How to integrate Kibana MCP with LangChain

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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.

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.

Supported Tools & Triggers

Tools
Delete ActionTool to delete an action in kibana.
Delete Alerting RuleTool to delete an alerting rule in kibana.
Delete ConnectorTool to delete a connector in kibana.
Delete Fleet OutputTool to delete a specific output configuration in kibana fleet.
Delete Fleet ProxyTool to delete a specific fleet proxy configuration by its id.
Delete ListDeletes a list.
Delete Osquery Saved QueryTool to delete a saved osquery query by its id.
Delete Saved ObjectTool to delete a saved object in kibana.
Find Kibana AlertsTool to find and/or aggregate detection alerts in kibana.
Get Action TypesTool to fetch the list of available action types (e.
Get Alerting RulesTool to retrieve a list of alerting rules in kibana.
Get Alert TypesTool to retrieve available alert types in kibana.
Get CasesTool to retrieve a list of cases in kibana.
Get All ConnectorsTool to retrieve a list of all connectors in kibana.
Get Data ViewsTool to retrieve a list of data views available in kibana.
Find Detection Engine RulesRetrieves a list of detection engine rules based on specified criteria.
Get Endpoint List ItemsTool to retrieve all items from an endpoint exception list.
Get Entity Store EnginesRetrieves the list of engines from the entity store.
List Entity Store EntitiesTool to list entity records in the entity store with support for paging, sorting, and filtering.
Get Entity Store StatusTool to retrieve the status of the entity store in kibana.
Get Fleet Agent PoliciesFetches a list of agent policies in fleet.
Get Fleet Agents Available VersionsTool to retrieve the available versions for fleet agents.
Get Fleet Agents Setup StatusTool to check if the fleet agents are set up.
Check Fleet PermissionsTool to check the permissions for the fleet api.
Get Fleet Data StreamsRetrieves the list of data streams in fleet.
Get Fleet Enrollment API KeyTool to retrieve details of a specific enrollment api key by its id.
Get Fleet Enrollment API KeysTool to fetch a list of enrollment api keys.
Get Fleet EPM CategoriesTool to fetch the list of categories in the elastic package manager.
Get Fleet EPM Data StreamsTool to retrieve the list of data streams in the elastic package manager.
Get Fleet EPM Package DetailsTool to fetch details of a specific package and version in the elastic package manager (epm).
Get Fleet EPM Package FileTool to retrieve a specific file from a package in the elastic package manager.
Get Fleet EPM PackagesTool to fetch the list of available packages in the elastic package manager.
Get Installed EPM PackagesTool to retrieve the list of installed packages in the elastic package manager.
Get Fleet EPM Packages (Limited)Tool to fetch a limited list of packages from the elastic package manager.
Get EPM Package StatisticsTool to retrieve statistics for a specific package in the elastic package manager.
Get Fleet Package PoliciesTool to retrieve a list of all package policies (agent & epm), providing their ids and associated details.
Get Fleet Server HostTool to fetch details of a specific fleet server host by its item id.
Get Fleet Server HostsTool to retrieve the list of fleet server hosts.
Get Index Management IndicesTool to fetch information about indices managed by kibana's index management feature.
Get Node MetricsTool to retrieve statistics for nodes in an elasticsearch cluster, often visualized in kibana.
Get Reporting JobsTool to retrieve a list of reporting jobs in kibana.
Get Saved ObjectsTool to retrieve a list of saved objects in kibana based on specified criteria.
Get Kibana StatusTool to get the current status of kibana.
Create Alerting RuleTool to create a new alerting rule in kibana.
Create CaseTool to create a new case in kibana.
Create Kibana ConnectorTool to create a new connector in kibana.
Create DashboardTool to create a new dashboard in kibana.
Create Data ViewTool to create a new data view (index pattern) in kibana.
Create or Update Saved ObjectTool to create or update a saved object in kibana.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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

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

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.

Install dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-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 is the core agent framework
  • python-dotenv loads environment variables

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

Import dependencies

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

Initialize Composio client

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")
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

Create a Tool Router session

# Create Tool Router session for Kibana
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['kibana']
)

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

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "kibana-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)
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
  • get_tools() retrieves all available Kibana tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Kibana 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")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts 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 ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

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

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=['kibana']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "kibana-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 Kibana 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())

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.

How to build Kibana MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Kibana MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Kibana MCP with Claude Agent SDK

Google ADK

Google ADK

Use Kibana MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Kibana MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Kibana MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Kibana MCP with LlamaIndex

CrewAI

CrewAI

Use Kibana MCP with CrewAI

Claude Code

Claude Code

Use Kibana MCP with Claude Code

Codex

Codex

Use Kibana MCP with Codex

FAQ

What are the differences in Tool Router MCP and Kibana MCP?

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.

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 Kibana tools.

Can I manage the permissions and scopes for Kibana while using Tool Router?

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.

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 Kibana data and credentials are handled as safely as possible.

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