How to integrate Honeyhive MCP with OpenAI Agents SDK

This guide walks you through connecting Honeyhive to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Honeyhive agent that can add new datapoints to your evaluation dataset, list all datasets in your honeyhive project, log a batch of model events for analysis through natural language commands. This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Honeyhive account through Composio's Honeyhive MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Honeyhive is an AI observability and evaluation platform for analyzing LLM apps. It helps teams monitor, debug, and improve AI system reliability faster.

42 Tools

Introduction

This guide walks you through connecting Honeyhive to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Honeyhive agent that can add new datapoints to your evaluation dataset, list all datasets in your honeyhive project, log a batch of model events for analysis through natural language commands.

This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Honeyhive account through Composio's Honeyhive MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

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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 Honeyhive
  • Configure an AI agent that can use Honeyhive as a tool
  • Run a live chat session where you can ask the agent to perform Honeyhive 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 Honeyhive MCP server, and what's possible with it?

The Honeyhive MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Honeyhive account. It provides structured and secure access to your AI observability platform, so your agent can perform actions like managing datasets, logging model and tool events, evaluating runs, and configuring project settings on your behalf.

  • Dataset management and organization: Create, retrieve, and delete datasets for your AI projects, helping you maintain organized and up-to-date evaluation data.
  • Efficient event logging: Log batches of model or external tool events, enabling comprehensive tracking and analysis of AI system interactions in real-time.
  • Data curation and cleanup: Add new datapoints to datasets or remove specific datapoints, ensuring your evaluation data remains accurate and relevant.
  • Streamlined evaluation workflows: Mark evaluation runs as completed and fetch project configuration details, making it easy to track progress and update run statuses automatically.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK 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

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Composio API Key and OpenAI API Key
  • Primary know-how of OpenAI Agents SDK
  • A live Honeyhive project
  • Some knowledge of Python or Typescript
2

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
3

Install dependencies

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

Install the Composio SDK and the OpenAI Agents SDK.

4

Set up environment variables

bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com

Create a .env file and add your OpenAI and Composio API keys.

5

Import dependencies

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';
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 Honeyhive.
6

Set up the Composio instance

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(),
});
What's happening:
  • dotenv.config() loads your .env file so COMPOSIO_API_KEY and USER_ID are available as environment variables.
  • Creating a Composio instance using the API Key and OpenAIAgentsProvider class.
7

Create a Tool Router session

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

What is happening:

  • You give the Tool Router the user id and the toolkits you want available. Here, it is only honeyhive.
  • The router checks the user's Honeyhive connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Honeyhive.
  • This approach keeps things lightweight and lets the agent request Honeyhive tools only when needed during the conversation.
8

Configure the agent

// Configure agent with MCP tool
const agent = new Agent({
  name: 'Assistant',
  model: 'gpt-5',
  instructions:
    'You are a helpful assistant that can access Honeyhive. Help users perform Honeyhive operations through natural language.',
  tools: [
    hostedMcpTool({
      serverLabel: 'tool_router',
      serverUrl: mcpUrl,
      headers: { 'x-api-key': composioApiKey },
      requireApproval: 'never',
    }),
  ],
});
What's happening:
  • We're creating an Agent instance with a name, model (gpt-5), and clear instructions about its purpose.
  • The agent's instructions tell it that it can access Honeyhive and help with queries, inserts, updates, authentication, and fetching database information.
  • The tools array includes a hostedMcpTool that connects to the MCP server URL we created earlier.
  • The headers object includes the Composio API key for secure authentication with the MCP server.
  • requireApproval: 'never' means the agent can execute Honeyhive operations without asking for permission each time, making interactions smoother.
9

Start chat loop and handle conversation

// 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);
});
What's happening:
  • The program prints a session URL that you visit to authorize Honeyhive.
  • After authorization, the chat begins.
  • Each message you type is processed by the agent using run().
  • The responses are printed to the console.
  • Typing exit, quit, or q cleanly ends the chat.

Complete Code

Here's the complete code to get you started with Honeyhive and OpenAI Agents SDK:

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: ['honeyhive'],
  });
  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 Honeyhive. Help users perform Honeyhive 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 Honeyhive MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Honeyhive.

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.
TOOLS

Supported Tools

Every Honeyhive action and event your agent gets out of the box.

Add datapoints to dataset

Tool to add datapoints to a dataset.

Compare Experiment Runs

Tool to retrieve experiment comparison between two evaluation runs.

Compare Runs Events

Tool to compare events between two experiment runs side-by-side.

Batch Create Datapoints

Tool to create multiple datapoints in a single batch operation.

Create Batch Model Events

Tool to create multiple model events in a single request.

Create Batch Tool Events

Tool to log a batch of external API calls as tool events.

Create Configuration

Creates a new configuration in HoneyHive for managing LLM or pipeline settings.

Create Datapoint

Tool to create a new datapoint with input-output pairs.

Create Dataset

Tool to create a dataset.

Create Event

Tool to create a new event in HoneyHive to track execution of different parts of your application.

Create Metric

Tool to create a new metric in HoneyHive.

Create Model Event

Tool to create a new model event to log LLM call data.

Create Tool

Creates a new tool definition in a HoneyHive project.

Delete Datapoint

Tool to delete a specific datapoint by its ID.

Delete Dataset

Tool to delete a dataset by ID.

End Evaluation Run

Tool to update an evaluation run's status and metadata.

Get Configurations

Tool to retrieve a list of configurations.

Get Datasets

Retrieve datasets from HoneyHive for a specified project.

Get Events

Tool to query events with filters and projections from HoneyHive.

Get Events By Session ID

Tool to retrieve the complete tree of nested events for a specific session.

Get Events Chart

Tool to retrieve charting and analytics data for events over time.

Get Metrics

Retrieves all metrics associated with a HoneyHive project.

Get Projects

Tool to retrieve all projects in the HoneyHive account.

Get Evaluation Run Details

Tool to get details of an evaluation run by its UUID.

Get Run Metrics

Tool to get event metrics for an experiment run.

Get Evaluation Runs

Tool to retrieve a list of evaluation runs from HoneyHive.

Get Runs Schema

Tool to retrieve the schema for experiment runs in HoneyHive.

Get Session

Retrieve a complete session tree by session ID from HoneyHive.

List Tools

Tool to list all available Honeyhive tools.

Retrieve Datapoint

Retrieve a specific datapoint by its ID from HoneyHive.

Retrieve Datapoints

Retrieve datapoints from a HoneyHive project.

Retrieve Events

Retrieve and export events from a HoneyHive project.

Retrieve Experiment Result

Tool to retrieve the result of a specific experiment run.

Start Evaluation Run

Creates a new evaluation run to group and track multiple session events for analysis.

Start Session

Start a new HoneyHive session for tracing and observability.

Update Configuration

Tool to update an existing HoneyHive configuration.

Update Datapoint

Update an existing datapoint by ID.

Update Dataset

Tool to update an existing dataset.

Update Event

Update an existing HoneyHive event by ID.

Update Metric

Tool to update an existing metric.

Update Project

Updates an existing HoneyHive project's name or description.

Update Tool

Tool to update an existing tool in HoneyHive.

FAQ

Frequently asked questions

With a standalone Honeyhive MCP server, the agents and LLMs can only access a fixed set of Honeyhive tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Honeyhive and many other apps based on the task at hand, all through a single MCP endpoint.

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 Honeyhive tools.

Yes, absolutely. You can configure which Honeyhive 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.

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 Honeyhive data and credentials are handled as safely as possible.

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