GBrain

Search gives you raw pages. GBrain gives you the answer. It's the brain layer your AI agent has been missing — the only one that does synthesis, graph traversal, and gap analysis in one box. Run a full autonomous agent on top of it, or just wire it into Claude Code or Codex as a supercharged retrieval layer in one command; either way your coding agent stops being amnesiac about everything that isn't code.

I'm Garry Tan, President and CEO of Y Combinator. I built GBrain to run my own AI agents. It's the production brain behind my OpenClaw and Hermes deployments: 155,795 pages, 24,589 people, 5,340 companies, 66 cron jobs running autonomously. My agent ingests meetings, emails, tweets, voice calls, and original ideas while I sleep. It enriches every person and company it encounters. It fixes its own citations and consolidates memory overnight. I wake up smarter than when I went to bed — and so will you.

And now it works as a company brain too. Each person on the team gets their own slice of the brain, scoped by login. When you query, you only see what you're allowed to see — never another person's notes, never another team's data. We fuzz-tested this across every way you can read the brain (search, list, lookup, multi-source reads) and got zero leaks. Drop GBrain in as your team's shared institutional memory — the company-brain shape YC just put on its Request for Startups. If you're building in that space, you might as well build on this. Tutorial: set up GBrain as your company brain →

Lots of personal-knowledge systems give you keyword matching and grep in a box. GBrain does that, and adds two things nobody else ships together:

• A synthesis layer that gives you the actual answer. Synthesized, well-cited prose across people, companies, deals, and ideas. Not "here are 10 chunks that mention your query"; an actual answer with citations and an explicit note on what the brain doesn't know yet. The gap analysis is the part that changes how you use the brain.
• A self-wiring knowledge graph. Every page write extracts entity refs and creates typed edges (attended, works_at, invested_in, founded, advises) with zero LLM calls. Ask "who works at Acme AI?" or "what did Bob invest in this quarter?" and get answers vector search alone can't reach. Benchmarked: P@5 49.1%, R@5 97.9% on a 240-page Opus-generated rich-prose corpus, +31.4 points P@5 over its graph-disabled variant and over ripgrep-BM25 + vector-only RAG by a similar margin. Full BrainBench scorecards live in the sibling gbrain-evals repo.

The point of building a 150K-page brain is to use it as a strategic moat. To never lose context. To query what's in your own head without re-reading it. The brain layer is what makes the moat usable. The 24/7 dream cycle is what keeps it sharp. Both run on your hardware, your DB, your keys.

It's easier to ship a daemon that runs 24/7 to ingest, enrich, and consolidate than it is to keep an agent in chat working hard. GBrain is that daemon, generalized. Install in 30 minutes. Your agent does the work. As my personal agent gets smarter, so does yours.

~30 minutes to a fully working brain. Database ready in 2 seconds (PGLite, no server). You just answer questions about API keys.

LLMs: fetch llms.txt for the documentation map, or llms-full.txt for the same map with core docs inlined in one fetch. Agents: start with AGENTS.md (or CLAUDE.md if you're Claude Code).

What this looks like

Say you have a meeting with Alice tomorrow. You want to walk in remembering what she works on, when you last talked, and what's still open between you. Here's what you'd type, and what you'd get back.

You ask:

"What do I need to know before my meeting with Alice tomorrow?"

Most personal-knowledge tools give you back a list of pages. Something like:

1. people/alice — Alice runs engineering at Acme...
2. meetings/2026-03-15-alice-q1 — Q1 product review with Alice...
3. meetings/2026-01-08-acme-kickoff — Kickoff meeting with Acme team...
4. customers/acme — Acme is a series-B fintech we work with...
5. notes/2026-04-22 — Quick chat with Alice about pricing...

Five pages you now have to open and read yourself to actually prepare. The tool found the right material, but it didn't do the work.

GBrain gives you back the answer, with sources:

Alice runs engineering at Acme (a series-B fintech). You last spoke
on April 22 in a quick pricing chat. Three things are still open
from that conversation:

1. She owes you the security review for the new tier
   (deadline was May 1; no update since).
2. You committed to pricing for a 500-seat tier
   (you sent it April 25; no response yet).
3. She mentioned they're hiring a CISO; you said you'd intro
   someone from your network.

Heads up: nothing's been added to the brain about Alice or Acme
since April 22, six weeks ago. She may have replied through email
or Slack DM, channels the brain doesn't see. Worth asking her to
catch up before assuming any of this is still current.

Every claim has a source page behind it. The "heads up" at the end tells you what the brain doesn't know yet, so you can ask Alice about it directly instead of being surprised. The brain just did your meeting prep.

This is the difference between a search engine and a brain. Search finds the pages. The brain reads them for you and writes the answer.

Install

Warning

GBrain is NOT distributed on npm. The npm package named gbrain is an unrelated package with no connection to this project. Do not run npm install -g gbrain or bun add -g gbrain — you'll get something else, and it can shadow the real binary on your PATH. Install and upgrade ONLY via the documented paths below (bun install -g github:garrytan/gbrain, or git clone + bun install && bun link). If you already ran the npm install by mistake: npm uninstall -g gbrain / bun remove -g gbrain, then reinstall from GitHub. gbrain doctor detects a shadowing npm install and prints the fix.

GBrain is designed to be installed and operated by an AI agent. New to GBrain? Start with Codex — it runs on the ChatGPT subscription you already have, takes ~15 minutes, and deploys nothing. Already living in Claude Code? Its path is identical. Want GBrain running the way it was designed to run — always on, enriching your brain around the clock? That's OpenClaw or Hermes, at real server + API cost. Each path below is complete on its own. (Wiring it up by hand instead? Jump to CLI standalone or the MCP table.)

For Codex — the recommended first step

Turn Codex into your persistent personal agent. Works in the ChatGPT desktop app (open Codex on a folder) and in the Codex CLI (codex in a terminal) — same install, same result. Open Codex in a new, empty folder (not an existing code project) — that folder becomes your agent's own private GitHub repo, which bootstrap creates and privacy-verifies for you. Then paste:

Read and follow every step of:
https://raw.githubusercontent.com/garrytan/gbrain/latest-stable/BOOTSTRAP_FOR_AGENTS.md
Goal: set yourself up as my persistent personal agent in this folder, with gbrain
as your memory. Interview me before writing any identity file — never invent
answers. Ask before anything destructive. You are not done until
`gbrain bootstrap verify` exits 0.

Codex will ask for command approvals during the install — approving them is the sandbox working as intended. What you get, in about 15 minutes: a short interview (6 required questions) → your agent's identity (SOUL.md, USER.md, MEMORY.md) rendered from your own answers, never invented → a local PGLite brain (2 seconds, no server, no Docker) → MCP wired so every session can search and write memory → a private GitHub repo, created and privacy-verified, as your agent's durable body. Works with zero API keys — keyword search plus memory your agent writes itself; one optional key (OpenAI, Anthropic, or Voyage) upgrades to semantic search and automatic fact extraction. Codex reads brain context through its tools each turn (pull-based).

Prefer to make the repo yourself? Create a new empty private repo under your own GitHub account (no README/.gitignore/license), clone it, open the clone in Codex, and paste the same block — bootstrap detects your empty repo and adopts it instead of creating one. The repo must be empty and personal-account-owned; org-owned repos are refused (create one under your account, or let bootstrap make it).

For Claude Code — turn it into your persistent personal agent

Works in the desktop app and in the CLI (claude in a terminal) — identical harness, identical result. Open Claude Code in a new, empty folder (not an existing code project) — that folder becomes your agent's own private GitHub repo, created and privacy-verified for you. Then paste the same block:

Read and follow every step of:
https://raw.githubusercontent.com/garrytan/gbrain/latest-stable/BOOTSTRAP_FOR_AGENTS.md
Goal: set yourself up as my persistent personal agent in this folder, with gbrain
as your memory. Interview me before writing any identity file — never invent
answers. Ask before anything destructive. You are not done until
`gbrain bootstrap verify` exits 0.

Everything from the Codex path applies — interview, identity from your own answers, local brain, private repo, keyless mode — plus Claude Code gets per-turn context hooks (consent-gated): your brain loads automatically into every prompt, and each session persists itself to your private repo at exit. Restart the session after install and ask "what did I tell you my top jobs were?" — that's the moment it clicks. Full contract, security posture, and uninstall: docs/guides/bootstrap.md.

Prefer to make the repo yourself? Create a new empty private repo under your own GitHub account (no README/.gitignore/license), clone it, open the clone in Claude Code (CLI or the desktop app's open-a-repo flow), and paste the same block — bootstrap adopts your empty repo instead of creating one. The repo must be empty and personal-account-owned; org-owned repos are refused.

For OpenClaw or Hermes — GBrain as intended, always on

This is GBrain used the way it was designed to be used: a server-hosted agent with 24/7 crons, continuous ingestion, and the overnight dream cycle that enriches your brain while you sleep — your agent works whether your laptop is open or not. It's also the highest-cost path: a deployed server (8GB+ RAM) plus raw API token usage that scales with how hard your agent runs, well beyond a chat subscription. Start here if you want the full experience from day one; start with Codex above if you want to feel it first. If you don't have a platform running yet, both deploy in one click:

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

The agent installs GBrain, creates the brain, asks for your API keys, loads the 50+ bundled skills, configures the dream cycle, and verifies the install end-to-end. ~30 minutes. You answer questions, it does the work.

Never set up an AI agent platform before? The personal-brain tutorial walks the whole path end-to-end — picking OpenClaw vs Hermes, deploying it, pointing it at INSTALL_FOR_AGENTS.md, getting the API keys, and verifying the first query. Start there if any of the above is new.

Lighter ways in

Just want a memory for your coding agent — no identity, no repo. Spin up a local brain and connect it in two commands — zero server, zero token, zero tunnel. --surface verbs gives your agent the five-verb memory protocol (recall, remember, entity, synthesize, forget — MEMORY_VERBS v1, frozen + additive-forever) instead of the full tool wall; drop the flag for every operation:

gbrain init --pglite                                    # 2-second local brain (no Docker)
claude mcp add gbrain -- gbrain serve --surface verbs   # or: codex mcp add gbrain -- gbrain serve --surface verbs

If claude is not found, install Claude Code first — or use the per-harness blocks in the protocol doc. Heads-up: memories agents save default to brain-wide visibility (every connected agent can recall them); pass visibility: "private" for local-only facts.

Already have a brain on a remote host (OpenClaw, Hermes, or any gbrain serve --http)? Point your laptop agents at it with one command each — --install wires it up and smoke-tests the token before handoff:

gbrain connect https://your-host/mcp --token gbrain_xxx --install               # Claude Code
gbrain connect https://your-host/mcp --token gbrain_xxx --agent codex --install # Codex

Brain-only install into another coding agent (Cursor, Claude Cowork, or anything that can fetch a URL and run shell commands) — paste the OpenClaw/Hermes block above (INSTALL_FOR_AGENTS.md); it installs the brain, skills, and dream cycle without the personal-agent identity layer. Tested with Codex, Claude Code, Claude Cowork, Cursor, and AlphaClaw.

→ Full walkthrough: give your coding agent a memory — the memory-only paths end to end, plus the brain-first protocol you paste into CLAUDE.md / AGENTS.md and the four habits that make it actually change how you work.

CLI standalone (no agent)

bun install -g github:garrytan/gbrain
gbrain init --pglite     # 2 seconds; no server, no Docker
gbrain doctor            # verify health
gbrain import ~/notes/   # index your markdown
gbrain query "what themes show up across my notes?"

Postgres-at-scale, Supabase, and thin-client setup paths live in docs/INSTALL.md.

Connect GBrain to your AI client (MCP)

GBrain exposes nearly all of its 100+ operations as MCP tools (stdio and HTTP; a handful of local-only ops stay CLI-side) — or exactly the five memory verbs with --surface verbs. The specific snippet depends on which client you use:

• Claude Code — local: one command, claude mcp add gbrain -- gbrain serve (zero server, zero tunnel). Remote with just a bearer token: gbrain connect https://your-host/mcp --token gbrain_xxx prints a paste-ready block (or --install wires it up and smoke-tests the token).
• Codex — gbrain connect https://your-host/mcp --token gbrain_xxx --agent codex (or --install). Codex reads the bearer from $GBRAIN_REMOTE_TOKEN at runtime, so the token never lands in Codex config.
• Cursor / Windsurf / any stdio MCP client — same shape, add {"command": "gbrain", "args": ["serve"]} to your MCP config.
• Claude Desktop (Cowork) — Settings → Integrations → add the URL of your HTTP server. Remote only; the local claude_desktop_config.json does not work for remote servers.
• Claude Cowork (team plan) — org Owner adds the connector under Organization Settings → Connectors.
• Perplexity Computer — gbrain connect https://your-host/mcp --agent perplexity --oauth --register mints a least-privilege OAuth client and prints the Issuer/Client ID/Secret to paste into Settings → Connectors (OAuth is the right path for a cloud connector; a bearer token also works for local use). Pro subscription required.
• ChatGPT — uses OAuth 2.1 with PKCE (the hard requirement). Register a chatgpt client from the admin dashboard with grant type authorization_code.

For the HTTP server itself:

gbrain serve              # stdio MCP (local subprocess; for Claude Code, Cursor, Windsurf)
gbrain serve --http       # HTTP MCP with OAuth 2.1 + admin dashboard at /admin
                          # (required for Claude Desktop, Cowork, Perplexity, ChatGPT)

The HTTP server includes DCR-style client registration, scope-gated access (read / write / admin), and rate limiting. Deployment guides (ngrok, Railway, Fly.io) live under docs/mcp/.

Two ways to query your brain

Raw retrieval (what most personal-knowledge tools ship) and a synthesis layer that gives you an actual answer. They serve different jobs.

# raw retrieval: top pages by hybrid score, fast, no LLM cost
gbrain search "who's working on AI agents at portfolio companies?"

# brain layer: synthesized answer with citations and gap analysis
gbrain think "who's working on AI agents at portfolio companies?"

gbrain search returns the top retrieved pages, ranked by hybrid scoring (vector + keyword + RRF + source-tier boost + reranker). Use it when you want raw material to skim: agent context windows, citation lookups, finding a specific quote.

gbrain think runs the same retrieval, then composes a synthesized answer across the results with explicit citations to the source pages AND an honest note on what the brain doesn't know yet. The gap analysis is the differentiator: the answer tells you when a page is stale, when a claim is uncited, when two pages contradict each other, when there's a hole you should fill.

Why it compounds. Pair the brain layer with find_trajectory and you get answers like "how have the company's metrics changed AND what does the team look like right now AND what did they promise / share AND when did we last meet AND what's the value-add I can offer here": well-scored, well-cited, in one shot. That's the strategic moat. That's why building a 150K-page brain is worth the effort.

gbrain agent run "..." exposes the same surface to a sub-agent through the Minions queue, with crash-safe two-phase persistence. Same answers, durable.

How to get data in

One command, local or hosted, synchronous receipt:

gbrain capture "the thought I want to remember"
gbrain capture --file ./notes/today.md
echo "from a pipe" | gbrain capture --stdin
SLUG=$(gbrain capture "..." --quiet)

The page lands in the database and on disk in one move. Default slug inbox/YYYY-MM-DD-<hash8> so captures cluster in a predictable triage location. On thin-client installs the verb routes through MCP to the server: same command, same UX.

For webhook ingestion (Zapier / IFTTT / Apple Shortcuts):

curl -X POST https://your-brain/ingest \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: text/markdown" \
  -d "# a thought from a Shortcut"

For mobile capture, the inbox folder source picks up anything dropped into ~/.gbrain/inbox/ from iOS Shortcuts / AirDrop / Drafts / Finder.

Third-party skillpacks can ship custom ingestion sources (Granola, Linear, voice, OCR) against the versioned IngestionSource contract at gbrain/ingestion. See docs/skillpack-anatomy.md.

Your brain's shape (schema packs)

Most personal-knowledge tools force one fixed layout: their idea of "notes" + "people" + "tags." Drop a Notion export or your own years-old Obsidian vault on top, and the agent doesn't know what a Projects/ folder means or whether Reading/ is people or sources.

gbrain doesn't have a fixed layout. It ships with bundled schema packs and lets you author your own when none fit:

• gbrain-base-v2 (default as of v0.41.22) — 15-type DRY/MECE canonical taxonomy (14 canonical + note catch-all): person, company, media, tweet, social-digest, analysis, atom, concept, source, deal, email, slack, writing, project, note. Subtypes/format/origin pushed to frontmatter. The taxonomy that responds to issue #1479.
• gbrain-base (legacy, v0.41 and earlier brains) — the original 24-type layout. Stays bundled for back-compat; brains on it can upgrade via gbrain onboard --check --explain → gbrain jobs submit unify-types --allow-protected --params '{"target_pack":"gbrain-base-v2","apply":true}' (omit "apply":true for a dry-run preview — that is the default).
• gbrain-recommended — extends gbrain-base with the 13 additional directories from docs/GBRAIN_RECOMMENDED_SCHEMA.md (source, place, trip, conversation, personal, civic, project, etc.). Activate with gbrain schema use gbrain-recommended.
• Your own pack — gbrain schema detect clusters your actual filesystem into proposed types, gbrain schema suggest runs an LLM pass over them, and gbrain schema review-candidates --apply promotes the ones you like. Three commands and the brain knows your shape. Authoring a successor pack (declares migration_from: so existing brains can opt in): see docs/architecture/pack-upgrade-mechanism.md.

gbrain schema active                # which pack is running, which tier set it
gbrain schema list                  # bundled + installed packs
gbrain schema detect                # propose types matching your filesystem
gbrain schema suggest               # LLM-refined proposals on top of detect
gbrain schema review-candidates     # human gate: promote / rename / ignore
gbrain schema use my-pack           # activate

The active pack threads through every read + write path: parseMarkdown infers page type from the pack's path prefixes; whoknows scopes expert routing to types declared expert_routing: true; extract_facts runs only on extractable: true types; the search cache folds the pack name + version into its key so cross-pack contamination is structurally impossible. Switch packs and the brain re-interprets itself; switch back and nothing's lost.

Seven-tier resolution chain (per-call flag → env var → per-source DB key → brain-wide DB key → gbrain.yml → ~/.gbrain/config.json → gbrain-base default). Full reference + authoring guide: docs/architecture/schema-packs.md.

Tutorials

Step-by-step walkthroughs for getting the most out of GBrain. Each one takes you from zero to a working outcome, with concrete commands and real numbers.

• Set up your personal AI agent + brain from zero — the canonical full-stack install. Two GitHub repos, a Telegram bot, AlphaClaw on Render, OpenClaw + GBrain + Supabase. End-to-end in about 2 hours.
• Set up GBrain as your company brain — federated, multi-user, OAuth-scoped institutional memory for a 10-50 person team. About 90 minutes end-to-end.
• Auto-improve a skill with gbrain skillopt — treat a SKILL.md as a trainable parameter. Generate a starter benchmark straight from the skill with --bootstrap-from-skill (or write your own), strengthen the judges, then watch the optimizer propose edits and keep only the ones that measurably score higher. ~20 minutes, ~$1 in API calls. Flag + cost + safety reference: docs/guides/skillopt.md.

More walkthroughs in progress: connecting an existing agent (Claude Code, Cursor, OpenClaw, Hermes) to a GBrain memory layer; setting up GBrain for VC dealflow with founder scorecards and meeting prep; migrating an existing Notion or Obsidian vault; indexing a codebase as a queryable code brain. Full tutorial index: docs/tutorials/.

Want to see a tutorial that isn't here yet? Open an issue describing the workflow you want documented.

What it does (the loop)

  signal   →   search   →   respond   →   write   →   auto-link   →   sync
  (every    (brain-first  (informed     (page +    (typed edges     (cron
  message)  retrieval)    by context)   timeline)  + backlinks)     keeps fresh)

• Signal detector runs on every message your agent receives. Captures ideas, entity mentions, time-sensitive todos, names, links.
• Brain-first lookup before any external API call. The cheapest, fastest, most personal information source you have.
• Auto-link fires on every page write. No LLM calls; pure pattern matching on [[wiki/people/bob]] style references. New entity → new page stub → graph grows.
• Cron-driven enrichment runs while you sleep: dedup people pages, fix citations, score salience, find contradictions, prep tomorrow's tasks.

The whole loop is described in docs/architecture/topologies.md with diagrams.

Capabilities

Hybrid search. Vector (HNSW on pgvector) + BM25 keyword + reciprocal-rank fusion + source-tier boost + intent-aware query rewriting. Three named search modes (conservative, balanced, tokenmax) bundle the cost/quality knobs into a single config key. Live cost/recall comparisons in docs/eval/SEARCH_MODE_METHODOLOGY.md. The install picker default-applies tokenmax (it recommends conservative for Haiku-class subagent tiers or keyless setups); a brain with search.mode unset resolves to balanced at query time. The ZeroEntropy reranker is on in balanced and tokenmax, off in conservative. Per-query graph signals notice when a top result is a hub for THAT query (adjacency boost), is corroborated across team brains (cross-source boost), or is being crowded out by weak chunks from a chatty session (session demote). Run gbrain search "<query>" --explain to see per-stage attribution: base score, every boost that fired, what it multiplied. gbrain doctor ships a graph_signals_coverage check; gbrain search stats shows fire counts and failure breakdowns. Vector retrieval pools the best chunk per page, so a page surfaces on its strongest evidence instead of losing to a neighbor on one weak chunk. Queries that match a page's title phrase or a declared free-text alias (gbrain reindex --aliases backfills existing pages) get boosted to the page they name. Every result carries an evidence tag (why it matched) and a create_safety hint (exists / probable / unknown) so an agent decides whether a page already exists instead of guessing from a raw score. gbrain search diagnose "<query>" --target <slug> traces which retrieval layer surfaces (or misses) a page.

Self-wiring knowledge graph. Every put_page extracts entity refs from markdown/wikilinks/typed-link syntax and writes edges with zero LLM calls. Typed edges (attended, works_at, invested_in, founded, advises, mentions, …). Multi-hop traversal via gbrain graph-query. The graph is what produces the +31.4 P@5 lift over vector-only RAG. Obsidian-style vaults: bare [[note-name]] wikilinks that point across folders — you wrote [[struktura]] but the page lives at projects/struktura.md — resolve by basename once you opt in with gbrain config set link_resolution.global_basename true. Off by default; gbrain doctor tells you how many edges you'd gain before you flip it. See migrating an Obsidian vault.

Job queue (Minions). BullMQ-shaped, Postgres-native job queue. Durable subagents (LLM tool loops that survive crashes via two-phase pending→done persistence), shell jobs with audit, child jobs with cascading timeouts, rate leases for outbound providers, attachments via S3/Supabase storage. Replaces "spawn subagent as fire-and-forget Promise" with something that recovers from anything.

Non-English brains (FTS language config). The Postgres full-text search tokenizer is configurable via GBRAIN_FTS_LANGUAGE. Defaults to english. Set it to any text-search configuration that exists in your Postgres instance:

export GBRAIN_FTS_LANGUAGE=portuguese     # uses built-in portuguese stemmer
export GBRAIN_FTS_LANGUAGE=spanish        # built-in spanish stemmer
export GBRAIN_FTS_LANGUAGE=pt_br          # custom config (e.g. unaccent + portuguese)

List available configs: psql -c "SELECT cfgname FROM pg_ts_config". Both the query side (websearch_to_tsquery) and the write side (the trigger functions that populate pages.search_vector and content_chunks.search_vector) honor GBRAIN_FTS_LANGUAGE. On first install (or upgrade), the configurable_fts_language schema migration reads the env var and creates trigger functions in the configured language; subsequent inserts/updates tokenize using that setting. To change language on a brain that has already run the migration, use the dedicated CLI command:

export GBRAIN_FTS_LANGUAGE=portuguese
gbrain reindex-search-vector --dry-run    # preview row counts
gbrain reindex-search-vector --yes        # recreate triggers + backfill

The command is idempotent (re-running with the same language is a no-op for vector content) and uses the same recreate-and-backfill primitives as the migration. For accent-insensitive Portuguese (pt_br), see docs/guides/multi-language-fts.md for the unaccent + portuguese stemmer recipe.

50+ curated skills (the current list lives in skills/manifest.json). Routing lives in skills/RESOLVER.md. Covers signal capture, ingest (idea / media / meeting), enrichment, querying, brain ops, citation fixing, daily task management, cron scheduling, reports, voice, soul audit, skill creation, eval framework, and migrations. Skills are markdown files (tool-agnostic), packaged as a single skillpack the installer drops into your agent workspace.

Eval framework. gbrain eval longmemeval runs the public LongMemEval benchmark against your hybrid retrieval. gbrain eval export + gbrain eval replay capture real queries and replay them against code changes (set GBRAIN_CONTRIBUTOR_MODE=1). gbrain eval cross-modal cross-checks an output against the task using three different-provider frontier models. gbrain eval retrieval-quality runs NamedThingBench, which hard-gates the named-thing retrieval families (title-substring, alias-synonym, generic-to-named, multi-chunk-dilution) so a regression in "find the page this query names" fails CI loudly. gbrain eval brainbench runs the cross-harness memory conformance suite: know-to-ask, push precision/recall, write-back fidelity, and cross-session continuity, scored per harness seam (your OpenClaw's production pipeline plus Claude Code and Codex injection contracts) against a committed 141-fixture synthetic corpus — hermetic by default (in-memory PGLite, no keys, seconds), and CI gates every PR against master's committed baseline. Methodology in docs/eval/BRAINBENCH.md; search-mode methodology in docs/eval/SEARCH_MODE_METHODOLOGY.md.

Brain consistency. gbrain eval suspected-contradictions samples retrieval pairs, layered date pre-filter, query-conditioned LLM judge, persistent cache. Surfaces conflicts between takes + facts the agent has written. Wired into the daily dream cycle.

Agent-authored schema (v0.40.7.0). Your brain has a shape — what page types exist (person, meeting, paper, case, lab-result), what they link to (attended, authored, prescribed-by), what facts get extracted automatically. The default ships with 22 universal types, but your brain's actual shape is not the default shape. Agents can now evolve that shape on your behalf via 14 gbrain schema CLI verbs + a batched MCP op (schema_apply_mutations, admin scope, NOT localOnly so remote agents reach it over HTTPS). Atomic file locks, audit log with the agent's identity, chunked UPDATE backfill in 1000-row batches that never wedge concurrent writers. The brain stops being a pile of notes and becomes something with structure. Why it matters: docs/what-schemas-unlock.md — 7 killer use cases (4000 invisible meetings, founder ops brain, research brain, legal brain, team brain, agent-as-co-curator). 5-minute walkthrough: docs/schema-author-tutorial.md. Agent skill: skills/schema-author/SKILL.md.

Integrations

Data flowing into the brain. Each integration is a recipe — markdown + setup hints — that ships in recipes/ and is discoverable via gbrain integrations list.

• Voice: Phone calls create brain pages via Twilio + OpenAI Realtime (or DIY STT+LLM+TTS). Setup recipe: recipes/twilio-voice-brain.md.
• Email + calendar: webhook handlers that route to brain signals. docs/integrations/meeting-webhooks.md.
• Embedding providers: a dozen providers covered — OpenAI (default fallback), OpenRouter, Voyage, ZeroEntropy (default), Google Gemini, Azure OpenAI, MiniMax, Alibaba DashScope, Zhipu, Ollama (local), llama.cpp llama-server (local), LiteLLM proxy. Pricing matrix + decision tree in docs/integrations/embedding-providers.md.
• Rerankers: ZeroEntropy zerank-2 hosted (the default; on in balanced and tokenmax modes) plus the llama-server-reranker recipe for fully-local cross-encoder rerank via llama.cpp — runs Qwen3-Reranker or self-hosted ZeroEntropy weights against the same gateway.rerank() seam. Setup walkthrough in docs/ai-providers/llama-server-reranker.md.
• Credential gateway: vault-aware secret distribution. docs/integrations/credential-gateway.md.
• MCP clients: every major MCP client is supported. docs/mcp/ per-client setup.

Architecture

Two engines, one contract. PGLite (Postgres 17 via WASM, zero-config, default) for personal brains up to ~50K pages. Postgres + pgvector (Supabase or self-hosted) for shared / large / multi-machine deployments. The contract-first BrainEngine interface in src/core/engine.ts defines the 140+ methods both engines implement; CLI and MCP server are generated from one source.

Brain repo is the system of record. Your knowledge lives in a regular git repo (your "brain repo") as markdown files. GBrain syncs the repo into Postgres for retrieval; deletes in git become soft-deletes in DB. You can publish public subsets, share team mounts, run thin-client setups pointing at a colleague's brain server. Topologies in docs/architecture/topologies.md.

Two organizational axes (brain ⊥ source). A brain is a database (your personal brain, a team mount you joined). A source is a repo inside that brain (wiki, gstack, an essay, a knowledge base). Routing lives in .gbrain-source dotfiles and resolves via a documented 6-tier precedence chain. Full diagrams in docs/architecture/brains-and-sources.md.

Why the graph matters. Vector search returns chunks that are semantically close. The graph returns chunks that are factually connected. Hybrid search pulls from both; auto-linking on every write keeps the graph fresh. Deep dive: docs/architecture/RETRIEVAL.md.

Troubleshooting

PGLite crashes at startup with RuntimeError: Aborted() (often right after a macOS upgrade)? Not a macOS incompatibility — the OS-upgrade reboot killed gbrain mid-write and tore the data dir's WAL. gbrain now repairs this automatically on the next command (data preserved, backup kept); if auto-repair is disabled or skipped, run gbrain pglite-repair --dry-run to diagnose and gbrain pglite-repair --yes to repair in place. Full recovery ladder (repair → rebuild → engine switch) in docs/ENGINES.md — Troubleshooting: startup abort and docs/INSTALL.md.

gbrain import fails with expected N dimensions, not M? Run gbrain doctor. It will print the exact gbrain config set ... or gbrain retrieval-upgrade command to repair the mismatch. You should not need to delete ~/.gbrain. Fresh gbrain init --pglite auto-detects your embedding provider from API keys in your environment: set OPENAI_API_KEY (or ZEROENTROPY_API_KEY / VOYAGE_API_KEY) before running init, or pass --embedding-model <provider>:<model> explicitly. With multiple keys set, init fires an interactive picker. In non-TTY contexts (CI, Docker) with no keys, init exits 1 with a paste-ready setup hint; pass --no-embedding to defer setup until runtime. See docs/integrations/embedding-providers.md for the full provider matrix and docs/operations/headless-install.md for Docker/CI sequencing.

Hourly cron sync keeps timing out on a federated brain? Switch your cron to a per-source loop with shell timeout(1) doing the OS-level kill and gbrain self-terminating gracefully half-a-minute earlier:

gbrain sync --break-lock --all --max-age 1800
for src in $(gbrain sources list --json | jq -r '.[].id'); do
  timeout 600 gbrain sync --source "$src" --timeout 540 || true
done

When --timeout fires mid-import, gbrain sync exits 0 with status partial and last_commit UNCHANGED — the next run re-walks the same diff and content_hash short-circuits already-imported files. The --max-age 1800 first command self-heals any wedged-but-alive locks left by a hung previous run, keyed on the lock's last refresh time (NOT when it was acquired) so healthy long-running holders are safe by construction. Scope note: the extract + embed phases still run to completion once started; --timeout interrupts the import walk only.

Dream cycle silently losing wiki links on Supabase? The engine self-retries every bulk batch write (addLinksBatch / addTimelineEntriesBatch / upsertChunks) on Supavisor pooler blips, with a 12s worst-case wait that covers the full 5-10s circuit-breaker recovery window. gbrain doctor surfaces incidents via the batch_retry_health check (reads the last 24h of ~/.gbrain/audit/batch-retry-YYYY-Www.jsonl). To tune for an unusually slow pooler:

# Defaults: 3 retries, base 1s, max 10s, decorrelated jitter.
# Override per operator without a release:
export GBRAIN_BULK_MAX_RETRIES=5       # int >= 0; 0 disables retries
export GBRAIN_BULK_RETRY_BASE_MS=2000  # int > 0
export GBRAIN_BULK_RETRY_MAX_MS=15000  # int >= base

Bad values surface at gbrain doctor startup with a paste-ready fix (not at first-retry mid-cycle). PGLite-only installs pay zero cost — the retry wrap is engine-level, but PGLite has no pooler so retries never fire in practice.

Dream cycle losing ~150 link rows per run with 'No database connection: connect() has not been called' errors in the log? The retry layer self-heals on a nulled-out database singleton: a reconnect callback on withRetry rebuilds the connection between attempts, and PostgresEngine.batchRetry injects () => this.reconnect() so engine-level batch writes survive a mid-cycle disconnect by something else in the same process. gbrain capture also no longer trails a 'No database connection' stderr line from a background facts:absorb worker firing after CLI exit — op dispatch awaits getFactsQueue().drainPending({timeout: 1000}) before engine.disconnect(). To find which code path is still calling disconnect mid-process, run gbrain doctor --json | jq '.checks[] | select(.id=="batch_retry_health")'; the check surfaces the 24h disconnect-call count and the most-recent caller frame from the ~/.gbrain/audit/db-disconnect-YYYY-Www.jsonl audit.

gbrain brainstorm returning judge_failed: true with 0 scored ideas? Two historical bugs caused it, both fixed: the judge hard-coded a 4K-token output cap (any run past ~40 ideas truncated mid-JSON and the parser threw), and slash-form model ids (gbrain brainstorm --judge-model anthropic/claude-sonnet-4-6 --max-cost 5) failed with BudgetExhausted reason=no_pricing because pricing lookups only matched the colon form. Both shapes work now. No config change, no schema migration — gbrain upgrade is the whole fix.

gbrain reindex --markdown wiped your auto/dream/signal-detector tags? Upgrade — tag reconciliation is add-only now. Re-import and reindex --markdown ADD current frontmatter tags and never delete, so enrichment tags written to the DB (auto-tag, dream synthesize, signal-detector) survive a re-chunk. The reindex DB-only fallback also reconstructs the full markdown (frontmatter + body + timeline) before re-chunking, so a page with no on-disk source keeps its frontmatter, title, and timeline instead of getting overwritten with empty frontmatter. Trade-off: removing a tag from a page's frontmatter no longer removes it from the DB on the next sync (frontmatter-tag removal needs a provenance column, deferred).

gbrain sync wedges on a large brain (no progress, high CPU)? Three tools. First, name the stalling file:

GBRAIN_SYNC_TRACE=1 gbrain sync --no-pull --no-embed --yes

The last [sync] begin import: <path> line with no following completion is the file being processed when the hang hit. Second, if you suspect a schema-pack inference.regex with catastrophic backtracking, complete the sync with the pack disabled and re-run extraction later:

gbrain sync --no-schema-pack --no-pull --no-embed --yes

gbrain schema lint warns on the classic nested-quantifier ReDoS shapes ((a+)+, (a*)*, …) in pack regexes, and the runtime caps inference-regex input length (override via GBRAIN_MAX_REGEX_INPUT_CHARS). Third, on a PGLite brain, stop gbrain serve before a large sync — PGLite is single-writer and a live MCP server contends for the write lock. See docs/architecture/serve-sync-concurrency.md for the full triage.

gbrain init --migrate-only / a schema migration fails on Windows with getaddrinfo ENOTFOUND? Upgrade — schema bring-up now runs its phases in-process instead of spawning a child gbrain init --migrate-only per phase. The spawned child died on Windows + bun + Supabase pooler with a DNS-resolution failure even though the parent connected fine; running in-process removes the spawn entirely. The grandfather migration that used to hang 70+ minutes on an 80K-page PGLite brain also runs as a chunked bulk SQL pass now (keyed on the page PK, soft-delete-filtered, source-safe) and completes in seconds.

Docs

• docs/INSTALL.md — every install path, end to end
• docs/guides/bootstrap.md — the persistent-personal-agent bootstrap contract (interview, identity files, hooks, private repo, security posture, uninstall)
• docs/what-schemas-unlock.md — why schemas matter: 7 killer use cases, the structural argument for typed page kinds, the agent-co-curates pattern (v0.40.7.0)
• docs/schema-author-tutorial.md — 5-minute walkthrough: fork the bundled pack, add a custom type, backfill existing pages, prove the wiring via gbrain whoknows
• docs/architecture/ — system design, topologies, retrieval theory
• docs/guides/ — how-to runbooks (sub-agent routing, minion deployment, skill development, brain-first lookup, idea capture, diligence ingestion)
• docs/integrations/ — connecting external data sources (voice, email, calendar, embedding providers)
• docs/mcp/ — per-client MCP setup (Claude Desktop, Code, Cursor, ChatGPT, Perplexity, Cowork)
• docs/eval/ — eval framework, metric glossary, methodology
• docs/ethos/ — philosophy (thin harness, fat skills, markdown as recipes, origin story)
• AGENTS.md — entry point for non-Claude agents
• CLAUDE.md — entry point for Claude Code (deep operating context)
• CONTRIBUTING.md — contributor guide, test discipline, eval-capture mode
• SECURITY.md — OAuth threat model, hardening defaults

Contributing

Run bun run test for the fast loop, bun run verify for the pre-push gate, bun run ci:local to run the full Docker-backed CI stack locally. Detailed test discipline in CONTRIBUTING.md.

Community PRs are batched into release waves rather than merged one-by-one — see the "PR wave workflow" section in CLAUDE.md. Contributor attribution stays attached via Co-Authored-By: trailers. We credit every accepted contribution in CHANGELOG.md.

If you find a bug or want a feature: open an issue first. Quick fixes (typo, doc bug, obvious regression) can go straight to a PR. Anything touching schema, retrieval ranking, MCP protocol, or the security boundary needs a design discussion in the issue first.

License + credit

MIT. I built GBrain to run my OpenClaw and Hermes deployments — the production brain behind my AI agents.

Origin story: docs/ethos/ORIGIN.md.

Community PR contributors are credited in CHANGELOG.md per release. ZeroEntropy (@zeroentropy) for the embedding + reranker stack that ships as the default. Voyage AI for the asymmetric-encoding recipe template. Ramp Labs for the search quality improvements lineage.