# Launch HN: Coasty (YC S26) – An API for computer-use agents

> Source: <https://coasty.ai/docs>
> Published: 2026-07-15 15:51:20+00:00

# API reference

Run autonomous tasks on managed machines, compose them into workflows, and drop to prediction primitives only when you need direct control.

[llms.txt](/docs/llms.txt)

## Introduction

Start with a [task run](#runs): give Coasty a goal and a machine, then let the agent drive to completion. Use [workflows](#workflows) when an automation needs many tasks, branches, loops, approvals, or shared outputs. [Machines](#machines) provide the managed computer those tasks operate.

The prediction endpoints are lower-level primitives for teams that need to own the control loop themselves. Use [sessions](#sessions) for a stateful screenshot loop, [predict](#predict) for a stateless step, grounding for coordinates, and parse for structured actions. Everything is normal HTTPS to `https://coasty.ai/v1`

, so you can choose the highest-level surface that fits the job and drop down only when you need finer control.

## Authentication

Every API-key-authenticated request must include your secret key. The four health probes are public, and webhook ingress uses its documented `Coasty-Signature`

HMAC credential instead. For API-key operations, the canonical form is the `X-API-Key`

header, but `Authorization: Bearer <key>`

works too: a blank `X-API-Key`

falls through to the Bearer header. Pick one form and send the raw key. Do not paste the literal text `Bearer `

inside `X-API-Key`

; that is the single most common first-day mistake and it returns `401 INVALID_API_KEY`

. Keys are created and revoked from the [API keys](/developers/keys) page. Treat a key like a password: keep it server-side, store it in an environment variable, and never commit it or ship it in client-side code.

```
X-API-Key: sk-coasty-live-your_key_here
```

`sk-coasty-test-`

key never bills and runs against mock VMs, yet exercises the exact same request and response shapes (its `X-Credits-Charged`

and `usage.cost_cents`

are always `0`

), so you can build and run CI confidently before flipping to a live key.## Run your first task

Your first autonomous task needs an API key and a machine. Grab a test key from the [API keys](/developers/keys) page (it never bills), then use an existing machine or [provision one](#machines). Set the key in your shell:

```
export COASTY_API_KEY="sk-coasty-test-your_key_here"
```

Start the task with `POST /v1/runs`

. Replace the example `machine_id`

, describe the outcome in `task`

, and send an `Idempotency-Key`

so a retried create cannot start a duplicate run. The complete example starts the run and follows it to a terminal state:

``` python
import os, time, requests

BASE = "https://coasty.ai/v1"
HEADERS = {"X-API-Key": os.environ["COASTY_API_KEY"]}
TERMINAL = {"succeeded", "failed", "cancelled", "timed_out"}

# 1. Start a run. Idempotency-Key makes a retried create safe.
run = requests.post(
    f"{BASE}/runs",
    headers={**HEADERS, "Idempotency-Key": "order-4821"},
    json={
        "machine_id": "mch_test_0123456789abcdef",
        "task": "Open the billing page and download the latest invoice as PDF",
        "cua_version": "v5",         # any of v1/v3/v4/v5, all tiers; omit to use the v5 default
        "max_steps": 40,
        "on_awaiting_human": "pause",
    },
    timeout=30,
).json()
run_id = run["id"]
print(run["status"])                 # "queued"
webhook_secret = run.get("webhook_secret")   # shown once; store it now

# 2. Poll until terminal.
while True:
    run = requests.get(f"{BASE}/runs/{run_id}", headers=HEADERS, timeout=30).json()
    print(run["status"], run["steps_completed"], "steps")
    if run["status"] in TERMINAL:
        break
    time.sleep(2)

print(run["result"])                 # {"passed": ..., "status": ..., "summary": ...}
```

The create response begins at `queued`

. Coasty then drives the machine, records each step, and finishes as `succeeded`

, `failed`

, `cancelled`

, or `timed_out`

. Your application can poll, subscribe to the event stream, or receive signed webhooks; it does not need to execute each prediction itself.

## Task runs

A run hands the agent a task and a machine, then drives it to completion on our side. The agent loops autonomously, verifies its own work (pass or fail), can pause for a human when it hits a wall, bills $0.05 per completed step from your dollar API wallet ($0.08/step on the legacy v1 engine), and streams every event live. You start one call and watch, instead of running the predict loop yourself.

Create a run with `POST /v1/runs`

. The two required fields are `machine_id`

and `task`

. The response is an `agent.run`

object with `status`

of `queued`

, plus a one-time `webhook_secret`

you store to verify [webhooks](#run-webhooks). Send an `Idempotency-Key`

header to make a retried create safe.

``` python
import os, time, requests

BASE = "https://coasty.ai/v1"
HEADERS = {"X-API-Key": os.environ["COASTY_API_KEY"]}
TERMINAL = {"succeeded", "failed", "cancelled", "timed_out"}

# 1. Start a run. Idempotency-Key makes a retried create safe.
run = requests.post(
    f"{BASE}/runs",
    headers={**HEADERS, "Idempotency-Key": "order-4821"},
    json={
        "machine_id": "mch_test_0123456789abcdef",
        "task": "Open the billing page and download the latest invoice as PDF",
        "cua_version": "v5",         # any of v1/v3/v4/v5, all tiers; omit to use the v5 default
        "max_steps": 40,
        "on_awaiting_human": "pause",
    },
    timeout=30,
).json()
run_id = run["id"]
print(run["status"])                 # "queued"
webhook_secret = run.get("webhook_secret")   # shown once; store it now

# 2. Poll until terminal.
while True:
    run = requests.get(f"{BASE}/runs/{run_id}", headers=HEADERS, timeout=30).json()
    print(run["status"], run["steps_completed"], "steps")
    if run["status"] in TERMINAL:
        break
    time.sleep(2)

print(run["result"])                 # {"passed": ..., "status": ..., "summary": ...}
{
  "id": "run_7a1b2c3d",
  "object": "agent.run",
  "status": "queued",
  "machine_id": "mch_test_0123456789abcdef",
  "task": "Open the billing page and download the latest invoice as PDF",
  "cua_version": "v5",
  "instructions": null,
  "max_steps": 40,
  "on_awaiting_human": "pause",
  "steps_completed": 0,
  "credits_charged": 0,
  "cost_cents": 0,
  "result": null,
  "error": null,
  "awaiting_human_reason": null,
  "metadata": {
    "team": "finance"
  },
  "webhook_url": "https://example.com/hooks/coasty",
  "created_at": "2026-06-01T12:00:00Z",
  "started_at": null,
  "awaiting_human_since": null,
  "finished_at": null,
  "request_id": "req_4f9a2b1c",
  "webhook_secret": "whsec_one_time_value_shown_here"
}
```

`queued`

to `running`

, can bounce between `running`

and `awaiting_human`

, and ends in one of `succeeded`

, `failed`

, `cancelled`

, or `timed_out`

. Terminal states are immutable, so it is always safe to stop polling once you reach one. Runs need the `runs:read`

and `runs:write`

scopes, granted to new keys by default.## Streaming events

`GET /v1/runs/{id}/events`

returns a Server-Sent Events stream so you can follow a run as it happens, instead of polling. Each event has a type and a numeric `id`

(the sequence number). If your connection drops, reconnect and replay everything you missed by sending the last sequence you saw as a `Last-Event-ID`

header, or as the `?after=`

query parameter. The stream closes after the `done`

event.

``` python
import os, httpx

BASE = "https://coasty.ai/v1"
HEADERS = {"X-API-Key": os.environ["COASTY_API_KEY"]}
run_id = "run_7a1b"
last_seq = 0  # persist this so a reconnect can replay

# httpx streams the SSE body line by line. Reconnect with Last-Event-ID.
with httpx.stream(
    "GET",
    f"{BASE}/runs/{run_id}/events",
    headers={**HEADERS, "Last-Event-ID": str(last_seq)},
    timeout=None,
) as resp:
    event_type = "message"
    for line in resp.iter_lines():
        if line.startswith("id:"):
            last_seq = int(line[3:].strip())
        elif line.startswith("event:"):
            event_type = line[6:].strip()
        elif line.startswith("data:"):
            data = line[5:].strip()
            print(event_type, data)
            if event_type == "done":
                break
```

## Human takeover

Some steps need a person: a captcha, a one-time code, a judgment call. When the agent reaches one and `on_awaiting_human`

is `pause`

, the run moves to `awaiting_human`

and emits an `awaiting_human`

event with a reason. A human completes the blocking step (in the same machine session), then you hand control back with `POST /v1/runs/{id}/resume`

and an optional `note`

. Resume is only valid while the status is `awaiting_human`

.

``` python
import os, requests

BASE = "https://coasty.ai/v1"
HEADERS = {"X-API-Key": os.environ["COASTY_API_KEY"]}
run_id = "run_7a1b"

run = requests.get(f"{BASE}/runs/{run_id}", headers=HEADERS, timeout=30).json()

# resume is only valid while status == "awaiting_human".
if run["status"] == "awaiting_human":
    print("paused:", run["awaiting_human_reason"])
    # ... a human completes the blocking step out of band ...
    resumed = requests.post(
        f"{BASE}/runs/{run_id}/resume",
        headers=HEADERS,
        json={"note": "Solved the captcha; continue"},
        timeout=30,
    ).json()
    print(resumed["status"])         # back to "running"
```

`status == awaiting_human`

with `awaiting_human_reason`

set), the SSE `awaiting_human`

event, or the `run.awaiting_human`

webhook. After resume, the run returns to `running`

and emits a `resumed`

event. Set `on_awaiting_human`

to `fail`

or `cancel`

at create time if you would rather the run stop than wait for a human.## Webhooks

Pass a `webhook_url`

(https only) when you create a run and we POST a signed callback at each lifecycle transition. The response to your create call includes a `webhook_secret`

exactly once: store it, because every callback is signed with it. Each request carries a `Coasty-Signature`

header of the form `t=<unix_ts>,v1=<hex>`

.

To verify, build the signed payload as `"<t>." + raw_request_body`

, compute `HMAC-SHA256`

over it keyed by the `webhook_secret`

, and compare against `v1`

with a constant-time check. Always hash the raw body bytes, before any JSON re-serialisation.

``` python
import hashlib, hmac, os, requests

BASE = "https://coasty.ai/v1"
HEADERS = {"X-API-Key": os.environ["COASTY_API_KEY"]}

# 1. Create a run with a webhook_url. webhook_secret is returned exactly once.
run = requests.post(
    f"{BASE}/runs",
    headers=HEADERS,
    json={
        "machine_id": "mch_test_0123456789abcdef",
        "task": "Reconcile the invoice against the order",
        "webhook_url": "https://example.com/hooks/coasty",
    },
    timeout=30,
).json()
webhook_secret = run["webhook_secret"]   # persist this securely

# 2. In your webhook handler, verify the Coasty-Signature header.
def verify(raw_body: bytes, signature_header: str, secret: str) -> bool:
    parts = dict(p.split("=", 1) for p in signature_header.split(","))
    signed = f"{parts['t']}.".encode() + raw_body
    expected = hmac.new(secret.encode(), signed, hashlib.sha256).hexdigest()
    return hmac.compare_digest(expected, parts["v1"])

# Example (your framework supplies the raw body + header):
# ok = verify(request.body, request.headers["Coasty-Signature"], webhook_secret)
```

## Bring your own model

By default every LLM call in the computer-use harness runs on Coasty's managed models. BYOK (bring your own key) flips that: opt in and the entire harness (the worker, grounding, the code agent, and compaction; every LLM call) runs on your own Anthropic or OpenAI account instead. Opt-in is always explicit, per request or per stored key. `provider: "managed"`

(or omitting `llm`

entirely) keeps the platform default, unchanged.

There are two ways to hand over a key. Store it once with `PUT /v1/llm/keys/{provider}`

(encrypted with AES-256-GCM at rest; only a sha256-prefix fingerprint is ever echoed back), or send it per request in headers. A header key takes precedence over the stored key. Sending a key without a provider returns `422`

.

```
X-LLM-Provider: anthropic
X-LLM-Api-Key: sk-ant-your_key_here
X-LLM-Model: claude-sonnet-4-6
```

The headers work on `POST /v1/predict`

, `POST /v1/runs`

, `POST /v1/workflows/{id}/runs`

, `POST /v1/sessions`

, and `POST /v1/schedules`

. Stored keys are managed through three endpoints, gated by the `llm_keys`

scope (granted to new live keys by default). These endpoints require a live key; sandbox keys cannot read, overwrite, or delete the production credential store:

``` python
import os, requests

BASE = "https://coasty.ai/v1"
HEADERS = {"X-API-Key": os.environ["COASTY_API_KEY"]}

# Store (upsert) your own Anthropic key. Encrypted at rest; never echoed back.
stored = requests.put(
    f"{BASE}/llm/keys/anthropic",
    headers=HEADERS,
    json={"api_key": os.environ["ANTHROPIC_API_KEY"]},
    timeout=30,
).json()
print(stored)   # {"provider": "anthropic", "key_fingerprint": "a1b2c3d4", "stored": true}

# List stored keys: provider, fingerprint, timestamps. Never the key itself.
keys = requests.get(f"{BASE}/llm/keys", headers=HEADERS, timeout=30).json()
for k in keys["keys"]:
    print(k["provider"], k["key_fingerprint"])

# Delete when you rotate away (404 LLM_KEY_NOT_FOUND when none is stored)
requests.delete(f"{BASE}/llm/keys/anthropic", headers=HEADERS, timeout=30)
```

On the request body, the same endpoints accept an `llm`

object that selects the provider and, optionally, a model per harness role. It deliberately has no api_key field (a `422`

if you attempt one): keys ride headers or the encrypted store only, so they can never be echoed in run objects, webhooks, or idempotency replays.

``` python
import os, requests

BASE = "https://coasty.ai/v1"
HEADERS = {"X-API-Key": os.environ["COASTY_API_KEY"]}

# Start a run on YOUR Anthropic key (stored earlier via PUT /llm/keys/anthropic).
# The llm block deliberately has NO api_key field (422 if you try): keys ride
# headers or the encrypted store only, never request bodies.
run = requests.post(
    f"{BASE}/runs",
    headers=HEADERS,
    json={
        "machine_id": "mch_test_0123456789abcdef",
        "task": "Open the billing page and download the latest invoice as PDF",
        "llm": {
            "provider": "anthropic",             # or "openai"; "managed" = platform default
            "model": "claude-sonnet-4-6",        # any model on your account (vision-capable)
            "compaction_model": "claude-haiku-4-5",  # optional per-role override
        },
    },
    timeout=30,
).json()

# Per-request variant: send the key in headers instead of storing it.
# A header key takes precedence over the stored key for this request only.
byok_headers = {
    **HEADERS,
    "X-LLM-Provider": "anthropic",
    "X-LLM-Api-Key": os.environ["ANTHROPIC_API_KEY"],
    "X-LLM-Model": "claude-sonnet-4-6",
}

# The run echoes a non-secret llm block; the key itself is never returned.
run = requests.get(f"{BASE}/runs/{run['id']}", headers=HEADERS, timeout=30).json()
print(run["llm"])   # {"provider": ..., "model": ..., "key_fingerprint": ..., "key_source": ..., "key_scrubbed": ...}
```

Per-role overrides exist for tuning cost against quality: run compaction on a cheaper model while the worker stays on the default, for example. For runs, workflows, and schedules the key is snapshotted encrypted into the run, so crash-recovery on another replica keeps using your key; it is scrubbed the moment the run reaches a terminal state. `GET /v1/runs/{id}`

echoes a non-secret `llm`

block: {`provider, model, key_fingerprint, key_source, key_scrubbed`

}. Schedules store only the non-secret preference; at fire time the current stored key is used. Delete the stored key and future firings fail loudly with `LLM_KEY_NOT_CONFIGURED`

; they never silently run on platform keys.

`grounding_model`

to experiment before committing a cheaper or different model to that role.`LLM_KEY_NOT_CONFIGURED`

, `LLM_KEY_INVALID`

, `LLM_PROVIDER_AUTH_FAILED`

, `LLM_PROVIDER_RATE_LIMITED`

, `LLM_PROVIDER_QUOTA_EXCEEDED`

, and `LLM_PROVIDER_ERROR`

.Billing: Coasty's per-call and per-step platform charges are unchanged with BYOK. The model tokens are billed by your provider account directly.

## Workflows

A workflow composes many runs into one versioned program, with branching, loops, and guards expressed as a JSON DSL. Each `task`

step is itself an agent run, so a workflow is the way to chain tasks, gate them on conditions, and pass results between them. Workflows are versioned: re-creating the same `slug`

bumps the version, and a `PUT`

does too.

Create one with `POST /v1/workflows`

. The `slug`

must match `[a-z0-9_-]`

. The response is a `Workflow`

carrying an `id`

, a `version`

, and the current `dsl_version`

(`2026-06-01`

).

``` python
import os, requests

BASE = "https://coasty.ai/v1"
HEADERS = {"X-API-Key": os.environ["COASTY_API_KEY"]}

definition = {
    "steps": [
        {
            "id": "fetch",
            "type": "task",
            "task": "Open order {{inputs.order_id}} and read the invoice total",
            "save_as": "invoice",
        },
        {
            "id": "check",
            "type": "assert",
            "condition": {"op": "truthy", "value": "{{invoice.passed}}"},
            "message": "Agent failed to read the invoice",
        },
        {
            "id": "branch",
            "type": "if",
            "condition": {"op": "contains", "left": "{{invoice.result}}", "right": "PAID"},
            "then": [{"id": "ok", "type": "succeed", "output": {"state": "paid"}}],
            "else": [{"id": "no", "type": "fail", "message": "Invoice not marked paid"}],
        },
    ],
}

# 1. Create the workflow. Re-using the same slug bumps its version.
wf = requests.post(
    f"{BASE}/workflows",
    headers=HEADERS,
    json={
        "name": "Invoice reconciliation",
        "slug": "invoice-reconcile",
        "inputs_schema": {"type": "object", "properties": {"order_id": {"type": "string"}}},
        "definition": definition,
    },
    timeout=30,
).json()
print(wf["id"], "v", wf["version"], wf["dsl_version"])

# 2. Start a run of the saved workflow.
run = requests.post(
    f"{BASE}/workflows/{wf['id']}/runs",
    headers=HEADERS,
    json={"inputs": {"order_id": "ord_4821"}, "machine_id": "mch_test_0123456789abcdef", "budget_cents": 500},
    timeout=30,
).json()
print(run["id"], run["status"])
```

`workflows:read`

and `workflows:write`

scopes, granted to new keys by default. See the [Workflow DSL](#workflow-dsl)for the full step and condition catalogue.

## Workflow DSL

The DSL (`dsl_version`

`2026-06-01`

) is a JSON object with a `steps`

array and an optional `output`

. Each step has an `id`

and a `type`

. A `task`

step runs the agent and binds its result (`{ status, passed, result, run_id, steps, error }`

) under both its `save_as`

name and its step id, so later steps can read it.

```
{
  "dsl_version": "2026-06-01",
  "definition": {
    "steps": [
      {
        "id": "fetch",
        "type": "task",
        "task": "Open order {{inputs.order_id}} and read the invoice total",
        "save_as": "invoice"
      },
      {
        "id": "check",
        "type": "assert",
        "condition": {
          "op": "truthy",
          "value": "{{invoice.passed}}"
        },
        "message": "Agent failed to read the invoice"
      },
      {
        "id": "branch",
        "type": "if",
        "condition": {
          "op": "contains",
          "left": "{{invoice.result}}",
          "right": "PAID"
        },
        "then": [
          {
            "id": "ok",
            "type": "succeed",
            "output": {
              "state": "paid"
            }
          }
        ],
        "else": [
          {
            "id": "no",
            "type": "fail",
            "message": "Invoice not marked paid"
          }
        ]
      }
    ],
    "output": {
      "paid": "{{invoice.result}}"
    }
  }
}
```

Conditions are structured rather than expression strings, which keeps them injection-safe. Each `left`

, `right`

, or `value`

is either a literal or a `{{path}}`

reference. Paths are dotted lookups into `inputs.*`

, `vars.*`

, and any step id or `save_as`

name.

`budget_cents`

(spend cap in USD cents; 0 means unlimited), `max_iterations`

(loop cap), and `deadline_seconds`

(wall-clock). A breach ends the run as `failed`

or `timed_out`

.A definition is validated before it is accepted. The limits below are enforced at create and ad-hoc time, so an invalid definition is rejected with `422 VALIDATION_ERROR`

rather than failing mid-run.

`definition`

is snapshotted into that run, so editing or replacing the workflow (which bumps its `version`

) never changes runs already in flight. Each run records the `workflow_version`

it executed.## Running workflows

Start a saved workflow with `POST /v1/workflows/{id}/runs`

, or run a definition inline (without saving) with `POST /v1/workflows/runs`

by adding a `definition`

(and optional `inputs_schema`

) to the same body. Both return a `workflow.run`

. The body accepts `inputs`

, a default `machine_id`

for task steps, and the `budget_cents`

, `max_iterations`

, and `deadline_seconds`

guards. An `Idempotency-Key`

header is honoured here too.

``` python
import os, requests

BASE = "https://coasty.ai/v1"
HEADERS = {"X-API-Key": os.environ["COASTY_API_KEY"]}

# POST /v1/workflows/runs runs a definition inline, without saving a workflow.
run = requests.post(
    f"{BASE}/workflows/runs",
    headers=HEADERS,
    json={
        "machine_id": "mch_test_0123456789abcdef",
        "inputs": {"url": "https://status.example.com"},
        "max_iterations": 5,
        "definition": {
            "steps": [
                {
                    "id": "open",
                    "type": "task",
                    "save_as": "page",
                    "task": "Open {{inputs.url}} and report whether all systems are operational",
                },
                {
                    "id": "gate",
                    "type": "assert",
                    "condition": {"op": "truthy", "value": "{{page.passed}}"},
                },
            ],
        },
    },
    timeout=30,
).json()
print(run["id"], run["status"])      # object == "workflow.run"
{
  "id": "wfr_5e6f7a8b",
  "object": "workflow.run",
  "status": "running",
  "workflow_id": "wf_1a2b3c",
  "workflow_version": 3,
  "machine_id": "mch_test_0123456789abcdef",
  "inputs": {
    "order_id": "ord_4821"
  },
  "output": null,
  "error": null,
  "awaiting_human_reason": null,
  "awaiting_step_id": null,
  "iterations_used": 0,
  "spent_cents": 0,
  "budget_cents": 500,
  "created_at": "2026-06-01T12:00:00Z",
  "started_at": "2026-06-01T12:00:01Z",
  "finished_at": null,
  "request_id": "req_9c8b7a6d"
}
```

## Provisioning

A machine is a Coasty-managed cloud VM that an agent can see and control. You provision one, poll until it is `running`

, then either hand it to a [task run](#runs) (the agent drives it to done) or drive it yourself with the [action endpoints](#machine-connect). Machines are optional: `/predict`

, `/sessions`

, and `/ground`

run against *your* screen. You only need a Coasty machine when you want the agent to execute on a VM Coasty hosts.

Provision with `POST /v1/machines`

. Only `display_name`

is required; everything else has a sensible default. The body rejects unknown fields, so a typo returns `422 VALIDATION_ERROR`

rather than being silently ignored.

```
curl -X POST https://coasty.ai/v1/machines \
  -H "X-API-Key: $COASTY_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: $(uuidgen)" \
  -d '{"display_name": "agent-box", "os_type": "linux", "desktop_enabled": true, "ttl_minutes": 120}'
```

Provisioning is asynchronous. The response returns immediately with the machine in `creating`

status and a `connection`

object whose secrets are redacted. The VM is not drivable yet — poll `GET /v1/machines/{id}`

until `status`

is `running`

before you send actions or start a run.

```
{
  "machine": {
    "id": "9f2c1e7a-3b6d-4c81-9a0e-2d5f8b1c4e90",
    "display_name": "agent-box",
    "status": "creating",
    "os_type": "linux",
    "desktop_enabled": true,
    "cpu_cores": 2,
    "memory_gb": 4,
    "storage_gb": 16,
    "public_ip": null,
    "auto_destroy_at": "2026-06-17T14:30:00Z",
    "ttl_minutes": 120,
    "is_test": false,
    "created_at": "2026-06-17T12:30:00Z"
  },
  "connection": {
    "public_ip": null,
    "ssh_port": 22,
    "ssh_username": "ubuntu",
    "has_ssh_key": true,
    "has_vnc_password": true
  },
  "request_id": "req_2f9c1a7b3e4d"
}
```

The machine object — returned by provision, list, and get:

List your machines with `GET /v1/machines`

(newest first, `?limit=`

1–200, default 50) — it returns `{ data, has_more, request_id }`

. Fetch one with `GET /v1/machines/{id}`

. Both read straight from the registry, so they keep working even when provisioning is busy.

`machines:write`

scope, a wallet balance of at least `20`

credits (Unlimited-tier keys skip this), and room under your plan's concurrent-machine cap. Building? An `sk-coasty-test-`

key returns a fully-shaped mock VM (id `mch_test_…`

, `is_test: true`

) with zero billing — up to 5 at a time.## Lifecycle & TTL

A machine moves through a small set of statuses. `status`

is the field you poll: drive the machine only while it is `running`

. The runtime rate that applies in each status is shown below; exact per-hour USD numbers are in the [Pricing](#pricing) section.

Start, stop, restart are `POST /v1/machines/{id}/start`

(and `/stop`

, `/restart`

). They are asynchronous: the call returns a transitional status (`starting`

/ `stopping`

) and you poll until it settles. They are state-checked — starting a machine that is already running, or stopping one that is not running, returns `409 INVALID_STATE`

with `current_state`

and `allowed_from`

in the body, so you can react without guessing. `start`

is allowed from `stopped`

or `error`

; `stop`

only from `running`

.

Terminate with `DELETE /v1/machines/{id}`

. This is permanent — the VM and its disk are destroyed and a later GET returns `404 MACHINE_NOT_FOUND`

. Delete is idempotent: deleting an already-gone machine still succeeds, so retries are safe.

```
curl -X POST https://coasty.ai/v1/machines/$MACHINE_ID/stop   -H "X-API-Key: $COASTY_API_KEY"
curl -X DELETE https://coasty.ai/v1/machines/$MACHINE_ID       -H "X-API-Key: $COASTY_API_KEY"
```

Auto-destroy (TTL). A machine left running bills until you destroy it, so set `ttl_minutes`

as a safety net. A background sweep (every ~60s) terminates a machine once its `auto_destroy_at`

passes. Adjust it any time with `PATCH /v1/machines/{id}`

: `ttl_minutes`

is measured *from now* (so it doubles as a lease extension), accepts `5`

–`10080`

(5 min to 7 days), and `0`

clears auto-destroy entirely. Anything else is `400 INVALID_TTL`

. Provisioning without a TTL works but adds a `Warning`

response header nudging you to set one.

```
curl -X PATCH https://coasty.ai/v1/machines/$MACHINE_ID \
  -H "X-API-Key: $COASTY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"ttl_minutes": 30}'
```

Runtime billing. Machines bill the developer API wallet (separate from any subscription credits) by the minute, rounded down to whole credits in your favour. Running Linux is `$0.05/hr`

, running Windows `$0.09/hr`

, and a `stopped`

or `suspended`

machine the keep-alive rate of `$0.01/hr`

; `creating`

, `error`

, and `terminated`

are free. Transitional states (starting/stopping/restarting) bill at the running rate. The per-call control endpoints (actions, terminal, files, browser, screenshot, connection) are never billed — you pay for runtime only.

`suspended`

) rather than destroying it — your disk and snapshots are preserved. Top up the wallet and `POST /start`

to resume. You can watch live accrual for every metered machine at `GET /v1/billing/active`

.## Connect & control

Once a machine is `running`

you can reach it three ways: connect directly over SSH/VNC, drive it through Coasty's control endpoints, or hand it to an agent run. Normal machine responses redact secrets and only expose `has_ssh_key`

/ `has_vnc_password`

booleans plus ports.

Connection secrets. `GET /v1/machines/{id}/connection`

returns the full `ssh_private_key_pem`

, `vnc_password`

, public IP, and ports. It is gated by the opt-in `connection:read`

scope (not granted by default — request it when you mint the key), and the response is sent `Cache-Control: no-store`

. Treat that payload like a password: never log it. SSH usernames are `ubuntu`

on Linux and `Administrator`

on Windows; VNC details exist only on `desktop_enabled`

machines.

```
curl https://coasty.ai/v1/machines/$MACHINE_ID/connection \
  -H "X-API-Key: $COASTY_API_KEY"
```

Screenshots & snapshots. `GET /v1/machines/{id}/screenshot`

returns the current screen of a desktop machine (a still-booting VM returns `502 SCREENSHOT_FAILED`

— poll for `running`

first). `POST /v1/machines/{id}/snapshot`

captures a restorable image (Linux), needs the `snapshots:write`

scope, and is the one machine op with a flat fee (`$0.01`

per snapshot). A conclusive pre-creation failure is refunded, confirmed by `X-Credits-Refunded`

. A timeout, an upstream 5xx, or malformed post-dispatch result may already have created the image and instead returns terminal `503 SNAPSHOT_OUTCOME_UNKNOWN`

without a blind refund or re-execution. Boot a future machine from a confirmed snapshot with `restore_from_snapshot: true`

.

The control surface. Drive the VM directly with these endpoints. Each enforces a specific scope, and the high-risk ones — `browser_execute`

(arbitrary JS) and anything under `connection:read`

— are opt-in. `/actions/batch`

runs up to 50 steps and stops on the first error by default (`stop_on_error: false`

to continue); `/terminal`

truncates output to 5000 chars.

```
curl -X POST https://coasty.ai/v1/machines/$MACHINE_ID/terminal \
  -H "X-API-Key: $COASTY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"command": "ls -la /home/ubuntu"}'
```

Idempotency & safety. The machine operations in the exact reserve-and-replay set — provision, snapshot, actions, action batches, browser, terminal, and file operations — accept an `Idempotency-Key`

(≤128 chars). Lifecycle start/stop/restart, TTL updates, and termination do not. For supported operations, a duplicate key replays the original result without re-executing; a duplicate that arrives while the first is still running waits up to ~25s, then returns `409 IDEMPOTENCY_IN_FLIGHT`

. One nuance worth knowing: a command that never reached the VM (a dispatch failure) is *not* cached, so retrying the same key runs it fresh; a command the VM actually ran (even if it errored) *is* cached. Every machine lookup is ownership-scoped, so a wrong or someone-else's id returns `404`

— never a leak. Full code list in [Errors](#errors).

## Predict

`POST /v1/predict`

is the low-level, stateless prediction primitive. Each call is independent: you provide the full context, execute the returned actions, capture the next screenshot, and decide when to call again. Use it when your application must own that loop. For an autonomous goal, start a [task run](#runs). When a manually driven loop needs server-side trajectory memory, reach for [sessions](#sessions) instead.

The response is the standard prediction shape, covered in [Response format](#responses).

## Sessions

A session keeps the trajectory — the running history of screenshots and actions — on our side, so each step only needs the latest screenshot and instruction. This produces better multi-step behaviour on long tasks and keeps your request bodies small. Create a session once, step through the task, then delete it to release your concurrency quota.

``` python
import base64, os, requests

BASE = "https://coasty.ai/v1"
HEADERS = {"X-API-Key": os.environ["COASTY_API_KEY"]}

def screenshot() -> str:
    with open("screen.png", "rb") as f:
        return base64.b64encode(f.read()).decode()

# 1. Open a session — it remembers the trajectory across steps
session = requests.post(f"{BASE}/sessions", headers=HEADERS, json={
    "screen_width": 1920,
    "screen_height": 1080,
}, timeout=60).json()
session_id = session["session_id"]

# 2. Drive the task one step at a time
try:
    for _ in range(20):  # safety cap
        res = requests.post(
            f"{BASE}/sessions/{session_id}/predict",
            headers=HEADERS,
            json={
                "screenshot": screenshot(),
                "instruction": "Book a meeting tomorrow at 3pm",
            },
            timeout=60,
        ).json()

        for action in res["actions"]:
            perform(action)          # your action executor

        if res["status"] != "continue":
            break
finally:
    # 3. Always release the session to free your concurrency quota
    requests.delete(f"{BASE}/sessions/{session_id}", headers=HEADERS, timeout=30)
```

`finally`

block. Sessions count against your tier's concurrent-session limit. The 2-hour (7200-second) idle TTL is reset on each predict and reset; deleting the session releases the slot immediately.## Grounding

Grounding answers a narrower question than predict: “where is this element?” Give it a screenshot and a description and it returns the exact `x`

, `y`

coordinate to target. It is faster and cheaper than a full prediction ($0.03 instead of $0.05), which makes it ideal when you already know what to do and only need a pixel to click.

``` python
import os, requests

res = requests.post(
    "https://coasty.ai/v1/ground",
    headers={"X-API-Key": os.environ["COASTY_API_KEY"]},
    json={
        "screenshot": screenshot,   # base64 PNG (see Predict)
        "element": "the blue Submit button below the form",
    },
    timeout=60,
).json()

print(res["x"], res["y"])           # exact click coordinates
```

The response is `{ x, y, usage, request_id }`

. Coordinates are in the same pixel space as the screenshot you sent.

## Parse

Parse converts a block of `pyautogui`

code into the same structured action objects the model returns. It is deterministic, runs no model, and is free. Use it to migrate existing automation scripts onto Coasty's executor, or to normalise hand-written steps into the canonical action schema.

``` python
import os, requests

res = requests.post(
    "https://coasty.ai/v1/parse",
    headers={"X-API-Key": os.environ["COASTY_API_KEY"]},
    json={"code": "pyautogui.click(100, 200)\npyautogui.typewrite('hello')"},
    timeout=30,
).json()

for action in res["actions"]:
    print(action["action_type"], action["params"])
```

## Action types

Every action the model can return uses an `action_type`

from the table below, paired with a `params`

object. Your executor switches on the type and applies the parameters. The terminal types — `done`

and `fail`

— set the response `status`

and signal you to stop looping.

## Response format

Predict and session-predict return the same shape. `actions`

is the ordered list to execute; `status`

tells you whether to keep going (`continue`

), stop successfully (`done`

), or stop because the task is impossible (`fail`

). `usage`

reports tokens and the dollar cost of the call (`cost_cents`

).

Billed success responses also carry two headers you can read without parsing the body: `X-Credits-Charged`

(what this call cost) and `X-Credits-Remaining`

(your wallet balance after it). In the body, the same numbers appear as `usage.credits_charged`

and `usage.cost_cents`

. On an `sk-coasty-test-`

key both are always `0`

. Every response (success or error) additionally carries an `X-Coasty-Request-Id`

header that mirrors `request_id`

; quote it when contacting support.

On a failed billed request, `X-Credits-Refunded`

is the authoritative confirmation that credits were returned, and `X-Credits-Charged`

is then`0`

. If settlement cannot be confirmed, the API returns`503 BILLING_UNAVAILABLE`

without the refund header. Follow the error's`retry_with_same_idempotency_key`

field rather than retrying by status alone.

```
{
  "request_id": "req_8f2c1e9a",
  "status": "continue",
  "reasoning": "The login form is visible. I'll click the email field, then type the address.",
  "actions": [
    {
      "action_type": "click",
      "params": {
        "x": 512,
        "y": 340
      },
      "description": "Click the email field"
    },
    {
      "action_type": "type_text",
      "params": {
        "text": "[email protected]"
      },
      "description": "Type the email address"
    }
  ],
  "raw_code": [
    "pyautogui.click(512, 340)",
    "pyautogui.typewrite('[email protected]')"
  ],
  "usage": {
    "input_tokens": 1523,
    "output_tokens": 245,
    "credits_charged": 5,
    "cost_cents": 5
  }
}
```

## Errors

Errors return a non-2xx status and a JSON envelope under an `error`

key. The `code`

is stable and safe to branch on; `message`

is human-readable and may change. Every error also carries an `error.request_id`

(mirrored in the `X-Coasty-Request-Id`

response header), plus `error.suggestion`

and `error.docs_url`

for self-service. A `Link: <url>; rel="help"`

header mirrors `docs_url`

. Always log the request id: it is the fastest way for us to trace a failed call.

Some codes attach machine-readable context to the body. A `402`

(`INSUFFICIENT_CREDITS`

) reports `required`

and `balance`

; a `403`

reports `required_scope`

and `current_scopes`

; a `422`

`VALIDATION_ERROR`

lists the offending field path under `error.details`

; and a `409`

state conflict carries `current_state`

with `allowed_from`

or `required_state`

.

```
{
  "error": {
    "code": "INSUFFICIENT_CREDITS",
    "message": "Your API wallet does not have enough funds to complete this request.",
    "type": "billing_error",
    "suggestion": "Add funds in the dashboard, or use an sk-coasty-test- key while building (test keys never bill).",
    "docs_url": "https://coasty.ai/docs#errors",
    "required": 5,
    "balance": 2,
    "request_id": "req_8f2c1e9a",
    "retryable": false,
    "retry_with_same_idempotency_key": false
  }
}
```

`429`

, `503`

(`UPSTREAM_UNAVAILABLE`

), and `504`

(`UPSTREAM_TIMEOUT`

) as retryable: honor `Retry-After`

on a 429, and use an `Idempotency-Key`

with exponential backoff on the upstream codes only when the response's `retryable`

and`retry_with_same_idempotency_key`

fields permit it. A`500`

model failure (`PREDICTION_FAILED`

or `GROUNDING_FAILED`

) is refunded only when `X-Credits-Refunded`

is present; after a confirmed refund, use a new idempotency key for a new execution.### Troubleshooting

Five mistakes account for almost every first-week support ticket. Each maps to one status and one fix:

## Pricing

Requests are billed in US dollars from your prepaid API wallet. The charge is taken before the model runs. On a conclusive server-side failure, the API submits a refund; treat it as complete only when `X-Credits-Refunded`

is present. Ambiguous provider mutations may retain the debit until reconciliation. Internally each request unit is `$0.01`

(the granularity behind every price below), but everything you pay and see is dollars. Every price on this page is exact; test keys (`sk-coasty-test-`

) always bill `$0.00`

.

### Surcharges

Four fixed surcharges can apply on top of a base price, all on the vision endpoints (predict, session steps, ground). Each is an exact USD amount:

### Machines

Machines bill for runtime only, metered per minute and rounded down: `$0.05/hr`

for a running Linux machine, `$0.09/hr`

for a running Windows machine, and `$0.01/hr`

while stopped or suspended. The starting, stopping, and restarting transitions bill at the running rate; the creating, error, and terminated states bill nothing, and TTL auto-destroy is free. Snapshots are a one-time `$0.01`

each, and every per-call operation (actions, batch, browser, terminal, files, screenshot, connection) is free. Provisioning requires a `$0.20`

wallet minimum, which is a gate, not a charge. If the wallet empties mid-flight the machine is automatically stopped, never destroyed, and resumes after you top up. The live rate card is always at `GET /v1/machines/pricing`

.

### Schedules

Schedules have no per-fire fee: webhook fires are free (limited to 60/min), and create, run-now, and webhook fires only require the same `$0.20`

wallet minimum as a gate. The execution itself is billed differently from everything else on this page: scheduled agent runtime is charged to your subscription credit balance at 10 credits per minute ($0.10 of subscription value per minute, at 1 credit = $0.01), not to this USD API wallet. Keep both balances funded if you rely on schedules.

## Cookbook

Curated open-source repositories for building on the Coasty API. Clone a repo, mint a key, and ship.

[coasty-ai/computer-use-cookbookExamplesPythonNodecURLView on GitHub](https://github.com/coasty-ai/computer-use-cookbook)

### Computer Use Cookbook

Runnable, copy-paste examples for every part of the Coasty API: predict, sessions, task runs, workflows, and driving machines. Start here.

[coasty-ai/open-coworkOpen sourceAgentsReference appView on GitHub](https://github.com/coasty-ai/open-cowork)

### Open Cowork

The open-source Coasty project for computer-use agents that work alongside you. Build on it, fork it, or contribute.