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E2B

Secure cloud sandboxes for AI agents

freemiumopen sourceupdated Aug 16, 2026

E2B provides secure cloud sandboxes that let AI agents execute code, run terminal commands, and interact with filesystems in isolated environments. Each sandbox spins up in ~150ms with its own OS, giving agents a safe space to run untrusted code. Supports Python, JavaScript, and any language via custom Dockerfiles. Used by AI coding assistants, data analysis agents, and code interpreters. SDK available for Python and JavaScript with a simple API for programmatic sandbox control.

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E2B provides cloud-based sandboxed environments specifically designed for AI agents and LLM applications. Each sandbox is a lightweight micro-VM with its own isolated OS that starts in approximately 150 milliseconds, giving AI agents a secure space to execute code and interact with filesystems.

The platform solves a critical challenge in agentic AI: allowing AI agents to execute arbitrary code safely. Agents can run Python scripts, install packages, execute terminal commands, read and write files, and even run web servers — all within an isolated environment that cannot affect the host system.

Custom sandbox templates built from Dockerfiles enable pre-configuring environments with specific tools, libraries, and configurations. The SDK provides programmatic control over sandboxes through Python and JavaScript/TypeScript, with simple APIs for code execution, file operations, and process management.

E2B is used as the code execution backend for AI coding assistants, data analysis agents, code interpreters, computer-use agents, and automated testing pipelines. Public pricing currently starts with a free Hobby tier that includes one-time usage credits, then Pro at $150/month plus usage and custom Enterprise/BYOC options.

Pricing

Hobby free with one-time $100 usage credits + usage costs. Pro $150/mo + usage. Enterprise/BYOC custom.

Platforms

API, Python SDK, JS/TS SDK, Docker

Categories

Tags

Use Cases

Dagger Container Use logo

Dagger Container Use

Containerized sandboxes for AI coding agents

Dagger Container Use provides isolated container environments for AI coding agents, enabling multiple agents to work in separate sandboxed branches simultaneously. Built by the Dagger team, it ensures reproducibility and safety for autonomous code execution by giving each agent its own containerized workspace with full toolchain access.

Open Source
Steel logo

Steel

Open-source browser infrastructure for AI agents at scale

Steel is an open-source browser API purpose-built for AI agents, providing managed headless browser sessions with anti-bot bypass, proxy rotation, CAPTCHA solving, and session persistence. It handles the infrastructure layer that browser automation agents like Browser Use and Stagehand run on top of. Self-hostable or available as a cloud service. Over 6,000 GitHub stars.

Open Source
Microsandbox logo

Microsandbox

Local microVM sandboxes for AI agent code execution

Microsandbox provides hardware-level isolated sandboxes for AI agents to execute code safely on local machines. Using libkrun microVMs and a 320ms bare-metal Linux/KVM homepage benchmark, it offers stronger isolation than Docker containers while staying lightweight enough for dev workstations. OCI-compatible with Python and Node.js runtimes. Apache-2.0 licensed with 6.6K+ GitHub stars.

Open Source
NVIDIA OpenShell logo

NVIDIA OpenShell

Secure sandboxed runtime for AI agent execution

NVIDIA OpenShell provides kernel-level isolation for AI agent workloads with Landlock, seccomp, and network namespace sandboxing. Announced at GTC 2026 with 17 enterprise partners including Adobe, Atlassian, SAP, and Salesforce, it offers declarative YAML policy enforcement, L7 HTTP inspection, and GPU passthrough — purpose-built to contain the blast radius when autonomous coding agents interact with filesystems and networks.

Open Source
Lume logo

Lume

macOS and Linux VM runtime for AI agents on Apple Silicon

Lume is an open-source CLI for creating and managing macOS and Linux virtual machines on Apple Silicon, built specifically for AI agent sandboxing, CI/CD pipelines, and desktop automation. Using Apple's native Virtualization.Framework for near-native performance, it provides the missing isolation layer for running coding agents safely — so an accidental destructive command doesn't affect your host machine.

Open Source
OpenSandbox logo

OpenSandbox

Enterprise-grade sandbox for AI agent code execution

OpenSandbox is an open-source sandbox platform from Alibaba providing secure, isolated execution environments for AI coding agents. It supports Python, Java, JavaScript, and C# SDKs with a unified Sandbox Protocol for custom runtimes. Integrates with Docker and Kubernetes, offering isolation through gVisor, Kata Containers, and Firecracker microVMs with per-sandbox network controls.

Open Source

Related Tools

computed discovery: shared active categories · kept separate from editor-verified Alternatives

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Agent Skills

Open standard for portable skills across AI agents

Agent Skills is the open SKILL.md folder specification for packaging reusable instructions, scripts, references, and assets that compatible AI agents load through progressive disclosure. Originally developed by Anthropic and released as an open standard, it defines the portable format itself—not an example library, marketplace, or hosted agent product.

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HyperAgent

Open-source natural-language browser automation framework for AI agents

An AGPL-3.0 TypeScript framework that drives a real browser from natural-language tasks — the self-hostable OSS framework from Hyperbrowser, distinct from the vendor's paid cloud browser product.

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KaibanJS

JavaScript framework for building and visualizing multi-agent workflows on a Kanban board

KaibanJS is an MIT-licensed JavaScript framework for defining AI agents, tasks, tools, and teams, then orchestrating their work through a Kanban-inspired runtime and visual board. It can run inside Node.js, React, or Next.js projects, supports custom UIs and headless workflows, and provides real-time task-state visibility for multi-agent applications.

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Sakana Fugu

Multi-agent model API that orchestrates frontier models behind one OpenAI-compatible endpoint

Sakana Fugu is a hosted model-provider API that exposes a learned multi-agent system as one OpenAI-compatible model. It dynamically routes coding, code review, research, and reasoning tasks across a frontier-model pool, with Fugu for lower-latency work and Fugu Ultra for harder workloads where answer quality matters more than cost or speed.

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LangChain logo

OpenWiki

LangChain CLI for maintaining agent-friendly codebase documentation

OpenWiki is LangChain’s open-source CLI for generating and maintaining an agent-focused wiki inside a codebase. It can create an openwiki/ documentation folder, update it from repository changes, add guidance to AGENTS.md or CLAUDE.md, and run via an interactive CLI or daily GitHub Action so coding agents have durable context without stuffing every detail into prompts.

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Accomplish Coworker

Open-source desktop AI coworker for browsing and code execution.

Accomplish Coworker is an MIT-licensed open-source AI coworker that runs on the desktop, combining computer-use style browsing with code execution so agents can research, implement, run, and debug workflows in one local environment.

Open SourceTelemetry

Comparisons

Freestyle vs E2B — Agent-Native VM Stack or Mature Code Execution

Freestyle and E2B both promise secure sandboxes for AI coding agents, but they make different bets about what that sandbox should contain. E2B is the mature, container-based runtime trusted across the agent ecosystem — LangChain, LlamaIndex, OpenAI cookbooks — while Freestyle is the newer, heavier stack that bundles Linux VMs, Git, deploys, and execution as one trust boundary.

E2B vs Daytona — Ephemeral Code Sandboxes vs Stateful Development Environments for AI

E2B and Daytona provide isolated environments for AI code execution with different persistence models. E2B offers ephemeral Firecracker microVM sandboxes destroyed after use for clean-slate execution. Daytona provides stateful Docker-based workspaces that persist across sessions, treating each environment as a long-lived development workspace rather than a disposable execution unit.

Lume vs E2B — macOS VM Runtime vs Cloud Sandbox Platform

Lume and E2B both provide isolated environments for running AI agents safely, but their architectures serve different deployment models. Lume creates native macOS and Linux VMs on Apple Silicon for local agent sandboxing, while E2B offers cloud-hosted micro-VMs optimized for code execution. The choice depends on whether you need local Apple Silicon isolation or scalable cloud sandboxes.

LumeE2B

FAQ

What is E2B?

E2B provides secure cloud sandboxes that let AI agents execute code, run terminal commands, and interact with filesystems in isolated environments. Each sandbox spins up in ~150ms with its own OS, giving agents a safe space to run untrusted code. Supports Python, JavaScript, and any language via custom Dockerfiles. Used by AI coding assistants, data analysis agents, and code interpreters. SDK available for Python and JavaScript with a simple API for programmatic sandbox control.

Is E2B free?

E2B offers a free tier alongside paid plans. Hobby free with one-time $100 usage credits + usage costs. Pro $150/mo + usage. Enterprise/BYOC custom.

Is E2B open source?

Yes — E2B is open source.

What are the best E2B alternatives?

The top editor-verified E2B alternatives are Dagger Container Use, Steel, Microsandbox, and more.

How does E2B score in our review?

Our hands-on review scores E2B 87/100 overall, based on speed, privacy, and developer-experience testing.