Build a text-to-video app

What you're building

a text-to-video app that calls a video-model API and gives users a feed of creations Modelled on Sora, by OpenAI.

A text prompt is sent to a video-model API you bring; the app shows progress, stores the clip, and offers a feed of creations to remix.

Primitives you'll use: Sandbox · Agent and harness · Deployment (App Engine)

• Text to video through a model API
• A feed of creations to remix
• Job status while it renders
• Short-form output

Source: openai.com/sora. Sora is a trademark of OpenAI; this guide is not affiliated with or endorsed by them.

What you need on MIOSA

Each row is one thing to create before you start. The number matches the step that uses it.

1. 1 Organization and API key Scopes every call; a workspace key is all a worker needs. miosa api-key create app-key --preset agent Docs
2. 2 Sandbox The isolated Linux workspace the agent writes code and runs commands in. miosa create app-box --template nextjs --wait Docs
3. 2 A workspace per customer Isolates each customer’s machines, deployments, and data as they onboard. miosa workspace create customer-1 Docs
4. 2 Branding and white-label Your name and slug on previews, deployments, and the desktop; customers never see MIOSA. miosa org Docs
5. 2 Usage metering and bill-to Usage per customer, and which account pays for new machines. miosa org bill Docs
6. 3 Agent and harness Turns a prompt into work: pick the harness and model a run uses. miosa agent harnesses Docs
7. 5 Deployment (App Engine) Publishes the app to an immutable, versioned URL with rollback. miosa deploy create --from-sandbox app-box --name app --wait Docs

Architecture

How it maps onto MIOSA

Prompt to asset -> agent runs (`miosa prompt`, client.runs) streaming events that call your generation provider and write results into the sandbox

Heavy rendering and encoding -> a MIOSA sandbox jobs (ffmpeg or headless rendering), sized up with `--size`

Projects and assets -> managed Postgres (`DATABASE_URL`) for job metadata; files and volumes for the media

Preview before export -> a sandbox preview URL (`miosa preview create`)

Data and storage

Jobs (status, inputs, output path) are metadata; the media itself is large, so keep it as files in the render sandbox and a volume, not in Postgres.

Assets are content-addressed by hash so identical inputs are never regenerated.

Auth and tenancy

Your organization is the platform; customers never get a MIOSA account, they sign into YOUR product. One workspace per customer, created as they onboard, isolates their machines, runs and data.

miosa workspace create sora-customer-1

Tag every machine and run with the customer id in `metadata`, and never query across customers. Meter usage per customer with GET /api/v1/usage and set who pays with PUT /api/v1/bill-to (/docs/platform/usage-and-billing).

Agent loop

Harness: OSA is MIOSA's own harness and works with any model provider you connect, including your own model.

Model: Anthropic (Claude). Calls use my own provider key (`miosa connections add models`); MIOSA platform keys are never used.

Sessions: one chat per project or conversation, so the agent keeps its context. The first `miosa prompt` on a sandbox uses `--new-chat` (a chat id is printed); every later turn passes `--chat <chat-id>`. `--reuse chat` keeps one new machine per chat so files persist.

Streaming: follow a run with `miosa run follow <run-id>` or client.runs.streamEvents(run.id), and steer or stop it with `miosa run steer` and `miosa run interrupt`.

The generation model is a provider you bring; the harness orchestrates the job in a sandbox and runs ffmpeg for everything deterministic. Keep generation and rendering as separate, retryable steps.

Steps

1. Create the render worker: a larger sandbox for encoding and compositing.

   miosa create sora-box --size large --wait

   Check: The sandbox is running.

2. Call the generation model you bring (your provider's API) with the prompt or image; store the returned clip.

   Check: The provider returns a clip or transcript and you store its job id.

3. Post-process in the sandbox: trim, stitch, add captions and audio, and encode with ffmpeg (install it in your template once, not per job).

   miosa prompt --sandbox sora-box --harness osa --model <anthropic-model-id> --chat <chat-id> "Assemble the final video from /workspace/jobs/<id> with ffmpeg and write it to /workspace/out"

   Check: The output plays, has the right duration, and the job row points to it.

4. Show progress: stream run events to the UI so a long render never looks hung.

   miosa run follow <run-id>

   Check: The UI shows progress while the job runs and the final file when done.

5. Preview and export: serve the output for playback, and offer a download.

   miosa preview create sora-box 3000 --name web

   Check: The preview plays the clip; the download has the right size and format.

6. Publish the app that accepts jobs and plays results.

   miosa deploy create --from-sandbox sora-box --name sora --dir /workspace --port 3000 --run-command "npm start" --wait

   Check: The public_url accepts a job and plays the finished result.

Limits and costs

Rendering is the cost: size the sandbox to the job, set `timeout_sec` up to the longest render, and pause or destroy workers when the queue is empty.

Provider usage (generation, speech) is billed by those providers; store your own job-level cost.

Prototype: keep it to one machine at the default size, skip replicas and custom hostnames you do not need, and delete everything when you are done.

Acceptance checks

A job runs from input to a playable output file.

A failed provider call leaves the job retryable, not stuck.

Progress is visible while a job runs.

Each customer is isolated in its own workspace and usage is metered against them.

Everything it created can be deleted with nothing left running.

Next steps

• Agent prompt builder Pick a product shape and hand the brief to your agent.
• Run coding agents Run Claude Code, Codex or Cursor in the cloud, or build a product on them.
• Compare MIOSA How MIOSA compares to other sandbox and agent platforms.
• What you can build Every product pattern MIOSA supports.