# How to integrate Fal.ai MCP with CrewAI ```json { "title": "How to integrate Fal.ai MCP with CrewAI", "toolkit": "Fal.ai", "toolkit_slug": "fal_ai", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/fal_ai/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/fal_ai/framework/crew-ai.md", "updated_at": "2026-03-29T06:33:16.683Z" } ``` ## Introduction This guide walks you through connecting Fal.ai to CrewAI using the Composio tool router. By the end, you'll have a working Fal.ai agent that can generate a photorealistic portrait of a cat, create a 15-second ai-generated promo video, synthesize an audio clip saying 'welcome home!' through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Fal.ai account through Composio's Fal.ai MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Fal.ai with - [ChatGPT](https://composio.dev/toolkits/fal_ai/framework/chatgpt) - [Antigravity](https://composio.dev/toolkits/fal_ai/framework/antigravity) - [OpenAI Agents SDK](https://composio.dev/toolkits/fal_ai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/fal_ai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/fal_ai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/fal_ai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/fal_ai/framework/codex) - [Cursor](https://composio.dev/toolkits/fal_ai/framework/cursor) - [VS Code](https://composio.dev/toolkits/fal_ai/framework/vscode) - [OpenCode](https://composio.dev/toolkits/fal_ai/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/fal_ai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/fal_ai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/fal_ai/framework/cli) - [Google ADK](https://composio.dev/toolkits/fal_ai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/fal_ai/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/fal_ai/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/fal_ai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/fal_ai/framework/llama-index) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Fal.ai connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Fal.ai - Build a conversational loop where your agent can execute Fal.ai 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 Fal.ai MCP server, and what's possible with it? The Fal.ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Fal.ai account. It provides structured and secure access so your agent can perform Fal.ai operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `FAL_AI_CANCEL_QUEUE_REQUEST` | Cancel Queue Request | Tool to cancel a queued or in-progress request in fal.ai's queue system. Use when you need to stop a request before it completes. Note that cancellation only succeeds if the request hasn't started processing; if already completed, returns an error status. Even with successful cancellation, the request may still execute if it was near the front of the queue. | | `FAL_AI_ESTIMATE_PRICING` | Estimate Pricing | Tool to estimate pricing for fal.ai model endpoints. Use when you need to calculate expected costs for API calls or unit-based usage across one or more endpoints. | | `FAL_AI_GET_JWKS` | Get JWKS for Webhook Verification | Tool to retrieve public keys for webhook signature verification. Returns a JSON Web Key Set containing ED25519 public keys. Use when you need to verify webhook signatures from fal.ai. The keys are cacheable but should be refreshed at least every 24 hours. | | `FAL_AI_GET_MODELS` | Get Models | Tool to discover and search fal.ai model endpoints. Use when you need to list all models, find specific models by ID, or search by category/query. Supports pagination and optional expansion of OpenAPI schemas. | | `FAL_AI_GET_MODEL_PRICING` | Get Model Pricing | Tool to retrieve unit pricing for model endpoints. Returns pricing information including unit price, billing unit, and currency. Use when you need to check costs for specific fal.ai models. | | `FAL_AI_GET_QUEUE_REQUEST_RESULT` | Get Queue Request Result | Tool to retrieve the final result of a completed queue request. Use when you need to get the output of a model request that was submitted to the queue and has finished processing. Only works after request status transitions to COMPLETED. | | `FAL_AI_GET_QUEUE_REQUEST_STATUS_WITH_LOGS` | Get Queue Request Status With Logs | Tool to retrieve the current status of a queued request with detailed logging information. Use when you need to monitor a queued request's progress and access execution logs for debugging or tracking purposes. Logs include timestamps, severity levels, and detailed messages about request processing. | | `FAL_AI_CHECK_QUEUE_REQUEST_STATUS` | Check Queue Request Status | Tool to check the status of a queued request in fal.ai. Use when you need to monitor the progress of an async request. Returns different information based on status: queue position when IN_QUEUE, logs when IN_PROGRESS or COMPLETED. | | `FAL_AI_STREAM_REQUEST_STATUS_UPDATES` | Stream Request Status Updates | Tool to stream request status updates via SSE. Use when you need real-time updates on a queued request's processing state. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Fal.ai MCP server is an implementation of the Model Context Protocol that connects your AI agent to Fal.ai. It provides structured and secure access so your agent can perform Fal.ai 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 Fal.ai 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 Fal.ai 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 Fal.ai 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 Fal.ai **What's happening:** - You create a Fal.ai only session through Composio - Composio returns an MCP HTTP URL that exposes Fal.ai tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["fal_ai"]) 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=["fal_ai"], ) 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 Fal.ai through Composio's Tool Router. The agent can perform Fal.ai 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 Fal.ai MCP Agent with another framework - [ChatGPT](https://composio.dev/toolkits/fal_ai/framework/chatgpt) - [Antigravity](https://composio.dev/toolkits/fal_ai/framework/antigravity) - [OpenAI Agents SDK](https://composio.dev/toolkits/fal_ai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/fal_ai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/fal_ai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/fal_ai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/fal_ai/framework/codex) - [Cursor](https://composio.dev/toolkits/fal_ai/framework/cursor) - [VS Code](https://composio.dev/toolkits/fal_ai/framework/vscode) - [OpenCode](https://composio.dev/toolkits/fal_ai/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/fal_ai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/fal_ai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/fal_ai/framework/cli) - [Google ADK](https://composio.dev/toolkits/fal_ai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/fal_ai/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/fal_ai/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/fal_ai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/fal_ai/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. - [Youtube](https://composio.dev/toolkits/youtube) - YouTube is a leading video-sharing platform for uploading, streaming, and discovering content. It empowers creators and businesses to reach global audiences and monetize their work. - [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. - [Figma](https://composio.dev/toolkits/figma) - Figma is a collaborative interface design tool for teams and individuals. It streamlines design workflows with real-time collaboration and easy sharing. - [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. - [Abyssale](https://composio.dev/toolkits/abyssale) - Abyssale is a creative automation platform for generating images, videos, GIFs, PDFs, and HTML5 content programmatically. It streamlines and scales visual content production for marketing, design, and operations teams. - [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. - [Alttext ai](https://composio.dev/toolkits/alttext_ai) - AltText.ai is a service that generates alt text for images automatically. It helps boost accessibility and SEO for your visual content. - [Amara](https://composio.dev/toolkits/amara) - Amara is a collaborative platform for creating and managing subtitles and captions for videos. It helps make content accessible and multilingual for global audiences. - [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. - [Bannerbear](https://composio.dev/toolkits/bannerbear) - Bannerbear is an API-driven platform for generating images and videos automatically at scale. It helps businesses create custom graphics, social visuals, and marketing assets using powerful templates. - [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. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Fal.ai MCP? With a standalone Fal.ai MCP server, the agents and LLMs can only access a fixed set of Fal.ai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Fal.ai 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 Fal.ai tools. ### Can I manage the permissions and scopes for Fal.ai while using Tool Router? Yes, absolutely. You can configure which Fal.ai 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 Fal.ai data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)