How to integrate PromptLayer MCP with LangChain

This guide walks you through connecting PromptLayer to LangChain using the Composio tool router. By the end, you'll have a working PromptLayer agent that can browse your prompt templates, inspect request logs, list registered tools, and review saved workflows through natural language commands. This guide will help you understand how to give your LangChain agent real control over a PromptLayer account through Composio's PromptLayer MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

Get started for free Get a demo

PromptLayer

Api Key

PromptLayer is a prompt management and observability platform for LLM apps. Use it to version prompts, run evaluations, trace requests, and analyze model behavior.

16 Tools

Managed auth

Connect PromptLayer without auth hassles

We manage OAuth, API keys, token refresh, and scopes — you just build.

Try for free

Introduction

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

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

Also integrate PromptLayer with

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK Vercel AI SDK Mastra AI LlamaIndex CrewAI

TL;DR

Here's what you'll learn:

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

The PromptLayer MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your PromptLayer account. It provides structured and secure access to your prompts, request logs, workflows, tools, skill collections, and tables, so your agent can find prompt templates, inspect LLM activity, browse workflows, review registered resources, and analyze table data on your behalf.

Prompt discovery and inspection: Have your agent search prompt templates and retrieve specific versions, messages, model settings, snippets, or release details for review.
Request monitoring and analysis: Instruct your agent to search LLM request logs and inspect recorded inputs, outputs, timing, usage, metadata, tags, and scores.
Workflow and tool exploration: Let the agent find workflows and registered tools, then retrieve their definitions without running them.
Skill and workspace browsing: Direct your agent to browse folders, registry items, and skill collections, including the files stored in a selected collection.
Table data review: Have your agent find tables, inspect their sheets and columns, and read rows with optional schema and execution summaries.

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:

Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step10 STEPS

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

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

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

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 PromptLayer functionality through MCP

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 PromptLayer tools
Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

const url = session.mcp.url;

What's happening:

We're creating a Tool Router session that gives your agent access to PromptLayer 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 PromptLayer tools as needed

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "promptlayer-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 PromptLayer MCP server via HTTP
The client is configured with a name and the URL from our Tool Router session
getTools() retrieves all available PromptLayer tools that the agent can use
We're creating a LangChain agent using the GPT-5 model

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 PromptLayer 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

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 PromptLayer 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: ['promptlayer']
        }
    );

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

Get Prompt Template

Retrieve a prompt template version, release label, messages, model metadata, snippets, and optionally provider-ready kwargs without executing it.

Get Registered Tool

Retrieve a PromptLayer registered tool definition by ID or name at a selected version or release label without test execution.

Get Request Log

Retrieve one PromptLayer request log with its recorded input, output, timing, usage, metadata, tags, and scores.

Get Skill Collection

Retrieve a PromptLayer skill collection and its files as JSON at a selected version or release label.

Get Table

Retrieve one PromptLayer Table's metadata and per-sheet row counts by UUID.

Get Workflow

Retrieve a PromptLayer workflow definition by ID or name at a specific version or release label without running it.

List Folder Entities

Browse or search PromptLayer folders and registry entities in the API-key workspace, optionally recursively and with metadata.

List Prompt Templates

Search and page through PromptLayer prompt templates and snippets without executing a model.

List Registered Tools

List and filter tools defined in PromptLayer's tool registry without executing them.

List Skill Collections

List and filter PromptLayer skill collections available in the API-key workspace.

List Table Columns

List one page of user and optionally system columns for a PromptLayer Table sheet.

List Table Rows

Read one page of rows and cells from a PromptLayer Table sheet, with optional schema and execution aggregates.

List Tables

Search and page through PromptLayer Tables by folder, name, or referenced prompt.

List Table Sheets

List one page of sheets in a PromptLayer Table, optionally filtering by referenced prompt.

List Workflows

Search and page through PromptLayer workflows without running them.

Search Request Logs

Search PromptLayer request logs by text or structured filters and return one rate-limited page sorted by operational fields.

FRAMEWORKS

How to build PromptLayer MCP Agent with another framework

OpenAI Agents SDK

Use PromptLayer MCP with OpenAI Agents SDK

Claude Agent SDK

Use PromptLayer MCP with Claude Agent SDK

Claude Code

Use PromptLayer MCP with Claude Code

Claude Cowork

Use PromptLayer MCP with Claude Cowork

Codex

Use PromptLayer MCP with Codex

OpenClaw

Use PromptLayer MCP with OpenClaw

Hermes

Use PromptLayer MCP with Hermes

CLI

Use PromptLayer MCP with CLI

Google ADK

Use PromptLayer MCP with Google ADK

Vercel AI SDK

Use PromptLayer MCP with Vercel AI SDK

Mastra AI

Use PromptLayer MCP with Mastra AI

LlamaIndex

Use PromptLayer MCP with LlamaIndex

CrewAI

Use PromptLayer MCP with CrewAI

MORE TOOLKITS

Explore Other Toolkits

Toolkit marketplace

AltTextLab

Api Key

AltTextLab is an AI service that generates accessible, SEO-aware alt text for images. It helps teams make image content easier to understand, index, and publish.

Apiframe

Api Key

Apiframe is an API platform for generating and managing AI images, videos, music, uploads, assets, jobs, and custom LoRAs. It gives developers one clean API for creative AI workflows without juggling multiple model providers.

Arize AX

Api Key

Arize AX is an AI engineering platform for tracing, evaluating, and improving AI applications. Use it to debug LLM behavior, compare experiments, and improve production AI quality.

AssemblyAI

Api Key

AssemblyAI is a speech-to-text and audio intelligence API for uploaded or hosted media. Use it to turn audio and video into accurate transcripts, summaries, and insights.

FAQ

Frequently asked questions

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

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

Start with PromptLayer.It takes 30 seconds.

Managed auth, hosted MCP servers, and every PromptLayer tool your agent needs.Free to start.

Start building

Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREE BOOK A DEMO

Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

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.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

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.

Ryan Yu

Founder, Extra Thursday

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.

Tomisin Jenrola

Founder & CEO, SwarmZero

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.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate PromptLayer MCP with LangChain

Connect PromptLayer without auth hassles

Introduction

Also integrate PromptLayer with

TL;DR

What is LangChain?

What is the PromptLayer MCP server, and what's possible with it?

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

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies

Initialize Composio client

Create a Tool Router session

Configure the agent with the MCP URL

Set up interactive chat interface

Run the application

Complete Code

Conclusion

Supported Tools

How to build PromptLayer MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

Explore Other Toolkits

AltTextLab

Apiframe

Arize AX

AssemblyAI

Frequently asked questions

What are the differences in Tool Router MCP and PromptLayer MCP?+

Can I use Tool Router MCP with LangChain?+

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

How safe is my data with Composio Tool Router?+

Start with PromptLayer.It takes 30 seconds.

Teams ship real agents on Composio