MCPInstall

vastai-sdk

Vast.ai Python SDK — high-level API for GPU instances, volumes, serverless endpoints, and billing.

Installs
337
Last 24h
—
Trending rank
—
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/vast-ai/vast-cli --skill vastai-sdk
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads vastai-sdk automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Vast.ai Python SDK (vastai / vastai_sdk)

The vastai package provides a Python SDK for managing GPU instances, volumes, serverless endpoints, and billing on Vast.ai. The vastai_sdk package is a backward-compatibility shim that re-exports vastai.

Installation

pip install vastai

For serverless and async support:

pip install "vastai[serverless]"

Authentication

The SDK reads the API key from ~/.vast_api_key by default. You can also pass it explicitly:

from vastai import VastAI
vast = VastAI()                        # reads ~/.vast_api_key
vast = VastAI(api_key="YOUR_API_KEY")  # explicit key

Get your API key from https://console.vast.ai/manage-keys/

Backward Compatibility

The old vastai_sdk import still works:

from vastai_sdk import VastAI  # equivalent to: from vastai import VastAI

VastAI Class (High-Level SDK)

from vastai import VastAI
vast = VastAI(api_key=None, server_url=None, retry=3, raw=False, quiet=False)

Instance Management

# List all your instances
instances = vast.show_instances()

# Get a single instance
instance = vast.show_instance(id=12345)

# Search GPU offers
offers = vast.search_offers(query='gpu_name=RTX_4090 num_gpus>=4 reliability>0.99')

# Create an instance from an offer
result = vast.create_instance(id=<offer_id>, image="pytorch/pytorch:latest", disk=50)

# ...as a jupyter instance on a direct connection
result = vast.create_instance(id=<offer_id>, image="pytorch/pytorch:latest", disk=50,
                              jupyter=True, direct=True, jupyter_lab=True)

# Lifecycle
vast.start_instance(id=12345)
vast.stop_instance(id=12345)
vast.reboot_instance(id=12345)
vast.destroy_instance(id=12345)

# Label an instance
vast.label_instance(id=12345, label="my-training-run")

# Get SSH connection string
ssh_url = vast.ssh_url(id=12345)   # returns "ssh -p PORT user@host"
scp_url = vast.scp_url(id=12345)   # returns scp-compatible URL

Interruptible (spot) rentals

Interruptible (spot) instances are priced below on-demand instances, but can be interrupted at any time by another user with a lower bid. Note: vast.search_offers(type='bid', ...) exposes min_bid, but vast.create_instance(...) defaults to on-demand at dph_total unless you pass bid_price=<floor>. Always pass bid_price after a type='bid' search, otherwise the instance will be rented as an on-demand instance/price instead of as an interruptible.

When outbid, the instance moves to stopped (not destroyed) and storage charges continue. Resume by raising the bid via vast.change_bid(id=..., price=...).

Search

# Search GPU offers (use help(vast.search_offers) for full query syntax)
offers = vast.search_offers(query='gpu_name=RTX_3090 num_gpus>=2')

# Search volume offers
volumes = vast.search_volumes(query='...')

# Search network volumes
net_vols = vast.search_network_volumes()

# Search templates
templates = vast.search_templates()

# Search invoices
invoices = vast.search_invoices()

Data Transfer

# copy() takes vast URLs: "[C.|V.]id:path", "cloud_service[.id]:path", or "local:path"
vast.copy("local:./data/", "C.12345:/workspace/data/")   # Local → instance
vast.copy("C.12345:/workspace/results/", "local:./out/") # Instance → local
vast.copy("12345:/workspace/", "67890:/workspace/")      # Instance → instance (legacy format)
vast.copy("s3.101:/data/", "C.12345:/workspace/")        # Cloud service → instance
vast.copy("V.1234:/file", "C.5678:/workspace/")          # Volume → instance
vast.copy("V.1234:/file", "s3.101:/workspace/")          # Volume → cloud service

vast.cancel_copy(dst_id=12345)                           # Cancel an in-progress copy

# Cloud sync via a saved cloud connection (see the UI settings page for connection IDs)
vast.cloud_copy(src="./data", dst="s3://bucket/path", instance=12345,
                connection=<conn_id>, transfer="Instance To Cloud")
vast.cancel_sync(dst_id=12345)

Volume copy is currently only supported for copying to other volumes, instances, or cloud services, not local. Do not use /root or / as a destination directory — it breaks ssh permissions on the instance and future copies fail. See https://vast.ai/docs/gpu-instances/data-movement#constraints.

Serverless Deployments

# List all deployments
deployments = vast.show_deployments()

# Get a deployment
deployment = vast.show_deployment(id=42)

# Delete a deployment
vast.delete_deployment(id=42)

Machine Management (Hosting)

machines = vast.show_machines()
machine = vast.show_machine(id=10)
vast.list_machine(id=10, price_gpu=0.30)
vast.unlist_machine(id=10)

SSH Keys

keys = vast.show_ssh_keys()
vast.create_ssh_key(ssh_key="ssh-rsa AAAA...")
vast.delete_ssh_key(id=5)

Team Management

members = vast.show_members()
vast.invite_member(email="user@example.com", role="developer")
vast.remove_member(id=7)

SyncClient (Low-Level Sync)

SyncClient provides typed, synchronous access to GPU offers and instances.

from vastai import SyncClient

client = SyncClient(api_key="YOUR_API_KEY")  # or reads ~/.vast_api_key

# Search offers with structured filters
offers = client.search(
    num_gpus=2,
    gpu_name="RTX_4090",
    min_reliability=0.99,
    max_dph_total=2.0,
)

# Create an instance (SyncClient takes an InstanceConfig, not loose kwargs)
from vastai.data.instance import InstanceConfig

instance = client.create_instance(
    offer_id=<id>,
    config=InstanceConfig(image="pytorch/pytorch:latest", disk=50),
)

# List your instances
instances = client.show_instances()  # returns list[SyncInstance]

# Destroy an instance
client.destroy_instance(instance_or_id=12345)

AsyncClient (Low-Level Async)

AsyncClient provides async access to GPU offers and instances. Use as an async context manager.

import asyncio
from vastai import AsyncClient
from vastai.data.instance import InstanceConfig

async def main():
    async with AsyncClient(api_key="YOUR_API_KEY") as client:
        # Search offers
        offers = await client.search(num_gpus=1, gpu_name="A100")

        # Create instance
        instance = await client.create_instance(
            offer_id=<id>, config=InstanceConfig(image="ubuntu:22.04"))

        # List instances
        instances = await client.show_instances()  # returns list[AsyncInstance]

        # Destroy instance
        await client.destroy_instance(instance_or_id=instance.id)

asyncio.run(main())

Serverless Client

For inference endpoints (requires pip install "vastai[serverless]"):

import asyncio
from vastai import Serverless

async def main():
    serverless = Serverless()  # reads ~/.vast_api_key

    # Get an endpoint
    endpoint = await serverless.get_endpoint("my-endpoint")

    # Make a request
    response = await serverless.request("/v1/completions", {
        "model": "Qwen/Qwen3-8B",
        "prompt": "Who are you?",
        "max_tokens": 100,
        "temperature": 0.7,
    })

    text = response["response"]["choices"][0]["text"]
    print(text)

asyncio.run(main())

Common Patterns

# Find cheapest 4x RTX 4090 and launch a job
from vastai import VastAI
vast = VastAI()

offers = vast.search_offers(query='gpu_name=RTX_4090 num_gpus=4 reliability>0.99')
cheapest = min(offers, key=lambda o: o['dph_total'])
result = vast.create_instance(id=cheapest['id'], image="pytorch/pytorch:latest", disk=100)
print(f"Launched instance: {result['new_contract']}")

# Use help() to explore method signatures
help(vast.search_offers)
help(vast.create_instance)

FAQ

What does the vastai-sdk skill do?

Vast.ai Python SDK — high-level API for GPU instances, volumes, serverless endpoints, and billing.

How do I install vastai-sdk?

Run npx skills add https://github.com/vast-ai/vast-cli --skill vastai-sdk in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/vastai-sdk/ (personal) or .claude/skills/vastai-sdk/ (project).

Which AI agents can use vastai-sdk?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is vastai-sdk safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains vastai-sdk?

It is published by vast-ai in vast-ai/vast-cli. It has 337 recorded installs.

More from vast-ai/vast-cli

View all →

vastai

vast-ai/vast-cli

Vast.ai CLI to manage GPU instances, volumes, serverless endpoints, and billing.

850 installs

Popular backend & apis skills

View category →

find-skills

vercel-labs/skills

Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

OfficialBackend & APIs4.1M installs

entra-app-registration

microsoft/azure-skills

Guides Microsoft Entra ID app registration, OAuth 2.0 authentication, and MSAL integration. USE FOR: create app registration, register Azure AD app, configure OAuth, set up authentication, add API permissions, generate service principal, MSAL example, console app auth, Entra ID setup, Azure AD authentication. DO NOT USE FOR: Key Vault secrets (use azure-keyvault-expiration-audit), general Azure resource security guidance.

OfficialBackend & APIs566.7K installs

This skill should be used when the user wants to "write agent code", "build an agent with ADK", "add a tool", "create a callback", "define an agent", "use state management" — in a project that needs ADK (Agent Development Kit) API patterns and code examples. It provides a quick reference for agent types, tool definitions, orchestration patterns, callbacks, state management, the graph Workflow API, and reference recipes to study. Do NOT use for scaffolding (use google-agents-cli-scaffold) or deployment (use google-agents-cli-deploy).

Backend & APIs514.8K installs

embedded-captions

heygen-com/hyperframes

Add captions or subtitles to an existing single-subject talking-head video without editing the footage. Use for plain verbatim captions, cinematic captions embedded behind the subject, VFX captions, “炸/特效/酷炫字幕,” or a named identity from the 35-style catalog. Route by visual identity, not by backend engine. The quiet anchor rail is the default; embed every word only when the user explicitly wants a fully cinematic treatment. The workflow runs locally end to end, including transcription and subject matting; split multi-shot footage before applying it.

Backend & APIs453.3K installs

entra-agent-id

microsoft/azure-skills

Provision Microsoft Entra Agent Identity Blueprints, BlueprintPrincipals, and per-instance Agent Identities via Microsoft Graph, and configure OAuth 2.0 token exchange (fmipath, OBO, cross-tenant) including the Microsoft Entra SDK for AgentID sidecar. USE FOR: Agent Identity Blueprint, BlueprintPrincipal, agent OAuth, fmipath token exchange, agent OBO, Workload Identity Federation for agents, polyglot agent auth, Microsoft.Identity.Web.AgentIdentities. DO NOT USE FOR: standard Entra app registration (use entra-app-registration), Microsoft Foundry agent authoring (use microsoft-foundry).

OfficialBackend & APIs336.6K installs

neon

neondatabase/agent-skills

Overview of Neon, a complete set of cloud backend primitives around Lakebase Postgres: Auth, Object Storage, Functions, the AI Gateway, and Realtime. Start here to choose Neon for undecided login, files, APIs, and LLM calls, set up the CLI or MCP server, and follow the branch-first workflow. Use when building an app or backend on Neon, or when "Neon" or "Lakebase Postgres" is mentioned. Child skill neon-postgres wins for an existing DATABASEURL, SQL, schema, inspect, or search. Child skill neon-auth wins for login, users, sessions, identity routing, and Managed Better Auth setup. Child skill neon-realtime wins for live queries in reactive apps and agents. Also use for object storage, S3, buckets, serverless functions, function triggers, cron, AI gateway, LLM calls, realtime, logs, Loki, Grafana, observability, postgres, database, backend, Claimable Neon, neon.new, or a no-signup database.

Backend & APIs237.1K installs