gbrain/eval

BrainBench

Public benchmark for personal knowledge brain agent stacks. Ships 4 adapter configurations scored side-by-side on a 240-page rich-prose fictional corpus (twin-amara). Measures retrieval, extraction quality, and per-link-type accuracy.

What this answers: "Does the knowledge graph layer do useful work, or is gbrain just a thin wrapper over vector+keyword hybrid?" Headline: gbrain beats the closest external baseline (hybrid-without-graph, same embedder, same chunking) by +31 points P@5. The graph layer is load-bearing.

5-minute quickstart

# 1. Run the full benchmark (4 adapters × 5 runs, ~15 min wall clock)
bun run eval:run

# 2. Fast iteration (N=1 single run)
bun run eval:run:dev

# 3. Just the type-accuracy report
bun run eval:type-accuracy

# 4. Explore the canonical world (contributor-facing UI)
bun run eval:world:view

What's in the box

eval/
├── data/world-v1/          Canonical world (committed). 240 sharded JSON files.
│                            One file per entity + _ledger.json metadata.
├── generators/
│   ├── gen.ts              Opus-backed corpus generator (run once; output cached)
│   ├── world.ts            World-schema scaffolder
│   └── world-html.ts       World explorer HTML renderer (XSS-safe)
├── runner/
│   ├── multi-adapter.ts    4-adapter side-by-side scorer (N=5)
│   ├── type-accuracy.ts    Per-link-type accuracy vs gold from _facts
│   ├── before-after.ts     Original v1 BEFORE/AFTER retrieval run
│   ├── types.ts            Adapter, Page, Query, RankedDoc interfaces
│   ├── adapters/
│   │   ├── ripgrep-bm25.ts       EXT-1: classic IR baseline (BM25 over grep hits)
│   │   ├── vector-only.ts        EXT-2: pure cosine similarity, same embedder
│   │   └── hybrid-nograph.ts     EXT-3: gbrain hybrid with graph disabled
│   └── queries/
│       ├── tier5-fuzzy.ts          30 vague-recall queries (hand-authored)
│       ├── tier5_5-synthetic.ts    50 synthetic outsider queries (AI-authored, labeled)
│       ├── validator.ts            Query schema enforcement (temporal as_of_date rule)
│       └── index.ts                Aggregator + validateAll()
├── cli/
│   ├── world-view.ts       Render + open world.html
│   ├── query-validate.ts   Validate a Query[] file
│   └── query-new.ts        Scaffold a Query template
└── reports/                Benchmark scorecards (gitignored)

Three contributor paths

Path 1: Reproduce a published scorecard

# 1. Check out the specific gbrain commit referenced in the scorecard
git checkout <commit-sha>
# 2. Run the full benchmark
bun run eval:run
# 3. Compare your numbers to the scorecard. Deterministic adapters should
#    match exactly. Embedding-based adapters should land within tolerance bands.

Path 2: Submit a new external adapter

See CONTRIBUTING.md for the adapter submission flow. Short version:

1. Implement eval/runner/adapters/<your-adapter>.ts conforming to the Adapter interface in eval/runner/types.ts.
2. Add a unit test file alongside.
3. Wire your adapter into eval/runner/multi-adapter.ts (one line).
4. bun run eval:run:dev to verify.
5. Open a PR.

Path 3: Write Tier 5.5 externally-authored queries

The T5.5 queries currently in the repo are AI-authored (author: "synthetic-outsider-v1") as a placeholder. Real outside researchers should:

1. bun run eval:world:view to understand the canonical world
2. bun run eval:query:new --tier externally-authored --author "@your-handle"
3. Edit the scaffolded template with a real query + gold slugs
4. bun run eval:query:validate path/to/your.json
5. Submit via eval/external-authors/<your-handle>/queries.json in a PR

See CONTRIBUTING.md for the query-submission template.

Methodology one-pager

• Corpus: 240 Opus-generated fictional biographical pages. Fixed, committed, zero private data. Reproducibility baseline for any run.
• Gold: Each page's _facts metadata defines canonical relationships. The scorer never shows _facts to the adapters — raw pages only cross the ingestion boundary (structural enforcement in Adapter.init).
• Metrics: P@5 and R@5 on relational queries (145 canonical from _facts, 80 tier-5 + tier-5.5). Type accuracy on extracted edges (eval/runner/type-accuracy.ts).
• N=5 runs per adapter with page-order shuffle (seeded LCG; runs are reproducible). Stddev surfaces order-dependent adapter bugs. Deterministic adapters correctly show stddev=0.
• Temporal queries require explicit as_of_date (validated at query authoring time; rejected at load if a temporal verb is present without it).

Adapter scorecard (most recent, N=5)

See docs/benchmarks/2026-04-18-brainbench-v1.md for the full report. Quick summary from bun run eval:run:

Adapter	P@5	R@5
gbrain-after	49.1%	97.9%
hybrid-nograph	17.8%	65.1%
ripgrep-bm25	17.1%	62.4%
vector-only	10.8%	40.7%

The graph layer beats vector+keyword hybrid on relational queries by ~31 points; hybrid-without-graph barely edges BM25. That's the story.