The best AI documentation tools for legacy code

Quick answer: For very large or mainframe codebases with on-prem or air-gapped requirements, start with Swimm. For a legacy application hosted on GitHub, especially an older PHP, Python, or JavaScript monolith, Moxie Docs writes architecture docs from the code and keeps them current as you modernize it. For a one-off pass over many document types, DocuWriter.ai. For reading an unfamiliar public repository, DeepWiki or Google Code Wiki.

Legacy code is where documentation tools earn their price. Nobody who wrote the code is still around, the docs that exist describe a system from three rewrites ago, and every change risks breaking something nobody understands. AI tools can read the code faster than a new hire, but they differ sharply in how much code they handle, where that code can live, and what happens to the docs after the first draft.

Checked October 2026 against each vendor's own site. Plans and features change, so confirm before you buy.

What to check before you pick a tool#

1. Where the code lives. GitHub, GitLab, a self-hosted server, or a mainframe. Many AI tools only connect to GitHub.
2. How big it is. Count files, not repositories. A tool that reads a few hundred files well may sample or skip the rest of a large monolith.
3. Language coverage. "Supports any language" usually means the tool reads the text. Deeper analysis (symbols, imports, call graphs) often covers a short list of languages.
4. Deployment and data rules. Regulated legacy systems may need on-prem, air-gapped, or bring-your-own-model deployment.
5. What happens after the first draft. Legacy code changes during modernization. Docs generated once go stale as soon as the refactor starts.

Legacy code documentation tools compared#

Tool	Best for	Code location	Keeps docs current	Pricing
Swimm	Large legacy and mainframe systems	Enterprise deployments, including on-prem and air-gapped	See vendor	Quoted by lines of code
Moxie Docs	Legacy apps on GitHub that are still changing	GitHub	Re-checks on every merge, docs-only PRs	Free on 1 repo; paid from $29/mo
DocuWriter.ai	Many doc types from one codebase	Upload or connect a repository	Autopilot on Enterprise	From $20/mo
DeepWiki	Understanding a public repository	Public GitHub; private via Devin	Regenerate	Free for public repos
Google Code Wiki	Exploring open-source code	Public repositories	Regenerated by Google	Free
Coding agents (Claude Code, Codex, Cursor)	Explaining one module while you work	Your local checkout	Only when you ask	Your existing subscription

1. Swimm: best for large and mainframe codebases#

Swimm has narrowed its focus to understanding large and legacy codebases, including mainframe modernization. It generates documentation and process-flow explanations of what the code does, can run with your company's own LLM, and supports on-prem, cloud, and air-gapped deployment.

• Choose it when: the code is large, older than your version control, or under deployment rules that rule out a SaaS GitHub app.
• Trade-off: sales-led, with pricing quoted by lines of code and no self-serve trial. Compare: Moxie Docs vs Swimm.

2. Moxie Docs: best for legacy apps on GitHub that are still changing#

Moxie Docs is a GitHub App. It indexes the source, tests, existing docs, and history of a repository, then writes architecture pages and conventions with citations back to the code. The legacy-specific value comes after that: on every merge it re-checks the docs against the code and proposes fixes as docs-only pull requests, so the documentation keeps up while you refactor. The same context is served to Claude Code, Cursor, and Codex over MCP, which helps when an agent is doing the modernization work.

• Choose it when: the legacy app is on GitHub and you are actively changing it, for example an older PHP or Python monolith.
• Language coverage: documentation and search work on any language. Structural analysis (symbols, import graphs, library conventions, API routes) covers TypeScript/JavaScript, Python, and PHP; Go, Rust, and Ruby get dependency conventions. Java, Kotlin, and other languages are indexed as source without language-specific analysis.
• Size limits: indexed files per repository are 500 on Free, 2,000 on Starter, 5,000 on Pro, and 15,000 on Team.
• Trade-off: GitHub only, with no on-prem option. For a mainframe or a multi-million-line system, Swimm is the better fit.

3. DocuWriter.ai: best for a one-time documentation pass#

DocuWriter.ai generates code docs, API documentation, and other document types from source into Knowledge Spaces you can chat with. Plans run from $20/mo (Starter) to $499/mo (Unlimited).

• Choose it when: you need a large batch of first-draft documents from an undocumented codebase.
• Trade-off: automatic updates (Autopilot) need the Enterprise plan. Without it, keeping the docs current means re-running generation. Compare: Moxie Docs vs DocuWriter.ai.

4. DeepWiki: best for reading a public legacy project#

DeepWiki turns a repository into a wiki with architecture diagrams and source links. It is free for public GitHub repositories; private ones go through a Devin account.

• Choose it when: you inherited, forked, or depend on an old open-source project and need to understand it fast.
• Trade-off: the wiki is read-only and lives outside your repository. Compare: Moxie Docs vs DeepWiki.

5. Google Code Wiki: best free option for open-source code#

Google Code Wiki generates wiki pages, diagrams, and a Gemini chat for public repositories, regenerated as the code changes.

• Choose it when: the legacy code is public and you want a free, browsable overview.
• Trade-off: public repositories only at our last check. Compare: Moxie Docs vs Google Code Wiki.

6. Coding agents: best for explaining one module at a time#

Claude Code, Codex, and Cursor can read a legacy module and explain it, add docstrings, or draft a README on request. That is often the fastest way through a single unfamiliar file.

• Choose it when: you need an answer about one area of the code right now.
• Trade-off: the explanation lives in a chat session. Nothing records it in the repository or checks it again after the code changes. Writing the findings into an AGENTS.md or a docs folder keeps them for the next person.

A practical order for documenting a legacy codebase#

1. Map before you document. Generate an architecture overview first (module boundaries, entry points, data stores) so later pages have a shared frame.
2. Record decisions as you discover them. When you work out why the code is the way it is, write an architecture decision record instead of leaving it in a chat log.
3. Write down the conventions. Legacy code has unwritten rules. Capture them in an AGENTS.md so people and coding agents follow them.
4. Keep the docs moving with the refactor. Check the docs on every merge, or they drift within weeks. What is documentation drift? explains the failure mode.

Frequently asked questions#

Can AI document legacy code accurately?#

AI tools are good at describing what code does: structure, data flow, and entry points. They are weaker on why it was built that way, which often lives only in people's heads or old tickets. Review generated docs against the code, and prefer tools that cite the source files they used.

What is the best AI tool for documenting COBOL or mainframe code?#

Among the tools here, Swimm is the one built for mainframe modernization and on-prem or air-gapped deployment. Most other tools in this list, including Moxie Docs, connect to code hosted on GitHub.

How do I keep legacy documentation from going stale during a rewrite?#

Tie documentation checks to merges. Moxie Docs re-checks docs on every merge and opens docs-only pull requests; DocuWriter.ai's Autopilot (Enterprise) and DeepWiki or Code Wiki regeneration take other approaches. Docs generated once and never checked again go stale as soon as the refactor starts.

Is there a free AI tool for documenting legacy code?#

Yes. Moxie Docs is free on one GitHub repository (up to 500 indexed files). DeepWiki and Google Code Wiki are free for public repositories. For other AI documentation tools, see our roundup of AI documentation platforms.