Three verified backlog fixes: - #3219: `gbrain models doctor` probed only the global chat + expansion models, so a per-task background route (models.dream.synthesize, models.think, facts.extraction_model, ...) configured to a distinct unreachable provider still reported green. New resolvePerTaskProbePlan resolves every PER_TASK_KEYS route the same way buildReport does, dedups against already-probed models and across routes, and probes each distinct resolved model once with the comma-joined route keys as the probe touchpoint for attribution. - #3221 (takeover of open PR #3250): the doctor chat/expansion probe used maxTokens: 1, which falsely fails reasoning models (output budget spent on internal reasoning; some providers reject sub-minimum caps outright). Probe budget raised to a shared PROBE_MAX_OUTPUT_TOKENS constant, widened above any configured extended-thinking budget, and a length-exhausted empty completion is classified reachable with the limitation surfaced. Added on top: a catch-branch classification so the probe stays coherent with #3249's gateway-side contentless-completion rejection if that lands. - #3206: reconfigureGatewayWithEngine passed the file-plane chat_model / expansion_model as resolveModel's bottom-rung `fallback`, so TIER_DEFAULTS silently replaced explicit user config on every engine-backed startup (and every chat-gated feature reported unavailable on non-Anthropic brains, with no error). New `userFallback` slot ranks explicit user config above the tier default while env/config-key overrides still win. The silent-zero half is also fixed: the facts extraction and classifier gates now check availability of the model the call will actually use, not the global chat default. Co-authored-by: Masashi-Ono0611 <Masashi-Ono0611@users.noreply.github.com> Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Install
Three install paths. Pick one. Mix later if needed.
1. Run with an agent platform (recommended)
Already running OpenClaw or Hermes?
bun install -g github:garrytan/gbrain
gbrain init --pglite # 2 seconds; no server
gbrain skillpack scaffold --all # 43 skills scaffolded into your agent workspace
gbrain doctor # green checks all the way down
Your agent now reads skills/RESOLVER.md once per request, routes intent to the right skill, executes. New entity mentions create new pages. Daily cron runs enrichment overnight.
Scaffolded skills are first-class files in your agent repo — edit freely. To pull upstream gbrain improvements later, gbrain skillpack reference <name> diffs your local copy vs the bundle. The legacy skillpack install managed-block model was retired in v0.36.0.0; if you're upgrading from an older release, run gbrain skillpack migrate-fence once to strip the legacy fence and keep your existing skill rows.
To upgrade later: gbrain upgrade runs schema migrations + post-upgrade prompts (chunker bumps, the v0.36.2.0 ZeroEntropy switch). Always TTY-only; non-TTY upgrades skip prompts with informational stderr lines.
2. CLI standalone
No agent platform, just shell + MCP-aware editor.
bun install -g github:garrytan/gbrain
gbrain init --pglite
If
bun install -ghits a postinstall error (Bun blocks postinstall hooks in some environments), the CLI prints a recovery hint pointing at #218. Rungbrain doctorto diagnose, thengbrain apply-migrations --yesmanually. The deterministic fallback isgit clone https://github.com/garrytan/gbrain.git ~/gbrain && cd ~/gbrain && bun install && bun link.
The init flow detects your repo size and suggests Supabase for brains > 1000 markdown files. To switch later:
gbrain migrate --to supabase # PGLite → Postgres
gbrain migrate --to pglite # Postgres → PGLite (rare)
For shared / large / multi-machine deployments (a team or company brain with multiple users hitting one server over HTTP MCP with OAuth scoping per user), follow the dedicated walkthrough: Tutorial: set up GBrain as your company brain.
API keys live in ~/.gbrain/config.json (file plane) or env vars (OPENAI_API_KEY, ZEROENTROPY_API_KEY, VOYAGE_API_KEY, ANTHROPIC_API_KEY). Set via CLI:
gbrain config set zeroentropy_api_key sk-...
gbrain config set anthropic_api_key sk-ant-...
Common follow-ups:
gbrain import ~/my-knowledge # bulk-import a markdown folder
gbrain sync --watch # live-sync a git repo (autopilot mode)
gbrain autopilot --install # background daemon for nightly enrichment
Wire this same local brain into your coding agent — zero server, zero token:
claude mcp add gbrain -- gbrain serve # Claude Code
codex mcp add gbrain -- gbrain serve # Codex
The agent spawns gbrain serve as a stdio subprocess against your local brain. Full walkthrough (both this local path and connecting to a remote brain), plus the brain-first protocol to paste into CLAUDE.md / AGENTS.md: Give your coding agent a memory.
3. MCP server (any MCP client)
gbrain serve # stdio MCP (Claude Desktop / Code / Cursor)
gbrain serve --http # HTTP MCP with OAuth 2.1 + admin dashboard
Wire a coding agent to a remote brain in one command (when you have an HTTP
server + a bearer token): gbrain connect prints a paste-ready setup block, or
--install runs it and smoke-tests the token.
gbrain auth create "claude-code"
gbrain connect https://your-host/mcp --token gbrain_xxx # Claude Code (default)
gbrain connect https://your-host/mcp --token gbrain_xxx --agent codex # Codex (env-var bearer)
gbrain connect https://your-host/mcp --agent perplexity --oauth --register # Perplexity (OAuth)
Per-client setup guides live in docs/mcp/:
docs/mcp/CLAUDE_CODE.mddocs/mcp/CODEX.mddocs/mcp/CLAUDE_DESKTOP.mddocs/mcp/CHATGPT.mddocs/mcp/PERPLEXITY.mddocs/mcp/DEPLOY.md— production deploy patterns
The HTTP server ships with an admin SPA at /admin, an SSE activity feed at /admin/events, DCR-style client registration, scope-gated read/write/admin access, and rate limiting.
Thin-client mode
Connect to someone else's brain without running a local engine:
gbrain init --mcp-only # configures remote MCP, skips local DB
Useful for: team mounts, brain-as-a-service deployments, dev machines without disk space. Most local commands refuse with a paste-ready hint. See docs/architecture/topologies.md.
Verifying the install
gbrain doctor --json # full health check
gbrain models # which AI models are configured for what
gbrain models doctor # minimal reachability probe per configured model
If anything's yellow, gbrain doctor names the fix command in the message. Most issues are missing API keys or stale schema (gbrain upgrade --force-schema).
Troubleshooting
PGLite crashes on macOS 26.x (Tahoe)
PGLite's embedded WASM engine is incompatible with macOS 26.x (Tahoe) on Apple Silicon. If gbrain init --pglite crashes during engine initialization, switch to native Homebrew PostgreSQL:
# Install PostgreSQL + pgvector
brew install postgresql@17
brew services start postgresql@17
createdb gbrain
# Build pgvector from source (required for vector search)
cd /tmp && git clone --branch v0.8.0 https://github.com/pgvector/pgvector.git
cd pgvector && make && make install
psql gbrain -c "CREATE EXTENSION IF NOT EXISTS vector;"
# Point gbrain at your local Postgres
cat > ~/.gbrain/config.json << 'EOF'
{
"engine": "postgres",
"database_url": "postgresql://localhost:5432/gbrain",
"schema_pack": "gbrain-base-v2"
}
EOF
# Run migrations and verify
gbrain apply-migrations --yes
gbrain doctor
All 102 migrations run on first try. Once gbrain doctor shows green, the brain works identically to PGLite — same commands, same skills, same data model. The only difference is the storage backend.
Note: This workaround is temporary. When the upstream WASM runtime fix ships (likely via a Bun update),
--pglitewill work on Tahoe again.