Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal
  Predefinition and Maximal Self-Evolution
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 Alita--a generalist agent designed with the principle
of "Simplicity is the ultimate sophistication," enabling scalable agentic
reasoning through minimal predefinition and 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 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 75.15% pass@1 and 87.27% pass@3
accuracy, which is top-ranking among general-purpose agents, on the GAIA
benchmark validation dataset, 74.00% and 52.00% pass@1, respectively, on
Mathvista and PathVQA, outperforming many agent systems with far greater
complexity. More details will be updated at
https://github.com/CharlesQ9/Alita{https://github.com/CharlesQ9/Alita}.
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Edify-Kd2024
Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal
  Predefinition and Maximal Self-Evolution
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 Alita--a generalist agent designed with the principle
of "Simplicity is the ultimate sophistication," enabling scalable agentic
reasoning through minimal predefinition and 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 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 75.15% pass@1 and 87.27% pass@3
accuracy, which is top-ranking among general-purpose agents, on the GAIA
benchmark validation dataset, 74.00% and 52.00% pass@1, respectively, on
Mathvista and PathVQA, outperforming many agent systems with far greater
complexity. More details will be updated at
https://github.com/CharlesQ9/Alita{https://github.com/CharlesQ9/Alita}.
Alita, a simplicity-driven generalist agent, achieves high performance across multiple benchmarks through minimal predefinition and self-evolution using task-related model context protocols.
large language models LLMs generalist agent scalable agentic reasoning minimal predefinition self-evolution task-related model context protocols MCPs GAIA benchmark Mathvista PathVQA