How to integrate Crowdin MCP with LangChain

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Introduction

This guide walks you through connecting Crowdin to LangChain using the Composio tool router. By the end, you'll have a working Crowdin agent that can create a new crowdin project for our app, add new source file to the translations project, assign sprint label to specific string ids, delete obsolete translation branch from project through natural language commands.

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

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

TL;DR

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

The Crowdin MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Crowdin account. It provides structured and secure access to your localization projects, so your agent can manage branches, organize files, label content, automate webhooks, and orchestrate translation workflows on your behalf.

  • Branch and project management: Easily have your agent create, delete, or organize Crowdin projects and branches to streamline new releases or features.
  • Dynamic file handling: Let your agent add new files to projects, ensuring your translation assets are always up to date and properly organized by branch or directory.
  • Labeling and content categorization: Direct your agent to create, assign, or remove labels on resources and strings, helping you segment and track translation tasks with precision.
  • Workflow automation with webhooks: Automate your translation process by having the agent set up or remove webhooks for real-time notifications and integrations.
  • Resource cleanup and maintenance: Empower your agent to delete obsolete branches, labels, webhooks, or entire projects, keeping your Crowdin workspace clean and focused.

Supported Tools & Triggers

Tools
Add BranchTool to create a new branch in a crowdin project.
Add FileTool to add a new file to a crowdin project.
Add LabelTool to create a new label in a crowdin project.
Create Crowdin ProjectTool to create a new project in crowdin.
Add WebhookTool to create a new webhook in a crowdin project.
Assign Label to StringsTool to assign the specified label to provided string ids in a project.
Delete BranchTool to delete a specific branch from a crowdin project.
Delete LabelTool to delete the label identified by the specified label id in a project.
Delete ProjectTool to delete a crowdin project by its id.
Delete WebhookTool to delete the webhook identified by the specified webhook id in a crowdin project.
Edit FileTool to update file details in a project.
Edit LabelTool to edit a label in a crowdin project.
Edit ProjectTool to update project details using json-patch.
Edit StringTool to update string details in a crowdin project.
Get LabelTool to retrieve information about the label identified by the specified label id in a project.
Get LanguageTool to retrieve details of a specific language.
Get Member InfoTool to retrieve information about a project member.
Get ProjectTool to retrieve details of a specific crowdin project.
Get StringTool to retrieve details of a specific string in a crowdin project.
Get WebhookTool to retrieve information about the webhook identified by the specified webhook id in a project.
List BranchesTool to list all branches in a crowdin project.
List FilesTool to list files in a crowdin project.
List LabelsTool to list labels in a crowdin project.
List LanguagesTool to retrieve a list of supported languages.
List Project MembersTool to list members in a crowdin project.
List ProjectsTool to retrieve a list of all crowdin projects with optional filters.
List ReportsTool to list reports for a given crowdin project.
Upload StorageTool to upload a file to crowdin storage.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router 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 Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router 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 Crowdin 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 Crowdin tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "crowdin-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 Crowdin 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 Crowdin 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 Crowdin MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Crowdin MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Crowdin MCP with Claude Agent SDK

Google ADK

Google ADK

Use Crowdin MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Crowdin MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Crowdin MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Crowdin MCP with LlamaIndex

CrewAI

CrewAI

Use Crowdin MCP with CrewAI

FAQ

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

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

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

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

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