Blaxel is a perpetual sandbox platform that achieves near instant latency by keeping infinite secure sandboxes on automatic standby, while co-hosting your agent logic to cut network overhead.
This repository contains Blaxel's TypeScript SDK, which lets you create and manage sandboxes and other resources on Blaxel.
# npm
npm install @blaxel/core
# yarn
yarn add @blaxel/core
# bun
bun add @blaxel/coreThe SDK authenticates with your Blaxel workspace using these sources (in priority order):
- Blaxel CLI, when logged in
- Environment variables in
.envfile (BL_WORKSPACE,BL_API_KEY) - System environment variables
- Blaxel configuration file (
~/.blaxel/config.yaml)
When developing locally, the recommended method is to just log in to your workspace with the Blaxel CLI:
bl login YOUR-WORKSPACEThis allows you to run Blaxel SDK functions that will automatically connect to your workspace without additional setup. When you deploy on Blaxel, this connection persists automatically.
When running Blaxel SDK from a remote server that is not Blaxel-hosted, we recommend using environment variables as described in the third option above.
Sandboxes are secure, instant-launching compute environments that scale to zero after inactivity and resume in under 25ms.
Base image contents: The default
blaxel/base-image:latestis Alpine Linux with Node.js 22 and git pre-installed. It does not include Python or other language runtimes. To use Python, either specifyblaxel/py-app:latestas your image (Python 3.12) or install it in the base image withapk add --no-cache python3 py3-pip.
import { SandboxInstance } from "@blaxel/core";
// Create a new sandbox
const sandbox = await SandboxInstance.createIfNotExists({
name: "my-sandbox",
image: "blaxel/base-image:latest",
memory: 4096,
region: "us-pdx-1",
ports: [{ target: 3000, protocol: "HTTP" }],
labels: { env: "dev", project: "my-project" },
ttl: "24h"
});
// Get existing sandbox
const existing = await SandboxInstance.get("my-sandbox");
// Delete sandbox (using class)
await SandboxInstance.delete("my-sandbox");
// Delete sandbox (using instance)
await existing.delete();Generate public preview URLs to access services running in your sandbox:
import { SandboxInstance } from "@blaxel/core";
// Get existing sandbox
const sandbox = await SandboxInstance.get("my-sandbox");
// Start a web server in the sandbox
await sandbox.process.exec({
command: "npm run dev -- --port 3000",
workingDir: "/app",
waitForPorts: [3000]
});
// Create a public preview URL
const preview = await sandbox.previews.createIfNotExists({
metadata: { name: "app-preview" },
spec: {
port: 3000,
public: true
}
});
console.log(preview.spec?.url); // https://xyz.preview.bl.runPreviews can also be private, with or without a custom prefix. When you create a private preview URL, a token is required to access the URL, passed as a request parameter or request header.
// ...
// Create a private preview URL
const privatePreview = await sandbox.previews.createIfNotExists({
metadata: { name: "private-app-preview" },
spec: {
port: 3000,
public: false
}
});
// Create a public preview URL with a custom prefix
const customPreview = await sandbox.previews.createIfNotExists({
metadata: { name: "custom-app-preview" },
spec: {
port: 3000,
prefixUrl: "my-app",
public: true
}
});Execute and manage processes in your sandbox:
import { SandboxInstance } from "@blaxel/core";
// Get existing sandbox
const sandbox = await SandboxInstance.get("my-sandbox");
// Execute a command
const process = await sandbox.process.exec({
name: "build-process",
command: "npm run build",
workingDir: "/app",
waitForCompletion: true,
timeout: 60000 // 60 seconds
});
// Kill a running process
await sandbox.process.kill("build-process");Restart a process if it fails, up to a maximum number of restart attempts:
// ...
// Run with auto-restart on failure
process = await sandbox.process.exec({
name: "web-server",
command: "npm run dev -- --host 0.0.0.0 --port 3000",
restartOnFailure: true,
maxRestarts: 5
});Manage files and directories within your sandbox:
import { SandboxInstance } from "@blaxel/core";
import * as fs from "fs";
// Get existing sandbox
const sandbox = await SandboxInstance.get("my-sandbox");
// Write and read text files
await sandbox.fs.write("/app/config.json", '{"key": "value"}');
const content = await sandbox.fs.read("/app/config.json");
// Write and read binary files
const binaryData = fs.readFileSync("./image.png");
await sandbox.fs.writeBinary("/app/image.png", binaryData);
const blob = await sandbox.fs.readBinary("/app/image.png");
// Create directories
await sandbox.fs.mkdir("/app/uploads");
// List files
const { subdirectories, files } = await sandbox.fs.ls("/app");
// Search for text within files
const matches = await sandbox.fs.grep("pattern", "/app", {
caseSensitive: true,
contextLines: 2,
maxResults: 5,
filePattern: "*.ts",
excludeDirs: ["node_modules"]
});
// Find files and directories matching specified patterns:
const results = await sandbox.fs.find("/app", {
type: "file",
patterns: ["*.md", "*.html"],
maxResults: 1000
});
// Watch for file changes
const handle = sandbox.fs.watch("/app", (event) => {
console.log(event.op, event.path);
}, {
withContent: true,
ignore: ["node_modules", ".git"]
});
// Close watcher
handle.close();Persist data by attaching and using volumes:
import { VolumeInstance, SandboxInstance } from "@blaxel/core";
// Create a volume
const volume = await VolumeInstance.createIfNotExists({
name: "my-volume",
size: 1024, // MB
region: "us-pdx-1",
labels: { env: "test", project: "12345" }
});
// Attach volume to sandbox
const sandbox = await SandboxInstance.createIfNotExists({
name: "my-sandbox",
image: "blaxel/base-image:latest",
volumes: [
{ name: "my-volume", mountPath: "/data", readOnly: false }
]
});
// List volumes
const volumes = await VolumeInstance.list();
// Delete volume (using class)
await VolumeInstance.delete("my-volume");
// Delete volume (using instance)
await volume.delete();Blaxel lets you support agentic workflows by offloading asynchronous batch processing tasks to its scalable infrastructure, where they can run in parallel. Jobs can run multiple times within a single execution and accept optional input parameters.
import { blJob } from "@blaxel/core";
// Create and run a job execution
const job = blJob("job-name");
const executionId = await job.createExecution({
tasks: [
{ name: "John" },
{ name: "Jane" },
{ name: "Bob" }
]
});
// Get execution status
// Returns: "pending" | "running" | "completed" | "failed"
const status = await job.getExecutionStatus(executionId);
// Get execution details
const execution = await job.getExecution(executionId);
console.log(execution.status, execution.metadata);
// Wait for completion
try {
const result = await job.waitForExecution(executionId, {
maxWait: 300000, // 5 minutes (milliseconds)
interval: 2000, // Poll every 2 seconds (milliseconds)
});
console.log(`Completed: ${result.status}`);
} catch (error) {
console.log(`Timeout: ${error.message}`);
}
// List all executions
const executions = await job.listExecutions();
// Delete an execution
await job.deleteExecution(executionId);Blaxel provides additional packages for framework-specific integrations and telemetry:
@blaxel/telemetry- OpenTelemetry instrumentation@blaxel/vercel- Vercel AI SDK integration@blaxel/llamaindex- LlamaIndex integration@blaxel/langgraph- LangGraph integration@blaxel/mastra- Mastra integration
Blaxel acts as a unified gateway for model APIs, centralizing access credentials, tracing and telemetry. You can integrate with any model API provider, or deploy your own custom model. When a model is deployed on Blaxel, a global API endpoint is also created to call it.
The SDK includes a helper function that creates a reference to a model deployed on Blaxel and returns a framework-specific model client that routes API calls through Blaxel's unified gateway.
import { blModel } from "@blaxel/core";
// With Vercel AI SDK
import { blModel } from "@blaxel/vercel";
const model = await blModel("gpt-5-mini");
// With LangChain
import { blModel } from "@blaxel/langgraph";
const model = await blModel("gpt-5-mini");
// With LlamaIndex
import { blModel } from "@blaxel/llamaindex";
const model = await blModel("gpt-5-mini");
// With Mastra
import { blModel } from "@blaxel/mastra";
const model = await blModel("gpt-5-mini");Blaxel lets you deploy and host Model Context Protocol (MCP) servers, accessible at a global endpoint over streamable HTTP.
The SDK includes a helper function that retrieves and returns tool definitions from a Blaxel-hosted MCP server in the format required by specific frameworks.
// With Vercel AI SDK
import { blTools } from "@blaxel/vercel";
const tools = await blTools(['sandbox/my-sandbox'])
// With Mastra
import { blTools } from "@blaxel/mastra";
const tools = await blTools(['sandbox/my-sandbox'])
// With LlamaIndex
import { blTools } from "@blaxel/llamaindex";
const tools = await blTools(['sandbox/my-sandbox'])
// With LangChain
import { blTools } from "@blaxel/langgraph";
const tools = await blTools(['sandbox/my-sandbox'])Here is an example of retrieving tool definitions from a Blaxel sandbox's MCP server in the format required by Vercel's AI SDK:
import { SandboxInstance } from "@blaxel/core";
import { blTools } from "@blaxel/vercel";
// Create a new sandbox
const sandbox = await SandboxInstance.createIfNotExists({
name: "my-sandbox",
image: "blaxel/base-image:latest",
memory: 4096,
region: "us-pdx-1",
ttl: "24h"
});
// Get sandbox tools
const tools = await blTools(['sandbox/my-sandbox'])Instrumentation happens automatically when workloads run on Blaxel.
Enable automatic telemetry by importing the @blaxel/telemetry package:
import "@blaxel/telemetry";The SDK includes error tracking that captures exceptions originating from the SDK itself (not your application code). It collects data including the error type, message, stack trace, SDK version, workspace name, and so on. No user or application data is collected.
Error tracking is off by default since v0.2.76. To explicitly disable it in older versions:
export DO_NOT_TRACK=1Or add to ~/.blaxel/config.yaml:
tracking: falseWhere both settings exist, the DO_NOT_TRACK variable takes precedence.
Telemetry, delivered via OpenTelemetry, is controlled by the BL_ENABLE_OPENTELEMETRY environment variable.
When you deploy an agent to Blaxel, the platform automatically injects BL_ENABLE_OPENTELEMETRY=true into the environment.
When developing locally, this environment variable is not set and therefore defaults to false.
To explicitly disable telemetry, override the variable in your Blaxel deployment:
export BL_ENABLE_OPENTELEMETRY=falseFor more information, refer to our documentation.
- Node.js v18 or later
Contributions are welcome! Please feel free to submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.