# For Python Teams

URL: /bengal/docs/about/for-python-teams/
Section: about
Tags: python, teams, documentation, jupyter, autodoc

--------------------------------------------------------------------------------

For Python Teams If your codebase is Python and your team thinks in Python, your documentation toolchain should be Python too. Bengal is built for this: same language end to end, same package manager, same debugging tools, same CI runner. One Language, One Toolchain Most Python documentation setups involve hidden dependencies on other ecosystems: Tool Python? But also... Sphinx Yes reStructuredText DSL, Make/Tox wrappers, extension debugging is archaeology MkDocs Yes Jinja2 GIL contention at scale, limited programmatic content Docusaurus No Node.js, npm, React, MDX Astro No Node.js, npm, Vite Bengal's stack is pure Python at every layer: Layer Library What it replaces Markdown parsing Patitas mistune, markdown-it, MyST-Parser Syntax highlighting Rosettes Pygments (3.75x faster, O(n) guaranteed) Templates Kida Jinja2 (1.81x faster under concurrency) Dev server Pounce livereload, mkdocs serve Every library is readable Python source you can breakpoint() into. No compiled extensions in the critical path. No JavaScript toolchains to maintain. Jupyter Notebooks as Pages Bengal renders .ipynb files as first-class pages — code cells, outputs, and Markdown cells become documentation without export steps: content/ tutorials/ data-pipeline.ipynb # Rendered as /tutorials/data-pipeline/ model-training.ipynb # Rendered as /tutorials/model-training/ Notebooks get the same treatment as Markdown pages: frontmatter, table of contents, syntax highlighting, search indexing, and navigation. Add Binder and Colab launch links from config. Autodoc for Python APIs Generate API reference from your source code, CLI tools, and OpenAPI specs: # config/_default/content.yaml autodoc: enabled: true sources: - type: python module: mypackage paths: ["src/mypackage"] - type: cli module: mypackage.cli - type: openapi path: openapi.yaml Bengal parses your Python AST directly — no import-time side effects, no autodoc_mock_imports, no sys.path manipulation. The parsed AST is cached between builds. Free-Threading Performance Bengal is designed for Python 3.14t. On free-threaded builds, page rendering achieves true parallelism: Configuration 1,000 pages Relative Python 3.14 (GIL enabled) ~3.5s 1.0x Python 3.14t (GIL disabled) ~2.0s 1.75x uv python install 3.14t uv run --python=3.14t bengal build This works because every layer — Patitas, Rosettes, Kida — was written from scratch for thread safety. No GIL assumptions, no shared mutable state in the hot path. See Free-Threading and Benchmarks for details. AI-Native by Default Your docs are consumed by AI agents as much as by humans. Bengal generates machine-readable output automatically: llms.txt — curated site overview for AI agents (spec) agent.json — hierarchical navigation manifest with content hashes Per-page JSON — metadata, plain text, optional heading-level chunks for RAG Content Signals — per-page/section control over search, AI input, and training (spec) See AI-Native Output for the full story. Getting Started pip install bengal bengal new site my-project-docs --template docs cd my-project-docs bengal serve The docs template creates a ready-to-use structure with getting-started, guides, and reference sections. Add your content, configure autodoc, and deploy. Info Seealso Benchmarks — measured build times and scaling curves Free-Threading — how Bengal uses Python 3.14t The Bengal Ecosystem — the full b-stack architecture AI-Native Output — machine-readable output formats

--------------------------------------------------------------------------------

Metadata:
- Author: lbliii
- Word Count: 482
- Reading Time: 2 minutes