second-brain-graph 0.9.0

A living, always-fresh, low-token map of every project: files, links, areas and mechanics, with a navigable 2D community map.

Second Brain (SB)

A living, always-fresh, low-token map of every project — files, links, areas and mechanics — that an AI assistant can query instead of re-reading everything, and that a human can explore as a navigable 2D community map.

🇮🇹 Leggi in italiano →

Not "another place to store stuff". It's the canonical, per-project picture that stays in sync with your files so you never lose track: no forgotten pieces, no "we discussed that three chats ago", no stale docs.

_{The offline viewer on a real, multi-project workspace (names anonymized): a flat 2D map with files coloured and clustered into auto-detected communities, and a click-through detail panel.}

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Why this exists

A project gets more complex over time. Files drift, some go orphaned or quietly broken, the mental model of "what's where and how it connects" gets fuzzier, and an AI assistant loses the thread between one chat and the next — so every session starts by re-reading and re-searching files. That is slow, incomplete, and burns tokens repeatedly — and it only gets worse as the project grows.

Second Brain builds the project's graph and keeps it honest as the project changes — a content-hash gate flags exactly what drifted, and a rebuild is a fast full re-walk (outside the model, at near-zero token cost). The assistant queries it and gets compact answers; a human opens the 2D community map and sees the whole project at a glance.

It is not a RAG system: no embeddings, no vector store, no LLM needed to build the graph. It maps the structural relationships between files, which makes it complementary to RAG and purpose-built for one thing: situational awareness at a very low token cost.

What makes it different

• Read-only on your sources — it indexes, it never modifies your files. Run it on anything without risk.
• Files are the truth — the graph is derived (in .secondbrain/) and always regenerable; contents are never duplicated into it.
• Zero runtime dependencies — the core runs on the Python standard library alone. No conflicts, instant install, works in CI / containers / air-gapped boxes.
• Low token cost by design — queries return ids, types, sizes and connections, never file contents, so orienting an assistant costs a few hundred tokens, not tens of thousands.
• Anti-drift gate — refuses to call the graph "fine" while something is stale, orphaned, or broken.
• Communities & impact — auto-detects the project's real modules from how files link (not folders), and answers "what breaks if I change this?" (upstream/downstream impact) in one call.
• GRAPH_REPORT.md one-pager — the artifact an agent reads first instead of grepping: god nodes, communities, surprising links, decisions, and problems, regenerated on every build.
• Offline graph viewer — an interactive force-directed map (vis-network), coloured by community, with search, a click-to-inspect node panel with neighbour navigation, and a per-community show/hide legend. Data and library are inlined in one HTML file, so it works fully offline. The viewer is adapted from Graphify (MIT) — see References & sources.
• Importance ranking (PageRank) — "god nodes" are ranked by structural importance (the files the important files depend on), not raw link count — a pure-Python, deterministic implementation.
• Task-aware retrieval (focus) — give it a task and it returns the minimal high-value subgraph for that task within a token budget (personalised PageRank seeded on matching files), instead of the whole digest.
• Self-refreshing reads — every query checks a cheap size+mtime signature and rebuilds only when the project actually changed (uncommitted edits included), so an assistant is never served a stale map — with no scheduler and no dependencies.
• Opt-in symbol call-graph — build --symbols adds function/class nodes and intra-file calls edges for Python (conservative resolution, no guessed edges); off by default to keep the map light.
• GraphML export — export --format graphml opens the graph in Gephi / yEd / Cytoscape / networkx.
• Optional MCP server — exposes the same low-token queries to MCP-aware assistants, behind an optional extra so the core stays dependency-free.

See it on a real project (anonymized)

A read-only measurement on a mature, multi-repo project (identity withheld): ~1,684 knowledge files across 17 top-level areas, indexed in ~1.3 s (index graph.json = 0.91 MB).

Three ways to answer the same four questions about the project — what's here, list every recorded decision, which files are truncated/empty, the most-connected files — in three separate clean chats:

Metric	NOTHING (manual)	TODAY (manual)	WITH Second Brain
Time	~8.5 min	~9 min	~3–4 min
Working tokens	opaque	opaque	~3–4k, self-measured
Decisions found	112	131	112 (exact, every run)
Truncated files	3	0 (missed)	2 (exact)
Files counted	2,174	2,174	1,684 (exact)
Reproducible / verifiable	no	no	yes

Two things stand out. (1) The two manual runs disagree with each other — 112 vs 131 decisions, 3 vs 0 truncated files (the second missed them entirely) — so the by-hand method is non-deterministic and unverifiable: you can't tell the right answer (112) from a wrong one (131). (2) Second Brain returns the same answer every run — 112, the correct count — with far fewer tokens and in less than half the time. The win isn't a number nobody else could reach; it's an exact, reproducible, queryable one instead of a coin-flip.

Just to orient an assistant on the whole project — something you pay for every session — reading the curated source-of-truth docs costs ~229,000 tokens; the second-brain map digest costs ~270 tokens: ~800× less, and roughly constant as the project grows (the full index is queried, never loaded into context).

And it surfaces what even curated docs miss: genuinely truncated/corrupted files (with UTF-16/encoding false positives excluded), ~45 empty files, ~1,390 orphan files (~80%), 112 decisions and ~626 cross-references now explicit and queryable, plus 13 files already stale within seconds of indexing (a live system constantly writing) — which is exactly why the map has to update itself.

_{Illustrative — the continuity problem SB removes: over many sessions a hand-kept map drifts as orphans and stale files pile up, while a queryable graph stays current.}

The same structure, the code layer

Second Brain's code-import layer renders the whole workspace as a graph you can actually read:

Install

pip install second-brain-graph              # from PyPI
pip install "second-brain-graph[mcp]"       # + optional MCP server
pip install -e .                            # or from a clone

On PyPI. Requires Python 3.10+. Runtime dependencies: none (standard library only). The package installs the second-brain command and the second_brain import module.

Commands

Every command takes an optional project path (default .). Queries are self-refreshing — they auto-build on first use and rebuild only when the project actually changed — so an assistant never answers from a stale map (see Always fresh below).

Build & freshness

second-brain build .              # index the project -> .secondbrain/ (graph + GRAPH_REPORT.md)
second-brain build . --symbols    # also index the Python symbol layer (functions/classes + calls)
second-brain gate .               # anti-drift check: broken refs, stale files, orphans (exit≠0 if drifted)

Orient — read these first

second-brain report .   # GRAPH_REPORT.md: god nodes (PageRank), communities, churn, decisions, problems
second-brain communities .   # the project's real modules (clusters from imports+refs) + cross-module bridges
second-brain map .      # compact digest: areas, sizes, most-connected files
second-brain assess .   # before/after: problems + token savings
second-brain stats .    # quick counts by node/edge type

Query

second-brain find util .                            # nodes whose name/path matches "util"
second-brain neighbors second_brain/model.py .      # a node and its connections
second-brain impact second_brain/model.py .         # blast radius: what breaks / what it depends on
second-brain impact second_brain/model.py . --up    # only what depends on it (run before editing!)
second-brain impact --diff . --up                    # blast radius of your UNCOMMITTED changes (git diff)
second-brain impact second_brain/model.py . --budget 300   # rank impacted nodes + trim to ~300 tokens
second-brain why second_brain/cli.py second_brain/model.py .   # shortest path: how are two nodes linked?
second-brain focus "token budget in the report" .   # task-aware: the minimal high-value subgraph
second-brain focus "auth flow" . --budget 4000      #   ...within ~4000 tokens (default 2000)
second-brain focus "auth flow" . --signatures        #   ...also list the top Python files' signatures
second-brain symbols second_brain/model.py .        # function/class signatures of one Python file

View & export

second-brain view .             # offline 2D community-map viewer -> .secondbrain/view.html
second-brain view ./src/api     # drill into one area in full detail (top-level view stays light)
second-brain view . --focus "auth flow"   # render only the focus slice (what an assistant receives)
second-brain export . --format graphml --out graph.graphml   # GraphML for Gephi/yEd/Cytoscape/networkx

Agent integration & automatic freshness

second-brain agent install .    # add the SB directive to CLAUDE.md/AGENTS.md + a Claude Code hook
second-brain hook install .     # git post-commit/post-checkout: rebuild the graph on every commit

Drill down by pointing any command at a subfolder — second-brain map ./src/api works on just that area; the top-level view stays light via backbone mode (areas + the knowledge-connected core; isolated data files are summarized on their area node).

Per-project tuning (.secondbrain.json)

Drop a .secondbrain.json at the project root to extend the classification taxonomy or pin a specific file's node type when the heuristic guesses wrong (fail-safe: unknown type names are ignored, and with no file behaviour is byte-identical):

{ "classify": { "type_overrides": { "data/seed.json": "data", "notes/SPEC.md": "design" } } }

Set "respect_gitignore": true (top-level) to also skip files matched by the project's root .gitignore — handy on big repos to keep build artefacts and generated data out of the map. Opt-in; default off = byte-identical. It honours a pragmatic subset of .gitignore (root file only): comments/blanks, globs and **, a leading / (anchored) and trailing / (directories only), and ! negation; nested .gitignore files are not read.

{ "respect_gitignore": true }

focus --signatures and view --focus

focus --signatures (and the MCP focus tool with signatures=true) additionally lists the key function/class signatures of the top-ranked files. It is Python-only (stdlib ast), reads the sources on demand and read-only, does not require a build --symbols graph, respects an internal token budget for the signatures, and leaves the plain focus output unchanged — so an assistant sees the API of the relevant files without opening them; non-Python files are skipped.

view --focus "<task>" writes the usual .secondbrain/view.html, but renders only the slice focus would return for that task — a temporary in-memory subgraph that never changes graph.json. It lets a human see exactly the context an assistant receives.

Recency-weighted recall

focus … --recency W (W in 0..1; MCP focus(recency=…)) blends a deterministic, git-derived recency/frequency signal into the ranking, so a recently or often-changed file can outrank a structurally important but dormant one — the base-level activation of human memory, on top of the associative recall personalised PageRank already provides. The signal is anchored to the most recent commit in the graph (never the wall clock), so results stay reproducible; it adds no weight (it reuses the session/touches data already indexed from git) and is a no-op without git history. --half-life DAYS (default 30) tunes how fast older changes fade. Off by default (--recency 0) — plain focus is byte-identical.

Always fresh (auto-refresh)

Queries (map / find / neighbors / impact / focus / report, and the MCP tools) check a cheap size+mtime signature before answering and rebuild only if the project changed — including uncommitted edits. So the map is current whenever the assistant uses it, with no scheduler and no dependencies. First use of a project auto-builds. To serve the stored graph as-is (skip the check), set SECOND_BRAIN_AUTO_REFRESH=0. On a huge single-graph project you can throttle the check with SECOND_BRAIN_REFRESH_TTL=<seconds> (default 150); the long-running MCP server also keeps the graph in an in-process cache within that window, so repeated tool calls are served from memory instead of re-scanning the whole tree. The stat-only check has one narrow blind spot — a same-size edit within the same filesystem tick as the last build — which second-brain gate (content-hash) catches exactly.

Switch	Effect
SECOND_BRAIN_AUTO_REFRESH=0	turn auto-refresh off (serve the stored graph, faster, may be stale)
SECOND_BRAIN_REFRESH_TTL=<sec>	throttle the freshness check to once per window (default 150s; the long-running MCP server also keeps the graph in an in-process cache within the window)
build --symbols	include the function/class + call layer (off by default to keep the map light)
focus … --budget N	size the task-context returned by focus (default 2000 tokens)
focus … --recency W	blend git recency/frequency into recall, W in 0..1 (default 0; --half-life DAYS tunes decay)
impact … --up / --down / --depth N	restrict/limit the blast-radius walk
view --backbone	force backbone rendering on any size (auto above ~8000 nodes)

Viewing the graph

1. Generate the viewer: second-brain view .
2. Open it: double-click the file it writes — .secondbrain/view.html — in any browser. No server, no install: the data and the rendering library are inlined into the single page, so it works fully offline.
3. Explore: the graph is laid out as a flat 2D map, with files coloured and clustered into communities (auto-detected from how they link). Scroll to zoom, right-drag to pan, double-click a node for its details (including its impact radius). Use the left panel to search, switch grouping (community / area / folder / type), focus a single community, or show only orphans.

Query layer (for AI assistants)

second-brain map, find, neighbors, impact, why, focus, and report return compact, budgeted answers (ids, types, sizes, connections — never file contents). An optional MCP server exposes the same queries to MCP-aware assistants:

pip install "second-brain-graph[mcp]"
second-brain-mcp .   # project_map / find / neighbors / subgraph / impact / impact_diff / why / communities / focus / report / health

See docs/mcp.md for the tools and their shapes.

How it works

1. Index — walk the project, classify each file into a typed node, and extract edges: Python imports (via ast), JS/TS imports, documentation references (markdown links, [[wikilinks]], and plain path mentions in prose — the part standard tools miss), and area membership. Operational nodes (decisions found in the docs, sessions from git commits) are added too.
2. Stay fresh — content-hash diffing tells the gate exactly what changed since the last build; a rebuild is a fast full re-walk (outside the model). Queries also self-refresh: they rebuild automatically when the project changed (uncommitted edits included), so an assistant never works from a stale map.
3. Query / view — a human gets the 2D community map; an assistant queries the low-token layer, and asks focus "<task>" for just the slice that matters for the work at hand.

On false positives: plain path mentions in prose are inherently noisy. Second Brain handles this asymmetrically — markdown links and wikilinks are intentional (an unresolved one is reported as broken), but a plain prose mention is used only if it resolves to a real file; otherwise it is dropped as noise and never creates a broken reference. Deep import parsing is Python and JS/TS today; other languages contribute via documentation links.

The full node/edge taxonomy, the graph.json schema, and the classification rules are documented in docs/graph-format.md.

Try the before/after yourself

Run these in separate clean chats (read-only), then compare the answers and the token/time cost. Replace /path/to/project with a real project.

TODAY (your current method):

READ-ONLY: do not modify, create, delete or move any file. On the project at
/path/to/project, work with your NORMAL method (reference docs, memory, the tools you
usually use). Give me a COMPLETE, ACCURATE picture answering these 4 questions:
1) how many files (excluding images, venvs, caches, .git) and the breakdown by type;
2) list ALL decisions recorded in the docs (D-XXX, ADR-N, RFC-N);
3) which files are truncated/corrupted (null-byte) or empty (zero-byte);
4) the 10 most-connected files. When done, tell me the time and tokens you used.

WITH Second Brain (index already built — query it, don't re-read files):

READ-ONLY. Second Brain's index is already built — only query it. Use ONLY:
  python -m second_brain map   "/path/to/project"
  python -m second_brain stats "/path/to/project"
  python -m second_brain find <text> "/path/to/project"
and read /path/to/project/.secondbrain/assessment.md. Answer the same 4 questions, then
tell me the time and tokens you used.

Architecture & modules

Read-only on your sources, zero runtime dependencies, deterministic. Everything lives in the second_brain/ package — every public module and what it does:

Module	Responsibility
cli.py	Command-line interface — every second-brain command.
mcp_server.py	Optional MCP server: the low-token query tools over stdio (extra [mcp]).
model.py	Graph data model: typed nodes/edges, colours, the JSON-serializable container + adjacency index.
indexer.py	Builds the graph: file nodes, areas, import/reference edges, the opt-in symbol layer.
classify.py	Classifies each file into a typed node (heuristic, configurable per project).
config.py	Per-project .secondbrain.json config (extend/replace the classification taxonomy + per-file type_overrides).
ignore.py	.secondbrainignore patterns + sensible default ignores.
references.py	Extracts doc references: markdown links, [[wikilinks]], and plain path-in-prose.
pycode.py	Import edges: Python via ast, JS/TS best-effort (comments stripped).
pysymbols.py	Same-file Python call graph (functions/classes + conservative calls).
symbols.py	Function/class signatures of a single Python file (the symbols command).
freshness.py	Content-hash manifest, cheap signature, and self-refreshing reads.
gate.py	The anti-drift gate: broken refs / stale files / orphans.
store.py	Persists the derived store under .secondbrain/ (graph, manifest, signature, mode).
query.py	Low-token query layer: map / find / neighbors / subgraph / impact (+ impact --diff) / why / communities / focus (BM25-seeded, optional symbol signatures).
bm25.py	Okapi BM25 lexical relevance (stdlib) — the task-to-file signal that seeds focus.
budget.py	Token-cost estimate + budget-fit (stdlib) — the one place focus / impact count tokens.
rank.py	PageRank importance (global + personalised) — engine behind god-nodes and focus.
recency.py	Deterministic git recency/frequency weighting (ACT-R base-level) — the opt-in focus --recency blend.
communities.py	Community detection (deterministic label propagation) + surprising cross-community edges.
operational.py	Operational nodes: decisions found in docs, sessions from git commits.
report.py	Generates GRAPH_REPORT.md (god nodes, communities, churn, decisions, problems).
assess.py	One-shot before/after assessment: hidden problems + token savings.
viewer.py	Offline 2D community-map viewer (vis-network, adapted from Graphify).
export.py	Exports the graph to GraphML.
agent_integration.py	Wires SB into AI agents: CLAUDE.md/AGENTS.md directive, Claude Code hook, git hooks.

Reference docs: the graph.json schema & taxonomy and the MCP tools.

Status & roadmap

Beta — v0.9.0. Working today: the typed graph; the anti-drift gate; self-refreshing reads (queries rebuild only when the project changed, no scheduler); the offline 2D community-map viewer; the low-token query layer (map / find / neighbors / subgraph / impact — plus --diff for the blast radius of your uncommitted changes — / why (shortest path between two nodes) / focus / communities — the project's real modules + cross-module bridges); PageRank importance ranking; recency-weighted recall (focus --recency — a deterministic git base-level signal, off by default); community detection; the GRAPH_REPORT.md one-pager; operational nodes (decisions/sessions); the opt-in Python symbol call-graph (build --symbols); GraphML export; configurable .secondbrain.json taxonomy with per-file type_overrides and opt-in respect_gitignore; agent integration (agent install + git hook install); and the optional MCP server. Next: a truly incremental build for very large graphs, richer reference-resolution, symbol layers for more languages, and an optional local semantic layer.

What it deliberately is not

Second Brain stays lean on purpose. The core will not grow:

• no embeddings / vector store — it maps structure and importance, not semantics (complementary to RAG);
• no tree-sitter / multi-language symbol parsing in the core — Python symbols use the stdlib ast;
• no mandatory watcher process — freshness is a cheap signature check plus an optional git hook;
• no file contents in query results — only ids, types, sizes and connections;
• no LLM required to build or query the graph.

These keep it zero-dependency, read-only, deterministic and low-token — its whole point.

Development

pip install -e ".[dev,mcp]"
ruff check second_brain tests
pytest -q

Contributions are welcome — see CONTRIBUTING.md and the design principles (read-only, zero-deps, low-token, deterministic). Security reports: SECURITY.md. Full version history: CHANGELOG.md.

References & sources

Built on / adapted from

• Graphify by Safi Shamsi (MIT) — the interactive graph viewer is modelled on Graphify's viewer; with thanks.
• vis-network (Apache-2.0 OR MIT) — the rendering library, bundled offline. Full license texts in THIRD_PARTY_NOTICES.md.

Concepts & specifications

• BM25 (lexical relevance) — S. Robertson & H. Zaragoza, The Probabilistic Relevance Framework: BM25 and Beyond (2009); see Okapi BM25. Ranks the anchor files of a task for focus.
• Repo map design — the PageRank-ranked, token-budgeted, signature-bearing map is inspired by aider's repo map and the sibling project veridge.
• PageRank — S. Brin & L. Page, The Anatomy of a Large-Scale Hypertextual Web Search Engine (1998); see PageRank. Used for importance ranking and task-aware focus (personalised PageRank).
• Label propagation — Raghavan, Albert & Kumara, Near linear time algorithm to detect community structures (2007); see Label propagation. Used for deterministic community detection.
• Model Context Protocol — modelcontextprotocol.io; the MCP server speaks it.
• GraphML — graphml.graphdrawing.org; the export format.
• Python ast — docs.python.org/3/library/ast; precise Python import + symbol parsing.
• BLAKE2 — blake2.net; the stdlib content hash used for freshness.
• Keep a Changelog — keepachangelog.com · Semantic Versioning — semver.org.

Project docs

• docs/graph-format.md — on-di