How to integrate Honeycomb MCP with LangChain

Connect LangChain to Honeycomb MCP. Investigate latency spikes in production dataset, compare error rates by endpoint today, and more using natural language, with authentication handled for you.

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Honeycomb is an observability platform for high-cardinality event data. It helps teams debug production systems fast with rich telemetry analysis.

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Introduction

This guide walks you through connecting Honeycomb to LangChain using the Composio tool router. By the end, you'll have a working Honeycomb agent that can investigate latency spikes in production dataset, compare error rates by endpoint today, find anomalous traces in checkout service through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Honeycomb account through Composio's Honeycomb 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
  • Connect your Honeycomb project to Composio
  • Create a Tool Router MCP session for Honeycomb
  • Initialize an MCP client and retrieve Honeycomb tools
  • Build a LangChain agent that can interact with Honeycomb
  • 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 Honeycomb MCP server, and what's possible with it?

The Honeycomb MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Honeycomb account. It provides structured and secure access so your agent can perform Honeycomb operations on your behalf.

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 step10 STEPS
1

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
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
  • 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.
3

Install dependencies

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters 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/core is the core agent framework
  • dotenv/config loads environment variables
4

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
5

Import dependencies

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();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Honeycomb functionality through MCP
6

Initialize Composio client

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()
    });
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 Honeycomb tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

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

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Honeycomb 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 Honeycomb tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "honeycomb_mcp-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 });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Honeycomb MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Honeycomb tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Honeycomb 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);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept 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 invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

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

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: ['honeycomb_mcp']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "honeycomb_mcp-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 Honeycomb 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 Honeycomb 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.
TOOLS

Supported Tools

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

Canvas agent invoke

Kick off a single-turn run of the Honeycomb Canvas agent.

Canvas agent poll response

Poll for the result of a previously-issued canvas_agent_invoke call.

Create board

Create a board (dashboard) with query, SLO, and text panels.

Create marker

Creates a marker (a vertical annotation on charts) to mark a point or window in time, such as a deploy, incident, or config change.

Create recipient

Create a notification recipient (email, Slack, PagerDuty, or webhook) so it can be attached to triggers and SLO burn alerts.

Create slo

Create an SLO (Service Level Objective) backed by an auto-created SLI derived column.

Create trigger

Create a trigger that fires alerts when a query result crosses a threshold.

Feedback

Submit feedback about Honeycomb's MCP server to the agentic-intelligence team.

Find columns

Search for columns and calculated fields by intent across one or all datasets in an environment.

Find queries

Search query history and saved queries by intent; returns matching queries with their run PKs.

Get aiconversation

Fetch the full event timeline for a single AI conversation, identified by its OpenTelemetry gen_ai.

Get dataset

Get dataset metadata and its full column schema (columns + calculated fields), sorted by most recent write activity.

Get dataset columns

Get the full column schema for one dataset, with optional sample values for specific columns.

Get environment

Get details for a specific environment, including its dataset list sorted by most recent activity.

Get query results

Retrieve results and metadata from an existing query execution.

Get semconv attribute

Get the full definitions of one or more semantic convention attributes by their exact names.

Get span details

Summarize attributes and their common values observed on spans with a specific name.

Get trace

Retrieve all spans for a specific trace ID and render them as a waterfall.

Get triggers

List triggers (alert rules) for the team, or fetch full configuration for a single trigger.

Get workspace context

Call this tool first to orient yourself in a Honeycomb workspace.

List aiconversations

Discover recent AI agent conversations (gen_ai.

List boards

List boards (saved dashboards) in an environment, or fetch one board's full contents by ID.

List recipients

List all pre-registered notification recipients (email, Slack, PagerDuty, webhook) for the team.

List semconv namespaces

List the top-level semantic convention namespaces available in this team's registry.

List spans

List span names in trace data, ranked by count, with how often each is a trace root and which dataset the count came from.

Refinery docs

Read Honeycomb Refinery documentation.

Run bubbleup

Run BubbleUp analysis to find what makes a selected data subset different from the baseline.

Run query

Run a time-series aggregation query against a Honeycomb dataset and return computed results.

Search semconv

Search the semantic convention registry for attributes matching a query.

Update board

Edit an existing board (dashboard) in place.

Update slo

Update an existing SLO.

Update trigger

Update an existing trigger.

FRAMEWORKS

How to build Honeycomb MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

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Claude Agent SDK logo

Claude Agent SDK

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Claude Code logo

Claude Code

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Claude Cowork logo

Claude Cowork

Use Honeycomb MCP with Claude Cowork

Codex logo

Codex

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OpenClaw logo

OpenClaw

Use Honeycomb MCP with OpenClaw

Hermes logo

Hermes

Use Honeycomb MCP with Hermes

Google ADK logo

Google ADK

Use Honeycomb MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Honeycomb MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Honeycomb MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Honeycomb MCP with LlamaIndex

CrewAI logo

CrewAI

Use Honeycomb MCP with CrewAI

Pydantic AI logo

Pydantic AI

Use Honeycomb MCP with Pydantic AI

AutoGen logo

AutoGen

Use Honeycomb MCP with AutoGen

FAQ

Frequently asked questions

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

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 Honeycomb tools.

Yes, absolutely. You can configure which Honeycomb 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 Honeycomb data and credentials are handled as safely as possible.

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