# How to integrate Groqcloud MCP with CrewAI ```json { "title": "How to integrate Groqcloud MCP with CrewAI", "toolkit": "Groqcloud", "toolkit_slug": "groqcloud", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/groqcloud/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/groqcloud/framework/crew-ai.md", "updated_at": "2026-05-12T10:14:26.189Z" } ``` ## Introduction This guide walks you through connecting Groqcloud to CrewAI using the Composio tool router. By the end, you'll have a working Groqcloud agent that can transcribe this spanish audio file to english, list all available ai models on groqcloud, generate the next chat reply from conversation history through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Groqcloud account through Composio's Groqcloud MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Groqcloud with - [OpenAI Agents SDK](https://composio.dev/toolkits/groqcloud/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/groqcloud/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/groqcloud/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/groqcloud/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/groqcloud/framework/codex) - [OpenClaw](https://composio.dev/toolkits/groqcloud/framework/openclaw) - [Hermes](https://composio.dev/toolkits/groqcloud/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/groqcloud/framework/cli) - [Google ADK](https://composio.dev/toolkits/groqcloud/framework/google-adk) - [LangChain](https://composio.dev/toolkits/groqcloud/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/groqcloud/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/groqcloud/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/groqcloud/framework/llama-index) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Groqcloud connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Groqcloud - Build a conversational loop where your agent can execute Groqcloud 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 Groqcloud MCP server, and what's possible with it? The Groqcloud MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Groqcloud account. It provides structured and secure access to your deployed AI models and inference services, so your agent can perform actions like running chat completions, translating audio, exploring models, and discovering TTS voices on your behalf. - Run chat-based completions: Let your agent generate context-aware replies or continue conversations using Groqcloud's high-performance AI models. - Translate audio to English: Quickly transcribe and translate non-English audio files into accurate English text for downstream processing or review. - Discover and explore available models: Ask your agent to list all supported AI models, then fetch detailed metadata for any model to inform your workflows. - Find available TTS voices: Retrieve a curated list of supported text-to-speech voices so your agent can select the best fit for voice synthesis tasks. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `GROQCLOUD_CREATE_AUDIO_TRANSCRIPTION` | Create Audio Transcription | Tool to transcribe audio into text in the same language as the audio. Use when you need to convert speech to text while preserving the original language. Supports multiple formats including mp3, mp4, wav, and webm. | | `GROQCLOUD_CREATE_RESPONSE` | Create Response | Tool to create a model response for the given input. Beta endpoint with simplified interface compared to chat completions. Use when you need a streamlined API for generating model responses. | | `GROQCLOUD_GROQ_CREATE_AUDIO_TRANSLATION` | Create Audio Translation | Tool to translate an audio file into English text. Use when you have a non-English recording and need an accurate English transcript. Use after confirming the file path. | | `GROQCLOUD_GROQ_CREATE_CHAT_COMPLETION` | Create Chat Completion | Tool to generate a chat-based completion for a conversation. Use when you have a list of prior messages and need the model's next reply. Response completion text is at choices[0].message.content in the returned envelope. | | `GROQCLOUD_GROQ_RETRIEVE_MODEL` | Retrieve Model | Tool to retrieve detailed information about a specific model. Use after listing models when you need metadata for a chosen model. Returned metadata may change as models update; do not cache. | | `GROQCLOUD_LIST_MODELS` | List Models | Tool to list all available models and their metadata. Always call this to retrieve current model IDs rather than using hard-coded or cached identifiers, as deprecated names cause failures in GROQCLOUD_GROQ_RETRIEVE_MODEL and GROQCLOUD_GROQ_CREATE_CHAT_COMPLETION. Returns availability and metadata only — excludes usage stats, latency metrics, and pricing. Response may include many models; filter client-side by provider, family, modality, or context length. Frequent polling combined with high-volume requests risks HTTP 429 rate_limit_exceeded; use backoff and minimize call frequency. | | `GROQCLOUD_LIST_VOICES` | List TTS Voices | Tool to retrieve available TTS voices for Groq PlayAI models. Use when you need to discover voice options before calling text-to-speech. Note: static list maintained manually; no live endpoint exists. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Groqcloud MCP server is an implementation of the Model Context Protocol that connects your AI agent to Groqcloud. It provides structured and secure access so your agent can perform Groqcloud 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 Groqcloud 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 Groqcloud 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 Groqcloud 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 Groqcloud **What's happening:** - You create a Groqcloud only session through Composio - Composio returns an MCP HTTP URL that exposes Groqcloud tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["groqcloud"]) 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=["groqcloud"], ) 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 Groqcloud through Composio's Tool Router. The agent can perform Groqcloud 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 Groqcloud MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/groqcloud/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/groqcloud/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/groqcloud/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/groqcloud/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/groqcloud/framework/codex) - [OpenClaw](https://composio.dev/toolkits/groqcloud/framework/openclaw) - [Hermes](https://composio.dev/toolkits/groqcloud/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/groqcloud/framework/cli) - [Google ADK](https://composio.dev/toolkits/groqcloud/framework/google-adk) - [LangChain](https://composio.dev/toolkits/groqcloud/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/groqcloud/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/groqcloud/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/groqcloud/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 Groqcloud MCP? With a standalone Groqcloud MCP server, the agents and LLMs can only access a fixed set of Groqcloud tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Groqcloud 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 Groqcloud tools. ### Can I manage the permissions and scopes for Groqcloud while using Tool Router? Yes, absolutely. You can configure which Groqcloud 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 Groqcloud data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)