graphify

graphify turns code, data schemas, and documents into a knowledge graph for AI assistants and project analysis.

What it is

graphify is a tool for building a project knowledge graph. It reads code, SQL schemas, scripts, documents, and other material, then connects entities so an AI assistant can understand the system structure better.

The repository appeared in 2026, its main language is Python, and the license is MIT. Its topics include Claude Code, Codex, Gemini, knowledge graphs, RAG, and tree-sitter.

What is inside

Inside are a CLI, entity extraction, clustering, report export, a global graph across projects, and integrations with several agent environments. The README contains many commands for Codex, Claude Code, OpenCode, Cursor, and other tools.

Extracting a project graph

The commands show graphify’s core idea: build a graph from a folder, then export a view that a human or agent can read.

Language: Bash

graphify extract ./docs --backend ollama
graphify export callflow-html --output docs/architecture.html
graphify global add graphify-out/graph.json myrepo

How people use it

The practical value is reducing agent blindness in a large codebase. Instead of reading the project in fragments every time, a team can build a map of modules, tables, scripts, documents, and architecture areas.

Its strength is that it targets real working folders, not only one programming language. Source code, SQL, documentation, and process descriptions can all enter the graph.

Project details

graphify is especially interesting because of its link to development agents. A large model can reason well over visible files, but it struggles to hold an entire system in memory. A project graph becomes extra memory that can be updated and reused.

SQL schemas, scripts, and documents matter because product architecture rarely lives only in source code. Database tables, migrations, launch instructions, and internal docs often explain the system as much as code does.

The risk is a false map. If extraction misses an important relation or the model names a node community incorrectly, the agent may confidently go in the wrong direction. The graph should be treated as navigation, not absolute truth.

Strengths and limitations

The limitation is that graph quality depends on input data and the selected model. If the project is poorly structured or extraction is wrong, the graph can confidently show incorrect relationships.

graphify is best read as infrastructure for agent-assisted development: it does not write code for the user, but tries to give the model a more stable memory of the project.