GBrain

Your AI agent is smart but forgetful. GBrain gives it a brain.

Ask your agent about a meeting from six weeks ago, a paper you skimmed in March, an idea you jotted down late one night. GBrain finds it. And it knows the connections — who attended that meeting, who that paper's author has invested in, what other ideas in your brain it overlaps with.

On LongMemEval, the public benchmark for AI memory systems, the right answer lands in gbrain's top 5 results 97.6% of the time. Better than every comparable system that doesn't pay for a language-model call on every retrieval, at $0.50 per thousand queries.

For relational questions ("who works at this company?", "what did this founder invest in this quarter?"), gbrain's self-wiring knowledge graph makes the answer roughly 4× more relevant than plain vector RAG. The graph isn't hand-curated. Every page write extracts entity references and creates typed edges (works_at, attended, invested_in, founded, advises) with zero language-model calls.

Receipts on the evals →

I'm the President and CEO of Y Combinator, and I use this 16 hours a day. The production brain powering my OpenClaw and Hermes deployments has grown into the largest documented personal-AI knowledge graph in active use:

~100K	total items in the brain
~16,000	people pages, auto-enriched
~5,000	companies
~8,000	concepts
~4,000	original essays + ideas + drafts
~3,500	daily notes
~31,000	media items (tweets, books, papers, interviews, films, articles, calls)
~2,200	LLM-conversation transcripts (ChatGPT, Claude, Perplexity, agent forks)
108	cron jobs running autonomously
273	skills in the live agent fork (35 from gbrain bundle + 238 user-built on top)

The agent ingests meetings, emails, tweets, voice calls, books, papers, and original ideas while you sleep. It enriches every person and company it encounters. It fixes its own citations, consolidates memory overnight, detects contradictions in your typed claims about people and companies, and flags trajectory regressions. You wake up and the brain is smarter than when you went to bed.

GBrain is those patterns, generalized. As my personal agent gets smarter, so does yours.

What it is, technically

• Open-source (MIT) TypeScript CLI + MCP server. One binary, no service to host. Mac and Linux. Requires Bun 1.3+.
• Local-first. Your brain lives in ~/.gbrain on your machine. Embedded Postgres (PGLite via WASM) so there's no database to install — ready in ~2 seconds. Or point at your own Postgres / Supabase for big brains.
• Your data stays on disk. Embedding calls hit OpenAI / Voyage / Anthropic only if you configure those keys. Sync, search, and graph extraction all run locally with zero outbound traffic.
• Speaks MCP. Plugs into Claude Code, Cursor, Windsurf, ChatGPT desktop, and any agent that consumes the Model Context Protocol — stdio for local, HTTP + OAuth 2.1 for multi-user.

LLMs: fetch llms.txt for the doc map, or llms-full.txt for the map with core docs inlined. Agents: start with AGENTS.md (or CLAUDE.md for Claude Code).

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Install

Three paths, ordered by how much you're committing.

Path 1 — 60 seconds, just try it (standalone CLI)

git clone https://github.com/garrytan/gbrain.git
cd gbrain && bun install && bun link
gbrain init                     # brain ready in ~2 seconds (PGLite)
gbrain import ~/notes/          # index any directory of markdown
gbrain query "what themes show up across my notes?"

What you'll see — gbrain finds the relevant pages, ranks them by hybrid search, and prints the top hits with snippets:

$ gbrain query "what themes show up across my notes?"
Found 8 results in 124ms (hybrid: vector + keyword + RRF)

1. concepts/durable-systems.md           (score 0.91)
   "Software that survives its maintainers has three properties: ..."
2. essays/2025-thinking-out-loud.md      (score 0.87)
   "I keep coming back to the idea that the best companies ..."
3. meetings/2026-02-12-alice-call.md     (score 0.83)
   "Discussed how Alice's framework for ..."
...

Add gbrain extract all after import to build the knowledge graph (entity links + timeline edges). gbrain dream runs the full 9-phase maintenance cycle.

Path 2 — Plug it into Claude Code / Cursor (MCP)

# Local single-user via stdio MCP
claude mcp add gbrain "$(which gbrain) serve"

# Multi-user via HTTP MCP with OAuth 2.1 + admin dashboard
gbrain serve --http                              # prints admin bootstrap token
gbrain serve --http --bind 0.0.0.0 --public-url https://brain.example.com

Now ask Claude / Cursor "what did Alice and I discuss last quarter?" and it queries your brain. The HTTP server supports OAuth 2.1 (client_credentials, authorization_code with PKCE, refresh rotation, revocation, RFC 7591 DCR). Source-scoped clients (--source dept-x) tie write authority to one source; --federated-read S1,S2,S3 adds orthogonal read scopes. Loopback bind by default — pass --bind 0.0.0.0 to publish.

Path 3 — Full agentic install (~30 min)

If you want the nightly auto-enrichment, cron jobs, and overnight synthesis I described above, the brain needs an agent to drive it.

• OpenClaw — deploy AlphaClaw on Render (one click, 8GB+ RAM)
• Hermes Agent — deploy on Railway (one click)

Then paste this into your agent:

Retrieve and follow the instructions at:
https://raw.githubusercontent.com/garrytan/gbrain/master/INSTALL_FOR_AGENTS.md

It clones the repo, installs GBrain, sets up the brain, scaffolds 35 skills, configures recurring jobs, and asks you for the API keys it needs. Budget ~30 minutes for the conversation.

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Receipts on the evals

The lead's numbers come from published, reproducible eval runs. Sibling repo gbrain-evals has the full reports, corpora, and cross-system tables. Glossary so the metric jargon below doesn't get in the way:

• R@5 — did the right answer land in gbrain's top 5? Higher is better.
• P@5 — what fraction of the top 5 are actually correct? Higher is better.
• No LLM in the loop — retrieval uses embeddings + keyword search + ranking, with no language model called per query. Cheaper, deterministic, reproducible.

LongMemEval — the standard public memory benchmark

500 questions across 6 question types, each against a haystack of ~50 conversation sessions. Built by Wu et al., the published yardstick for memory systems.

System	R@5	LLM in retrieval?	Notes
gbrain-hybrid	97.60%	no	this run, 2026-05-07
MemPalace hybrid+rerank (held-out)	98.4%	yes	0.8pt ahead because they pay for an LLM call per query
MemPalace raw	96.6%	no	gbrain wins this head-to-head by 1pt
Hindsight	91.4%	yes	per their release
Stella (academic)	~85%	no	dense retriever
Contriever (academic)	~78%	no	dense retriever
BM25 (sparse)	~70%	no	keyword baseline

Mastra (94.87%) and Supermemory (~99%) publish on this dataset but measure end-to-end QA accuracy with an LLM judge, which is a different thing than "did retrieval find the right session." Not directly comparable; flagged honestly in the cross-system table. Reports + reproduction: 2026-05-07 LongMemEval.

BrainBench — relational queries (in-house corpus)

LongMemEval is conversation-style. The other half of the proof is relational questions over a structured knowledge base. We built a 240-page rich-prose corpus (80 people, 80 companies, 50 meetings, 30 concepts) and wrote 145 relational gold queries — "who attended this meeting?", "what's this person invested in?" — against it. Reproducible, deterministic.

Retrieval system	P@5	Δ vs gbrain
gbrain (hybrid + knowledge graph)	49.1%	—
Vector + keyword RRF (graph turned off)	17.8%	−31.4
Keyword search only (BM25)	17.1%	−32.0
Vector-only RAG	10.8%	−38.4

The knowledge graph layer is worth 31 points P@5 on these queries. Separable, measured, load-bearing. Flat across seven releases (v0.16 → v0.20) — zero retrieval regression while ops + infra changed underneath. Full report.

Curated content vs bulk swamp

Personal brains accumulate bulk content (tweet dumps, daily chat transcripts, archived articles). When a query phrase appears in both a short curated essay AND a long chat dump, which wins? Source-aware ranking keeps the essay on top. 93.3% top-1 hit (vs 80% on grep-only). Report.

Calibration loop — the brain that knows how you've been wrong

v0.36.1.0's calibration wave extracts gradeable claims from your prose, grades them against reality over time, and applies the resulting bias profile when the AI gives you advice. First published benchmark for AI memory systems that reason about user track records.

Eval	Result	Cost
cat14 — advice quality A/B	75% calibrated wins / 0% baseline wins / 25% tie	~$0.05 per run
cat15 — propose_takes F1	0.952 training / 0.922 holdout (gap 0.03, no overfitting)	~$0.10 per run

Voice gate 100%, force-fit prevention 100%. Hindsight introduced the concept as a skill demo without quantified evaluation; cat14 + cat15 stake out the category. The iteration log at cat14-calibration/ captures three same-day prompt variants where the eval caught two regressions before either could ship. Full report.

Prompt compression

Routing rule: the functional-area-resolver skill compresses a fat AGENTS.md into a two-layer dispatch table. A real-world 25KB resolver compressed to 13KB (48% the size) and gained 13–17 percentage points of routing accuracy across Opus 4.7, Sonnet 4.6, and Haiku 4.5. The (dispatcher for: ...) clause is load-bearing — strip it (same compression, no dispatcher signal) and Sonnet's accuracy collapses to 41.7%. Cross-model receipts in this repo.

Run your own evals

# Public benchmark, in-process, ~30s warm
gbrain eval longmemeval ~/Downloads/longmemeval_s.jsonl

# Multi-model output quality gate (3 frontier models score the same output)
gbrain eval cross-modal --task "..." --output report.md

# Capture real query/search traffic as regression-eval data
GBRAIN_CONTRIBUTOR_MODE=1 gbrain eval export > snapshot.ndjson
gbrain eval replay --against snapshot.ndjson

# In-house BrainBench corpus + multi-adapter runner
cd ~/git/gbrain-evals && bun eval/runner/multi-adapter.ts

Full BrainBench corpus + per-category scorecards (Cats 1–13) in the sibling gbrain-evals repo.

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What it does

1. Ingests everything, enriches automatically

Markdown sync from a git brain. Meeting transcripts (Whisper / Groq Whisper). EPUB / PDF books (book-mirror produces personalized chapter-by-chapter analysis). PDFs of academic papers with citation extraction. Articles via Reader / Perplexity / archive.is. Tweets (timeline + bookmarks). Voice notes. ChatGPT / Claude / Perplexity / agent-fork conversation exports. Email threads. Calendar events. Foursquare exports. Slack channel archives.

Every page write extracts entity references → typed links (attended, works_at, invested_in, founded, advises, mentions) with zero LLM calls. The graph wires itself.

2. Hybrid search that beats vector-only RAG

Per-query: vector + keyword + RRF fusion + multi-query expansion (Haiku) + source-aware ranking + reranking (ZeroEntropy, optional). Two-stage CTE so the HNSW index stays usable when source-boost re-ranks the top-K. Hard exclude prefixes (test/, archive/, etc.) at retrieval. Intent classifier auto-selects detail level by query type (entity / temporal / event / general). P@5 49.1%, R@5 97.9% on the BrainBench corpus.

3. Self-maintaining via the dream cycle

A 9-phase nightly cycle: lint → backlinks → sync → synthesize → extract → patterns → recompute_emotional_weight → embed → orphans (+ a purge phase for the destructive-guard lifecycle). Each phase is independently runnable via gbrain dream --phase <name>. Subagents fan out under a rate-leased Anthropic client; protected job names gate trust; per-job + per-cycle timeouts; auto-replay on stalled jobs. Conversation transcripts become brain pages with content-hash dedup so re-runs are no-ops.

4. Multi-source brains + temporal trajectory

gbrain sources add <id> --local-path <path> mounts additional brains alongside your host brain (team-published, CEO-class, departmental). Each source has its own RLS-isolated DB rows. OAuth clients can write to one source and read federated.

Author typed claims in the ## Facts fence: mrr=50000, arr=2000000, team_size=12, valuation_usd=15000000. gbrain eval trajectory companies/<slug> prints the chronological history with regressions auto-flagged. gbrain founder scorecard <entity> rolls up claim accuracy, consistency, growth direction, and red flags into a stable JSON contract.

5. Durable background work

gbrain agent run <prompt> submits LLM-loop subagents. gbrain jobs submit shell --params '{"cmd":"..."}' (operator-only) submits shell jobs. gbrain jobs supervisor start runs a self-restarting worker daemon with crash-cause classification, OOM detection, RSS watchdog, and per-cause metrics surfaced in gbrain doctor. gbrain autopilot --install registers the dream cycle as a launchd / systemd job.

Children + aggregator parents, child_done inbox for fan-in, per-job timeouts, idempotency keys, cascade-kill. Supervisor classifies exit codes into runtime_error / oom_or_external_kill / graceful_shutdown / clean_exit so 120 crashes/24h reads correctly instead of false-alarming on clean worker drains.

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Multi-player and company brains

Three ways to share a brain across multiple agents, machines, or teammates. Most setups want pattern 1 (single server, multiple MCP clients). The other two patterns exist for specific reasons.

Pattern 1 — One GBrain server, many MCP clients (recommended)

The GBrain server runs on the same machine or container as your primary agent (OpenClaw, Hermes, AlphaClaw). It publishes an MCP endpoint over HTTP with OAuth 2.1. Other agents — on other machines, in other apps, across teammates' laptops — connect as thin MCP clients.

# On the box that hosts your brain + primary agent:
gbrain serve --http --bind 0.0.0.0 --public-url https://brain.example.com
# Prints a one-time admin bootstrap token; paste it into the admin UI
# at /admin to register OAuth clients.

# On any other machine, set up a thin client:
gbrain init --mcp-only
# Walks you through pointing at the server URL + OAuth credentials.
# No local database, no scaffolded skills — just a routing shim.

Network: use Tailscale. Run the server on a Tailscale-private address so the brain is reachable from your other machines without exposing anything to the public internet. Public-internet exposure works fine (OAuth + loopback-bind-by-default + admin dashboard handle it), it's just a larger blast radius. For teams, Tailscale + OAuth is the cheap path to "my engineering team's agents all read from one shared brain."

What thin clients CAN do (subject to OAuth scope: read / write / admin):

• search, query, think — full retrieval pipeline, including the knowledge graph + reranking
• get_page, list_pages, put_page, delete_page, restore_page — page CRUD (put_page/delete_page require write scope)
• find_experts (whoknows), find_trajectory, find_orphans, find_anomalies, find_contradictions — the analytical surfaces
• get_recent_salience, get_brain_identity — observability
• apply-migrations, restore_page — admin scope, OAuth-gated
• Anything else marked localOnly: false in src/core/operations.ts

What thin clients CANNOT do (server-side only, by design):

• sync_brain — needs filesystem access to the git brain repo
• file_upload, file_list, file_url — server-side S3 / Supabase Storage credentials don't cross the trust boundary
• get_recent_transcripts — raw .txt transcripts; privacy gate keeps these local
• purge_deleted_pages — destructive, admin-only, local-only
• code_traversal_cache_clear — local maintenance

If a thin client tries one of these, the server returns a clear "local-only" error pointing at the local CLI.

Source-scoped clients (v0.34+). A teammate's OAuth client can be tied to a single source with --source dept-x; its writes are isolated to that source's RLS-protected rows. --federated-read S1,S2,S3 adds orthogonal read scopes so the client can read across departments while writing to one canon. Useful when a team has a shared brain AND contributes back to a CEO-class brain.

Pattern 2 — Local PGLite for code search (GStack)

For an engineering agent that wants symbol-aware code search on the repo it's currently working in, run a local PGLite instance instead of hitting a central GBrain server. GStack supports this directly: one PGLite per worktree, indexed against that worktree's source tree.

# In the worktree you want to index:
gbrain init --pglite              # ~2 seconds, no server, no creds
gbrain import .                    # indexes the worktree's code
echo "$(pwd)" > .gbrain-source     # pins this PGLite to this worktree

/sync-gbrain (a GStack skill) keeps the PGLite current as you work. Code-aware queries (gbrain code-def, gbrain code-refs, gbrain code-callers) walk tree-sitter call-graph edges across 29 languages. Symbol-aware retrieval works better than grep for "where is X defined" / "what calls Y" queries.

Don't use this pattern for shared knowledge. PGLite is single-process, worktree-scoped, no concurrent writers. The right tool for "my agent needs the brain my team uses" is pattern 1.

Pattern 3 — Federated repos (advanced)

Multiple GBrain servers, each indexing the same brain repo from its own git clone. Each server has its own DB; they sync to the same markdown source of truth via gbrain sync.

Use this when:

• You want a personal brain AND a team brain that share some content (the team's knowledge wiki) but diverge on private notes.
• You're crossing a hard trust boundary (your personal brain is read-write to you; the team brain is read-only to most teammates, write-gated to a few).
• The shared content is genuinely repo-shaped (markdown files in git), not "we both query each other's brains via MCP."

The mechanics: clone the same brain repo on each server, run gbrain init against each, configure both to sync from the same git remote. Pages land in both databases; the knowledge graph wires independently on each side from the same source markdown.

Most teams should NOT pick this. The MCP-client path (pattern 1) gives you one source of truth, one set of skills, one knowledge graph, and OAuth-gated multi-user access. Federated repos make sense when the shared content is the source of truth (a published team wiki) and the agents are operating against their own private overlays.

My personal setup runs both: a personal brain on my laptop and a team brain on a separate box, indexing two different git repos. The team brain is what teammates' thin MCP clients connect to; the personal brain stays local.

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Skills

35 curated skills ship in the bundle. The agent installs them via gbrain skillpack scaffold <name> (one-time additive copy — you own the files after). See docs/guides/skillpacks-as-scaffolding.md for the model.

Always-on

Skill	What it does
signal-detector	Catches ideas, entities, and TODOs on every message — the always-on capture surface.
brain-ops	Brain-first lookup before web search. Enforces the read-enrich-write loop.
repo-architecture	File-placement decisions follow the primary subject, not the format.

Content ingestion

Skill	What it does
idea-ingest	Links / articles / tweets. Mandatory people page for the author.
media-ingest	Video / audio / PDF / book with entity extraction.
meeting-ingestion	Transcripts → attendee enrichment chaining.
voice-note-ingest	Whisper → brain page with entity refs.
book-mirror	EPUB / PDF → personalized chapter-by-chapter two-column analysis.
book-mirror-extreme	Synthesis pass: lift the book's principles into your own framework.
brain-pdf	Render any brain page as a publication-quality PDF.

Research + synthesis

Skill	What it does
perplexity-research	Bounded Perplexity calls with brain-side dedup.
academic-verify	Citation verification + DOI resolution + claim cross-check.
strategic-reading	Reading-list curation tuned to active concept clusters.
archive-crawler	Crawl + extract from archive.is / archive-org bookmarks.
article-enrichment	Article → people / companies / concepts links.
concept-synthesis	Cross-source synthesis: 3+ pages on one concept → unified writeup.
data-research	Email-to-tracker pipeline with parameterized YAML recipes.

Brain operations

Skill	What it does
query	The default query path. Walks intent classifier + hybrid search.
enrich	Tier-escalating enrichment for people / companies / deals.
maintain	Background brain hygiene: backlinks, citations, orphans.
citation-fixer	Audit and fix citation format issues across the brain.
frontmatter-guard	Validate every page's frontmatter against the contract.

Operational

Skill	What it does
daily-task-manager	Task lifecycle with priority levels (P0–P4).
daily-task-prep	Morning prep with calendar context + agenda generation.
cron-scheduler	Schedule staggering, quiet hours, idempotency keys.
reports	Timestamped reports with keyword routing.
cross-modal-review	Quality gate via second model.
webhook-transforms	External events → brain signals.
minion-orchestrator	Background work: shell jobs + LLM subagents.

Identity + meta

Skill	What it does
soul-audit	6-phase interview → SOUL.md / USER.md / ACCESS_POLICY.md / HEARTBEAT.md.
testing	Skill validation framework.
skillify	The meta-skill: turn any feature into a properly-skilled, tested unit.
skill-creator	Create conforming skills with MECE check.
skillpack-check	Agent-readable bundle health report.
skillpack-harvest	Lift a proven skill from your fork back into gbrain (privacy-linted).
functional-area-resolver	Two-layer dispatch for compressing AGENTS.md / RESOLVER.md.

Conventions (shared deps, installed alongside every skill)

• _AGENT_README.md — agent operating contract: how to discover skills via frontmatter
• _brain-filing-rules.md — file-placement rules (primary subject wins)
• _output-rules.md — output quality standards (no LLM slop)
• _friction-protocol.md — log user friction to ~/.gstack/friction/
• conventions/ — cross-cutting rules (quality, brain-first, model-routing, test-before-bulk, cross-modal)

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How it works

Architecture

Contract-first: src/core/operations.ts defines ~47 shared operations. CLI and MCP server are both generated from that one source. Engine factory dynamically imports the configured engine (pglite for zero-config local, postgres for Supabase / self-hosted).

Trust boundary: OperationContext.remote distinguishes trusted local CLI (remote: false) from untrusted agent-facing callers (remote: true). Security-sensitive operations tighten when remote: true is set.

Knowledge model

Every brain page is markdown with YAML frontmatter:

• Frontmatter: name, type (person / company / deal / concept / ...), tags, optional sources:, triggers: (for skills), ## Facts fenced block with typed claims (mrr=50000, arr=2000000).
• Compiled truth: stable section the agent updates with consensus knowledge.
• Timeline: append-only event log, sentinel-separated (<!-- timeline -->).

Pages are chunked by recursive markdown structure (default) or LLM-guided (opt-in). 29 programming languages get tree-sitter semantic chunking with embedded WASM grammars and tiktoken token budgeting.

Knowledge graph

Every page write fires extract.ts:

• Matches [Name](people/slug) markdown links AND [[people/slug|Name]] Obsidian wikilinks.
• Heuristics infer link type: attended, works_at, invested_in, founded, advises, source, mentions.
• Bulk-insert via unnest() arrays — 4-5 params regardless of batch size, sidesteps the 65535-param cap.

Query: gbrain graph-query alice --depth 2 --direction in returns who attended what with Alice, transitively.

Search

Per-query pipeline:

1. Query intent classifier (entity / temporal / event / general) selects detail level.
2. Multi-query expansion via Haiku (deterministic, off by default in conservative mode).
3. Hybrid: keyword (ts_rank × source-factor) + vector (HNSW + source-boost re-rank) + RRF fusion.
4. Optional reranker (ZeroEntropy zerank-2 flagship or zerank-1).
5. Source-aware ranking: curated content (originals/, concepts/, writing/) outranks bulk content (chat transcripts, daily/, media/x/). Hard-exclude test/, archive/, attachments/ by default.
6. Dedup + token budget enforcement per mode.

Engines

• PGLite — embedded Postgres 17.5 via WASM. Zero-config default. Single- file backup. Forward-reference bootstrap walks schema migrations cleanly across the v0.13–v0.35 wave.
• Postgres + pgvector — Supabase / self-hosted. pg_trgm + HNSW indexes. Migrations include 60+ schema bumps with sqlFor.postgres / sqlFor.pglite branches for engine-specific DDL.

gbrain migrate --to supabase / --to pglite does the round-trip.

Storage tiering

For brains crossing 100K files where bulk machine-generated content dominates the size: declare which directories are db_tracked (committed) vs db_only (DB-only, gitignored). gbrain sync manages .gitignore automatically; gbrain export --restore-only repopulates missing DB-only files from the database.

See docs/guides/storage-tiering.md for the config shape and per-tier defaults.

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Commands

SETUP
  gbrain init [--pglite | --supabase]   Set up a brain. Picks search mode.
  gbrain migrate --to {supabase,pglite} Round-trip between engines.
  gbrain doctor [--fast] [--fix]        Health check + auto-repair.

CONTENT
  gbrain sync [--workers N]             Pull from the git brain.
  gbrain import <path>                  Index any directory of markdown.
  gbrain embed [--stale]                Compute / refresh embeddings.
  gbrain extract {links,timeline,all}   Build the knowledge graph.

SEARCH + QUERY
  gbrain query "<question>"             Hybrid search + RRF + reranking.
  gbrain search "<text>"                Lower-level: vector | keyword | hybrid.
  gbrain whoknows <topic>               Expertise routing across the graph.
  gbrain graph-query <slug>             Typed-edge traversal.
  gbrain orphans                        Pages with zero inbound links.
  gbrain salience [--days N]            Pages ranked by emotional + activity.
  gbrain anomalies [--lookback-days N]  Cohort-level outlier detection.

AGENTS + JOBS
  gbrain agent run "<prompt>"           Submit a subagent run.
  gbrain agent logs <id>                Tail the run's heartbeat + transcript.
  gbrain jobs submit shell --params ... Submit a shell job (operator-only).
  gbrain jobs supervisor start          Self-restarting worker daemon.

CYCLE + AUTOPILOT
  gbrain dream [--phase NAME]           Run the 9-phase brain cycle.
  gbrain autopilot --install            Register cycle as launchd / systemd job.

SKILLPACK
  gbrain skillpack list                 What's in the bundle.
  gbrain skillpack scaffold <name>      Copy a skill into your agent repo.
  gbrain skillpack reference <name>     Diff vs bundle. Or --all --since <ver>.
  gbrain skillpack migrate-fence        One-shot upgrade from pre-v0.36 model.
  gbrain skillpack harvest <slug> --from <host>
                                        Lift a proven fork skill into gbrain.

SERVE
  gbrain serve                          Stdio MCP (single-user, Claude Code).
  gbrain serve --http [--bind HOST]     HTTP MCP + OAuth 2.1 + admin dashboard.

EVAL + TRAJECTORY
  gbrain eval longmemeval <dataset>     LongMemEval benchmark, in-memory PGLite.
  gbrain eval cross-modal --task "..."  3 frontier models score the same output.
  gbrain eval whoknows <fixture>        Two-layer gate for whoknows accuracy.
  gbrain eval suspected-contradictions  Cached contradiction probe.
  gbrain eval trajectory <entity>       Typed-claim history + regression flags.
  gbrain founder scorecard <entity>     Claim accuracy / consistency / growth.

INTEGRATIONS + SOURCES
  gbrain integrations list              Recipe catalog (Reader, Perplexity, etc).
  gbrain sources {list,add,remove,...}  Multi-source brain management.
  gbrain auth register-client <name>    Mint an OAuth client for the HTTP MCP.

Run gbrain <command> --help for per-command options.

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Docs

• Architecture: docs/architecture/
• Skills as scaffolding (v0.36): docs/guides/skillpacks-as-scaffolding.md
• Search modes + cost matrix: docs/eval/SEARCH_MODE_METHODOLOGY.md
• Storage tiering: docs/guides/storage-tiering.md
• Embedding providers (14 recipes): docs/integrations/embedding-providers.md
• Eval capture (BrainBench-Real): docs/eval-bench.md
• MCP per-client setup: docs/mcp/
• Two-repo pattern (agent vs brain): docs/guides/repo-architecture.md
• Skill development cycle: docs/guides/skill-development.md
• Per-agent install guide: AGENTS.md, CLAUDE.md, INSTALL_FOR_AGENTS.md
• Full changelog: CHANGELOG.md

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Origin story

I needed a brain that could remember every meeting, every founder encounter, every essay I'd ever drafted, every idea I wanted to chase later. Off-the-shelf knowledge tools (Roam, Obsidian, Notion) handle storage. None of them ingest a meeting, enrich the attendees, build the graph, fix the citations, and surface the connection three weeks later when it matters. So I built one. The first 12 days produced a working brain. The next 12 months produced 100,000 pages, a self-wiring knowledge graph, 108 cron jobs, and the multi-engine architecture that ships here.

GBrain is the generalized version of that work. You scaffold the skills. You bring your data. Your agent runs the patterns. The brain compounds nightly.

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Contributing

PRs welcome. See CONTRIBUTING.md for the dev loop, test taxonomy, and how to ship. Privacy rules in CLAUDE.md are non-negotiable — no real names, fork names, or private references in public artifacts. The harvest CLI's privacy linter exists to catch accidental leaks before they merge.

License

MIT.