LoCoBench-Agent: An Interactive Benchmark for LLM Agents in Long-Context Software Engineering
Authors: name: Jielin Qiu, name: Zuxin Liu, name: Zhiwei Liu, name: Rithesh Murthy, name: Jianguo Zhang, name: Haolin Chen, name: Shiyu Wang, name: Ming Zhu, name: Liangwei Yang, name: Juntao Tan, name: Roshan Ram, name: Akshara Prabhakar, name: Tulika Awalgaonkar, name: Zixiang Chen, name: Zhepeng Cen, name: Cheng Qian, name: Shelby Heinecke, name: Weiran Yao, name: Silvio Savarese, name: Caiming Xiong, name: Huan Wang
Organization: Salesforce AI Research

As large language models (LLMs) evolve into sophisticated autonomous agents capable of complex software development tasks, evaluating their real-world capabilities becomes critical. While existing benchmarks like LoCoBench~qiu2025locobench assess long-context code understanding, they focus on single-turn evaluation and cannot capture the multi-turn interactive nature, tool usage patterns, and adaptive reasoning required by real-world coding agents. We introduce LoCoBench-Agent, a comprehensive evaluation framework specifically designed to assess LLM agents in realistic, long-context software engineering workflows. Our framework extends LoCoBench's 8,000 scenarios into interactive agent environments, enabling systematic evaluation of multi-turn conversations, tool usage efficiency, error recovery, and architectural consistency across extended development sessions. We also introduce an evaluation methodology with 9 metrics across comprehension and efficiency dimensions. Our framework provides agents with 8 specialized tools (file operations, search, code analysis) and evaluates them across context lengths ranging from 10K to 1M tokens, enabling precise assessment of long-context performance. Through systematic evaluation of state-of-the-art models, we reveal several key findings: (1) agents exhibit remarkable long-context robustness; (2) comprehension-efficiency trade-off exists with negative correlation, where thorough exploration increases comprehension but reduces efficiency; and (3) conversation efficiency varies dramatically across models, with strategic tool usage patterns differentiating high-performing agents. As the first long-context LLM agent benchmark for software engineering, LoCoBench-Agent establishes a rigorous foundation for measuring agent capabilities, identifying performance gaps, and advancing autonomous software development at scale.