  Autonomous agents powered by large language models (LLMs) have garnered
significant research attention. However, fully harnessing the potential of LLMs
for agent-based tasks presents inherent challenges due to the heterogeneous
nature of diverse data sources featuring multi-turn trajectories. In this
paper, we introduce \textbf{AgentOhana} as a comprehensive solution to address
these challenges. \textit{AgentOhana} aggregates agent trajectories from
distinct environments, spanning a wide array of scenarios. It meticulously
standardizes and unifies these trajectories into a consistent format,
streamlining the creation of a generic data loader optimized for agent
training. Leveraging the data unification, our training pipeline maintains
equilibrium across different data sources and preserves independent randomness
across devices during dataset partitioning and model training. Additionally, we
present \textbf{xLAM-v0.1}, a large action model tailored for AI agents, which
demonstrates exceptional performance across various benchmarks. Begin the
exploration at \url{https://github.com/SalesforceAIResearch/xLAM}.
