How to integrate Deepgram MCP with LangChain

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Introduction

This guide walks you through connecting Deepgram to LangChain using the Composio tool router. By the end, you'll have a working Deepgram agent that can transcribe meeting audio from this url, summarize podcast episode for quick review, convert blog post text to speech audio, detect topics in uploaded interview recording through natural language commands.

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

The Deepgram MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Deepgram account. It provides structured and secure access to Deepgram's speech AI services, so your agent can transcribe audio, generate summaries, convert text to speech, detect topics, and analyze project usage on your behalf.

  • Automated audio transcription: Your agent can transcribe pre-recorded audio files into accurate, readable text for effortless documentation and analysis.
  • Audio summarization: Have your agent generate quick, concise summaries of audio content, making it easy to review lengthy recordings in seconds.
  • Natural-sounding text-to-speech: Let your agent convert written text into lifelike speech audio using Deepgram's public TTS models for accessibility or creative applications.
  • Topic detection from audio: Enable your agent to identify and report on the main topics discussed within audio files, streamlining content review and categorization.
  • Usage analytics and project insights: Retrieve and analyze project usage metrics, scope permissions, and available models to optimize your Deepgram integration and monitor account health.

Supported Tools & Triggers

Tools
Get Public ModelsTool to retrieve metadata on all the latest public deepgram speech-to-text models.
List Deepgram ProjectsTool to list all deepgram projects.
Get Project Usage SummaryTool to retrieve a summary of usage data for a specified deepgram project.
Get Public TTS ModelsTool to fetch metadata about all latest public tts voice models.
List Project ScopesTool to list all scopes for a specified deepgram project.
Transcribe Pre-recorded AudioTool to transcribe pre-recorded audio files into text.
Summarize AudioTool to generate concise summaries from audio content.
Text-to-Speech (REST)Tool to convert text into natural-sounding speech.
Topic DetectionTool to transcribe audio and detect topics.

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

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

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

OpenAI Agents SDK

OpenAI Agents SDK

Use Deepgram MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Deepgram MCP with Claude Agent SDK

Google ADK

Google ADK

Use Deepgram MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Deepgram MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Deepgram MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Deepgram MCP with LlamaIndex

CrewAI

CrewAI

Use Deepgram MCP with CrewAI

FAQ

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

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

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

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

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