# How to integrate Gladia MCP with CrewAI ```json { "title": "How to integrate Gladia MCP with CrewAI", "toolkit": "Gladia", "toolkit_slug": "gladia", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/gladia/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/gladia/framework/crew-ai.md", "updated_at": "2026-05-12T10:13:13.125Z" } ``` ## Introduction This guide walks you through connecting Gladia to CrewAI using the Composio tool router. By the end, you'll have a working Gladia agent that can transcribe this recorded meeting audio file, start real-time transcription from your microphone, list all recent transcription jobs with status through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Gladia account through Composio's Gladia MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Gladia with - [OpenAI Agents SDK](https://composio.dev/toolkits/gladia/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/gladia/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/gladia/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/gladia/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/gladia/framework/codex) - [OpenClaw](https://composio.dev/toolkits/gladia/framework/openclaw) - [Hermes](https://composio.dev/toolkits/gladia/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/gladia/framework/cli) - [Google ADK](https://composio.dev/toolkits/gladia/framework/google-adk) - [LangChain](https://composio.dev/toolkits/gladia/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/gladia/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/gladia/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/gladia/framework/llama-index) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Gladia connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Gladia - Build a conversational loop where your agent can execute Gladia operations ## What is CrewAI? CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews. Key features include: - Agent Roles: Define specialized agents with specific goals and backstories - Task Management: Create tasks with clear descriptions and expected outputs - Crew Orchestration: Combine agents and tasks into collaborative workflows - MCP Integration: Connect to external tools through Model Context Protocol ## What is the Gladia MCP server, and what's possible with it? The Gladia MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Gladia account. It provides structured and secure access to advanced audio transcription and intelligence services, so your agent can perform actions like uploading audio, transcribing recordings, retrieving live transcription results, and organizing jobs on your behalf. - Real-time and asynchronous transcription: Instantly start live or pre-recorded transcription jobs for audio and video files, letting your agent process spoken content with ease. - Audio and video file uploads: Seamlessly upload audio or video files to Gladia's servers as a first step before transcription or analysis. - Fetch detailed transcription results: Retrieve comprehensive results and metadata from both live and pre-recorded transcription sessions, including job status and transcribed text. - Job management and status tracking: List, filter, and monitor all your live and pre-recorded transcription jobs so your agent can keep tabs on ongoing or completed work. - Effortless workflow automation: Combine audio file handling, job initiation, and result retrieval into streamlined, agent-driven automations—saving you time and manual effort. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `GLADIA_DELETE_LIVE_SESSION` | Delete Live Session | Tool to delete a live transcription session and all its associated data (audio file, transcription). Use when permanently removing a live session from Gladia. | | `GLADIA_DELETE_PRE_RECORDED_JOB` | Delete Pre-recorded Job | Tool to delete a pre-recorded transcription job and all its associated data (audio file, transcription). Use when permanently removing a transcription job from Gladia. | | `GLADIA_GET_LIVE_TRANSCRIPTION_RESULT` | Get live transcription result | Tool to retrieve metadata and results of a live transcription job. Use when you need detailed status or results for a specific live transcription session. | | `GLADIA_GET_PRE_RECORDED_JOB` | Get Pre-recorded Job | Tool to retrieve a pre-recorded transcription job's metadata, status, and results by ID. Use when checking the status or retrieving results of a specific pre-recorded job. | | `GLADIA_INITIATE_LIVE_SESSION` | Initiate Live Transcription Session | Initiates a live (real-time) transcription session with Gladia's API. Returns a WebSocket URL that you can connect to for streaming audio data in real-time. The WebSocket URL includes an embedded authentication token and should be used promptly. After initiating, connect to the returned WebSocket URL and send audio chunks. Use GET /v2/live/{id} to retrieve the full transcription results after the session ends. | | `GLADIA_INITIATE_PRE_RECORDED_TRANSCRIPTION` | Initiate Pre-Recorded Transcription | Tool to initiate a pre-recorded transcription job. Use when you have an audio URL and need asynchronous transcription results. | | `GLADIA_LIST_LIVE_TRANSCRIPTIONS` | List live transcription jobs | Tool to list live transcription jobs. Use when you need an overview of live transcription sessions with optional filtering and pagination. Use after setting up live transcription. | | `GLADIA_LIST_PRE_RECORDED_JOBS` | List Pre-Recorded Jobs | Tool to list all pre-recorded transcription jobs matching the query parameters. Use when you need to retrieve a paginated list of transcription jobs with optional filtering by date, status, or custom metadata. | | `GLADIA_UPLOAD_AUDIO_VIDEO_FILE` | Upload Audio/Video File | Upload an audio or video file to Gladia for speech-to-text transcription. Returns a URL that can be used with the pre-recorded transcription endpoint. Supports common audio formats (WAV, MP3, FLAC, OGG) and video formats (MP4, MKV, etc.). | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Gladia MCP server is an implementation of the Model Context Protocol that connects your AI agent to Gladia. It provides structured and secure access so your agent can perform Gladia operations on your behalf through a secure, permission-based interface. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 1. Prerequisites Before starting, make sure you have: - Python 3.9 or higher - A Composio account and API key - A Gladia connection authorized in Composio - An OpenAI API key for the CrewAI LLM - Basic familiarity with Python ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies **What's happening:** - composio connects your agent to Gladia via MCP - crewai provides Agent, Task, Crew, and LLM primitives - crewai-tools[mcp] includes MCP helpers - python-dotenv loads environment variables from .env ```bash pip install composio crewai crewai-tools[mcp] python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates with Composio - USER_ID scopes the session to your account - OPENAI_API_KEY lets CrewAI use your chosen OpenAI model ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key_here ``` ### 4. Import dependencies **What's happening:** - CrewAI classes define agents and tasks, and run the workflow - MCPServerHTTP connects the agent to an MCP endpoint - Composio will give you a short lived Gladia MCP URL ```python import os from composio import Composio from crewai import Agent, Task, Crew from crewai_tools import MCPServerAdapter import dotenv dotenv.load_dotenv() COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set") ``` ### 5. Create a Composio Tool Router session for Gladia **What's happening:** - You create a Gladia only session through Composio - Composio returns an MCP HTTP URL that exposes Gladia tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["gladia"]) url = session.mcp.url ``` ### 6. Initialize the MCP Server **What's Happening:** - Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication. - MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format. - Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools. - Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops. - Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries. ```python server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users search the internet effectively", backstory="You are a helpful assistant with access to search tools.", tools=tools, verbose=False, max_iter=10, ) ``` ### 7. Create a CLI Chatloop and define the Crew **What's Happening:** - Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable. - Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean. - Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses. - Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context. - Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response. - Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity. ```python print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Complete Code ```python from crewai import Agent, Task, Crew, LLM from crewai_tools import MCPServerAdapter from composio import Composio from dotenv import load_dotenv import os load_dotenv() GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not GOOGLE_API_KEY: raise ValueError("GOOGLE_API_KEY is not set in the environment.") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set in the environment.") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set in the environment.") # Initialize Composio and create a session composio = Composio(api_key=COMPOSIO_API_KEY) session = composio.create( user_id=COMPOSIO_USER_ID, toolkits=["gladia"], ) url = session.mcp.url # Configure LLM llm = LLM( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY"), ) server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users with internet searches", backstory="You are an expert assistant with access to Composio Search tools.", tools=tools, llm=llm, verbose=False, max_iter=10, ) print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Conclusion You now have a CrewAI agent connected to Gladia through Composio's Tool Router. The agent can perform Gladia operations through natural language commands. Next steps: - Add role-specific instructions to customize agent behavior - Plug in more toolkits for multi-app workflows - Chain tasks for complex multi-step operations ## How to build Gladia MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/gladia/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/gladia/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/gladia/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/gladia/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/gladia/framework/codex) - [OpenClaw](https://composio.dev/toolkits/gladia/framework/openclaw) - [Hermes](https://composio.dev/toolkits/gladia/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/gladia/framework/cli) - [Google ADK](https://composio.dev/toolkits/gladia/framework/google-adk) - [LangChain](https://composio.dev/toolkits/gladia/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/gladia/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/gladia/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/gladia/framework/llama-index) ## Related Toolkits - [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed. - [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service. - [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows. - [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow. - [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows. - [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application. - [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities. - [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization. - [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications. - [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations. - [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key. - [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient. - [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions. - [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences. - [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement. - [Cody](https://composio.dev/toolkits/cody) - Cody is an AI assistant built for businesses, trained on your company's knowledge and data. It delivers instant answers and insights, tailored for your team. - [Context7 MCP](https://composio.dev/toolkits/context7_mcp) - Context7 MCP delivers live, version-specific code docs and examples right from the source. It helps developers and AI agents instantly retrieve authoritative programming info—no more out-of-date docs. - [Customgpt](https://composio.dev/toolkits/customgpt) - CustomGPT.ai lets you build and deploy chatbots tailored to your own data and business needs. Get precise and context-aware AI conversations without writing code. - [Datarobot](https://composio.dev/toolkits/datarobot) - Datarobot is a machine learning platform that automates model development, deployment, and monitoring. It empowers organizations to quickly gain predictive insights from large datasets. - [Deepgram](https://composio.dev/toolkits/deepgram) - Deepgram is an AI-powered speech recognition platform for accurate audio transcription and understanding. It enables fast, scalable speech-to-text with advanced audio intelligence features. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Gladia MCP? With a standalone Gladia MCP server, the agents and LLMs can only access a fixed set of Gladia tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Gladia and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with CrewAI? Yes, you can. CrewAI 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 Gladia tools. ### Can I manage the permissions and scopes for Gladia while using Tool Router? Yes, absolutely. You can configure which Gladia 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 Gladia data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)