E2B

Overview

E2B is an open-source cloud platform that gives AI agents secure, isolated sandbox environments to execute code. Each sandbox is a lightweight Firecracker microVM that spins up in under 200 milliseconds. You get Python and JavaScript SDKs to integrate sandboxed code execution into your AI apps. E2B works with any LLM provider including OpenAI, Anthropic, and open-source models, and plugs into frameworks like LangChain and LlamaIndex. Used by 88% of Fortune 100 companies, it handles everything from data analysis to autonomous multi-step agent workflows.

Key Features

Instant Sandbox Startup: Sandboxes boot in under 200ms using Firecracker microVMs, the same technology behind AWS Lambda, so your AI agents get near-instant compute access.
Code Interpreter SDK: Python and JavaScript/TypeScript SDKs let you add sandboxed code execution to any AI app with just a few lines of code.
Desktop Sandbox: A full graphical desktop environment you can connect to any LLM, enabling AI agents to interact with GUI applications like a human would.
LLM-Agnostic Design: Works with OpenAI, Anthropic, Mistral, Llama, and any other model. No vendor lock-in for your AI stack.
Framework Integrations: Native support for LangChain, LlamaIndex, and other popular AI frameworks, so you can plug E2B into your existing agent pipelines.
Custom Templates: Build reusable sandbox templates with pre-installed dependencies, custom configurations, and specific tooling for your use case.
Per-Second Billing: Pay only for actual compute time. A 1 vCPU sandbox runs about $0.05/hour, billed by the second.
Open Source: Core infrastructure and SDKs are open source on GitHub, giving you full transparency and the ability to contribute.

Pros

Extremely Fast Startup: Sub-200ms sandbox boot times mean your AI agents don't wait around for environments to spin up.
Easy SDK Support: Python and JavaScript/TypeScript SDKs let developers control sandboxes programmatically, adding code execution to apps with familiar languages.
Strong Isolation: Firecracker microVMs provide hardware-level isolation, keeping AI-generated code completely contained from your production systems.
Flexible Model Support: No lock-in to a specific LLM provider. Swap between OpenAI, Anthropic, or open-source models without changing your sandbox setup.
Generous Free Tier: The Hobby plan includes $100 in usage credits with no credit card required, giving you real room to experiment.
Open Source and Community‑Oriented: Core tools, SDKs, and resources are open source on GitHub, encouraging contribution and transparency.

Cons

Ephemeral Sandboxes: Sandboxes don't persist state by default. Files and data created during a session are lost when the sandbox shuts down, requiring workarounds for stateful workflows.
No GPU Support: You can't run GPU-accelerated workloads. If your AI agents need to do model training or inference on GPUs, you'll need a different solution.
Session Time Limits: Free tier caps sessions at 1 hour. Even the Pro plan limits sessions to 24 hours, which can disrupt very long-running agent tasks.

Use Cases

AI Data Analysis Apps: Build AI assistants that let users upload datasets and get instant analysis with generated charts and insights. Perplexity shipped advanced data analysis for their Pro users in just one week using E2B sandboxes.
Autonomous Multi-Step Agents: Create agents that can execute complex, multi-tool workflows in a full virtual computer environment. Manus uses E2B to give their agents access to 27 different tools running inside isolated sandboxes.
AI-Powered Code Generation: Build platforms where AI writes, tests, and validates code in real time. The sandbox ensures generated code runs safely without touching your production environment.
AI Model Evaluation at Scale: Run thousands of concurrent sandboxes for benchmarking and evaluating AI models. Hugging Face uses E2B to launch hundreds of parallel sandboxes for their Open R1 training experiments.
Interactive Learning Platforms: Create AI tutors that generate and execute code examples live, letting students experiment with programming concepts in a safe environment.
Automated Workflow Pipelines: Use E2B to power AI agents that handle API integrations, data processing, and report generation. Gumloop relies on E2B for their LLM-generated API integration workflows.

Frequently Asked Questions

What programming languages does E2B support?

E2B sandboxes support any programming language you can install in a Linux environment. The Code Interpreter SDK has built-in support for Python and JavaScript/TypeScript, but you can create custom templates with any language runtime, including Go, Rust, Java, Ruby, and more.

How does E2B pricing work?

E2B uses usage‑based pricing — you pay by the second for compute while sandboxes run, and you get a one‑time $100 free credit on signup. The Hobby tier is free with limits (e.g., 1‑hour session max), the Pro plan is about $150/month with longer sessions and higher concurrency, and Enterprise plans have custom pricing.

Can I use E2B with any AI model?

Yes. E2B is LLM-agnostic and works with OpenAI, Anthropic Claude, Mistral, Llama, and any other language model. It also integrates natively with AI frameworks like LangChain and LlamaIndex.

How fast do E2B sandboxes start?

Sandboxes boot in under 200 milliseconds. This near-instant startup makes E2B practical for real-time AI applications where users expect quick responses.

Is E2B open source?

Yes. E2B's core infrastructure, SDKs, and CLI are all open source and available on GitHub. You can inspect the code, contribute, and even self-host, though self-hosting is experimentally supported.

What is E2B Desktop?

E2B Desktop is a sandbox variant that provides a full graphical desktop environment. You can connect it to any LLM to build AI agents that interact with GUI applications, browse the web, and perform tasks that require visual interaction, similar to how a human uses a computer.