How to integrate Amara MCP with LangChain

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Introduction

This guide walks you through connecting Amara to LangChain using the Composio tool router. By the end, you'll have a working Amara agent that can list all subtitle languages for video id, fetch english subtitles for given video, create new spanish subtitle track for video, get details for specific team by slug through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Amara account through Composio's Amara 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 Amara project to Composio
  • Create a Tool Router MCP session for Amara
  • Initialize an MCP client and retrieve Amara tools
  • Build a LangChain agent that can interact with Amara
  • 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 Amara MCP server, and what's possible with it?

The Amara MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Amara account. It provides structured and secure access to your subtitle and caption management tools, so your agent can perform actions like creating subtitles, managing languages, fetching video metadata, and handling teams on your behalf.

  • Subtitle creation and editing: Direct your agent to add notes, create new subtitle languages, and fetch subtitle data for any supported video.
  • Language management: Effortlessly list all available subtitle languages for a given video, retrieve supported language options, or fetch details about specific language tracks.
  • Video metadata retrieval: Ask your agent to get detailed information about any video URL, including its Amara ID, title, duration, and thumbnails.
  • Team and user management: Let your agent list all accessible teams, pull details for a specific team, or fetch user data by username or ID for streamlined collaboration.

Supported Tools & Triggers

Tools
Add Subtitle NoteTool to add a note to a subtitle set.
Create Subtitle LanguageTool to create a new subtitle language for a video.
Fetch Subtitles DataTool to fetch subtitle data for a video in a specific language.
Get Subtitle Language DetailsTool to retrieve details for a single subtitle language.
Get Team DetailsTool to get details on a specific team by slug.
Get User DataTool to get user data by username or id.
Get Video URL DetailsTool to get details for a specific video url.
List Available LanguagesTool to get a list of all supported languages.
List Subtitle LanguagesTool to list all subtitle languages for a video.
List TeamsTool to list all teams.
List VideosTool to list all videos.
List Video URLsTool to list all urls associated with a video.
Make Video URL PrimaryTool to set a video url as the primary url.
Update Subtitle LanguageTool to update a subtitle language for a video.
View Video DetailsTool to view details of a specific video by id.

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 Amara 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 Amara tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

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

OpenAI Agents SDK

OpenAI Agents SDK

Use Amara MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Amara MCP with Claude Agent SDK

Google ADK

Google ADK

Use Amara MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Amara MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Amara MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Amara MCP with LlamaIndex

CrewAI

CrewAI

Use Amara MCP with CrewAI

FAQ

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

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

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

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

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