Many teams struggle with complex AI pipelines that require juggling multiple models, managing different APIs, and staying compliant when a provider changes access or policies. This adds overhead, increases risk of vendor lock‑in, and makes it hard to guarantee consistent performance for tasks like code review, reasoning, or research.
Sakana Fugu solves these problems by presenting a single endpoint that behaves like one model while internally orchestrating a pool of expert language models. Users send a request to the familiar OpenAI‑compatible chat/completions URL and specify either fugu or fugu‑ultra as the model. The system decides automatically whether a single expert is sufficient or whether a team of thinkers, workers, and verifiers should be assembled, coordinated, and their output synthesized into one answer.
For everyday coding, chatbots, or code review, Fugu balances quality and latency. Teams can opt out specific providers from its pool to meet data‑privacy, compliance, or internal policy requirements without changing any client code. When higher accuracy is needed on multi‑step problems, Fugu Ultra engages a deeper, fixed set of experts and delivers top‑tier results on coding and reasoning benchmarks.
Because the orchestration logic is learned, not hard‑coded, Fugu adapts to new tasks and can route around a restricted or unavailable provider instantly, preserving service continuity. The internal routing and model selection remain proprietary, so users benefit from a resilient system without exposing their workflow to unnecessary complexity.
In short, Fugu gives developers a simple, vendor‑agnostic API that hides multi‑agent complexity, offers opt‑out controls for compliance, provides a high‑quality ultra mode for hard tasks, and automatically mitigates provider disruptions—all without requiring changes to existing integrations.
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