Render TSX to video
curl --request POST \
--url https://api.varg.ai/v2/render \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"code": "import { Render, Clip, Image, Video } from \"vargai/react\";\nimport { fal } from \"vargai/ai\";\nconst img = Image({ prompt: \"cute orange cat, Pixar style\", aspectRatio: \"9:16\", model: fal.imageModel(\"nano_banana_pro\") });\nexport default <Render width={1080} height={1920}><Clip duration={3}><Video prompt={{ text: \"cat waves hello\", images: [img] }} model={fal.videoModel(\"kling_v3\")} /></Clip></Render>;",
"mode": "strict",
"output": "video"
}
'import requests
url = "https://api.varg.ai/v2/render"
payload = {
"code": "import { Render, Clip, Image, Video } from \"vargai/react\";
import { fal } from \"vargai/ai\";
const img = Image({ prompt: \"cute orange cat, Pixar style\", aspectRatio: \"9:16\", model: fal.imageModel(\"nano_banana_pro\") });
export default <Render width={1080} height={1920}><Clip duration={3}><Video prompt={{ text: \"cat waves hello\", images: [img] }} model={fal.videoModel(\"kling_v3\")} /></Clip></Render>;",
"mode": "strict",
"output": "video"
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
code: 'import { Render, Clip, Image, Video } from "vargai/react";\nimport { fal } from "vargai/ai";\nconst img = Image({ prompt: "cute orange cat, Pixar style", aspectRatio: "9:16", model: fal.imageModel("nano_banana_pro") });\nexport default <Render width={1080} height={1920}><Clip duration={3}><Video prompt={{ text: "cat waves hello", images: [img] }} model={fal.videoModel("kling_v3")} /></Clip></Render>;',
mode: 'strict',
output: 'video'
})
};
fetch('https://api.varg.ai/v2/render', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.varg.ai/v2/render",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'code' => 'import { Render, Clip, Image, Video } from "vargai/react";
import { fal } from "vargai/ai";
const img = Image({ prompt: "cute orange cat, Pixar style", aspectRatio: "9:16", model: fal.imageModel("nano_banana_pro") });
export default <Render width={1080} height={1920}><Clip duration={3}><Video prompt={{ text: "cat waves hello", images: [img] }} model={fal.videoModel("kling_v3")} /></Clip></Render>;',
'mode' => 'strict',
'output' => 'video'
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.varg.ai/v2/render"
payload := strings.NewReader("{\n \"code\": \"import { Render, Clip, Image, Video } from \\\"vargai/react\\\";\\nimport { fal } from \\\"vargai/ai\\\";\\nconst img = Image({ prompt: \\\"cute orange cat, Pixar style\\\", aspectRatio: \\\"9:16\\\", model: fal.imageModel(\\\"nano_banana_pro\\\") });\\nexport default <Render width={1080} height={1920}><Clip duration={3}><Video prompt={{ text: \\\"cat waves hello\\\", images: [img] }} model={fal.videoModel(\\\"kling_v3\\\")} /></Clip></Render>;\",\n \"mode\": \"strict\",\n \"output\": \"video\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.varg.ai/v2/render")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"code\": \"import { Render, Clip, Image, Video } from \\\"vargai/react\\\";\\nimport { fal } from \\\"vargai/ai\\\";\\nconst img = Image({ prompt: \\\"cute orange cat, Pixar style\\\", aspectRatio: \\\"9:16\\\", model: fal.imageModel(\\\"nano_banana_pro\\\") });\\nexport default <Render width={1080} height={1920}><Clip duration={3}><Video prompt={{ text: \\\"cat waves hello\\\", images: [img] }} model={fal.videoModel(\\\"kling_v3\\\")} /></Clip></Render>;\",\n \"mode\": \"strict\",\n \"output\": \"video\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.varg.ai/v2/render")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"code\": \"import { Render, Clip, Image, Video } from \\\"vargai/react\\\";\\nimport { fal } from \\\"vargai/ai\\\";\\nconst img = Image({ prompt: \\\"cute orange cat, Pixar style\\\", aspectRatio: \\\"9:16\\\", model: fal.imageModel(\\\"nano_banana_pro\\\") });\\nexport default <Render width={1080} height={1920}><Clip duration={3}><Video prompt={{ text: \\\"cat waves hello\\\", images: [img] }} model={fal.videoModel(\\\"kling_v3\\\")} /></Clip></Render>;\",\n \"mode\": \"strict\",\n \"output\": \"video\"\n}"
response = http.request(request)
puts response.read_body{
"id": "job_a1b2c3d4e5f6",
"status": "queued",
"stage": "queued",
"attempts": [
{
"n": 1,
"provider": "byteplus",
"model_key": "byteplus:dreamina-seedance-2-5-260628",
"status": "running",
"reason": "real person in the reference images",
"reason_code": "content_privacy",
"references_uploaded": 123,
"resubmitted": true,
"started_at": "2023-11-07T05:31:56Z",
"ended_at": "2023-11-07T05:31:56Z"
}
],
"tool": "tool_2001a2b3c4d5",
"input": {},
"output": {
"version": "v1",
"outputs": [
{
"url": "<string>",
"file_id": "<string>",
"media_type": "video/mp4",
"size_bytes": 123,
"thumbnail_url": "<string>",
"data": "<unknown>",
"raw": "<unknown>"
}
]
},
"error": "<string>",
"progress_message": "stitching 3/8 (40%)",
"progress": 123,
"progress_metadata": {},
"step_state": {
"current_step": "extend",
"steps": {}
},
"estimated_duration_ms": 123,
"estimated_cost_cents": 123,
"actual_cost_cents": 123,
"pricing": {
"estimated": 123,
"actual": 123,
"billing": "<string>",
"cached": true,
"billed_units": {
"images": 1
},
"pricing_id": "<string>"
},
"provider": "fal",
"provider_model": "fal:flux_schnell",
"provider_status": "<string>",
"provider_job_id": "<string>",
"completed_by_provider_model_id": "<string>",
"last_attempt_provider_model_id": "<string>",
"workspace_id": "<string>",
"created_at": "2023-11-07T05:31:56Z",
"started_at": "2023-11-07T05:31:56Z",
"completed_at": "2023-11-07T05:31:56Z",
"warnings": [
"<string>"
],
"urls": {
"self": "<string>",
"status": "<string>",
"refresh": "<string>",
"cancel": "<string>",
"retry": "<string>",
"confirm": "<string>",
"rerun": "<string>"
}
}{
"id": "job_a1b2c3d4e5f6",
"status": "queued",
"stage": "queued",
"attempts": [],
"tool": "tool_a1b2c3d4e5f6",
"input": {
"model": "flux_schnell",
"prompt": "a cat astronaut"
},
"output": null,
"error": null,
"progress_message": null,
"progress": null,
"progress_metadata": null,
"step_state": null,
"estimated_duration_ms": 8000,
"estimated_cost_cents": 4,
"actual_cost_cents": null,
"pricing": {
"estimated": 4,
"actual": null,
"billing": "metered",
"cached": null,
"billed_units": null,
"pricing_id": "ppr_abc123"
},
"provider": "fal",
"provider_model": "fal:flux_schnell",
"provider_status": null,
"provider_job_id": null,
"completed_by_provider_model_id": null,
"last_attempt_provider_model_id": null,
"workspace_id": null,
"created_at": "2026-07-01T10:00:00Z",
"started_at": null,
"completed_at": null,
"urls": {
"self": "https://api.varg.ai/v2/jobs/job_a1b2c3d4e5f6",
"status": "https://api.varg.ai/v2/jobs/job_a1b2c3d4e5f6/status",
"refresh": "https://api.varg.ai/v2/jobs/job_a1b2c3d4e5f6/refresh",
"cancel": "https://api.varg.ai/v2/jobs/job_a1b2c3d4e5f6/cancel",
"retry": "https://api.varg.ai/v2/jobs/job_a1b2c3d4e5f6/retry"
}
}{
"error": {
"code": "model_not_found",
"message": "Unknown model: flux-shnell",
"details": "<unknown>"
}
}{
"error": {
"code": "unauthorized",
"message": "Invalid or missing API key or JWT"
}
}{
"error": {
"code": "insufficient_balance",
"message": "Insufficient balance to run this job",
"details": {
"needed_cents": 120,
"available_cents": 40,
"reserved_cents": 300,
"total_cents": 340,
"held_jobs": 2,
"job_id": "job_a1b2c3d4e5f6"
}
}
}{
"error": {
"code": "model_not_found",
"message": "Unknown model: flux-shnell",
"details": "<unknown>"
}
}{
"error": {
"code": "model_not_found",
"message": "Unknown model: flux-shnell",
"details": "<unknown>"
}
}{
"error": {
"code": "invalid_request",
"message": "Body validation failed (text: Required)"
}
}{
"error": {
"code": "model_not_found",
"message": "Unknown model: flux-shnell",
"details": "<unknown>"
}
}Generation
Render TSX to video
Submit varg TSX code and get back a rendered video (or image frames). No local setup needed — the render runs in the cloud, generating AI assets and stitching them together.
Billing: a flat stitching fee (~5 credits) plus each AI sub-generation billed as its own job. Sub-generation assets are linked to the render job, so partial work is recoverable even if the render fails.
POST
/
render
Render TSX to video
curl --request POST \
--url https://api.varg.ai/v2/render \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"code": "import { Render, Clip, Image, Video } from \"vargai/react\";\nimport { fal } from \"vargai/ai\";\nconst img = Image({ prompt: \"cute orange cat, Pixar style\", aspectRatio: \"9:16\", model: fal.imageModel(\"nano_banana_pro\") });\nexport default <Render width={1080} height={1920}><Clip duration={3}><Video prompt={{ text: \"cat waves hello\", images: [img] }} model={fal.videoModel(\"kling_v3\")} /></Clip></Render>;",
"mode": "strict",
"output": "video"
}
'import requests
url = "https://api.varg.ai/v2/render"
payload = {
"code": "import { Render, Clip, Image, Video } from \"vargai/react\";
import { fal } from \"vargai/ai\";
const img = Image({ prompt: \"cute orange cat, Pixar style\", aspectRatio: \"9:16\", model: fal.imageModel(\"nano_banana_pro\") });
export default <Render width={1080} height={1920}><Clip duration={3}><Video prompt={{ text: \"cat waves hello\", images: [img] }} model={fal.videoModel(\"kling_v3\")} /></Clip></Render>;",
"mode": "strict",
"output": "video"
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
code: 'import { Render, Clip, Image, Video } from "vargai/react";\nimport { fal } from "vargai/ai";\nconst img = Image({ prompt: "cute orange cat, Pixar style", aspectRatio: "9:16", model: fal.imageModel("nano_banana_pro") });\nexport default <Render width={1080} height={1920}><Clip duration={3}><Video prompt={{ text: "cat waves hello", images: [img] }} model={fal.videoModel("kling_v3")} /></Clip></Render>;',
mode: 'strict',
output: 'video'
})
};
fetch('https://api.varg.ai/v2/render', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.varg.ai/v2/render",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'code' => 'import { Render, Clip, Image, Video } from "vargai/react";
import { fal } from "vargai/ai";
const img = Image({ prompt: "cute orange cat, Pixar style", aspectRatio: "9:16", model: fal.imageModel("nano_banana_pro") });
export default <Render width={1080} height={1920}><Clip duration={3}><Video prompt={{ text: "cat waves hello", images: [img] }} model={fal.videoModel("kling_v3")} /></Clip></Render>;',
'mode' => 'strict',
'output' => 'video'
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.varg.ai/v2/render"
payload := strings.NewReader("{\n \"code\": \"import { Render, Clip, Image, Video } from \\\"vargai/react\\\";\\nimport { fal } from \\\"vargai/ai\\\";\\nconst img = Image({ prompt: \\\"cute orange cat, Pixar style\\\", aspectRatio: \\\"9:16\\\", model: fal.imageModel(\\\"nano_banana_pro\\\") });\\nexport default <Render width={1080} height={1920}><Clip duration={3}><Video prompt={{ text: \\\"cat waves hello\\\", images: [img] }} model={fal.videoModel(\\\"kling_v3\\\")} /></Clip></Render>;\",\n \"mode\": \"strict\",\n \"output\": \"video\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.varg.ai/v2/render")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"code\": \"import { Render, Clip, Image, Video } from \\\"vargai/react\\\";\\nimport { fal } from \\\"vargai/ai\\\";\\nconst img = Image({ prompt: \\\"cute orange cat, Pixar style\\\", aspectRatio: \\\"9:16\\\", model: fal.imageModel(\\\"nano_banana_pro\\\") });\\nexport default <Render width={1080} height={1920}><Clip duration={3}><Video prompt={{ text: \\\"cat waves hello\\\", images: [img] }} model={fal.videoModel(\\\"kling_v3\\\")} /></Clip></Render>;\",\n \"mode\": \"strict\",\n \"output\": \"video\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.varg.ai/v2/render")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"code\": \"import { Render, Clip, Image, Video } from \\\"vargai/react\\\";\\nimport { fal } from \\\"vargai/ai\\\";\\nconst img = Image({ prompt: \\\"cute orange cat, Pixar style\\\", aspectRatio: \\\"9:16\\\", model: fal.imageModel(\\\"nano_banana_pro\\\") });\\nexport default <Render width={1080} height={1920}><Clip duration={3}><Video prompt={{ text: \\\"cat waves hello\\\", images: [img] }} model={fal.videoModel(\\\"kling_v3\\\")} /></Clip></Render>;\",\n \"mode\": \"strict\",\n \"output\": \"video\"\n}"
response = http.request(request)
puts response.read_body{
"id": "job_a1b2c3d4e5f6",
"status": "queued",
"stage": "queued",
"attempts": [
{
"n": 1,
"provider": "byteplus",
"model_key": "byteplus:dreamina-seedance-2-5-260628",
"status": "running",
"reason": "real person in the reference images",
"reason_code": "content_privacy",
"references_uploaded": 123,
"resubmitted": true,
"started_at": "2023-11-07T05:31:56Z",
"ended_at": "2023-11-07T05:31:56Z"
}
],
"tool": "tool_2001a2b3c4d5",
"input": {},
"output": {
"version": "v1",
"outputs": [
{
"url": "<string>",
"file_id": "<string>",
"media_type": "video/mp4",
"size_bytes": 123,
"thumbnail_url": "<string>",
"data": "<unknown>",
"raw": "<unknown>"
}
]
},
"error": "<string>",
"progress_message": "stitching 3/8 (40%)",
"progress": 123,
"progress_metadata": {},
"step_state": {
"current_step": "extend",
"steps": {}
},
"estimated_duration_ms": 123,
"estimated_cost_cents": 123,
"actual_cost_cents": 123,
"pricing": {
"estimated": 123,
"actual": 123,
"billing": "<string>",
"cached": true,
"billed_units": {
"images": 1
},
"pricing_id": "<string>"
},
"provider": "fal",
"provider_model": "fal:flux_schnell",
"provider_status": "<string>",
"provider_job_id": "<string>",
"completed_by_provider_model_id": "<string>",
"last_attempt_provider_model_id": "<string>",
"workspace_id": "<string>",
"created_at": "2023-11-07T05:31:56Z",
"started_at": "2023-11-07T05:31:56Z",
"completed_at": "2023-11-07T05:31:56Z",
"warnings": [
"<string>"
],
"urls": {
"self": "<string>",
"status": "<string>",
"refresh": "<string>",
"cancel": "<string>",
"retry": "<string>",
"confirm": "<string>",
"rerun": "<string>"
}
}{
"id": "job_a1b2c3d4e5f6",
"status": "queued",
"stage": "queued",
"attempts": [],
"tool": "tool_a1b2c3d4e5f6",
"input": {
"model": "flux_schnell",
"prompt": "a cat astronaut"
},
"output": null,
"error": null,
"progress_message": null,
"progress": null,
"progress_metadata": null,
"step_state": null,
"estimated_duration_ms": 8000,
"estimated_cost_cents": 4,
"actual_cost_cents": null,
"pricing": {
"estimated": 4,
"actual": null,
"billing": "metered",
"cached": null,
"billed_units": null,
"pricing_id": "ppr_abc123"
},
"provider": "fal",
"provider_model": "fal:flux_schnell",
"provider_status": null,
"provider_job_id": null,
"completed_by_provider_model_id": null,
"last_attempt_provider_model_id": null,
"workspace_id": null,
"created_at": "2026-07-01T10:00:00Z",
"started_at": null,
"completed_at": null,
"urls": {
"self": "https://api.varg.ai/v2/jobs/job_a1b2c3d4e5f6",
"status": "https://api.varg.ai/v2/jobs/job_a1b2c3d4e5f6/status",
"refresh": "https://api.varg.ai/v2/jobs/job_a1b2c3d4e5f6/refresh",
"cancel": "https://api.varg.ai/v2/jobs/job_a1b2c3d4e5f6/cancel",
"retry": "https://api.varg.ai/v2/jobs/job_a1b2c3d4e5f6/retry"
}
}{
"error": {
"code": "model_not_found",
"message": "Unknown model: flux-shnell",
"details": "<unknown>"
}
}{
"error": {
"code": "unauthorized",
"message": "Invalid or missing API key or JWT"
}
}{
"error": {
"code": "insufficient_balance",
"message": "Insufficient balance to run this job",
"details": {
"needed_cents": 120,
"available_cents": 40,
"reserved_cents": 300,
"total_cents": 340,
"held_jobs": 2,
"job_id": "job_a1b2c3d4e5f6"
}
}
}{
"error": {
"code": "model_not_found",
"message": "Unknown model: flux-shnell",
"details": "<unknown>"
}
}{
"error": {
"code": "model_not_found",
"message": "Unknown model: flux-shnell",
"details": "<unknown>"
}
}{
"error": {
"code": "invalid_request",
"message": "Body validation failed (text: Required)"
}
}{
"error": {
"code": "model_not_found",
"message": "Unknown model: flux-shnell",
"details": "<unknown>"
}
}Authorizations
varg API key (varg_...). Get one at app.varg.ai
or run bunx vargai login. App sessions may alternatively send a
Supabase JWT.
Headers
Any unique string. Retrying a request with the same key returns the existing job (HTTP 200) instead of creating and charging a new one (HTTP 202).
Folder id (uuid) or slug. The job's output is filed into this folder
when the job completes. An unknown folder returns 404
workspace_not_found and no job is created. If you cannot set
headers, send a top-level workspace_id field in the JSON body
instead. Not part of the generation input, so it does not affect
caching.
Body
application/json
TSX source. Import components from vargai/react and providers
from vargai/ai, and default-export a <Render> element.
strict (default) fails on errors; preview substitutes placeholders
Available options:
strict, preview frames skips stitching for image-only renders
Available options:
video, frames Response
Idempotency replay — the existing job for this Idempotency-Key (no new charge).
Example:
"job_a1b2c3d4e5f6"
The status only moves forward: queued → running → completed,
failed or cancelled. Everything a running job goes through —
sending to the provider, uploading references, switching to another
provider — is a stage of running; a job never goes back to
queued. (Older API builds also reported submitting; treat it as
running.)
awaiting_confirmation (multi-step pipelines only): the job finished a
step that asks for review and waits for POST /jobs/{id}/confirm. It
is not terminal.
Available options:
queued, running, awaiting_confirmation, completed, failed, cancelled What the job is doing right now. null once the job is terminal, and
while the job is awaiting_confirmation.
queued— waiting for a slot; no attempt yetsubmitting— being sent to the provideruploading_references— the provider is taking the reference files into its asset library (progress_messageshowsn/m)rendering— the provider accepted the task and is generatingretrying— an attempt failed (the provider refused, failed or rate limited it); the job waits for its next attempt, usually on another provider. Seeattempts.finalizing— the result is being saved and billed
Available options:
queued, submitting, uploading_references, rendering, retrying, finalizing, null Every provider tried for this job, oldest first. provider / provider_model are the newest one.
Show child attributes
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Internal tool id (e.g. tool_2001a2b3c4d5 for video), not the tool_key. Do not compare it against GET /tools keys.
Example:
"tool_2001a2b3c4d5"
The request body as submitted (model, prompt, ...)
Canonical output envelope. Present only when the job is completed.
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Human-readable failure message. null unless the job failed or was cancelled.
Example:
"stitching 3/8 (40%)"
Fraction 0..1
Multi-step pipeline jobs only: the current step and each step's
status, intermediate output and cost. null for ordinary jobs.
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Structured pricing. 1 credit = 1 cent = $0.01.
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Example:
"fal"
Example:
"fal:flux_schnell"
The provider's raw status string
Provider model route that completed the job
Provider model route of the most recent attempt
Folder the outputs are filed into (see X-Workspace-Id)
Present on creation responses when the request was adjusted (for example a resolution fallback)
Lifecycle URLs (present on creation responses)
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