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How to integrate Bamboohr MCP with Autogen

Connect Autogen to Bamboohr MCP. Add new dependent for employee john doe, update employee details for sarah smith, and more using natural language, with authentication handled for you.

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BambooHR is a cloud-based HR management platform for small and mid-sized businesses. It streamlines employee data, HR workflows, and reporting in one easy interface.

42 Tools

Introduction

This guide walks you through connecting Bamboohr to AutoGen using the Composio tool router. By the end, you'll have a working Bamboohr agent that can look up and update employees, manage time off requests, create job openings, track applications, and run custom reports through natural language commands.

This guide will help you understand how to give your AutoGen 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

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TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Bamboohr
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Bamboohr tools
  • Run a live chat loop where you ask the agent to perform Bamboohr operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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 employee records, hiring data, time off, benefits, reports, and company files, so your agent can manage employees, track candidates, submit time off requests, run reports, and organize HR documents on your behalf.

  • Employee records and organization: Have your agent retrieve or create employee records, find recent employee changes, and review departments, divisions, locations, job titles, and employment statuses.
  • Hiring and candidate management: Direct your agent to create job openings and candidate applications, list applications, check applicant statuses, and review hiring leads or job summaries.
  • Time off administration: Let the agent submit time off requests, review requests within a date range, check employee balances, and inspect available time off types and policies.
  • Reports, benefits, and company data: Instruct your agent to run company reports, review benefit coverages and member events, or retrieve company information and tax identifiers.
  • HR files and employee details: Have your agent list or upload company files, create file categories, retrieve employee photos and custom fields, or add and review employee dependents.

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 step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Bamboohr account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
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

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Bamboohr via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Bamboohr connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Bamboohr session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["bamboohr"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Bamboohr tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Bamboohr assistant agent with MCP tools
    agent = AssistantAgent(
        name="bamboohr_assistant",
        description="An AI assistant that helps with Bamboohr operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Bamboohr tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Bamboohr related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Bamboohr tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Bamboohr and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Bamboohr session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["bamboohr"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Bamboohr assistant agent with MCP tools
        agent = AssistantAgent(
            name="bamboohr_assistant",
            description="An AI assistant that helps with Bamboohr operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Bamboohr related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Bamboohr through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Bamboohr, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every Bamboohr action and event your agent gets out of the box.

Create Candidate Application

Tool to create a candidate application.

Create Job Opening

Tool to create a new job opening in BambooHR ATS.

List Job Applications

Tool to list job applications with optional filters.

Get Benefit Coverages

Tool to retrieve standard benefit coverage options.

Get Member Benefit Events

Lists member benefit enrollment events for employees and their dependents over the past year, with optional offset and limit on returned members.

Get Company EINs

Tool to retrieve company Employer Identification Numbers (EINs).

Get Company Information

Tool to retrieve company information.

Create File Category

Tool to create new company file categories.

Create Time Off Request

Tool to submit a new time off request.

List Datasets

Lists available datasets via the BambooHR Datasets API, returning dataset names and labels for discovery before querying.

Create Employee Dependent

Tool to add a dependent to an employee.

Get All Employee Dependents

Tool to retrieve all employee dependents.

Create Employee

Tool to create a new employee record.

Create Employee File Category

Tool to create new employee file categories.

Get Changed Employees

Get employee IDs grouped by inserted, updated, or deleted changes since a given timestamp.

List Company Files

List company file categories and their files that the requesting user is permitted to see.

Upload Company File

Tool to upload a new company file.

Get All Employees

Retrieves all employees from the BambooHR employee directory including their basic information and status.

Get Applicant Statuses

Tool to retrieve applicant statuses.

Get Custom Employee Fields

Lists employee field definitions available in the account, including standard and custom fields.

Run Custom Report

Tool to run a custom report by ID or ad-hoc fields.

Get Employee

Tool to retrieve detailed information for a specific employee.

Get Employee Photo

Retrieve an employee's profile photo by ID and size, returning its content as Base64 with a filename and MIME type.

Get Hiring Leads

Tool to retrieve potential hiring leads (employees who can manage job openings) for use in creating a new job opening.

Get Job Summaries

Retrieve ATS job opening summaries, optionally filtered by status group and sorted.

Get Departments Metadata

Tool to list department metadata.

Get Meta Divisions

Tool to list all division metadata.

List Employment Status Metadata

Tool to list all employment status metadata.

Get Meta Job Titles

Tool to retrieve job title metadata.

Get Meta Locations

Tool to list location metadata.

Get Time-Off Types Metadata

Tool to list time-off type metadata.

Get Report

Tool to fetch a built-in or published report in JSON or other formats.

Get Time-Off Balances

Retrieve estimated time-off balances for specified employees as of an optional end date.

Get Time-Off Policies

List time-off policies, with accrual and rollover details when available.

Get Time-Off Requests

Tool to list time-off requests within a date range.

List Company Reports

Lists a page of saved custom reports available in the account.

Get Country Options

Retrieve BambooHR country options for use when updating or validating country fields.

Get List Field Details

Tool to get details for all list fields.

Get Tabular Fields Metadata

Tool to list tabular table fields metadata.

Get Users

List company users with basic information, including IDs, names, email addresses, and account status.

Update Employee

Tool to update fields on a specified employee record.

Update Time Off Request

Tool to update the status of an existing time-off request.

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Vercel AI SDK

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LlamaIndex

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CrewAI

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Pydantic AI

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FAQ

Frequently asked questions

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