Machine learning systems are now consuming technical documentation at scale, forcing maintainers to rethink how they structure and publish information. Docsy, an open source documentation framework created by Google and widely adopted across cloud native projects, is transitioning to the Linux Foundation while introducing capabilities tailored specifically for AI consumption.
Erin McKean, senior developer relations engineer at Google and steering committee member for Docsy, unveiled the transition during a keynote address Wednesday at the Linux Foundation's Open Source Summit Europe in Prague.
Since its 2019 debut, Docsy has served as a Hugo static site generator theme built for technical documentation. While applicable to any documentation need, the platform has gained particular traction in open source communities. By the close of 2024, approximately 2,200 projects had adopted Docsy, with significant uptake among Cloud Native Computing Foundation members such as Kubernetes, OpenTelemetry, gRPC and Jaeger.
The decision to move Docsy under the Linux Foundation umbrella reflects the platform's deep roots within the foundation's ecosystem. McKean explained that proximity to existing users strengthens open source initiatives. "Open source projects work best when they are close to the users," she stated.
Documentation as infrastructure for AI systems
McKean's presentation emphasized how artificial intelligence has emerged as a significant new audience for technical documentation. While some technical writers have expressed frustration that it required AI adoption to secure additional documentation resources, McKean argues the underlying goal remains unchanged: ensuring information reaches and assists developers through whatever channel proves most effective.
When we're making technical documentation, it really doesn't matter how the information becomes useful to humans, as long as it does it.
Erin McKean
Docsy has already begun modifying its output to accommodate AI systems. Starting with version 0.15.0 in May, the platform generates a Markdown rendering of each page in addition to standard HTML output, plus an llms.txt file that indexes site content for machine learning tools. Both capabilities are optional and currently marked as experimental.
This approach creates a more efficient pathway for AI systems to access the specific information projects want them to reference. "You can redirect your LLMs and agents to the text that tells them how to use the project," McKean noted.
The platform has continued expanding its agent-focused functionality. Version 0.16.0, shipped in July, introduced an upgrade guide formatted for AI assistant comprehension, with explicit conditions, steps and verification checks embedded in the instructions. Version 0.17.0, released in August, advanced the concept further by automatically inserting a hidden directive at the top of pages with llms.txt enabled, guiding visiting agents toward the site's content index. This feature remains experimental.
Measuring agent-friendly documentation
Docsy's upcoming roadmap includes "AF" (agent-friendly) documentation scores, a measurement system designed to help project maintainers evaluate how effectively AI tools can discover, navigate and process their documentation. McKean indicated this would provide teams with concrete targets for improvement.
You won't have to guess. You can measure how agent friendly your docs are.
Erin McKean
Beyond serving machine audiences, improved documentation delivers conventional benefits. McKean emphasized that comprehensive documentation reduces the volume of routine inquiries reaching maintainers, lowering operational overhead. "When you have good docs, it reduces the number of questions that can be easily answered by documentation, reducing the burden on maintainers," she explained.