Title: Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution
Primary area: generative models
Keywords: Generalist agent, Self-evolution

Abstract: Recent advances in large language models (LLMs) have enabled agents to autonomously perform complex, open-ended tasks. However, many existing frameworks depend heavily on manually predefined tools and workflows, which hinder their adaptability, scalability, and generalization across domains. In this work, we introduce $\textbf{Alita}$—a generalist agent designed with the principle of $\textit{Simplicity is the ultimate sophistication,}$ enabling scalable agentic reasoning through $\textit{minimal predefinition}$ and $\textit{maximal self-evolution}$. For minimal predefinition, Alita is equipped with only one component for direct problem-solving, making it much simpler and neater than previous approaches that relied heavily on hand-crafted, elaborate tools and workflows. This clean design enhances its potential to generalize to challenging questions, without being limited by tools. For $\textit{Maximal self-evolution}$, we enable the creativity of Alita by providing a suite of general-purpose components to autonomously construct, refine, and reuse external capabilities by generating task-related model context protocols (MCPs) from open source, which contributes to scalable agentic reasoning. Notably, Alita achieves 72.73\% pass@1 and 86.06\% pass@3 accuracy, which ranks top 1 among all open-source frameworks temporarily, on the GAIA benchmark, 74.00\% and 52.00\% pass@1, respectively, on Mathvista and PathVQA, outperforming many agent systems with far greater complexity. Our code is open-sourced.