Coasty (YC S26): 컴퓨터 제어 에이전트 API

Coasty API 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 Building with an AI assistant? Generate a ready-made prompt tailored to Cursor, Claude Code, ChatGPT, or any LLM. Create with AI Introduction Start with a task run : give Coasty a goal and a machine, then let the agent drive to completion. Use workflows when an automation needs many tasks, branches, loops, approvals, or shared outputs. 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 for a stateful screenshot loop, 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. Start with Use it when Task runs One goal should be driven autonomously from start to verified completion. Workflows Many tasks need sequencing, branching, loops, budgets, or human approval. Machines The agent needs a managed Linux or Windows computer with browser, terminal, and files. Primitives Your application needs direct control over every screenshot, prediction, and action. 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 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. Header Copy X-API-Key: sk-coasty-live-your_key_here Prefix Kind Behaviour sk-coasty-live- Live Runs the real model and draws down your USD wallet balance. sk-coasty-test- Test Returns mock responses and never bills. Ideal for local dev and CI. Prefer test keys while you wire up your integration. An 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 page (it never bills), then use an existing machine or provision one . Set the key in your shell: Shell Copy 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: cURL Python Node Go Ruby PHP Copy 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. Next Go to Understand task fields, lifecycle, and results Task runs Follow progress without polling Streaming events Turn the task into repeatable multi-step automation Workflows Own every screenshot and action yourself Prediction primitives Task runs are the default starting point for autonomous work. Workflows build on them, machines host them, and prediction primitives remain available when your application needs direct control over the loop. 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 . Send an Idempotency-Key header to make a retried create safe. cURL Python Node Go Ruby PHP Copy 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": ...} Field Required Description machine_id Yes The machine the agent will drive. task Yes The natural-language goal to accomplish. cua_version No Model family. v5 by default; v1 / v3 / v4 / v5 on all tiers. instructions No Extra guidance appended to the base prompt. system_prompt No A preamble placed ahead of the base prompt. max_steps No Hard cap on agent steps (default 50). deadline_seconds No Wall-clock budget; the run becomes timed_out if breached. on_awaiting_human No What to do when a human is needed: pause (default), fail , or cancel . awaiting_human_timeout_seconds No How long to wait for a human before timing out. webhook_url No HTTPS endpoint for lifecycle callbacks (https only). metadata No Arbitrary JSON echoed back on the run object. Endpoint Purpose POST /v1/runs Start a run. Returns the run plus a one-time webhook_secret. GET /v1/runs List runs. Filter with ?status= and ?limit= . GET /v1/runs/ {id} Fetch a single run and its current status. GET /v1/runs/ {id} /events Server-Sent Events stream of the run (see Streaming events). POST /v1/runs/ {id} /cancel Cancel a run that has not reached a terminal state. POST /v1/runs/ {id} /resume Hand control back after a human takeover. JSON Copy { "id": "run_7a1b2c3d", "object": "agent.run", "status": "queued", "machine_id": "mch_test_0123456789abcdef",