CONNECT APP
Build with fal.ai
Artificial Intelligence (AI)
- API key
MCP
Give your agent fal.ai tools
Every fal.ai action is exposed as an MCP tool on Pipedream's remote server. Point a client at it with your end user's ID and Connect resolves that user's fal.ai account for each tool call — you store no tokens.
// accessToken: mint a short-lived token with the Connect SDK — see the MCP guide
const transport = new StreamableHTTPClientTransport(
new URL("https://remote.mcp.pipedream.net/v3"),
{
requestInit: {
headers: {
Authorization: `Bearer ${accessToken}`,
"x-pd-project-id": "{project_id}",
"x-pd-environment": "production",
"x-pd-external-user-id": "{external_user_id}", // any stable ID for this user in your system
"x-pd-app-slug": "fal_ai",
},
},
},
)
const mcp = new Client({ name: "my-agent", version: "1.0.0" })
await mcp.connect(transport)
const { tools } = await mcp.listTools()
// e.g. run Cancel Request:
const result = await mcp.callTool({
name: "fal_ai-cancel-request",
arguments: {
appId: "App ID",
requestId: "Request ID",
},
})# access_token: mint a short-lived token with the Connect SDK — see the MCP guide
headers = {
"Authorization": f"Bearer {access_token}",
"x-pd-project-id": "{project_id}",
"x-pd-environment": "production",
"x-pd-external-user-id": "{external_user_id}", # any stable ID for this user in your system
"x-pd-app-slug": "fal_ai",
}
async with streamablehttp_client("https://remote.mcp.pipedream.net/v3", headers=headers) as (read, write, _):
async with ClientSession(read, write) as session:
await session.initialize()
tools = await session.list_tools()
# e.g. run Cancel Request:
result = await session.call_tool("fal_ai-cancel-request", {
"appId": "App ID",
"requestId": "Request ID",
})API PROXY
Call the fal.ai API directly
For an endpoint with no pre-built tool, the Connect proxy forwards your request to the fal.ai API with the connected user's credentials attached. You store no tokens and write no refresh logic.
const resp = await pd.proxy.get({
externalUserId: "{external_user_id}", // any stable ID for this user in your system
accountId: "apn_xxxxxxx",
url: "https://api.fal.ai/v1/models",
})
// Any allowed fal.ai endpoint works here. Pipedream attaches the
// connected account's credentials to the outgoing request.# The path segment is the target URL, URL-safe base64 encoded:
# https://api.fal.ai/v1/models
curl "https://api.pipedream.com/v1/connect/{project_id}/proxy/aHR0cHM6Ly9hcGkuZmFsLmFpL3YxL21vZGVscw?external_user_id={external_user_id}&account_id=apn_xxxxxxx" \
-H "Authorization: Bearer {access_token}" \
-H "x-pd-environment: production"SDK
Run fal.ai actions from your backend
Connect a user's fal.ai account once, then run Cancel Request on their behalf from your own code — TypeScript, Python, or plain HTTP.
import { PipedreamClient } from "@pipedream/sdk"
const pd = new PipedreamClient({
projectId: process.env.PIPEDREAM_PROJECT_ID!,
clientId: process.env.PIPEDREAM_CLIENT_ID!,
clientSecret: process.env.PIPEDREAM_CLIENT_SECRET!,
projectEnvironment: "production",
})
const result = await pd.actions.run({
id: "fal_ai-cancel-request",
externalUserId: "{external_user_id}", // any stable ID for this user in your system
configuredProps: {
fal_ai: { authProvisionId: "apn_xxxxxxx" },
appId: "App ID",
requestId: "Request ID",
},
})from pipedream import Pipedream
pd = Pipedream(
client_id="{oauth_client_id}",
client_secret="{oauth_client_secret}",
project_id="{project_id}",
project_environment="production",
)
result = pd.actions.run(
id="fal_ai-cancel-request",
external_user_id="{external_user_id}", # any stable ID for this user in your system
configured_props={
"fal_ai": {"authProvisionId": "apn_xxxxxxx"},
"appId": "App ID",
"requestId": "Request ID",
},
)curl -X POST https://api.pipedream.com/v1/connect/{project_id}/actions/run \
-H "Content-Type: application/json" \
-H "X-PD-Environment: production" \
-H "Authorization: Bearer {access_token}" \
-d '{
"external_user_id": "{external_user_id}",
"id": "fal_ai-cancel-request",
"configured_props": {
"fal_ai": { "authProvisionId": "apn_xxxxxxx" },
"appId": "App ID",
"requestId": "Request ID"
}
}'TOOLS
fal.ai actions
On-demand operations your product or agent can configure and run on behalf of a connected user.
-
Cancel Request
actionCancels a request in the queue. This allows you to stop a long-running task if it's no longer needed. See the documentation.Writev1.0.1 -
Get Request Response
actionGets the response of a completed request in the queue. This retrieves the results of your asynchronous task. See the documentation.Read-onlyv1.0.1 -
Get Request Status
actionGets the status of a request in the queue. This allows you to monitor the progress of your asynchronous tasks. See the documentation.Read-onlyv1.0.1
No fal.ai triggers are available yet.
MULTI-APP
Use fal.ai with other popular apps
Most products don't stop at one integration. Pair fal.ai with the other apps your users rely on, and ship use cases that span both.
- App slug
- fal_ai
- Authentication
- API key
- Categories
- Artificial Intelligence (AI)
- Actions
- 3
- Triggers
- 0
- API proxy
- Available