LogCopilot: Automating Log Aggregation Analysis through Large Language Models

2026-06-13

AI Summary

This work proposes an automated log aggregation and analysis framework based on large language models to address the growing challenge of log analysis in increasingly complex systems, where engineers traditionally rely on domain expertise to manually craft intricate LogQL queries. The framework enables end-to-end generation of LogQL queries from natural language instructions by integrating a hierarchical log knowledge base, natural language understanding, knowledge retrieval, and tool invocation mechanisms. Evaluated on four real-world log datasets, the approach achieves an average accuracy of 76.8%, significantly outperforming existing baselines and demonstrating its effectiveness and practicality for log analysis tasks.

Abstract

Logs record the runtime behavior of software and are widely used in various tasks such as debugging, testing, and fault diagnosis. With the increase in system size and complexity, log analysis has gradually become a challenging task. Current industrial systems typically use log aggregation systems such as Grafana Loki and ELK to simplify the log collection and analysis process. Engineers write queries using the DSL query language provided by these systems can complete a variety of log analysis tasks. However, writing these queries is often time-consuming and labor-intensive, as it requires engineers to have a thorough understanding of the DSL syntax and the detailed information contained in the logs. To address these challenges, this paper proposes LogCopilot, an automated log aggregation analysis framework based on large language models (LLMs). LogCopilot accepts natural language log analysis instructions and accomplishes automated log analysis through knowledge retrieval and tool calling. LogCopilot constructs a hierarchical knowledge base to represent and provide key knowledge in logs. And it achieves automated log aggregation analysis by generating and executing LogQL queries. The evaluation based on four log datasets confirm the effectiveness of LogCopilot, which achieves an average accuracy of 76.8% and outperforms baseline approaches. Moreover, experiment results shows that LogCopilot is effective in LogQL query generation.

Problem

• log analysis
• DSL query
• log aggregation
• software debugging
• fault diagnosis

Innovation

• Large Language Models
• Log Analysis
• Log Aggregation
• Natural Language to Query
• LogQL