# How to integrate Fal.ai MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Fal.ai MCP with Vercel AI SDK v6", "toolkit": "Fal.ai", "toolkit_slug": "fal_ai", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/fal_ai/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/fal_ai/framework/ai-sdk.md", "updated_at": "2026-03-29T06:33:16.683Z" } ``` ## Introduction This guide walks you through connecting Fal.ai to Vercel AI SDK v6 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 Vercel AI SDK 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) - [Mastra AI](https://composio.dev/toolkits/fal_ai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/fal_ai/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/fal_ai/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with Fal.ai integration - Using Composio's Tool Router to dynamically load and access Fal.ai tools - Creating an MCP client connection using HTTP transport - Building an interactive CLI chat interface with conversation history management - Handling tool calls and results within the Vercel AI SDK framework ## What is Vercel AI SDK? The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state. Key features include: - streamText: Core function for streaming responses with real-time tool support - MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp - Step Counting: Control multi-step tool execution with stopWhen: stepCountIs() - OpenAI Provider: Native integration with OpenAI models ## 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 you begin, make sure you have: - Node.js and npm installed - A Composio account with API key - An OpenAI API key ### 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 required dependencies First, install the necessary packages for your project. What you're installing: - @ai-sdk/openai: Vercel AI SDK's OpenAI provider - @ai-sdk/mcp: MCP client for Vercel AI SDK - @composio/core: Composio SDK for tool integration - ai: Core Vercel AI SDK - dotenv: Environment variable management ```bash npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's needed: - OPENAI_API_KEY: Your OpenAI API key for GPT model access - COMPOSIO_API_KEY: Your Composio API key for tool access - COMPOSIO_USER_ID: A unique identifier for the user session ```bash OPENAI_API_KEY=your_openai_api_key_here COMPOSIO_API_KEY=your_composio_api_key_here COMPOSIO_USER_ID=your_user_id_here ``` ### 4. Import required modules and validate environment What's happening: - We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio - The dotenv/config import automatically loads environment variables - The MCP client import enables connection to Composio's tool server ```typescript import "dotenv/config"; import { openai } from "@ai-sdk/openai"; import { Composio } from "@composio/core"; import * as readline from "readline"; import { streamText, type ModelMessage, stepCountIs } from "ai"; import { createMCPClient } from "@ai-sdk/mcp"; const composioAPIKey = process.env.COMPOSIO_API_KEY; const composioUserID = process.env.COMPOSIO_USER_ID; if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set"); if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set"); if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set"); const composio = new Composio({ apiKey: composioAPIKey, }); ``` ### 5. Create Tool Router session and initialize MCP client What's happening: - We're creating a Tool Router session that gives your agent access to Fal.ai tools - The create method takes the user ID and specifies which toolkits should be available - The returned mcp object contains the URL and authentication headers needed to connect to the MCP server - This session provides access to all Fal.ai-related tools through the MCP protocol ```typescript async function main() { // Create a tool router session for the user const session = await composio.create(composioUserID!, { toolkits: ["fal_ai"], }); const mcpUrl = session.mcp.url; ``` ### 6. Connect to MCP server and retrieve tools What's happening: - We're creating an MCP client that connects to our Composio Tool Router session via HTTP - The mcp.url provides the endpoint, and mcp.headers contains authentication credentials - The type: "http" is important - Composio requires HTTP transport - tools() retrieves all available Fal.ai tools that the agent can use ```typescript const mcpClient = await createMCPClient({ transport: { type: "http", url: mcpUrl, headers: session.mcp.headers, // Authentication headers for the Composio MCP server }, }); const tools = await mcpClient.tools(); ``` ### 7. Initialize conversation and CLI interface What's happening: - We initialize an empty messages array to maintain conversation history - A readline interface is created to accept user input from the command line - Instructions are displayed to guide the user on how to interact with the agent ```typescript let messages: ModelMessage[] = []; console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n"); console.log( "Ask any questions related to fal_ai, like summarize my last 5 emails, send an email, etc... :)))\n", ); const rl = readline.createInterface({ input: process.stdin, output: process.stdout, prompt: "> ", }); rl.prompt(); ``` ### 8. Handle user input and stream responses with real-time tool feedback What's happening: - We use streamText instead of generateText to stream responses in real-time - toolChoice: "auto" allows the model to decide when to use Fal.ai tools - stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations - onStepFinish callback displays which tools are being used in real-time - We iterate through the text stream to create a typewriter effect as the agent responds - The complete response is added to conversation history to maintain context - Errors are caught and displayed with helpful retry suggestions ```typescript rl.on("line", async (userInput: string) => { const trimmedInput = userInput.trim(); if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) { console.log("\nGoodbye!"); rl.close(); process.exit(0); } if (!trimmedInput) { rl.prompt(); return; } messages.push({ role: "user", content: trimmedInput }); console.log("\nAgent is thinking...\n"); try { const stream = streamText({ model: openai("gpt-5"), messages, tools, toolChoice: "auto", stopWhen: stepCountIs(10), onStepFinish: (step) => { for (const toolCall of step.toolCalls) { console.log(`[Using tool: ${toolCall.toolName}]`); } if (step.toolCalls.length > 0) { console.log(""); // Add space after tool calls } }, }); for await (const chunk of stream.textStream) { process.stdout.write(chunk); } console.log("\n\n---\n"); // Get final result for message history const response = await stream.response; if (response?.messages?.length) { messages.push(...response.messages); } } catch (error) { console.error("\nAn error occurred while talking to the agent:"); console.error(error); console.log( "\nYou can try again or restart the app if it keeps happening.\n", ); } finally { rl.prompt(); } }); rl.on("close", async () => { await mcpClient.close(); console.log("\n👋 Session ended."); process.exit(0); }); } main().catch((err) => { console.error("Fatal error:", err); process.exit(1); }); ``` ## Complete Code ```typescript import "dotenv/config"; import { openai } from "@ai-sdk/openai"; import { Composio } from "@composio/core"; import * as readline from "readline"; import { streamText, type ModelMessage, stepCountIs } from "ai"; import { createMCPClient } from "@ai-sdk/mcp"; const composioAPIKey = process.env.COMPOSIO_API_KEY; const composioUserID = process.env.COMPOSIO_USER_ID; if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set"); if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set"); if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set"); const composio = new Composio({ apiKey: composioAPIKey, }); async function main() { // Create a tool router session for the user const session = await composio.create(composioUserID!, { toolkits: ["fal_ai"], }); const mcpUrl = session.mcp.url; const mcpClient = await createMCPClient({ transport: { type: "http", url: mcpUrl, headers: session.mcp.headers, // Authentication headers for the Composio MCP server }, }); const tools = await mcpClient.tools(); let messages: ModelMessage[] = []; console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n"); console.log( "Ask any questions related to fal_ai, like summarize my last 5 emails, send an email, etc... :)))\n", ); const rl = readline.createInterface({ input: process.stdin, output: process.stdout, prompt: "> ", }); rl.prompt(); rl.on("line", async (userInput: string) => { const trimmedInput = userInput.trim(); if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) { console.log("\nGoodbye!"); rl.close(); process.exit(0); } if (!trimmedInput) { rl.prompt(); return; } messages.push({ role: "user", content: trimmedInput }); console.log("\nAgent is thinking...\n"); try { const stream = streamText({ model: openai("gpt-5"), messages, tools, toolChoice: "auto", stopWhen: stepCountIs(10), onStepFinish: (step) => { for (const toolCall of step.toolCalls) { console.log(`[Using tool: ${toolCall.toolName}]`); } if (step.toolCalls.length > 0) { console.log(""); // Add space after tool calls } }, }); for await (const chunk of stream.textStream) { process.stdout.write(chunk); } console.log("\n\n---\n"); // Get final result for message history const response = await stream.response; if (response?.messages?.length) { messages.push(...response.messages); } } catch (error) { console.error("\nAn error occurred while talking to the agent:"); console.error(error); console.log( "\nYou can try again or restart the app if it keeps happening.\n", ); } finally { rl.prompt(); } }); rl.on("close", async () => { await mcpClient.close(); console.log("\n👋 Session ended."); process.exit(0); }); } main().catch((err) => { console.error("Fatal error:", err); process.exit(1); }); ``` ## Conclusion You've successfully built a Fal.ai agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback. Key features of this implementation: - Real-time streaming responses for a better user experience with typewriter effect - Live tool execution feedback showing which tools are being used as the agent works - Dynamic tool loading through Composio's Tool Router with secure authentication - Multi-step tool execution with configurable step limits (up to 10 steps) - Comprehensive error handling for robust agent execution - Conversation history maintenance for context-aware responses You can extend this further by adding custom error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows. ## 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) - [Mastra AI](https://composio.dev/toolkits/fal_ai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/fal_ai/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/fal_ai/framework/crew-ai) ## 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 Vercel AI SDK v6? Yes, you can. Vercel AI SDK v6 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)