This week we made improvements in our SDK, added new features for sandboxes, and fixed bugs.

Start command

Custom sandboxes now allow you to specify a command that will be running when a sandbox is spawned. This way you can run a command that will start your app or any specific process.

When building a sandbox template, you can specify the start command using the -c option.

e2b build -c "npm start"

Delete sandbox template

We released an option for deleting sandbox templates.

e2b delete

This week we are announcing native support for the new OpenAI's Assistants API. That means you can now define actions for the AI assistant and we will automatically execute them inside the E2B Sandbox.

Combined with the custom sandboxes feature, this allows you to create powerful AI assistants with tools completely predefined by you.

To try the OpenAI Assistants with E2B, start here. You can follow our guide in Python or JavaScript.

See the complete code in the E2B Cookbook.

New feature: Reconnect to running sandbox

This week we launched a new experimental SDK feature. If you want to keep the sandbox alive after disconnecting from it, you can now explicitly say how long you want to keep it alive. You can then reconnect to the sandbox later.

Read more here.

This feature is available in versions:

• JS: 0.10.0

• Python: 0.11.0

For implementation assistance or feedback, contact us at hello@e2b.dev or on our Discord.

Docs and SDK improvements

We also made a few updates to our SDK and documentation.

Docs

• Shorter time for code snippets to be ready for running (using custom sandbox)

JS SDK

• Pass sandbox id directly

• Pass command directly to process

Both SDKs

• Add StartAndWait for process.

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

This week we launched AI Playgrounds, supporting Python and JS/TS. 

It is a sandboxed cloud environment where an AI agent or copilot can freely run code, use a filesystem, use a headless browser, and more.

Try the AI playgrounds here: https://e2b.dev/docs.

Every playground has its own URL, which allows your agent for example to start a web app. AI playgrounds are especially useful for coding copilots and agents. They can easily run linters, formatters, or debug code and feed all this information back to LLM to make the next iteration better.

Quality of life improvements

• An API key is now required for our SDK (get your API key here)

• Added timeouts to asynchronous methods in our SDK

• Improved Python SDK debug logging

• Improved internal overview of the usage of our infrastructure

• Added periodic end to end test of our infra via calling SDKs

This week, we worked on the following:

SDK

• Improved the reliability of our SDK and fixed bugs regarding thread and WebSocket communication

• Brought both the JavaScript SDK and Python SDK to parity

• We improved our release process in our monorepo

Documentation

• Added an option to retrieve API keys

• Added more content based on user feedback

UX improvement

Docs, changelog, and blog are now accessible through e2b.dev/docs, e2b.dev/blog, and e2b.dev/changelog instead of subdomains. 

Community

We talked with the founders of Superagent and Aomni about LLM agents' orchestration, debugging, tracing, and monitoring. Read the published interviews on our blog.

We hosted a community event in Prague where local startups talked about developer tools and AI.

We released new documentation for the E2B SDK that helps you with getting started.

With a single line of our SDK, you can give your AI agent a sandboxed cloud environment where your agent can do any of the following (and more!):

• Run any code

• Run any terminal command

• Install dependencies and programs

• Use filesystem

• Upload and download files

• Access the internet

• Start a web server that's accessible from the internet

• Clone git repositories

• Start any process (even long-running such as a database)

The new docs help you with installation and basic usage and examples are to be added soon. We have fixed reliability-related issues in the SDK.

We’ve been iterating with our early users and fixed a bunch of critical bugs.

If you want early access to the SDK, join our Discord and send us a message!

Agent Protocol Documentation

We put the Agent Protocol on a dedicated website with a nice domain and proper documentation. 

The documentation goes deeper into the incentives of the protocol, guides for adoption, and the details of how the protocol works.


Discuss the updates on our Discord, and if you want to appreciate our work, star our repositories on GitHub.

This week, we have for you an early access version of the E2B SDK.

The E2B REST API gives AI agents them their own sandboxed cloud environment. Agents can use these environments to run code, terminal commands, long-running tasks like databases, start servers, and upload and download files.

You can run pip install e2b to get our SDK.

The E2B SDK is available in Python and we are finalizing a JavaScript/TypeScript version. See the Python SDK also in PyPi or go through the examples.

We recently tried out our API in the e2b plugin for ChatGPT which you can check out if you have developer access to ChatGPT plugins.

Next on our roadmap

• Make the API generally available

• Integrate the API and the protocol into popular agent-building frameworks

Contributors our welcome, so in case you are interested, just do a pull request on our GitHub, or discuss more info on our Discord.

The e2b ChatGPT Plugin gives your ChatGPT access to a full cloud environment that is sandboxed and running on e2b infrastructure.

We’ve built these cloud environments to be used specifically by AI Agents. With the plugin, you can accomplish for instance deploying websites, managing databases, executing shell commands, or installing programs via the terminal.

Try out the Plugin here and tell us how you liked it via Discord or by tagging @e2b_dev on Twitter. Contributors and beta testers are welcome.

Agent Protocol Update

We did an overhaul of the Agent Protocol repository. Main updates include:

• Better communication of incentives, roadmap, and usage

• RFC template

• Better code examples 

• New JS SDK

• New agent-protocol CLI to check compliance with the spec.

The protocol serves as a common interface for interacting with any agent. It is techstack-agnostic and open-source.

We're now a few months into building e2b, which means we will be sharing weekly updates. Here's some news we think you'll love in this release.

Get your own instance of smol developer

This week, we have launched a smol developer - your own personal junior developer which you can deploy via e2b. You can connect the developer with three simple steps.

Let us know how you like the smol developer.

Agent Protocol

We have made first steps in developing the Agent Protocol.

The protocol defines an interface for interacting with your agent. Adopting the protocol makes your agent automatically deployable to the e2b platform.

Our goal is to make this protocol open and usable in the whole agent field. This is a collaborative project. We're working on it with the most popular open-source agents, e.g., Auto-GPT, smol developer, or babyagi.

Objectives:

• Common endpoints to communicate with an agent - "start", "step", "stop" for starters. More will come, especially around debugging, tracing, and monitoring

• Tech-stack agnostic - no specific framework required - you can adopt this without using the rest of SDK

• Great DX - easy to adopt and implement - no need to rewrite your existing agent

• Open - any agent builder can implement this and instantly become compatible with any platform supporting this protocol

If you like the protocol, please appreciate the GitHub repo with a star. Contributors are welcome.

Smol Developer Update

With more than 1500 smol developer pull requests, we hit OpenAI API rate limits. We consequently added an option to pass your own OpenAI key in case e2b is hitting OpenAI's rate limits.

Additionally, you can now select which OpenAI model you want to use with smol developer when using your own OpenAI key. Try the updated smol developer here.