Open-Source Stack for Building Micro-Apps: Tools, Templates, and Integration Recipes

Build micro-apps fast without reinventing the wheel — a curated open-source stack

You're under pressure to ship a small, useful app in days, not months. You need auth that just works, a lightweight backend, a frontend that snaps together, and—now—an LLM connector that doesn't leak secrets or explode your bill. This guide gives you a tested, open-source stack and plug-and-play integration recipes to build micro-apps in 2026: frontend, backend, auth, LLM connectors, deployment, and observability.

Why micro-apps matter in 2026

Micro-apps—personal apps, internal utilities, or ephemeral consumer experiences—exploded as a category in 2024–2026 because AI tooling and local LLMs made it possible for small teams (or individuals) to deliver value fast. Non-developers and devs alike are "vibe-coding" prototypes using LLMs and minimal infra. Hardware advances (for example, the AI HAT+ 2 for Raspberry Pi 5) and local-AI browsers show a parallel trend: compute is moving to the edge and device layer, letting micro-apps run offline or cheaply at scale.

"Micro apps are fast, personal, and fleeting — they solve a specific need without a full product roadmap."

The curated open-source stack at a glance

Below is a compact, practical stack that balances developer DX and operational simplicity. Each tool is chosen for adoption in 2026, open-source licensing, and integration fit for micro-apps.

• Frontend: Vite + SvelteKit or SolidStart for small, fast UIs; Astro for content-first micro-apps.
• Backend / API: Deno (serverless/edge) or Fastify/Bun for node-based microservices; tRPC or REST for API contract.
• Database: Postgres (managed or self-hosted) with Drizzle or Prisma for type-safe queries; Supabase as an option for realtime & auth-adjacent features.
• Auth: Ory (Kratos + Oathkeeper) for full control, or NextAuth/Auth.js and SuperTokens for fast integration if you use Next.js/SvelteKit.
• LLM connectors: LangChain.js for orchestration, OpenAI/HuggingFace for hosted models, and llama.cpp / gpt4all / text-generation-inference for local models.
• Deployment: Docker + GitHub Actions to Render / Fly / self-hosted k3s / CapRover; Cloudflare Workers or Deno Deploy for edge micro-apps.
• Observability: Prometheus + Grafana, OpenTelemetry + a managed or open backend for traces and metrics.

Why these choices?

They minimize vendor lock-in, have active OSS communities in 2026, and cover the most common micro-app needs: tiny latency, low-cost compute, and easy local development. You can swap components depending on constraints (e.g., use Supabase to skip DB ops entirely, or host a local LLM for privacy-sensitive apps).

Starter templates & repositories (project-based learning)

Pick a starter that matches your app’s constraints. Each template below is trimmed to be forkable and deployable in a day.

• SvelteKit + tRPC + Drizzle + Postgres — fast SSR micro-app with type-safe backend. Ideal for internal tools and dashboards.
• Astro + Edge Functions + Supabase — content-first micro-app with zero backend ops; good for personal apps and docs with small dynamic features.
• Deno + Oak + LangChain — lightweight LLM-first API to run on Deno Deploy or an edge platform.
• Raspberry Pi local LLM starter — bootstrap for running llama.cpp (GGML) on a Pi 5 with the AI HAT+ 2 and exposing a secure local HTTP inference endpoint.

Commands (example):

git clone https://github.com/yourorg/microapp-sveltekit-trpc
cd microapp-sveltekit-trpc
pnpm install
pnpm dev

Integration Recipe 1 — Full stack micro-app (SvelteKit + tRPC + Postgres + Ory + LangChain)

Step-by-step to get a production-ready micro-app that uses an LLM for a specific feature (e.g., summarization or recommendation):

1. Scaffold: Use Vite/SvelteKit template and add tRPC for backend routes.
2. DB: Start Postgres (local or managed). Use Drizzle for migrations and types.

   // drizzle config (drizzle.config.ts)
   import { defineConfig } from 'drizzle-orm/pg-core'
   export default defineConfig({ /* connection */ })
   

3. Auth: Deploy Ory Kratos (Docker) for account management and Oathkeeper to protect endpoints. Example quick-start: run Kratos + Postgres via Docker Compose and use Kratos' SDK to register/login users.
4. LLM connector: Add LangChain.js to the backend. Use environment switching: if DEPLOY_ENV=local -> call local llama.cpp HTTP server; else -> call HF/OpenAI.

   // minimal LangChain usage (Node/Deno)
   import { OpenAI } from 'langchain/llms'
   const llm = new OpenAI({ apiKey: process.env.OPENAI_API_KEY })
   const res = await llm.call('Summarize this note in 3 bullet points: ...')
   

5. Backend endpoint: Expose a tRPC route that receives a prompt and returns an LLM response. Secure it with Kratos session middleware.

   // tRPC handler (pseudo)
   const appRouter = createRouter()
     .mutation('summarize', {
       input: z.object({ text: z.string() }),
       async resolve({ input, ctx }) {
         // check ctx.user from Kratos
         const summary = await llm.call(`Summarize: ${input.text}`)
         return { summary }
       }
     })
   

6. Frontend: Call tRPC from SvelteKit. Show spinner, handle rate-limit errors.

Result: a secure micro-app with a single LLM-powered feature, deployable by pushing Docker images and updating secrets.

Integration Recipe 2 — Local LLM on Raspberry Pi 5 (AI HAT+ 2) for on-device micro-apps

When privacy and cost matter, run the model locally. This recipe is ideal for a personal micro-app that never leaves the owner’s device.

1. Hardware: Raspberry Pi 5 + AI HAT+ 2 (available late 2025) — gives you usable NN acceleration for GGML-style models.
2. Model: Choose a compact GGML model (e.g., llama-2-7b-ggml or a 3B) compatible with llama.cpp or gpt4all.
3. Service: Run text-generation-inference or a tiny HTTP wrapper around llama.cpp. Example using llama.cpp simple server:

   git clone https://github.com/ggerganov/llama.cpp
   cd llama.cpp
   make && ./server --model /models/your.ggml
   # exposes local port 8080
   

4. Secure the endpoint: Use a reverse proxy (Caddy) with automatic local TLS, or keep it on LAN only behind your router. Use API keys stored in Pi's secure storage.
5. Client: Micro-app (mobile or web) connects to the Pi's HTTP API. Use short prompt templates and inexpensive context windows to keep inference fast.

Why this matters: by 2026, local runs are practical for many micro-apps and greatly reduce recurring costs and privacy risk. ZDNET and other reviews highlighted the arrival of practical AI HAT hardware in late 2025, making this pattern mainstream.

Integration Recipe 3 — Edge micro-app with Deno Deploy + Hugging Face inference

Serverless edge app that proxies requests to a hosted HF model and caches responses for repeated prompts.

1. API: Implement an edge function in Deno that validates a session (JWT from Supabase or Ory) and forwards to HF Inference or OpenAI.

   import { serve } from 'std/server'
   serve(async (req) => {
     // validate JWT
     // forward to HF Inference
     const hfRes = await fetch('https://api.huggingface.co/models/xxx', { method: 'POST', body: req.body, headers: { Authorization: `Bearer ${Deno.env.get('HF_KEY')}` } })
     return new Response(await hfRes.text())
   })
   

2. Caching: Use a short LRU cache or Cloudflare cache for identical prompts to reduce bills.
3. Deploy: Deploy to Deno Deploy or Cloudflare Workers for single-digit ms cold starts and regional proximity to users.

Deployment & CI/CD: practical tips

• Dockerfile pattern: multi-stage build; keep runtime minimal (distroless or node:alpine). Use build cache for dependencies to speed CI.
• Secrets: store LLM keys in GitHub Actions secrets or your hosting provider’s secret store. Never commit API keys.

  # GitHub Actions snippet
  - uses: actions/setup-node@v4
  - name: Build and push
    env:
      HF_KEY: ${{ secrets.HF_KEY }}
  

• Ephemeral environments: create preview environments for PRs (Render / Fly / Vercel offer this) — critical for micro-app iteration.
• Scaling: micro-apps usually need burstable concurrency. Use horizontal scale with a small memory footprint, and set up rate limits for LLM endpoints.

Security: protect auth and LLM usage

Micro-apps often have sensitive flows. Apply these minimal but high-impact controls:

• Least privilege for API keys: create separate keys/scopes for inference vs. admin operations.
• Server-side LLM calls: avoid exposing keys to the browser. Proxy requests through a server/edge function and validate sessions.
• Rate limiting & quota: throttle per-user and per-IP to prevent runaway bills and abuse.
• Prompt injection: sanitize user content before using it as instructions, and enforce a system message that prevents exfiltration of PII.
• CORS & CSP: minimal allowed origins and strict Content-Security-Policy headers on the frontend.

Observability & cost control

Micro-apps need light but effective observability:

• Metrics: instrument request counts, latencies, LLM token usage. Push to Prometheus/Grafana or use Honeycomb.
• Tracing: OpenTelemetry for distributed traces across edge + backend + LLM calls.
• Logging: structured logs (JSON) and a short retention policy to keep costs low.

Starter checklist before shipping a micro-app

1. Secrets rotated and not in repo
2. Authentication enforced on sensitive endpoints
3. LLM requests proxied through a server and rate-limited
4. Telemetry enabled (at least basic metrics)
5. Preview environments configured for PR testing

Future-proofing: 2026 trends and what to watch

Plan for these shifts so your micro-apps remain maintainable:

• Local-first models: expect more micro-apps to optionally run models locally using NN accelerators (AI HAT+ 2, mobile NPUs). Design an easy switch between hosted and local inference.
• Composability: small apps will call specialized microservices (vector search, embeddings, OCR). Use clear API contracts like OpenAPI or tRPC for safe composition.
• LLM Ops: invest in simple tooling for prompt versioning, cost tracking, and evaluation. Libraries like LangChain are becoming central to operational workflows.
• Privacy-first deployments: on-device and on-prem inference will be a differentiator for enterprise micro-apps.

Real-world example: shipping a recommendation micro-app in 48 hours

Plan:

1. Day 0: scaffold SvelteKit frontend, route skeleton, basic UI with Tailwind.
2. Day 0.5: add Postgres via Supabase (free tier), enable Supabase Auth for quick email login.
3. Day 1: add an LLM route in a node microservice using LangChain and HF Inference, proxied via a Deno edge function for latency.
4. Day 1.5: integrate rate limiting, add caching for repeated prompts, and wire up PR preview deployment.
5. Day 2: QA, attach basic metrics, and release to a small group for feedback.

Outcome: a focused feature (recommendations) with minimal infra and reasonable cost controls. If privacy is required, swap HF Inference for a local llama.cpp instance running on a Pi 5 with an AI HAT+ 2.

Actionable takeaways

• Start small: pick one LLM-powered feature and keep auth and DB simple (Supabase or Ory for full control).
• Use proven templates: fork a SvelteKit/tRPC starter or Deno LangChain example to avoid boilerplate.
• Design for switchability: implement an adapter pattern for LLMs so you can flip between local and hosted providers without rewriting app code.
• Deploy cheap & fast: use edge functions or small Docker containers and preview environments for rapid iteration.

Resources & starter repos

• SvelteKit + tRPC + Drizzle starter (forkable) — good for internal tools.
• Deno + LangChain minimal LLM API — great for edge deployments.
• Raspberry Pi LLM starter — includes llama.cpp build and simple HTTP server for local inference.
• Ory Kratos quickstart — for self-hosted auth with advanced flows.

Final notes

In 2026, micro-apps are an ideal way to deliver value quickly: they combine low infrastructure cost, rapid iteration, and increasingly powerful on-device AI options. Use the curated open-source stack in this guide to get from idea to working prototype in days, not months.

Get started — call to action

Fork the SvelteKit + tRPC starter, add an LLM connector using the adapter pattern shown here, and deploy a preview environment from your first PR. Join our community at thecoding.club to share your starter, get feedback, and find collaborators for micro-app projects.

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