graph-build 0.2.0

Interactive code+docs dependency graph with doc-sync tooling and an AI-agent-friendly JSON export

build-graph

Architectural memory for your refactors. See the blast radius of your changes across code, docs, and git — on one interactive map that both you and your AI agent can read.

build-graph renders a single-file interactive HTML graph connecting three layers no other tool combines:

• code → code — Python imports (AST-based, TYPE_CHECKING-aware)
• code ↔ docs — which markdown files mention which source files
• git drift — added / modified / renamed / deleted overlay with ghost nodes for files that no longer exist

…and exports the same map as a compact, token-efficient JSON designed to drop into an LLM agent's context.

All of that with zero dependencies — pure Python stdlib, pip install brings in nothing else. The only third-party code is D3.js in the browser, SRI-pinned from CDN or fully embedded with --no-cdn.

▶ Live demo — the graph of this very repository (dogfood), with a synthetic --mock-git overlay so the Git mode is clickable too. 📖 UI guide — every feature, one by one.

Install

pip install graph-build        # or: uv tool install graph-build

No PyPI needed — install straight from GitHub:

pip install git+https://github.com/Mr-Freewan/build-graph.git

# or from a clone:
git clone https://github.com/Mr-Freewan/build-graph.git
pip install ./build-graph

Zero dependencies — stdlib only, Python 3.11+. The HTML output needs only a browser (D3.js from CDN with SRI pinning, or fully embedded via --no-cdn).

The PyPI distribution is named graph-build (the straight name is taken); the installed commands keep their names: build-graph, find-related-docs, verify-doc-links.

Quick start

cd your-project
build-graph                    # autodiscovery, no config needed → docs/graph.html
build-graph --compact          # + docs/graph-compact.json for AI agents
build-graph --init             # optional: pin discovered structure to graph.toml

Two companion CLIs — find-related-docs (reverse lookup: code → docs) and verify-doc-links (broken-reference gate for CI) — ship in the same package; see Companion tools.

Why not X?

• pydeps / Import Linter — imports only; no docs layer, no git drift.
• lychee & co. — dead-URL checkers; no map, no code layer.
• Obsidian graph view — notes only; doesn't see your code.
• Repomix / Gitingest — pack the repo text for LLMs; build-graph gives the structure: ~2 % of the tokens the raw text would cost (see the numbers).

Designed for AI agents

--compact writes a self-documenting JSON snapshot (embedded legend, indexed nodes, 3-letter type codes) that agents use for:

1. Blast radius — incoming imports of the file you're about to change, without grep.
2. Docs routing — which ADR / reference doc to read before editing a file.
3. Three-way doc-sync — the graph reveals (1) what's documented, (2) what should be documented but isn't, and (3) what's documented but no longer exists (ghost nodes = staleness detector).

Add build-graph --compact to a pre-commit hook or CI step to keep the map fresh for every agent session.

The compact format

--compact writes graph-compact.json (schema v2): nodes as an indexed array, edges as [source_idx, target_idx, type, [line_numbers]] rows, 3-letter codes for every category and edge type. The legend key embeds the full decoding table — an agent needs no external schema, the file explains itself:

{
  "v": "2.0",
  "legend": { "...": "what every field and code below means" },
  "stats": { "nodes": 1070, "ghosts": 0, "edges": 6279 },
  "n": [
    { "p": "smm_bot_async/core/security/access.py", "t": "cor", "d": 56 },
    { "p": "docs/explanation/adr/0009-parser-framework.md", "t": "adr",
      "d": 11, "s": "mod" }
  ],
  "e": [
    [ 1, 75, "d2d", [186] ]
  ]
}

p — path, t — category, d — degree, s — git status (omitted when clean). Edge types: c2c imports, c2d doc mentions, d2d doc links, dcs docstring refs, typ TYPE_CHECKING-only, ren git renames. Deleted-but-still-referenced files ride along as ghost nodes ("G": 1).

What it costs in context

Real numbers from a production repo — 1,070 mapped files, 6,279 edges (tokens ≈ bytes / 4, the usual rough estimate):

What you put in context	Size	≈ Tokens
The mapped files themselves	15 MB	~3,700,000
--json (verbose snapshot)	1.6 MB	~410,000
--compact	0.33 MB	~80,000

The whole architecture — every import, every doc mention, every stale reference — lands in ~2 % of what the raw text would cost, and fits in a single 200 k-context session with room to work. Without the map an agent rediscovers this structure every session: dozens of speculative greps and file reads that burn comparable tokens per question, not once. On small projects the map is almost free — the compact snapshot of this very repo is 4 KB ≈ ~1,000 tokens.

Don't take these numbers on faith — measure your own repo:

$ build-graph --root . --bench

Context cost on this repo (tokens ~= bytes / 4):

  What you put in context            Size      ~Tokens  vs corpus
  raw corpus (1070 files)         14.3 MB    3,757,913     100.0%
  --json export (schema v1)        1.5 MB      397,419      10.6%
  --compact export (schema v2)   311.4 KB       79,729       2.1%

--bench only measures — it writes no files.

A prompt to start from

graph-compact.json is a dependency map of this repository: nodes are
files, edges are imports and documentation mentions. Read the embedded
"legend" key first — it explains every field and code.

Using the map (before any grep):
1. Lay of the land: the 10 highest-degree hubs, grouped by category,
   with one line each on why they're central.
2. I'm about to modify <path/to/file.py>. List the blast radius:
   direct and 2-hop incoming importers, plus every doc that mentions
   the file — and tell me which of those docs to read first.
3. Anything suspicious: ghost nodes (docs pointing at deleted files),
   zero-degree modules, docs nothing links to.

Verify any surprising claim against the actual source before acting.

The interactive graph

• Canvas renderer — smooth at 1000+ nodes / 6000+ edges (pre-warmed layout, viewport culling, label LOD).
• 6 edge types — doc→doc, code→doc, code→code, type-only (TYPE_CHECKING), docstring mentions, git renames.
• Git overlay — status colours + ghost nodes + rename edges; --mock-git for a synthetic demo.
• Analysis aids — dead-code candidates, orphan ring, shortest path between two nodes (Shift+click), isolate-a-type, exclude-by-name.
• Sharing — URL-encoded views (Copy link), Mermaid export of the focused subgraph, full/compact JSON export.
• Comfort — 10 UI languages, dark/light themes, hue-aligned pastel/saturated palettes, draggable glass panels, IDE deep links (VS Code / Cursor / PyCharm), FAQ built in (?).

Everything lands in one self-contained HTML file — attach it to a PR, send it in chat, open it offline.

Configuration (optional)

Autodiscovery classifies every tracked file by kind (code / doc / config / locale) × location, detects your package and docs layout, and generates deterministic colours. A graph.toml is only an override:

build-graph --init           # generate graph.toml pinning current structure
build-graph --init --diff    # report drift (new folders, stale pins), change nothing
build-graph --init --merge   # append coverage for new folders, keep your edits

See graph.example.toml for the annotated format ([docs] categories, [[code]] dirs, [[rules]], [scan] excludes, [dead_code] exemptions, colour pins).

Two optional plain-text companions, both looked up in the project root:

• known-brokens.txt — whitelist for verify-doc-links false positives (one exact path per line).
• exclude-dirs.txt — directory-name skip list used only when git is unavailable (with git, .gitignore is the source of truth).

CLI reference (build-graph)

Flag	Effect
--root PATH	project root to scan (default: cwd)
--config PATH	graph.toml location (default: <root>/graph.toml)
--output PATH	HTML output (default: docs/graph.html or [output].path)
--scope full|package	whole repo (default) or package+tests+docs only
--json / --compact	verbose / agent-oriented JSON snapshots next to the HTML
--docs-only / --no-tests	trim the node set
--no-cdn	fully offline output: embed D3.js inline (SHA-256 verified) and drop the external font link
--mock-git	synthetic git overlay for demos/testing
--init [--diff|--merge|--force]	config lifecycle (see above)

Companion tools

find-related-docs and verify-doc-links run the same reference scanner the graph is built from — what the map draws as a code↔docs edge is exactly what they look up and verify. graph-query answers questions over an already-built snapshot.

find-related-docs

Reverse lookup: which docs mention a given code file. Run it before editing a file to know which pages need updating afterwards, or wire --git-added into a pre-commit hook so undocumented changes get flagged before they land.

find-related-docs src/mypkg/core/access.py   # single file (bare filename works too)
find-related-docs --git-added -v             # pre-commit: staged files, with doc line numbers
find-related-docs --git-modified             # working tree: staged + unstaged modifications

Flag	Effect
path	file or directory to look up (a bare filename is searched project-wide)
--docs-dir PATH	documentation directory (default: docs)
--exclude DIRNAME	skip a folder name anywhere under the docs dir (repeatable)
--git-added	check all staged files; also warns about deleted files still mentioned in docs
--git-modified	check all modified files (staged + unstaged)
-v / --verbose	print docs/<file>.md:<line> for every mention

verify-doc-links

Check that every file reference in your .md files points to a real file. Exit codes make it a drop-in CI gate:

Exit	Meaning
0	all references valid
1	broken references found
2	target path invalid (not found, or not a .md file)

verify-doc-links                     # whole docs/ against the project root
verify-doc-links docs/reference -v   # one subtree, with the offending lines

# CI step (GitHub Actions)
- run: pip install graph-build
- run: verify-doc-links --root .

Flag	Effect
path	.md file or directory to check (default: docs)
--root PATH	project root the references resolve against (default: cwd)
--known-brokens PATH	whitelist file (default: <root>/known-brokens.txt)
-v / --verbose	show the offending lines

Besides known-brokens.txt, false positives can be silenced inline with HTML comments (invisible in rendered Markdown): <!-- broken-link-ok --> on the same line, <!-- broken-links-ok-start --> / <!-- broken-links-ok-end --> around a block, or <!-- ignore-ref: path/to/file.py --> anywhere in the file.

graph-query

Ask the graph questions without opening a browser. Works on the JSON written by --json or --compact (auto-detected; defaults to docs/graph-compact.json):

graph-query blast-radius app/core.py   # transitive importers + every doc mentioning them
graph-query hubs --top 15              # most-connected files, in/out breakdown
graph-query stale-docs --check         # docs older than the code they describe (CI gate: exit 1)
graph-query orphans --type code        # files with no edges at all

Command	Answers
blast-radius <path>	"what breaks if I touch this file" — transitive incoming imports (--depth, --edges to tune), plus affected docs
hubs	"where is the center of gravity" — top nodes by in+out edges (--top N)
stale-docs	"which docs lag behind the code" — compares last-commit times (one git log pass; mtime fallback), --check for CI
orphans	"what is connected to nothing" — degree-0 nodes, filterable by category

Every command takes --json for machine-readable output — pipe it to jq or feed it to an agent.

Known limitations

Static analysis has natural borders — the graph is a referential map, not a semantic one:

• Dynamic imports resolve only literal / top-level-constant module names (f-strings, dict lookups, conditional rebinding are skipped).
• eval / exec and DI-by-string are invisible. [project.scripts] / [project.gui-scripts] entry points in pyproject.toml are read, but only to exempt those modules from dead-code flagging — they don't create edges.
• Markdown templating ({{ ref }}, Jekyll/Hugo shortcodes) isn't parsed.
• Links resolve to whole files — section anchors (file.md#part) map to the file node.
• code→code edges are Python-only for now (the markdown/doc layers are language-agnostic).
• One repo per graph; symlinks are treated as physical paths.

License

MIT © Yuriy Totyshev