Build and explore a small graph

This example works without an API key. It makes the first query reproducible, instead of depending on names that may not exist in your repository.

Create a project

Create a folder containing app.py:

app.py

def normalize_email(email: str) -> str:
    return email.strip().lower()


def find_user(email: str) -> dict:
    return {"email": normalize_email(email)}


def login(email: str) -> dict:
    return find_user(email)

Build the graph

From that folder, with Graphify installed:

graphify extract . --code-only
graphify cluster-only . --no-label
graphify export html

Open graphify-out/graph.html, or read graphify-out/GRAPH_REPORT.md. The graph should contain the three functions and their call relationships. Exact IDs and report formatting can vary by release.

Ask for context

graphify query "how does login normalize an email?"
graphify explain "normalize_email"
graphify path "login" "normalize_email"

Look for the relationship from login to find_user, then to normalize_email. Check the associated source references. If a label matches several nodes, use the node ID returned by the query for a more precise lookup.

Make a change

Add another helper or change a call, then refresh:

graphify update .
graphify export html

Re-run the query and compare the connections. Graphify queries the saved graph, so changing a source file without updating the graph can leave an old answer.

Move to your repository

Repeat the workflow from your repository root. Use the assistant skill to include model-assisted document context, or connect MCP for native agent tool calls.

Guides

Team workflows

Build and explore a small graph

Create a project

Build the graph

Ask for context

Make a change

Move to your repository

​Create a project

​Build the graph

​Ask for context

​Make a change

​Move to your repository

Create a project

Build the graph

Ask for context

Make a change

Move to your repository