pystdoc: Python Structural & Topological Documentation Engine

pystdoc (Python Structural & Topological Documentation Engine) is an enterprise-grade, high-precision codebase and architectural documentation generator powered by LLMs, AST parsing, and libclang.

It analyzes codebases in bottom-up + top-down topological passes, constructs hierarchical execution/data models, and synthesizes clean, human-centric Markdown and Mermaid diagrams within a 16K context window.

[!WARNING] ⚠️ Caution: Potential LLM API Usage Costs pystdoc performs thorough, multi-pass analysis by sending prompts for individual symbols, modules, and architecture synthesis. When using commercial paid API endpoints (such as OpenAI GPT-4, Claude, etc.), analyzing large codebases can consume a significant amount of tokens and may incur substantial financial costs. We strongly recommend using self-hosted/local LLM backends (e.g., LiteRT-LM, Ollama, vLLM, or LocalAI) or setting strict API budget limits before running against large projects.

📁 Output Directory: .docgen/

[!IMPORTANT] All generated documentation, architecture designs, and caches are automatically centralized inside the .docgen/ directory of your target project. Your existing source code files are never modified.

When pystdoc finishes, you can explore the complete documentation suite starting from .docgen/README.md:

your_project/
├── .docgen/                          # <-- Centralized output directory
│   ├── README.md                     # Executive summary: "What does this project actually do?"
│   ├── design/                       # System architecture and design documentation
│   │   ├── overview.md               # Architecture overview & inter-module Mermaid diagram
│   │   ├── data_models.md            # Data structure design, models, lifecycle & integrity
│   │   ├── execution_model.md        # Runtime execution model, paradigms, & control flow
│   │   └── modules/                  # Module-by-module detailed design documents
│   │       ├── module_a.md
│   │       └── ...
│   ├── documents/                    # Granular symbol & source code documentation
│   │   ├── src/main.c.md             # File-level overview and symbol list
│   │   ├── src/main.c.fn.main.md     # Individual symbol document (with call graph & context)
│   │   └── ...
│   ├── files.txt                     # List of scanned source files
│   └── index.db                      # SQLite WAL database for instantaneous incremental caching
├── src/
└── ...

🌟 Key Features

1. Topological & Structural Ordering (Tarjan SCC + Kahn DAG):
   • Evaluates call graphs in $O(V+E)$ linear time.
   • Automatically breaks cyclic mutual recursions and organizes code symbols into dependency-safe execution levels.
   • Level-by-level parallel LLM execution guarantees context-rich bottom-up summaries without race conditions.
2. 3-in-1 Unified Documentation Pipeline:
   • docgen: Bottom-up & top-down symbol-level documentation with SHA-256 and SQLite caching (.docgen/documents/).
   • designgen: Map-Reduce architectural synthesis (.docgen/design/).
   • reportgen / pystdoc: Executive summary README (.docgen/README.md) answering "What does this project actually do?"
3. C/C++, Python & Shell Deep Understanding:
   • compile_commands.json Integration: Full include path resolution and macro expansion via libclang.
   • Fully Qualified Domain Names (FQDN): Disambiguates identical symbol names across large monorepos.
4. Standard LLM Options & Multi-Language Support:
   • Works with Ollama, LiteRT-LM, vLLM, and OpenAI API.
   • Supports --host, --model, --token / --api-key, --context-size, and --language (e.g. English, Japanese, 日本語).

🚀 Quick Start

Installation

pip install pystdoc

Basic Usage

1. Generate Full Documentation & README (One Command)

pystdoc --dir ./my_project/

Output will be created at ./my_project/.docgen/README.md.

2. Generate in Japanese

pystdoc --dir ./my_project/ --language 日本語

3. Run Individual Steps

# Generate symbol-level docs into .docgen/documents/
docgen --dir ./my_project/ -j 4

# Synthesize architecture design docs into .docgen/design/
designgen --dir ./my_project/

4. Practical Real-World Example (Dedicated Remote LLM Server)

pystdoc --dir ./target_project/ --host 192.168.0.123:11434 --model gemma4-26b-a4b --language 日本語

⚙️ CLI Options

📄 License

MIT License. Author: tab4moji.