Migrate to @fal-ai/client
The @fal-ai/serverless-client
package has been deprecated in favor of @fal-ai/client
. Please check the migration guide for more information.
fal-ai/any-llm
Run any large language model with fal, powered by OpenRouter.
The client provides a convenient way to interact with the model API.
npm install --save @fal-ai/client
@fal-ai/client
The @fal-ai/serverless-client
package has been deprecated in favor of @fal-ai/client
. Please check the migration guide for more information.
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("fal-ai/any-llm", {
input: {
prompt: "What is the meaning of life?"
},
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("fal-ai/any-llm", {
input: {
prompt: "What is the meaning of life?"
},
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("fal-ai/any-llm", {
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("fal-ai/any-llm", {
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.
model
ModelEnum
Name of the model to use. Premium models are charged at 10x the rate of standard models, they include: anthropic/claude-3.5-sonnet, anthropic/claude-3-5-haiku, google/gemini-pro-1.5, openai/gpt-4o. Default value: "google/gemini-flash-1.5"
Possible enum values: anthropic/claude-3.5-sonnet, anthropic/claude-3-5-haiku, anthropic/claude-3-haiku, google/gemini-pro-1.5, google/gemini-flash-1.5, google/gemini-flash-1.5-8b, meta-llama/llama-3.2-1b-instruct, meta-llama/llama-3.2-3b-instruct, meta-llama/llama-3.1-8b-instruct, meta-llama/llama-3.1-70b-instruct, openai/gpt-4o-mini, openai/gpt-4o
prompt
string
* requiredPrompt to be used for the chat completion
system_prompt
string
System prompt to provide context or instructions to the model
{
"model": "google/gemini-flash-1.5",
"prompt": "What is the meaning of life?"
}
output
string
* requiredGenerated output
partial
boolean
Whether the output is partial
error
string
Error message if an error occurred
{
"output": "The meaning of life is subjective and depends on individual perspectives."
}