# How to integrate Connecteam MCP with LangChain

```json
{
  "title": "How to integrate Connecteam MCP with LangChain",
  "toolkit": "Connecteam",
  "toolkit_slug": "connecteam",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/connecteam/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/connecteam/framework/langchain.md",
  "updated_at": "2026-05-12T10:07:22.192Z"
}
```

## Introduction

This guide walks you through connecting Connecteam to LangChain using the Composio tool router. By the end, you'll have a working Connecteam agent that can archive users who have left the company, create new staff accounts for onboarding, list all available time-off policy types through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Connecteam account through Composio's Connecteam MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Connecteam with

- [OpenAI Agents SDK](https://composio.dev/toolkits/connecteam/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/connecteam/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/connecteam/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/connecteam/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/connecteam/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/connecteam/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/connecteam/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/connecteam/framework/cli)
- [Google ADK](https://composio.dev/toolkits/connecteam/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/connecteam/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/connecteam/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/connecteam/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/connecteam/framework/crew-ai)

## TL;DR

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

The Connecteam MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Connecteam account. It provides structured and secure access to your workforce management data, so your agent can perform actions like managing users, retrieving chats, processing forms, handling jobs, and analyzing performance metrics on your behalf.
- User management and automation: Quickly add new employees, archive inactive users, or update user details to keep your workforce roster up to date.
- Team communications monitoring: Retrieve chat conversations and team channels, making it easy for your agent to help you stay on top of internal messages and updates.
- Form and workflow processing: List and review all existing forms, so your agent can help automate routine HR tasks and data collection.
- Job and scheduling insights: Access job objects tied to your schedules or time clocks, letting your agent assist with workforce planning and role assignments.
- Performance and policy analytics: Fetch performance indicators and available time-off policy types, enabling your agent to surface key metrics and streamline HR policy management.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `CONNECTEAM_ARCHIVE_USERS` | Archive Users | Tool to archive one or more users by their unique IDs. Use when you need to deactivate users without deleting their records. |
| `CONNECTEAM_CREATE_USERS` | Create Users | Tool to create multiple users in Connecteam. Use when you need to add several staff or admin accounts at once. |
| `CONNECTEAM_GENERATE_UPLOAD_URL` | Generate Upload URL | Tool to generate a pre-signed URL for uploading a file. Use when you need a secure, time-limited URL prior to file upload. |
| `CONNECTEAM_GET_CHAT` | Get Chat | Tool to retrieve chat conversations. Use when you need to list all team chats/channels after confirming your Communications hub is on Expert plan. |
| `CONNECTEAM_GET_CUSTOM_FIELD_CATEGORIES` | Get Custom Field Categories | Tool to retrieve all custom field categories. Use when you need to list or filter custom field categories in your Connecteam account. |
| `CONNECTEAM_GET_CUSTOM_FIELDS` | Get Custom Fields | Tool to retrieve all custom fields associated with the account. Use when you need to filter, sort, or page through custom fields after authentication. |
| `CONNECTEAM_GET_FORMS` | Get Forms | Tool to retrieve all form definitions from Connecteam. Use when you need to list all existing forms after enabling the Forms API. |
| `CONNECTEAM_GET_JOBS` | Get Jobs | Tool to retrieve a list of job objects relevant to a specific instance ID. Use after confirming scheduler or time clock instance ID when you need to filter and page through jobs. |
| `CONNECTEAM_GET_PERFORMANCE_INDICATORS` | Get Performance Indicators | Tool to retrieve the list of performance metric indicators. Use when you need to list available performance indicators for data analysis. Examples: "List performance metrics". |
| `CONNECTEAM_GET_POLICY_TYPES` | Get Policy Types | Tool to retrieve available time-off policy types. Use before filtering or creating time-off requests by policyTypeId. |
| `CONNECTEAM_GET_PUBLISHERS` | Get Publishers | Tool to retrieve a list of all custom publishers. Use when you need to list custom publishers after confirming API access. |
| `CONNECTEAM_GET_SCHEDULERS` | Get Schedulers | Tool to retrieve a list of job schedulers associated with the account. Use after authentication when you need to enumerate all schedulers. |
| `CONNECTEAM_GET_SMART_GROUPS` | Get Smart Groups | Tool to retrieve all smart groups associated with the account. Use when you need to list all smart groups after authenticating with a valid API key. |
| `CONNECTEAM_GET_TASK_BOARDS` | Get Task Boards | Tool to retrieve all task boards. Use after authenticating with a valid API key to list available task boards. |
| `CONNECTEAM_GET_USERS` | Get Users | Tool to retrieve a list of all users associated with your account. Use when you need to fetch and filter user data. |
| `CONNECTEAM_LIST_ME` | List Me | Tool to retrieve account information including company name and company ID. Use when you need to get details about the authenticated account. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Connecteam MCP server is an implementation of the Model Context Protocol that connects your AI agent to Connecteam. It provides structured and secure access so your agent can perform Connecteam operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

No description provided.

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

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
```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
```

### 4. Import dependencies

No description provided.
```python
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()
```

```typescript
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();
```

### 5. Initialize Composio client

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 Connecteam tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
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")
```

```typescript
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()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to Connecteam 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 Connecteam tools as needed
```python
# Create Tool Router session for Connecteam
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['connecteam']
)

url = session.mcp.url
```

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

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "connecteam-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)
```

```typescript
const client = new MultiServerMCPClient({
    "connecteam-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 });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Connecteam 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")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Connecteam 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);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Complete Code

```python
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=['connecteam']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "connecteam-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 Connecteam 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())
```

```typescript
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: ['connecteam']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "connecteam-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 Connecteam 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 Connecteam 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.

## How to build Connecteam MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/connecteam/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/connecteam/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/connecteam/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/connecteam/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/connecteam/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/connecteam/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/connecteam/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/connecteam/framework/cli)
- [Google ADK](https://composio.dev/toolkits/connecteam/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/connecteam/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/connecteam/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/connecteam/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/connecteam/framework/crew-ai)

## Related Toolkits

- [Ashby](https://composio.dev/toolkits/ashby) - Ashby is an applicant tracking system that handles job postings, candidate management, and hiring analytics.
- [Async interview](https://composio.dev/toolkits/async_interview) - Async interview is an on-demand video interview platform for streamlined hiring. Candidates record responses on their schedule, so employers can review anytime.
- [Bamboohr](https://composio.dev/toolkits/bamboohr) - 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.
- [Breathe HR](https://composio.dev/toolkits/breathehr) - Breathe HR is cloud-based HR software for SMEs to manage employee data, absences, and performance. It simplifies HR admin, making it easy to keep employee records accurate and up to date.
- [Lever](https://composio.dev/toolkits/lever) - 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.
- [Recruitee](https://composio.dev/toolkits/recruitee) - Recruitee is collaborative hiring software that centralizes recruitment tasks for teams. It streamlines sourcing, interviewing, and hiring so you can fill roles faster.
- [Remote retrieval](https://composio.dev/toolkits/remote_retrieval) - Remote retrieval is a logistics automation tool for managing laptop and monitor returns. It streamlines return tracking, saving time and hassle for IT and ops teams.
- [Sap successfactors](https://composio.dev/toolkits/sap_successfactors) - Sap successfactors is a cloud-based human capital management suite for HR, payroll, recruiting, and talent management. It helps organizations centralize employee data and streamline the entire employee lifecycle.
- [Talenthr](https://composio.dev/toolkits/talenthr) - TalentHR is an intuitive, all-in-one HR tool for managing employee records, leave, and HR workflows. It streamlines HR operations so businesses can focus on people, not paperwork.
- [Workable](https://composio.dev/toolkits/workable) - Workable is an all-in-one HR software platform that streamlines hiring, employee management, and payroll. It helps teams simplify recruiting, onboarding, and staff operations in one place.
- [Workday](https://composio.dev/toolkits/workday) - Workday is a cloud-based ERP platform for HR, finance, and workforce analytics. It streamlines employee management, payroll, and business operations in a single system.
- [Gmail](https://composio.dev/toolkits/gmail) - Gmail is Google's email service with powerful spam protection, search, and G Suite integration. It keeps your inbox organized and makes communication fast and reliable.
- [Google Calendar](https://composio.dev/toolkits/googlecalendar) - Google Calendar is a time management service for scheduling meetings, events, and reminders. It streamlines personal and team organization with integrated notifications and sharing options.
- [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices.
- [Outlook](https://composio.dev/toolkits/outlook) - Outlook is Microsoft's email and calendaring platform for unified communications and scheduling. It helps users stay organized with powerful email, contacts, and calendar management.
- [Twitter](https://composio.dev/toolkits/twitter) - Twitter is a social media platform for sharing real-time updates, conversations, and news. Stay connected, informed, and engaged with communities worldwide.
- [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly.
- [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure.
- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Connecteam MCP?

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

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

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

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)