e60b60244f
* feat(cross-modal/0): batched multimodal + query helpers + SSRF helper
Commit 0 of the cross-modal search wave. Foundation for Phase 1-3:
- embedMultimodal accepts MultimodalInput text variant + EmbedMultimodalOpts
with inputType: 'document' | 'query' (D22-2). Default unchanged so
importImageFile keeps document-side embedding.
- embedQueryMultimodal(text) + embedQueryMultimodalImage(input) wrappers
for hybridSearch + searchByImage query paths.
- embedMultimodalSafe binary-search retry on transient batch failure +
failed_indices surfacing. Phase 3 reindex uses this so a single bad
chunk doesn't discard the 31 in-flight embeddings around it.
- Voyage path: text + image inputs in one batch via content arrays.
- openai-compat path: text + image inputs in one request per input.
- src/core/ssrf-validate.ts (D19): DNS-resolve-and-fetch-by-IP defense
for redirect chains. Closes the DNS-rebinding gap that url-safety.ts'
static check leaves open. Uses node:dns/promises with {all: true,
family: 0} to inspect every A and AAAA record before connecting.
fetchWithSSRFGuard helper validates per-redirect-hop and limits chain
depth (default 3).
- Re-exports from src/core/embedding.ts public seam.
Tests:
- test/embed-multimodal-batching.test.ts (13 cases): text variant, query
inputType discipline, mixed text+image batches, embedQueryMultimodal,
embedQueryMultimodalImage, embedMultimodalSafe happy/empty/all-fail/
mid-batch-recovery/permanent-misconfig.
- test/ssrf-validate.test.ts (20 cases): static rejections via
isInternalUrl, scheme + credentials rejection, DNS rebinding defense
(single-record + multi-record), public happy path, IPv6 literals,
malformed URLs.
No regression in existing voyage-multimodal.test.ts or
openai-compat-multimodal.test.ts (33 cases all pass).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(cross-modal/1): Phase 1 text→image routing + knobsHash + RRF + backfill
Phase 1 of the cross-modal search wave. Wires the existing 1024d Voyage
multimodal embedding space (already populated for image chunks via
importImageFile) into the user-facing query path. Text queries that match
cross-modal intent regex route through Voyage multimodal-3 instead of the
text embedding model, then search content_chunks.embedding_image.
- query-intent.ts: new `suggestedModality: 'text' | 'image' | 'both'`
axis on `QuerySuggestions`. Module-scope CROSS_MODAL_PATTERNS regex
array (D15 — compiled once at module load). Conservative on purpose;
LLM intent escalation (Commit 4) catches genuinely ambiguous phrasings.
- query-intent.ts: new `isAmbiguousModalityQuery(query)` pure heuristic
for Commit 4's escalation gate. Returns true ONLY when regex misses
AND a visual noun + reference marker both fire.
- types.ts: `SearchOpts.crossModal: 'text' | 'image' | 'both' | 'auto'`
+ `SearchResult.modality: 'text' | 'image'` for downstream renderers.
- mode.ts: 7 new knobs in ModeBundle (D2): cross_modal_both_text_weight,
cross_modal_both_image_weight, image_query_text_refinement_weight,
image_query_image_refinement_weight, unified_multimodal,
unified_multimodal_only, cross_modal_llm_intent. All three mode
bundles default to the same values (cross-modal is opt-in).
- mode.ts: D2 cache-key fix — KNOBS_HASH_VERSION bumped 2→3, all 7 new
knobs participate in knobsHash so a text-mode cache hit can't be
served to an image-mode caller.
- mode.ts: D3 registry — all 7 keys land in SEARCH_MODE_CONFIG_KEYS so
`gbrain search modes` / `stats` / `tune` see them.
- hybrid.ts: routing branch at the embed step. Resolves effective
modality from (per-call opts → suggestions → 'text'). Image route:
embedQueryMultimodal + searchVector(embedding_image), skip expansion
+ keyword (D9 mode-bundle override). Both route: parallel text + image
vector searches merged via weighted RRF (D6) with cross_modal_both_*
weights. Fail-open: multimodal misconfigured → structured warn + text
fallback. 'auto' literal normalized to undefined (D22-1).
- operations.ts: thread `cross_modal` param through `query` op.
- backfill-registry.ts: new `modality` backfill kind. SQL filter requires
`chunk_source='image_asset'` (D22-7 defensive guard). Idempotent.
- doctor.ts: `cross_modal_modality_backfill` check surfaces unflagged
image-asset chunks with paste-ready `gbrain backfill modality` hint.
Tests:
- cross-modal-phase1.test.ts (45 cases): regex classification (positive
+ negative + plural-safe), isAmbiguousModalityQuery, D3 registry, D2
knobsHash diffs across all 7 new knobs, MODE_BUNDLES defaults,
resolveSearchMode precedence chain.
- cross-modal-hybrid-integration.test.ts (7 cases): PGLite + stubbed
gateway. Verifies image-modality calls Voyage and not OpenAI, text
calls OpenAI and not Voyage, 'auto' literal normalizes, 'both' mode
hits both endpoints, fail-open routes to text on multimodal misconfig.
- search-mode.test.ts: updated MODE_BUNDLES + KNOBS_HASH_VERSION
assertions (148 cross-suite tests still pass; no regression).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(cross-modal/2): Phase 2 image-as-query + D18 path ban + D23-#6 spend cap
Phase 2 of the cross-modal search wave. Adds the `search_by_image` MCP op,
the SSRF-defended image loader, and the daily per-OAuth-client spend cap
on paid Voyage multimodal calls. D17 honest framing applied: Phase 2 ships
image→similar-images + image-OCR-text retrieval. True image→full-text-
knowledge requires Phase 3's unified column.
- src/core/search/image-loader.ts: loadImageInput accepts local path,
data: URI, or http(s):// URL. Magic-byte sniff for PNG/JPEG/WebP (no
other formats). Hard size cap (10MB local default, 2MB remote default).
http(s) path uses fetchWithSSRFGuard from Commit 0: every redirect hop
re-resolved via DNS lookup + every record checked against the internal
IP deny list. Max 3 redirect hops. 5s total fetch timeout. Pre-flight
Content-Length check + post-fetch size guard for lying servers.
- src/core/search/by-image.ts: searchByImage runs the image branch
always; D13 hybrid intersect runs a parallel text branch when
`query` is provided, merged via weighted RRF. Phase 3 will widen
the column routing to embedding_multimodal once that lands.
- src/core/operations.ts: new search_by_image op (scope: read, NOT
localOnly). D18 P0 — when ctx.remote === true AND image_path is set,
rejects with permission_denied at handler entry (validateParams would
catch it again at dispatch). D5 source-id thread via sourceScopeOpts.
D12 per-param length cap enforced via remote-vs-local maxBytes config
read at handler entry. D23-#6 pre-flight checkBudget + post-call
recordSpend (best-effort; failures don't block response).
- src/core/spend-log.ts: BudgetExceededError + checkBudget + recordSpend
+ getTodaySpendCents. UTC day-aligned aggregation so the cap rolls
over deterministically. Local CLI callers (no clientId) bypass the
gate entirely. Pre-v0.36 brains without the mcp_spend_log table fail
open to spend=0; the migration brings the table in on first start.
- src/core/migrate.ts: new migration v67 mcp_spend_log table + indexes
for the (client_id, day) and (token_name, day) hot reads. PGLite
parity via sqlFor.pglite.
- src/core/search/hybrid.ts: RRF_K constant exported so by-image.ts can
share the same effective-K math as the main hybrid path.
Tests:
- cross-modal-phase2.test.ts (15 cases): magic-byte sniffing (PNG +
JPEG + WebP positive, GIF rejection), oversized rejection (default +
custom cap), data: URI happy path + malformed + decoded-non-image
+ oversized, invalid input shapes (empty + ftp), SSRF defense via
DNS rebinding stub.
- search-by-image-op.test.ts (7 cases): D18 remote image_path
rejection + local CLI accepts; input validation (missing all three /
multiple together); D23-#6 budget block-at-cap + allow-under-cap +
local-CLI-bypass; migration v67 mcp_spend_log table applied cleanly.
All 166 tests across the cross-modal suite pass; no regression in
existing voyage-multimodal / openai-compat-multimodal / search-mode suites.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(cross-modal/3): Phase 3 unified column + reindex + D8 fail-open + D23-#2
Phase 3 of the cross-modal search wave. Adds the unified multimodal column
on content_chunks + the `gbrain reindex --multimodal` sweep + the
`search.unified_multimodal` routing flag with D8 source-aware coverage
guard + fail-open behavior. D17 honest framing: this is the phase that
unlocks true image→full-text-knowledge — Phase 2's searchByImage
transparently upgrades to the richer retrieval once the unified column
has coverage.
D10 reindex-core extraction filed as a follow-up TODO. The existing
markdown reindex walks pages and re-imports via importFromFile; this
walks content_chunks and re-embeds via the gateway. Patterns rhyme but
cores diverge enough that extraction balloons the diff. Both commands
stand alone with their own checkpoint + cost-prompt logic.
- migrate.ts v68 (embedding_multimodal_column): column-only ALTER on
content_chunks. HNSW partial index deferred to post-reindex build
(D20: pgvector docs recommend post-load build for HNSW). Both engines.
- types.ts SearchOpts.embeddingColumn type widened to include
'embedding_multimodal'.
- postgres-engine.ts + pglite-engine.ts searchVector: route to
embedding_multimodal column when opts.embeddingColumn set. NO modality
filter (unified column carries both text + image content).
- hybrid.ts unified routing branch: when search.unified_multimodal=true,
bypasses dual-column branching and runs embedQueryMultimodal +
searchVector(embedding_multimodal). D8 fail-open: zero rows + not
strict-mode → falls through to dual-column text path with structured
warning. search.unified_multimodal_only=true bypasses the fallback.
- src/commands/reindex-multimodal.ts: `gbrain reindex --multimodal`.
D7 lock via tryAcquireDbLock('gbrain-reindex-multimodal'); 6h TTL.
Cost prompt + 10s Ctrl-C grace window in TTY; auto-proceeds non-TTY.
GBRAIN_NO_REEMBED=1 bypass. Checkpoint at
~/.gbrain/reindex-multimodal-checkpoint.json for resume. D23-#2
auto-flip prompt at coverage=100% completion.
- cli.ts: `gbrain reindex --multimodal` dispatch with --limit, --dry-run,
--cost-estimate, --no-embed, --yes, --json flags.
- doctor.ts: unified_multimodal_coverage check (D21 source-aware) +
reports per-source % when search.unified_multimodal is on. Warns at
<95% lowest source; fails when unified_multimodal_only=true AND
lowest source <99%. Falls open to OK when column not yet present.
Tests:
- unified-multimodal.test.ts (8 cases): schema migration v68 applies,
reindex --dry-run + --cost-estimate + GBRAIN_NO_REEMBED bypass +
zero-pending fast-path, hybridSearch unified routing forces voyage
endpoint, D8 fail-open routes to text on empty unified, D8 strict
blocks text fallback.
All 211 tests across the cross-modal + related suite pass; no
regression in voyage-multimodal / openai-compat-multimodal / search-mode
/ intent / search base suites.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(cross-modal/4): LLM intent escalation for ambiguous modality
Commit 4 of the cross-modal search wave (opt-in default off).
When `search.cross_modal.llm_intent` is true AND the regex classifier
returned 'text' AND `isAmbiguousModalityQuery(query)` fires, hybridSearch
awaits a Haiku tie-break via gateway.chat() before routing. The
ambiguous-modality gate (introduced in Commit 1) ensures the LLM call
only fires on the narrow band where regex misses but a visual noun +
reference marker both fire — roughly <1% of queries with the flag on.
- src/core/search/llm-intent.ts: new module. `classifyModalityWithLLM`
routes through gateway.chat() with a fixed system prompt ("Output
exactly one word: text, image, or both"). 1s timeout via AbortController.
`parseModality` is a pure exported helper that tolerates trailing
punctuation + casing. Fail-open on every error path (gateway
unavailable, timeout, parse failure, unrecognized output).
- src/core/search/hybrid.ts: escalation branch slots BEFORE the unified
routing branch. Gated by: no explicit per-call crossModal opt, regex
result == 'text', config flag on, ambiguity heuristic fires. Fail-open
to regex result on any error from the LLM tie-break.
Tests:
- llm-intent-escalation.test.ts (14 cases): parseModality tolerance
matrix (text / image / both / trailing punct / whitespace /
unrecognized / empty), classifyModalityWithLLM happy paths for all 3
outputs, fail-open on throw / unrecognized output / gateway-not-
configured, explicit-fallback-honored.
- llm-intent-hybrid-integration.test.ts (6 cases): hybridSearch
escalation gate fires ONLY when flag-on + ambiguous; off when flag-off,
unambiguous, regex-confident, or explicit per-call opt set; fail-open
on LLM throw.
All 231 tests across the cross-modal + related suite pass; no
regression in voyage-multimodal / openai-compat-multimodal /
search-mode / intent / search base suites.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(cross-modal/3): verify-gate fixes for full test suite
Three small fixes to pass the full unit + E2E sweep after the cross-modal
wave commits land.
- migrate.ts v67: drop date_trunc('day', created_at) from
mcp_spend_log indexes. TIMESTAMPTZ truncation depends on session
timezone and isn't IMMUTABLE, so Postgres rejects the function in
the index expression with SQLSTATE 42P17. BTREE on
(client_id, created_at) covers the per-day rollup query via range
scan on created_at — same performance, no IMMUTABLE constraint.
- pglite-schema.ts + src/schema.sql: shorten the embedding_multimodal
column comment. The longer version contained a comma inside a SQL
line comment ("...search.unified_multimodal=true, all queries..."),
which broke parseBaseTableColumns in test/schema-bootstrap-coverage
(the parser splits on commas at depth-0 before stripping comments,
so the comma inside the comment shortened the column-definition part
and an "all" token from "all queries" got picked up as the next
column name — silently hiding embedding_multimodal from coverage).
- schema-embedded.ts: regenerated via `bun run build:schema`.
- test/e2e/v030_1-integration-pglite.test.ts: listBackfills assertion
extended to include the new `modality` entry registered in
src/core/backfill-registry.ts as part of Commit 1.
- test/search/knobs-hash-reranker.test.ts: KNOBS_HASH_VERSION assertion
updated from 2→3 to match the cross-modal-wave hash-key extension
(D2 cache contamination fix). Same shape as the prior
v0.32→v0.35 bump.
- test/unified-multimodal.test.ts: migrated process.env mutation to
withEnv() helper to satisfy the scripts/check-test-isolation R1
rule.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* docs(cross-modal): VERSION + CHANGELOG + CLAUDE.md + spec doc + llms regen
Final docs commit for the cross-modal wave (v0.36.0.0).
- VERSION + package.json: bump 0.35.5.1 → 0.36.0.0
- CHANGELOG.md: full Garry-voice release entry with five-commit breakdown,
the-numbers-that-matter table, what-this-means-for-you, and the
required to-take-advantage-of-v0.36.0.0 block
- docs/issues/cross-modal-search.md: cherry-picked from PR #1127 head
(164 lines, the original spec doc preserved as historical reference
for Phase 2 + 3 background)
- CLAUDE.md: Key Files entries for src/core/ssrf-validate.ts,
src/core/search/image-loader.ts, src/core/search/by-image.ts,
src/core/search/llm-intent.ts, src/core/spend-log.ts,
src/commands/reindex-multimodal.ts, plus extension annotations on
src/core/search/query-intent.ts, src/core/search/mode.ts,
src/core/search/hybrid.ts, src/core/backfill-registry.ts,
src/core/migrate.ts (v67 + v68)
- llms-full.txt + llms.txt: regenerated via `bun run build:llms`
`bun run verify` clean (privacy + proposal-pii + test-names + jsonb +
source-id-projection + progress + test-isolation + wasm + admin-build +
admin-scope-drift + cli-exec + system-of-record + eval-glossary +
typecheck). `bun test test/build-llms.test.ts` clean (7/7).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(cross-modal): renumber migrations 67→69 + 68→70 post-master-merge
Master shipped its own v67 (`facts_typed_claim_columns`) during the
cross-modal wave's review cycle. The merge picked up both side's v67
entries, breaking the migration-distinct-versions test. Renumbering
moves cross-modal's table + column ALTER off the collision:
- v67 mcp_spend_log → v69 mcp_spend_log
- v68 embedding_multimodal_column → v70 embedding_multimodal_column
References updated in CHANGELOG, CLAUDE.md, pglite-schema.ts, schema.sql.
schema-embedded.ts regenerated. llms-full.txt regenerated.
7006 unit tests pass, 0 fail. No test code touched — just version
renumbering plus comment refs.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* chore: bump version 0.36.0.0 → 0.36.4.0
Bumping to v0.36.4.0 to land in the queue slot the user requested.
No behavior change; pure version bump across VERSION, package.json,
CHANGELOG.md header, llms-full.txt regen.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
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 v0.36.4.0 — Your agent drives the brain to 90/100 by itself. One command does the loop you used to run by hand: gbrain doctor --remediate --yes --target-score 90 --max-usd 5. It computes a dependency-ordered plan (sync before extract, embed after consolidate), submits each step as a Minion job, re-checks score between every step, and refuses to spend past your cost cap. Cron can drive it unattended. gbrain doctor --remediation-plan --json previews what would run. Autopilot now does the same thing on its 5-minute tick: small problems get targeted handlers, big problems get the full cycle, a healthy brain sleeps for 60 minutes instead of grinding through synthesize+patterns+embed every tick. Eleven new things you can submit as background jobs (reindex, repair-jsonb, orphans, integrity, purge, plus six cycle phases); three of them (synthesize, patterns, consolidate) are PROTECTED so an MCP-connected agent can't silently burn Anthropic credits. New --background flag on gbrain embed submits the job and exits with job_id=N for shell composition.
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 scaffold into your agent's workspace.
gbrain init --pglite
gbrain skillpack scaffold --all # or: scaffold <name> per skill
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. Scaffolded skills are first-class members of your agent repo — you own them, edit freely; gbrain skillpack reference <name> diffs your copy against gbrain's bundle when you want to pull upstream improvements. (The legacy gbrain skillpack install managed-block model was retired in v0.36.0.0; run gbrain skillpack migrate-fence once if you're upgrading from an older release.)
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: Phone calls create brain pages via Twilio + OpenAI Realtime (or DIY STT+LLM+TTS). Setup recipe:
recipes/twilio-voice-brain.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.