Garry TanandClaude Opus 5
4d6599dd4c
ZeroEntropy's hosted API shuts down 2026-09-04. `zeroentropyai:zembed-1` has been DEFAULT_EMBEDDING_MODEL since v0.36.2.0, so every brain that never picked a model explicitly was going to lose semantic retrieval on that date — query embedding runs through the same endpoint, so existing vectors go unqueryable too, not just new writes. New default: openai:text-embedding-3-small at 1280 dimensions. 1280, not 1536, is load-bearing. OpenAI text-embedding-3-* is Matryoshka and `isValidOpenAITextEmbedding3Dim` accepts any integer width up to the model's native size (1536 for -small), so a brain created under the previous 1280-wide ZE default keeps its existing vector(1280) column AND its HNSW index. `applyEmbeddingMigration` only calls `runSchemaTransition` when `col.dims !== plan.to_dims`, so migrating at the same width rebuilds vectors only: no dimension transition, no ALTER, no index rebuild. Two things had to move for the new default to actually work: - The openai recipe's `dims_options` omitted 1280. That list is Tier 1 in `isCustomDimValidForProvider` and wins over the Tier-2 Matryoshka range check, so `resolveSchemaEmbeddingDim` REJECTED the shipped default config — `gbrain init` would have refused its own default. Verified by probe before and after. - The recipe listed text-embedding-3-large first. `init`'s env detection picks `models[0]` and only adopts DEFAULT_EMBEDDING_DIMENSIONS when that equals the canonical default, so a fresh OPENAI_API_KEY-only install would have landed on 3-large@1536 and the declared default would have been unreachable. Sunset banner (scaffolded in #3459) now names the concrete target and passes `--dim` at the brain's current width, and reads the DB config plane as well as the file plane — with the default no longer a ZE model, brains that never wrote `embedding_model` to ~/.gbrain/config.json would otherwise stop being detected. Adds a Default-provider policy to CLAUDE.md: a gbrain DEFAULT embedding or reranking model must be either open-weight, or from the vendor with the longest proven model-lifetime record. Novel/startup providers ship as opt-in recipes, never as the default. Also fixes a pre-existing ambient-env flake: test/e2e/fresh-install-pglite.test.ts cleared a hardcoded pair of provider keys, so it failed on any machine with a third provider key set (reproduced on master). It now clears every non-OpenAI embedding provider key enumerated from the recipe registry. The ZeroEntropy reranker default is deliberately unchanged — no replacement has been chosen. Its sunset is called out in the banner, README, and provider doc. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Tutorials
Step-by-step walkthroughs that take you from zero to a working outcome. Concrete commands, real numbers, no abstraction-first jargon. Each tutorial assumes no prior GBrain knowledge.
Shipped
- Set up your personal AI agent + brain from zero — the canonical solo install. Two GitHub repos, a Telegram bot, AlphaClaw on Render, OpenClaw + GBrain + Supabase. End-to-end in about 2 hours; about $100 to $150 a month sustained. The full-stack install I'd run today.
- Set up GBrain as your company brain — federated, multi-user, OAuth-scoped institutional memory for a 10-50 person team. Three sources (shared / customers / internal-only), per-user scope, first synthesized query as a teammate. About 90 minutes end-to-end, about $5 in API calls for the demo, under $100 a month sustained for a 25-person company.
- Auto-improve a skill with
gbrain skillopt— treat aSKILL.mdas the trainable parameter of a frozen agent. Write your first benchmark from scratch (the part everyone gets stuck on), preview the cost, run the optimizer, read accepted vs no_improvement vs aborted, and accept a measurably better skill. About 20 minutes, about $1 in API calls. Reference:../guides/skillopt.md. - Give your coding agent a memory: GBrain + Claude Code / Codex — the two-funnel walkthrough for coding-agent users. Path A: connect Claude Code / Codex to a brain you already run (OpenClaw, Hermes, any
gbrain serve --http). Path B: start from nothing with a 2-second local PGLite brain. Both end with the brain-first protocol you paste intoCLAUDE.md/AGENTS.mdand the four habits (brain-first lookup, ambient capture, briefing-from-your-brain, whoknows) that make it worth it. About 10 minutes.
In progress
These are the next tutorials on the roadmap. Open an issue if one of them is the one you need most; that's how we'll prioritize.
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Set up GBrain for VC dealflow — the operator's recipe. People pages for founders, companies with typed Facts fence carrying ARR / team-size / runway across dates, meetings auto-ingested, deal pages linking everything. Shows
gbrain whoknows,gbrain find_trajectory, andgbrain founder scorecardon real workflows. -
Migrate your existing vault into GBrain — for Notion / Obsidian / Roam users with a vault that doesn't match GBrain's default layout. Walks through
gbrain schema detect→suggest→review-candidatesso the brain learns your shape instead of forcing you to learn its. -
Index your codebase as a code brain — for developers. Initialize a brain in a code repo, swap to
voyage-code-3for embeddings, usegbrain code-def/gbrain code-refs/gbrain code-callersto navigate the codebase semantically from any MCP-aware editor. -
Run GBrain fully local with Ollama or llama.cpp — for privacy-first deployments. No cloud calls, no API keys, no telemetry. Trades some retrieval quality for full local control. Useful for regulated industries, air-gapped environments, or just paranoia.
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Set up the dream cycle — the overnight enrichment daemon that makes the brain self-maintaining. Fixes citations, dedupes people pages, surfaces contradictions, generates founder scorecards on the schedule you configure. The piece that turns a static knowledge base into a brain that gets smarter while you sleep.
Want to write one?
Tutorials follow the Diataxis tutorial pattern: learning-oriented, walks a learner from zero to a working result in one session, every step produces a visible change. If you've used GBrain for something interesting and want to write the walkthrough, the existing company-brain.md is the model. Open a PR.
Related documentation
- Reference:
docs/architecture/— system design, topologies, retrieval theory - How-to:
docs/guides/— task-oriented runbooks (sub-agent routing, minion deployment, skill development, brain-first lookup, idea capture, diligence ingestion). Highlight: scaling skills past 300 — the three-tier architecture for agents that have outgrown the always-loaded skill manifest. - Integrations:
docs/integrations/— connecting external data sources (voice, email, calendar, embedding providers) - MCP setup:
docs/mcp/— per-client setup (Claude Desktop, Code, Cursor, ChatGPT, Perplexity, Cowork) - Install paths:
docs/INSTALL.md— every install path, end to end