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Agents working on GBrain

This is your install + operating protocol. Claude Code reads ./CLAUDE.md automatically. Everyone else (Codex, Cursor, OpenClaw, Aider, Continue, or an LLM fetching via URL): start here.

Install (5 min)

  1. Install gbrain via Bun (the canonical path): 
    curl -fsSL https://bun.sh/install | bash
export PATH="$HOME/.bun/bin:$PATH"
bun install -g github:garrytan/gbrain
    If bun install -g aborts or gbrain doctor reports schema_version: 0, the CLI prints a recovery hint pointing at #218. Run gbrain apply-migrations --yes to recover, or fall back to the deterministic install: git clone https://github.com/garrytan/gbrain.git ~/gbrain && cd ~/gbrain && bun install && bun link.
  2. Init the brain: gbrain init (defaults to PGLite, zero-config). For 1000+ files or multi-machine sync, init suggests Postgres + pgvector via Supabase.
  3. STOP — ask the user about search mode. gbrain init auto-applied a default but printed a 9-cell cost matrix (mode × downstream model) preceded by [AGENT] markers. You MUST relay the matrix to the operator and confirm their choice before continuing. Cost spread between corners is 25x — silent acceptance is the wrong default. See ./INSTALL_FOR_AGENTS.md Step 3.5 for the exact ask-the-user protocol. Same banner fires on gbrain post-upgrade for existing users (search modes were added in v0.32.3).
  4. Read ./INSTALL_FOR_AGENTS.md for the full 9-step flow (API keys, identity, cron, verification).

Read this order

  1. ./AGENTS.md (this file) — install + operating protocol.
  2. ./CLAUDE.md — architecture reference, key files, trust boundaries, test layout.
  3. ./docs/architecture/brains-and-sources.md — the two-axis mental model (brain = which DB, source = which repo in the DB). Every query routes on both axes. Read before writing anything that touches brain ops.
  4. ./skills/conventions/brain-routing.md — agent-facing decision table: when to switch brain, when to switch source, how cross-brain federation works (latent-space only; the agent decides).
  5. ./skills/RESOLVER.md — skill dispatcher. Read before any task.

Trust boundary (critical)

GBrain distinguishes trusted local CLI callers (OperationContext.remote = false, set by src/cli.ts) from untrusted agent-facing callers (remote = true, set by src/mcp/server.ts). Security-sensitive operations like file_upload tighten filesystem confinement when remote = true and default to strict behavior when unset. If you are writing or reviewing an operation, consult src/core/operations.ts for the contract.

Common tasks

  • Configure: docs/ENGINES.md, docs/guides/live-sync.md, docs/mcp/DEPLOY.md.
  • Debug: docs/GBRAIN_VERIFY.md, docs/guides/minions-fix.md, gbrain doctor --fix.
  • Migrate / upgrade: gbrain upgrade (binary self-update + schema migrations + post-upgrade prompts), docs/UPGRADING_DOWNSTREAM_AGENTS.md, skills/migrations/, gbrain apply-migrations --yes (manual schema-only).
  • Eval retrieval changes: capture is off by default. To benchmark a retrieval change against real captured queries, set GBRAIN_CONTRIBUTOR_MODE=1, then gbrain eval export --since 7d > base.ndjson and gbrain eval replay --against base.ndjson. For public benchmark coverage (LongMemEval, ground-truth scoring), gbrain eval longmemeval <dataset.jsonl> (v0.28.8) runs against an isolated in-memory PGLite per question — your ~/.gbrain is never opened. Full guide: docs/eval-bench.md.
  • Drive the brain to a target health score (v0.36.4.0): the one-command loop. gbrain doctor --remediation-plan --json previews what would be fixed; gbrain doctor --remediate --yes --target-score 90 --max-usd 5 walks a dependency-ordered plan (sync before extract, embed after consolidate), re-checking score between every step, refusing to spend past the cost cap. Empty brains (no entity pages) or unconfigured embedding keys hit a max_reachable_score ceiling and bail with what's missing. Three phase handlers (synthesize / patterns / consolidate) are PROTECTED — only trusted local callers can submit them; MCP cannot. Reference: docs/architecture/topologies.md and the CHANGELOG entry for v0.36.4.0.
  • Track a founder/company over time (v0.35.7): when an entity has typed metric claims in its ## Facts fence (metric: mrr, value: 50000, unit: USD, period: monthly columns), run gbrain eval trajectory <entity-slug> for the chronological history with regressions auto-flagged, or gbrain founder scorecard <entity-slug> for a four-signal JSON rollup (claim_accuracy / consistency / growth_trajectory / red_flags). MCP op find_trajectory exposes the same data — read scope, visibility-filtered for remote callers.
  • Everything else: ./llms.txt is the full documentation map. ./llms-full.txt is the same map with core docs inlined for single-fetch ingestion.

Before shipping

Easiest path: bun run ci:local runs the full CI gate inside Docker (gitleaks, unit tests with DATABASE_URL unset, then all 29 E2E files sequentially against a fresh pgvector container) and tears down. Use bun run ci:local:diff for the diff-aware subset during fast iteration on a focused branch. Requires Docker (Docker Desktop / OrbStack / Colima) and gitleaks (brew install gitleaks).

Manual path: bun test plus the E2E lifecycle described in ./CLAUDE.md (spin up the test Postgres container, run bun run test:e2e, tear it down).

Ship via the /ship skill, not by hand.

Privacy

Never commit real names of people, companies, or funds into public artifacts. See the Privacy rule in ./CLAUDE.md. GBrain pages reference real contacts; public docs must use generic placeholders (alice-example, acme-example, fund-a).

Forks

If you are a fork, regenerate llms.txt + llms-full.txt with your own URL base before publishing: LLMS_REPO_BASE=https://raw.githubusercontent.com/your-org/your-fork/main bun run build:llms.