Fugu-Ultra v1.1 Integrations

Sakana Fugu Ultra is the advanced, performance-focused model in the Sakana Fugu platform, designed to coordinate multiple expert AI agents for difficult and high-stakes work. It is built for users who need stronger results on complex multi-step tasks than a single model or basic AI assistant can usually provide. Through one OpenAI-compatible API, Fugu Ultra dynamically selects and coordinates agents from a powerful model pool while presenting the experience as one model. This allows teams to use multi-agent intelligence without manually building agent workflows, assigning roles, or switching between different providers. Fugu Ultra is optimized for demanding use cases such as software engineering, code review, Kaggle competitions, paper reproduction, cybersecurity analysis, scientific problem solving, literature investigations, patent analysis, and autonomous research. The system is grounded in research-driven orchestration methods, including TRINITY and the Conductor, which focus on learning how to route tasks, coordinate agents, and create effective collaboration patterns. Compared with the standard Fugu model, Fugu Ultra uses a deeper expert pool to prioritize quality on harder problems. It is designed for workloads where precision, reasoning depth, completeness, and reliability are more important than low latency alone. Organizations can opt out of specific models or providers in the agent pool to meet data, privacy, compliance, procurement, or internal governance requirements. Fugu Ultra also includes fixed pay-as-you-go pricing for input, output, and cached input tokens, with higher rates for very long context usage. Sakana Fugu Ultra helps technical teams plug advanced multi-agent orchestration into existing workflows while reducing single-vendor dependency and improving performance on challenging AI tasks.

Sakana Fugu is a multi-agent AI system that operates like one model while coordinating many underlying expert models behind a single API. The platform is designed to deliver frontier-level performance without forcing users to depend on one model provider or manually manage several separate AI tools. Fugu dynamically chooses which agents should participate in each task and coordinates them through learned collaboration patterns. This approach allows the system to handle complex work such as coding, reasoning, scientific problem solving, code review, security assessment, literature analysis, patent research, and autonomous research workflows. Sakana Fugu is grounded in research on learned orchestration, including TRINITY and the Conductor, which explore how AI systems can route tasks, assign roles, and coordinate communication among multiple agents. Users can access the system through an OpenAI-compatible API and choose between Fugu and Fugu Ultra depending on their workload. Fugu is built for everyday coding, chatbot, review, and productivity use cases where strong performance and lower latency are both important. Fugu Ultra uses a deeper pool of expert agents to improve quality on harder tasks such as Kaggle competitions, paper reproduction, cybersecurity analysis, and technical investigations. Organizations can control which agents, providers, or models are allowed in the pool to meet privacy, data handling, compliance, and procurement needs. The platform offers pay-as-you-go and subscription pricing options, with Fugu Ultra priced separately for input, output, and cached input tokens. Sakana Fugu gives developers, researchers, and enterprises a way to plug multi-agent intelligence into existing workflows while maintaining flexibility, control, and stronger performance on demanding tasks.