Sakana AI has introduced a new technique that allows multiple large language models (LLMs) to cooperate on a single task, effectively creating a “dream team” of AI agents. The method, called Multi-LLM AB-MCTS, enables models to perform trial-and-error and combine their unique strengths to solve problems that are too complex for any individual model. For enterprises, this approach provides a means to develop more robust and capable AI systems. Instead of being locked into a single provider or model, businesses could dynamically leverage the best aspects of different frontier models, assigning the right AI for the right part of a task to achieve superior results. Sakana AI’s new algorithm is an “inference-time scaling” technique. On tasks where a clear path to a solution existed, the algorithm quickly identified the most effective LLM and used it more frequently. More impressively, the team observed instances where the models solved problems that were previously impossible for any single one of them. To help developers and businesses apply this technique, Sakana AI has released the underlying algorithm as an open-source framework called TreeQuest, available under an Apache 2.0 license (usable for commercial purposes). TreeQuest provides a flexible API, allowing users to implement Multi-LLM AB-MCTS for their own tasks with custom scoring and logic.