Skip to main content

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/

⚙️ CLI Options

Option Alias / Env Default Description
--dir ./ Target project directory path
--language -l English Output documentation language (English, Japanese, 日本語)
--host -H, --base-url http://127.0.0.1:11434 LLM server host endpoint URL
--model -m, LLM_MODEL gemma4-26b-a4b LLM model identifier
--token --api-key, OPENAI_API_KEY None API Bearer token
--context-size --ctx-size 16384 Context window size
--concurrency -j 1 Number of parallel LLM workers
--force -f false Force regenerate all documents ignoring cache
--compile-commands None Path to compile_commands.json

📄 License

MIT License. Author: tab4moji.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pystdoc-0.5.1.tar.gz (40.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pystdoc-0.5.1-py3-none-any.whl (41.9 kB view details)

Uploaded Python 3

File details

Details for the file pystdoc-0.5.1.tar.gz.

File metadata

  • Download URL: pystdoc-0.5.1.tar.gz
  • Upload date:
  • Size: 40.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/7.0.0 CPython/3.14.6

File hashes

Hashes for pystdoc-0.5.1.tar.gz
Algorithm Hash digest
SHA256 0d6d9422e6444dedaa0398934755ac0b03656fa34dcce53920f3a026b728091c
MD5 f14439887fc110a5f9e08c7ba2696ce9
BLAKE2b-256 46bc281819cffdd83ef4a621f54959dabd43f69d914524363b6738ec7fd49ffd

See more details on using hashes here.

File details

Details for the file pystdoc-0.5.1-py3-none-any.whl.

File metadata

  • Download URL: pystdoc-0.5.1-py3-none-any.whl
  • Upload date:
  • Size: 41.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/7.0.0 CPython/3.14.6

File hashes

Hashes for pystdoc-0.5.1-py3-none-any.whl
Algorithm Hash digest
SHA256 a5ebef31d93f3626ac62bb7360acf2391eb28dff1eaea4c31773dbc2295e0fb7
MD5 0cfb3669432de4f4d19f4e3596308418
BLAKE2b-256 a1314477569b235dccda7c52ccf0bef6bd801246dd01ffdce57ae97abdf06886

See more details on using hashes here.

Release history Release notifications | RSS feed

0.5.4

2 files

0.5.3

2 files

This release

0.5.1 This release

2 files

0.5.0

2 files

0.4.0

2 files

0.3.0

2 files

0.2.0

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page