How to integrate Lever MCP with LangChain

This guide walks you through connecting Lever to LangChain using the Composio tool router. By the end, you'll have a working Lever agent that can list all open job postings, get candidate details by email, schedule interview for specific candidate through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Lever account through Composio's Lever MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Lever is an applicant tracking system that blends sourcing, CRM, and analytics for recruiting. It helps companies scale hiring with collaborative workflows and actionable insights.

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Introduction

This guide walks you through connecting Lever to LangChain using the Composio tool router. By the end, you'll have a working Lever agent that can list all open job postings, get candidate details by email, schedule interview for specific candidate through natural language commands.

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

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

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

The Lever MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Lever account. It provides structured and secure access to your recruiting pipeline, so your agent can perform actions like managing candidates, scheduling interviews, updating job postings, tracking offers, and analyzing hiring metrics on your behalf.

  • End-to-end candidate management: Let your agent add, update, or move candidates through different stages of your hiring process seamlessly.
  • Automated interview scheduling: Have the agent create, modify, or cancel interviews and coordinate with both candidates and interviewers to streamline the process.
  • Job posting and requisition updates: Direct your agent to create new job postings, update existing requisitions, or close filled roles instantly.
  • Offer and feedback tracking: Enable your agent to manage offer letters, track acceptance rates, and collect structured feedback from interviewers.
  • Recruiting analytics and reporting: Ask the agent to generate reports on pipeline activity, source effectiveness, and diversity metrics—helping you make data-driven hiring decisions.

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 Lever 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 Lever 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: ['lever']
    }
);

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

Configure the agent with the MCP URL

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

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

Add Opportunity Links

Tool to add links to a contact associated with an opportunity.

Add Opportunity Sources

Tool to add sources to an opportunity.

Add Opportunity Tags

Tool to add tags to an opportunity.

Create Form Submission

Tool to create a completed profile form submission for a candidate's opportunity profile.

Create Form Template

Tool to create a profile form template for an account.

Create Interview

Tool to create an interview on an externally-managed panel in Lever.

Create Note

Tool to create a note on an opportunity profile or add a threaded comment to an existing note.

Create Opportunity

Tool to create a new candidate opportunity in Lever.

Create Panel

Tool to create a new interview panel for an opportunity.

Create Requisition

Tool to create a new requisition in Lever for tracking hiring needs.

Create Requisition Field

Tool to create a custom requisition field schema for use across requisitions.

Create Requisition Field Option

Tool to add new options to a dropdown requisition field without replacing existing options.

Upload File

Tool to upload a file temporarily to Lever for use with posting applications.

Create User

Tool to create a new user in the Lever system.

Deactivate User

Tool to deactivate a user in the Lever system.

Delete Form Template

Tool to delete a profile form template from account.

Delete Interview

Tool to delete an interview from an opportunity panel.

Delete Note

Tool to delete a note on an opportunity.

Delete Panel

Tool to delete a panel from an opportunity.

Delete Requisition

Tool to delete or archive a requisition from Lever account.

Delete Requisition Field

Tool to delete a requisition field from the account.

Delete Requisition Field Option

Tool to remove specific options from a dropdown requisition field.

Download File

Tool to download a file associated with an opportunity.

Get File Metadata

Tool to retrieve metadata for a single file on an opportunity.

Get Form

Tool to retrieve a specific profile form for an opportunity.

Get Form Template

Tool to retrieve a single form template by unique identifier.

Get Interview

Tool to retrieve a single interview for an opportunity.

Get Note

Tool to retrieve a single note for an opportunity.

Get Opportunity

Tool to retrieve detailed information about a single opportunity.

Get Panel

Tool to retrieve a single interview panel for an opportunity.

Get Requisition

Tool to retrieve detailed information about a single requisition by ID.

Get Requisition Field

Tool to retrieve detailed information about a single custom requisition field by ID.

Get Stage

Tool to retrieve detailed information about a single stage by its UUID.

Get User

Tool to retrieve detailed information about a single user by their UUID.

List Opportunity Files

Tool to list all files on an opportunity.

List Forms

Tool to list all profile forms for an opportunity.

List Form Templates

Tool to list all active form templates.

List Interviews

Tool to list all interviews for an opportunity.

List Notes

Tool to list notes on an opportunity profile.

List Offers

Tool to list offers for an opportunity.

List Opportunities

Tool to list all opportunities in the hiring pipeline.

List Panels

Tool to list all interview panels for an opportunity.

List Postings

Tool to list all job postings including published, internal, closed, draft, pending, and rejected postings.

List Referrals

Tool to list all referrals for an opportunity.

List Requisition Fields

Tool to list all requisition field schemas in your Lever account with optional filtering.

List Requisitions

Tool to list all requisitions with filtering and pagination.

List Opportunity Resumes

Tool to list all resumes for an opportunity.

List Sources

Tool to list all recruitment sources in your Lever account.

List Stages

Tool to retrieve all pipeline stages in your Lever account.

List Tags

Tool to list all tags in your Lever account.

List Users

Tool to retrieve all active users in your Lever account with optional filters.

Reactivate User

Tool to reactivate a previously deactivated user in the Lever system.

Remove Contact Links by Opportunity

Tool to remove links from a contact associated with an opportunity.

Remove Opportunity Sources

Tool to remove sources from an opportunity.

Remove Opportunity Tags

Tool to remove tags from an opportunity.

Update Form Template

Tool to update an existing profile form template.

Update Interview

Tool to update an interview on an externally-managed panel.

Update Note

Tool to update a note on an opportunity profile.

Update Panel

Tool to update an externally-managed panel for an opportunity.

Update Requisition

Tool to update an existing requisition in Lever.

Update Requisition Field

Tool to update an existing requisition field in Lever.

Update Requisition Field Option

Tool to update existing options in a dropdown requisition field without replacing the entire field object.

Update User

Tool to update an existing user in the Lever system.

Upload File to Opportunity

Tool to upload a file permanently to an opportunity.

FRAMEWORKS

How to build Lever MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Lever MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Lever MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Lever MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Lever MCP with Claude Cowork

Codex logo

Codex

Use Lever MCP with Codex

OpenClaw logo

OpenClaw

Use Lever MCP with OpenClaw

Hermes logo

Hermes

Use Lever MCP with Hermes

CLI logo

CLI

Use Lever MCP with CLI

Google ADK logo

Google ADK

Use Lever MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Lever MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Lever MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Lever MCP with LlamaIndex

CrewAI logo

CrewAI

Use Lever MCP with CrewAI

FAQ

Frequently asked questions

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

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

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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
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
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
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
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
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
Jerome Leclanche
Co-Founder, Ingram Technologies