E2B

Transforming AI code execution with safe, flexible SDKs.

Freemiume2b.devJun 4, 2026

Code Interpreter SDK

Offers robust Python and JavaScript/TypeScript SDKs that let you add a full code-execution backend to your AI application with just a few lines of code.

Secure Sandboxes

Every code execution runs in a completely isolated environment, preventing unauthorized file access, network escapes, and supply-chain risks.

Open-Source Codebase

The entire platform is open-source, encouraging transparency, community audits, and the ability to self-host or customize the stack.

Ease of Integration

Designed to plug into existing AI workflows—whether it’s a LangChain agent, a custom LLM app, or a research pipeline—through straightforward APIs and SDKs.

Comprehensive Documentation

The project includes step-by-step guides, API references, and tutorials to accelerate onboarding and troubleshooting.

GitHub Integration

Out-of-the-box support for version control and collaboration, allowing you to sync sandboxes with repositories effortlessly.

What is E2B?

E2B is an open-source platform that redefines how AI applications run and interpret code. It provides secure, isolated sandboxes where AI-generated code can be executed safely, along with software development kits (SDKs) for Python and JavaScript/TypeScript. The tool is designed to simplify the integration of code-interpreting capabilities into AI-powered apps, data pipelines, and educational environments, all while keeping execution airtight through containerized environments.

Core Features

Code Interpreter SDK: Offers robust Python and JavaScript/TypeScript SDKs that let you add a full code-execution backend to your AI application with just a few lines of code.
Secure Sandboxes: Every code execution runs in a completely isolated environment, preventing unauthorized file access, network escapes, and supply-chain risks.
Open-Source Codebase: The entire platform is open-source, encouraging transparency, community audits, and the ability to self-host or customize the stack.
Ease of Integration: Designed to plug into existing AI workflows—whether it’s a LangChain agent, a custom LLM app, or a research pipeline—through straightforward APIs and SDKs.
Comprehensive Documentation: The project includes step-by-step guides, API references, and tutorials to accelerate onboarding and troubleshooting.
GitHub Integration: Out-of-the-box support for version control and collaboration, allowing you to sync sandboxes with repositories effortlessly.

Use Cases & Considerations

Use Cases

AI Developers: Integrate E2B into AI agents or chatbot backends so the model can write and execute code on the fly without exposing the host system.
Data Scientists: Run AI-generated analysis scripts inside a sandbox to safely test hypotheses or prototype data transformations without risking local environments.
Educational Institutions: Provide students with an interactive coding environment where they can experiment with AI-suggested code examples in a controlled, reproducible setup.
Research Labs: Execute experimental AI programs in a reproducible sandbox, ensuring that unexpected code behavior does not compromise shared lab infrastructure.
Cybersecurity Professionals: Use E2B to safely analyze and test potentially malicious AI-generated scripts in a throwaway, isolated environment.
Financial Analysts: Run proprietary predictive models generated by AI in a secure sandbox, keeping sensitive data isolated from public cloud services.

Limitations & Considerations

Limited Language Support: Out-of-the-box SDKs are currently limited to Python and JavaScript/TypeScript, which may not suit projects that rely on other languages like R or Go.
Initial Learning Curve: Developers new to sandboxed execution or AI‑agent patterns may need time to understand the SDK’s workflow and error handling.
Resource Caps: Free-tier sandboxes come with memory and time constraints; long-running or memory-intensive AI code might require a paid plan or workarounds.
Dependency on External Infrastructure: While the sandboxes are secure, the overall performance depends on internet connectivity and the cloud backend unless you self-host the open-source version.
Not a Full IDE Replacement: E2B focuses on code execution, not on editing, debugging, or project management—you will still need a separate development environment for authoring complex code.
Community-Driven Documentation: As an open-source project, some advanced topics may rely on community tutorials and examples, though the official docs are steadily growing.

How to use E2B

Install the SDK: Choose your language (Python or JavaScript/TypeScript) and install the corresponding package via pip or npm.
Initialize a Sandbox: Use the SDK to create a new sandbox instance. This returns a unique ID and an isolated execution environment.
Write or Send Code: Pass the AI-generated code (or any string) to the sandbox using the SDK’s run method, along with any required dependencies.
Execute and Capture Results: The sandbox runs the code and returns the output, errors, and any generated files—all visible through the API.
Retrieve Files and Clean Up: If your code produces artifacts, download them back to your app. When done, terminate the sandbox to free resources.
Iterate and Integrate: Embed the SDK calls inside your AI agent logic, letting the model decide when to execute code and how to use the results.

Pricing & Plans

E2B follows a freemium model. The Free Tier gives you open-source access to the core sandbox engine and SDKs, suitable for prototyping and personal projects. A Pro Tier with higher usage limits, advanced security controls, and priority support is available, but specific pricing details are not publicly listed. For the most up-to-date information on limits and paid features, check the official E2B website.

Platforms

Python SDK: Full-featured SDK for building AI-backed code interpreters in Python applications (pip installable).
JavaScript/TypeScript SDK: Equivalent SDK for Node.js and browser-based apps, enabling code execution on the frontend with security guarantees.
REST API: Direct HTTP access to create, manage, and execute sandboxes, usable from any language or environment.
Web Dashboard: A management interface at e2b.dev for monitoring sandbox usage, API keys, and account settings.
GitHub Integration: Native support for linking sandboxes to repositories, making it easy to run and test code from version-controlled projects.

Tips & Best Practices

Validate AI-Generated Code Before Execution: While the sandbox adds a safety layer, a quick static check can prevent infinite loops or excessive resource use.
Set Timeouts and Resource Limits: Configure execution time limits and memory caps to avoid runaway processes that could affect performance or costs.
Use Ephemeral Sandboxes for Untrusted Code: Always create a fresh sandbox for user-supplied or model-generated code rather than reusing an environment.
Pre-warm Sandboxes for Low Latency: If your application requires instant execution, keep a pool of idle sandboxes ready to reduce cold-start delays.
Leverage Community Extensions: Explore the open-source ecosystem for plugins, templates, and examples that speed up development of common patterns.
Monitor API Usage: Track your sandbox consumption and output sizes to stay within your plan’s limits and optimize for cost-efficiency.

Who is E2B for?

AI Application Developers who need a safe, scalable way to let large language models write and run code inside a user-facing product.
Data Scientists and ML Engineers looking to test AI-generated scripts or run untrusted code without risking their local or cloud data environments.
Educators and Coding Bootcamps that want to offer an interactive code-execution playground tightly integrated with AI-assisted learning tools.
Researchers in AI and Autonomous Agents who require a clean, reproducible environment for evaluating and benchmarking code-generating models.
Security Professionals who need an isolated environment to detonate and analyze suspicious code snippets produced by AI or users.
Enterprises that want to embed code execution capabilities into internal tools while maintaining strict data governance and isolation policies.

Alternatives

View all

Replit

A browser-based coding platform that offers collaborative environments and AI assistance, though not specifically focused on secure AI-generated code execution.

Modal

A serverless cloud platform designed for data and ML workloads, providing ephemeral containers that can run arbitrary code at scale.

Piston

An open-source code execution engine that supports many languages via a simple API, often used for competitive programming and code playgrounds.

CodeSandbox

A cloud development environment optimized for web development, with sandboxed execution and instant previews.

Google Colab

A free Jupyter notebook environment with GPU support, useful for Python-based AI code execution but with less isolation control.

AWS Lambda

A serverless compute service that can run code in response to events, offering fine-grained security but requiring more setup for AI agent integration.

FAQ

Q1. How does E2B keep AI-generated code from harming my system?

E2B runs every code snippet inside a tightly isolated sandbox built with containers. The sandbox has no access to your local filesystem, network ports, or host processes unless you explicitly configure it. Even if the AI generates malicious code, it can only affect that temporary environment, which is destroyed after use.

Q2. Is E2B really free, and what are its limits?

Yes, the core platform and SDKs are open-source and free to use. The hosted Free Tier at e2b.dev has soft limits on execution time, memory, and concurrent sandboxes. For higher limits and additional features you can upgrade to a paid plan; check the official website for current details.

Q3. Can I install third-party libraries inside a sandbox?

Absolutely. The SDKs support passing a list of packages (e.g., pip install requirements or npm packages) as part of the execution request. The sandbox will install them on the fly before running your code, making the environment fully customizable.

Q4. What kind of AI models work best with E2B?

E2B is model-agnostic. It works behind the scenes as a tool for any large language model that outputs code—like GPT-4, Claude, or open-source models via frameworks such as LangChain. You just need to write a connector that passes the model’s code output to the E2B sandbox.

Q5. Where can I find tutorials and community support?

Start with the official documentation and tutorial series on the E2B website. The project also has a GitHub repository with examples, and you can join the community on Discord or GitHub Discussions for troubleshooting and sharing use cases.