a55de71221
* feat(core): Levenshtein helper + preflight schema-dim resolvers Foundation for v0.37.10.0 env-detection wave. Two pure modules: - src/core/levenshtein.ts: editDistance(a,b) + suggestNearest(input, candidates, maxDistance). Used by config-set "did you mean" suggestions and env-var typo detection at init. - src/core/embedding-dim-check.ts: resolveSchemaEmbeddingDim() + resolveSchemaMultimodalDim() pure functions. Validate resolved dim against recipe default_dims + per-provider Matryoshka allow-lists (OpenAI text-3, Voyage flexible-dim, ZeroEntropy zembed-1) BEFORE any DB write. Plus EmbeddingDisabledError + assertEmbeddingEnabled() runtime guard for the deferred-setup path (D9). New PGVECTOR_COLUMN_MAX_DIMS=16000 exported. Tests: 41 unit cases across both modules. * feat(providers): extract formatRecipeTable + add init provider picker Two changes prepping the env-detection wave: - providers.ts: extract formatRecipeTable() helper from runList(). Picker reuses it so UI can't drift from \`gbrain providers list\`. Also adds the codex finding #10 warn-line to \`providers test\` when the tested model differs from the configured default ("Note: tested X in isolation; gbrain's configured embedding is Y — this test does NOT verify your brain's active path."). envReady() takes an explicit env arg for testing. - init-provider-picker.ts (NEW): interactive picker mirroring init-mode-picker.ts. Filters candidate recipes to env-ready ones (codex finding #3), prompts via readLineSafe, exports printSubagentAnthropicCaveat() for shared use from initPGLite/initPostgres. Tests: 17 unit cases (10 providers + 7 picker). * feat(config): embedding_disabled sentinel + strict unknown-key rejection Two changes for the v0.37.10.0 wave: - src/core/config.ts: add embedding_disabled?:boolean to GBrainConfig (D9 deferred-setup sentinel, mutually exclusive with embedding_model). Export KNOWN_CONFIG_KEYS (60+ canonical keys, file-plane + DB-plane) and KNOWN_CONFIG_KEY_PREFIXES (search., models., dream., cycle., etc.) for validation use. - src/commands/config.ts: D6 strict-default unknown-key rejection. Unknown key + no --force → exit 1 with Levenshtein suggestion against KNOWN_CONFIG_KEYS. Prefix matches accepted without --force. --force escape hatch accepts arbitrary keys with stderr WARN. Closes the silent-no-op class the bug reporter hit (embedding.provider, embedding.model, embedding.dimensions all exit 1 with right suggestion). Tests: 19 unit cases pinning the bug-reporter regression + gate logic. * feat(init): env-detection auto-pick + preflight + atomic persist + --no-embedding Core of the v0.37.10.0 wave (D1-D7, D9-D11). Closes the bug where a fresh \`gbrain init --pglite\` silently produced a broken brain when no provider key matched the v0.36 default. resolveAIOptions rewritten with per-touchpoint env detection: - Explicit flag → shorthand → env auto-pick (group by provider id, codex #2) - Picker fires when multiple providers env-ready (D1+D2 hybrid) - Non-TTY zero-key exits 1 with paste-ready setup hint (D3) + Levenshtein typo detection for OPENAPI_API_KEY → OPENAI_API_KEY (D13) - All three touchpoints covered (embedding + expansion + chat, D4) - Local-only providers (Ollama/llama-server) excluded from auto-pick; picking Ollama silently when user has OPENAI_API_KEY set was wrong UX initPGLite + initPostgres: - Drop conditional configureGateway gate → always call before initSchema - Preflight resolveSchemaEmbeddingDim() BEFORE engine.initSchema() (D11) — invalid dim refuses with paste-ready hint, no disk write - Atomic embedding-config persistence (codex #13): either resolved tuple or embedding_disabled:true sentinel, never partial state - Post-initSchema invariant assertion stays as regression guardrail - --no-embedding opt-in flag (D9) for deferred-setup mode - Subagent-Anthropic caveat (D7) fires post-init when chat_model is non-Anthropic AND ANTHROPIC_API_KEY missing Exported groupReadyByProvider() + findEnvKeyTypos() for unit testing. Tests: 21 unit cases covering provider grouping + typo detection edge cases. * feat(embed,import): refuse cleanly when --no-embedding deferred-setup is active T7 of the v0.37.10.0 wave. Both runEmbedCore and runImport now call assertEmbeddingEnabled(loadConfig()) at entry. When the brain was init'd with --no-embedding (config has embedding_disabled:true), they exit 1 with a paste-ready hint: gbrain config set embedding_model <provider>:<model> gbrain config set embedding_dimensions <N> gbrain init --force --embedding-model <provider>:<model> \`gbrain import --no-embed\` flag still works (chunks land without vectors), so users can still ingest in deferred-setup mode and backfill embeddings later with \`gbrain embed --stale\`. * feat(doctor): empty-config drift detection + subagent-Anthropic caveat extension Two doctor check extensions for v0.37.10.0: T9 — embedding_provider check extended for the v0.36 silent-default repair case. When config is empty AND schema column dim differs from the gateway-resolved default, surface the mismatch with empty-brain vs non-empty-brain repair branching (codex finding #7 nuance): - Empty brain (0 embedded chunks) → \`gbrain init --force --pglite --embedding-model <id> --embedding-dimensions <N>\` (drop and re-init) - Non-empty brain → \`gbrain retrieval-upgrade --to <id> --reindex\` Gated on totalChunks > 0 so pristine empty brains aren't pre-warned. Never recommend rm -rf ~/.gbrain. T10 — subagent_provider check (v0.31.12) extended per D7. When chat_model is non-Anthropic AND ANTHROPIC_API_KEY is missing, warn that subagent features (gbrain dream, gbrain agent run, gbrain autopilot) will fail at job submission. Chat alone (gbrain think) still works. * feat(reindex,test): multimodal preflight + E2E suite for fresh PGLite init T11 — reindex-multimodal.ts: hook resolveSchemaMultimodalDim() preflight BEFORE the reindex sweep. Mirrors the text-side contract from initPGLite — if the configured multimodal model can't produce a dim matching the schema column, fail loud here with a \`gbrain config set\` hint rather than mid-reindex with a vector(N) INSERT error. T12 — test/e2e/init-fresh-pglite.test.ts (NEW, 14 cases): subprocess-driven E2E verification of the bug-reporter's repro scenarios: - Happy path: OPENAI_API_KEY set → auto-pick OpenAI, persists config - D3 non-TTY fail-loud (with and without env-key typos) - D6 regression: bug-reporter's three no-op config keys all exit 1 with Levenshtein suggestions - D9 deferred-setup mode + gbrain import refusal (and --no-embed bypass) - D11 preflight refuses BEFORE any disk write - Explicit --embedding-model wins over env detection Each test uses its own throw-away GBRAIN_HOME for hermetic runs. * docs: env-detection + headless-install + close v0.32 picker TODO T13/T14 docs sync for v0.37.10.0: - docs/integrations/embedding-providers.md: TL;DR table refreshed to reflect ZE as v0.36 default; added "Init resolves your provider from env keys" section explaining the auto-pick → picker → fail-loud chain; added "If first import fails" troubleshooting block pointing at gbrain doctor instead of \`rm -rf ~/.gbrain\`. - docs/operations/headless-install.md (NEW): Docker/CI sequencing guide. Two acceptable patterns — provider key at build time (Pattern 1) or --no-embedding opt-in + runtime config (Pattern 2). Codex finding #11. - README.md: Troubleshooting section with one-paragraph repair hint and links to embedding-providers.md + headless-install.md. - TODOS.md: closed v0.32.x "interactive provider chooser" entry as SUPERSEDED by this wave. Added four follow-up entries (dedicated v0.36 broken-install migration, namespaced ext fields, runtime config-key audit, value-level Levenshtein on config set). * chore: bump version and changelog (v0.37.10.0) Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> * fix(doctor): empty brain scores 100/100 + hermetic doctor-report-remote test Two fixes coupled because the test couldn't pass without the formula fix: src/core/pglite-engine.ts + src/core/postgres-engine.ts — empty brain (pageCount === 0) now gets FULL marks (100/100), not 0/100. Semantically an empty brain has no coverage problem to penalize — there's nothing to embed, nothing to link, nothing to orphan. Vacuous truth applies. The pre-fix "empty = 0" caused fresh-init brains to score as critically unhealthy on \`gbrain doctor\`, which was a structural surprise to users who'd just run init successfully. Same fix on both engines. test/brain-score-breakdown.test.ts — updated the "empty brain" assertion to match the new contract (was: 0/0/0/0/0/0; is: 100/35/25/15/15/10). test/doctor-report-remote.test.ts → renamed to .serial.test.ts and made hermetic. The pre-fix test pulled audit data from the host ~/.gbrain (reranker_health, sync_failures, etc.), which made the assertion non-deterministic depending on whoever ran the suite. Now isolates GBRAIN_HOME to a tempdir via beforeAll/afterAll; env mutation requires serial-quarantine per scripts/check-test-isolation.sh R1. Closes the master-state flake that was failing on every \`bun run test\` run regardless of my branch contents. * docs: update CLAUDE.md and TODOS.md for v0.37.10.0 empty-brain fix - CLAUDE.md: annotate src/core/pglite-engine.ts + src/core/postgres-engine.ts entries with v0.37.10.0 empty-brain 100/100 contract. Vacuous truth: an empty brain has no coverage to penalize, so getBrainScore returns full marks (35/25/15/15/10 breakdown) when pageCount === 0. Pre-fix 0/100 was structurally surprising on fresh init and caused the v0.37.8.0 doctor-report-remote.test.ts flake. - TODOS.md: mark P0 doctor-report-remote.test.ts:65 TODO completed (resolved by commit 9aa571f3's empty-brain-100/100 fix; test renamed to .serial.test.ts and made hermetic per scripts/check-test-isolation.sh R1). - llms-full.txt: regenerated from updated CLAUDE.md per CLAUDE.md "Auto-derived files" rule. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> * fix(init): D5 persisted-config-wins on re-init + CI mechanical E2E Two coupled fixes for the v0.37.10.0 wave's interaction with CI's Tier-1 mechanical E2E suite (which runs without any embedding-provider env var). src/commands/init.ts — Honor D5 properly at resolveAIOptions entry. Pre-fix the env-detection branch fired on EVERY init regardless of persisted config. A non-TTY re-init with no env keys exited 1 (D3 fail-loud) even when ~/.gbrain/config.json already had embedding_model set from a prior successful init. Now resolveAIOptions reads loadConfig() first and seeds out.embedding_model / embedding_dimensions / expansion_model / chat_model from the file plane BEFORE running env detection. Also honors embedding_disabled (D9 sentinel) on re-init so deferred-setup brains don't re-trigger fail-loud. test/e2e/mechanical.test.ts:722 — Setup Journey's first init runs against a fresh DB with no persisted config. Pass --embedding-model explicitly (openai:text-embedding-3-large) so the preflight resolves offline. After this init writes config, subsequent inits in the file (RLS self-heal v24, RLS event-trigger probes, etc.) honor the persisted config via the D5 fix above. Verified locally: full test/e2e/mechanical.test.ts → 78 pass / 0 fail. --------- Co-authored-by: Claude Opus 4.7 <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: 16 recipes covering OpenAI (default fallback), OpenRouter, 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.
Troubleshooting
gbrain import fails with expected N dimensions, not M? Run gbrain doctor. It will print the exact gbrain config set ... or gbrain retrieval-upgrade command to repair the mismatch. You should not need to delete ~/.gbrain. As of v0.37, fresh gbrain init --pglite auto-detects your embedding provider from API keys in your environment — set OPENAI_API_KEY (or ZEROENTROPY_API_KEY / VOYAGE_API_KEY) before running init, or pass --embedding-model <provider>:<model> explicitly. With multiple keys set, init fires an interactive picker. In non-TTY contexts (CI, Docker) with no keys, init exits 1 with a paste-ready setup hint; pass --no-embedding to defer setup until runtime. See docs/integrations/embedding-providers.md for the full provider matrix and docs/operations/headless-install.md for Docker/CI sequencing.
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.