Build or update a knowledge graph from a folder. (Writes to graphify-out/.)

Args: path: Folder to extract the graph from (relative to the project dir or absolute). mode: "deep" -> more aggressive INFERRED edges; empty -> default. update: True -> re-extract only changed files and merge into the existing graph. cluster_only: True -> rerun clustering only, without re-extraction. no_viz: True -> skip the HTML visualization (faster for development).

Run a natural-language query against the graph.

Args: question: Natural-language question, e.g. "what connects attention to the optimizer?" dfs: True -> trace a specific path in depth. budget: If >0, cap the number of tokens returned (e.g. 1500).

Find the exact path between two nodes (e.g. "DigestAuth" -> "Response").

Return everything Graphify knows about a node.

Add an external source to the graph (arXiv paper, tweet, etc.). http/https only.

Args: url: Source URL to add. author: Original author tag (optional). contributor: Tag for who added it (optional).

One-shot orientation: call this FIRST.

Returns graph size, top god nodes, community count, surprise-edge count and suggested starting questions — enough to plan further exploration cheaply.

List the highest-degree (most connected) 'god nodes'.

List unexpected cross-file/cross-domain connections (surprise edges).

Summarize Leiden communities with sizes and sample members.

Capability test: how can semantic naming be produced in this session?

Reports whether the connected client supports host-LLM sampling (so the server needs no API key), whether a backend API key is configured as a fallback, and which method graphify_label_communities will pick.

Give the Leiden communities human-readable names.

Args: method: "auto" -> host-LLM sampling if the client supports it, else a configured backend key (graphify CLI), else "Community N" placeholders. "sampling" -> force host-LLM sampling (no API key needed). "cli" -> force the graphify backend (GEMINI_API_KEY/OPENAI_API_KEY/... or a local ollama). "placeholder" -> no LLM at all. limit: Only the largest limit communities are named, to stay cheap. sample_size: Member labels per community handed to the model.

Search nodes by text in their name/label (case-insensitive).

List the direct (1-hop) neighbors of a node, with relations.

Extract a BFS subgraph around a node, capped at a token budget.

This is the token-cheap way to hand the model just the relevant slice of a large codebase instead of the whole graph.

Args: node: Center node (exact or fuzzy match). hops: BFS depth from the center. budget_tokens: Approximate cap on returned size; expansion stops when hit.

Show a node's full metadata: type, source file/line, docstring, community.

Check whether graph.json is stale relative to the current git HEAD.

Prefers the commit graphify recorded the graph was built from (built_at_commit) over the file mtime — robust across checkouts where mtime is reset — and flags both modified and newly-added (untracked) files. Recommends graphify_build(update=True) if stale.