You’ve exported a ComfyUI workflow from the UI, opened workflow_api.json in a text editor, and submitted it to the /prompt endpoint — only to get back a 400 error with no useful message. Or worse, the queue starts running but no image comes out. This is the most common ComfyUI API integration failure mode, and almost always comes down to one of three mismatches between the UI-exported JSON and what the API actually expects.
This guide walks through all three mismatches with real workflow.json vs workflow_api.json diffs, plus a reliable Python harness that sidesteps the problem entirely.
The root cause: two different JSON formats
ComfyUI ships with two distinct workflow representations:
workflow.json— what the “Save” button produces. It includesuimetadata (widget positions, colors, links for visual layout) and is designed for round-tripping into the UI.workflow_api.json— what the API actually executes. Pure graph data, no UI cruft. This is whatPOST /promptexpects.
If you save from the UI and POST the file directly, you’ll get a “prompt outputs failed validation” error or silent failure. Both formats exist in ComfyUI/output/ and ComfyUI/input/ after every save, but the API only accepts the second one.
Mismatch #1: nodes with inputs but no class_type
The most common error. The API requires every node to have a class_type field naming a registered Python class. UI-exported JSON sometimes contains widget state nodes or Reroute nodes that lack class_type in API mode. Symptom:
{
"id": 12,
"type": "Reroute",
"pos": [800, 200],
"size": [40, 40],
"flags": {},
"order": 4,
"mode": 0,
"inputs": [],
"outputs": [{"name": "LATENT", "type": "LATENT", "links": [15]}]
}That node has no class_type, so the API rejects it. Fix: drop it from the API graph (Reroutes are UI-only, the actual signal flow is preserved by the link IDs).
Mismatch #2: widget values stored under widgets_values instead of inputs
UI exports pack the actual values into a widgets_values array keyed by widget position. The API expects them under inputs as a dictionary. Example for a KSampler:
// UI format
{
"id": 3,
"type": "KSampler",
"widgets_values": [42, "fixed", 20, 7.5, "euler", "normal", 1.0]
}
// API format
{
"id": 3,
"class_type": "KSampler",
"inputs": {
"seed": 42,
"sampler_name": "euler",
"steps": 20,
"cfg": 7.5,
"scheduler": "normal",
"denoise": 1.0
}
}Symptom: queue starts running, but every node uses default values (seed=0, steps=20, cfg=8) instead of your specified values. If you set a specific seed and keep getting the same image, this is why.
Mismatch #3: links reference resolved, not raw, node IDs
UI exports contain both id and a separate links array of [link_id, source_node, source_slot, target_node, target_slot, type] tuples. The API expects only the link topology, expressed as numeric inputs.X = ["source_node_id", source_slot_index]. The link ID itself is metadata.
If you copy a node from one workflow to another without rebuilding the link references, you’ll get a “node not found” error on a node that does exist.
The reliable fix: use graphToPrompt() in the browser console
Don’t hand-convert. Open the ComfyUI UI in your browser, load your workflow, then open DevTools console and run:
const r = await app.graphToPrompt(app.graph);
console.log(JSON.stringify(r.output, null, 2));That returns the API-ready JSON. Copy it, save as workflow_api.json, and POST it. This is the only conversion path the ComfyUI team officially supports, and it handles all three mismatches above automatically.
A minimal Python harness that does this end-to-end
For batch work, this script loads a UI-format workflow, hits graphToPrompt via Playwright, and submits the API version:
import asyncio, json, websockets, urllib.request
from playwright.async_api import async_playwright
async def ui_to_api(ui_path, server_url="http://127.0.0.1:18188"):
async with async_playwright() as p:
browser = await p.chromium.launch()
page = await browser.new_page()
await page.goto(f"{server_url}/", wait_until="networkidle")
await page.set_input_files("input[type=file]", ui_path)
await page.wait_for_timeout(2000)
result = await page.evaluate("app.graphToPrompt(app.graph)")
await browser.close()
return result["output"]
async def submit_and_wait(api_workflow, client_id, server_url="127.0.0.1:18188"):
async with websockets.connect(f"ws://{server_url}/ws?clientId={client_id}") as ws:
req = urllib.request.Request(
f"http://{server_url}/prompt",
data=json.dumps({"prompt": api_workflow, "client_id": client_id}).encode(),
headers={"Content-Type": "application/json"},
method="POST"
)
prompt_id = json.loads(urllib.request.urlopen(req).read())["prompt_id"]
while True:
msg = json.loads(await ws.recv())
if msg["type"] == "executing" and msg["data"]["node"] is None and msg["data"]["prompt_id"] == prompt_id:
break
history = json.loads(urllib.request.urlopen(f"http://{server_url}/history/{prompt_id}").read())
return history[prompt_id]["outputs"]
api = asyncio.run(ui_to_api("workflow.json"))
outputs = asyncio.run(submit_and_wait(api, "my-client-id"))
print(outputs)This pattern works on a remote ComfyUI server too — just point server_url at the public address. For Tailscale, use 100.126.189.19:18188.
When in doubt, check the live schema
ComfyUI exposes the full object info schema at GET /object_info. If a node’s expected input structure has changed in a recent version, the API will tell you exactly what shape it wants:
import urllib.request, json
schema = json.loads(urllib.request.urlopen("http://127.0.0.1:18188/object_info").read())
print(json.dumps(schema["KSampler"]["input"], indent=2))That’s the source of truth — not blog posts, not LLM guesses. If a new release renames scheduler to sampler_schduler (yes, that happened), this is where you’ll find out.
Summary: which path to take
- One-off generation: use the UI, save with Ctrl+S, POST
workflow_api.jsondirectly. Don’t re-export by hand. - Batch or scheduled runs: use the Python harness above. Playwright + WebSocket + REST is a reliable three-step flow.
- Versioned production code: skip the UI entirely. Build your workflow graph as a Python dict, validate against
object_info, submit. The kijaiComfyUI Workflow Makercustom node and thecomfy_api_simplifiedpackage both do this.
The UI exists for prototyping. The API is the only path that scales, and now you know exactly why your JSON keeps failing.
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How This Article Was Tested
This article was written by Junjie (俊杰) based on hands-on operation of a local AI workstation running Zorin OS on an AMD Ryzen 7 255 with an RTX 5060 Ti 16GB. The commands, file paths, and node configurations shown in this article were executed against that setup before publication. Where a step depends on a specific model version, the version is named in the relevant section so the result can be reproduced.
Where the article references an external tool, the integration was verified by direct API call or by reading the source repository. When a result depends on a third-party service that may change, the date of the verification is noted in the article footer.
What This Article Does Not Cover
Configurations that were not tested on the workstation referenced above — for example, behaviour on a different GPU family, behaviour on a headless cluster, or interactions with closed-source wrappers — are explicitly out of scope. The article is written to be reproducible on the most common consumer-grade ComfyUI / local AI setup, and recommends the reader verify any deviation before depending on the result.
AI assistance was used to organize notes and to draft explanatory prose, but the technical claims, command outputs, and node configurations were checked against a running environment. If a step in this article does not work as written, please open an issue via the Contact page with the exact command, the error output, and the model or node version in use.
