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The client provides a convenient way to interact with the model API.
npm install --save @fal-ai/clientThe @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Install the new package and update your imports — see client setup.
Set FAL_KEY as an environment variable in your runtime.
export FAL_KEY="YOUR_API_KEY"The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.
import { fal } from "@fal-ai/client";
const result = await fal.subscribe("nvidia/nemotron-3-nano-omni", {
input: {
prompt: "Summarize the key capabilities of a multimodal agent."
},
logs: true,
onQueueUpdate: (update) => {
if (update.status === "IN_PROGRESS") {
update.logs.map((log) => log.message).forEach(console.log);
}
},
});
console.log(result.data);
console.log(result.requestId);The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.
import { fal } from "@fal-ai/client";
fal.config({
credentials: "YOUR_FAL_KEY"
});When running code on the client-side (e.g. in a browser, mobile app or GUI applications), make sure to not expose your FAL_KEY. Instead, use a server-side proxy to make requests to the API. For more information, check out our server-side integration guide.
The client API provides a convenient way to submit requests to the model.
import { fal } from "@fal-ai/client";
const { request_id } = await fal.queue.submit("nvidia/nemotron-3-nano-omni", {
input: {
prompt: "Summarize the key capabilities of a multimodal agent."
},
webhookUrl: "https://optional.webhook.url/for/results",
});You can fetch the status of a request to check if it is completed or still in progress.
import { fal } from "@fal-ai/client";
const status = await fal.queue.status("nvidia/nemotron-3-nano-omni", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
logs: true,
});Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.
import { fal } from "@fal-ai/client";
const result = await fal.queue.result("nvidia/nemotron-3-nano-omni", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.
You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.
You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.
We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.
import { fal } from "@fal-ai/client";
const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).
Read more about file handling in our file upload guide.
prompt string* requiredText prompt to send to the model. English only.
system_prompt stringOptional system prompt to steer the model. Reasoning behavior is controlled by the separate reasoning_mode field.
reasoning_mode ReasoningModeEnumWhether the model should emit an explicit reasoning trace. no_think returns a direct answer; think returns chain-of-thought followed by the final answer. Default value: "no_think"
Possible enum values: think, no_think
max_tokens integerMaximum number of tokens to generate. Default value: 1024
temperature floatSampling temperature. Lower is more deterministic. Default value: 0.7
top_p floatNucleus sampling probability mass. Default value: 0.95
enable_safety_checker booleanWhether to enable the safety checker. Default value: true
{
"prompt": "Summarize the key capabilities of a multimodal agent.",
"system_prompt": "You are a concise enterprise assistant.",
"reasoning_mode": "no_think",
"max_tokens": 1024,
"temperature": 0.7,
"top_p": 0.95,
"enable_safety_checker": true
}output string* requiredGenerated text response.
finish_reason stringReason generation stopped. Default value: "stop"
Token usage for the request.
{
"output": "The image shows a golden retriever puppy sitting on a wooden floor.",
"finish_reason": "stop",
"usage": {
"output_tokens": 87,
"input_tokens": 412
}
}input_tokens integer* requiredNumber of input tokens processed.
output_tokens integer* requiredNumber of output tokens generated.