# AutoLogger: A Multi-Agent Framework for the End-to-End Automated Logging

arXiv: https://arxiv.org/abs/2511.18528 · HTML: https://mlsanity.com/paper/2511.18528/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 45 references that are in the map.
- First posted on arXiv 23 Nov 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
- [LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead](https://mlsanity.com/paper/2404.04834/) (2024)
- [MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution](https://mlsanity.com/paper/2403.17927/) (2024)
- [Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge in Software Engineering](https://mlsanity.com/paper/2502.06193/) (2025)
- [A Survey on Automated Log Analysis for Reliability Engineering](https://mlsanity.com/paper/2009.07237/) (2020)
- [Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications](https://mlsanity.com/paper/2403.16073/) (2024)
- [Using Deep Learning to Generate Complete Log Statements](https://mlsanity.com/paper/2201.04837/) (2022)
- [Go Static: Contextualized Logging Statement Generation](https://mlsanity.com/paper/2402.12958/) (2024)
- [A Multi-Agent Approach to Fault Localization via Graph-Based Retrieval and Reflexion](https://mlsanity.com/paper/2409.13642/) (2024)
- [FastLog: An End-to-End Method to Efficiently Generate and Insert Logging Statements](https://mlsanity.com/paper/2311.02862/) (2023)
- [LogUpdater: Automated Detection and Repair of Specific Defects in Logging Statements](https://mlsanity.com/paper/2408.03101/) (2024)
- [AL-Bench: A Benchmark for Automatic Logging](https://mlsanity.com/paper/2502.03160/) (2025)
- [Log Statements Generation via Deep Learning: Widening the Support Provided to Developers](https://mlsanity.com/paper/2311.04587/) (2023)
- [Larger Is Not Always Better: Exploring Small Open-source Language Models in Logging Statement Generation](https://mlsanity.com/paper/2505.16590/) (2025)

## Built on by (most cited first, 1 of 1)

- [Towards Automatically Pruning Logging Code with Coding Agents: How Far Are We?](https://mlsanity.com/paper/2610.04716/) (2026)

## Abstract (arXiv)

Software logging is critical for system observability, yet developers face a dual crisis of costly overlogging and risky underlogging. Existing automated logging tools often overlook the fundamental whether-to-log decision and struggle with the composite nature of logging. In this paper, we propose Autologger, a novel hybrid framework that addresses the complete the end-to-end logging pipeline. Autologger first employs a fine-tuned classifier, the Judger, to accurately determine if a method requires new logging statements. If logging is needed, a multi-agent system is activated. The system includes specialized agents: a Locator dedicated to determining where to log, and a Generator focused on what to log. These agents work together, utilizing our designed program analysis and retrieval tools. We evaluate Autologger on a large corpus from three mature open-source projects against state-of-the-art baselines. Our results show that Autologger achieves 96.63% F1-score on the crucial whether-to-log decision. In an end-to-end setting, Autologger improves the overall quality of generated logging statements by 16.13% over the strongest baseline, as measured by an LLM-as-a-judge score. We also demonstrate that our framework is generalizable, consistently boosting the performance of various backbone LLMs.