Recently, several people have asked about the syslog-ng project’s position on AI. In short, the answer is one of cautious optimism. We are open to using AI, but we do not allow it to take over any critical responsibilities from human developers. What does this approach look like in practice?
First and foremost, all syslog-ng code continues to be written by humans. While we do not reject AI tools outright, we deliberately avoid using them to directly generate production code. There are two main reasons for this.
The first is licensing. Syslog-ng’s source code is distributed under a combination of GPLv2 and LGPLv2.1 licenses. At present, there is no reliable way to guarantee that code produced by AI tools would be compliant with these licensing requirements.
The second reason is code quality. Syslog-ng is built using high-performance C code and is often deployed in highly secure environments. Even if AI-generated code were to run correctly, there would be no guarantee that it would meet our efficiency and security standards. Retrofitting performance optimizations and security measures after the fact is significantly more difficult than building them in from the very beginning.
Portability is another major concern. While x86_64 Linux is one of the supported platforms, it is far from the only one. Syslog-ng also runs on ARM, RISC-V, POWER, s390, and other architectures, including big-endian systems. In addition, it is not limited to Linux — it also supports operating systems such as macOS and FreeBSD. Writing code that behaves correctly and efficiently across all these environments is a complex task and one that current AI tools are not equipped to manage reliably.
So where does AI fit into the syslog-ng ecosystem?
Although it is not used to write core code, AI is part of our broader quality and support processes. We maintain a large suite of automated test cases, and each pull request is reviewed by humans before being merged. In parallel, various automated tools continuously analyze our code, both on GitHub and internally. These tools provide suggestions on how to improve code quality and security.
Of course, such tools are known to generate a significant number of false positives. Still, they remain useful, as they sometimes identify legitimate issues that require attention. Importantly, every final decision — whether to accept or reject a recommendation — is made by a human, since AI-based tools often lack full contextual understanding of the codebase.
Another critical pillar of the syslog-ng project is its documentation. Many of our most active users have chosen syslog-ng specifically because of the high quality of its documentation, which is written by humans. That said, we are exploring the use of AI as a way to improve how users access that information. We have already run a proof of concept on a local machine, where users could ask questions through an AI-powered interface and find relevant information much faster than by manually browsing the documentation.
While this is not directly tied to syslog-ng development itself, I recently realized that sequence-rtg — the tool I use to generate syslog-ng PatternDB rules from large volumes of log messages — would also be classified as an AI tool under many modern definitions. Interestingly, its documentation never mentions AI at all, likely because it was created before “AI” became a mainstream buzzword. If you’d like to learn more about sequence and how it helps streamline PatternDB creation for syslog-ng, you can read more here:
https://www.syslog-ng.com/community/b/blog/posts/sequence-making-patterndb-creation-for-syslog-ng-easier
In summary: yes, we do use AI. Probably less than what AI enthusiasts would hope for, but certainly more than what AI skeptics would be comfortable with. We have chosen a balanced, pragmatic position — one where AI enhances our work but does not replace the critical thinking, responsibility, and expertise of human developers.
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