DeepDoc

DeepDoc turns a code repository into a documentation site that can be navigated by topic and refreshed as code changes. Its documentation can cover areas such as architecture, endpoints, integrations, and schemas. The tool scans source files, plans and generates pages, adds OpenAPI references when available, and builds a site. Supported inputs include Python, TypeScript, JavaScript, Go, PHP, Vue, GraphQL, and OpenAPI or Swagger specifications. The output can be a MkDocs Material site with navigation and search, optionally including an AI chatbot, as well as Markdown, Mermaid diagrams, a static Next.js/Fumadocs site, or an interactive API reference. AI answers can cite repository file paths and line ranges. DeepDoc is a Python package installed with pip and runs in a terminal; it requires Python 3.10 or later and an LLM provider supplied by the user. The free plan is free. Generate and update use the chosen LLM, so usage costs depend on the model and documentation pages. Other listed commands do not call an LLM. Deploy creates a static site for any host.

DeepDoc combines repository scanning, structured documentation, and targeted regeneration in a free, MIT-licensed package. Account for the Python requirement and LLM costs when choosing it.

DeepDoc suits developers and teams who want repository-based documentation they can regenerate when code changes. It is relevant to users comfortable with a terminal, Python 3.10 or later, and configuring their own LLM provider.

DeepDoc is a terminal-based package that turns a code repository into a navigable documentation site. It suits developers who want explanations tied directly to source files and are comfortable supplying an LLM provider. The main trade-off is that generation and updates consume the model budget you choose.

Overview

DeepDoc scans source files, plans a documentation structure, generates pages, stages OpenAPI references when specifications are present, and builds a site. Topics such as architecture, endpoints, integrations, and schemas give the result a useful information hierarchy instead of a single undifferentiated stream of generated text.

The output is a MkDocs Material site with navigation and search, plus an optional AI chatbot widget. Chatbot answers cite repository file paths and line ranges, so readers can verify an explanation against the code. DeepDoc can also produce a static site for deployment to any host, making it suitable for self-hosted publication rather than requiring a vendor-hosted portal.

It runs in the terminal with no account or cloud requirement and is MIT licensed. Installation is provided as a Python package through pip install deepdoc; Python 3.10 or later is required.

Key features

Repository-aware generation

Supported inputs include Python, JavaScript, TypeScript, Go, PHP, Vue, GraphQL, and OpenAPI/Swagger specifications. API-spec support and interactive API references make DeepDoc a sensible fit for projects where endpoint documentation must sit alongside implementation details. The homepage names a slightly narrower set of supported languages, so teams with an unusual repository should confirm that their code is covered before committing to the workflow.

Traceable answers

Source paths and line ranges attached to AI answers provide a practical checking mechanism. That is more useful for maintenance and code review than uncited prose, although the citations do not remove the need for an engineer to assess the underlying implementation.

Targeted regeneration

The update command regenerates pages affected by code changes rather than rebuilding every page conceptually. This is the right approach for repositories that change frequently and have documentation broad enough for full regeneration to be wasteful. DeepDoc does not provide automatic updates, so a team must invoke the update process as part of its own workflow.

Local pipeline and deployment

Commands such as init, serve, deploy, status, config, performance, and clean do not call an LLM, while generate and update do. That separation helps control spend during setup and inspection. DeepDoc's static output can be hosted anywhere, and the package's self-hosted format avoids an account dependency.

Pricing

The sole DeepDoc plan costs free. It is MIT licensed, requires no account, and has no cloud requirement. There is no free trial because the product itself is free, and no subscription tiers or seat charges are published.

Free does not mean unlimited model usage. You bring your own LLM provider, and the cost of generate and update depends on the selected model and the number of documentation pages involved. The maker provides no dollar estimates, so organizations should budget from their provider's rates. Readers seeking a fixed monthly allowance, included AI credits, or automatic synchronization will need a different product.

Platforms

DeepDoc supports Linux, macOS, Windows, web, and self-hosted deployment. Its core workflow is terminal-based and requires Python 3.10 or later, while the generated site is static and can be deployed to any host.

Who it's for

DeepDoc is aimed at developers and engineering teams maintaining repositories that need structured, source-linked reference material. It is particularly well suited to teams comfortable managing an LLM account themselves, hosting a static site, and running updates when code changes. It is a weaker fit for nontechnical documentation authors, teams that require a hosted editor, or organizations that want vendor-managed automatic refreshes and predictable bundled AI usage.

Pros and cons

Pros

• Source citations: File paths and line ranges make generated answers easier to audit.
• Focused updates: The update command targets pages affected by changes, limiting unnecessary regeneration.
• Broad code and API inputs: Language, GraphQL, and OpenAPI/Swagger support covers both implementation and interface documentation.
• Portable publishing: MkDocs Material and static output can be deployed to any host.
• Open, local operation: MIT licensing, terminal execution, and no account or cloud requirement suit self-hosting.

Cons

• LLM costs are external: Generate and update use your chosen provider, with spend varying by model and page count.
• No automatic updates: Teams must run the update workflow themselves.
• Python dependency: Python 3.10 or later is required for the package.
• Coverage needs checking: The homepage and source-type list name different sets of supported inputs.

Alternatives

AI Documentation Generators is the broader category for comparing other approaches.

Choose Guidde instead if you want a freemium web-and-device product for capturing up to 25 how-to videos on its free plan, with voiceover recording and shareable links.

GitBook is preferable for a hosted documentation service: its free plan is for individuals, includes one free user, unlimited page views, and 10 GitBook Agent messages per week.

Pick AutomaDocs when GitHub integration and automatic sync on push matter more than a local, bring-your-own-provider workflow; its free plan covers one repository and 50 AI chat messages per month.

Docframe suits teams wanting a web editor and a low-cost paid tier, while its free plan omits AI editing and static-site export.

Documentation.AI is the alternative for a hosted starter plan with five editor seats, one project, and 10,000 one-time AI credits.

Fern is worth choosing for a hosted or self-hosted team plan with 10 members, 1,000 pages, monthly AI credits, and page-generation quotas on its free tier.

Hyperdocs offers a hosted free site for one member with 2,000 one-time Explore Credits and a basic third-party integration.

InkLoom is the better match for unlimited pages on a free plan with an inkloom.dev subdomain and one free user per project.

Verdict

Choose DeepDoc if you want open-source, repository-aware documentation with verifiable source citations, targeted regeneration, and static self-hosting. Look elsewhere if you need automatic synchronization, a hosted authoring experience, or predictable AI costs bundled into a subscription.