Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for research groups and engineering teams who want to understand and replicate the Fugu mechanism (Sakana AI's policy-over-models orchestrator) without taking on a vendor lock-in: OpenFugu is the Apache-2.0 independent reimplementation from the Sakana papers + released artifacts, with the architecture, the training path, the serving endpoint, and the trained Conductor weights all on GitHub
Who should use it
Who should skip it
Skip trotsky1997/OpenFugu for now if your priority is a tool you can use today without configuring a build pipeline or development environment.
About this signal
trotsky1997/OpenFugu is tracked by RepoRadar as a open reverse-engineering of saka in the Apache-2.0 independent reimplementation of Sakan section. It was first seen on 2026-06-25 and last updated on 2026-06-25. The current verdict is 'try now' with a Gold tier and hard setup difficulty. trotsky1997/OpenFugu leads on workflow potential (9.1) and novelty (9.0); its lowest signal is setup ease (4.2), so factor that in before investing setup time. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned trotsky1997/OpenFugu a composite score of 8.0 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 183.0 and never affects the composite score or tier. The risk label of 'low' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Risk explanation
**Trained Conductor weights are under the Llama 3.2 Community License, not Apache-2.0.** The `NOTICE` is explicit: the OpenFugu code is Apache-2.0, but the trained Conductor weights published on Hugging Face (`huggingface.co/di-zhang-fdu/openfugu-conductor-3b`) are a fine-tune of Llama-3.2-3B-Instruct and carry the Llama 3.2 Community License. Adopters who want to ship a derived model need to comply with the Llama 3.2 license (acceptable use policy + the 700M-monthly-active-user clause for derivatives). Review the `NOTICE` license-layering section before redistributing any checkpoint; **Per-question routing +107% is query-level, not per-step coordination; Fugu-Ultra held-out shows TIE.** The eval is honest about scope: `eval/eval_orchestration.py` reports +107% over best single worker on the trained router, but the maintainer is explicit that this is query-level routing, not per-step coordination. The held-out Fugu-Ultra recursion test (`eval/eval_recursion_real.py`) shows TIE between round-0 and round-1, the honest negative result the maintainer publishes. Adopters betting a production routing decision on the pattern should reproduce the per-question orchestration eval on their own worker pool and read the Fugu-Ultra caveats before claiming the workflow-DAG pattern delivers the same lift; **Third-party material is fetched, not redistributed; verify the upstream license terms before distribution.** The OpenFugu code does not redistribute Qwen3-0.6B, `model_iter_60.npy`, or the 37-case fixture — `scripts/fetch_artifacts.py` pulls them from their licensed sources at install time. Adopters who want to redistribute the OpenFugu bundle need to verify the upstream license terms for each fetched artifact (Qwen3-0.6B's license, the original Sakana paper figures, the ToolScale dataset) and pass the licenses through to the redistribution.