TensorFlow Code Documentation Generator - AI Docs & Docstrings

Generate TensorFlow documentation with AI: add comments and docstrings to your code, or create a README, API reference, architecture overview or usage guide — for a single file or an entire project.

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The AI model that does the work. Stronger models cost more credits.

Add files · folder or .zip
Code, text and ZIP files are read in your browser and added to your input, free for everyone. Images and documents go to the AI model and need an account. To upload to the Agent mode sandbox, turn on Agent mode first.
Import from GitHub The AI clones the repository inside its Agent mode sandbox and works on it there — Agent mode turns on automatically. A token is only needed for private repos: it is placed in the ephemeral sandbox and never stored in our database. Still, use a temporary fine-grained token scoped to just that repository, and revoke it after. Optional
Private repo? Create a fine-grained token scoped to just that repo.
In-code documentation (comments and docstrings woven into your code) or a standalone document generated from your code, ready to drop into your repository.
Auto follows your language's standard convention (PEP 257 for Python, JSDoc for JavaScript, Javadoc for Java...). Pick a specific style to match your project's existing docs.
Your code or project Works on entire projects: add a .zip or folder with "Add files" above (parsed in your browser into a file tree where you pick what to include) or import a GitHub repository. Huge projects go to the AI's sandbox automatically. Or simply paste code here.
Brief covers the essentials; In detail explains purpose, behavior and edge cases.
BriefModerateIn detail
Tell the AI about your audience, conventions, or sections to include or skip. Optional
Lets the AI read current docs, APIs and the URLs you paste. Learn more
Free account The AI works in its own cloud computer: it writes and runs code, edits files and installs packages. Sign up free to use it.
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Your documentation will appear here

Paste code or add a whole project, pick the type of docs, and press the button.

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TensorFlow code documentation, generated — not hand-written

Most AI tools stop at sprinkling comments over a snippet. This documentation generator reads your TensorFlow code the way a new team member would — single file or entire project — and produces the documentation you actually need: docstrings and comments woven into the code itself, or standalone Markdown documents (README, API reference, architecture overview, usage guide) ready to commit to your repository.

Five kinds of documentation, one tool

Pick a type above
  • Comments & docstrings — your complete code returned with documentation added, in the docstring style you choose (Google, NumPy, reST/Sphinx, JSDoc, Javadoc, Doxygen, XML doc comments) or your language's default.
  • README.md — what the project does, features, installation, usage with real examples from your code.
  • API reference — every public class, function and constant: signatures, parameters, returns, errors, examples.
  • Architecture overview — components, responsibilities and data flow, with a Mermaid diagram.
  • Usage guide — a step-by-step tutorial for new users of your code.

Document a whole project, not one file at a time

Multi-file aware

Drop a project .zip or a folder into the upload area and it's parsed instantly in your browser — nothing is uploaded. You get a file tree that auto-skips the junk (node_modules, binaries, lockfiles) where you tick exactly which files to document, with a live character budget. The AI treats the selection as one codebase — it infers the project's purpose, entry points and structure, and documents how the pieces fit together instead of describing each file in isolation.

For the best result

  • Include the entry point (main, index, app) so the AI understands how the project is used.
  • Set the depth slider to match your audience — brief for experienced developers, in-depth for onboarding docs.
  • Use the instructions field for team conventions (tone, sections, what to skip).
  • Generating several documents? Run once per type — each produces a focused, complete document.

When to turn on Web Access and Agent Mode

These tools provide different kinds of context and verification. Enable them when they improve the task.

Web Access

Use for current docs

Enable Web Access when the task depends on current documentation, an external API, or a third-party package—especially a new, niche, or less familiar library.

For the best result

  • Paste the official documentation URL into your prompt.
  • Name the package and version you intend to use.
  • Ask the model to follow the linked API instead of relying on memory.
  • For multiple sources, say which source is authoritative.

Example: “Use version 4.x and follow the official documentation at https://docs.example.com/.”

Agent Mode

Use for automatic checks

Turn on Agent Mode when you want the model to run the code, execute tests, reproduce an error, or compare the result with an expected output.

Particularly useful for

  • Algorithms with testable inputs and outputs.
  • Parsing, data transformation, and numerical tasks.
  • Debugging a reproducible error.
  • Checking that dependencies install and tests pass.

Credit note: Running and iterating on code may consume more credits than a single response alone. Leave execution off when you only need a small snippet you can inspect yourself.

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Browse public code generations for inspiration, or contact us if you have any questions.