Defer record_node_startup_error in prestartup error path; add docstrings

Buffer prestartup failures into a module-level list inside main.py
instead of importing 'nodes' (and therefore 'torch') from within the
exception handler. After the normal 'import nodes' line, drain the
buffer via nodes.record_node_startup_error so bootstrap order stays
deterministic regardless of whether a prestartup script succeeded.

Also convert the explanatory '#' comment on the new
/node_startup_errors endpoint into a proper docstring and add a
docstring to execute_prestartup_script, addressing CodeRabbit's
docstring-coverage warning on this PR.

Addresses review feedback on PR #13184.

Amp-Thread-ID: https://ampcode.com/threads/T-019e2f90-26fe-7048-9855-5ff39d08a3e0
Co-authored-by: Amp <amp@ampcode.com>

app/preview3d_bridge.py

@@ -1,116 +0,0 @@
-"""Round-trip render bridge for Preview3D / SaveGLB IMAGE+MASK outputs.
-
-Flow: a node's execute() calls bridge.request_render(), which sends a
-websocket "preview3d.render_request" event to the client and awaits a
-Future. The client renders the file with a fixed camera at fixed
-resolution, uploads the PNGs to /temp/, and POSTs {render_id, image,
-mask} to /3d/render_response, which resolves the Future. The node then
-loads image/mask via the standard LoadImage path.
-"""
-
-from __future__ import annotations
-
-import asyncio
-import concurrent.futures
-import uuid
-from typing import Any
-
-from aiohttp import web
-
-
-_RENDER_TIMEOUT_SEC = 60
-
-
-class Preview3DBridge:
-    """Server-side half of the Preview3D / SaveGLB render round-trip.
-
-    Owns the pending Future registry and the POST /3d/render_response
-    route. Instantiated once on PromptServer and surfaced as
-    PromptServer.instance.preview3d_bridge.
-    """
-
-    def __init__(self) -> None:
-        self._pending: dict[str, concurrent.futures.Future] = {}
-
-    def add_routes(self, routes: web.RouteTableDef) -> None:
-        @routes.post("/3d/render_response")
-        async def render_response(request: web.Request) -> web.Response:
-            try:
-                data = await request.json()
-            except Exception:
-                return web.Response(status=400, text="invalid json")
-            render_id = data.get("render_id")
-            if not render_id or render_id not in self._pending:
-                return web.Response(status=404, text="unknown render_id")
-            future = self._pending.pop(render_id)
-            if future.done():
-                return web.Response(status=409, text="already resolved")
-            if "error" in data:
-                future.set_exception(RuntimeError(str(data["error"])))
-            else:
-                future.set_result({
-                    "image": data.get("image"),
-                    "mask": data.get("mask"),
-                })
-            return web.json_response({"ok": True})
-
-    async def request_render(
-        self,
-        node_id: str,
-        file_path: str,
-        file_type: str = "output",
-        camera_info: dict | None = None,
-        timeout: float = _RENDER_TIMEOUT_SEC,
-    ) -> dict[str, Any]:
-        """Send a render request and await the client's response.
-
-        node_id should be the executor-provided cls.hidden.unique_id of
-        the requesting Preview3D / SaveGLB node — the client uses it to
-        find the matching viewer instance.
-        """
-        from server import PromptServer
-        server = PromptServer.instance
-        if server is None:
-            raise RuntimeError("PromptServer is not initialized")
-
-        render_id = uuid.uuid4().hex
-        future: concurrent.futures.Future = concurrent.futures.Future()
-        self._pending[render_id] = future
-
-        payload = {
-            "render_id": render_id,
-            "node_id": node_id,
-            "file_path": file_path,
-            "type": file_type,
-            "camera_info": camera_info,
-        }
-        server.send_sync("preview3d.render_request", payload, server.client_id)
-
-        try:
-            return await asyncio.wait_for(
-                asyncio.wrap_future(future), timeout=timeout
-            )
-        except asyncio.TimeoutError:
-            self._pending.pop(render_id, None)
-            raise RuntimeError(
-                f"Preview3D render bridge: client did not respond in {timeout}s "
-                f"(node_id={node_id}, file={file_path}). Is the workflow open "
-                "in a browser tab?"
-            )
-        except Exception:
-            self._pending.pop(render_id, None)
-            raise
-
-
-def load_image_and_mask(image_ref: str, mask_ref: str):
-    import nodes
-    loader = nodes.LoadImage()
-    image_tensor, _ = loader.load_image(image=image_ref)
-    _, mask_tensor = loader.load_image(image=mask_ref)
-    return image_tensor, mask_tensor
-
-
-__all__ = [
-    "Preview3DBridge",
-    "load_image_and_mask",
-]

comfy_api_nodes/apis/anthropic.py

@@ -35,6 +35,19 @@ class AnthropicMessage(BaseModel):
     content: list[AnthropicTextContent | AnthropicImageContent] = Field(...)
 
 
+class AnthropicThinkingConfig(BaseModel):
+    type: Literal["enabled", "disabled", "adaptive"] = Field(...)
+    budget_tokens: int | None = Field(
+        None, ge=1024,
+        description="Reasoning budget in tokens. Used when type is 'enabled'. Must be less than max_tokens.",
+    )
+
+
+class AnthropicOutputConfig(BaseModel):
+    """Used with `thinking.type='adaptive'` on models like Opus 4.7."""
+    effort: Literal["low", "medium", "high"] | None = Field(None)
+
+
 class AnthropicMessagesRequest(BaseModel):
     model: str = Field(...)
     messages: list[AnthropicMessage] = Field(...)
@@ -44,6 +57,8 @@ class AnthropicMessagesRequest(BaseModel):
     top_p: float | None = Field(None, ge=0.0, le=1.0)
     top_k: int | None = Field(None, ge=0)
     stop_sequences: list[str] | None = Field(None)
+    thinking: AnthropicThinkingConfig | None = Field(None)
+    output_config: AnthropicOutputConfig | None = Field(None)
 
 
 class AnthropicResponseTextBlock(BaseModel):
@@ -51,6 +66,14 @@ class AnthropicResponseTextBlock(BaseModel):
     text: str = Field(...)
 
 
+class AnthropicResponseThinkingBlock(BaseModel):
+    type: Literal["thinking"] = "thinking"
+    thinking: str = Field(...)
+
+
+AnthropicResponseBlock = AnthropicResponseTextBlock | AnthropicResponseThinkingBlock
+
+
 class AnthropicCacheCreationUsage(BaseModel):
     ephemeral_5m_input_tokens: int | None = Field(None)
     ephemeral_1h_input_tokens: int | None = Field(None)
@@ -69,7 +92,7 @@ class AnthropicMessagesResponse(BaseModel):
     type: str | None = Field(None)
     role: str | None = Field(None)
     model: str | None = Field(None)
-    content: list[AnthropicResponseTextBlock] | None = Field(None)
+    content: list[AnthropicResponseBlock] | None = Field(None)
     stop_reason: str | None = Field(None)
     stop_sequence: str | None = Field(None)
     usage: AnthropicMessagesUsage | None = Field(None)

comfy_api_nodes/apis/openrouter.py

@@ -0,0 +1,93 @@
+"""Pydantic models for the OpenRouter chat completions API.
+
+See: https://openrouter.ai/docs/api/api-reference/chat/send-chat-completion-request
+"""
+
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class OpenRouterTextContent(BaseModel):
+    type: Literal["text"] = "text"
+    text: str = Field(...)
+
+
+class OpenRouterImageUrl(BaseModel):
+    url: str = Field(...)
+
+
+class OpenRouterImageContent(BaseModel):
+    type: Literal["image_url"] = "image_url"
+    image_url: OpenRouterImageUrl = Field(...)
+
+
+class OpenRouterVideoUrl(BaseModel):
+    url: str = Field(...)
+
+
+class OpenRouterVideoContent(BaseModel):
+    type: Literal["video_url"] = "video_url"
+    video_url: OpenRouterVideoUrl = Field(...)
+
+
+OpenRouterContentBlock = OpenRouterTextContent | OpenRouterImageContent | OpenRouterVideoContent
+
+
+class OpenRouterMessage(BaseModel):
+    role: Literal["system", "user", "assistant"] = Field(...)
+    content: str | list[OpenRouterContentBlock] = Field(...)
+
+
+class OpenRouterReasoningConfig(BaseModel):
+    effort: str | None = Field(None)
+    exclude: bool | None = Field(None, description="If true, model reasons but reasoning is excluded from response.")
+
+
+class OpenRouterWebSearchOptions(BaseModel):
+    search_context_size: str | None = Field(None)
+
+
+class OpenRouterChatRequest(BaseModel):
+    model: str = Field(...)
+    messages: list[OpenRouterMessage] = Field(...)
+    seed: int | None = Field(None)
+    reasoning: OpenRouterReasoningConfig | None = Field(None)
+    web_search_options: OpenRouterWebSearchOptions | None = Field(None)
+    stream: bool = Field(False)
+
+
+class OpenRouterUsage(BaseModel):
+    prompt_tokens: int | None = Field(None)
+    completion_tokens: int | None = Field(None)
+    total_tokens: int | None = Field(None)
+    cost: float | None = Field(None, description="Server-side authoritative USD cost of the call.")
+
+
+class OpenRouterResponseMessage(BaseModel):
+    role: str | None = Field(None)
+    content: str | None = Field(None)
+    reasoning: str | None = Field(None)
+    refusal: str | None = Field(None)
+
+
+class OpenRouterChoice(BaseModel):
+    index: int | None = Field(None)
+    message: OpenRouterResponseMessage | None = Field(None)
+    finish_reason: str | None = Field(None)
+
+
+class OpenRouterError(BaseModel):
+    code: int | str | None = Field(None)
+    message: str | None = Field(None)
+    metadata: dict | None = Field(None)
+
+
+class OpenRouterChatResponse(BaseModel):
+    id: str | None = Field(None)
+    model: str | None = Field(None)
+    object: str | None = Field(None)
+    provider: str | None = Field(None)
+    choices: list[OpenRouterChoice] | None = Field(None)
+    usage: OpenRouterUsage | None = Field(None)
+    error: OpenRouterError | None = Field(None)

comfy_api_nodes/nodes_anthropic.py

@@ -9,8 +9,11 @@ from comfy_api_nodes.apis.anthropic import (
     AnthropicMessage,
     AnthropicMessagesRequest,
     AnthropicMessagesResponse,
+    AnthropicOutputConfig,
+    AnthropicResponseTextBlock,
     AnthropicRole,
     AnthropicTextContent,
+    AnthropicThinkingConfig,
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
@@ -32,15 +35,29 @@ CLAUDE_MODELS: dict[str, str] = {
     "Haiku 4.5": "claude-haiku-4-5-20251001",
 }
 
+_THINKING_UNSUPPORTED = {"Haiku 4.5"}
+# Models that use the newer "adaptive" thinking mode (Opus 4.7 requires it; older models keep the explicit budget API).
+# Anthropic decides the actual budget when adaptive is used, based on the `output_config.effort` hint.
+_ADAPTIVE_THINKING_MODELS = {"Opus 4.7", "Opus 4.6", "Sonnet 4.6"}
 
-def _claude_model_inputs():
-    return [
+# Budget mode (Sonnet 4.5): effort -> reasoning budget in tokens. Must be < max_tokens.
+# Sized so even the "high" budget fits comfortably under the default max_tokens=32768.
+_REASONING_BUDGET: dict[str, int] = {
+    "low": 2048,
+    "medium": 8192,
+    "high": 16384,
+}
+_REASONING_EFFORTS = ["off", "low", "medium", "high"]
+
+
+def _claude_model_inputs(model_label: str):
+    inputs: list = [
         IO.Int.Input(
             "max_tokens",
-            default=16000,
-            min=32,
-            max=32000,
-            tooltip="Maximum number of tokens to generate before stopping.",
+            default=32768,
+            min=4096,
+            max=64000,
+            tooltip="Maximum number of tokens to generate (includes reasoning tokens when enabled).",
             advanced=True,
         ),
         IO.Float.Input(
@@ -49,10 +66,24 @@ def _claude_model_inputs():
             min=0.0,
             max=1.0,
             step=0.01,
-            tooltip="Controls randomness. 0.0 is deterministic, 1.0 is most random. Ignored for Opus 4.7.",
+            tooltip=(
+                "Controls randomness. 0.0 is deterministic, 1.0 is most random. "
+                "Ignored for Opus 4.7 and any model when reasoning_effort is set."
+            ),
             advanced=True,
         ),
     ]
+    if model_label not in _THINKING_UNSUPPORTED:
+        inputs.append(
+            IO.Combo.Input(
+                "reasoning_effort",
+                options=_REASONING_EFFORTS,
+                default="off",
+                tooltip="Extended thinking effort. 'off' disables reasoning.",
+                advanced=True,
+            )
+        )
+    return inputs
 
 
 def _model_price_per_million(model: str) -> tuple[float, float] | None:
@@ -95,7 +126,11 @@ def calculate_tokens_price(response: AnthropicMessagesResponse) -> float | None:
 def _get_text_from_response(response: AnthropicMessagesResponse) -> str:
     if not response.content:
         return ""
-    return "\n".join(block.text for block in response.content if block.text)
+    # Thinking blocks are silently dropped — we never want reasoning in the output.
+    return "\n".join(
+        block.text for block in response.content
+        if isinstance(block, AnthropicResponseTextBlock) and block.text
+    )
 
 
 async def _build_image_content_blocks(
@@ -133,7 +168,10 @@ class ClaudeNode(IO.ComfyNode):
                 ),
                 IO.DynamicCombo.Input(
                     "model",
-                    options=[IO.DynamicCombo.Option(label, _claude_model_inputs()) for label in CLAUDE_MODELS],
+                    options=[
+                        IO.DynamicCombo.Option(label, _claude_model_inputs(label))
+                        for label in CLAUDE_MODELS
+                    ],
                     tooltip="The Claude model used to generate the response.",
                 ),
                 IO.Int.Input(
@@ -207,8 +245,29 @@ class ClaudeNode(IO.ComfyNode):
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
         model_label = model["model"]
-        max_tokens = model["max_tokens"]
-        temperature = None if model_label == "Opus 4.7" else model["temperature"]
+        max_tokens = model.get("max_tokens", 32768)
+        reasoning_effort = model.get("reasoning_effort", "off")
+        thinking_enabled = reasoning_effort not in ("off", None) and model_label not in _THINKING_UNSUPPORTED
+
+        # Anthropic requires temperature to be unset (defaults to 1.0) when thinking is enabled.
+        # Opus 4.7 also rejects user-supplied temperature.
+        if thinking_enabled or model_label == "Opus 4.7":
+            temperature = None
+        else:
+            temperature = model.get("temperature", 1.0)
+
+        thinking_cfg: AnthropicThinkingConfig | None = None
+        output_cfg: AnthropicOutputConfig | None = None
+        if thinking_enabled:
+            if model_label in _ADAPTIVE_THINKING_MODELS:
+                # Adaptive mode - Anthropic chooses the budget based on effort hint
+                thinking_cfg = AnthropicThinkingConfig(type="adaptive")
+                output_cfg = AnthropicOutputConfig(effort=reasoning_effort)
+            else:
+                # Budget mode (Sonnet 4.5). Leave at least 1024 tokens for the actual response
+                budget = _REASONING_BUDGET[reasoning_effort]
+                budget = min(budget, max(1024, max_tokens - 1024))
+                thinking_cfg = AnthropicThinkingConfig(type="enabled", budget_tokens=budget)
 
         image_tensors: list[Input.Image] = [t for t in (images or {}).values() if t is not None]
         if sum(get_number_of_images(t) for t in image_tensors) > CLAUDE_MAX_IMAGES:
@@ -229,6 +288,8 @@ class ClaudeNode(IO.ComfyNode):
                 messages=[AnthropicMessage(role=AnthropicRole.user, content=content)],
                 system=system_prompt or None,
                 temperature=temperature,
+                thinking=thinking_cfg,
+                output_config=output_cfg,
             ),
             price_extractor=calculate_tokens_price,
         )

comfy_api_nodes/nodes_bytedance.py

@@ -43,15 +43,16 @@ from comfy_api_nodes.util import (
     ApiEndpoint,
     download_url_to_image_tensor,
     download_url_to_video_output,
+    downscale_video_to_max_pixels,
     get_number_of_images,
     image_tensor_pair_to_batch,
     poll_op,
-    resize_video_to_pixel_budget,
     sync_op,
     upload_audio_to_comfyapi,
     upload_image_to_comfyapi,
     upload_images_to_comfyapi,
     upload_video_to_comfyapi,
+    upscale_video_to_min_pixels,
     validate_image_aspect_ratio,
     validate_image_dimensions,
     validate_string,
@@ -110,12 +111,13 @@ def _validate_ref_video_pixels(video: Input.Video, model_id: str, resolution: st
     max_px = limits.get("max")
     if min_px and pixels < min_px:
         raise ValueError(
-            f"Reference video {index} is too small: {w}x{h} = {pixels:,}px. " f"Minimum is {min_px:,}px for this model."
+            f"Reference video {index} is too small: {w}x{h} = {pixels:,} total pixels. "
+            f"Minimum for this model is {min_px:,} total pixels."
         )
     if max_px and pixels > max_px:
         raise ValueError(
-            f"Reference video {index} is too large: {w}x{h} = {pixels:,}px. "
-            f"Maximum is {max_px:,}px for this model. Try downscaling the video."
+            f"Reference video {index} is too large: {w}x{h} = {pixels:,} total pixels. "
+            f"Maximum for this model is {max_px:,} total pixels. Try downscaling the video."
         )
 
 
@@ -1676,14 +1678,14 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
                     "first_frame_asset_id",
                     default="",
                     tooltip="Seedance asset_id to use as the first frame. "
-                            "Mutually exclusive with the first_frame image input.",
+                    "Mutually exclusive with the first_frame image input.",
                     optional=True,
                 ),
                 IO.String.Input(
                     "last_frame_asset_id",
                     default="",
                     tooltip="Seedance asset_id to use as the last frame. "
-                            "Mutually exclusive with the last_frame image input.",
+                    "Mutually exclusive with the last_frame image input.",
                     optional=True,
                 ),
                 IO.Int.Input(
@@ -1865,11 +1867,20 @@ def _seedance2_reference_inputs(resolutions: list[str], default_ratio: str = "16
         IO.Boolean.Input(
             "auto_downscale",
             default=False,
-            advanced=True,
             optional=True,
             tooltip="Automatically downscale reference videos that exceed the model's pixel budget "
             "for the selected resolution. Aspect ratio is preserved; videos already within limits are untouched.",
         ),
+        IO.Boolean.Input(
+            "auto_upscale",
+            default=False,
+            advanced=True,
+            optional=True,
+            tooltip="Automatically upscale reference videos that are below the model's minimum pixel count "
+            "for the selected resolution. Aspect ratio is preserved; videos already meeting the minimum are "
+            "untouched. Note: upscaling a low-resolution source does not add real detail and may produce "
+            "lower-quality generations.",
+        ),
         IO.Autogrow.Input(
             "reference_assets",
             template=IO.Autogrow.TemplateNames(
@@ -2030,7 +2041,13 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
             max_px = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id, {}).get(model["resolution"], {}).get("max")
             if max_px:
                 for key in reference_videos:
-                    reference_videos[key] = resize_video_to_pixel_budget(reference_videos[key], max_px)
+                    reference_videos[key] = downscale_video_to_max_pixels(reference_videos[key], max_px)
+
+        if model.get("auto_upscale") and reference_videos:
+            min_px = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id, {}).get(model["resolution"], {}).get("min")
+            if min_px:
+                for key in reference_videos:
+                    reference_videos[key] = upscale_video_to_min_pixels(reference_videos[key], min_px)
 
         total_video_duration = 0.0
         for i, key in enumerate(reference_videos, 1):

comfy_api_nodes/nodes_openrouter.py

@@ -0,0 +1,374 @@
+"""API Nodes for OpenRouter LLM chat completions."""
+
+from dataclasses import dataclass
+from typing import Literal
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.openrouter import (
+    OpenRouterChatRequest,
+    OpenRouterChatResponse,
+    OpenRouterContentBlock,
+    OpenRouterImageContent,
+    OpenRouterImageUrl,
+    OpenRouterMessage,
+    OpenRouterReasoningConfig,
+    OpenRouterTextContent,
+    OpenRouterVideoContent,
+    OpenRouterVideoUrl,
+    OpenRouterWebSearchOptions,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    get_number_of_images,
+    sync_op,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_string,
+)
+
+OPENROUTER_CHAT_ENDPOINT = "/proxy/openrouter/api/v1/chat/completions"
+
+
+Profile = Literal["standard", "reasoning", "frontier_reasoning", "perplexity", "perplexity_reasoning"]
+
+
+@dataclass(frozen=True)
+class _ModelSpec:
+    slug: str  # exact OpenRouter model id
+    profile: Profile
+    price_in: float  # USD per token (prompt)
+    price_out: float  # USD per token (completion)
+    max_images: int = 0  # 0 = no image input; otherwise max URL-passed images supported
+    max_videos: int = 0  # 0 = no video input; otherwise max URL-passed videos supported
+
+
+MODELS: list[_ModelSpec] = [
+    _ModelSpec("anthropic/claude-opus-4.7", "frontier_reasoning", 0.000005, 0.000025, max_images=20),
+    _ModelSpec("openai/gpt-5.5-pro", "frontier_reasoning", 0.00003, 0.00018, max_images=20),
+    _ModelSpec("openai/gpt-5.5", "frontier_reasoning", 0.000005, 0.00003, max_images=20),
+    _ModelSpec("google/gemini-3.5-flash", "reasoning", 0.0000015, 0.000009, max_images=20, max_videos=4),
+    _ModelSpec("x-ai/grok-4.20", "reasoning", 0.00000125, 0.0000025, max_images=20),
+    _ModelSpec("x-ai/grok-4.3", "reasoning", 0.00000125, 0.0000025, max_images=20),
+    _ModelSpec("deepseek/deepseek-v4-pro", "reasoning", 0.000000435, 0.00000087),
+    _ModelSpec("deepseek/deepseek-v4-flash", "reasoning", 0.000000112, 0.000000224),
+    _ModelSpec("deepseek/deepseek-v3.2", "reasoning", 0.000000252, 0.000000378),
+    _ModelSpec("qwen/qwen3.6-max-preview", "reasoning", 0.00000104, 0.00000624),
+    _ModelSpec("qwen/qwen3.6-plus", "reasoning", 0.000000325, 0.00000195, max_images=10, max_videos=4),
+    _ModelSpec("qwen/qwen3.6-flash", "reasoning", 0.0000001875, 0.000001125, max_images=10, max_videos=4),
+    _ModelSpec("mistralai/mistral-large-2512", "standard", 0.0000005, 0.0000015, max_images=8),
+    _ModelSpec("mistralai/mistral-medium-3-5", "reasoning", 0.0000015, 0.0000075, max_images=8),
+    _ModelSpec("z-ai/glm-4.6", "reasoning", 0.00000043, 0.00000174),
+    _ModelSpec("z-ai/glm-5", "reasoning", 0.0000006, 0.00000192),
+    _ModelSpec("moonshotai/kimi-k2.6", "reasoning", 0.00000073, 0.00000349, max_images=10),
+    _ModelSpec("moonshotai/kimi-k2-thinking", "reasoning", 0.0000006, 0.0000025),
+    _ModelSpec("perplexity/sonar-pro", "perplexity", 0.000003, 0.000015),
+    _ModelSpec("perplexity/sonar-reasoning-pro", "perplexity_reasoning", 0.000002, 0.000008),
+    _ModelSpec("perplexity/sonar-deep-research", "perplexity_reasoning", 0.000002, 0.000008),
+]
+
+_MODELS_BY_SLUG: dict[str, _ModelSpec] = {m.slug: m for m in MODELS}
+_REASONING_EFFORTS = ["off", "low", "medium", "high"]
+_SEARCH_CONTEXT_SIZES = ["low", "medium", "high"]
+
+
+def _reasoning_extra_inputs() -> list:
+    return [
+        IO.Combo.Input(
+            "reasoning_effort",
+            options=_REASONING_EFFORTS,
+            default="off",
+            tooltip="Reasoning effort. 'off' disables reasoning entirely.",
+            advanced=True,
+        ),
+    ]
+
+
+def _perplexity_extra_inputs() -> list:
+    return [
+        IO.Combo.Input(
+            "search_context_size",
+            options=_SEARCH_CONTEXT_SIZES,
+            default="medium",
+            tooltip="How much web search context to retrieve. Larger = more grounded but slower/pricier.",
+            advanced=True,
+        ),
+    ]
+
+
+def _profile_inputs(profile: Profile) -> list:
+    if profile == "standard":
+        return []
+    if profile in ("reasoning", "frontier_reasoning"):
+        return _reasoning_extra_inputs()
+    if profile == "perplexity":
+        return _perplexity_extra_inputs()
+    if profile == "perplexity_reasoning":
+        return _perplexity_extra_inputs() + _reasoning_extra_inputs()
+    raise ValueError(f"Unknown profile: {profile}")
+
+
+def _media_inputs(spec: _ModelSpec) -> list:
+    extras: list = []
+    if spec.max_images > 0:
+        extras.append(
+            IO.Autogrow.Input(
+                "images",
+                template=IO.Autogrow.TemplateNames(
+                    IO.Image.Input("image"),
+                    names=[f"image_{i}" for i in range(1, spec.max_images + 1)],
+                    min=0,
+                ),
+                tooltip=f"Optional reference image(s) — up to {spec.max_images}. Sent as URLs.",
+            )
+        )
+    if spec.max_videos > 0:
+        extras.append(
+            IO.Autogrow.Input(
+                "videos",
+                template=IO.Autogrow.TemplateNames(
+                    IO.Video.Input("video"),
+                    names=[f"video_{i}" for i in range(1, spec.max_videos + 1)],
+                    min=0,
+                ),
+                tooltip=f"Optional reference video(s) — up to {spec.max_videos}. Sent as URLs.",
+            )
+        )
+    return extras
+
+
+def _inputs_for_model(spec: _ModelSpec) -> list:
+    return _profile_inputs(spec.profile) + _media_inputs(spec)
+
+
+def _build_model_options() -> list[IO.DynamicCombo.Option]:
+    return [IO.DynamicCombo.Option(spec.slug, _inputs_for_model(spec)) for spec in MODELS]
+
+
+def _calculate_price(response: OpenRouterChatResponse) -> float | None:
+    if response.usage and response.usage.cost is not None:
+        return float(response.usage.cost)
+    return None
+
+
+def _price_badge_jsonata() -> str:
+    rates_pairs = []
+    for spec in MODELS:
+        prompt_per_1k = spec.price_in * 1000
+        completion_per_1k = spec.price_out * 1000
+        rates_pairs.append(f'  "{spec.slug}": [{prompt_per_1k:.8g}, {completion_per_1k:.8g}]')
+    rates_block = ",\n".join(rates_pairs)
+    return (
+        "(\n"
+        "  $rates := {\n"
+        f"{rates_block}\n"
+        "  };\n"
+        "  $r := $lookup($rates, widgets.model);\n"
+        "  $r ? {\n"
+        '    "type": "list_usd",\n'
+        '    "usd": $r,\n'
+        '    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }\n'
+        '  } : {"type": "text", "text": "Token-based"}\n'
+        ")"
+    )
+
+
+async def _build_image_blocks(
+    cls: type[IO.ComfyNode], spec: _ModelSpec, images: list[Input.Image]
+) -> list[OpenRouterImageContent]:
+    urls = await upload_images_to_comfyapi(
+        cls,
+        images,
+        max_images=spec.max_images,
+        total_pixels=2048 * 2048,
+        mime_type="image/png",
+        wait_label="Uploading reference images",
+    )
+    return [OpenRouterImageContent(image_url=OpenRouterImageUrl(url=url)) for url in urls]
+
+
+async def _build_video_blocks(cls: type[IO.ComfyNode], videos: list[Input.Video]) -> list[OpenRouterVideoContent]:
+    blocks: list[OpenRouterVideoContent] = []
+    total = len(videos)
+    for idx, video in enumerate(videos):
+        label = "Uploading reference video"
+        if total > 1:
+            label = f"{label} ({idx + 1}/{total})"
+        url = await upload_video_to_comfyapi(cls, video, wait_label=label)
+        blocks.append(OpenRouterVideoContent(video_url=OpenRouterVideoUrl(url=url)))
+    return blocks
+
+
+def _user_message(prompt: str, media_blocks: list[OpenRouterContentBlock]) -> OpenRouterMessage:
+    if not media_blocks:
+        return OpenRouterMessage(role="user", content=prompt)
+    blocks: list[OpenRouterContentBlock] = list(media_blocks)
+    blocks.append(OpenRouterTextContent(text=prompt))
+    return OpenRouterMessage(role="user", content=blocks)
+
+
+def _build_messages(
+    system_prompt: str, prompt: str, media_blocks: list[OpenRouterContentBlock]
+) -> list[OpenRouterMessage]:
+    messages: list[OpenRouterMessage] = []
+    if system_prompt:
+        messages.append(OpenRouterMessage(role="system", content=system_prompt))
+    messages.append(_user_message(prompt, media_blocks))
+    return messages
+
+
+def _build_request(
+    slug: str,
+    system_prompt: str,
+    prompt: str,
+    media_blocks: list[OpenRouterContentBlock],
+    *,
+    seed: int,
+    reasoning_effort: str | None,
+    search_context_size: str | None,
+) -> OpenRouterChatRequest:
+    reasoning_cfg: OpenRouterReasoningConfig | None = None
+    if reasoning_effort and reasoning_effort != "off":
+        # exclude=True asks providers to reason internally but not return the trace
+        reasoning_cfg = OpenRouterReasoningConfig(effort=reasoning_effort, exclude=True)
+    web_search_cfg: OpenRouterWebSearchOptions | None = None
+    if search_context_size:
+        web_search_cfg = OpenRouterWebSearchOptions(search_context_size=search_context_size)
+    return OpenRouterChatRequest(
+        model=slug,
+        messages=_build_messages(system_prompt, prompt, media_blocks),
+        seed=seed if seed > 0 else None,
+        reasoning=reasoning_cfg,
+        web_search_options=web_search_cfg,
+    )
+
+
+def _extract_text(response: OpenRouterChatResponse) -> str:
+    if response.error:
+        code = response.error.code if response.error.code is not None else "unknown"
+        raise ValueError(f"OpenRouter error ({code}): {response.error.message or 'no message'}")
+    if not response.choices:
+        raise ValueError("Empty response from OpenRouter (no choices).")
+    message = response.choices[0].message
+    if not message:
+        raise ValueError("Empty response from OpenRouter (no message).")
+    if message.refusal:
+        raise ValueError(f"Model refused to respond: {message.refusal}")
+    return message.content or ""
+
+
+class OpenRouterLLMNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="OpenRouterLLMNode",
+            display_name="OpenRouter LLM",
+            category="api node/text/OpenRouter",
+            essentials_category="Text Generation",
+            description=(
+                "Generate text responses through OpenRouter. Routes to a curated set of popular "
+                "models from xAI, DeepSeek, Qwen, Mistral, Z.AI (GLM), Moonshot (Kimi), and "
+                "Perplexity Sonar."
+            ),
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text input to the model.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=_build_model_options(),
+                    tooltip="The OpenRouter model used to generate the response.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed for sampling. Set to 0 to omit. Most models treat this as a hint only.",
+                ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default="",
+                    optional=True,
+                    advanced=True,
+                    tooltip="Foundational instructions that dictate the model's behavior.",
+                ),
+            ],
+            outputs=[IO.String.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr=_price_badge_jsonata(),
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+        system_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        slug: str = model["model"]
+        spec = _MODELS_BY_SLUG.get(slug)
+        if spec is None:
+            raise ValueError(f"Unknown OpenRouter model: {slug}")
+
+        reasoning_effort: str | None = model.get("reasoning_effort")
+        search_context_size: str | None = model.get("search_context_size")
+
+        image_tensors: list[Input.Image] = [t for t in (model.get("images") or {}).values() if t is not None]
+        if image_tensors and sum(get_number_of_images(t) for t in image_tensors) > spec.max_images:
+            raise ValueError(f"Up to {spec.max_images} images are supported for {slug}.")
+        video_inputs: list[Input.Video] = [v for v in (model.get("videos") or {}).values() if v is not None]
+        if video_inputs and len(video_inputs) > spec.max_videos:
+            raise ValueError(f"Up to {spec.max_videos} videos are supported for {slug}.")
+
+        media_blocks: list[OpenRouterContentBlock] = []
+        if image_tensors:
+            media_blocks.extend(await _build_image_blocks(cls, spec, image_tensors))
+        if video_inputs:
+            media_blocks.extend(await _build_video_blocks(cls, video_inputs))
+
+        request = _build_request(
+            slug,
+            system_prompt,
+            prompt,
+            media_blocks,
+            seed=seed,
+            reasoning_effort=reasoning_effort,
+            search_context_size=search_context_size,
+        )
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=OPENROUTER_CHAT_ENDPOINT, method="POST"),
+            response_model=OpenRouterChatResponse,
+            data=request,
+            price_extractor=_calculate_price,
+        )
+        return IO.NodeOutput(_extract_text(response))
+
+
+class OpenRouterExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [OpenRouterLLMNode]
+
+
+async def comfy_entrypoint() -> OpenRouterExtension:
+    return OpenRouterExtension()

comfy_api_nodes/util/__init__.py

@@ -16,16 +16,17 @@ from .conversions import (
     convert_mask_to_image,
     downscale_image_tensor,
     downscale_image_tensor_by_max_side,
+    downscale_video_to_max_pixels,
     image_tensor_pair_to_batch,
     pil_to_bytesio,
     resize_mask_to_image,
-    resize_video_to_pixel_budget,
     tensor_to_base64_string,
     tensor_to_bytesio,
     tensor_to_pil,
     text_filepath_to_base64_string,
     text_filepath_to_data_uri,
     trim_video,
+    upscale_video_to_min_pixels,
     video_to_base64_string,
 )
 from .download_helpers import (
@@ -88,16 +89,17 @@ __all__ = [
     "convert_mask_to_image",
     "downscale_image_tensor",
     "downscale_image_tensor_by_max_side",
+    "downscale_video_to_max_pixels",
     "image_tensor_pair_to_batch",
     "pil_to_bytesio",
     "resize_mask_to_image",
-    "resize_video_to_pixel_budget",
     "tensor_to_base64_string",
     "tensor_to_bytesio",
     "tensor_to_pil",
     "text_filepath_to_base64_string",
     "text_filepath_to_data_uri",
     "trim_video",
+    "upscale_video_to_min_pixels",
     "video_to_base64_string",
     # Validation utilities
     "get_image_dimensions",

comfy_api_nodes/util/conversions.py

@@ -415,14 +415,48 @@ def trim_video(video: Input.Video, duration_sec: float) -> Input.Video:
         raise RuntimeError(f"Failed to trim video: {str(e)}") from e
 
 
-def resize_video_to_pixel_budget(video: Input.Video, total_pixels: int) -> Input.Video:
-    """Downscale a video to fit within ``total_pixels`` (w * h), preserving aspect ratio.
+def downscale_video_to_max_pixels(video: Input.Video, max_pixels: int) -> Input.Video:
+    """Downscale a video to fit within ``max_pixels`` (w * h), preserving aspect ratio.
 
     Returns the original video object untouched when it already fits. Preserves frame rate, duration, and audio.
     Aspect ratio is preserved up to a fraction of a percent (even-dim rounding).
     """
     src_w, src_h = video.get_dimensions()
-    scale_dims = _compute_downscale_dims(src_w, src_h, total_pixels)
+    scale_dims = _compute_downscale_dims(src_w, src_h, max_pixels)
+    if scale_dims is None:
+        return video
+    return _apply_video_scale(video, scale_dims)
+
+
+def _compute_upscale_dims(src_w: int, src_h: int, total_pixels: int) -> tuple[int, int] | None:
+    """Return upscaled (w, h) with even dims meeting at least ``total_pixels``, or None if already large enough.
+
+    Source aspect ratio is preserved; output may drift by a fraction of a percent because both dimensions
+    are rounded up to even values (many codecs require divisible-by-2). The result is guaranteed to be at
+    least ``total_pixels``.
+    """
+    pixels = src_w * src_h
+    if pixels >= total_pixels:
+        return None
+    scale = math.sqrt(total_pixels / pixels)
+    new_w = math.ceil(src_w * scale)
+    new_h = math.ceil(src_h * scale)
+    if new_w % 2:
+        new_w += 1
+    if new_h % 2:
+        new_h += 1
+    return new_w, new_h
+
+
+def upscale_video_to_min_pixels(video: Input.Video, min_pixels: int) -> Input.Video:
+    """Upscale a video to meet at least ``min_pixels`` (w * h), preserving aspect ratio.
+
+    Returns the original video object untouched when it already meets the minimum. Preserves frame rate,
+    duration, and audio. Aspect ratio is preserved up to a fraction of a percent (even-dim rounding).
+    Note: upscaling a low-resolution source does not add real detail; downstream model quality may suffer.
+    """
+    src_w, src_h = video.get_dimensions()
+    scale_dims = _compute_upscale_dims(src_w, src_h, min_pixels)
     if scale_dims is None:
         return video
     return _apply_video_scale(video, scale_dims)

comfy_extras/nodes_load_3d.py

@@ -6,9 +6,6 @@ import uuid
 from typing_extensions import override
 from comfy_api.latest import IO, UI, ComfyExtension, InputImpl, Types
 
-from app.preview3d_bridge import load_image_and_mask
-from server import PromptServer
-
 from pathlib import Path
 
 
@@ -104,15 +101,11 @@ class Preview3D(IO.ComfyNode):
                 IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
                 IO.Image.Input("bg_image", optional=True, advanced=True),
             ],
-            outputs=[
-                IO.Image.Output(display_name="image"),
-                IO.Mask.Output(display_name="mask"),
-            ],
-            hidden=[IO.Hidden.unique_id],
+            outputs=[],
         )
 
     @classmethod
-    async def execute(cls, model_file: str | Types.File3D, **kwargs) -> IO.NodeOutput:
+    def execute(cls, model_file: str | Types.File3D, **kwargs) -> IO.NodeOutput:
         if isinstance(model_file, Types.File3D):
             filename = f"preview3d_{uuid.uuid4().hex}.{model_file.format}"
             model_file.save_to(os.path.join(folder_paths.get_output_directory(), filename))
@@ -120,20 +113,7 @@ class Preview3D(IO.ComfyNode):
             filename = model_file
         camera_info = kwargs.get("camera_info", None)
         bg_image = kwargs.get("bg_image", None)
-
-        rendered = await PromptServer.instance.preview3d_bridge.request_render(
-            node_id=cls.hidden.unique_id,
-            file_path=filename,
-            file_type="output",
-            camera_info=camera_info,
-        )
-        image_tensor, mask_tensor = load_image_and_mask(rendered["image"], rendered["mask"])
-
-        return IO.NodeOutput(
-            image_tensor,
-            mask_tensor,
-            ui=UI.PreviewUI3D(filename, camera_info, bg_image=bg_image),
-        )
+        return IO.NodeOutput(ui=UI.PreviewUI3D(filename, camera_info, bg_image=bg_image))
 
     process = execute  # TODO: remove
 

comfy_extras/nodes_save_3d.py

@@ -14,8 +14,6 @@ from typing_extensions import override
 import folder_paths
 from comfy.cli_args import args
 from comfy_api.latest import ComfyExtension, IO, Types
-from app.preview3d_bridge import load_image_and_mask
-from server import PromptServer
 
 
 def pack_variable_mesh_batch(vertices, faces, colors=None, uvs=None, texture=None):
@@ -331,17 +329,12 @@ class SaveGLB(IO.ComfyNode):
                     tooltip="Mesh or 3D file to save",
                 ),
                 IO.String.Input("filename_prefix", default="3d/ComfyUI"),
-                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
             ],
-            outputs=[
-                IO.Image.Output(display_name="image"),
-                IO.Mask.Output(display_name="mask"),
-            ],
-            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo, IO.Hidden.unique_id]
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo]
         )
 
     @classmethod
-    async def execute(cls, mesh: Types.MESH | Types.File3D, filename_prefix: str, camera_info=None) -> IO.NodeOutput:
+    def execute(cls, mesh: Types.MESH | Types.File3D, filename_prefix: str) -> IO.NodeOutput:
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, folder_paths.get_output_directory())
         results = []
 
@@ -390,24 +383,7 @@ class SaveGLB(IO.ComfyNode):
                     "type": "output"
                 })
                 counter += 1
-
-        if not results:
-            raise ValueError("SaveGLB: no mesh was written; cannot render image output")
-
-        first = results[0]
-        rel_path = (first["subfolder"] + "/" + first["filename"]) if first["subfolder"] else first["filename"]
-        rendered = await PromptServer.instance.preview3d_bridge.request_render(
-            node_id=cls.hidden.unique_id,
-            file_path=rel_path,
-            file_type="output",
-            camera_info=camera_info,
-        )
-        image_tensor, mask_tensor = load_image_and_mask(rendered["image"], rendered["mask"])
-
-        ui_out: dict = {"3d": results}
-        if camera_info is not None:
-            ui_out["camera_info"] = [camera_info]
-        return IO.NodeOutput(image_tensor, mask_tensor, ui=ui_out)
+        return IO.NodeOutput(ui={"3d": results})
 
 
 class Save3DExtension(ComfyExtension):

main.py

@@ -27,6 +27,7 @@ from utils.mime_types import init_mime_types
 import faulthandler
 import logging
 import sys
+import traceback
 from comfy_execution.progress import get_progress_state
 from comfy_execution.utils import get_executing_context
 from comfy_api import feature_flags
@@ -135,7 +136,20 @@ def apply_custom_paths():
         folder_paths.set_user_directory(user_dir)
 
 
+# Buffer for prestartup failures. Recorded into `nodes.NODE_STARTUP_ERRORS`
+# only AFTER the normal `import nodes` line below, so a failing prestartup
+# script never triggers an early `import nodes` (and therefore `import torch`)
+# on the error path.
+_PRESTARTUP_FAILURES: list[dict] = []
+
+
 def execute_prestartup_script():
+    """Run every custom_nodes/*/prestartup_script.py once, before importing nodes.
+
+    Failures are buffered into the module-level ``_PRESTARTUP_FAILURES`` list and
+    must be flushed via ``record_node_startup_error`` after ``import nodes`` has
+    happened at its normal bootstrap point.
+    """
     if args.disable_all_custom_nodes and len(args.whitelist_custom_nodes) == 0:
         return
 
@@ -148,6 +162,15 @@ def execute_prestartup_script():
             return True
         except Exception as e:
             logging.error(f"Failed to execute startup-script: {script_path} / {e}")
+            # Buffer the failure - do NOT `import nodes` here, that would drag
+            # torch in before the intended bootstrap point.
+            _PRESTARTUP_FAILURES.append({
+                "module_path": os.path.dirname(script_path),
+                "source": "custom_nodes",
+                "phase": "prestartup",
+                "error": e,
+                "tb": traceback.format_exc(),
+            })
         return False
 
     node_paths = folder_paths.get_folder_paths("custom_nodes")
@@ -207,6 +230,16 @@ import execution
 import server
 from protocol import BinaryEventTypes
 import nodes
+
+# Flush any prestartup failures that were buffered before `nodes` was
+# importable. Doing this here (rather than from the prestartup error
+# handler) keeps the bootstrap order deterministic: `nodes` (and torch)
+# import at this single line whether prestartup succeeded or failed.
+if _PRESTARTUP_FAILURES:
+    for _failure in _PRESTARTUP_FAILURES:
+        nodes.record_node_startup_error(**_failure)
+    _PRESTARTUP_FAILURES.clear()
+
 import comfy.model_management
 import comfyui_version
 import app.logger

nodes.py

@@ -2158,6 +2158,71 @@ EXTENSION_WEB_DIRS = {}
 # Dictionary of successfully loaded module names and associated directories.
 LOADED_MODULE_DIRS = {}
 
+# Dictionary of custom node startup errors, keyed by "<source>:<module_name>"
+# so that name collisions across custom_nodes / comfy_extras / comfy_api_nodes
+# do not overwrite each other. Each value contains: source, module_name,
+# module_path, error, traceback, phase.
+#
+# `source` is the same string as the internal `module_parent` used at load
+# time (e.g. "custom_nodes", "comfy_extras", "comfy_api_nodes"). It is
+# intentionally a free-form string rather than a fixed enum so the contract
+# survives node-source layouts evolving (e.g. comfy_api_nodes eventually
+# moving out of core). Consumers should treat any new value as a new bucket
+# rather than rejecting it.
+NODE_STARTUP_ERRORS: dict[str, dict] = {}
+
+
+def _read_pyproject_metadata(module_path: str) -> dict | None:
+    """Best-effort extraction of node-pack identity from pyproject.toml.
+
+    Returns a dict with the Comfy Registry-style identity (pack_id,
+    display_name, publisher_id, version, repository) when the module
+    directory contains a pyproject.toml. Returns None when no toml is
+    present or parsing fails for any reason — startup-error tracking
+    must never itself raise.
+    """
+    if not module_path or not os.path.isdir(module_path):
+        return None
+    toml_path = os.path.join(module_path, "pyproject.toml")
+    if not os.path.isfile(toml_path):
+        return None
+    try:
+        from comfy_config import config_parser
+
+        cfg = config_parser.extract_node_configuration(module_path)
+        if cfg is None:
+            return None
+        meta = {
+            "pack_id": cfg.project.name or None,
+            "display_name": cfg.tool_comfy.display_name or None,
+            "publisher_id": cfg.tool_comfy.publisher_id or None,
+            "version": cfg.project.version or None,
+            "repository": cfg.project.urls.repository or None,
+        }
+        # Drop empty fields so the API payload stays compact.
+        return {k: v for k, v in meta.items() if v}
+    except Exception:
+        return None
+
+
+def record_node_startup_error(
+    *, module_path: str, source: str, phase: str, error: BaseException, tb: str
+) -> None:
+    """Record a startup error for a node module so it can be exposed via the API."""
+    module_name = get_module_name(module_path)
+    entry = {
+        "source": source,
+        "module_name": module_name,
+        "module_path": module_path,
+        "error": str(error),
+        "traceback": tb,
+        "phase": phase,
+    }
+    pyproject = _read_pyproject_metadata(module_path)
+    if pyproject:
+        entry["pyproject"] = pyproject
+    NODE_STARTUP_ERRORS[f"{source}:{module_name}"] = entry
+
 
 def get_module_name(module_path: str) -> str:
     """
@@ -2267,14 +2332,30 @@ async def load_custom_node(module_path: str, ignore=set(), module_parent="custom
                         NODE_DISPLAY_NAME_MAPPINGS[schema.node_id] = schema.display_name
                 return True
             except Exception as e:
+                tb = traceback.format_exc()
                 logging.warning(f"Error while calling comfy_entrypoint in {module_path}: {e}")
+                record_node_startup_error(
+                    module_path=module_path,
+                    source=module_parent,
+                    phase="entrypoint",
+                    error=e,
+                    tb=tb,
+                )
                 return False
         else:
             logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS or comfy_entrypoint (need one).")
             return False
     except Exception as e:
-        logging.warning(traceback.format_exc())
+        tb = traceback.format_exc()
+        logging.warning(tb)
         logging.warning(f"Cannot import {module_path} module for custom nodes: {e}")
+        record_node_startup_error(
+            module_path=module_path,
+            source=module_parent,
+            phase="import",
+            error=e,
+            tb=tb,
+        )
         return False
 
 async def init_external_custom_nodes():

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.43.18
-comfyui-workflow-templates==0.9.79
+comfyui-workflow-templates==0.9.82
 comfyui-embedded-docs==0.5.0
 torch
 torchsde

server.py

@@ -44,7 +44,6 @@ from app.model_manager import ModelFileManager
 from app.custom_node_manager import CustomNodeManager
 from app.subgraph_manager import SubgraphManager
 from app.node_replace_manager import NodeReplaceManager
-from app.preview3d_bridge import Preview3DBridge
 from typing import Optional, Union
 from api_server.routes.internal.internal_routes import InternalRoutes
 from protocol import BinaryEventTypes
@@ -210,7 +209,6 @@ class PromptServer():
         self.custom_node_manager = CustomNodeManager()
         self.subgraph_manager = SubgraphManager()
         self.node_replace_manager = NodeReplaceManager()
-        self.preview3d_bridge = Preview3DBridge()
         self.internal_routes = InternalRoutes(self)
         self.supports = ["custom_nodes_from_web"]
         self.prompt_queue = execution.PromptQueue(self)
@@ -767,6 +765,29 @@ class PromptServer():
                 out[node_class] = node_info(node_class)
             return web.json_response(out)
 
+        @routes.get("/node_startup_errors")
+        async def get_node_startup_errors(request):
+            """Return startup errors recorded during node loading, grouped by source.
+
+            Group errors by source so the frontend/Manager can render them in
+            distinct sections. ``source`` is the same string as the
+            ``module_parent`` used at load time (e.g. ``"custom_nodes"``,
+            ``"comfy_extras"``, ``"comfy_api_nodes"``) and is left as a
+            free-form string so the contract survives node-source layouts
+            evolving. The response only contains source buckets that actually
+            had a failure; consumers should not assume any particular set of
+            keys is always present.
+
+            ``module_path`` is stripped because the absolute on-disk path is
+            internal detail that the frontend has no use for.
+            """
+            grouped: dict[str, dict[str, dict]] = {}
+            for entry in nodes.NODE_STARTUP_ERRORS.values():
+                source = entry.get("source", "custom_nodes")
+                public_entry = {k: v for k, v in entry.items() if k != "module_path"}
+                grouped.setdefault(source, {})[entry["module_name"]] = public_entry
+            return web.json_response(grouped)
+
         @routes.get("/api/jobs")
         async def get_jobs(request):
             """List all jobs with filtering, sorting, and pagination.
@@ -1052,7 +1073,6 @@ class PromptServer():
         self.custom_node_manager.add_routes(self.routes, self.app, nodes.LOADED_MODULE_DIRS.items())
         self.subgraph_manager.add_routes(self.routes, nodes.LOADED_MODULE_DIRS.items())
         self.node_replace_manager.add_routes(self.routes)
-        self.preview3d_bridge.add_routes(self.routes)
         self.app.add_subapp('/internal', self.internal_routes.get_app())
 
         # Prefix every route with /api for easier matching for delegation.

tests-unit/node_startup_errors_test.py

@@ -0,0 +1,146 @@
+"""Tests for the custom node startup error tracking introduced for
+Comfy-Org/ComfyUI-Launcher#303.
+
+Covers:
+- load_custom_node populates NODE_STARTUP_ERRORS with the correct source
+  for each module_parent (custom_nodes / comfy_extras / comfy_api_nodes).
+- Composite keying prevents collisions between modules with the same name
+  in different sources.
+- record_node_startup_error stores the expected fields.
+- pyproject.toml metadata is attached when present and omitted when absent.
+"""
+import textwrap
+
+import pytest
+
+import nodes
+
+
+@pytest.fixture(autouse=True)
+def _clear_startup_errors():
+    nodes.NODE_STARTUP_ERRORS.clear()
+    yield
+    nodes.NODE_STARTUP_ERRORS.clear()
+
+
+def _write_broken_module(tmp_path, name: str) -> str:
+    path = tmp_path / f"{name}.py"
+    path.write_text(textwrap.dedent("""\
+        # Deliberately broken module to exercise startup-error tracking.
+        raise RuntimeError("boom from " + __name__)
+    """))
+    return str(path)
+
+
+def test_record_node_startup_error_fields(tmp_path):
+    err = ValueError("kaboom")
+    nodes.record_node_startup_error(
+        module_path=str(tmp_path / "my_pack"),
+        source="custom_nodes",
+        phase="import",
+        error=err,
+        tb="traceback-text",
+    )
+    assert "custom_nodes:my_pack" in nodes.NODE_STARTUP_ERRORS
+    entry = nodes.NODE_STARTUP_ERRORS["custom_nodes:my_pack"]
+    assert entry["source"] == "custom_nodes"
+    assert entry["module_name"] == "my_pack"
+    assert entry["phase"] == "import"
+    assert entry["error"] == "kaboom"
+    assert entry["traceback"] == "traceback-text"
+    assert entry["module_path"].endswith("my_pack")
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "module_parent",
+    ["custom_nodes", "comfy_extras", "comfy_api_nodes"],
+)
+async def test_load_custom_node_records_source(tmp_path, module_parent):
+    # `source` in the entry should be the same string as `module_parent`.
+    module_path = _write_broken_module(tmp_path, "broken_pack")
+
+    success = await nodes.load_custom_node(module_path, module_parent=module_parent)
+    assert success is False
+
+    key = f"{module_parent}:broken_pack"
+    assert key in nodes.NODE_STARTUP_ERRORS, nodes.NODE_STARTUP_ERRORS
+    entry = nodes.NODE_STARTUP_ERRORS[key]
+    assert entry["source"] == module_parent
+    assert entry["module_name"] == "broken_pack"
+    assert entry["phase"] == "import"
+    assert "boom from" in entry["error"]
+    assert "RuntimeError" in entry["traceback"]
+
+
+@pytest.mark.asyncio
+async def test_load_custom_node_collision_across_sources(tmp_path):
+    # Same module name registered as both a custom node and a comfy_extra;
+    # composite keying should keep both entries.
+    cn_dir = tmp_path / "cn"
+    extras_dir = tmp_path / "extras"
+    cn_dir.mkdir()
+    extras_dir.mkdir()
+    cn_path = _write_broken_module(cn_dir, "nodes_audio")
+    extras_path = _write_broken_module(extras_dir, "nodes_audio")
+
+    assert await nodes.load_custom_node(cn_path, module_parent="custom_nodes") is False
+    assert await nodes.load_custom_node(extras_path, module_parent="comfy_extras") is False
+
+    assert "custom_nodes:nodes_audio" in nodes.NODE_STARTUP_ERRORS
+    assert "comfy_extras:nodes_audio" in nodes.NODE_STARTUP_ERRORS
+    assert (
+        nodes.NODE_STARTUP_ERRORS["custom_nodes:nodes_audio"]["module_path"]
+        != nodes.NODE_STARTUP_ERRORS["comfy_extras:nodes_audio"]["module_path"]
+    )
+
+
+@pytest.mark.asyncio
+async def test_load_custom_node_attaches_pyproject_metadata(tmp_path):
+    pack_dir = tmp_path / "MyCoolPack"
+    pack_dir.mkdir()
+    (pack_dir / "__init__.py").write_text("raise RuntimeError('boom')\n")
+    (pack_dir / "pyproject.toml").write_text(textwrap.dedent("""\
+        [project]
+        name = "comfyui-mycoolpack"
+        version = "1.2.3"
+
+        [project.urls]
+        Repository = "https://github.com/example/comfyui-mycoolpack"
+
+        [tool.comfy]
+        PublisherId = "example"
+        DisplayName = "My Cool Pack"
+    """))
+
+    success = await nodes.load_custom_node(str(pack_dir), module_parent="custom_nodes")
+    assert success is False
+
+    entry = nodes.NODE_STARTUP_ERRORS["custom_nodes:MyCoolPack"]
+    assert "pyproject" in entry, entry
+    py = entry["pyproject"]
+    assert py["pack_id"] == "comfyui-mycoolpack"
+    assert py["display_name"] == "My Cool Pack"
+    assert py["publisher_id"] == "example"
+    assert py["version"] == "1.2.3"
+    assert py["repository"] == "https://github.com/example/comfyui-mycoolpack"
+
+
+@pytest.mark.asyncio
+async def test_load_custom_node_no_pyproject_skips_metadata(tmp_path):
+    # Single-file extras-style module: no pyproject.toml exists alongside it,
+    # so the entry must not contain a 'pyproject' key.
+    module_path = _write_broken_module(tmp_path, "lonely")
+    assert await nodes.load_custom_node(module_path, module_parent="comfy_extras") is False
+    entry = nodes.NODE_STARTUP_ERRORS["comfy_extras:lonely"]
+    assert "pyproject" not in entry
+
+
+@pytest.mark.asyncio
+async def test_load_custom_node_arbitrary_module_parent_passes_through(tmp_path):
+    # `source` is a free-form string — an unknown module_parent (e.g. a future
+    # node-source bucket) should be recorded as-is, not coerced or rejected.
+    module_path = _write_broken_module(tmp_path, "future_pack")
+    assert await nodes.load_custom_node(module_path, module_parent="future_source") is False
+    entry = nodes.NODE_STARTUP_ERRORS["future_source:future_pack"]
+    assert entry["source"] == "future_source"