How to integrate Bamboohr MCP with LangChain

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Introduction

This guide walks you through connecting Bamboohr to LangChain using the Composio tool router. By the end, you'll have a working Bamboohr agent that can add new dependent for employee john doe, update direct deposit details for sarah smith, log overtime hours for marketing team members through natural language commands.

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

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

Also integrate Bamboohr with

TL;DR

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

The BambooHR MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your BambooHR account. It provides structured and secure access to your HR data, so your agent can perform actions like managing employee benefits, updating payroll records, tracking time, and assisting with applicant management on your behalf.

  • Employee benefits administration: Automatically enroll employees in benefit groups, create or update benefit records, and manage company-wide benefit offerings with ease.
  • Payroll and direct deposit management: Enable your agent to create paystubs, add unpaid pay periods, and update employee direct deposit information for seamless payroll processing.
  • Dependent and tax record updates: Empower your agent to add employee dependents and modify withholding details, keeping employee records accurate and compliant.
  • Time tracking automation: Let your agent log new time tracking records for employees, ensuring precise attendance and overtime data for reporting and payroll.
  • Applicant and recruitment collaboration: Allow your agent to post comments on applicant records, streamlining feedback and communication during the hiring process.

Supported Tools & Triggers

Tools
Create Candidate ApplicationTool to create a candidate application.
Create Job OpeningTool to create a new job opening in BambooHR ATS.
List Job ApplicationsTool to list job applications with optional filters.
Get Benefit CoveragesTool to retrieve standard benefit coverage options.
Get Member Benefit EventsTool to list member benefit events.
Get Company EINsTool to retrieve company Employer Identification Numbers (EINs).
Get Company InformationTool to retrieve company information.
Create File CategoryTool to create new company file categories.
Create Time Off RequestTool to submit a new time off request.
List DatasetsTool to list available datasets via the Datasets API.
Create Employee DependentTool to add a dependent to an employee.
Get All Employee DependentsTool to retrieve all employee dependents.
Create EmployeeTool to create a new employee record.
Create Employee File CategoryTool to create new employee file categories.
Get Changed EmployeesTool to get employees inserted, updated, or deleted since a given timestamp.
List Company FilesTool to list company file categories and their files.
Upload Company FileTool to upload a new company file.
Get All EmployeesRetrieves all employees from the BambooHR employee directory including their basic information and status.
Get Applicant StatusesTool to retrieve applicant statuses.
Get Custom Employee FieldsTool to fetch custom employee field values.
Run Custom ReportTool to run a custom report by ID or ad-hoc fields.
Get EmployeeTool to retrieve detailed information for a specific employee.
Get Employee PhotoTool to retrieve an employee's profile photo by size.
Get Hiring LeadsTool to retrieve potential hiring leads (employees who can manage job openings) for use in creating a new job opening.
Get Job SummariesTool to retrieve a list of ATS job summaries.
Get Departments MetadataTool to list department metadata.
Get Meta DivisionsTool to list all division metadata.
List Employment Status MetadataTool to list all employment status metadata.
Get Meta Job TitlesTool to retrieve job title metadata.
Get Meta LocationsTool to list location metadata.
Get Time-Off Types MetadataTool to list time-off type metadata.
Get ReportTool to fetch a built-in or published report in JSON or other formats.
Get Time-Off BalancesTool to retrieve time-off balances for employees.
Get Time-Off RequestsTool to list time-off requests within a date range.
List Company ReportsTool to list all available company and custom reports.
Get Country OptionsTool to retrieve all available country options.
Get List Field DetailsTool to get details for all list fields.
Get Tabular Fields MetadataTool to list tabular table fields metadata.
Get UsersTool to list active users with basic info.
Update EmployeeTool to update fields on a specified employee record.
Update Time Off RequestTool to update the status of an existing time-off request.

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

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 Bamboohr 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 Bamboohr tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

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

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "bamboohr-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 Bamboohr MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Bamboohr 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 Bamboohr 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 Bamboohr 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=['bamboohr']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "bamboohr-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 Bamboohr 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 Bamboohr 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.

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FAQ

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

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

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

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

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