想看看编码Agent真实的调用模式？TraceLab用4300个会话告诉你Claude Code和Codex的工作负载细节，还指明了KV缓存优化的具体方向。
华盛顿大学发布了TraceLab数据集，收录约4,300个编码Agent会话、350,000个LLM步骤和430,000次工具调用，数据来自日常使用的Claude Code和Codex。分析显示，工作负载具有长自主循环、长上下文短输出、多样且长尾的工具调用等特征，前缀缓存命中率虽高但存在不完美之处。基于这些发现，研究者提出了低开销工具调用、追加长度感知的预填充、语义感知工具延迟预测以及改进KV缓存管理四项优化方向。数据集和分析代码已开源在GitHub。
TraceLab: Characterizing Coding Agent Workloads for LLM Serving
Coding agents are rapidly becoming a major application of agentic LLMs, but serving them efficiently remains challenging. Progress on this challenge requires understanding real workload patterns, yet the data needed for such analysis is largely absent. Existing public traces and benchmarks do not capture real, day-to-day coding-agent usage across multiple agents and model families for serving-system analysis. To help fill this gap, we collect and release a trace of roughly 4,300 coding-agent sessions, containing about 350,000 LLM steps and 430,000 tool calls from our own day-to-day use of Claude Code and Codex. Our analysis shows that coding-agent workloads feature long autonomous loops, long contexts with short outputs, diverse and heavily-tailed tool calls, and high but imperfect prefix cache hit rates. These findings point to concrete opportunities for optimizing serving, including lower-overhead tool calling, append-length-aware prefill, semantic-aware tool-latency prediction, and improved KV-cache management around human-paced gaps. We release the dataset, trace collection pipeline, and analysis code at https://github.com/uw-syfi/TraceLab.git; the project website is https://tracelab.cs.washington.edu.