DeepWiki
Generated wiki
Best for quickly reading a public repository as structured documentation.
Both tools help engineers and AI assistants read unfamiliar code, from opposite directions. DeepWiki turns public GitHub repositories into browsable wiki pages. Graphify builds downloadable graph artifacts from a project ZIP, including private code, docs, and diagrams.
Graphify stores inspectable artifacts so codebase knowledge can be reviewed by humans and reused by AI coding assistants.

DeepWiki
Best for quickly reading a public repository as structured documentation.
Graphify
Best for private code, structural queries, and portable outputs.
Decision
Public repo you want to read? Private repo you must map? Choose accordingly.
DeepWiki writes documentation for you to read: wiki pages and Q&A for public repositories. Graphify builds structure for you to query and keep: graph.html, graph.json, and GRAPH_REPORT.md that capture dependencies, communities, and rationale from any project ZIP you upload.
DeepWiki is built around public GitHub repositories. Teams with private code typically need an upload-based workflow instead. Graphify accepts a project ZIP with code, docs, PDFs, and diagrams, and runs the build in the cloud without a local install.
Choose DeepWiki for a quick guided read of a public repository, for example when evaluating an open-source dependency. Choose Graphify when the repository is private, large, or architecturally important, and when you need artifacts that assistants and teammates can reuse.
DeepWiki turns public GitHub repositories into generated wiki pages with Q&A, so a reader can browse someone else's project as structured documentation instead of raw files. Its natural zone is open code you want to read: evaluating a dependency, exploring a popular library, or getting oriented in a public project without cloning it first.
This site runs a hosted Graphify workflow on top of the open-source Graphify engine. You upload a project ZIP, private code and mixed documents included, and the cloud build returns three artifacts: graph.html for exploration, graph.json for tooling, and GRAPH_REPORT.md for reading. The artifacts are files you keep and point your AI assistants at, rather than pages hosted away from the project.
Yes, and the split is natural. Read public dependencies with DeepWiki while you evaluate them; when code becomes yours, vendored, forked, or replaced by internal work, build a Graphify graph so structure and rationale stay with the project. Reading stays cheap on the public side, and structure becomes durable on the side you maintain.
DeepWiki is built around public GitHub repositories. When code is private, use an upload-based workflow such as Graphify, which builds from your own ZIP.
Yes. graph.html, graph.json, and GRAPH_REPORT.md are kept in your account and can be downloaded into the project, so the map travels with the code.
A wiki is written to be read; a graph is built to be queried and traced. Prose explains a project, while nodes and edges let you ask what connects to what and keep the answer as an artifact.
Use each where it is strongest: public reading on the wiki side, structural memory for repositories you own. Teams that maintain long-lived code get the most from graph artifacts.
Continue through the Graphify topic cluster with pages that explain assistant context, architecture review, documentation, and graph-based codebase understanding.
Graphify vs Vector RAG for codebases
Compare Graphify and Vector RAG for codebases across structure, persistent context, multimodal inputs, and architecture review workflows.
Graphify vs Repomix for AI coding assistants
Repomix packs a repository into one AI-ready file. Graphify builds a persistent graph you can query again and again. Compare the two and see when to use both.
Codebase knowledge graphs for AI assistants
Learn how codebase knowledge graphs connect source files, docs, diagrams, and rationale so AI coding assistants can answer with structure.
Start from the use cases page or upload a project ZIP to build a graph that your team can inspect and reuse.