How to integrate Crowdin MCP with Autogen

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Introduction

This guide walks you through connecting Crowdin to AutoGen 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 AutoGen 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
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Crowdin
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Crowdin tools
  • Run a live chat loop where you ask the agent to perform Crowdin 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 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

You will need:

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

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

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 Crowdin via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

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 Crowdin connections to use

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 Crowdin session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["crowdin"]
    )
    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 Crowdin tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to

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

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 Crowdin assistant agent with MCP tools
    agent = AssistantAgent(
        name="crowdin_assistant",
        description="An AI assistant that helps with Crowdin 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 Crowdin tools from the workbench

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Crowdin 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 Crowdin 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 Crowdin and AutoGen:

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 Crowdin session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["crowdin"]
    )
    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 Crowdin assistant agent with MCP tools
        agent = AssistantAgent(
            name="crowdin_assistant",
            description="An AI assistant that helps with Crowdin 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 Crowdin 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 Crowdin 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 Crowdin, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

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

LangChain

LangChain

Use Crowdin MCP with LangChain

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 Autogen?

Yes, you can. Autogen 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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