graphLM

Point it at a codebase. Get back a map.

You've cloned an unfamiliar repo and you're staring at 400 files wondering where anything is. graphLM reads the project the way you would — but faster — and hands you a map: what the modules are, how they depend on each other, where data flows, which imports form nasty little cycles, and "where do I find X?" answers. It comes out as Markdown to read, JSON to script against, and an interactive HTML graph to click around in.

It's built for the age of coding agents, too: the map stamps itself with the git commit it was generated against, so an agent (or you) can tell at a glance when it's gone stale and regenerate. Under the hood it pairs an OpenAI-compatible LLM with deterministic Tree-sitter parsing — the AST is ground truth the model isn't allowed to contradict, so the dependency edges are real, not hallucinated.

$ graphlm ~/code/some-project
Scanning ~/code/some-project...
Wrote .graphlm/GRAPH.md, .graphlm/GRAPH.json, .graphlm/GRAPH.html

By default the map is written into a .graphlm/ folder inside the project (so it stays out of your way); point it elsewhere with -o.

What it produces

• Directory tree — annotated tree of the project
• Import edges — dependency relationships between files
• Modules — named components and what they do
• Data flow — how data moves through the system
• Database schema — tables and columns (if applicable)
• Test organization — test files mapped to what they cover
• Architecture notes — key decisions and patterns
• Quick reference — "where do I find X?" lookups
• Import cycles — strongly-connected components with SLOC-based risk scores
• Mermaid module graph — a directory-level flowchart of the parser's import edges inside GRAPH.md, with import-cycle members in red. GitHub renders it natively, so a committed map shows a picture with no CDN and no extra file
• Interactive HTML — D3 force graph (GRAPH.html) with zoom/pan, search, theme toggle, and layer toggles for parser-proven imports vs LLM-inferred imports vs data flow; cycle members are ringed red
• Provenance stamp — GRAPH.json records when and against which git commit the map was generated, and GRAPH.md opens with a refresh directive so a coding agent can tell when the map is stale (see Self-refreshing graph)
• Graph-vs-graph diff — every run also writes GRAPH_DIFF.md / GRAPH_DIFF.json: what changed in the map (modules, edges, cycles, data flows, entry points, file summaries added and removed) since the prior run, so you see a new entry point or a broken import cycle at a glance without re-reading the whole graph (see Graph diff)

Install

graphLM is a Python 3.11+ CLI. The friendliest way to get it on your PATH is a tool installer that keeps it in its own isolated environment:

uv tool install graphlm      # via uv (https://github.com/astral-sh/uv)
# or
pipx install graphlm         # via pipx

Either one gives you a global graphlm command. Prefer plain pip? pip install graphlm works too — just mind your virtualenvs.

Want your coding agent to query the map over MCP (see Serve the map to your agent)? Install the mcp extra: uv tool install 'graphlm[mcp]'. Parser edges for languages other than Python are opt-in extras — see Language packs.

No PyPI, no problem. Every release also ships the wheel and sdist as GitHub Release assets. Grab the latest graphlm-*.whl from that page and pipx install the file (or its URL).

Hacking on graphLM itself? Clone it and let uv sync the dev deps. Add the extras if you want the MCP and language-pack tests to run rather than skip:

git clone https://github.com/ggrace519/graphLM && cd graphLM
uv sync --group dev --extra mcp --extra all
uv run graphlm --version

Language packs

Python import edges ship in the base install. JavaScript/TypeScript, Java, Rust, C#, and C/C++ are opt-in extras that pull only the Tree-sitter grammar wheel; the resolver is in-tree. Without the extra, those files still go to the model but contribute no parser edges (one log line per language, never a crash). graphlm[all] installs every language pack; it does not include mcp.

uv tool install 'graphlm[js]'         # or [java], [rust], [csharp], [cpp], [all]
uv tool install 'graphlm[mcp,all]'    # MCP server + every language pack

Quick start

graphLM needs an OpenAI-compatible LLM endpoint to do its thing. Point it at one with three environment variables (or the matching -b / -k / -m flags):

export GRAPHLM_BASE_URL="https://your-endpoint/v1"
export GRAPHLM_API_KEY="sk-..."
export GRAPHLM_MODEL="your-model-name"

graphlm ~/code/some-project        # writes the map into ~/code/some-project/.graphlm/

Want to see what it would send the model without spending a token? Add --dry-run — it scans, parses the AST, and prints the context stats, no network call. See Configuration for the full list of settings and the user-level ~/.config/graphlm/.env.

Usage

CLI

# Analyze a project; writes GRAPH.md, GRAPH.json, GRAPH.html into <project>/.graphlm/
graphlm /path/to/project

# Write to a different directory
graphlm /path/to/project -o ./output

# Dry run — see context stats without calling the LLM
graphlm /path/to/project --dry-run

# Override LLM settings from the command line
graphlm /path/to/project -b https://api.example.com/v1 -k sk-xxx -m my-model

# Exclude test files and custom patterns
graphlm /path/to/project --no-tests --exclude __pycache__ --exclude .git

# Skip a project-level .graphlmignore
graphlm /path/to/project --no-graphlmignore

# Skip Tree-sitter AST import edges
graphlm /path/to/project -o ./output --no-ast

# Skip writing GRAPH.html
graphlm /path/to/project -o ./output --no-html

# Skip writing the GRAPH_DIFF.* graph-vs-graph diff
graphlm /path/to/project --no-diff

Library API

from graphlm import generate_graph
from pathlib import Path

result = generate_graph("/path/to/project")
written = result.write(Path("./output"))
md_path, json_path, html_path = written
# html_path is Path | None (None if include_html=False)
diff_md = written.diff_md      # Path | None (None if include_diff=False)
diff_json = written.diff_json  # Path | None

GraphResult.write accepts str | Path and returns a WriteResult — the (Markdown, JSON, HTML) path tuple you can unpack three ways, with .diff_md / .diff_json attributes for the graph diff (None when include_diff=False).

result = generate_graph(
    "/path/to/project",
    base_url="https://api.example.com/v1",
    api_key="sk-xxx",
    model="your-model-name",
    output_dir="./output",
    ast=True,              # Tree-sitter import edges + SLOC cycle scores (default)
    include_html=True,     # skip GRAPH.html when False (default: write it)
    include_diff=True,     # skip GRAPH_DIFF.* when False (default: write it)
    show_cycles=True,      # skip the cycle section when False
    cycle_threshold=0.0,   # min cycle risk score
)

print(len(result.graph.modules), "modules found")

AST parsing is on by default: graphLM runs Tree-sitter, attaches graph.deterministic_edges, passes those edges into the pass-2 prompt as ground truth, and runs cycle detection on the AST edges with SLOC-based risk scores. Pass ast=False or --no-ast to skip. include_html=False skips writing GRAPH.html when output_dir is set (HTML is on by default).

How it works

graphLM uses a two-pass LLM strategy to stay within context windows while still producing comprehensive graphs:

1. Pass 1 — The directory tree (no file contents) is sent to the LLM, which identifies the most important files to read.
2. Pass 2 — The tree + those key files are sent to the LLM, which produces the final structured graph.

This keeps the first pass lightweight (~tree tokens) and ensures the second pass only includes files that matter.

A Tree-sitter pass runs by default (Python imports always; JavaScript/TypeScript with graphlm[js]; Java with graphlm[java]; Rust with graphlm[rust]; C# with graphlm[csharp]; C/C++ with graphlm[cpp]). It does not replace the LLM: the two-pass analysis still runs, and AST edges are extra ground truth plus cycle detection. Pass --no-ast to skip. Without a language extra, those files are still sent to the model but contribute no parser edges.

Big files are sent as signature skeletons, not heads. A file longer than --max-file-chars (default 4000) used to be cut at the cap, so the model saw the imports and the first class of a large module and guessed the rest. Now a Python file over the cap is rendered with Tree-sitter as its API surface — every import, every class/def signature (decorators and multi-line headers intact), the first line of each docstring, short constants — with bodies elided to .... That is exact where the head was partial, and usually smaller. The skeleton starts with a # [graphlm skeleton: …] marker, and the pass-2 prompt tells the model to summarize the API from it rather than invent behaviour for the elided bodies. Secret redaction runs on the skeleton too. Python only for now (other languages still send the head); --no-skeleton restores head-truncation.

Teach your coding agent to use it

The map is most useful when your coding agent reads it automatically before it starts spelunking through a codebase. One command sets that up:

graphlm --install-skill claude    # writes ~/.claude/skills/graphlm/SKILL.md
graphlm --install-skill codex     # writes ~/.codex/graphlm.md + a snippet to paste into AGENTS.md

It drops a short guide telling the agent to look for .graphlm/GRAPH.md when it opens a repo, follow the map's refresh directive, and regenerate with graphlm . when the map is missing or stale. Installs user-global by default (so every repo benefits); add --skill-local to write into the current project instead, and --skill-force to overwrite an existing guide.

graphLM only ever creates its own files — it will never edit your existing CLAUDE.md or AGENTS.md. For Codex (whose config is a user-owned AGENTS.md), it writes a standalone guide and prints the one line for you to paste in yourself.

Serve the map to your agent (MCP)

Reading GRAPH.md costs an agent the whole document — tens of thousands of tokens — to answer one question. graphlm --serve exposes the same map as a stdio MCP server with typed, zero-LLM tools, so the agent asks "who imports scanner.py?" and gets a few hundred tokens back:

uv tool install 'graphlm[mcp]'                       # the extra pulls in the MCP SDK
graphlm .                                            # generate the map first (serving never calls the LLM)
claude mcp add graphlm -- graphlm --serve /path/to/repo   # register with Claude Code (once per repo)

The server reads <project>/.graphlm/GRAPH.json (or the -o directory) and picks up a regenerated map automatically — no restart. It never runs the LLM: if there is no map yet it says so and tells the agent to run graphlm .. The --install-skill guide tells the agent to prefer these tools when they are registered.

Self-refreshing graph

A generated graph goes stale the moment the code moves on. graphLM makes the output self-refreshing without any hook or flag: it stamps its own provenance and rides the refresh nudge along in the loop an agent already uses to read GRAPH.md.

• The stamp. GRAPH.json carries a versioned meta block — created_at (UTC), commit_sha (the git HEAD the graph was generated against, or null outside a git repo), graphlm_version, and schema_version. GRAPH.md opens with a short refresh directive rendered from that stamp.
• The agent is the scheduler. graphLM has no staleness logic — invoked, it always regenerates and re-stamps. The directive tells a reading agent to compare the repo's current git rev-parse HEAD to the stamped commit and, if they differ, regenerate with graphlm .. Staleness = SHA mismatch.
• It's advisory. The agent may ignore the directive; the map is best-effort, not guaranteed current. Non-git projects have no SHA, so the directive falls back to "regenerate when you believe the code has changed."
• Honest wording. The stamp says "generated against commit X", not "reflects X": the graph is built from files on disk, which may include uncommitted changes, so a graph can be SHA-fresh yet not match the working tree.

Run telemetry

The stamp also records what the run actually cost and how far to trust the LLM's edge table. Directly under the refresh directive, GRAPH.md carries one line like:

Run telemetry. pass 2 prompt: 41,920 tokens (graphlm estimated 47,300); output: 9,812 tokens. LLM import edges vs parser ground truth: precision 0.93, recall 0.81 (n=15 LLM / 16 AST, 14 matched).

• Usage is the endpoint's own token count (requested via stream_options.include_usage), shown beside graphlm's estimate for the same prompt so you can see how the built-in estimator tracks your model. An endpoint that reports no usage shows "not reported by endpoint".
• Faithfulness scores the model's import_edges against the parser's deterministic edges: precision is the share of the model's comparable import edges the parser confirms, recall the share of the parser's edges the model reproduced. Comparable means kinds the parser emits (import / from / require / static / include) between files whose extensions the parser actually produced edges for on this run (.py always; .js/.ts/… when graphlm[js] ran, and so on). Low precision means invented dependencies. Absent under --no-ast (no ground truth) and on --dry-run (no LLM edges).

Both live in GRAPH.json under meta.usage / meta.faithfulness as additive, optional fields — older graphs without them still diff normally — and the CLI echoes them as Usage: / Faithfulness: lines.

Adoption — one line for an AGENTS.md / rules file (or just run graphlm --install-skill claude / --install-skill codex, below):

A codebase map lives at .graphlm/GRAPH.md — read it before exploring the code, and follow its refresh directive (regenerate with graphlm . when the stamped commit differs from the current HEAD, or when the map is missing).

Graph diff

Once the map is self-stamped and regenerated as the code moves, the natural next question is "what changed in the map since last time?" Every real run answers it by also writing GRAPH_DIFF.md and GRAPH_DIFF.json — a graph-vs-graph diff, not a code diff (git already does code diffs better).

• What it reports. Per dimension — modules, import edges (LLM and AST), import cycles, data flows, entry points, file summaries — the entities added and removed since the prior GRAPH.json. So a new entry point, a dropped module, or a newly broken/resolved import cycle is visible at a glance without re-reading the whole graph.
• Added/removed only. Identity is structural (a module's path, an edge's (from, to, kind), a cycle's node set), so a pure prose rewrite — a description or summary the LLM regenerates every run — is intentionally invisible. It would otherwise drown the structural signal in nondeterministic churn. Renames show as remove + add (no rename-matching).
• Three baseline states, never conflated. First run ("initial graph — no prior version to compare"), uncomparable (the prior file is corrupt or an unrecognized schema_version — this is not silently treated as a first run), and normal. An agent can always tell "nothing changed" from "never compared."
• Commit range. The diff header shows the old→new commit_sha range (a null side — non-git or an old graph — reads as unknown).
• --no-ast safety. Toggling AST parsing off between runs reports the AST edge dimension as "not compared" rather than fabricating a mass deletion.
• Reads graphlm's own prior output. This is why the meta block is a versioned input contract (above): a future format change is detected, not misparsed.

On by default. Pass --no-diff (or include_diff=False) to skip it. --dry-run writes no diff — it makes no LLM call and produces no authoritative graph. The diff is pure local computation over the two graphs: no extra network or LLM call. Opting out only skips writing — like --no-html, it does not delete a GRAPH_DIFF.* left by a previous run, so a stale diff can linger on disk; regenerate (or remove it) if that matters.

Committing vs. gitignoring the graph. The refresh check is stamped_sha != HEAD, so if you commit GRAPH.*, the stamp is invalidated by the very commit that ships it — HEAD moves to that commit, so the map immediately reads as one commit stale, and stays perpetually one commit behind. Two sane options:

• Gitignore .graphlm/ (this repo's own choice) and regenerate on demand. The stamp then always reflects a real, current SHA. (One line — echo '.graphlm/' >> .gitignore — covers the whole output folder.)
• Commit it and regenerate as the final step of the same commit so the map ships fresh — but expect it to show one-commit staleness until the next regen, and treat that as normal.

Committing a graph that goes stale on every push (with no regeneration step) is the one workflow to avoid — it reintroduces exactly the per-session refresh tax this design set out to remove.

Note on -o: the default (.graphlm/ inside the scanned project) keeps the map in the repo it describes, so the staleness check works. If you redirect output elsewhere (-o <elsewhere>), an agent reading that GRAPH.md and running git rev-parse HEAD in its own directory will compare against the wrong repo — keep the graph in the project it describes for the staleness check to work.

Configuration

graphLM reads its LLM settings from environment variables. Copy .env.example to ~/.config/graphlm/.env and fill in your endpoint, key, and model — those three have no built-in defaults:

mkdir -p ~/.config/graphlm
cp .env.example ~/.config/graphlm/.env

Settings are resolved in this order (first non-empty wins):

1. Exported shell environment — export GRAPHLM_BASE_URL=… etc.
2. User-level .env at ~/.config/graphlm/.env (or $XDG_CONFIG_HOME/graphlm/.env).
3. Built-in defaults for the numeric budgets and timeout only.

A .env in the working directory or in the project being scanned is not read. graphLM's LLM config is yours, not the target repo's.

CLI flags (-b, -k, -m, --max-context, --max-output-tokens, --timeout) and the matching generate_graph(...) arguments override the env var.

Project ignore file

Drop a .graphlmignore at the project root to record patterns that should stay out of every scan — a big sibling worktree, a game-engine cache, generated files — without passing --exclude on every run.

# one glob per line; # comments and blanks are skipped
.worktrees/
.godot/
*.generated.py

Patterns are merged with the built-in exclude set and any --exclude flags (union). Matching is the same as --exclude: the full relative path or any path component. A trailing slash is stripped, so .godot/ matches a directory named .godot. The file itself is never sent to the model (same as .gitignore). Missing file → no change. --no-graphlmignore opts out.

Options

Project structure

graphlm/
├── __init__.py           # Library API — generate_graph()
├── _html_template.html   # D3 visualization template
├── cli.py                # CLI entry point — Typer
├── config.py             # Settings from environment variables
├── context.py            # Two-pass prompt assembly
├── cycles.py             # Import cycle detection (Tarjan + SLOC risk)
├── diff.py               # Graph-vs-graph diff (GRAPH_DIFF.*)
├── faithfulness.py       # LLM-vs-parser edge score in the stamp
├── html_render.py        # Interactive D3 HTML visualization
├── llm.py                # LLM client with retry and JSON recovery
├── mcp_server.py         # --serve: thin MCP wrapper over query.py
├── mermaid.py            # Directory-level Mermaid flowchart for GRAPH.md
├── models.py             # Pydantic v2 data models
├── parser.py             # Thin shim re-exporting parsers.base
├── parsers/              # Tree-sitter registry + per-language resolvers
│   ├── base.py
│   ├── python.py         # core (always on)
│   ├── javascript.py     # graphlm[js]
│   ├── java.py           # graphlm[java]
│   ├── rust.py           # graphlm[rust]
│   ├── csharp.py         # graphlm[csharp]
│   └── cpp.py            # graphlm[cpp]
├── prompts.py            # System prompt (injection guard)
├── provenance.py         # Git SHA / timestamp / version capture for the stamp
├── query.py              # Map-query helpers used by --serve
├── redact.py             # Sensitive-file skip + secret redaction
├── render.py             # Markdown + JSON + HTML output rendering
├── scanner.py            # Project directory scanner
└── skills.py             # --install-skill: agent-guide installer
tests/
├── conftest.py
├── test_*.py
└── fixtures/             # small, medium, large, cyclic, skeleton, ts, java, rust, csharp, cpp, ignore

Requirements

• Python 3.11, 3.12, or 3.13
• An OpenAI-compatible LLM endpoint (base URL + API key + model name)
• uv — recommended for installing (uv tool install) and required for development

Contributing

Contributions are welcome — bug reports, fixes, docs, and new language support especially. See CONTRIBUTING.md for setup, the test/mypy commands, and the security invariants to preserve. Please also read the Code of Conduct.

Security

Found a vulnerability? Please don't open a public issue. Report it privately via GitHub's Report a vulnerability button — see SECURITY.md for scope and details. graphLM reads code it didn't write, so its sensitive-file, redaction, symlink, and prompt-injection guards are the surface that matters most.

License

GPLv3 — see LICENSE.