tools
Report Check-InTool to report a check-in (ping) to Honeybadger.
Report DeploymentTool to report a new deployment to Honeybadger.
Report EventTool to send custom events to Honeybadger Insights.
Report ExceptionTool to report an exception notice to Honeybadger.
Upload File to S3Tool to upload a local file to a managed S3 bucket.
Upload Source MapTool to upload JavaScript source maps for error de-minification.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Honeybadger functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Honeybadger tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Honeybadger
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['honeybadger']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Honeybadger tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Honeybadger tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "honeybadger-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Honeybadger MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Honeybadger tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Honeybadger related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Honeybadger and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['honeybadger']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "honeybadger-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Honeybadger related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've successfully built a LangChain agent that can interact with Honeybadger through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

How to build Honeybadger MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Honeybadger MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Honeybadger MCP with Claude Agent SDK

Google ADK

Google ADK

Use Honeybadger MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Honeybadger MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Honeybadger MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Honeybadger MCP with LlamaIndex

CrewAI

CrewAI

Use Honeybadger MCP with CrewAI

FAQ

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 LangChain?

Yes, you can. LangChain 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.

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