AgentOhana is a comprehensive solution designed to address the challenges of training autonomous agents using large language models (LLMs). The paper introduces AgentOhana, a unified data and training pipeline that aggregates agent trajectories from diverse environments, standardizes and unifies these trajectories into a consistent format, and streamlines the creation of a generic data loader optimized for agent training. This pipeline ensures equilibrium across different data sources and preserves independent randomness across devices during dataset partitioning and model training. Additionally, the paper presents xLAM-v0.1, a large action model tailored for AI agents, which demonstrates strong performance across various benchmarks. The paper highlights the challenges of training LLM agents due to the heterogeneous nature of data sources, which include multi-turn trajectories. These challenges include data structure diversity, syntax differences, labeling conventions, and processing methods across datasets, which complicate the training and fine-tuning processes of LLMs. To address these challenges, the paper proposes a unified agent data format, a structured definition of a step for capturing interaction details, and a method called AgentRater to assess and filter agent trajectories based on public or close-world models. The paper also introduces a generic dataloader that enables the seamless integration of various datasets into a distributed training process. The paper presents experiments on four benchmarks: Webshop, HotpotQA, ToolEval, and MINT-Bench. The results show that xLAM-v0.1 outperforms other models in these benchmarks, demonstrating its effectiveness in function calling and handling complex tool usage tasks. The paper concludes that AgentOhana provides a comprehensive and high-quality dataset that empowers researchers and practitioners to push the boundaries of AI capabilities, ultimately contributing to the advancement of autonomous agents powered by LLMs.AgentOhana is a comprehensive solution designed to address the challenges of training autonomous agents using large language models (LLMs). The paper introduces AgentOhana, a unified data and training pipeline that aggregates agent trajectories from diverse environments, standardizes and unifies these trajectories into a consistent format, and streamlines the creation of a generic data loader optimized for agent training. This pipeline ensures equilibrium across different data sources and preserves independent randomness across devices during dataset partitioning and model training. Additionally, the paper presents xLAM-v0.1, a large action model tailored for AI agents, which demonstrates strong performance across various benchmarks. The paper highlights the challenges of training LLM agents due to the heterogeneous nature of data sources, which include multi-turn trajectories. These challenges include data structure diversity, syntax differences, labeling conventions, and processing methods across datasets, which complicate the training and fine-tuning processes of LLMs. To address these challenges, the paper proposes a unified agent data format, a structured definition of a step for capturing interaction details, and a method called AgentRater to assess and filter agent trajectories based on public or close-world models. The paper also introduces a generic dataloader that enables the seamless integration of various datasets into a distributed training process. The paper presents experiments on four benchmarks: Webshop, HotpotQA, ToolEval, and MINT-Bench. The results show that xLAM-v0.1 outperforms other models in these benchmarks, demonstrating its effectiveness in function calling and handling complex tool usage tasks. The paper concludes that AgentOhana provides a comprehensive and high-quality dataset that empowers researchers and practitioners to push the boundaries of AI capabilities, ultimately contributing to the advancement of autonomous agents powered by LLMs.