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

Built by the President and CEO of Y Combinator to run his actual AI agents. The production brain powering his OpenClaw and Hermes deployments: 17,888 pages, 4,383 people, 723 companies, 21 cron jobs running autonomously, built in 12 days. The agent ingests meetings, emails, tweets, voice calls, and original ideas while you sleep. It enriches every person and company it encounters. It fixes its own citations and consolidates memory overnight. You wake up and the brain is smarter than when you went to bed.

The brain wires itself. Every page write extracts entity references and creates typed links (attended, works_at, invested_in, founded, advises) with zero LLM calls. Hybrid search. Self-wiring knowledge graph. Structured timeline. Backlink-boosted ranking. Ask "who works at Acme AI?" or "what did Bob invest in this quarter?" and get answers vector search alone can't reach. Benchmarked end-to-end: Recall@5 jumps from 83% to 95%, Precision@5 from 39% to 45%, +30 more correct answers in the agent's top-5 reads on a 240-page Opus-generated rich-prose corpus. Graph-only F1: 86.6% vs grep's 57.8% (+28.8 pts). Full report.

GBrain is those patterns, generalized. 26 skills. Install in 30 minutes. Your agent does the work. As Garry's 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.

GBrain is designed to be installed and operated by an AI agent. If you don't have one running yet:

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

Paste this into your agent:

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

That's it. The agent clones the repo, installs GBrain, sets up the brain, loads 26 skills, and configures recurring jobs. You answer a few questions about API keys. ~30 minutes.

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

3 results (hybrid search, 0.12s):

1. concepts/do-things-that-dont-scale (score: 0.94)
   PG's argument that unscalable effort teaches you what users want.
   [Source: paulgraham.com, 2013-07-01]

2. originals/founder-mode-observation (score: 0.87)
   Deep involvement isn't micromanagement if it expands the team's thinking.

3. concepts/build-something-people-want (score: 0.81)
   The YC motto. Connected to 12 other brain pages.

GBrain exposes 30+ MCP tools via stdio:

{
  "mcpServers": {
    "gbrain": { "command": "gbrain", "args": ["serve"] }
  }
}

Add to ~/.claude/server.json (Claude Code), Settings > MCP Servers (Cursor), or your client's MCP config.

ngrok http 8787 --url your-brain.ngrok.app
bun run src/commands/auth.ts create "claude-desktop"
claude mcp add gbrain -t http https://your-brain.ngrok.app/mcp -H "Authorization: Bearer TOKEN"

Per-client guides: docs/mcp/. ChatGPT requires OAuth 2.1 (not yet implemented).

GBrain ships 26 skills organized by skills/RESOLVER.md. The resolver tells your agent which skill to read for any task.

Skill files are code. They're the most powerful way to get knowledge work done. A skill file is a fat markdown document that encodes an entire workflow: when to fire, what to check, how to chain with other skills, what quality bar to enforce. The agent reads the skill and executes it. Skills can also call deterministic TypeScript code bundled in GBrain (search, import, embed, sync) for the parts that shouldn't be left to LLM judgment. Thin harness, fat skills: the intelligence lives in the skills, not the runtime.

Cross-cutting rules in skills/conventions/:

• quality.md ... citations, back-links, notability gate, source attribution
• brain-first.md ... 5-step lookup before any external API call
• model-routing.md ... which model for which task
• test-before-bulk.md ... test 3-5 items before any batch operation
• cross-modal.yaml ... review pairs and refusal routing chain

Signal arrives (meeting, email, tweet, link)
  -> Signal detector captures ideas + entities (parallel, never blocks)
  -> Brain-ops: check the brain first (gbrain search, gbrain get)
  -> Respond with full context
  -> Write: update brain pages with new information + citations
  -> Auto-link: typed relationships extracted on every write (zero LLM calls)
  -> Sync: gbrain indexes changes for next query

Every cycle adds knowledge. The agent enriches a person page after a meeting. Next time that person comes up, the agent already has context. The difference compounds daily.

The system gets smarter on its own. Entity enrichment auto-escalates: a person mentioned once gets a stub page (Tier 3). After 3 mentions across different sources, they get web + social enrichment (Tier 2). After a meeting or 8+ mentions, full pipeline (Tier 1). The brain learns who matters without being told. Deterministic classifiers improve over time via a fail-improve loop that logs every LLM fallback and generates better regex patterns from the failures. gbrain doctor shows the trajectory: "intent classifier: 87% deterministic, up from 40% in week 1."

"Prep me for my meeting with Jordan in 30 minutes" ... pulls dossier, shared history, recent activity, open threads

"What have I said about the relationship between shame and founder performance?" ... searches YOUR thinking, not the internet

A durable, Postgres-native job queue built into the brain. Every long-running agent task is now a job that survives gateway restarts, streams progress, gets paused / resumed / steered mid-flight, and shows up in gbrain jobs list. Zero infra beyond your existing brain.

Here's my personal OpenClaw deployment: one Render container. Supabase Postgres holding a 45,000-page brain. 19 cron jobs firing on schedule. Real gateway load from real daily work. The task: pull a month of my social posts from an external API and ingest them end-to-end into the brain as a structured page.

Under that 19-cron load, sub-agent spawn couldn't clear the 10-second gateway wall. Minions landed it in under a second for zero tokens. Scaling: 19,240 posts across 36 months, single bash loop, ~15 min total, $0.00. Sub-agents: ~9 min best case, ~$1.08 in tokens, ~40% spawn failure. Lab: durability ∞ (SIGKILL mid-flight, 10/10 rescued), throughput ~10× faster, fan-out ~21× with no failure wall, memory ~400× less.

Full benchmarks: production and lab.

Deterministic (same input → same steps → same output) → Minions Judgment (input requires assessment or decision) → Sub-agents

Pull posts, parse JSON, write a brain page, run a sync — deterministic. $0 tokens, survives restart, millisecond runtime. Triage the inbox, assess meeting priority, decide if a cold email deserves a reply — judgment. What sub-agents are actually good at. minion_mode: pain_triggered (the default) automates the routing.

The six daily pains — spawn storms, agents that stop responding, forgotten dispatches, gateway crashes mid-run, runaway grandchildren, debugging soup — all belonged to the "deterministic work through a reasoning model" mistake. Minions fixes them by not making that mistake: max_children cap, timeout_ms + AbortSignal, child_done inbox, full parent_job_id/depth/transcript per job, Postgres durability with stall detection, cascade cancel via recursive CTE. Plus idempotency keys, attachment validation, removeOnComplete, and gbrain jobs smoke that proves the install in half a second.

gbrain jobs smoke                        # verify install
gbrain jobs submit sync --params '{}'    # fire a background job
gbrain jobs stats                        # health dashboard
gbrain jobs work --concurrency 4         # start a worker (Postgres only)

Read skills/minion-orchestrator/SKILL.md for parent-child DAGs, fan-in collection, steering via inbox.

Minions is not incrementally better than sub-agents for background work. It's categorically different. 753ms vs gateway timeout. $0 vs tokens. 100% vs couldn't-spawn. If your agent does deterministic work on a schedule, it runs on Minions now.

Minions is canonical as of v0.11.1 — every gbrain upgrade runs the migration automatically (schema → smoke → prefs → host rewrites → env-aware autopilot install). If you ever want to verify manually or wire a cron into your morning briefing:

gbrain doctor                    # half-migrated state? prints loud banner + exits non-zero
gbrain skillpack-check --quiet    # exit 0/1/2 for pipeline gating
gbrain skillpack-check | jq       # full JSON: {healthy, summary, actions[], doctor, migrations}

If anything's off, actions[] tells you the exact command to run. For deeper troubleshooting: docs/guides/minions-fix.md.

Hermes and similar agent frameworks auto-create skills as a background behavior. Fine until you don't know what the agent shipped. Checklists decay. Tests drift. Resolver entries get stale. Six months later you've got an opaque pile of "skills" that nobody has read, nobody has tested, and nobody is sure still work.

GBrain ships the same capability. Except the human stays in the loop.

• /skillify turns raw code into a properly-skilled feature: SKILL.md + deterministic script + unit tests + integration tests + LLM evals + resolver trigger + resolver trigger eval + E2E smoke + brain filing. Ten items. Every one required.
• gbrain check-resolvable walks the whole skills tree: reachability, MECE overlap, DRY violations, gap detection, orphaned skills. Exits non-zero if anything is off.
• scripts/skillify-check.ts — machine-readable audit. --json for CI, --recent for last-7-days files.

You decide when and what. The tooling keeps the checklist honest.

Auto-generated skills are a liability the first time a behavior breaks. Was it the skill? The test? The resolver trigger? The eval? You don't know, because you never read it. Debugging a black box is pure guesswork.

Skillify makes the black box legible. Every skill in your tree has: a contract (SKILL.md), tests that exercise that contract, an eval that grades LLM output against a rubric, a resolver trigger the user actually types, and a test that confirms the trigger routes right. If something breaks, you know which layer to look at. If anything goes stale, check-resolvable says so.

In practice this combo produces zero orphaned skills, every feature with tests + evals + resolver triggers + evals of the triggers. Compounding quality instead of compounding entropy.

# Audit a feature's skill completeness (10-item checklist)
bun run scripts/skillify-check.ts src/commands/publish.ts

# In CI: fail the build when a new feature isn't properly skilled
bun run scripts/skillify-check.ts --json --recent

# Validate the whole skills tree before shipping
gbrain check-resolvable

Skillify is not a nice-to-have. It's the piece that makes the skills tree survive six months of compounding work. Read skills/skillify/SKILL.md for the full 10-item checklist and the anti-patterns it catches.

GBrain ships integration recipes that your agent sets up for you. Each recipe tells the agent what credentials to ask for, how to validate, and what cron to register.

Data research recipes extract structured data from email into tracked brain pages. Built-in recipes for investor updates (MRR, ARR, runway, headcount), expense tracking, and company metrics. Create your own with gbrain research init.

Run gbrain integrations to see status.

GStack is the engine. GBrain is the mod.

• GStack = coding skills (ship, review, QA, investigate, office-hours, retro). 70,000+ stars, 30,000 developers per day. When your agent codes on itself, it uses GStack.
• GBrain = everything-else skills (brain ops, signal detection, ingestion, enrichment, cron, reports, identity). When your agent remembers, thinks, and operates, it uses GBrain.
• hosts/gbrain.ts = the bridge. Tells GStack's coding skills to check the brain before coding.

gbrain init detects if GStack is installed and reports mod status. If GStack isn't there, it tells you how to get it.

┌──────────────────┐    ┌───────────────┐    ┌──────────────────┐
│   Brain Repo     │    │    GBrain     │    │    AI Agent      │
│   (git)          │    │  (retrieval)  │    │  (read/write)    │
│                  │    │               │    │                  │
│  markdown files  │───>│  Postgres +   │<──>│  26 skills       │
│  = source of     │    │  pgvector     │    │  define HOW to   │
│    truth         │    │               │    │  use the brain   │
│                  │<───│  hybrid       │    │                  │
│  human can       │    │  search       │    │  RESOLVER.md     │
│  always read     │    │  (vector +    │    │  routes intent   │
│  & edit          │    │   keyword +   │    │  to skill        │
│                  │    │   RRF)        │    │                  │
└──────────────────┘    └───────────────┘    └──────────────────┘

The repo is the system of record. GBrain is the retrieval layer. The agent reads and writes through both. Human always wins... edit any markdown file and gbrain sync picks up the changes.

Every page follows the compiled truth + timeline pattern:

---
type: concept
title: Do Things That Don't Scale
tags: [startups, growth, pg-essay]
---

Paul Graham's argument that startups should do unscalable things early on.
The key insight: the unscalable effort teaches you what users actually
want, which you can't learn any other way.

---

- 2013-07-01: Published on paulgraham.com
- 2024-11-15: Referenced in batch W25 kickoff talk

Above the ---: compiled truth. Your current best understanding. Gets rewritten when new evidence changes the picture. Below: timeline. Append-only evidence trail. Never edited, only added to.

Pages aren't just text. Every mention of a person, company, or concept becomes a typed link in a structured graph. The brain wires itself.

Write a meeting page mentioning Alice and Acme AI
  -> Auto-link extracts entity refs from content (zero LLM calls)
  -> Infers types: meeting page + person ref => `attended`
                   "CEO of X" pattern        => `works_at`
                   "invested in"             => `invested_in`
                   "advises", "advisor"      => `advises`
                   "founded", "co-founded"   => `founded`
  -> Reconciles stale links: edits remove links no longer in content
  -> Backlinks rank well-connected entities higher in search

gbrain graph-query people/alice --type attended --depth 2
# returns who Alice met with, transitively

The graph powers questions vector search can't: "who works at Acme AI?", "what has Bob invested in?", "find the connection between Alice and Carol". Backfill an existing brain in one command:

gbrain extract links --source db        # wire up the existing 29K pages
gbrain extract timeline --source db     # extract dated events from markdown timelines

Then ask graph questions or watch the search ranking improve. Benchmarked: Recall@5 jumps from 83% to 95%, Precision@5 from 39% to 45%, +30 more correct answers in the agent's top-5 reads on a 240-page Opus-generated rich-prose corpus. Graph-only F1 hits 86.6% vs grep's 57.8% (+28.8 pts). See docs/benchmarks/2026-04-18-brainbench-v1.md.

Hybrid search: vector + keyword + RRF fusion + multi-query expansion + 4-layer dedup.

Query
  -> Intent classifier (entity? temporal? event? general?)
  -> Multi-query expansion (Claude Haiku)
  -> Vector search (HNSW cosine) + Keyword search (tsvector)
  -> RRF fusion: score = sum(1/(60 + rank))
  -> Cosine re-scoring + compiled truth boost
  -> 4-layer dedup + compiled truth guarantee
  -> Results

Keyword alone misses conceptual matches. Vector alone misses exact phrases. RRF gets both. Search quality is benchmarked and reproducible: gbrain eval --qrels queries.json measures P@k, Recall@k, MRR, and nDCG@k. A/B test config changes before deploying them.

The brain isn't one trick. Every retrieval question goes through ~20 deterministic techniques layered together. No single one is magic; the win comes from stacking them so each layer covers what the others miss.

Question
  │
  ├─ INGESTION (every put_page)
  │    ├─ Recursive markdown chunking (or semantic / LLM-guided)
  │    ├─ Embedding cache invalidation on edit
  │    └─ Idempotent imports (content-hash dedup)
  │
  ├─ GRAPH EXTRACTION (auto-link post-hook, zero LLM)
  │    ├─ Entity-ref regex (markdown links + bare slugs)
  │    ├─ Code-fence stripping (no false-positive slugs in code blocks)
  │    ├─ Typed inference cascade (FOUNDED → INVESTED → ADVISES → WORKS_AT)
  │    ├─ Page-role priors (partner-bio language → invested_in)
  │    ├─ Within-page dedup (same target collapses to one link)
  │    ├─ Stale-link reconciliation (edits remove dropped refs)
  │    └─ Multi-type link constraint (same person can works_at AND advises)
  │
  ├─ SEARCH PIPELINE (every query)
  │    ├─ Intent classifier (entity / temporal / event / general — auto-routes)
  │    ├─ Multi-query expansion (Haiku rephrases the question 3 ways)
  │    ├─ Vector search (HNSW cosine over OpenAI embeddings)
  │    ├─ Keyword search (Postgres tsvector + websearch_to_tsquery)
  │    ├─ Reciprocal Rank Fusion (score = sum 1/(60+rank) across both)
  │    ├─ Cosine re-scoring (re-rank chunks against actual query embedding)
  │    ├─ Compiled-truth boost (assessments outrank timeline noise)
  │    ├─ Backlink boost (well-connected entities rank higher)
  │    └─ Source-aware dedup (one CT chunk per page guaranteed)
  │
  ├─ GRAPH TRAVERSAL (relational queries)
  │    ├─ Recursive CTE with cycle prevention (visited-array check)
  │    ├─ Type-filtered edges (--type works_at, attended, etc.)
  │    ├─ Direction control (in / out / both)
  │    └─ Depth-capped (≤10 for remote MCP; DoS prevention)
  │
  └─ AGENT WORKFLOW (graph-confident hybrid)
       ├─ Graph-query first (high-precision typed answers)
       ├─ Grep fallback when graph returns nothing
       └─ Graph hits ranked first in top-K (better P@K and R@K)

End-to-end on the BrainBench v1 corpus (240 rich-prose pages, before/after PR #188):

Plus 5 orthogonal capability checks (identity resolution, temporal queries, performance at 10K-page scale, robustness to malformed input, MCP operation contract). All pass. Full report.

The point: each technique handles a class of inputs the others miss. Vector search misses exact slug refs; keyword catches them. Keyword misses conceptual matches; vector catches them. RRF picks the best of both. Compiled-truth boost keeps assessments above timeline noise. Auto-link extraction wires the graph that lets backlink boost rank well-connected entities higher. Graph traversal answers questions search alone can't reach. The agent picks graph-first for precision and falls back to keyword for recall. All deterministic, all in concert, all measured.

Call a phone number. Your AI answers. It knows who's calling, pulls their full context from the brain, and responds like someone who actually knows your world. When the call ends, a brain page appears with the transcript, entity detection, and cross-references.

See it in action

The voice recipe ships with GBrain: Voice-to-Brain. WebRTC works in a browser tab with zero setup. A real phone number is optional.

CLI / MCP Server
     (thin wrappers, identical operations)
              |
      BrainEngine interface (pluggable)
              |
     +--------+--------+
     |                  |
PGLiteEngine       PostgresEngine
  (default)          (Supabase)
     |                  |
~/.gbrain/           Supabase Pro ($25/mo)
brain.pglite         Postgres + pgvector
embedded PG 17.5

     gbrain migrate --to supabase|pglite
         (bidirectional migration)

PGLite: embedded Postgres, no server, zero config. When your brain outgrows local (1000+ files, multi-device), gbrain migrate --to supabase moves everything.

Brain repos accumulate binaries. GBrain has a three-stage migration:

gbrain files mirror <dir>       # copy to cloud, local untouched
gbrain files redirect <dir>     # replace local with .redirect pointers
gbrain files clean <dir>        # remove pointers, cloud only
gbrain files restore <dir>      # download everything back (undo)

Storage backends: S3-compatible (AWS, R2, MinIO), Supabase Storage, or local.

SETUP
  gbrain init [--supabase|--url]        Create brain (PGLite default)
  gbrain migrate --to supabase|pglite   Bidirectional engine migration
  gbrain upgrade                        Self-update with feature discovery

PAGES
  gbrain get <slug>                     Read a page (fuzzy slug matching)
  gbrain put <slug> [< file.md]         Write/update (auto-versions)
  gbrain delete <slug>                  Delete a page
  gbrain list [--type T] [--tag T]      List with filters

SEARCH
  gbrain search <query>                 Keyword search (tsvector)
  gbrain query <question>              Hybrid search (vector + keyword + RRF)

IMPORT
  gbrain import <dir> [--no-embed]      Import markdown (idempotent)
  gbrain sync [--repo <path>]           Git-to-brain incremental sync
  gbrain export [--dir ./out/]          Export to markdown

FILES
  gbrain files list|upload|sync|verify  File storage operations

EMBEDDINGS
  gbrain embed [<slug>|--all|--stale]   Generate/refresh embeddings

LINKS + GRAPH
  gbrain link|unlink|backlinks          Cross-reference management
  gbrain extract links|timeline|all     Batch backfill from existing pages
                                        (--source db|fs, --type, --since, --dry-run)
  gbrain graph-query <slug>             Typed traversal (--type T --depth N
                                        --direction in|out|both)

JOBS (Minions)
  gbrain jobs submit <name> [--params JSON] [--follow]  Submit a background job
  gbrain jobs list [--status S] [--queue Q]             List jobs with filters
  gbrain jobs get|cancel|retry|delete <id>              Manage job lifecycle
  gbrain jobs prune [--older-than 30d]                  Clean completed/dead jobs
  gbrain jobs stats                                     Job health dashboard
  gbrain jobs smoke                                     One-command health check
  gbrain jobs work [--queue Q] [--concurrency N]        Start worker daemon

ADMIN
  gbrain doctor [--json] [--fast]       Health checks (resolver, skills, DB, embeddings)
  gbrain doctor --fix                   Auto-fix resolver issues
  gbrain stats                          Brain statistics
  gbrain serve                          MCP server (stdio)
  gbrain integrations                   Integration recipe dashboard
  gbrain check-backlinks check|fix      Back-link enforcement
  gbrain lint [--fix]                   LLM artifact detection
  gbrain repair-jsonb [--dry-run]       Repair v0.12.0 double-encoded JSONB (Postgres)
  gbrain orphans [--json] [--count]     Find pages with zero inbound wikilinks
  gbrain transcribe <audio>             Transcribe audio (Groq Whisper)
  gbrain research init <name>           Scaffold a data-research recipe
  gbrain research list                  Show available recipes

Run gbrain --help for the full reference.

I was setting up my OpenClaw agent and started a markdown brain repo. One page per person, one page per company, compiled truth on top, timeline on the bottom. Within a week: 10,000+ files, 3,000+ people, 13 years of calendar data, 280+ meeting transcripts, 300+ captured ideas.

The agent runs while I sleep. The dream cycle scans every conversation, enriches missing entities, fixes broken citations, consolidates memory. I wake up and the brain is smarter than when I went to sleep.

The skills in this repo are those patterns, generalized. What took 11 days to build by hand ships as a mod you install in 30 minutes.

For agents:

• skills/RESOLVER.md ... Start here. The skill dispatcher.
• Individual skill files ... 25 standalone instruction sets
• GBRAIN_SKILLPACK.md ... Legacy reference architecture
• Getting Data In ... Integration recipes and data flow
• GBRAIN_VERIFY.md ... Installation verification

For humans:

• GBRAIN_RECOMMENDED_SCHEMA.md ... Brain repo directory structure
• Thin Harness, Fat Skills ... Architecture philosophy
• ENGINES.md ... Pluggable engine interface

Reference:

• GBRAIN_V0.md ... Full product spec
• CHANGELOG.md ... Version history

Benchmarks:

• BrainBench v1 (PR #188) ... single comprehensive before/after report on a 240-page Opus-generated corpus. 7 categories: relational queries, identity resolution, temporal queries, performance, robustness, MCP contract.

See CONTRIBUTING.md. Run bun test for unit tests. E2E tests: spin up Postgres with pgvector, run bun run test:e2e, tear down.

PRs welcome for: new enrichment APIs, performance optimizations, additional engine backends, new skills following the conformance standard in skills/skill-creator/SKILL.md.

MIT