An experimental toolkit that automatically generates and maintains codebase documentation using LLMs like GPT-4.
Autodoc is an experimental toolkit that automatically generates and maintains documentation for software codebases using Large Language Models like GPT-4. It solves the problem of outdated, missing, or inconsistent documentation by programmatically analyzing repository structures and content to produce detailed, queryable documentation. The generated docs are stored within the project, ensuring they evolve with the code.
Developers and engineering teams working on medium to large codebases who need to improve documentation quality, streamline onboarding, and enable better code understanding through conversational interfaces.
Developers choose Autodoc because it automates the tedious process of documentation writing, ensures docs stay in sync with code changes, and provides an interactive CLI for querying codebases—reducing knowledge silos and improving team productivity.
Experimental toolkit for auto-generating codebase documentation using LLMs
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Uses LLMs to generate detailed documentation for every file and folder through depth-first traversal, ensuring no part of the codebase is overlooked.
Automatically chooses the cheapest LLM that can handle each file's token count, balancing expense and accuracy by defaulting to GPT-3.5 for smaller files.
Only reindexes files that have changed on subsequent runs, reducing time and computational costs while keeping documentation up-to-date with code changes.
Stores generated documentation in the `.autodoc` folder within the repository, making it version-controlled, and provides a CLI for conversational querying to enhance developer onboarding.
The README explicitly states it's 'not ready for production use' and things may break, making it risky for critical projects that require stability.
Indexing can cost several hundred dollars for large projects, especially with the recommended GPT-4 model, and costs scale with codebase size and API usage.
Currently only supports OpenAI APIs, forcing code to be sent externally with no self-hosted options yet, which poses security concerns for proprietary code.