x LAM : A Family of Large Action Models to Empower AI Agent Systems (2025)

Abstract: Autonomous agents powered by large language models (LLMs) have attracted significant research interest. However, the open-source community faces many challenges in developing specialized models for agent tasks, driven by the scarcity of high-quality agent datasets and the absence of standard protocols in this area. We introduce xLAM, a series of large action models designed for AI agent tasks. The xLAM series includes five models with both dense and mixture-of-expert architectures, ranging from 1B to 8x22B parameters, trained using a scalable, flexible pipeline that unifies, augments, and synthesizes diverse datasets to enhance AI agents’ generalizability and performance across varied environments. Our experimental results demonstrate that xLAM consistently delivers exceptional performance across multiple agent ability benchmarks, notably securing the 1st position on the Berkeley Function-Calling Leaderboard, outperforming GPT-4, Claude-3, and many other models in terms of tool use. By releasing the xLAM series, we aim to advance the performance of open-source LLMs for autonomous AI agents, potentially accelerating progress and democratizing access to high-performance models for agent tasks.

## introduction the field of autonomous agents has witnessed significant advancements in recent years, with large language models (llms) playing a crucial role in enhancing agent capabilities across diverse tasks. researchers have made substantial progress in developing sophisticated frameworks @xcite @xcite and specialized environments @xcite @xcite to enhance agent capabilities, such as tool use @xcite ) and * co-first authors. while proprietary llms developed by industry leaders have demonstrated competitive performance in various agent tasks @xcite @xcite , the open-source community faces limited choices for specialized models in this domain. this scarcity stems from several challenges in adapting open-source llms to agent tasks, primarily due to the lack of comprehensive, high-quality datasets and the heterogeneity of existing data formats. these factors complicate the unification of diverse datasets and obstruct the learning of transferable knowledge across different agent tasks. recently, the agent research community has intensified efforts in open-source agent data processing and model training @xcite @xcite @xcite @xcite . however, these works still face challenges in managing complex environments and generalizing to new scenarios, primarily due to limitations in the collected agent data. a major obstacle is the homogeneity of content and format in existing datasets, resulting in models that lack diversity across various tasks and struggle to adapt to new or slightly different data structures in practical applications. while previous efforts have attempted to design pipelines for unifying data, they typically cover only a few scenarios or lack flexibility in their unified formats. for instance, lumos @xcite primarily addresses question answering, web agents, and mathematical tasks involving planning and grounding; while agentohana @xcite , despite encompassing a more diverse range of environments, lacks an extendable unified format to accommodate new environments. moreover, open-source datasets often suffer from quality issues, such as incorrect agent outputs, hallucinated actions, and repeated interaction turns within trajectories @xcite . the lack of detailed analysis and understanding of agent data further complicates these challenges, hindering the development of robust and versatile open-source agent models. addressing them is crucial for advancing the field of opensource agent models and bridging the performance gap with proprietary llms in agent tasks. in this work, we introduce and open-source xlam, a series of powerful models with varying sizes. this diverse set is tailored for a variety of applications, with smaller models (1b and 7b) optimized for on-device deployment, while larger models (8x7b and 8x22b) are designed to tackle more challenging tasks. alongside the model release, we offer several insights and lessons learned from our experience in agent model training: our developed data preprocess and augmentation pipeline significantly improves the generalizability of agent models across diverse environments. we evaluate the xlam series on public agent benchmarks, demonstrating exceptional performance across various agent tasks. by opensourcing these models, we aim to advance opensource agent models and provide valuable insights into data processing and synthesis techniques, addressing key challenges in developing competitive alternatives to proprietary models. ## data processing pipeline in this section, we discuss the data pipeline for training xlam, including data unification, augmentation, quality verification, general instruction data synthesis, and preference data generation. ## conclusion this paper introduces xlam series, a set of large action models for autonomous ai agents. our models, ranging from 1b to 8x22b parameters, were trained with a scalable and flexible data pipeline that unifies, augments, and synthesizes diverse datasets. evaluations show that xlam models consistently perform exceptionally across various benchmarks. the insights we learned from training these models highlight the importance of rigorous data processing and the potential of data synthesis in developing capable ai agents. by releasing the xlam series, we aim to democratize access to high-performance models for agent tasks, thereby accelerating progress in the field.