How to integrate Plausible Analytics MCP with LangChain

This guide walks you through connecting Plausible Analytics to LangChain using the Composio tool router. By the end, you'll have a working Plausible Analytics agent that can summarize yesterday's traffic for your site, list top referrers from last week, compare pageviews across marketing campaigns through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Plausible Analytics account through Composio's Plausible Analytics MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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13 Tools

Introduction

This guide walks you through connecting Plausible Analytics to LangChain using the Composio tool router. By the end, you'll have a working Plausible Analytics agent that can summarize yesterday's traffic for your site, list top referrers from last week, compare pageviews across marketing campaigns through natural language commands.

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

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

Also integrate Plausible Analytics 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 Plausible Analytics project to Composio
  • Create a Tool Router MCP session for Plausible Analytics
  • Initialize an MCP client and retrieve Plausible Analytics tools
  • Build a LangChain agent that can interact with Plausible Analytics
  • 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 Plausible Analytics MCP server, and what's possible with it?

The Plausible Analytics MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Plausible Analytics account. It provides structured and secure access to your website analytics, so your agent can monitor live visitors, analyze traffic trends, compare audience segments, review site settings, and record events on your behalf.

  • Live visitor monitoring: Have your agent check how many people visited your site during the last five minutes.
  • Traffic and trend analysis: Instruct your agent to query site statistics and retrieve visitor trends for a chosen time period.
  • Audience breakdowns: Let the agent break down your analytics by a selected dimension so you can compare traffic segments.
  • Site, goal, and access reviews: Direct your agent to list accessible sites, teams, goals, custom properties, and guests with dashboard access.
  • Pageview and custom event recording: Have your agent record pageviews or custom events in Plausible Analytics.

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 Plausible Analytics 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 Plausible Analytics 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: ['plausible_analytics']
    }
);

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

Configure the agent with the MCP URL

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

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

Check API Health

Tool to check the health status of the Plausible Analytics API.

Get Breakdown Stats

Tool to retrieve breakdown statistics for a specific property (dimension) from Plausible Analytics.

Get Plugin Capabilities

Tool to retrieve available capabilities for the Plausible Analytics Plugins API.

Get Realtime Visitors

Tool to retrieve the number of current visitors on your site in the last 5 minutes.

Get Site Details

Tool to retrieve details for a specific Plausible Analytics site.

Get Timeseries Stats

Tool to retrieve timeseries visitor data from Plausible Analytics over a specified time period.

List Custom Properties

Tool to list all custom properties configured for a site.

List Goals

Tool to list all goals configured for a site.

List Site Guests

Tool to list all guest users who have access to a site's dashboard in Plausible Analytics.

List Sites

Tool to list all sites the API key owner's Plausible account can access.

List Teams

Tool to list all teams available for the API key owner.

Query Analytics Stats

Tool to query analytics stats for a site using the Stats API v2.

Record Event

Tool to record a pageview or custom event via the Plausible Events API.

FRAMEWORKS

How to build Plausible Analytics MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

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Claude Agent SDK logo

Claude Agent SDK

Use Plausible Analytics MCP with Claude Agent SDK

Claude Code logo

Claude Code

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Claude Cowork logo

Claude Cowork

Use Plausible Analytics MCP with Claude Cowork

Codex logo

Codex

Use Plausible Analytics MCP with Codex

OpenClaw logo

OpenClaw

Use Plausible Analytics MCP with OpenClaw

Hermes logo

Hermes

Use Plausible Analytics MCP with Hermes

CLI logo

CLI

Use Plausible Analytics MCP with CLI

Google ADK logo

Google ADK

Use Plausible Analytics MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Plausible Analytics MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Plausible Analytics MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Plausible Analytics MCP with LlamaIndex

CrewAI logo

CrewAI

Use Plausible Analytics MCP with CrewAI

FAQ

Frequently asked questions

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

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

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