cdba533a04
* feat(dims): OpenAI text-embedding-3 Matryoshka range validation (D13) dimsProviderOptions now fail-loud at the embed boundary when the configured embedding_dimensions is outside the model's native range (1..1536 for -small, 1..3072 for -large). Paste-ready fix hint in the AIConfigError.fix field. Closes the silent-HTTP-400 path that would have bit OpenAI-fallback users on v0.36.0.0 ZE-default installs. 16 new test cases in test/ai/dims-openai.test.ts pinning the contract across native-openai and openai-compatible adapter paths. * feat(ai): flip defaults to ZeroEntropy zembed-1 1280d + zerank-2 reranker Default embedding model is now zeroentropyai:zembed-1 at 1280d via Matryoshka. Real-corpus benchmark: 2.2x faster than OpenAI, 2.6x cheaper at regular pricing, wins 11/20 head-to-head queries. 1280 is the closest valid ZE Matryoshka step to the prior OpenAI 1536d default (valid set: 2560/1280/640/320/160/80/40). 1024 (Voyage's step) is NOT on ZE's list — pinned by AIConfigError fail-loud in dims.ts. balanced mode bundle now defaults reranker_enabled=true. zerank-2 reshuffles 60% of top-1 results in benchmarks. Missing-key fail-open contract in src/core/search/rerank.ts handles unauthenticated cases. Opt out with: gbrain config set search.reranker.enabled false Existing tests updated (gateway.test.ts, search-mode.test.ts) and a new test/balanced-reranker-default.test.ts (10 cases) pins the fail- open invariants. * feat(retrieval-upgrade): RetrievalUpgradePlanner + interactive prompt UX New src/core/retrieval-upgrade-planner.ts is the consolidated planner that computes the brain's pending retrieval-upgrade work (chunker bumps + ZE switch) in one pass and applies the schema transition + config updates atomically. Tagged-union ApplyResult enum (D15): 'applied' | 'skipped_already_ applied' | 'skipped_no_work' | 'declined' | 'planned' | 'failed'. No string-parsing reasons. Three config keys (D12): ze_switch_prompt_shown (UI state), ze_switch_requested (user intent), ze_switch_applied (work done). Plus ze_switch_previous_snapshot (JSON, full prior config for --undo per D16) and ze_switch_declined_at (90-day re-ask window). Schema transition (D18) is atomic: DROP indexes + ALTER COLUMN + CREATE INDEX inside a single engine.transaction(). HNSW recreation is part of the same transaction — no silent slow-search window. C3 eligibility logic: ze_switch_offered iff NOT on ZE + NOT declined recently + NOT applied + (legacy default OR >100 pages). C4 cost math: MAX(chunker_pending, dim_pending) not SUM — one re-embed pass invalidates both surfaces simultaneously. New src/core/retrieval-upgrade-prompt.ts wires the planner to a TTY-only interactive prompt with two-line cost split (D10) and privacy callout for the reranker flip. Tests: test/retrieval-upgrade-planner.test.ts (24 cases) pins the state machine. test/asymmetric-encoding-contract.test.ts (6 cases) pins D17: search read path uses gateway.embedQuery() not embed(), asserted via __setEmbedTransportForTests mock. * feat(cli): gbrain ze-switch — manual lever for the ZE switch New gbrain ze-switch CLI with --dry-run, --json, --resume, --force, --undo, --non-interactive, --confirm-reembed, --ignore-missing-key flags. Mirrors the upgrade prompt's UX symmetry: --undo presents a cost-warning before re-embedding back to the prior width. src/cli.ts: dispatch case + CLI_ONLY entry. ze-switch owns its own engine lifecycle (mirrors the doctor pattern). test/ze-switch-cli.test.ts (11 cases): --help, --dry-run, --json, --non-interactive, --ignore-missing-key, --resume, --undo, --confirm-reembed. Uses captureExit harness to test process.exit() paths without breaking the test process. * feat(doctor): ze_embedding_health + embedding_width_consistency checks Two new doctor checks (D-A5): ze_embedding_health: when embedding_model starts with zeroentropyai:, verify ZEROENTROPY_API_KEY is set (env or config). Paste-ready setup hint with the signup URL on failure. embedding_width_consistency: cross-check that the configured embedding_dimensions matches the actual vector(N) column width on content_chunks.embedding. Catches the half-applied switch state (schema migrated but config write crashed) with a paste-ready gbrain ze-switch --resume hint. Wired into runDoctor between reranker_health and the existing sync_freshness checks. Both checks gracefully no-op on non-ZE embedding configs. test/doctor-ze-checks.test.ts (8 cases) pins both checks across happy + missing-key + missing-config + drift paths. Uses withEnv() helper to clear ZEROENTROPY_API_KEY for the no-key path so tests are hermetic against contributor env state. test/e2e/v0_28_5-fix-wave.test.ts + test/openai-compat-multimodal.test.ts: updated to explicit-configure the gateway when the test depends on specific dims that diverge from the v0.36.0.0 default (1280d). * docs: README zero-based rewrite (884 -> 139 lines) + new docs files Strip 4 months of accreted "New in v0.X.Y" hero blocks and reorganize around what gbrain does today. 33 H2s -> 8. The Commands section (136 lines duplicating gbrain --help) moved out; the 6-table skills enumeration collapsed to a one-paragraph capability description with a link to skills/RESOLVER.md. Hero retains load-bearing facts: OpenClaw + Hermes credit, production numbers (17,888 pages / 4,383 people / 723 companies), BrainBench numbers (P@5 49.1% / R@5 97.9% / +31.4 lift), ZE comparison numbers, 30-min install claim. Adds one paragraph announcing the v0.36.0.0 ZE default with the explicit gbrain config set escape for OpenAI/Voyage users. New files: - docs/INSTALL.md: every install path consolidated (agent platform, CLI standalone, MCP server). Thin-client mode covered. - docs/architecture/RETRIEVAL.md: why the hybrid + graph stack works. BrainBench numbers, why each strategy alone fails, the source-aware ranking + intent classification + multi-query expansion story. - docs/ethos/ORIGIN.md: origin story lifted from the old README so the front door stays factual + concrete. test/readme-hero-anchors.test.ts (5 cases) is the D9 regression guard. Five load-bearing strings: OpenClaw, Hermes, ZE, production-numbers regex, P@5/R@5. Light anchors that let voice/ structure evolve but block accidental loss of headline facts. scripts/check-test-real-names.sh: allowlist entries for OpenClaw + Hermes literals in the anchor test (it explicitly asserts those strings appear in README). * chore: bump version and changelog (v0.36.0.0) ZeroEntropy as the new default for embedding (zembed-1 at 1280d via Matryoshka) and reranker (zerank-2 cross-encoder, on by default in balanced mode bundle). README zero-based rewrite (884 -> 139 lines). 3 new docs files. Two new doctor checks. New gbrain ze-switch CLI with --undo for symmetric reversibility. skills/migrations/v0.36.0.0.md tells the agent how to surface the retrieval-upgrade prompt post-upgrade. llms-full.txt regenerated via bun run build:llms. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(docs): scrub Wintermute from RETRIEVAL.md per privacy rule * chore: rebump version 0.36.0.0 → 0.36.2.0 (queue collision) Three open PRs were claiming v0.36.0.0 (#1130 skillpack, #1139 hindsight, #1136 this PR). Ship-aware queue allocator says this branch lands at v0.36.2.0. Trio audit: VERSION 0.36.2.0 package.json 0.36.2.0 CHANGELOG ## [0.36.2.0] - 2026-05-17 Updates: VERSION, package.json, CHANGELOG header + body refs, README "New default in v0.36.2.0" announcement + credit line, skills/migrations/v0.36.0.0.md renamed to v0.36.2.0.md with frontmatter + body refs updated. llms-full.txt regenerated. * fix(test): pin gateway dim=1536 in cross-file-stateful PGLite tests CI shard 1 reported 10 failures across `query-cache.test.ts` (6) and `consolidate-valid-until.test.ts` (4). Both files hardcode 1536-dim vectors but rely on `PGLiteEngine.initSchema()` to size `vector(__EMBEDDING_DIMS__)` at the right width. Root cause: v0.36.2.0 flipped DEFAULT_EMBEDDING_DIMENSIONS from 1536 to 1280 (ZE Matryoshka step). The gateway module is process-singleton; when ANOTHER test file in the same shard's bun-test process configures the gateway before us, `pglite-engine.ts:216` reads `getEmbeddingDimensions() === 1280` and sizes the schema columns at vector(1280). The hardcoded 1536-dim INSERTs then fail with "expected 1280 dimensions, not 1536". Locally these tests pass in isolation because the gateway falls back through the try/catch at pglite-engine.ts:218 (1536 default). CI runs multiple test files in one process, so cross-file state poisons the schema width. Fix: explicit `resetGateway()` + `configureGateway({embedding_dimensions: 1536, ...})` at the top of `beforeAll`, plus `resetGateway()` in `afterAll`. Pins the schema width regardless of cross-file state. --------- Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
GBrain
Your AI agent is smart but forgetful. GBrain gives it a brain.
Built by the President and CEO of Y Combinator to run his actual AI agents. The production brain behind his OpenClaw and Hermes deployments: 17,888 pages, 4,383 people, 723 companies, 21 cron jobs running autonomously, built in 12 days. The agent ingests meetings, emails, tweets, voice calls, and original ideas while you sleep. It enriches every person and company it encounters. It fixes its own citations and consolidates memory overnight. You wake up smarter than when you went to bed.
The brain wires itself. Every page write extracts entity references and creates typed links (attended, works_at, invested_in, founded, advises) with zero LLM calls. Hybrid search. Self-wiring knowledge graph. Structured timeline. Backlink-boosted ranking. Ask "who works at Acme AI?" or "what did Bob invest in this quarter?" and get answers vector search alone can't reach. Benchmarked side-by-side: gbrain lands P@5 49.1%, R@5 97.9% on a 240-page Opus-generated rich-prose corpus, beating its graph-disabled variant by +31.4 points P@5 and ripgrep-BM25 + vector-only RAG by a similar margin. Full BrainBench scorecards live in the sibling gbrain-evals repo.
New default in v0.36.2.0: ZeroEntropy for both embedding (zembed-1 at 1280d via Matryoshka) and reranker (zerank-2). On a real-corpus benchmark vs OpenAI and Voyage: 2.2× faster (442ms vs OpenAI 973ms), 2.6× cheaper at regular pricing ($0.05/M vs OpenAI $0.13), wins 11 of 20 queries head-to-head, reshuffles 60% of top-1 results when used as a second-pass reranker. Bring your own key from zeroentropy.dev, or stay on OpenAI/Voyage via gbrain config set embedding_model <provider:model> — your choice is sticky.
GBrain is those patterns, generalized. Install in 30 minutes. Your agent does the work. As Garry's personal agent gets smarter, so does yours.
New in v0.35.7 — Temporal trajectory + founder scorecard. Author typed metric assertions in the ## Facts fence (mrr=50000, arr=2000000, team_size=12) and gbrain stores them as first-class typed columns. gbrain eval trajectory companies/acme-example prints the chronological history with regressions auto-flagged inline. gbrain founder scorecard companies/acme-example rolls up claim accuracy, consistency, growth direction, and red flags into a stable schema_version: 1 JSON contract. New MCP op find_trajectory exposes the same data to agents (read scope, visibility-filtered for remote callers). The consolidate cycle phase now writes valid_until on chronologically-superseded facts AND uses semantic upsert on (page_id, claim, since_date) — re-running the dream cycle on stable input is now a true no-op (fixed a pre-existing duplicate-takes bug from prior versions).
~30 minutes to a fully working brain. Database ready in 2 seconds (PGLite, no server). You just answer questions about API keys.
LLMs: fetch
llms.txtfor the documentation map, orllms-full.txtfor the same map with core docs inlined in one fetch. Agents: start withAGENTS.md(orCLAUDE.mdif you're Claude Code).
Install
GBrain runs in three shapes. Pick the one that matches how you use AI agents today.
Run with your agent platform
Already using OpenClaw or Hermes? GBrain installs as a skillpack into your agent's workspace.
gbrain init --pglite
gbrain skillpack install
That's it. Your agent picks up 43 skills (signal detection, brain-ops, ingest, enrich, citation-fixer, daily-task-manager, cron-scheduler, eval framework, and 35 more). Routing lives in skills/RESOLVER.md — the agent reads it once per request, picks the right skill, executes.
CLI standalone
Use gbrain from any shell, no agent platform required.
bun install -g github:garrytan/gbrain
gbrain init --pglite # 2 seconds; no server, no Docker
gbrain doctor # verify health
Then point any MCP-aware client (Claude Code, Cursor, Windsurf) at it, or use it from your shell:
gbrain search "who works at acme AI?"
gbrain query "what did bob invest in this quarter?"
gbrain graph-query people/garry-tan --depth 2
Detailed setup paths (Postgres at scale, Supabase, thin-client mode) live in docs/INSTALL.md.
MCP server (any MCP client)
gbrain serve # stdio MCP (Claude Desktop / Code / Cursor)
gbrain serve --http # HTTP MCP with OAuth 2.1 + admin dashboard
# at /admin, SSE activity feed at /admin/events
Per-client guides (Claude Desktop, Code, Cursor, ChatGPT, Perplexity, Cowork) live under docs/mcp/. HTTP server supports DCR-style client registration, scope-gated access (read/write/admin), and built-in rate limiting.
What it does (the loop)
signal → search → respond → write → auto-link → sync
(every (brain-first (informed (page + (typed edges (cron
message) retrieval) by context) timeline) + backlinks) keeps fresh)
- Signal detector runs on every message your agent receives. Captures ideas, entity mentions, time-sensitive todos, names, links.
- Brain-first lookup before any external API call. The cheapest, fastest, most personal information source you have.
- Auto-link fires on every page write. No LLM calls; pure pattern matching on
[[wiki/people/bob]]style references. New entity → new page stub → graph grows. - Cron-driven enrichment runs while you sleep: dedup people pages, fix citations, score salience, find contradictions, prep tomorrow's tasks.
The whole loop is described in docs/architecture/topologies.md with diagrams.
Capabilities
Hybrid search. Vector (HNSW on pgvector) + BM25 keyword + reciprocal-rank fusion + source-tier boost + intent-aware query rewriting. Three named search modes (conservative, balanced, tokenmax) bundle the cost/quality knobs into a single config key. Live cost/recall comparisons in docs/eval/SEARCH_MODE_METHODOLOGY.md. Default: balanced with ZeroEntropy reranker on.
Self-wiring knowledge graph. Every put_page extracts entity refs from markdown/wikilinks/typed-link syntax and writes edges with zero LLM calls. Typed edges (attended, works_at, invested_in, founded, advises, mentions, …). Multi-hop traversal via gbrain graph-query. The graph is what produces the +31.4 P@5 lift over vector-only RAG.
Job queue (Minions). BullMQ-shaped, Postgres-native job queue. Durable subagents (LLM tool loops that survive crashes via two-phase pending→done persistence), shell jobs with audit, child jobs with cascading timeouts, rate leases for outbound providers, attachments via S3/Supabase storage. Replaces "spawn subagent as fire-and-forget Promise" with something that recovers from anything.
43 curated skills. Routing lives in skills/RESOLVER.md. Covers signal capture, ingest (idea / media / meeting), enrichment, querying, brain ops, citation fixing, daily task management, cron scheduling, reports, voice, soul audit, skill creation, eval framework, and migrations. Skills are markdown files (tool-agnostic), packaged as a single skillpack the installer drops into your agent workspace.
Eval framework. gbrain eval longmemeval runs the public LongMemEval benchmark against your hybrid retrieval. gbrain eval export + gbrain eval replay capture real queries and replay them against code changes (set GBRAIN_CONTRIBUTOR_MODE=1). gbrain eval cross-modal cross-checks an output against the task using three different-provider frontier models. Full methodology in docs/eval/SEARCH_MODE_METHODOLOGY.md.
Brain consistency. gbrain eval suspected-contradictions samples retrieval pairs, layered date pre-filter, query-conditioned LLM judge, persistent cache. Surfaces conflicts between takes + facts the agent has written. Wired into the daily dream cycle.
Integrations
Data flowing into the brain. Each integration is a recipe — markdown + setup hints — that ships in recipes/ and is discoverable via gbrain integrations list.
- Voice: Whisper or Groq voice-to-brain capture. Setup in
docs/integrations/voice.md. - Email + calendar: webhook handlers that route to brain signals.
docs/integrations/meeting-webhooks.md. - Embedding providers: 14 recipes covering OpenAI (default fallback), Voyage, ZeroEntropy (default), Google Gemini, Azure OpenAI, MiniMax, Alibaba DashScope, Zhipu, Ollama (local), llama.cpp llama-server (local), LiteLLM proxy. Pricing matrix + decision tree in
docs/integrations/embedding-providers.md. - Credential gateway: vault-aware secret distribution.
docs/integrations/credential-gateway.md. - MCP clients: every major MCP client is supported.
docs/mcp/per-client setup.
Architecture
Two engines, one contract. PGLite (Postgres 17 via WASM, zero-config, default) for personal brains up to ~50K pages. Postgres + pgvector (Supabase or self-hosted) for shared / large / multi-machine deployments. The contract-first BrainEngine interface in src/core/engine.ts defines ~47 operations both engines implement; CLI and MCP server are generated from one source.
Brain repo is the system of record. Your knowledge lives in a regular git repo (your "brain repo") as markdown files. GBrain syncs the repo into Postgres for retrieval; deletes in git become soft-deletes in DB. You can publish public subsets, share team mounts, run thin-client setups pointing at a colleague's brain server. Topologies in docs/architecture/topologies.md.
Two organizational axes (brain ⊥ source). A brain is a database (your personal brain, a team mount you joined). A source is a repo inside that brain (wiki, gstack, an essay, a knowledge base). Routing lives in .gbrain-source dotfiles and resolves via a documented 6-tier precedence chain. Full diagrams in docs/architecture/brains-and-sources.md.
Why the graph matters. Vector search returns chunks that are semantically close. The graph returns chunks that are factually connected. Hybrid search pulls from both; auto-linking on every write keeps the graph fresh. Deep dive: docs/architecture/RETRIEVAL.md.
Docs
docs/INSTALL.md— every install path, end to enddocs/architecture/— system design, topologies, retrieval theorydocs/guides/— how-to runbooks (sub-agent routing, minion deployment, skill development, brain-first lookup, idea capture, diligence ingestion)docs/integrations/— connecting external data sources (voice, email, calendar, embedding providers)docs/mcp/— per-client MCP setup (Claude Desktop, Code, Cursor, ChatGPT, Perplexity, Cowork)docs/eval/— eval framework, metric glossary, methodologydocs/ethos/— philosophy (thin harness, fat skills, markdown as recipes, origin story)AGENTS.md— entry point for non-Claude agentsCLAUDE.md— entry point for Claude Code (deep operating context)CONTRIBUTING.md— contributor guide, test discipline, eval-capture modeSECURITY.md— OAuth threat model, hardening defaults
Contributing
Run bun run test for the fast loop, bun run verify for the pre-push gate, bun run ci:local to run the full Docker-backed CI stack locally. Detailed test discipline in CONTRIBUTING.md.
Community PRs are batched into release waves rather than merged one-by-one — see the "PR wave workflow" section in CLAUDE.md. Contributor attribution stays attached via Co-Authored-By: trailers. We credit every accepted contribution in CHANGELOG.md.
If you find a bug or want a feature: open an issue first. Quick fixes (typo, doc bug, obvious regression) can go straight to a PR. Anything touching schema, retrieval ranking, MCP protocol, or the security boundary needs a design discussion in the issue first.
License + credit
MIT. Built by Garry Tan to run his OpenClaw and Hermes deployments — the production brain behind his actual AI agents.
Origin story: docs/ethos/ORIGIN.md.
Community PR contributors are credited in CHANGELOG.md per release. ZeroEntropy (@zeroentropy) for the embedding + reranker stack that became the v0.36.2.0 default. Voyage AI for the asymmetric-encoding recipe template. Ramp Labs for the search quality improvements lineage.