LogCopilot: Automating Log Aggregation Analysis through Large Language Models

2026-06-17 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Expert, extended

Summary

LogCopilot is an automated log aggregation analysis framework that employs large language models (LLMs) to simplify complex log analysis tasks. It addresses the challenge of manually writing Domain-Specific Language (DSL) queries, such as LogQL for systems like Grafana Loki, by accepting natural language instructions. The framework constructs a hierarchical knowledge base from raw logs, summarizing variables, templates, and workflows. LogCopilot then uses LLMs for knowledge retrieval and generates LogQL queries to interact with log aggregation systems. Evaluated on four log datasets (HDFS, OpenSSH, OpenStack, TrainTicket), LogCopilot achieved an average accuracy of 76.8% with GPT-4o, significantly outperforming baseline methods. Its rectification mechanism also improved LogQL query syntax accuracy, reaching 1.000 on HDFS.

Key takeaway

For MLOps Engineers or AI Engineers struggling with manual LogQL query generation for systems like Grafana Loki, LogCopilot offers a compelling solution. You should consider integrating LLM-based frameworks that build hierarchical knowledge bases and automate query generation from natural language. This approach significantly reduces the learning curve and labor costs associated with log analysis, improving accuracy and operational efficiency. Explore LogCopilot's public code and dataset to assess its applicability to your specific log environments.

Key insights

LLM-driven LogCopilot automates log analysis by translating natural language into LogQL queries via a hierarchical knowledge base.

Principles

• Hierarchical knowledge bases enhance LLM log understanding.
• LLM-driven query rectification improves execution success.
• Semantic summarization bridges gap between logs and natural language.

Method

LogCopilot constructs a hierarchical knowledge base from logs, performs intent understanding and knowledge retrieval, generates LogQL queries for Grafana Loki, and rectifies errors before generating analysis reports.

In practice

• Use LLMs to summarize log variables, templates, and workflows.
• Implement query rectification for DSL generation tasks.
• Integrate LLMs with existing log aggregation systems like Loki.

Topics

• Log Analysis
• Large Language Models
• LogQL
• Grafana Loki
• Knowledge Retrieval
• Automated Query Generation

Code references

• FudanSELab/LogCopilot

Best for: Research Scientist, MLOps Engineer, AI Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.