miosa (Python)

Official Python SDK for MIOSA — the AI cloud platform for sandboxes, computers, deployments, and managed data.

Install

pip install miosa
# or
uv add miosa
# or
poetry add miosa

Requires Python 3.9+. Dependencies: httpx, pydantic v2.

Quickstart

from miosa import Miosa

miosa = Miosa(api_key="msk_live_...")

# Create or resume a persistent sandbox workspace, then work inside it.
sb = miosa.sandboxes.create_agent_workspace(
    "my-build",
    external_workspace_id="customer-workspace-123",
)

result = sb.exec.run("python --version")
print(result.stdout)   # Python 3.12.x

sb.files.write("/workspace/hello.py", "print('hello from miosa')")
out = sb.exec.run("python /workspace/hello.py")
print(out.stdout)      # hello from miosa

print(sb.preview.create(port=8000)["url"])
# https://8000-<slug>.sandbox.miosa.ai/

sb.snapshots.create("after-hello-world")
sb.pause()

What's included

Computers and Desktop Access

computer is the user-facing product. miosa-desktop is the desktop computer template/profile. The canonical fast desktop profile is:

computer = miosa.computers.create(
    name="agent-desktop",
    template_type="miosa-desktop",
    size="small",  # 2 vCPU / 4 GB
)

Authenticated platform desktop links do not need a viewer password. Raw external viewer links such as *.computer.miosa.ai/desktop/index.html are password-gated; rotate the password when you need to share that raw link:

status = computer.viewer_password()
rotated = computer.rotate_viewer_password()
print(rotated["viewer_password"])

Connect vs Egress

Use Connect when your product needs to manage provider credentials for agents, sandboxes, computers, or deployments. Connect owns the product-level contract: connectors, installations, project links, inherited defaults, short-lived tokens, white-label attribution, and runtime bindings.

Use Egress when you need the low-level security boundary: encrypted secret storage, outbound host allowlists, placeholder token exchange, and audit logs. Egress is the enforcement layer under Connect. Most white-label apps should call miosa.connectors and runtime binding helpers first; only call miosa.secrets, miosa.network, or miosa.audit when you are building security/admin controls directly.

import os

connector = miosa.connectors.create(
    "anthropic",
    name="workspace-claude",
    scope="workspace",
    external_workspace_id="clinic-iq",
    value=os.environ["ANTHROPIC_API_KEY"],
)

# Runtime-level materialization: the sandbox sees ANTHROPIC_API_KEY, but your
# app does not get the raw provider key back from MIOSA.
miosa.connectors.materialize_defaults(
    workspace_id="workspace-id",
    resource_type="sandbox",
    resource_id=sb.id,
    target="agent",
    external_workspace_id="clinic-iq",
)

Agent workspaces

For AI builders, code assistants, and white-label agent products, the sandbox is the development machine. Do not run builds on the user's laptop and upload the result as the primary flow. Create or resume a stable sandbox workspace, write files under /workspace, run package installs/tests/builds inside the sandbox, expose previews from the sandbox, and publish from that sandbox.

sb = miosa.sandboxes.get_or_create(
    "acme-marketing-page",
    template_id="miosa-sandbox",
    persistent=True,
    timeout_sec=86_400,
    idle_timeout_sec=1800,
    snapshot_expiration_days=30,
    keep_last_snapshots=1,
    external_workspace_id="acme",
    external_user_id="jane",
    wait_until_ready=True,
)

sb.files.write_files([
    {"path": "/workspace/package.json", "data": '{"scripts":{"dev":"vite --host 0.0.0.0"}}'},
    {"path": "/workspace/index.html", "data": '<main id="app"></main>'},
])

for event in sb.exec_stream("cd /workspace && npm install && npm run dev -- --port 3000"):
    print(event)

preview = sb.preview.create(port=3000)
print(preview["url"])

For platform agents that plan work outside the sandbox and then hand execution to a runtime inside the sandbox/computer, use the unified device API:

miosa.agent_runtime_profiles.create(
    workspace_id="clinic-workspace",
    name="ClinicIQ Claude Code",
    runtime="claude-code",
    is_default=True,
    connectors=[
        "anthropic/cliniciq",
        {
            "uid": "refero",
            "type": "mcp",
            "managed": True,
            "server_url": "https://api.refero.design/mcp",
        },
    ],
    env={"ANTHROPIC_API_KEY": "miosa-managed:anthropic/cliniciq"},
    metadata={"model": "claude-opus-4.8", "white_label_client": "cliniciq"},
)

miosa.devices.write_file(
    sb.id,
    "/workspace/PLAN.md",
    content="# Build plan\nUse the customer profile and export /workspace/out.",
)

build = miosa.devices.exec(
    sb.id,
    "npm install && npm run build",
    cwd="/workspace",
    timeout_ms=600_000,
)

artifact = miosa.devices.read_file(sb.id, "/workspace/out/report.pdf")

create_agent_workspace() is a convenience wrapper around get_or_create() with builder defaults: a 24-hour activity cap, thirty-minute idle snapshot/pause, readiness waiting, 30-day snapshot-retention metadata, keep-last-snapshot metadata, and metadata that marks the sandbox as an agent workspace.

That mirrors the Vercel-style persistent-sandbox model without keeping CPU running forever: activity extends the running session, idle workspaces are checkpointed and paused, and later sessions resume from the checkpoint. Use extend(86_400) before long builds, pause() when the user is done for now, resume() for the next session, snapshots.create() for manual checkpoints, and destroy() only when the workspace should be permanently deleted.

Agent SDK

The Python SDK includes a small agent loop plus provider adapters and a MIOSA tool catalogue. This is the quickest path when you want an LLM to operate MIOSA computers directly.

from miosa.agent import Agent, groq_provider

agent = Agent(
    provider=groq_provider(
        api_key="gsk_...",
        model="moonshotai/kimi-k2-instruct-0905",
    ),
    miosa_api_key="msk_live_...",
)

result = agent.run(
    "Create a sandbox workspace, write /workspace/hello.py, run it, show me "
    "the output, snapshot it, then pause the sandbox.",
    max_iterations=10,
)

print(result.final_text)

Built-in agent tools expose the computer primitive in plain terms:

Provider factories include openai_provider, groq_provider, deepseek_provider, openrouter_provider, together_provider, fireworks_provider, mistral_provider, cerebras_provider, perplexity_provider, xai_provider, ollama_provider, lm_studio_provider, and openai_compatible_provider for custom routers.

Sandbox-first builder examples live in examples/:

See BUILDER_GUIDE.md for the full “build your own Lovable with MIOSA sandboxes” product pattern.

Set GROQ_API_KEY and MIOSA_API_KEY, then run one of the examples. They keep the sandbox alive with miosa_tool_options={"allow_destroy": False} so your app can show previews, inspect generated files, or publish later.

The agent should treat a MIOSA sandbox like the actual development machine: write source files under /workspace, run package managers/tests/builds inside the sandbox, start dev servers inside the sandbox, stream logs/tool results back to your UI, and only publish after a sandbox preview passes a smoke check.

File operations

# Write / read
computer.files.write_file("/workspace/config.json", '{"key": "value"}')
text = computer.files.read_file("/workspace/config.json")

# Upload / download
computer.files.upload("/local/report.pdf", "/workspace/report.pdf")
raw_bytes = computer.files.download("/workspace/report.pdf")

# List / stat / mkdir / rename / chmod
entries = computer.files.list("/workspace")
stat    = computer.files.stat("/workspace/config.json")
computer.files.mkdir("/workspace/output", recursive=True)
computer.files.rename("/workspace/old.txt", "/workspace/new.txt")
computer.files.chmod("/workspace/run.sh", 0o755)

Services (background processes)

svc = computer.services.create(
    name="api",
    command="uvicorn app:main --port 8080",
    working_dir="/workspace",
    env={"DEBUG": "1"},
    port=8080,
)

for log in computer.services.logs(svc.id, follow=True):
    print(log.stream, log.line)

computer.services.restart(svc.id)
computer.services.delete(svc.id)

Publish from sandbox

Sandbox previews are mutable builder URLs. Publishing creates a durable deployment version with a stable url / public_url.

sb = miosa.sandboxes.create(name="clinic-intake", template_id="miosa-sandbox")
sb.files.write_file("/workspace/index.html", "<h1>Clinic intake</h1>")

result = sb.deploy(
    name="clinic-intake",
    output_path="/workspace",
    entrypoint="index.html",
)
print(result["url"])

For full-stack/dynamic apps:

result = sb.deploy(
    name="clinic-intake-api",
    output_path="/workspace",
    build_command="npm run build",
    run_command="npm start",
    port=3000,
    health_check_path="/",
    resources={
        "database": {"auto": True, "engine": "postgresql", "size": "xs"},
        "storage": {"auto": True},
    },
    domain="intake.apps.cliniciq.com",
)

If result["state"] == "building" for a dynamic app, poll miosa.deployments.get(result["deployment_id"]) until it is running. Always display the server-returned url / public_url; do not hardcode preview.miosa.app, api.miosa.app, or <tenant>.miosa.app.

To publish the exact snapshot that passed QA instead of whatever the editable sandbox holds right now, use deploy_snapshot. It forks the snapshot into a temporary release sandbox, deploys that fork, and destroys it again, so the source sandbox is never mutated:

snapshot = sb.snapshots.create("qa-approved")

result = sb.deploy_snapshot(
    snapshot["id"],
    name="clinic-intake",
    output_path="/workspace",
    entrypoint="index.html",
)
print(result["source_snapshot_id"], result["release_sandbox_id"])

The result always carries source_snapshot_id and release_sandbox_id for provenance. Pass cleanup=False to keep the release sandbox for inspection. If the release sandbox could not be destroyed, the deployment still succeeds and result["release_cleanup_error"] explains why; when the deploy itself fails and the release sandbox survives, the raised error carries the same detail so the leftover sandbox can be cleaned up by id.

For workspace App Engine, publish from the same sandbox but choose the App Engine target:

result = sb.deploy_docker(
    name="lead-magnet",
    path="/workspace",
    build_command="npm run build",
    run_command="npm start",
    port=3000,
)
print(result.get("url") or result.get("public_url"))

App Engine runs app containers inside the workspace's dedicated MIOSA App Engine appliance VM. It is separate from normal MIOSA dynamic runtime VMs and is useful when a workspace needs many small apps, funnels, lead magnets, APIs, or client sites.

Desktop control

screenshot = computer.screenshot()          # PNG bytes
computer.click(640, 400)
computer.double_click(640, 400)
computer.type("hello world")
computer.key("Return")
computer.scroll("down", 3)
computer.drag(100, 100, 400, 400)

cursor = computer.cursor()
windows = computer.windows()
computer.launch("firefox")

Async usage

import asyncio
from miosa import AsyncMiosa

async def main():
    async with AsyncMiosa(api_key="msk_live_...") as miosa:
        computer = await miosa.computers.create(
            name="async-build", template_type="miosa-sandbox"
        )
        await computer.files.write_file("/workspace/app.py", "print('async!')")
        result = await computer.exec.run("python /workspace/app.py")
        print(result.stdout)
        await computer.destroy()

asyncio.run(main())

White-label / multi-tenant

Tag resources with external_workspace_id and external_user_id to attribute usage to your downstream customers:

computer = miosa.computers.create(
    name="customer-build",
    template_type="miosa-sandbox",
    metadata={
        "external_workspace_id": "dental-office-123",
        "external_user_id": "dr-smith-456",
    },
)

White-label URL layers are separate:

sandbox preview:     https://<port>-<slug>.sandbox.<preview-domain>
durable deployment:  https://<slug>.<deployment-domain>
custom domain:       https://app.customer.com

Tenant preview-domain management is available through miosa.tenant. Tenant deployment-domain routing exists server-side; clients should consume deployment public_url instead of reconstructing it.

Runs

Use an idempotency key when a caller may retry run creation. The same key prevents a retry from creating duplicate work.

run = miosa.runs.run(
    sandbox_id="sbx_abc123",
    instruction="Build the ClinicIQ report",
    idempotency_key="clinic-iq-request-123",
)

events = miosa.runs.events(run["id"], limit=200)
for event in events:
    print(event["type"], event.get("metadata", {}))

# Continue from the last durable event without replaying earlier events.
new_events = miosa.runs.events(run["id"], after_id=events[-1]["id"])

The async client exposes the same contract through await client.runs.run(...) and await client.runs.events(...).

Environments

An environment is the template a new sandbox or computer inherits: which repositories are cloned, which variables and secret files are injected, and which of your credentials the machine may use. The same environments apply to sandboxes and computers.

# Create an environment and give it variables, a repo and a tool.
env = miosa.environments.create(
    "prod",
    variables={"STRIPE_KEY": "sk_live_..."},
    repositories=[{"repo": "octocat/hello-world", "setup_script": "npm ci"}],
    cli_tools={"gh": "latest"},
)

# Start a machine in it, with a one-off setup script and a create-time wait.
sb = miosa.sandboxes.create(
    "miosa-sandbox",
    environment="prod",
    setup_file="#!/bin/sh\nnpm ci\n",
    wait=45,  # wait up to 45s for ready; ?wait= on the create request itself
)

# One atomic save -> exactly one new version.
miosa.environments.update(
    "prod",
    variables_set={"REGION": "us-east-1"},
    variables_unset=["OLD"],
    pass_sandbox_credentials=True,
)

# Move pinned machines to the latest version.
miosa.environments.upgrade("prod")

# What a machine would receive, lowest layer first.
miosa.environments.effective("prod")

miosa.environments.billing() reports the account new machines bill, and miosa.deployment_environments.list() is the deployment-environment inventory (previously the deprecated data key on GET /environments).

Snapshots

# Account-wide history, newest first, searchable.
history = miosa.snapshot_history.list(q="builder", limit=50)

# Browse a snapshot's disk image without the machine.
tree = miosa.snapshot_history.tree("f3c1", path="/home/user")
blob = miosa.snapshot_history.file("f3c1", path="/home/user/notes.txt")

# Fork a snapshot into a new, independent machine.
fork = miosa.snapshot_history.fork("f3c1", idempotency_key="k1")

# Pin a snapshot under a name that outlives the machine.
miosa.named_snapshots.create("web-stack", sandbox_id="9a2e")
miosa.named_snapshots.deploy("web-stack")  # fork it into a new machine

Commands and agent steering

# The commands endpoint: synchronous, with cwd, stdin and a duration.
result = miosa.commands.run("sbx_abc123", "bash ./setup.sh", cwd="my-repo")
print(result["stdout"], result["exit_code"], result["duration_ms"])

# Steer the agent run attached to a machine.
miosa.agent_steering.prompt("sbx_abc123", "fix the failing test")
miosa.agent_steering.stop("sbx_abc123")

A machine that is not ready refuses a command with a retryable 409 → MachineStartingError; poll it using e.retry_after_ms.

Agents, connections and bill-to

# The Agents tab: harnesses, model catalog, shared credentials.
harnesses = miosa.agent_settings.harnesses()
miosa.agent_settings.put_credential("openrouter", api_key="sk-or-...")
miosa.agent_settings.update_harness("claude-code", default_model="claude-sonnet-5-5")

# One read over model providers, apps and tool servers.
miosa.connections.list(family="models", usable_by="agents")

# Who pays for a create.
miosa.bill_to.update("acme")
limits = miosa.bill_to.limits()

# Triggers start runs on a schedule, a webhook, an event, Forge or email.
miosa.agent_triggers.create(
    "agent_1",
    name="Every Monday",
    kind="schedule",
    instruction="Summarize last week's tickets.",
    config={"cron": "0 9 * * 1", "timezone": "America/Chicago"},
)

# A chat's harness context and compaction.
miosa.agent_chats.context("chat_1")
miosa.agent_chats.compact("chat_1", focus="keep the API notes")

Error handling

from miosa import (
    AuthenticationError, NotFoundError, RateLimitError, MiosaError
)

try:
    computer = miosa.computers.get("cmp_doesnt_exist")
except NotFoundError:
    print("Computer not found")
except RateLimitError as e:
    print(f"Rate limited; retry after {e.retry_after}s")
except AuthenticationError:
    print("Check your API key")
except MiosaError as e:
    print(f"API error {e.status}: {e.message}")

Exception hierarchy: MiosaError > AuthenticationError (401), InsufficientCreditsError (402), PermissionError (403), NotFoundError (404), ValidationError (422), RateLimitError (429), ServerError (5xx), ConnectionError, TimeoutError.

Every error carries the server's stable code and any details, plus retry hints: retryable (bool) and retry_after_ms (int, or the 429 retry_after). The sprint codes have typed subclasses, so a caller can catch exactly what it means:

from miosa import (
    CreditRequiredError,        # 402 needing credit (a named snapshot over the free allowance)
    MachineStartingError,       # 409 SANDBOX_STARTING / COMPUTER_STARTING (retry)
    OwnCredentialsRequiredError,# 422 the harness has nothing of yours to run on
    RunSessionNotResumableError,# 409 continue_from_run_id cannot resume
    SnapshotInUseError,         # 409 a snapshot still has dependents
)

try:
    miosa.commands.run("sbx_abc123", "echo hi")
except MachineStartingError as e:
    time.sleep((e.retry_after_ms or 2000) / 1000)

The environments, snapshots, bill-to, agents and triggers codes each have a typed class (EnvironmentNotFoundError, EnvironmentMemoryWithheldError, BillToLockedError, TeamMemberCapReachedError, UnknownHarnessError, TriggerNotFoundError, ...); see miosa.errors for the full list.

Pagination

List endpoints page with limit plus offset or a cursor. miosa.pagination drives that loop:

from miosa import collect, paginate

for snapshot in paginate(
    lambda **p: client.snapshot_history.list(**p), items_key="data", limit=100
):
    print(snapshot["id"])

all_runs = collect(lambda **p: client.runs.list(**p), items_key="data")

iter_cursor_pages follows an opaque cursor; iter_pages_async and iter_cursor_pages_async are the async twins.

Retries

A 429 or 5xx on an idempotent request (GET, HEAD, OPTIONS, PUT, DELETE) is retried with exponential backoff, honoring Retry-After. A POST or PATCH is retried only when it carries an Idempotency-Key, so a retried create dedups server-side instead of running twice. sandboxes.create() generates one for you; pass idempotency_key= to override, or add the header yourself for another write.

Configuration

Links

• Full documentation
• Quickstart
• GitHub
• Contact

Phase 1-4 methods (v1.1.0)

Preview tokens and share URLs

# Mint a scoped preview token (e.g. for iframe embed)
token = sb.preview_token(expires_in=3600, scope="read")
print(token["url"])  # https://...?mt=mp_<token>

# Create a public share link (no API key required)
share = sb.share.create(expires_in=7200)
print(share["share_url"])

File tree and batch write

tree = sb.files.tree("/workspace", depth=2)
sb.files.write_many([
    {"path": "/workspace/app.py", "content": "print('hello')"},
    {"path": "/workspace/cfg.json", "content": b"{}"},
])

Tenant events stream and quotas

for event in client.events.stream(types=["sandbox.*"]):
    print(event["_event_type"], event)

client.quotas.set("usr_abc", max_sandboxes=10, max_concurrent=3)

Webhook signature verification

from miosa.resources.webhooks import Webhooks

ok = Webhooks.verify_signature(request.body, request.headers["X-Miosa-Signature"], secret)

License

MIT