Build an AI Documentation Generator from Code
Automatically analyze codebases and generate comprehensive documentation using AI agents that understand code structure, extract docstrings, and produce human-readable docs.
Code Parsing & Ingestion
Parse and structure source code into LLM-friendly formats for analysis
Packs entire repositories into structured, LLM-ready formats — ideal for feeding codebases into documentation generators
Converts existing docs and code files into clean markdown, useful when the project already has partial documentation to merge
Lightweight file-to-markdown converter that handles diverse source formats as preprocessing step
AI Understanding & Generation
Use language models to analyze code semantics and generate documentation text
Vercel AI SDK provides structured output via generateText + Output.object(), perfect for generating typed documentation schemas from code analysis
Mature agent framework with document loaders and chains suited for multi-step doc generation pipelines
Programming-oriented LLM framework that can optimize documentation generation prompts automatically for consistent quality
Agent Orchestration
Coordinate multi-step workflows: analyze → outline → draft → review → finalize
Graph-based agent framework ideal for modeling the documentation pipeline as a DAG — parse, analyze, draft, review, and publish steps
Role-based agents (code analyst, tech writer, reviewer) naturally map to documentation team workflows
TypeScript-native agent framework with built-in workflow orchestration, fitting for JS/TS documentation toolchains
Quality Evaluation
Evaluate generated documentation for accuracy, completeness, and readability
LLM Observability
Monitor token usage, latency, and quality across documentation generation runs