gbrain/CONTRIBUTING.md

Contributing to GBrain

Setup

git clone https://github.com/garrytan/gbrain.git
cd gbrain
bun install
bun test

Requires Bun 1.0+.

Windows

bun run test, verify, ci:local and test:e2e all dispatch through bash, so the shell scripts under scripts/ must be checked out with Unix line endings. The root .gitattributes pins *.sh text eol=lf, which overrides the core.autocrlf=true that Git for Windows installs by default. A fresh clone is correct with no extra steps.

If you cloned before that pin existed, your working copy still has the old Windows line endings and bash will fail with $'\r': command not found. Refresh it once, from the repository root:

git rm --cached -r . -q
git reset --hard
bash -n scripts/run-unit-parallel.sh   # silence means bash can read the scripts

Every check:* entry in package.json invokes its script as bash scripts/<name>.sh rather than relying on the shebang, because bun on Windows cannot exec a .sh directly. Keep that prefix when you add a new shell-script check.

Project structure

src/
  cli.ts                  CLI entry point
  commands/               CLI-only commands (init, upgrade, import, export, etc.)
  core/
    operations.ts         Contract-first operation definitions (the foundation)
    engine.ts             BrainEngine interface
    postgres-engine.ts    Postgres implementation
    db.ts                 Connection management + schema loader
    import-file.ts        Import pipeline (chunk + embed + tags)
    types.ts              TypeScript types
    markdown.ts           Frontmatter parsing
    config.ts             Config file management
    storage.ts            Pluggable storage interface
    storage/              Storage backends (S3, Supabase, local)
    supabase-admin.ts     Supabase admin API
    file-resolver.ts      MIME detection + content hashing
    migrate.ts            Migration helpers
    yaml-lite.ts          Lightweight YAML parser
    chunkers/             3-tier chunking (recursive, semantic, llm)
    search/               Hybrid search (vector, keyword, hybrid, expansion, dedup)
    embedding.ts          OpenAI embedding service
  mcp/
    server.ts             MCP stdio server (generated from operations)
  schema.sql              Postgres DDL
skills/                   Fat markdown skills for AI agents
test/                     Unit tests (bun test, no DB required)
test/e2e/                 E2E tests (requires DATABASE_URL, real Postgres+pgvector)
  fixtures/               Miniature realistic brain corpus (16 files)
  helpers.ts              DB lifecycle, fixture import, timing
  mechanical.test.ts      All operations against real DB
  mcp.test.ts             MCP tool generation verification
  skills.test.ts          Tier 2 skill tests (requires OpenClaw + API keys)
docs/                     Architecture docs

Running tests

# Inner edit loop (~85s on a Mac dev box, 3700+ unit tests)
bun run test                      # parallel 8-shard fan-out + serial post-pass
bun test test/markdown.test.ts    # specific unit test

# Pre-push gate (matches what CI runs on shard 1 + typecheck)
bun run verify                    # privacy + jsonb + progress + test-isolation + wasm + admin-build + resolver + typecheck

# Pre-merge sanity (everything CI runs)
bun run test:full                 # verify + parallel unit + slow + smart e2e

# Slow / serial / e2e in isolation
bun run test:slow                 # *.slow.test.ts only (cold-path correctness)
bun run test:serial               # *.serial.test.ts only (--max-concurrency=1)
bun run test:e2e                  # real-Postgres E2E (requires DATABASE_URL)

# E2E setup (Postgres with pgvector)
docker compose -f docker-compose.test.yml up -d
DATABASE_URL=postgresql://postgres:postgres@localhost:5434/gbrain_test bun run test:e2e

# Or use your own Postgres / Supabase
DATABASE_URL=postgresql://... bun run test:e2e

Use bun run verify before pushing. The guard chain catches: banned fork-name leaks (scripts/check-privacy.sh), JSON.stringify(x)::jsonb interpolation patterns (scripts/check-jsonb-pattern.sh), \r progress bleed to stdout (scripts/check-progress-to-stdout.sh), test-isolation rule violations (scripts/check-test-isolation.sh — see "Writing tests that survive the parallel loop" below), silent fallback to recursive chunking in the compiled binary (scripts/check-wasm-embedded.sh), stale admin-dashboard build artifacts (scripts/check-admin-build.sh), and resolver drift on bundled skills (bun run check:resolver — strict-mode check-resolvable that exit-1s on any warning, added in v0.41.14.0 to catch SKILL.md frontmatter ↔ RESOLVER.md drift before merge). bun run check:all runs the full historical sweep including the trailing-newline and exports-count checks.

Writing tests that survive the parallel loop

bun run test shards 92+ unit-test files across 8 worker processes. Files in the same shard share a process, so process-global state leaks between them. Four lint rules (scripts/check-test-isolation.sh, R1-R4) enforce isolation:

Rule	What it bans	Fix
R1	Direct process.env.X = ... mutation	Use withEnv() from test/helpers/with-env.ts, or rename to *.serial.test.ts
R2	mock.module(...) anywhere in the file	Rename to *.serial.test.ts
R3	new PGLiteEngine( outside ~50 lines after beforeAll(	Use the canonical PGLite block (see below)
R4	new PGLiteEngine( without paired afterAll(disconnect)	Add the afterAll(() => engine.disconnect())

Canonical PGLite block (R3 + R4 compliant — paste this verbatim):

import { PGLiteEngine } from '../src/core/pglite-engine.ts';
import { resetPgliteState } from './helpers/reset-pglite.ts';

let engine: PGLiteEngine;

beforeAll(async () => {
  engine = new PGLiteEngine();
  await engine.connect({});
  await engine.initSchema();
});
afterAll(async () => { await engine.disconnect(); });
beforeEach(async () => { await resetPgliteState(engine); });

Env-touching tests:

import { withEnv } from './helpers/with-env.ts';

test('reads OPENAI_API_KEY', async () => {
  await withEnv({ OPENAI_API_KEY: 'sk-test' }, async () => {
    expect(loadConfig().openai_key).toBe('sk-test');
  });
});

withEnv saves and restores keys via try/finally including when the callback throws. Cross-test safe; NOT intra-file concurrent-safe (process.env is process-global). Files using withEnv stay outside the future test.concurrent() codemod's eligibility filter.

When to quarantine instead of fix: rename to *.serial.test.ts if the file uses mock.module(...), is genuinely env-coupled (module-load env readers + ESM caching defeat dynamic-import-after-env tricks), or intentionally shares state across it() boundaries. Quarantine count cap: 10 (informational).

Files that violated these rules at the v0.26.7 baseline are listed in scripts/check-test-isolation.allowlist. The allow-list MUST shrink over time ... never add new entries. v0.26.8 (env sweep) and v0.26.9 (PGLite sweep

• codemod) remove entries as files get fixed.

Local CI gate (recommended before pushing, v0.23.1+)

bun run ci:local         # full gate: gitleaks + unit + ALL 29 E2E files (sequential)
bun run ci:local:diff    # gate with diff-aware E2E selector
bun run ci:select-e2e    # print which E2E files the selector would run

ci:local spins up pgvector/pgvector:pg16 + oven/bun:1 via docker-compose.ci.yml, runs everything PR CI runs plus the full E2E suite, then tears down. Named volumes keep the install warm across runs (~16-20 min sequential E2E after the first cold pull). Requires Docker (Docker Desktop, OrbStack, or Colima) and gitleaks on host (brew install gitleaks). Override the postgres host port with GBRAIN_CI_PG_PORT=5435 bun run ci:local if 5434 collides.

Fail-closed selector: an unmapped src/ change runs all 29 E2E files. Hand-tune narrower mappings via scripts/e2e-test-map.ts.

PR-side security checks

Besides the test gate, PRs may trigger three security workflows: Semgrep CE SAST (every PR — advisory/non-blocking while the baseline is tuned, so a Semgrep finding won't fail your PR), OSV-Scanner (only when package.json or bun.lock change), and actionlint (only when .github/workflows/** change). See SECURITY.md → "Automated security scanning" for details.

Building

bun build --compile --outfile bin/gbrain src/cli.ts

Adding a new operation

GBrain uses a contract-first architecture. Add your operation to one file and it automatically appears in the CLI, MCP server, and tools-json:

1. Add your operation to src/core/operations.ts (define params, handler, cliHints)
2. Add tests
3. That's it. The CLI, MCP server, and tools-json are generated from operations.

For CLI-only commands (init, upgrade, import, export, files, embed, doctor, sync):

1. Create src/commands/mycommand.ts
2. Add the case to src/cli.ts

Parity tests (test/parity.test.ts) verify CLI/MCP/tools-json stay in sync.

Adding a new engine

See docs/ENGINES.md for the full guide. In short:

1. Create src/core/myengine-engine.ts implementing BrainEngine
2. Add to engine factory in src/core/engine.ts
3. Run the test suite against your engine
4. Document in docs/

The original SQLite engine plan was superseded by PGLite (embedded Postgres 17 via WASM), which uses the same SQL dialect as Postgres and eliminates the need for a separate FTS5/sqlite-vss translation layer. See docs/ENGINES.md for the engine architecture and the rationale.

CONTRIBUTOR_MODE — turn on the dev loop

gbrain captures retrieval traffic so you can replay real queries against your code changes before merging. This is off by default (production users get a quiet brain, no surprise data accumulation). Contributors turn it on with one shell rc line:

# In ~/.zshrc or ~/.bashrc:
export GBRAIN_CONTRIBUTOR_MODE=1

That's it. Every query / search you (or agents pointed at your dev brain) run from that shell now writes a row to eval_candidates, and the replay tool has data to work against.

What CONTRIBUTOR_MODE actually does:

• Turns on query/search capture into the local eval_candidates table. Without it the gate is closed and capture is a no-op.
• That's all. PII scrubbing, retention, and replay are independent.

Resolution order (most explicit wins):

1. eval.capture: true in ~/.gbrain/config.json → on
2. eval.capture: false in ~/.gbrain/config.json → off
3. GBRAIN_CONTRIBUTOR_MODE=1 → on
4. otherwise → off

Quick check that capture is actually running:

gbrain query "anything" >/dev/null
psql $DATABASE_URL -c 'SELECT count(*) FROM eval_candidates'
# (or `gbrain doctor` — surfaces silent capture failures cross-process)

To disable capture even with the env var set, write {"eval": {"capture": false}} to ~/.gbrain/config.json — explicit config beats the env var both directions.

Running real-world eval benchmarks (touching retrieval code)

If your PR touches retrieval — search ranking, RRF fusion, embeddings, intent classification, query expansion, source boost, or the query / search op handlers — run gbrain eval replay against a snapshot of real traffic before merging. Requires CONTRIBUTOR_MODE (above) so you have captured rows to replay against.

Quick loop:

gbrain eval export --since 7d > baseline.ndjson    # snapshot before your change
# ... make your change ...
gbrain eval replay --against baseline.ndjson       # diff retrieval, get Jaccard@k

Three numbers come back: mean Jaccard@k between captured and current slug sets, top-1 stability, and mean latency Δ. The replay tool flags the worst regressions so you can eyeball whether the change is hurting real queries.

Trigger paths (rerun if your diff touches any of these):

• src/core/search/hybrid.ts
• src/core/search/source-boost.ts, sql-ranking.ts
• src/core/search/intent.ts, expansion.ts, dedup.ts
• src/core/embedding.ts
• src/core/operations.ts (query / search handlers)
• src/core/postgres-engine.ts / pglite-engine.ts (searchKeyword / searchVector SQL)

See docs/eval-bench.md for the full guide including CI integration, hand-crafted NDJSON corpora (so a fresh checkout without captured data can still replay), and cost considerations. The NDJSON wire format is documented in docs/eval-capture.md.

For public benchmark coverage on top of replay, gbrain eval longmemeval <dataset.jsonl> (v0.28.1) runs LongMemEval against gbrain's hybrid retrieval. One in-memory PGLite per question, runtime-enumerated TRUNCATE between questions, ground-truth scoring via LongMemEval's published evaluate_qa.py. Use it alongside replay when changes affect retrieval quality on long-context conversational data — replay catches regressions on YOUR queries, LongMemEval catches them on a public set the benchmark community already cites. See the "Public benchmarks: LongMemEval" section in docs/eval-bench.md.

Welcome PRs

• SQLite engine implementation
• Docker Compose for self-hosted Postgres
• Additional migration sources
• New enrichment API integrations
• Performance optimizations