> ## Documentation Index > Fetch the complete documentation index at: https://docs.graphify.com/llms.txt > Use this file to discover all available pages before exploring further. > ## Agent Instructions > The PyPI package is graphifyy, with two y's. The command is graphify. > /graphify . is typed in an assistant chat, not in a shell. > Use only commands, flags, and endpoints shown on these pages. Do not invent one. > Check the current version on PyPI (https://pypi.org/project/graphifyy/) or in https://docs.graphify.com/changelog.md before you describe what shipped. > The official site is https://graphify.com. These docs are https://docs.graphify.com. > The local MCP server is the ten tools in the MCP tool reference and reads a graph on the machine. The hosted server is https://api.graphify.com/mcp (Streamable HTTP, OAuth, no API key). The VS Code, JetBrains, ChatGPT, OpenClaw, and Vercel Connect pages use the hosted server, not the ten-tool list. > Read https://docs.graphify.com/llms.txt, then open one page's .md link. Use https://docs.graphify.com/llms-full.txt only when one page is not enough. # Your first knowledge graph > Install Graphify, map your repository, and ask a question about your code.

Graphify turns a repository into a graph of its functions, classes, imports, and relationships. Your assistant can query that graph instead of repeatedly scanning the same files. This quickstart uses the **open-source engine** on your machine. You need Python 3.10 or later and a supported coding assistant. For the hosted product, see [Graphify Cloud](/platform/overview). ```bash theme={null} uv tool install graphifyy graphify --version ``` `graphify --version` prints the version you installed. The latest release is on [PyPI](https://pypi.org/project/graphifyy/) and in the [changelog](/changelog). The package is **`graphifyy`**, with two y's. The command is `graphify`. You can also use `pipx install graphifyy`. ```bash theme={null} graphify install ``` This installs the Graphify skill for a detected assistant. To choose one explicitly: ```bash theme={null} graphify install --platform cursor ``` See [all integrations](/integrations/overview) for your assistant’s setup. Open a repository in your assistant, then enter this in its chat: ```text theme={null} /graphify . ``` In Codex, select the Graphify skill with `$graphify` and ask it to map the current repository. This is an **assistant instruction**, not a shell command. Code is parsed locally. If the repository includes documents or media, the skill can use your assistant’s model for semantic extraction. For an explicit code-only run, use the [local CLI workflow](/guides/local-development). ```text theme={null} graphify-out/ ├── graph.html Interactive graph ├── GRAPH_REPORT.md Concepts, communities, and suggested questions └── graph.json The graph used by CLI and MCP queries ``` Open `graph.html` in your browser. Click a node to inspect its connections, or start with the report for a tour of the architecture. From a terminal at the repository root: ```bash theme={null} graphify query "how does authentication reach the database?" ``` Use names from **your graph** to investigate further: ```bash theme={null} graphify explain "YourService" graphify path "YourService" "YourRepository" ``` The names above are placeholders. A path only exists if those nodes and connecting edges exist in your project. Build a small example and follow a real relationship. Expose the graph as structured tools in your editor.