Graphify guide: query a codebase as a knowledge graph

Key takeaways#

• Graphify is a GitHub project that turns code, database schemas, shell scripts, documents, papers, images, and videos into a queryable knowledge graph for AI coding assistants.
• The repository targets Claude Code, Codex, OpenCode, Cursor, Gemini CLI, and similar coding-agent workflows.
• Treat it as a practical developer resource for understanding larger repositories, not as a replacement for code review or architecture docs.

What Graphify helps with#

Graphify is aimed at a common AI-coding problem: the assistant can only work with the context it can see. Large projects spread important facts across application code, SQL schemas, scripts, infra files, documents, and notebooks. Graphify gives builders a way to map those inputs into a knowledge graph so an assistant can answer questions about relationships instead of only matching text snippets.

The GitHub description is unusually direct. It says Graphify can turn a folder of code, SQL schemas, R scripts, shell scripts, docs, papers, images, or videos into a queryable knowledge graph. That makes it useful for onboarding, refactors, incident analysis, and research-heavy codebases where a normal file search misses dependencies.

When to use it#

Use Graphify when you need to ask structural questions about a repository. Good examples include: which services touch this database table, what scripts call this model endpoint, where a specific data field is transformed, or which documents describe a workflow that also appears in code. Those are graph-shaped questions, and they are hard to answer with plain keyword search.

Graphify also fits agentic coding sessions. Before asking Claude Code, Codex, Cursor, or another assistant to change a large project, you can build a graph from the folder and use that graph as a richer context source. The assistant gets better grounding, and the human reviewer gets a clearer explanation of why files are connected.

Setup checklist#

1. Review the README in the GitHub repository before installing anything. Confirm the current commands, supported file types, and required local services.
2. Start with a small folder, not a monorepo. Validate that the generated graph matches obvious relationships before indexing more data.
3. Keep secrets out of the input folder. A knowledge graph can preserve sensitive paths, comments, queries, and documents.
4. Ask concrete questions. Graph tools perform best when the prompt names the subsystem, schema, function, table, or document family you care about.
5. Compare answers against the source files. Graphify can improve retrieval, but it does not remove the need for human verification.

Best-fit users#

Graphify is most useful for senior developers, AI coding power users, staff engineers, data engineers, and consultants who inherit unfamiliar systems. It can shorten the time between opening a repository and understanding the dependency map. It is also useful for teams building internal coding-agent workflows because the graph can become a reusable context artifact.

Beginners can still use it, but they should treat the graph as a map, not a final answer. If the generated explanation says that two modules are related, open both files and verify the relationship before making a change.

Limitations#

Graphify depends on the quality of the source folder and the extraction pipeline. Generated graphs can miss dynamic runtime relationships, external services, private packages, environment variables, or conventions that only appear in team knowledge. It may also produce noisy graphs for large repos unless the input is scoped carefully.

For production use, teams should document what was indexed, when it was indexed, and which files were excluded. That keeps agent answers from drifting away from the current codebase.

Recommended workflow#

Start by graphing one service, package, or feature folder. Ask Graphify relationship questions, then verify each answer in the source tree. Once the output is trustworthy, connect it to your coding assistant workflow and use it before refactors, migrations, incident reviews, and onboarding sessions. The win is not that the assistant writes more code; the win is that it has a better map before it writes any code.