e2b

e2b is an advanced cloud infrastructure platform designed specifically for building and deploying enterprise-grade AI agents. It provides developers with secure, isolated, and high-performance environments called "AI Sandboxes" where AI-generated code can be executed safely. At its core, e2b addresses the critical need for a reliable and scalable runtime for AI agents, allowing them to perform complex tasks that go beyond simple text generation, such as interacting with filesystems, running terminal commands, accessing the internet, and executing code in any programming language.

The platform is built on Firecracker, the same microVM technology used by AWS Lambda, ensuring that each sandbox is fully isolated from others and the host system. This robust security model makes it possible to run untrusted code generated by Large Language Models (LLMs) without risking the underlying infrastructure. e2b is LLM-agnostic, meaning it seamlessly integrates with any model, including those from OpenAI, Anthropic, Google, Mistral, or custom-built models, providing developers with maximum flexibility.

How to use e2b

Integrating e2b into your AI application is a straightforward process designed for developers. Here is a typical workflow:

1. Sign Up and Get API Key: Create an account on the e2b website. Every new account receives a generous starting credit. Navigate to your dashboard to find and copy your unique API key.
2. Install the SDK: e2b provides easy-to-use SDKs for popular programming languages. Install the necessary package in your project environment (e.g., `npm install @e2b/code-interpreter` for Node.js or `pip install e2b` for Python).
3. Configure Environment: Set your E2B_API_KEY as an environment variable in your project to authenticate your requests.
4. Instantiate a Sandbox: In your code, import the Sandbox class from the e2b SDK and create a new sandbox instance with a single line of code, like `sandbox = await Sandbox.create()`.
5. Execute Code and Interact: Once the sandbox is running (which takes less than 200ms), you can use its methods to run code (`sandbox.runCode(...)`), manage files (`sandbox.files.list('/')`, `sandbox.files.write(...)`), or execute terminal commands. The results, logs, and any errors are returned directly to your application for processing.
6. Close the Sandbox: After the task is complete, you can close the sandbox to release the resources.

Core Features of e2b

• Secure AI Sandboxes: Each sandbox is a fully isolated microVM powered by Firecracker, providing a secure environment to run untrusted, AI-generated code.
• LLM Agnostic: Compatible with any Large Language Model, including OpenAI's GPT series, Anthropic's Claude, Llama, Mistral, and custom models.
• Multi-Language Code Execution: Natively supports running code in any language, such as Python, JavaScript/Node.js, Ruby, C++, and more. If it runs on Linux, it runs in e2b.
• High Performance and Scalability: Sandboxes launch in under 200ms with no cold starts. The platform is designed to run tens of thousands of concurrent sandboxes for large-scale applications.
• Full Environment Control: Provides complete control over the execution environment, including filesystem access (read, write, upload, download), terminal access for running shell commands, and the ability to install any necessary packages or libraries.
• Long-Running Sessions: Supports sessions lasting up to 24 hours (on paid plans), ideal for complex, time-consuming tasks like deep research or model training.
• Developer-Friendly SDKs: Offers intuitive SDKs for Python and JavaScript/TypeScript, making integration simple and fast.
• Flexible Deployment: Available as a cloud service or can be deployed on-premises or in your own cloud (BYOC) for maximum data control and security.

Use Cases for e2b

e2b empowers a wide range of advanced AI applications:

• Deep Research Agents: Enable AI agents to perform extensive research by browsing the web, processing large datasets, and synthesizing information over long periods.
• AI Data Analysis & Visualization: Connect data sources to a sandbox to let AI securely analyze data, run statistical models, and generate interactive charts and reports.
• Advanced Coding Agents: Create sophisticated agents that can write, debug, and execute code, interact with APIs, and manage software projects.
• AI-Generated App Runtimes: Use sandboxes as a secure runtime environment for entire applications generated by AI, supporting any language or framework.
• Reinforcement Learning: Scale up reinforcement learning tasks by running and evaluating thousands of reward function experiments in parallel sandboxes.
• Secure AI Chatbots: Go beyond OpenAI's Code Interpreter by giving chatbots powerful, secure code execution capabilities to answer complex queries and perform actions.

Advantages of e2b

e2b offers significant advantages over building a custom code execution solution or using more limited alternatives. Its primary benefits include enterprise-grade security, exceptional performance with near-instant startup times, and the flexibility to work with any AI model and programming language. This allows development teams to focus on their core product logic instead of spending months building and maintaining complex infrastructure. The platform's scalability and reliability are trusted by Fortune 100 companies and fast-growing AI startups alike.

Pricing and Plans

e2b uses a freemium pricing model that is accessible to individual developers and scalable for large enterprises.

• Hobby Plan: Free. This plan includes a one-time $100 in usage credits, community support, sandbox sessions up to 1 hour, and up to 20 concurrent sandboxes. No credit card is required to start.
• Pro Plan: $150 per month, plus usage costs. This includes everything in the Hobby plan, plus the ability to customize CPU & RAM, session lengths up to 24 hours, and up to 100 concurrent sandboxes.
• Ultimate (Enterprise) Plan: Custom pricing. This plan is for a bespoke solution with special pricing, including options for on-premise or BYOC (Bring Your Own Cloud) deployment.

Usage costs are charged per second of a running sandbox, with rates depending on the allocated CPU and RAM resources.