learning_ai_common_plat/docs/devops/vercel/ROADMAP_LOCALMEMGPT_WEB.md

LocalMemGPT Web — Vercel Deployment Roadmap

App: @localmemgpt/web Repo: learning_ai_local_memory_gpt → web/ Status: 🔴 Blocked — file: ref + hardcoded standalone + local-first architecture

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Current State

• Framework: Next.js 16.1.6, React 19.2.3, TailwindCSS v4
• Output: ❌ Hardcoded output: 'standalone'
• Dependencies: 7 @bytelyst/* — 1 via file: ref (ui), 6 via Gitea
• Build: next build --webpack
• Architecture: Thin web client that connects to local Fastify backend (port 4019)
• Backend dependency: SQLite + Ollama — both local-only, NOT cloud-compatible

Gaps to Fix

Gap 1: Convert file: ref (~5 min)

File: web/package.json

- "@bytelyst/ui": "file:../../learning_ai_common_plat/packages/ui",
+ "@bytelyst/ui": "^0.1.0",

• Update package.json
• Run pnpm install
• Verify build

Gap 2: Make output Vercel-aware (~5 min)

File: web/next.config.ts

- output: 'standalone',
+ ...(process.env.VERCEL ? {} : { output: 'standalone' }),

• Update next.config.ts

Gap 3: Registry Access (~5 min)

Once Caddy is configured on the Azure VM (see SECURE_API_EXPOSURE.md), Gitea will be accessible at https://gitea.bytelyst.com.

• Update .npmrc:

  @bytelyst:registry=https://gitea.bytelyst.com/api/packages/ByteLyst/npm/
  //gitea.bytelyst.com/api/packages/ByteLyst/npm/:_authToken=${GITEA_NPM_TOKEN}
  

• Set GITEA_NPM_TOKEN as a Vercel environment variable

Gap 4: Backend Connectivity (⚠️ Architecture concern)

The web app is a thin client — all data comes from the local Fastify backend (SQLite + Ollama). On Vercel, there's no backend to connect to unless:

Options:

• A) Deploy web as static shell — Vercel serves the UI, user configures NEXT_PUBLIC_BACKEND_URL to point to their self-hosted backend (e.g., home server, VPS)
• B) Skip Vercel — this is inherently a local-first app, Docker/self-hosted is the natural fit
• C) Hybrid — deploy landing page + docs on Vercel, keep app itself self-hosted

Recommended: Option A — deploy as configurable shell. Users point it at their own backend.

Deployment Steps

Step 1: Fix file: ref (~5 min)

Step 2: Fix output mode (~5 min)

Step 3: Fix registry access (~5 min)

Step 4: Create Vercel Project (~5 min)

• Connect learning_ai_local_memory_gpt repo
• Set Root Directory to web
• Framework preset: Next.js

Step 5: Environment Variables (~5 min)

• NEXT_PUBLIC_BACKEND_URL = https://api.bytelyst.com/localmemgpt (or user's self-hosted URL)

Step 6: Verify (~5 min)

• Verify app loads with "no backend" graceful error
• Test with a publicly accessible backend URL

Step 7: Domain (~5 min)

• Add custom domain (e.g., app.localmemgpt.app)

Risks

• Local-first tension: This app is designed to run locally. Vercel deployment is useful only as a hosted shell pointing to a user's backend.
• SSE streaming: Chat and compare features use SSE — same Vercel timeout constraints as ActionTrail.
• No server-side routes: The web app has no API routes — all logic is in the backend.

Estimated Total Effort: ~35 minutes (technical), but architectural decision needed first