How to integrate Kibana MCP with LangChain

This guide walks you through connecting Kibana to LangChain using the Composio tool router. By the end, you'll have a working Kibana agent that can visualize weekly sales data as a chart, list top error logs from last 24 hours, generate dashboard of user activity trends through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Kibana account through Composio's Kibana MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Kibana is a visualization and analytics platform for Elasticsearch data. It helps you explore, visualize, and monitor your data using intuitive dashboards and interactive tools.

47 Tools

Introduction

This guide walks you through connecting Kibana to LangChain using the Composio tool router. By the end, you'll have a working Kibana agent that can visualize weekly sales data as a chart, list top error logs from last 24 hours, generate dashboard of user activity trends through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Kibana account through Composio's Kibana MCP server.

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

Also integrate Kibana with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Kibana project to Composio
  • Create a Tool Router MCP session for Kibana
  • Initialize an MCP client and retrieve Kibana tools
  • Build a LangChain agent that can interact with Kibana
  • 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 Kibana MCP server, and what's possible with it?

The Kibana MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Kibana account. It provides structured and secure access so your agent can perform Kibana 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 Kibana 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 Kibana 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: ['kibana']
    }
);

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

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "kibana-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 Kibana MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Kibana 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 Kibana 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 Kibana 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: ['kibana']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "kibana-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 Kibana 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 Kibana 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 Kibana action and event your agent gets out of the box.

Delete Alerting Rule

Tool to delete an alerting rule in Kibana.

Delete Connector

Tool to delete a connector in Kibana.

Delete Fleet Output

Tool to delete a specific output configuration in Kibana Fleet.

Delete Fleet Proxy

Deletes a Fleet proxy configuration by its unique identifier.

Delete List

Deletes a list.

Delete Osquery Saved Query

Delete a saved Osquery query by its saved object ID.

Delete Saved Object

Tool to delete a saved object in Kibana.

Find Kibana Alerts

Tool to find and/or aggregate detection alerts in Kibana.

Get Action Types

Retrieves all available connector types (actions) in Kibana.

Get Alerting Rules

Tool to retrieve a list of alerting rules in Kibana.

Get Rule Types

Retrieves available rule types (alert types) in Kibana.

Get Cases

Tool to retrieve a list of cases in Kibana.

Get All Connectors

Tool to retrieve a list of all connectors in Kibana.

Get Data Views

Retrieves all data views (formerly known as index patterns) available in Kibana.

Find Detection Engine Rules

Retrieves a paginated list of Kibana detection engine rules with flexible filtering and sorting options.

Get Endpoint List Items

Retrieves Elastic Endpoint exception list items with filtering, pagination, and sorting capabilities.

Get Entity Store Engines

Retrieves all entity store engines configured in Kibana.

List Entity Store Entities

Tool to list entity records in the entity store with support for paging, sorting, and filtering.

Get Entity Store Status

Retrieves the current status of the Kibana Entity Store and its configured engines.

Get Fleet Agent Policies

Retrieves a paginated list of Fleet agent policies with filtering, sorting, and optional detailed information.

Get Fleet Agents Available Versions

Tool to retrieve the available versions for Fleet agents.

Get Fleet Agents Setup Status

Check Fleet setup readiness and identify missing requirements.

Check Fleet Permissions

Tool to check the permissions for the Fleet API.

Get Fleet Enrollment API Key

Tool to retrieve details of a specific enrollment API key by its ID.

Get Fleet Enrollment API Keys

Tool to fetch a list of enrollment API keys.

Get Fleet EPM Categories

Get all available package categories in the Elastic Package Manager (EPM) with package counts.

Get Fleet EPM Data Streams

Tool to retrieve the list of data streams in the Elastic Package Manager.

Get Fleet EPM Package Details

Retrieves comprehensive details for a specific Fleet integration package version from the Elastic Package Manager (EPM).

Get Fleet EPM Package File

Retrieves a specific file from an Elastic Package Manager (EPM) package.

Get Fleet EPM Packages

Tool to fetch the list of available packages in the Elastic Package Manager.

Get Installed EPM Packages

Tool to retrieve the list of installed packages in the Elastic Package Manager.

Get Fleet EPM Packages (Limited)

Retrieves a limited list of package names from the Elastic Package Manager (EPM) registry.

Get EPM Package Statistics

Retrieves usage statistics for a specific Fleet package in Kibana, including the number of package policies and agent policies using the package.

Get Fleet Package Policies

Retrieves a list of Fleet package policies (integration policies) in Kibana.

Get Fleet Server Host

Tool to fetch details of a specific Fleet server host by its item ID.

Get Fleet Server Hosts

Tool to retrieve the list of Fleet Server hosts.

Get Index Management Indices

Tool to fetch information about indices managed by Kibana's Index Management feature.

Get Node Metrics

Tool to retrieve statistics for nodes in an Elasticsearch cluster, often visualized in Kibana.

Get Reporting Jobs

Tool to retrieve a list of reporting jobs in Kibana.

Get Saved Objects

Tool to retrieve a list of saved objects in Kibana based on specified criteria.

Get Kibana Status

Tool to get the current status of Kibana.

Create Alerting Rule

Tool to create a new alerting rule in Kibana.

Create Case

Tool to create a new case in Kibana.

Create Kibana Connector

Tool to create a new connector in Kibana.

Create Dashboard

Tool to create a new dashboard in Kibana.

Create Data View

Tool to create a new data view (index pattern) in Kibana.

Create or Update Saved Object

Tool to create or update a saved object in Kibana.

FRAMEWORKS

How to build Kibana MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Kibana MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Kibana MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Kibana MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Kibana MCP with Claude Cowork

Codex logo

Codex

Use Kibana MCP with Codex

OpenClaw logo

OpenClaw

Use Kibana MCP with OpenClaw

Hermes logo

Hermes

Use Kibana MCP with Hermes

CLI logo

CLI

Use Kibana MCP with CLI

Google ADK logo

Google ADK

Use Kibana MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Kibana MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Kibana MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Kibana MCP with LlamaIndex

CrewAI logo

CrewAI

Use Kibana MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Kibana MCP server, the agents and LLMs can only access a fixed set of Kibana tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Kibana 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 Kibana tools.

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

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A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

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As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

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Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

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With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

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