setup

name	setup
description	Set up GBrain with auto-provision Supabase or PGLite, AGENTS.md injection, first import
triggers	["set up gbrain","initialize brain","gbrain setup"]
tools	["get_stats","get_health","sync_brain","put_page"]
mutating	true

Setup GBrain

Set up GBrain from scratch. Target: working brain in under 5 minutes.

Contract

• Setup completes with a working brain verified by gbrain doctor --json (all checks OK).
• The brain-first lookup protocol is injected into the project's AGENTS.md or equivalent.
• Live sync is configured and verified (a test change pushed and found via search).
• Schema state is tracked in ~/.gbrain/update-state.json so future upgrades know what the user adopted or declined.
• No Supabase anon key is requested; GBrain uses only the database connection string.

Install (if not already installed)

bun add github:garrytan/gbrain

How GBrain connects

GBrain connects directly to Postgres over the wire protocol. NOT through the Supabase REST API. You need the database connection string (a postgresql:// URI), not the project URL or anon key. The password is embedded in the connection string.

Use the Shared Pooler connection string (port 6543), not the direct connection (port 5432). The direct hostname resolves to IPv6 only, which many environments can't reach. Find it: go to the project, click Get Connected next to the project URL, then Direct Connection String > Session Pooler, and copy the Shared Pooler connection string.

Do NOT ask for the Supabase anon key. GBrain doesn't use it.

Why Supabase

Supabase gives you managed Postgres + pgvector (vector search built in) for $25/mo:

• 8GB database + 100GB storage on Pro tier
• No server to manage, automatic backups, dashboard for debugging
• pgvector pre-installed, just works
• Alternative: any Postgres with pgvector extension (self-hosted, Neon, Railway, etc.)

Prerequisites

• A Supabase account (Pro tier recommended, $25/mo) OR any Postgres with pgvector
• An OpenAI API key (for semantic search embeddings, ~$4-5 for 7,500 pages)
• A git-backed markdown knowledge base (or start fresh)

Available init options

• gbrain init --supabase -- interactive wizard (prompts for connection string)
• gbrain init --url <connection_string> -- direct, no prompts
• gbrain init --non-interactive --url <connection_string> -- for scripts/agents
• gbrain doctor --json -- health check after init

There is no --local, --sqlite, or offline mode. GBrain requires Postgres + pgvector (local PGLite or remote Supabase / self-hosted).

Phase A.5: Choose Topology (run BEFORE Phase A)

GBrain supports three deployment shapes. Pick the right one before installing, because picking wrong creates contention or duplicate work that's painful to unwind. Read docs/architecture/topologies.md for the full picture; the short version:

Ask the user this BEFORE running gbrain init:

"Three deployment shapes:

1. Single brain (default) — one machine, one DB, one agent. Pick this if unsure.
2. Cross-machine thin client — your brain lives on another machine (e.g. brain-host) running gbrain serve --http, and this install just calls it over MCP. No local DB on this machine.
3. Per-worktree code + shared remote artifacts — Conductor users with multiple worktrees indexing the same code repo. Each worktree owns its own code engine; artifacts live on a shared remote brain. For code engines, configure Voyage's code-tuned model: gbrain init --pglite --embedding-model voyage:voyage-code-3 --embedding-dimensions 1024 (full guidance in docs/architecture/topologies.md Topology 3).

Which fits?"

If the user picks 1 (single brain) — proceed to Phase A

Continue with the existing gbrain init --supabase / --pglite setup below.

If the user picks 2 (cross-machine thin client)

1. Confirm a host already exists. Ask: "Is the remote gbrain serve --http already running on the host machine?" If no, the user needs to set up the host first (Phases A-C on the host, then gbrain serve --http). Don't try to run init on this machine until the host is up.

2. Get OAuth credentials from the host operator. Ask the user to run on the host:

   gbrain auth register-client <name> \
     --grant-types client_credentials \
     --scopes read,write,admin
   

   The admin scope is required because gbrain remote ping and gbrain remote doctor (Tier B convenience commands) call MCP ops with admin scope. read,write alone breaks ping/doctor.

3. Run thin-client init on this machine:

   gbrain init --mcp-only \
     --issuer-url https://<host>:<port> \
     --mcp-url https://<host>:<port>/mcp \
     --oauth-client-id <id> \
     --oauth-client-secret <secret>
   

   Or set GBRAIN_REMOTE_CLIENT_SECRET env var instead of the flag (preferred for headless / scripted setup). Pre-flight runs three smoke probes; any failure surfaces an actionable error.

4. Configure your agent's MCP client. Add a server entry pointing at <mcp_url> with the bearer token. See docs/mcp/CLAUDE_DESKTOP.md, docs/mcp/CLAUDE_CODE.md, etc. for per-client snippets.

5. Verify with gbrain doctor. Thin-client doctor runs OAuth discovery, token round-trip, and MCP smoke against the host. Should report mode: thin-client with all checks green.

6. Skip Phases B, C, C.5, and H entirely. They're for local engines. The host's autopilot handles sync/extract/embed. Thin clients consume only.

7. Continue to Phase D (brain-first lookup). It works identically over MCP — the agent uses the same brain-ops skill to query/search/get_page, they just round-trip through the host's gbrain serve --http.

If init reports "thin-client config already present", a previous setup already configured this machine. Refusing without --force is the correct behavior; either accept the existing config or pass --force to refresh.

If the user picks 3 (split-engine per-worktree)

This shape requires per-worktree wiring that gstack handles, not gbrain directly. gbrain's role is just to run a local engine when GBRAIN_HOME is set — that already works.

Point the user at docs/architecture/topologies.md (the Topology 3 section) for the wiring recipe, then continue with Phase A as normal — gbrain init on this machine sets up the artifact brain (the "default" home). The per-worktree code engines are configured per-worktree as gstack creates them.

If the user has a remote artifact brain (Topology 2 + 3 combined), follow the thin-client setup above for the artifact brain instead of Phase A.

Phase A: Supabase Setup (recommended)

Guide the user through creating a Supabase project:

1. "Go to https://supabase.com and sign up or log in."
2. "Click 'New Project' in the top left."
   • Name: gbrain
   • Region: pick the one closest to you
   • Database password: generate a strong one and save it
3. "Wait about 2 minutes for the project to initialize."
4. "Find the connection string: go to your project, click Get Connected next to the project URL, then Direct Connection String > Session Pooler, and copy the Shared Pooler connection string (port 6543)."
5. Initialize gbrain:

   gbrain init --non-interactive --url "postgresql://postgres.[ref]:[password]@aws-0-[region].pooler.supabase.com:6543/postgres"
   

6. Verify: gbrain doctor --json

OpenClaw/Hermes agent instructions: Store the Supabase access token in your persistent env as SUPABASE_ACCESS_TOKEN. gbrain doesn't store it, you need it for future gbrain doctor runs. Generate at: https://supabase.com/dashboard/account/tokens

Phase B: BYO Postgres (alternative)

If the user already has Postgres with pgvector:

1. Get the connection string from the user.
2. Run: gbrain init --non-interactive --url "<connection_string>"
3. Verify: gbrain doctor --json

If the connection fails with ECONNREFUSED and the URL contains supabase.co, the user probably pasted the direct connection (IPv6 only). Guide them to the Session pooler string instead (see Phase A step 4).

Phase C: First Import

1. Discover markdown repos. Scan the environment for git repos with markdown content.

echo "=== GBrain Environment Discovery ==="
for dir in /data/* ~/git/* ~/Documents/* 2>/dev/null; do
  if [ -d "$dir/.git" ]; then
    md_count=$(find "$dir" -name "*.md" -not -path "*/node_modules/*" -not -path "*/.git/*" 2>/dev/null | wc -l | tr -d ' ')
    if [ "$md_count" -gt 10 ]; then
      total_size=$(du -sh "$dir" 2>/dev/null | cut -f1)
      echo "  $dir ($total_size, $md_count .md files)"
    fi
  fi
done
echo "=== Discovery Complete ==="

2. Import the best candidate. For large imports (>1000 files), use nohup to survive session timeouts:

   nohup gbrain import <dir> --no-embed --workers 4 > /tmp/gbrain-import.log 2>&1 &
   

   Then check progress: tail -1 /tmp/gbrain-import.log

   For smaller imports, run directly:

   gbrain import <dir> --no-embed
   

3. Prove search works. Pick a semantic query based on what you imported:

   gbrain search "<topic from the imported data>"
   

   This is the magical moment: the user sees search finding things grep couldn't.

4. Start embeddings. Refresh stale embeddings (runs in background). Keyword search works NOW, semantic search improves as embeddings complete.

5. Backfill the knowledge graph. Populate typed links and structured timeline from the imported pages. Auto-link maintains both going forward, but historical pages need a one-time backfill.

   gbrain extract links --source db --dry-run | head -20    # preview
   gbrain extract links --source db                         # commit
   gbrain extract timeline --source db                      # dated events
   gbrain stats                                             # verify links > 0
   

   After this, gbrain graph-query <slug> --depth 2 works and search ranks well-connected entities higher. Idempotent — safe to re-run anytime. Supports --since YYYY-MM-DD for incremental runs on huge brains.

   Skip if Phase C imported zero pages (auto-link handles new writes).

6. Offer file migration. If the repo has binary files (.raw/ directories with images, PDFs, audio):

   "You have N binary files (X GB) in your brain repo. Want to move them to cloud storage? Your git repo will drop from X GB to Y MB. All links keep working."

   If the user agrees, configure storage and run migration:

   # Configure storage backend (Supabase Storage recommended)
   gbrain config set storage.backend supabase
   gbrain config set storage.bucket brain-files
   gbrain config set storage.projectUrl <supabase-url>
   gbrain config set storage.serviceRoleKey <service-role-key>
   
   # Migrate binary files to cloud (3-step lifecycle)
   gbrain files mirror <brain-dir>       # Upload to cloud, keep local
   gbrain files redirect <brain-dir>     # Replace local with .redirect.yaml pointers
   # (optional) gbrain files clean <brain-dir> --yes   # Remove pointers too
   

   After migration, gbrain files upload-raw handles new files automatically: small text/PDFs stay in git, large/media files go to cloud with .redirect.yaml pointers. Files >= 100 MB use TUS resumable upload for reliability.

If no markdown repos are found, create a starter brain with a few template pages (a person page, a company page, a concept page) from docs/GBRAIN_RECOMMENDED_SCHEMA.md.

Phase C.5: One-step autopilot + Minions install (v0.11.1+)

Run the migration runner once, then install autopilot. Two commands, done:

gbrain apply-migrations --yes       # applies any pending migrations; idempotent on healthy installs
gbrain autopilot --install          # supervises itself + forks the Minions worker; env-aware

What gbrain autopilot --install does:

• On macOS: writes a launchd plist at ~/Library/LaunchAgents/com.gbrain.autopilot.plist.
• On Linux with systemd: writes ~/.config/systemd/user/gbrain-autopilot.service with Restart=on-failure.
• On ephemeral containers (Render / Railway / Fly / Docker): writes ~/.gbrain/start-autopilot.sh and prints the one-line your agent's bootstrap should source to launch autopilot on every container start. Auto-injects into OpenClaw's hooks/bootstrap/ensure-services.sh if detected (use --no-inject to opt out).
• On Linux without systemd: installs a crontab entry (every 5 min).

Autopilot then supervises the Minions worker as a child process. Users get sync + extract + embed + backlinks + durable Postgres-backed job processing from ONE install step. No separate gbrain jobs work daemon to manage.

On PGLite, autopilot runs inline (PGLite's exclusive file lock blocks a separate worker process). Everything else still works.

If apply-migrations prints "N host-specific items need your agent's attention," read ~/.gbrain/migrations/pending-host-work.jsonl + walk skills/migrations/v0.11.0.md + docs/guides/plugin-handlers.md to register host-specific handlers. Re-run apply-migrations after each batch.

Phase D: Brain-First Lookup Protocol

Inject the brain-first lookup protocol into the project's AGENTS.md (or equivalent). This replaces grep-based knowledge lookups with structured gbrain queries.

BEFORE (grep) vs AFTER (gbrain)

Task	Before (grep)	After (gbrain)
Find a person	grep -r "Pedro" brain/	gbrain search "Pedro"
Understand a topic	grep -rl "deal" brain/ | head -5 && cat ...	gbrain query "what's the status of the deal"
Read a known page	cat brain/people/pedro.md	gbrain get people/pedro
Find connections	grep -rl "Brex" brain/ | xargs grep "Pedro"	gbrain query "Pedro Brex relationship"

Lookup sequence (MANDATORY for every entity question)

1. gbrain search "name" -- keyword match, fast, works without embeddings
2. gbrain query "what do we know about name" -- hybrid search, needs embeddings
3. gbrain get <slug> -- direct page read when you know the slug from steps 1-2
4. grep fallback -- only if gbrain returns zero results AND the file may exist outside the indexed brain

Stop at the first step that gives you what you need. Most lookups resolve at step 1.

Sync-after-write rule

After creating or updating any brain page in the repo, sync immediately so the index stays current:

gbrain sync --no-pull --no-embed

This indexes new/changed files without pulling from git or regenerating embeddings. Embeddings can be refreshed later in batch (gbrain embed --stale).

gbrain vs memory_search

Layer	What it stores	When to use
gbrain	World knowledge: people, companies, deals, meetings, concepts, media	"Who is Pedro?", "What happened at the board meeting?"
memory_search	Agent operational state: preferences, decisions, session context	"How does the user like formatting?", "What did we decide about X?"

Both should be checked. gbrain for facts about the world. memory_search for how the agent should behave.

Phase E: Load the Production Agent Guide

Read docs/GBRAIN_SKILLPACK.md. This is the reference architecture for how a production agent uses gbrain: the brain-agent loop, entity detection, enrichment pipeline, meeting ingestion, cron schedules, and the five operational disciplines.

Inject the key patterns into the agent's system context or AGENTS.md:

1. Brain-agent loop (Section 2): read before responding, write after learning
2. Entity detection (Section 3): spawn on every message, capture people/companies/ideas
3. Source attribution (Section 7): every fact needs [Source: ...]

Convention: See skills/conventions/quality.md for Iron Law back-linking.

Tell the user: "The production agent guide is at docs/GBRAIN_SKILLPACK.md. It covers the brain-agent loop, entity detection, enrichment, meeting ingestion, and cron schedules. Read it when you're ready to go from 'search works' to 'the brain maintains itself.'"

Phase F: Health Check

Run gbrain doctor --json and report the results. Every check should be OK. If any check fails, the doctor output tells you exactly what's wrong and how to fix it.

Error Recovery

If any gbrain command fails, run gbrain doctor --json first. Report the full output. It checks connection, pgvector, RLS, schema version, and embeddings.

What You See	Why	Fix
Connection refused	Supabase project paused, IPv6, or wrong URL	Use Session pooler (port 6543), or supabase.com/dashboard > Restore
Password authentication failed	Wrong password	Project Settings > Database > Reset password
pgvector not available	Extension not enabled	Run CREATE EXTENSION vector; in SQL Editor
OpenAI key invalid	Expired or wrong key	platform.openai.com/api-keys > Create new
No pages found	Query before import	Import files into gbrain first
RLS not enabled	Security gap	Run gbrain init again (auto-enables RLS)

Phase G: Auto-Update Check (if not already configured)

If the user's install did NOT include setting up auto-update checks (e.g., they used the manual install path or an older version of the OpenClaw/Hermes paste), offer it:

"Would you like daily GBrain update checks? I'll let you know when there's a new version worth upgrading to — including new skills and schema recommendations. You'll always be asked before anything is installed."

If they agree:

1. Test: gbrain check-update --json
2. Register daily cron (see GBRAIN_SKILLPACK.md Section 17)

If already configured or user declines, skip.

Phase H: Live Sync Setup (MUST ADD)

The brain repo is the source of truth. If sync doesn't run automatically, the vector DB falls behind and gbrain returns stale answers. This phase is not optional.

Read docs/GBRAIN_SKILLPACK.md Section 18 for the full reference. Key points:

1. Check the connection pooler first. Sync uses transactions on every import. If DATABASE_URL uses Supabase's Transaction mode pooler, sync will throw .begin() is not a function and silently skip most pages. Verify the connection string uses Session mode (port 6543, Session mode) or direct (port 5432).

2. Set up automatic sync. Choose the approach that fits your environment:

   • Cron (recommended for agents): register a cron every 5-30 minutes: gbrain sync --repo /data/brain && gbrain embed --stale
   • Watch mode: gbrain sync --watch --repo /data/brain under a process manager. Pair with a cron fallback (watch exits after 5 consecutive failures).
   • Webhook or git hook: if available in your environment.

3. Verify sync works. Don't just check that the command ran. Check that it worked:

   • gbrain stats should show page count close to syncable file count in the repo.
   • If page count is way too low, the pooler bug is silently skipping pages.
   • Push a test change and confirm it appears in gbrain search.

4. Chain sync + embed. Always run both: gbrain sync --repo <path> && gbrain embed --stale. For small syncs, embeddings are generated inline. The embed --stale is a safety net for any stale chunks.

Tell the user: "Live sync is configured. The brain will stay current automatically. I'll verify it's working in the next phase."

Phase I: Full Verification

Run the full verification runbook to confirm the entire installation is working.

1. Read docs/GBRAIN_VERIFY.md
2. Execute each check in order
3. Report results to the user
4. Fix any failures before declaring setup complete

Every check in the runbook should pass. The most important one is check 4 (live sync actually works): push a change, wait for sync, search for the corrected text. "Sync ran" is not the same as "sync worked."

Tell the user: "I've verified the full GBrain installation. Here's the status of each check: [list results]. Everything is working / [specific item] needs attention."

If already configured or user declines, skip.

Phase J: Cold Start — Populate Your Brain (AUTOMATIC)

Setup is done. The brain works. But it's empty. This is the most important moment — an empty brain is useless. Transition directly to the cold-start skill to fill it with the user's actual data.

Do not end setup without offering cold-start. The user just invested 15+ minutes in setup. The payoff is seeing their brain come alive with their own data. Stopping here is like installing a phone and never adding contacts.

Present this immediately after verification passes:

"✅ GBrain is set up and verified. Now let's fill it with your data.

I can connect your Google services (contacts, calendar, email), import your existing notes, pull in conversations from ChatGPT/Claude, and archive your tweets — all in one session. Each step is optional.

Ready to populate your brain?"

If the user says yes (or anything affirmative): → Load and execute skills/cold-start/SKILL.md immediately. Do not just print a reference — actually run the cold-start skill.

If the user says no or wants to stop: → Record in ~/.gbrain/cold-start-state.json:

{"deferred": true, "deferred_at": "ISO-timestamp", "phases_completed": []}

→ Tell them: "You can run cold-start anytime by asking me to 'fill my brain' or 'cold start'."

Schema State Tracking

After presenting the recommended directories (Phase C/E) and the user selects which ones to create, write ~/.gbrain/update-state.json recording:

• schema_version_applied: current gbrain version
• skillpack_version_applied: current gbrain version
• schema_choices.adopted: directories the user created
• schema_choices.declined: directories the user explicitly skipped
• schema_choices.custom: directories the user added that aren't in the recommended schema

This file enables future upgrades to suggest new schema additions without re-suggesting things the user already declined.

Anti-Patterns

• Ending setup without offering cold-start. An empty brain is useless. Phase J (cold-start) is where setup pays off. Always present the "Ready to populate?" prompt after verification. Skipping this is like installing an app and never logging in.
• Asking for the Supabase anon key. GBrain connects directly to Postgres over the wire protocol, not through the REST API. Only the database connection string is needed.
• Skipping live sync setup. If sync doesn't run automatically, the vector DB falls behind and search returns stale answers. Phase H is not optional.
• Declaring setup complete without verification. "The command ran" is not the same as "it worked." Push a test change, wait for sync, search for the corrected text.
• Using Transaction mode pooler. Sync uses transactions on every import. Transaction mode pooler causes .begin() is not a function errors and silently skips pages. Always use Session mode (port 6543).
• Importing without proving search. The magical moment is the user seeing search find things grep couldn't. Don't skip it.

Output Format

GBRAIN SETUP COMPLETE
=====================

Engine: [PGLite / Supabase Postgres]
Connection: [verified / pooler mode confirmed]
Pages imported: N
Embeddings: N/N (keyword search active, semantic improving)
Live sync: [configured / method]
Health check: all OK / [specific failures]
Verification: [GBRAIN_VERIFY.md results]

🧠 Ready to populate your brain? I can connect your Google services,
import your notes, and pull in your conversations — all in one session.
→ Launching cold-start...

The output should transition directly into cold-start (Phase J), not end with a bullet list. The bullet list is for when the user defers cold-start.

Tools Used

• gbrain init --non-interactive --url ... -- create brain
• gbrain import <dir> --no-embed [--workers N] -- import files
• gbrain search <query> -- search brain
• gbrain doctor --json -- health check
• gbrain check-update --json -- check for updates
• gbrain embed refresh -- generate embeddings
• gbrain embed --stale -- backfill missing embeddings
• gbrain sync --repo <path> -- one-shot sync from brain repo
• gbrain sync --watch --repo <path> -- continuous sync polling
• gbrain config get sync.last_run -- check last sync timestamp
• gbrain stats -- page count + embed coverage