Skills
skills/blaxel-ai/agent-skills/blaxel

blaxel

SKILL.md

Blaxel Skill Reference

What is Blaxel

Blaxel (https://blaxel.ai) is a cloud platform that gives AI agents their own compute environments. Its flagship product is perpetual sandboxes: instant-launching microVMs that resume from standby in under 25ms and scale to zero after a few seconds of inactivity.

You use Blaxel primarily to:

  • Spin up a sandbox, install dependencies, run a dev server, and expose a live preview URL
  • Build and deploy sandbox templates (custom Docker images) for reusable environments
  • Deploy AI agents, MCP servers, and batch jobs as serverless endpoints

SDKs: TypeScript (@blaxel/core) and Python (blaxel) CLI: bl (install from https://docs.blaxel.ai/cli-reference/introduction) Docs: https://docs.blaxel.ai

Authentication

The SDK authenticates using these sources in priority order:

  1. Blaxel CLI, when logged in
  2. Environment variables in .env file (BL_WORKSPACE, BL_API_KEY)
  3. System environment variables
  4. Blaxel configuration file (~/.blaxel/config.yaml)

Log in locally (recommended for development):

bl login YOUR-WORKSPACE

Or set environment variables (for remote/CI environments):

export BL_WORKSPACE=your-workspace
export BL_API_KEY=your-api-key

When running on Blaxel itself, authentication is automatic.

Sandbox workflow (primary use case)

This is the most common workflow: create a sandbox, run commands in it, and get a preview URL.

Step 1: Create a sandbox

Use a public image from the Blaxel Hub (https://github.com/blaxel-ai/sandbox/tree/main/hub):

  • blaxel/base-image:latest — minimal Linux
  • blaxel/node:latest — Node.js
  • blaxel/nextjs:latest — Next.js
  • blaxel/vite:latest — Vite
  • blaxel/expo:latest — Expo (React Native)
  • blaxel/py-app:latest — Python

Or use a custom template image you deployed yourself.

Declare the ports you need at creation time. Ports cannot be added after creation. Ports 80, 443, and 8080 are reserved.

import { SandboxInstance } from "@blaxel/core";

const sandbox = await SandboxInstance.createIfNotExists({
  name: "my-sandbox",
  image: "blaxel/base-image:latest",
  memory: 4096,
  ports: [{ target: 3000, protocol: "HTTP" }],
});

from blaxel.core import SandboxInstance

sandbox = await SandboxInstance.create_if_not_exists({
  "name": "my-sandbox",
  "image": "blaxel/base-image:latest",
  "memory": 4096,
  "ports": [{"target": 3000, "protocol": "HTTP"}],
})

Use createIfNotExists / create_if_not_exists to reuse an existing sandbox by name or create a new one.

Step 2: Write files and run commands

// Write files
await sandbox.fs.write("/app/package.json", JSON.stringify({
  name: "my-app",
  scripts: { dev: "astro dev --host 0.0.0.0 --port 3000" },
  dependencies: { "astro": "latest" }
}));

// Or write multiple files at once
await sandbox.fs.writeTree([
  { path: "src/pages/index.astro", content: "<h1>Hello</h1>" },
  { path: "astro.config.mjs", content: "import { defineConfig } from 'astro/config';\nexport default defineConfig({});" },
], "/app");

// Execute a command and wait for it to finish
const install = await sandbox.process.exec({
  name: "install",
  command: "npm install",
  workingDir: "/app",
  waitForCompletion: true,
  timeout: 60000,
});

// Start a long-running dev server (don't wait for completion)
const devServer = await sandbox.process.exec({
  name: "dev-server",
  command: "npm run dev",
  workingDir: "/app",
  waitForPorts: [3000],  // returns once port 3000 is open
});

await sandbox.fs.write("/app/package.json", '{"name":"my-app","scripts":{"dev":"astro dev --host 0.0.0.0 --port 3000"},"dependencies":{"astro":"latest"}}')

await sandbox.fs.write_tree([
  {"path": "src/pages/index.astro", "content": "<h1>Hello</h1>"},
  {"path": "astro.config.mjs", "content": "import { defineConfig } from 'astro/config';\nexport default defineConfig({});"},
], "/app")

install = await sandbox.process.exec({
  "name": "install",
  "command": "npm install",
  "working_dir": "/app",
  "wait_for_completion": True,
  "timeout": 60000,
})

dev_server = await sandbox.process.exec({
  "name": "dev-server",
  "command": "npm run dev",
  "working_dir": "/app",
  "wait_for_ports": [3000],
})

IMPORTANT: Dev servers must bind to 0.0.0.0 (not localhost) to be reachable through preview URLs. Use --host 0.0.0.0 or the HOST env variable.

Step 3: Create a preview URL

const preview = await sandbox.previews.createIfNotExists({
  metadata: { name: "app-preview" },
  spec: { port: 3000, public: true },
});
const url = preview.spec?.url;
// url => https://xxxx.us-pdx-1.preview.bl.run

preview = await sandbox.previews.create_if_not_exists({
  "metadata": {"name": "app-preview"},
  "spec": {"port": 3000, "public": True},
})
url = preview.spec.url

For private previews, set public: false and create a token:

const preview = await sandbox.previews.createIfNotExists({
  metadata: { name: "private-preview" },
  spec: { port: 3000, public: false },
});
const token = await preview.tokens.create(new Date(Date.now() + 10 * 60 * 1000));
// Access: preview.spec?.url + "?bl_preview_token=" + token.value

Step 4: Manage the sandbox

// Reconnect to an existing sandbox
const sandbox = await SandboxInstance.get("my-sandbox");

// List files
const { subdirectories, files } = await sandbox.fs.ls("/app");

// Read a file
const content = await sandbox.fs.read("/app/src/pages/index.astro");

// Get process info / logs
const proc = await sandbox.process.get("dev-server");
const logs = proc.logs; // available if waitForCompletion was true

// Kill a process
await sandbox.process.kill("dev-server");

// Delete the sandbox (all data is erased)
await sandbox.delete();

sandbox = await SandboxInstance.get("my-sandbox")

result = await sandbox.fs.ls("/app")
content = await sandbox.fs.read("/app/src/pages/index.astro")

proc = await sandbox.process.get("dev-server")
# proc.logs available if wait_for_completion was True
await sandbox.process.kill("dev-server")
await sandbox.delete()

Sandbox templates (custom images)

When you need a reusable environment (e.g. an Astro project with all deps pre-installed), create a template:

bl new sandbox my-astro-template
cd my-astro-template

This creates: blaxel.toml, Dockerfile, entrypoint.sh, Makefile.

Customize the Dockerfile. Always include the sandbox-api binary:

FROM node:22-alpine
WORKDIR /app
COPY --from=ghcr.io/blaxel-ai/sandbox:latest /sandbox-api /usr/local/bin/sandbox-api
RUN npm install -g astro
COPY entrypoint.sh /entrypoint.sh
RUN chmod +x /entrypoint.sh
ENTRYPOINT ["/entrypoint.sh"]

The entrypoint.sh must start the sandbox-api:

#!/bin/sh
/usr/local/bin/sandbox-api &
while ! nc -z 127.0.0.1 8080; do sleep 0.1; done
echo "Sandbox API ready"
# Optionally start a process via the sandbox API:
# curl http://127.0.0.1:8080/process -X POST -d '{"workingDir":"/app","command":"npm run dev","waitForCompletion":false}' -H "Content-Type: application/json"
wait

Deploy the template:

bl deploy

Then retrieve the IMAGE_ID and use it to create sandboxes:

bl get sandboxes my-astro-template -ojson | jq -r '.[0].spec.runtime.image'

const sandbox = await SandboxInstance.createIfNotExists({
  name: "project-sandbox",
  image: "IMAGE_ID",
  memory: 4096,
  ports: [{ target: 3000, protocol: "HTTP" }],
});

Tutorials and Examples

Sandboxes

Astro: https://docs.blaxel.ai/Tutorials/Astro Expo: https://docs.blaxel.ai/Tutorials/Expo Next.js: https://docs.blaxel.ai/Tutorials/Nextjs

Agents

Overview: https://docs.blaxel.ai/Tutorials/Agents-Overview

Core CLI commands

For CLI commands that may prompt for input (like confirmations), add -y to auto-confirm when running in non-interactive / no-TTY environments (e.g. scripts, CI, agents).

Command Purpose
bl login Authenticate to workspace
bl new sandbox|agent|job|mcp NAME Initialize new resource from template
bl deploy Build and deploy resource to Blaxel
bl deploy -d DIR Deploy from a specific directory
bl serve Run resource locally for testing
bl serve --hotreload Run locally with hot reload
bl get sandboxes|agents|jobs|functions List resources
bl get sandbox NAME --watch Watch a sandbox deployment status
bl delete sandbox|agent|job|function NAME Remove resource
bl connect sandbox NAME Open interactive terminal in sandbox
bl chat AGENT-NAME Interactive chat with deployed agent
bl run job NAME --data JSON Execute a deployed batch job

blaxel.toml structure

name = "my-resource"
type = "sandbox"  # sandbox, agent, function, job, volume-template

[env]
NODE_ENV = "development"  # NOT for secrets — use Variables-and-secrets

[runtime]
memory = 4096       # MB
generation = "mk3"
# timeout = 900     # seconds (agents max 900, jobs max 86400)

# Ports (sandbox only)
[[runtime.ports]]
name = "dev-server"
target = 3000
protocol = "tcp"

Important gotchas

  • Ports must be declared at sandbox creation time — they cannot be added later
  • Ports 80, 443, 8080 are reserved by Blaxel
  • Dev servers must bind to 0.0.0.0, not localhost, for preview URLs to work
  • ~50% of sandbox memory is reserved for the in-memory filesystem (tmpfs). Use volumes for extra storage
  • Sandboxes auto-scale to zero after ~5s of inactivity. State is preserved in standby and resumes in <25ms
  • waitForCompletion has a max timeout of 60 seconds. For longer processes, use process.wait() with maxWait
  • Secrets should never go in [env] — use the Variables-and-secrets page in the Console

Agent Drive (shared filesystem)

Agent Drive is a distributed filesystem backed by SeaweedFS that can be mounted to multiple sandboxes or agents at any time, including while they are already running. Unlike volumes (block storage attached only at sandbox creation), drives support concurrent read-write access from multiple sandboxes and can be attached/detached dynamically.

This feature is currently in private preview. During the preview, Agent Drive is only available in the us-was-1 region. Both drive and sandbox must be in this region.

Use cases:

  • Passing data between sandboxes without intermediary services
  • Storing tool outputs and context histories for other agents
  • Sharing datasets across multiple agents
  • Creating a shared filesystem cache of package dependencies

Create a drive

import { DriveInstance } from "@blaxel/core";

const drive = await DriveInstance.createIfNotExists({
  name: "my-drive",
  region: "us-was-1",
  displayName: "My Project Drive",     // optional; defaults to name
  labels: { env: "dev", project: "x" }, // optional
});

from blaxel.core.drive import DriveInstance

drive = await DriveInstance.create_if_not_exists(
    {
        "name": "my-drive",
        "region": "us-was-1",
        "display_name": "My Project Drive",
        "labels": {"env": "dev", "project": "x"},
    }
)

Mount a drive to a sandbox

import { SandboxInstance } from "@blaxel/core";

const sandbox = await SandboxInstance.get("my-sandbox");

await sandbox.drives.mount({
  driveName: "my-drive",
  mountPath: "/mnt/data",
  drivePath: "/",   // optional; defaults to root of the drive
});

from blaxel.core import SandboxInstance

sandbox = await SandboxInstance.get("my-sandbox")

await sandbox.drives.mount(
    drive_name="my-drive",
    mount_path="/mnt/data",
    drive_path="/",
)

Once mounted, any file written to the mount path inside the sandbox is stored on the drive and persists even after the sandbox is deleted.

Mount a subdirectory

await sandbox.drives.mount({
  driveName: "my-drive",
  mountPath: "/app/project",
  drivePath: "/projects/alpha",
});

await sandbox.drives.mount(
    drive_name="my-drive",
    mount_path="/app/project",
    drive_path="/projects/alpha",
)

List, unmount, and delete drives

// List mounted drives on a sandbox
const mounts = await sandbox.drives.list();

// List all drives
const drives = await DriveInstance.list();

// Unmount
await sandbox.drives.unmount("/mnt/data");

// Delete a drive
await DriveInstance.delete("my-drive");
// or instance-level:
const drive = await DriveInstance.get("my-drive");
await drive.delete();

mounts = await sandbox.drives.list()

drives = await DriveInstance.list()

await sandbox.drives.unmount("/mnt/data")

await DriveInstance.delete("my-drive")
# or instance-level:
drive = await DriveInstance.get("my-drive")
await drive.delete()

CLI: bl get drives

Full Agent Drive example

import { SandboxInstance, DriveInstance } from "@blaxel/core";

// 1. Create a drive
const drive = await DriveInstance.createIfNotExists({
  name: "agent-storage",
  region: "us-was-1",
});

// 2. Create a sandbox (use image ID from custom template)
const sandbox = await SandboxInstance.createIfNotExists({
  name: "my-agent-sandbox",
  image: "my-sandbox-image-id",
  memory: 2048,
  region: "us-was-1",
});

// 3. Mount the drive
await sandbox.drives.mount({
  driveName: "agent-storage",
  mountPath: "/mnt/storage",
  drivePath: "/",
});

// 4. Write a file to the mounted drive
await sandbox.fs.write("/mnt/storage/hello.txt", "Hello from the drive!");

// 5. Read it back
const content = await sandbox.fs.read("/mnt/storage/hello.txt");
console.log(content); // "Hello from the drive!"

// 6. List mounted drives
const mounts = await sandbox.drives.list();
console.log(mounts);

import asyncio
from blaxel.core.drive import DriveInstance
from blaxel.core import SandboxInstance

async def main():
    drive = await DriveInstance.create_if_not_exists(
        {"name": "agent-storage", "region": "us-was-1"}
    )

    sandbox = await SandboxInstance.create_if_not_exists(
        {
            "name": "my-agent-sandbox",
            "image": "my-sandbox-image-id",
            "memory": 2048,
            "region": "us-was-1",
        }
    )

    await sandbox.drives.mount(
        drive_name="agent-storage",
        mount_path="/mnt/storage",
        drive_path="/",
    )

    await sandbox.fs.write("/mnt/storage/hello.txt", "Hello from the drive!")

    content = await sandbox.fs.read("/mnt/storage/hello.txt")
    print(content)

    mounts = await sandbox.drives.list()
    print(mounts)

asyncio.run(main())

Docs: https://docs.blaxel.ai/Agent-drive/Overview

Other Blaxel resources

Agents Hosting

Deploy AI agents as serverless auto-scaling HTTP endpoints. Framework-agnostic (LangChain, CrewAI, Claude SDK, etc.).

bl new agent
# develop in src/agent.ts or src/agent.py
bl serve            # test locally
bl deploy           # deploy
bl chat AGENT-NAME  # query

Sync endpoint handles requests up to 100s, async endpoint up to 10 minutes. Docs: https://docs.blaxel.ai/Agents/Overview

MCP Servers Hosting

Deploy custom tool servers following the MCP protocol.

bl new mcp
# implement in src/server.ts or src/server.py
bl serve --hotreload  # test locally
bl deploy

Agents connect to deployed MCP servers via SDK:

const tools = await blTools(["functions/my-mcp-server"]);

Every sandbox also exposes its own built-in MCP server at https://<SANDBOX_URL>/mcp with tools for process management, filesystem, and code generation. Docs: https://docs.blaxel.ai/Functions/Overview

Batch Jobs

Scalable compute for parallel background tasks (minutes to hours).

bl new job
# implement in src/index.ts or src/index.py
bl deploy
bl run job NAME --data '{"tasks": [...]}'

Max 24h per task. Set maxConcurrentTasks in blaxel.toml. Docs: https://docs.blaxel.ai/Jobs/Overview

Resources

Read individual SDK files for detailed explanations and code examples:

  • ./references/sdk-python.md
  • ./references/sdk-typescript.md

Each SDK README contains:

  • An overview of the SDK
  • Requirements
  • Code examples for working with sandboxes, volumes, agents, batch jobs, MCP
  • Additional useful information

For additional documentation, see: https://docs.blaxel.ai/llms.txt

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