Add native OpenAI Decisions and OpenRouter Decisions API routing to the image and text Decision Router nodes while keeping local in-process llama.cpp inference as the default.
Discover OpenRouter decision models by input modality and decision output capability, then enforce server profile image policy and optional model allowlists both in the UI model catalog and before inference. Keep credentials and endpoints in server-side profiles; apply profile input, image, and timeout limits; never fall back to chat-generated JSON.
Preserve saved workflow widget order, release any retained local model when switching to API mode, and normalize typed route choices, probabilities, and confidence into decision_json. Add API setup documentation and adapter/router/security regression coverage.
Validation: 240 tests passed across six test modules run individually in bundled ComfyUI Python 3.12; Python and JavaScript syntax checks, API catalog validation, version metadata checks, and git diff --check passed. Provider inference is covered with mocked responses; no authenticated live inference was run.
Decision Router memory and explanation controls
Add keep_model_loaded to the image and text routers. It defaults to releasing the llama.cpp model and MTMD handler after routing, including errors and empty batches; users can retain the model for faster queue reuse.
Add an opt-in include_reason pass that records a bounded post-hoc explanation in decision_json. The native decision head remains authoritative for route selection and probabilities. Explanation errors are reported without failing routing.
Document both controls and bump package, node registry, and runtime metadata to 3.0.1.
Validation: 227 tests passed in the bundled ComfyUI Python runtime (17.62 seconds), including 29 Decision Router tests. Version metadata parsing and git diff --check passed.
Limitation: explanation generation has not been smoke-tested with downloaded model weights; model-specific chat compatibility and added latency may vary.
Add separate image and text Decision Router nodes with configurable system prompts, up to 52 user-defined routes, per-item decisions, normalized decision_json output, and native IMAGE/STRING route sockets.
Run Clef-Flash 9B, Clef 27B, and OpenJev 27B in-process through llama.cpp. Add decision-head, logits, MTMD vision, and dynamic CUDA backend capability checks before model downloads. Keep model weights and vision projectors out of the repository; do not launch a separate llama-server or upgrade PyTorch/Transformers.
Block inactive route outputs so unused downstream branches are skipped. Keep dynamic route names synchronized with connected outputs and preserve stable socket identities. Register the commercial UI variant while keeping non-commercial OpenJev unavailable in commercial builds.
Update all 3.0.0 package metadata, README, runtime and workflow guides, the illustrated router example, and the detailed update log. Refresh regression expectations for commercial router registration and the current provider model catalog.
Validation: all 220 tests pass in the bundled ComfyUI Python 3.12 runtime. Python compilation, JavaScript syntax, version metadata, README image, and staged diff checks pass. The Image Router was manually exercised with Clef-Flash 9B on fox, dog, cat, and none routes.
Coverage limits: full-weight inference for Clef 27B and OpenJev 27B, text-mode smoke tests, and clean installs across other OS/Python/CUDA combinations remain unverified. The documented Windows x64, CPython 3.12, CUDA 13.0 JamePeng runtime is the only verified build matrix.
Marcel Hofer