# How to integrate Honeybadger MCP with LangChain

```json
{
  "title": "How to integrate Honeybadger MCP with LangChain",
  "toolkit": "Honeybadger",
  "toolkit_slug": "honeybadger",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/honeybadger/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/honeybadger/framework/langchain.md",
  "updated_at": "2026-05-12T10:15:01.607Z"
}
```

## 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 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.

## 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)
- [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
- 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

| 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

No description provided.

### 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

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

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
```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
```

### 4. Import dependencies

No description provided.
```python
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()
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
```

### 5. Initialize Composio client

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
```python
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")
```

```typescript
const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
```

### 6. Create a Tool Router session

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

url = session.mcp.url
```

```typescript
const session = await composio.create(
    userId as string,
    {
        toolkits: ['honeybadger']
    }
);

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
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)
```

```typescript
const client = new MultiServerMCPClient({
    "honeybadger-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
```

### 8. Set up interactive chat interface

No description provided.
```python
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")
```

```typescript
let conversationHistory: any[] = [];

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

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Complete Code

```python
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())
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['honeybadger']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "honeybadger-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Honeybadger related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## 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](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)
- [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)

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- [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 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.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)