E2B

Introduction

When you build an AI model capable of writing and executing code, a fundamental question arises: where will this code be executed? Do we allow an intelligent agent (AI Agent) to run Python commands or shell scripts directly on your production server? This is exactly the dilemma that E2B solved by providing isolated and secure execution environments (Sandboxes) designed specifically to run code produced by large language models (LLMs) without putting your infrastructure at risk. E2B was not built as a consumer tool for end users, but as an infrastructure layer that developers rely on to build real AI products such as code interpreters and autonomous coding agents.

What is the E2B tool?

E2B, officially hosted at e2b.dev, is a partially open-source platform that provides what are known as “Sandboxes,” i.e., secure isolation boxes—isolated runtime environments based on lightweight microVM technology (built on the Firecracker project developed by Amazon for AWS Lambda). These environments boot in under one second and provide a full filesystem, dedicated compute space, and the ability to install packages and libraries, so that any AI agent or language model can write Python or JavaScript code or even run Bash commands inside them with complete safety, with no possibility of data leakage or impact on the original host server.

The core idea behind E2B is complete separation between the model’s “mind” (the LLM) and its “hands” (the execution environment). Instead of the model merely proposing code as text, E2B gives it a real capability to execute that code, see the results, fix errors, and retry automatically—what the AI community calls a “Code Interpreter” or a “Code Execution Sandbox.” Companies such as Perplexity and GitHub Copilot Workspace and other tools use similar infrastructure, and E2B provides the same capability as a ready-made service that developers can call through a simple SDK.

The technology behind E2B

E2B relies on a tech stack built around Firecracker microVMs, a lightweight isolation technology that provides hardware-level isolation security while maintaining boot speed measured in milliseconds. This sets it apart from traditional isolation solutions such as standard Docker containers that share the system kernel with the host server, which makes them relatively less secure when running untrusted or automatically generated code.

Main features

• Ready-to-use Code Interpreter SDK: A ready software package that enables developers to integrate “code execution” capability into their LLM-based applications in minutes, with direct support for displaying text outputs, charts, and images produced by libraries like Matplotlib.
• Strong microVM-level isolation: Each execution box (Sandbox) runs in a fully isolated environment thanks to Firecracker, so the executed code cannot access data from other sandboxes or the host server.
• Ultra-fast startup: A brand-new full runtime environment can be created in under a second, which is critical when dealing with interactive applications that require instant responses from the intelligent agent.
• Multi-language support: In addition to Python and JavaScript/TypeScript, sandboxes can run code in other languages and install packages via pip or npm or even direct system commands.
• Full, customizable filesystem: Files (such as CSV or PDF files) can be uploaded to the sandbox, processed with code, then the results downloaded—making it suitable for data analysis scenarios.
• Custom Sandbox Templates: Developers can build an environment image (Template) that already contains specific libraries and requirements, to speed up boot time and standardize the runtime environment across sessions.
• Direct integrations with AI frameworks: E2B provides ready integrations with LangChain, LlamaIndex, AutoGPT, and other agent-building tools, in addition to direct compatibility with Function Calling in OpenAI and Anthropic Claude models.
• Command Line Interface (CLI): Enables managing sandboxes, their templates, and deployment directly from the terminal without needing a graphical dashboard.
• Partially open source: The core code for E2B’s infrastructure is available on GitHub, giving developers transparency into how isolation works and allowing contributions or security auditing.

How to use E2B step by step

1. Create an account: The developer goes to the official website e2b.dev and creates a free account using an email address or a GitHub account.
2. Get an API key: From the dashboard, the user creates their own API key, which will be used for authentication when calling the service programmatically.
3. Install the package: In Python, run pip install e2b-code-interpreter, or in Node.js use npm install @e2b/code-interpreter.
4. Initialize the sandbox: In code, the developer calls the function to create a new sandbox (Sandbox.create) while passing the API key, and an isolated runtime environment boots up ready for use.
5. Execute code: The code the developer wants to run (whether written manually or generated by a language model) is passed to the sandbox via an execution function (run_code), and results are returned immediately as text, structured data, or even images.
6. Connect it to an LLM: In practice, E2B is most often used as a tool invoked by the model via Function Calling; the model writes the code, the developer sends it to E2B for execution, then the result is returned to the model to decide the next step.
7. Close the sandbox or reuse it: After the task finishes, the sandbox can be closed automatically to free resources, or kept for a specific period if the session requires multiple successive operations.

Advantages and practical benefits

The benefit of E2B varies depending on the user type:

• AI product developers: Those building applications similar to “ChatGPT Code Interpreter” save weeks of engineering work related to building a secure isolation infrastructure from scratch, replacing it with calling a ready SDK.
• Data science teams: E2B can be integrated into internal apps that allow a data analyst to ask a question in natural language; the model generates Python code to process an Excel or CSV file, and the code is executed directly inside the sandbox to return a chart or a results table.
• Programming education and training teams: Interactive programming education platforms use E2B sandboxes to run students’ code without the risk of damaging the server or leaking other students’ data.
• AI researchers: When developing agents that work iteratively (Agentic Loops), E2B provides a stable execution environment where the agent’s performance can be measured objectively and repeatably.

The core shared benefit across these categories is reducing the time needed to go from an “intelligent agent idea” to a “securely deployable product,” as E2B handles the most security- and infrastructure-sensitive and complex part.

Drawbacks and challenges

• Developer-focused only: E2B does not include a ready end-user interface; it is entirely an API/SDK layer, which means it is not suitable at all for non-programmers or anyone looking for a ready-to-use tool.
• Cumulative cost with heavy usage: Since pricing depends on sandbox runtime duration and consumed resources, high-traffic applications may face rising bills that require careful monitoring.
• Dependency on network connectivity: Every code execution requires calling an external cloud service, adding latency that does not exist in fully local execution solutions.
• Initial learning curve: Understanding concepts like Sandbox Templates or the Sandbox Lifecycle requires new developers to read the documentation carefully before real production use.
• Limitations for some specialized environments: Some rare use cases that require access to special hardware (such as a dedicated GPU for training models) may not be supported to the same extent available in traditional runtime environments.

Comparing E2B with competing tools

E2B vs Modal

Modal offers a broader cloud infrastructure that includes running general compute tasks and training models, while E2B specializes more precisely in one specific scenario: executing LLM-produced code safely and quickly. For someone building a simple agent that only needs code execution, E2B is lighter and faster to adopt.

E2B vs traditional Docker/Kubernetes

Any developer can build an isolation solution themselves using Docker containers, but that requires significant engineering effort to ensure security against malicious code escaping the container (Container Escape), whereas E2B provides this security out of the box via microVMs with a boot speed that competes with containers themselves.

E2B vs ChatGPT Code Interpreter (Advanced Data Analysis)

The “Advanced Data Analysis” feature in ChatGPT is a closed end product that cannot be integrated into third-party applications, whereas E2B is an open infrastructure that developers can use to build a similar experience inside their own product, with full control over behavior and data.

E2B vs Replit

Replit focuses on an interactive code editing and execution experience for a human user directly through a full editor interface, while E2B is designed for automated programmatic invocation by AI models, usually without direct human intervention.

Practical examples of using E2B

• Financial data analysis assistant: The user uploads a sales data file in CSV format; the model writes Python code using the Pandas library to compute the top-selling products; the code is executed inside an E2B sandbox, and the result is returned along with an automatically generated chart.
• Autonomous coding agent (Coding Agent): The agent is asked to fix a bug in a Python project; it writes the modified code, executes it inside an E2B sandbox, reads the resulting error message, and retries automatically until the test passes.
• Interactive educational application: A Python learning platform lets the student write code directly in the browser, which is executed inside an E2B sandbox fully isolated from the platform’s core servers.
• Automated PDF file processing: An AI model receives a PDF file and writes a script to extract text and tables using a library like PyPDF2, and this script is run inside E2B to ensure the main server is not exposed to untrusted files.

Pricing

E2B offers a free plan (Hobby/Free Tier) that includes a limited monthly quota of sandbox runtime hours, which is enough for experimentation and building small prototypes. After that, paid plans follow a pay-as-you-go model, where cost is calculated based on the number of sandbox runtime hours and the amount of memory and CPU allocated to each sandbox. The platform also offers a “Pro” plan with additional features such as faster support and higher usage limits, and an “Enterprise” plan tailored for large companies that need special service-level agreements (SLA) and support for hosting within the customer environment (On-premise/VPC) when needed. It is always recommended to review the pricing page directly on the official website to get accurate and up-to-date numbers, as plan details are subject to change periodically.

Evaluation and tips to get started

E2B is clearly aimed at developers and technical product teams, and it is not suitable for anyone looking for a consumer-ready tool with a graphical interface. If you are a developer building an AI application that needs dynamic code execution—whether for data analysis, building an autonomous coding agent, or an interactive educational platform—E2B saves you weeks of engineering work related to security and isolation.

Practical tips to get started:

• Start with the free plan to try the Python or JavaScript SDK on a small scale before committing to any paid plan.
• Review the official documentation well to understand the Sandbox Lifecycle to avoid leaving sandboxes running unnecessarily and consuming cost.
• Use the Custom Templates feature if your application needs certain fixed libraries every time, to reduce boot time and cost.
• If your application handles sensitive data, check the private hosting options (VPC/On-premise) available in the Enterprise plan.

Those for whom the tool is not suitable are non-programmer users, or anyone looking for a ready end product for direct use without writing any integration code.

Conclusion

E2B represents one of the most important invisible infrastructure layers that makes AI applications capable of actually “acting”—not just replying with text—possible and safer. Thanks to its reliance on Firecracker microVM technology, it provides strong isolation and astonishing boot speed, a rare combination that brings together security and performance. The platform suits developers and intelligent-agent building teams more than any other group, and its closest competitors in spirit remain solutions like Modal or custom Docker setups, but it stands out for its precise focus on the specific scenario of executing LLM code. For anyone building a real AI product that needs to run code automatically, E2B is worth a serious try, and the journey begins simply by visiting e2b.dev and creating a free account.