Changelog

This week we released the Custom Sandboxes for AI-powered apps and agents.

The E2B Sandbox is an isolated long-running cloud environment where you can let any LLM use tools exactly like you would do locally.

You can now create a Custom Sandbox for any use case, e.g. your own AI coding copilot, data analyst, or Al Browser. The Custom Sandbox allows you to spawn a sandbox with an environment predefined by you and then control it with the E2B SDK.

We also offer two premade sandboxes (Default Sandbox and Code Interpreter Sandbox).

Example of running Python code with the E2B Sandbox

How to start with Custom Sandboxes:

1. Sign in with your GitHub account to get your free E2B API key.
2. Install the E2B SDK, either in JS or Python.
3. Create your custom sandbox (you can follow our guide) or try out one of our premade sandboxes.

Support 

• Need help or want to share feedback? Join our Discord.
• You can also reach us at hello@e2b.dev.

If you are on X (Twitter), we would appreciate if you support our work there.

We’re releasing a private beta of E2B Custom Sandbox. 

If you want to test them, please contact vasek@e2b.dev or the @e2b-team on our Discord.

Custom Sandboxes in private beta

LLM Sandbox is an isolated cloud environment for LLMs with support for long-running sessions.

You can customize sandboxes by creating a Sandbox Template, which is just a simple Dockerfile. This way, you can preinstall any needed libraries and packages, or prepare any files you need. Then you can simply control your Custom Sandbox with the E2B SDK.

This week, we announced the official E2B integration into LangChain and created a guide for trying out the E2B Data Analysis Sandbox with LangChain.

LangChain integration

E2B is now available as a tool on LangChain. The E2B LLM Sandbox is ideal for building your own AI Data Analysts and Code Interpreters.

A big advantage of the E2B Sandbox is a safe code execution - allowing for example installing packages dynamically, generating charts, or file upload & download.

Bug fixes

• Fix SDK browser compatibility
• Fix installing system packages inside Sandbox

Guides and examples

We wrote a tutorial for using E2B with LangChain to build your own AI data analyst.

We put together more examples of using E2B, which are now shared within the E2B Discord community. 

• Need help or want to share feedback? Join our Discord.
• If you like the guide, please support us with a star on GitHub.
• Follow us on X (Twitter).
• You can also reach us at hello@e2b.dev.

🎃 Happy Halloween hacking!

This week, we fixed various bugs in our SDK and worked on our infrastructure.

New design and communication

We made our docs a bit nicer and better structured. See how to get started with E2B, a useful step-by-step example,  or an overview of the sandbox API. Check out our updated landing page. How do you like the new E2B logo?

Read about AI agents’ SDKs and frameworks

We shared an update on key topics of developing LLM agents and apps, covering for example security, privacy, memory management, frameworks, and developers' perspectives.

You can read it also on Medium or go through the frameworks and SDKs here.

This week, we continued improving the E2B sandboxed environments and the Superagent integration of E2B. To try Superagent with E2B, create a Code interpreter API and then select it for your agent to use.

Additionally, we worked on the backend for building your custom Advanced Data Analysis/Code Interpreter, so stay tuned.

We published more content and expanded our blog to Medium. You can read the newest AutoGen tutorial for code-executing agents.

If you haven’t done it, try out our SDK here and tell us how you liked it via Discord or by tagging @e2b_dev on Twitter. 

• E2B is now integrated into Superagent, meaning there is added support for a Code Execution feature. You can try it here.
• This integration allows the AI agents to actually run code in a sandboxed environment, which helps for example with creating charts, doing requests, or analyzing files.
• Join the Superagent Discord to stay updated and discuss your creations.

The code execution API in Superagent is still in its alpha version, as we are still working on improvements. We are very grateful for your feedback - let us know on Discord or contact our CEO at vasek@e2b.dev if you have any questions.

More E2B updates

• We updated the E2B SDK - you can now set it on stdout, on stderr, and on exit for the whole session.

This week, we released a simple way to run code generated by LLMs in the cloud:

1. Import e2b
2. Select runtime and pass LLM-generated code
3. Run code in a sandboxed environment
4. Get stdout and stderr

This way, developers of AI agents and apps don’t need to handle any infrastructure.

Read more in our documentation and let us know on Discord or contact our CEO at vasek@e2b.dev if you have any questions.

SDK Bug Fixes & Improvements

• You don’t have to pass API key when starting session if you have the E2B_API_KEY env variable set
• Python SDK now supports both Pydantic v1 and v2
• You can use ~ and relative paths in the filesystem module
• You can set the working directory for the whole playground session via the cwd argument
• You can pass environment variables to the playground via the envVars and env_vars arguments for each SDK respectively
• Users can now use sudo in the playground properly
• Fixed user permissions inside the playground
• Fixed .bashrc/.profile not being loaded inside the playground
• Fixed the incorrect user’s home directory in the playground

This week we released a new version of our SDK, added examples of use cases, and continued working on the infrastructure and custom environments.

New methods

1. Write bytes
   • Writing bytes to a file in our playground allows you to upload binary files like images, audio files, or video files
2. Read bytes
   • Reading bytes from a file inside our playground allows you to download binary files like images, audio files, or video files

Added examples

We added runnable code snippets for the use case examples for AI agents to our documentation:

1. Executing code
2. Cloning GitHub repo & installing deps
3. Running shell commands

All use cases are available in Python and JS.

We continued working on the E2B infrastructure and made several smaller improvements in our docs. If you want to stay in touch, please join our Discord, where we publish updates in real time.

Documentation updates

• You can now run code samples in Python and JS directly in the docs 
  

• We added an example of how to run multiple processes with the SDK to our docs
• We made our docs easier to use by highlighting relevant parts

New insights about agents

• We closed the first part of our agent developers interview series with a conversation about building tools for LLM agents with the CEO at Lindy AI
  

This week we updated our infrastructure and SDK and worked on the E2B docs.

Infrastructure

• Worked on the custom playgrounds and CLI for managing the AI playgrounds

Updates in SDK

• Improved speed of starting playgrounds in the Python SDK
• Allowed users to set timeouts when using playgrounds in JS SDK to limit the maximum time playground methods can run
• Added logging to the JS SDK

Documentation

• Worked on improving user experience in the documentation, so users can quickly try E2B SDK right from their browser