# How to integrate Honeybadger MCP with OpenAI Agents SDK

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

## Introduction

This guide walks you through connecting Honeybadger to the OpenAI Agents SDK 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 OpenAI Agents SDK 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

- [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 necessary dependencies
- Initialize Composio and create a Tool Router session for Honeybadger
- Configure an AI agent that can use Honeybadger as a tool
- Run a live chat session where you can ask the agent to perform Honeybadger operations

## What is OpenAI Agents SDK?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.
Key features include:
- Hosted MCP Tools: Connect to external services through hosted MCP endpoints
- SQLite Sessions: Persist conversation history across interactions
- Simple API: Clean interface with Agent, Runner, and tool configuration
- Streaming Support: Real-time response streaming for interactive applications

## 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:
- Composio API Key and OpenAI API Key
- Primary know-how of OpenAI Agents SDK
- A live Honeybadger project
- Some knowledge of Python or Typescript

### 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).
- Go to Settings and copy your API key.

### 2. Install dependencies

Install the Composio SDK and the OpenAI Agents SDK.
```python
pip install composio_openai_agents openai-agents python-dotenv
```

```typescript
npm install @composio/openai-agents @openai/agents dotenv
```

### 3. Set up environment variables

Create a .env file and add your OpenAI and Composio API keys.
```bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com
```

### 4. Import dependencies

What's happening:
- You're importing all necessary libraries.
- The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Honeybadger.
```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';
```

### 5. Set up the Composio instance

No description provided.
```python
load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
```

```typescript
dotenv.config();

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

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});
```

### 6. Create a Tool Router session

What is happening:
- You give the Tool Router the user id and the toolkits you want available. Here, it is only honeybadger.
- The router checks the user's Honeybadger connection and prepares the MCP endpoint.
- The returned session.mcp.url is the MCP URL that your agent will use to access Honeybadger.
- This approach keeps things lightweight and lets the agent request Honeybadger tools only when needed during the conversation.
```python
# Create a Honeybadger Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["honeybadger"]
)

mcp_url = session.mcp.url
```

```typescript
// Create Tool Router session for Honeybadger
const session = await composio.create(userId as string, {
  toolkits: ['honeybadger'],
});
const mcpUrl = session.mcp.url;
```

### 7. Configure the agent

No description provided.
```python
# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Honeybadger. "
        "Help users perform Honeybadger operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
```

```typescript
// Configure agent with MCP tool
const agent = new Agent({
  name: 'Assistant',
  model: 'gpt-5',
  instructions:
    'You are a helpful assistant that can access Honeybadger. Help users perform Honeybadger operations through natural language.',
  tools: [
    hostedMcpTool({
      serverLabel: 'tool_router',
      serverUrl: mcpUrl,
      headers: { 'x-api-key': composioApiKey },
      requireApproval: 'never',
    }),
  ],
});
```

### 8. Start chat loop and handle conversation

No description provided.
```python
print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
// Keep conversation state across turns
const conversationSession = new OpenAIConversationsSession();

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

console.log('\nComposio Tool Router session created.');
console.log('\nChat started. Type your requests below.');
console.log("Commands: 'exit', 'quit', or 'q' to end\n");

try {
  const first = await run(agent, 'What can you help me with?', { session: conversationSession });
  console.log(`Assistant: ${first.finalOutput}\n`);
} catch (e) {
  console.error('Error:', e instanceof Error ? e.message : e, '\n');
}

rl.prompt();

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

  if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
    console.log('Goodbye!');
    rl.close();
    process.exit(0);
  }

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

  try {
    const result = await run(agent, text, { session: conversationSession });
    console.log(`\nAssistant: ${result.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();
});

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

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["honeybadger"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Honeybadger. "
        "Help users perform Honeybadger operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';

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

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});

async function main() {
  // Create Tool Router session
  const session = await composio.create(userId as string, {
    toolkits: ['honeybadger'],
  });
  const mcpUrl = session.mcp.url;

  // Configure agent with MCP tool
  const agent = new Agent({
    name: 'Assistant',
    model: 'gpt-5',
    instructions:
      'You are a helpful assistant that can access Honeybadger. Help users perform Honeybadger operations through natural language.',
    tools: [
      hostedMcpTool({
        serverLabel: 'tool_router',
        serverUrl: mcpUrl,
        headers: { 'x-api-key': composioApiKey },
        requireApproval: 'never',
      }),
    ],
  });

  // Keep conversation state across turns
  const conversationSession = new OpenAIConversationsSession();

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

  console.log('\nComposio Tool Router session created.');
  console.log('\nChat started. Type your requests below.');
  console.log("Commands: 'exit', 'quit', or 'q' to end\n");

  try {
    const first = await run(agent, 'What can you help me with?', { session: conversationSession });
    console.log(`Assistant: ${first.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();

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

    if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
      console.log('Goodbye!');
      rl.close();
      process.exit(0);
    }

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

    try {
      const result = await run(agent, text, { session: conversationSession });
      console.log(`\nAssistant: ${result.finalOutput}\n`);
    } catch (e) {
      console.error('Error:', e instanceof Error ? e.message : e, '\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

This was a starter code for integrating Honeybadger MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Honeybadger.
Key features:
- Hosted MCP tool integration through Composio's Tool Router
- SQLite session persistence for conversation history
- Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

## How to build Honeybadger MCP Agent with another framework

- [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)

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- [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.
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- [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 OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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)