Your AI agent is sensible however forgetful. Each new session begins from zero — no reminiscence of who you met, what you learn, what you determined final Tuesday. GBrain is an open-source repair for that. Constructed by Garry Tan (President and CEO of Y Combinator) to energy his personal OpenClaw and Hermes deployments, it’s a markdown-first, Postgres-backed information layer that ingests conferences, emails, tweets, and notes, then auto-wires a typed information graph on prime — with zero LLM requires the graph extraction. The manufacturing mind behind Garry’s precise brokers presently holds 146,646 pages, 24,585 individuals, 5,339 corporations, and 66 autonomous cron jobs. By itself benchmark (BrainBench, a 240-page rich-prose corpus), GBrain hits P@5 49.1% and R@5 97.9%, a +31.4-point P@5 lead over the identical codebase with the graph layer disabled.
This can be a hands-on tutorial. You’ll set up GBrain domestically, import a small notes folder, run an actual search, watch the information graph wire itself, and join it to Claude Code through MCP. About 20 minutes begin to end. All terminal outputs beneath had been captured from a reside set up of GBrain v0.38.2.0. The repository (MIT-licensed) lives at github.com/garrytan/gbrain.
What you’re constructing
By the top of the tutorial, you’ll have:
- An area
~/.gbrain/mind.pglitedatabase — embedded Postgres 17 (through WASM) with pgvector, zero server config. - A small “brain repo” of markdown notes about individuals, corporations, and ideas.
- A working hybrid-search CLI that mixes vector + BM25 key phrase + Reciprocal Rank Fusion (RRF), with a ZeroEntropy reranker on prime by default.
- A typed information graph (
works_at,based,invested_in,attended,advises,mentions) auto-extracted out of your notes. - An MCP server exposing 74 instruments so Claude Code, Cursor, and Windsurf can learn and write to the mind straight.
Stipulations
- macOS or Linux (Home windows customers: use WSL2).
- A code editor.
- Bun ≥ 1.3.10 (the runtime GBrain ships on; the repo’s
package deal.jsondeclares this because the minimal engine). We’ll set up it in Step 1. - An embedding API key from one of: ZeroEntropy (default), OpenAI, or Voyage. With out one, you possibly can nonetheless set up and run key phrase search, however
gbrain question(hybrid + vector) will return no outcomes. - Non-obligatory: an Anthropic API key for multi-query enlargement throughout search.
Step 1 — Set up Bun and GBrain
GBrain is written in TypeScript and runs on Bun. Set up it first:
curl -fsSL https://bun.sh/set up | bash
exec $SHELL # reload shell so `bun` is on PATH
bun --versionNow set up GBrain. As of v0.38, the canonical set up path is a single world Bun set up:
bun set up -g github:garrytan/gbrain
gbrain --version
# gbrain 0.38.2.0Step 2 — Initialize your mind
gbrain init --pglite provisions an area PGLite database in ~/.gbrain/. PGLite is full Postgres compiled to WASM — no server, no Docker, prepared in roughly two seconds.
For this tutorial we’ll defer the embedding supplier so you possibly can observe alongside with out an API key straight away — we’ll wire it up in Step 6 after we run hybrid search:
gbrain init --pglite --no-embedding(In the event you’d quite configure embeddings now, set certainly one of OPENAI_API_KEY, ZEROENTROPY_API_KEY, or VOYAGE_API_KEY in your surroundings earlier than operating plain gbrain init --pglite.)
Actual output captured from a recent set up (truncated for brevity — there are 81 migrations from schema v1 → v85):
Establishing native mind with PGLite (no server wanted)...
Schema model 1 → 85 (81 migration(s) pending)
[2] slugify_existing_pages...
[2] ✓ slugify_existing_pages
[3] unique_chunk_index...
[3] ✓ unique_chunk_index
...
Mind prepared at /residence/you/.gbrain/mind.pglite
0 pages. Engine: PGLite (native Postgres).You now have an empty mind. Verify:
gbrain stats
# Pages: 0
# Chunks: 0
# Embedded: 0
# Hyperlinks: 0
# Tags: 0
# Timeline: 0Step 3 — Create a tiny mind repo
The mind repo is only a listing of markdown information. Every file follows GBrain’s compiled reality + timeline sample: a present best-understanding part on prime, an append-only proof path beneath.
Necessary: wikilinks should use the total slug path (e.g., [[people/alice-chen]], not simply [[alice-chen]]) for the graph extractor to resolve them. This can be a actual gotcha — I examined each kinds; the quick type silently produces zero hyperlinks.
mkdir -p ~/my-brain/individuals ~/my-brain/corporations ~/my-brain/ideas
cd ~/my-brainCreate an individual web page:
cat > individuals/alice-chen.md An organization web page:
cat > corporations/acme-ai.md And an idea web page:
cat > ideas/inference-optimization.md Step 4 — Import the repo
gbrain import is idempotent (content-hash deduplicated). We’ll move --no-embed so this step is deterministic for readers who don’t have an embedding key set but — embeddings get backfilled in Step 6. Actual output:
gbrain import ~/my-brain/ --no-embed[gbrain phase] import.collect_files begin dir=/residence/you/my-brain/ technique=markdown
[gbrain phase] import.collect_files carried out 2ms information=3
Discovered 3 markdown information
[import.files] 3/3 (100%) imported=3 skipped=0 errors=0
Import full (0.3s):
3 pages imported
0 pages skipped (0 unchanged, 0 errors)
3 chunks createdVerify:
gbrain record
# corporations/acme-ai firm 2026-05-22 Acme AI
# ideas/inference-optimization idea 2026-05-22 Inference Optimization
# individuals/alice-chen particular person 2026-05-22 Alice ChenStep 5 — Wire the information graph
For a first-time import, run the hyperlink extractor explicitly to backfill the graph out of your wikilinks. That is pure regex + typed inference — zero LLM calls.
gbrain extract hyperlinks --source dbActual output:
[extract.links_db] 3/3 (100%) carried out
Hyperlinks: created 2 from 3 pages (db supply)
Executed: 2 hyperlinks, 0 timeline entries from 3 pagesTwo typed edges had been inferred from the wikilinks: alice-chen --works_at--> acme-ai (from “Founder and CEO of …”) and acme-ai --founded--> alice-chen (from “Founded by …”). The inference cascade fires so as: FOUNDED → INVESTED → ADVISES → WORKS_AT → MENTIONS. No mannequin within the loop.
Examine the graph straight:
gbrain graph-query individuals/alice-chen --depth 1
# [depth 0] individuals/alice-chen
# --works_at-> corporations/acme-ai (depth 1)gbrain backlinks corporations/acme-ai
# [
# {
# "from_slug": "people/alice-chen",
# "to_slug": "companies/acme-ai",
# "link_type": "works_at",
# "context": "Founder and CEO of [[companies/acme-ai]]...",
# "link_source": "markdown",
# ...
# }
# ]That is the distinction between vector search and structured retrieval. “Who works at Acme AI?” is now a one-hop typed-edge traversal, not a similarity rating. That structural channel is what drives the +31.4-point P@5 raise over the graph-disabled variant on BrainBench.
Step 6 — Run a search
GBrain ships two search verbs. gbrain search is keyword-only (BM25 on Postgres tsvector) and works with out embeddings:
gbrain search "inference"
# [0.3648] corporations/acme-ai -- YC W24 inference-optimization startup...
# [0.3648] individuals/alice-chen -- Founder and CEO of [[companies/acme-ai]]...gbrain question is the total hybrid pipeline: vector (HNSW on pgvector) + BM25 + Reciprocal Rank Fusion + non-compulsory multi-query enlargement (Anthropic Haiku) + an non-compulsory ZeroEntropy reranker. It wants embeddings, which we deferred in Step 2 — wire them up now:
# Set certainly one of: ZEROENTROPY_API_KEY (default), OPENAI_API_KEY, or VOYAGE_API_KEY
export OPENAI_API_KEY=sk-...
gbrain config set embedding_model openai:text-embedding-3-large
gbrain embed --all # one-time backfill in opposition to your embedding supplier
gbrain question "who works on small-model inference?"# Set certainly one of: ZEROENTROPY_API_KEY (default), OPENAI_API_KEY, or VOYAGE_API_KEY
export OPENAI_API_KEY=sk-...
gbrain config set embedding_model openai:text-embedding-3-large
gbrain embed --all # one-time backfill in opposition to your embedding supplier
gbrain question "who works on small-model inference?"Three search modes ship out of the field — conservative, balanced, tokenmax — bundling the fee/high quality knobs into one config key. Default is balanced with the ZeroEntropy reranker on. RRF formulation: rating = sum(1 / (60 + rank)).
Step 7 — Connect with Claude Code through MCP
The mind is extra helpful when an AI agent can learn and write to it straight. GBrain exposes 74 instruments over the Mannequin Context Protocol through stdio. The canonical setup is one command (not a hand-edited JSON file):
claude mcp add gbrain -- gbrain serveConfirm the set up:
claude mcp record
# gbrain stdio gbrain serveNow ask Claude Code one thing like “search the brain for inference optimization” and it’ll route by the search device and return your listed outcomes. The precise MCP device names are plain snake_case: get_page, put_page, delete_page, list_pages, search, question, add_link, get_backlinks, add_tag, and 65 extra.
Cursor and Windsurf use the usual MCP JSON config of their respective settings UIs. The server spec is similar:
{
"mcpServers": {
"gbrain": { "command": "gbrain", "args": ["serve"] }
}
}Claude Desktop makes use of claude_desktop_config.json for native stdio MCP servers with the identical JSON spec. Distant HTTP MCP servers should be added by Settings → Integrations with a bearer token. See docs/mcp/CLAUDE_DESKTOP.md within the repo for the GUI walkthrough.
If you need distant entry from any machine, swap stdio for HTTP:
gbrain serve --http --port 8787
# Bearer auth, default-deny CORS, two-bucket price restrict, per-request audit log.
# Postgres-only by design (PGLite is local-only).Step 8 — Let the mind run itself
GBrain ships an autopilot loop. As of v0.36.4, one command computes a dependency-ordered remediation plan, submits every step as a Minion job, re-checks the mind’s well being rating between steps, and refuses to spend previous your price cap:
gbrain physician --remediate --yes --target-score 90 --max-usd 5Or run it as a daemon:
gbrain autopilot --install # cron-driven, 5-minute tickWholesome brains sleep for 60 minutes between ticks. Unhealthy ones get the total in a single day cycle: sync, extract, embed, consolidate, synthesize. Three phases (synthesize, patterns, consolidate) are protected so an MCP-connected agent can’t silently burn API credit.
For ad-hoc background work, the Minions queue takes shell jobs and LLM subagent jobs aspect by aspect:
gbrain jobs submit sync --params '{}' --follow
gbrain jobs stats
gbrain jobs work --queue defaultOne PGLite caveat: gbrain jobs supervisor (the auto-restarting employee daemon) is Postgres-only. PGLite’s unique file lock blocks the separate employee course of — the CLI rejects with a transparent error if config.engine === 'pglite'. In the event you’re on PGLite, stick to inline --follow jobs for the tutorial, or run gbrain migrate --to supabase earlier than standing up a persistent employee.
Routing rule: deterministic work (pull tweets, parse JSON, write a web page) goes to Minions; judgment work (triage an inbox, assess precedence) goes to LLM sub-agents.
What simply occurred, in a single diagram
markdown information ──> PGLite + pgvector 43 expertise
(your repo, (hybrid retrieval + (HOW to make use of the mind;
supply of reality) typed graph) RESOLVER.md routes intent)
▲ │
└────────────── agent reads/writes ──────────┘The markdown repo is the system of document. GBrain is the retrieval + graph layer over it. The agent reads and writes by each, and people can at all times open any .md file and edit it straight — gbrain sync picks up the change.
The place to go subsequent
- One-line seize (new in v0.38):
gbrain seize "the thought I want to remember"lands straight ininbox/YYYY-MM-DD-. Additionally accepts--file,--stdin, and webhook ingestion throughgbrain serve --http /ingest. - Migrate to Supabase when your mind outgrows native (PGLite is nice as much as ~50K pages):
gbrain migrate --to supabase. - Ingest actual information with one of many recipes: voice (Twilio + OpenAI Realtime), electronic mail + calendar, 16 embedding suppliers, credential gateway.
- Run the benchmarks within the sibling repo gbrain-evals: BrainBench (artificial) and
gbrain eval longmemeval(the general public LongMemEval benchmark). - Creator your individual expertise. A talent is a fats markdown file that encodes a workflow — triggers, checks, high quality gate.
gbrain check-resolvablevalidates the talent tree for reachability / MECE / DRY.
The deeper wager behind GBrain is that skinny harness, fats expertise beats skinny expertise behind a fats agent. The runtime stays small; the intelligence lives in markdown information the agent reads at determination time. Every commit you make to your mind repo is everlasting context your agent inherits the following time it wakes up. The longer you run it, the smarter it will get.
Marktechpost’s Visible Explainer
Key Takeaways
- GBrain (v0.38.2.0) provides AI brokers a persistent, markdown-first reminiscence layer — constructed by Garry Tan to energy his personal OpenClaw/Hermes deployments holding 146,646 pages and 24,585 individuals.
- Set up runs domestically in ~half-hour on PGLite (Postgres 17 compiled to WASM, zero server) and scales to Supabase or self-hosted Postgres when wanted.
- Each wikilink is parsed by a regex inference cascade (
FOUNDED → INVESTED → ADVISES → WORKS_AT) that writes typed graph edges with zero LLM calls. - Hybrid search (vector + BM25 + RRF + ZeroEntropy reranker) hits P@5 49.1% / R@5 97.9% on BrainBench — a +31.4-point P@5 raise over the graph-disabled variant.
- Exposes 74 instruments over MCP — wire it into Claude Code with a single
claude mcp add gbrain -- gbrain serveand your agent can learn/write the mind straight.
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