How to integrate Teamcamp MCP with LangChain

This guide walks you through connecting Teamcamp to LangChain using the Composio tool router. By the end, you'll have a working Teamcamp agent that can create a new task in project alpha, list all active projects for your team, show tasks assigned to me due this week through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Teamcamp account through Composio's Teamcamp MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Teamcamp is an all-in-one project management tool for teams to plan, track, and collaborate. It streamlines workflows, making team projects more organized and productive.

14 Tools

Introduction

This guide walks you through connecting Teamcamp to LangChain using the Composio tool router. By the end, you'll have a working Teamcamp agent that can create a new task in project alpha, list all active projects for your team, show tasks assigned to me due this week through natural language commands.

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

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

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

The Teamcamp MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Teamcamp account. It provides structured and secure access to your projects and tasks, so your agent can manage projects, create and review tasks, fetch project overviews, and streamline team workflows on your behalf.

  • Project overview and listing: Instantly retrieve a full list of all projects within your workspace, making it easy to get a snapshot of ongoing work.
  • Automated task creation: Let your agent add new tasks to any project, complete with details like assignee, due date, and description—no manual entry needed.
  • Task tracking and filtering: Effortlessly pull lists of tasks for any project, filterable by status, assignee, or due date to stay on top of team priorities.
  • Project cleanup and deletion: Direct your agent to permanently delete projects that are finished or no longer needed, keeping your workspace neat and efficient.

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 Teamcamp 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 Teamcamp 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: ['teamcamp']
    }
);

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

Configure the agent with the MCP URL

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

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

Create Project

Tool to create a new project in the workspace.

Create Task

Tool to create a new task within a specified project.

Create Task (V2)

Tool to create a new task in a TeamCamp project.

Delete Project

Tool to delete a specific project by its ID.

Get Company Customers

Tool to retrieve all customers in the company/workspace.

Get Company Users

Tool to retrieve all users in the company/workspace.

Get Project Details

Tool to get comprehensive project details including core fields, configuration, user data, feature toggles, and related resources.

Get Project Groups

Tool to retrieve all groups within a specific project.

Get Project List

Tool to retrieve all projects within the workspace.

Get Task

Tool to retrieve details of a specific task by its unique identifier.

Get Task List (Filtered)

Tool to retrieve a list of tasks from a project with optional completion status filtering.

Post Task Comment

Tool to post a new comment on a task.

Update Project

Tool to update an existing project's details by its unique identifier.

Update Task

Tool to update an existing task by its unique identifier.

FRAMEWORKS

How to build Teamcamp MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Teamcamp MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Teamcamp MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Teamcamp MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Teamcamp MCP with Claude Cowork

Codex logo

Codex

Use Teamcamp MCP with Codex

OpenClaw logo

OpenClaw

Use Teamcamp MCP with OpenClaw

Hermes logo

Hermes

Use Teamcamp MCP with Hermes

CLI logo

CLI

Use Teamcamp MCP with CLI

Google ADK logo

Google ADK

Use Teamcamp MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Teamcamp MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Teamcamp MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Teamcamp MCP with LlamaIndex

CrewAI logo

CrewAI

Use Teamcamp MCP with CrewAI

FAQ

Frequently asked questions

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

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

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