gbrain/TODOS.md

TODOS

P1

Batch embedding queue across files

What: Shared embedding queue that collects chunks from all parallel import workers and flushes to OpenAI in batches of 100, instead of each worker batching independently.

Why: With 4 workers importing files that average 5 chunks each, you get 4 concurrent OpenAI API calls with small batches (5-10 chunks). A shared queue would batch 100 chunks across workers into one API call, cutting embedding cost and latency roughly in half.

Pros: Fewer API calls (500 chunks = 5 calls instead of ~100), lower cost, faster embedding.

Cons: Adds coordination complexity: backpressure when queue is full, error attribution back to source file, worker pausing. Medium implementation effort.

Context: Deferred during eng review because per-worker embedding is simpler and the parallel workers themselves are the bigger speed win (network round-trips). Revisit after profiling real import workloads to confirm embedding is actually the bottleneck. If most imports use --no-embed, this matters less.

Implementation sketch: src/core/embedding-queue.ts with a Promise-based semaphore. Workers await queue.submit(chunks) which resolves when the queue has room. Queue flushes to OpenAI in batches of 100 with max 2-3 concurrent API calls. Track source file per chunk for error propagation.

Depends on: Part 5 (parallel import with per-worker engines) -- already shipped.

P0

Fix bun build --compile WASM embedding for PGLite

What: Submit PR to oven-sh/bun fixing WASM file embedding in bun build --compile (issue oven-sh/bun#15032).

Why: PGLite's WASM files (~3MB) can't be embedded in the compiled binary. Users who install via bun install -g gbrain are fine (WASM resolves from node_modules), but the compiled binary can't use PGLite. Jarred Sumner (Bun founder, YC W22) would likely be receptive.

Pros: Single-binary distribution includes PGLite. No sidecar files needed.

Cons: Requires understanding Bun's bundler internals. May be a large PR.

Context: Issue has been open since Nov 2024. The root cause is that bun build --compile generates virtual filesystem paths (/$bunfs/root/...) that PGLite can't resolve. Multiple users have reported this. A fix would benefit any WASM-dependent package, not just PGLite.

Depends on: PGLite engine shipping (to have a real use case for the PR).

ChatGPT MCP support (OAuth 2.1)

What: Add OAuth 2.1 with Dynamic Client Registration to the self-hosted MCP server so ChatGPT can connect.

Why: ChatGPT requires OAuth 2.1 for MCP connectors. Bearer token auth is NOT supported. This is the only major AI client that can't use GBrain remotely.

Pros: Completes the "every AI client" promise. ChatGPT has the largest user base.

Cons: OAuth 2.1 is a significant implementation: authorization endpoint, token endpoint, PKCE flow, dynamic client registration. Estimated CC: ~3-4 hours.

Context: Discovered during DX review (2026-04-10). All other clients (Claude Desktop/Code/Cowork, Perplexity) work with bearer tokens. The Edge Function deployment was removed in v0.8.0. OAuth needs to be added to the self-hosted HTTP MCP server (or gbrain serve --http when implemented).

Depends on: gbrain serve --http (not yet implemented).

P1 (new from v0.7.0)

Constrained health_check DSL for third-party recipes

What: Replace shell command health_checks with a typed DSL: {type: "env_exists", name: "KEY"}, {type: "url_responds", url: "..."}, {type: "heartbeat_fresh", max_age: "24h"}.

Why: Shell commands in recipe frontmatter = arbitrary code execution from markdown. Currently trusted because recipes are first-party only. This DSL is the mandatory gate before opening community recipe submissions.

Pros: Eliminates RCE risk from third-party recipes. Health checks become machine-parseable.

Cons: Less flexible than shell commands for novel checks. Need to define enough check types to cover common cases.

Context: From CEO review + Codex outside voice (2026-04-11). User approved shell commands for first-party but explicitly requested constrained DSL before third-party recipes.

Depends on: v0.7.0 recipe format (shipped).

P2

Community recipe submission (gbrain integrations submit)

What: Package a user's custom integration recipe as a PR to the GBrain repo. Validates frontmatter, checks constrained DSL health_checks, creates PR with template.

Why: Turns GBrain from "Garry's integrations" into a community ecosystem. The recipe format IS the contribution format.

Pros: Community-driven integration library. Users build Slack-to-brain, RSS-to-brain, Discord-to-brain.

Cons: Support burden. Need constrained DSL (P1) before accepting third-party recipes. Need review process for recipe quality.

Context: From CEO review (2026-04-11). User explicitly deferred due to bandwidth constraints. Target v0.9.0.

Depends on: Constrained health_check DSL (P1).

Always-on deployment recipes (Fly.io, Railway)

What: Alternative deployment recipes for voice-to-brain and future integrations that run on cloud servers instead of local + ngrok.

Why: ngrok free URLs are ephemeral (change on restart). Always-on deployment eliminates the watchdog complexity and gives a stable webhook URL.

Pros: Stable URLs, no ngrok dependency, production-grade uptime.

Cons: Costs $5-10/mo per integration. Requires cloud account.

Context: From DX review (2026-04-11). v0.7.0 ships local+ngrok as v1 deployment path.

Depends on: v0.7.0 recipe format (shipped).

gbrain serve --http + Fly.io/Railway deployment

What: Add gbrain serve --http as a thin HTTP wrapper around the stdio MCP server. Include a Dockerfile/fly.toml for cloud deployment.

Why: The Edge Function deployment was removed in v0.8.0. Remote MCP now requires a custom HTTP wrapper around gbrain serve. A built-in --http flag would make this zero-effort. Bun runs natively, no bundling seam, no 60s timeout, no cold start.

Pros: Simpler remote MCP setup. Users run gbrain serve --http behind ngrok instead of building a custom server. Supports all 30 operations remotely (including sync_brain and file_upload).

Cons: Users need ngrok ($8/mo) or a cloud host (Fly.io $5/mo, Railway $5/mo). Not zero-infra.

Context: The production deployment at wintermute uses a custom Hono server wrapping gbrain serve. This TODO would formalize that pattern into the CLI. ChatGPT OAuth 2.1 support depends on this.

Depends on: v0.8.0 (Edge Function removal shipped).

Completed

Implement AWS Signature V4 for S3 storage backend

Completed: v0.6.0 (2026-04-10) — replaced with @aws-sdk/client-s3 for proper SigV4 signing.