Build an OpenSwarm-style multi-agent app

What you're building

an AI desktop app built around running several agents in parallel (proprietary; distributed as signed builds) Modelled on OpenSwarm, by OpenSwarm.

An orchestrator breaks a goal into parts and runs agents on them at the same time, then combines what they return.

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

• An orchestrator that runs agents in parallel
• Each agent works on a part of the goal
• A combined result
• A desktop to watch and steer it

Source: openswarm.com. OpenSwarm is a trademark of its owner; 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 Computer A persistent Linux desktop for browser, mouse, and screenshot work. miosa computer create app-desktop Docs
4. 2 A workspace per customer Isolates each customer’s machines, deployments, and data as they onboard. miosa workspace create customer-1 Docs
5. 2 Branding and white-label Your name and slug on previews, deployments, and the desktop; customers never see MIOSA. miosa org Docs
6. 2 Usage metering and bill-to Usage per customer, and which account pays for new machines. miosa org bill Docs
7. 3 Agent and harness Turns a prompt into work: pick the harness and model a run uses. miosa agent harnesses Docs
8. 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

An orchestrator that plans and delegates -> agent runs (`miosa prompt`, client.runs) streaming events from your orchestrator process in its own sandbox

Workers that run in parallel -> one sandbox per worker (`orchestration_role`, `agent_run_group_id` tie them together)

Shared starting state -> sandbox snapshots and fork: snapshot once, restore into each worker

Joined result -> client.runs.waitForCompletion, then run outputs and files from every worker

Data and storage

Keep the plan (subtasks, owners, status) and each worker's result keyed by the run group id, so a failed join can resume and the UI can show who did what.

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 openswarm-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 orchestrator is an agent that decides the subtasks; workers are separate harness runs, each on its own machine. Give each worker a narrow role and only the tools it needs. Cap the number of workers and the depth of delegation.

Steps

1. Create the orchestrator's own sandbox. It plans and delegates; it does not do the work.

   miosa create openswarm-orchestrator --wait

   Check: The orchestrator can create other sandboxes with the SDK.

2. Create one worker sandbox per child agent (a fresh, isolated machine each).

   miosa create openswarm-worker-1 --wait

   miosa create openswarm-worker-2 --wait

   Check: Two workers are running and cannot see each other's files.

3. Create ONE shared computer for children that need a desktop or browser.

   miosa computer create openswarm-desktop

   Check: A desktop child can open a browser; code children never touch it.

4. Snapshot the orchestrator workspace once and restore it into each worker so they start from the same state.

   sandbox.snapshots.create("shared start")

   Check: Every worker begins with identical files.

5. Fan out: dispatch one run per worker, tied together by a group id and a role.

   miosa prompt --sandbox openswarm-worker-1 --harness osa --model <anthropic-model-id> --chat <chat-id> "<subtask>"

   client.agentRuns.run({ provider: "osa", targetKind: "sandbox", targetId: worker1.id, orchestration_role: "researcher", agent_run_group_id: "grp_1", prompt: "<subtask>" })

   Check: The runs execute in parallel and show the same group id.

6. Join: wait for every run, collect each output and files, and let the orchestrator merge them. Retry only the worker that failed.

   client.runs.waitForCompletion(run.id)

   miosa run outputs <run-id>

   miosa run files <run-id>

   Check: One failed worker is retried without rerunning the others.

7. Publish the joined report or the orchestrator UI.

   miosa deploy create --from-sandbox openswarm-orchestrator --name openswarm --dir /workspace --wait

   Check: The public_url shows the joined result.

Limits and costs

Parallelism multiplies cost: cap workers per job and give every run `--max-time`.

Use `idempotency_key` on `sandboxes.create` in retry loops so a retried fan-out does not create duplicates.

Prefer fewer, better-scoped workers over many shallow ones.

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

Every child run completes and its output is collected.

Children are isolated from each other; only desktop children share the computer.

The orchestrator's final answer cites which worker produced what.

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.