graphifyy
AI coding assistant skill (Claude Code, CodeBuddy, Codex, OpenCode, Kilo Code, Cursor, Gemini CLI, Aider, OpenClaw, Factory Droid, Trae, Hermes, Kiro, Pi, Devin CLI, Google Antigravity) - turn any folder of code, docs, papers, images, or videos into a queryable knowledge graph
Decision gist · record as of 2026-08-14
Yes, if you use an AI coding assistant and want to explore or understand a codebase faster than grepping or reading files. The local-first code parsing, permissive license, and active maintenance make it low-risk. Install friction is minimal (pure Python wheel, standard dependencies). No security vulnerabilities reported. Start with a small project to verify the graph quality matches your needs.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Code parsing is local and LLM-free, but semantic passes over docs/media require either your AI assistant's model or a configured API key.
- Low friction install as a pure Python wheel.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use without restriction, modification, or redistribution obligations beyond license inclusion.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 106,339 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,834,260 downloads/mo, #3,506 on PyPI
Alternatives
Verify before relying
# Install
uv tool install graphifyy
# Register with AI assistant
graphify install
# In your AI assistant, run:
# /graphify .
# Outputs appear in graphify-out/:
# - graph.html (interactive browser view)
# - GRAPH_REPORT.md (highlights and suggested queries)
# - graph.json (full queryable graph)- Whether the 29 tree-sitter language parsers are all installed by default or selectively based on detected project languages.
- Performance characteristics and graph size limits for very large codebases.
- Whether graph.json can be queried programmatically via Python API or only through CLI commands (explain, path, query).
What it is and what it does
Graphifyy is a CLI tool that transforms a folder of source code, documentation, images, and video into an interactive knowledge graph you can query instead of searching files. It uses tree-sitter to parse code deterministically into an AST—no LLM involved, nothing leaves your machine—and optionally adds semantic analysis of non-code media via your AI assistant's model or a configured backend. The result is three files: an interactive HTML graph, a markdown report of key concepts and connections, and a JSON graph you can query later.
The package integrates as a skill into popular AI coding assistants (Claude Code, Cursor, Codex, Gemini CLI, and others) so you can invoke it with a slash command. Every edge in the graph is tagged as either EXTRACTED (explicit in source code) or INFERRED (resolved by graphify), so you know what was read directly versus inferred. It detects communities within the graph, identifies highly-connected nodes, and resolves cross-file relationships like calls, imports, inheritance, and mixins across multiple languages.
Use it for
- Map a large unfamiliar codebase to understand its architecture, key concepts, and how components connect without manually reading files.
- Query a knowledge graph to find how two unrelated functions or classes are connected, or trace the path between them.
- Generate a markdown report of surprising connections and suggested questions about a project's structure for onboarding or documentation.
- Index code, docs, PDFs, and images together so you can ask questions that span multiple media types within a single graph.
- Commit a project-scoped graph to version control so teammates can query the same knowledge graph without re-parsing the codebase.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you use an AI coding assistant and want to explore or understand a codebase faster than grepping or reading files.
The local-first code parsing, permissive license, and active maintenance make it low-risk. Install friction is minimal (pure Python wheel, standard dependencies). No security vulnerabilities reported. Start with a small project to verify the graph quality matches your needs.
Install
graphifyy on PyPI
Before you install
Low friction install as a pure Python wheel. Active maintenance with recent release. Substantial runtime dependency footprint (29 packages including tree-sitter parsers for multiple languages), but all are standard PyPI packages with no compiled system dependencies.
Requires Python 3.10 or later. Code parsing is local and LLM-free, but semantic passes over docs/media require either your AI assistant's model or a configured API key.
License in practice
Apache-2.0 permissive license allows commercial and private use without restriction, modification, or redistribution obligations beyond license inclusion.
Quickstart
# Install
uv tool install graphifyy
# Register with AI assistant
graphify install
# In your AI assistant, run:
# /graphify .
# Outputs appear in graphify-out/:
# - graph.html (interactive browser view)
# - GRAPH_REPORT.md (highlights and suggested queries)
# - graph.json (full queryable graph)Verify before relying
- Whether the 29 tree-sitter language parsers are all installed by default or selectively based on detected project languages.
- Performance characteristics and graph size limits for very large codebases.
- Whether graph.json can be queried programmatically via Python API or only through CLI commands (explain, path, query).
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 29 packagesnetworkxnumpyrapidfuzztree-sittertree-sitter-pythontree-sitter-javascripttree-sitter-typescripttree-sitter-gotree-sitter-rusttree-sitter-javatree-sitter-groovytree-sitter-ctree-sitter-cpptree-sitter-rubytree-sitter-c-sharptree-sitter-kotlintree-sitter-scalatree-sitter-phptree-sitter-swifttree-sitter-luatree-sitter-zigtree-sitter-powershelltree-sitter-elixirtree-sitter-objctree-sitter-juliatree-sitter-verilogtree-sitter-fortrantree-sitter-bashtree-sitter-json |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,834,260 / month, #3,506 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: graphifyy-0.9.43-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “code knowledge graph builder”
- graphifyyParses code, docs, PDFs, images, and video into a queryable knowledge…
- cogneeCognee builds a self-hosted knowledge graph from ingested data,…
- neomodelAn Object Graph Mapper (OGM) for Neo4j that lets you define graph…
Give your agent the search over MCP, or paste the wish link into any chat.
More Software Development packages
Provides backported and experimental type hints for Python 3.9+, allowing use of newer typing features on older Python versions and enabling early experimentation with type system PEPs before they enter the standard library.
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Distlib provides low-level packaging utilities for building, distributing, and managing Python software—including metadata handling, version specifiers, wheel support, script installation, and dependency resolution.
See also code-review-graph · trailmark · aider-chat · trinity-agent · code-puppy · tree-sitter-go · tree-sitter-zig · tree-sitter-java · cognee · memsearch