How to integrate Artificial Analysis MCP with LangChain

Connect LangChain to Artificial Analysis MCP. Compare model pricing and latency, rank providers by coding benchmark scores, and more using natural language, with authentication handled for you.

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Artificial Analysis is an independent benchmarking platform for AI models and API providers. Use it to compare model intelligence, coding, math, pricing, latency, throughput, and arena rankings.

12 Tools

Introduction

This guide walks you through connecting Artificial Analysis to LangChain using the Composio tool router. By the end, you'll have a working Artificial Analysis agent that can compare model pricing and latency, rank providers by coding benchmark scores, find cheapest high-intelligence language models through natural language commands.

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

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

Also integrate Artificial Analysis with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAIPydantic AIAutoGen

TL;DR

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

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

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

Configure the agent with the MCP URL

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

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

List Image-Editing Models (Arena)

List image-editing models ranked by the Artificial Analysis arena, with Elo score and 95% confidence interval.

List Image-to-Video-with-Audio Models (Arena)

List image-to-video-with-audio models ranked by the Artificial Analysis arena, with Elo score and 95% confidence interval.

List Image-to-Video Models (Arena)

List image-to-video models ranked by the Artificial Analysis image-to-video arena, with Elo score and 95% confidence interval.

List Language Models (Intelligence & Pricing)

List LLMs from the Artificial Analysis leaderboard with intelligence, coding and agentic indices, pricing, and performance (speed and latency).

List Instrumental Music Models (Arena)

List instrumental music-generation models ranked by the Artificial Analysis arena, with Elo score and 95% confidence interval.

List Music-with-Vocals Models (Arena)

List music-with-vocals generation models ranked by the Artificial Analysis arena, with Elo score and 95% confidence interval.

List Speech-to-Speech Models

List speech-to-speech models with Big Bench Audio (bba_score), Full Duplex Bench (fdb_score), and Tau-Voice (tau_voice_score) quality scores.

List Speech-to-Text Models (WER)

List speech-to-text (transcription) models with the Artificial Analysis overall word-error-rate index (aa_wer_index).

List Text-to-Image Models (Arena)

List text-to-image models ranked by the Artificial Analysis image arena, with Elo score and 95% confidence interval.

List Text-to-Speech Models (Arena)

List text-to-speech (TTS) models ranked by the Artificial Analysis arena, with Elo score and 95% confidence interval.

List Text-to-Video-with-Audio Models (Arena)

List text-to-video-with-audio models ranked by the Artificial Analysis arena, with Elo score and 95% confidence interval.

List Text-to-Video Models (Arena)

List text-to-video models ranked by the Artificial Analysis arena, with Elo score and 95% confidence interval.

FRAMEWORKS

How to build Artificial Analysis MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Artificial Analysis MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Artificial Analysis MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Artificial Analysis MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Artificial Analysis MCP with Claude Cowork

Codex logo

Codex

Use Artificial Analysis MCP with Codex

OpenClaw logo

OpenClaw

Use Artificial Analysis MCP with OpenClaw

Hermes logo

Hermes

Use Artificial Analysis MCP with Hermes

Google ADK logo

Google ADK

Use Artificial Analysis MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Artificial Analysis MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Artificial Analysis MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Artificial Analysis MCP with LlamaIndex

CrewAI logo

CrewAI

Use Artificial Analysis MCP with CrewAI

Pydantic AI logo

Pydantic AI

Use Artificial Analysis MCP with Pydantic AI

AutoGen logo

AutoGen

Use Artificial Analysis MCP with AutoGen

FAQ

Frequently asked questions

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

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

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