Hey everyone! I'm the CEO of the company that built this SDK.
We're a company called E2B [0]. We're building and open-source [1] secure environments for running untrusted AI-generated code and AI agents. We call these environments sandboxes and they are built on top of micro VM called Firecracker [2]. We specifically decided to use Firecrackers instead of containers because of their security and ability to do snapshots.
You can think of us as giving small cloud computers to LLMs.
We recently created a dedicated SDK for building custom code interpreters in Python or JS/TS. We saw this need after a lot of our users have been adding code execution capabilities to their AI apps with our core SDK [3]. These use cases were often centered around AI data analysis so code interpreter-like behavior made sense
The way our code interpret SDK works is by spawning an E2B sandbox with Jupyter Server. We then communicate with this Jupyter server through Jupyter Kernel messaging protocol [4]. We work with an LLM and AI framework. We have different examples on how to use the SDK with Llama models, Anthropic models, LangChain, LangGraph, and more in our cookbook [5].
We don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that to our users. We're really just a boring code execution layer that sits at the bottom. We're building for the future software that will be building another software.
Our long-term plan is to build an automated AWS for AI apps and agents where AI can build and deploy its own software while giving developers powerful observability into what's happening inside our sandboxes. With everything being open-source.
We're a company called E2B [0]. We're building and open-source [1] secure environments for running untrusted AI-generated code and AI agents. We call these environments sandboxes and they are built on top of micro VM called Firecracker [2]. We specifically decided to use Firecrackers instead of containers because of their security and ability to do snapshots.
You can think of us as giving small cloud computers to LLMs.
We recently created a dedicated SDK for building custom code interpreters in Python or JS/TS. We saw this need after a lot of our users have been adding code execution capabilities to their AI apps with our core SDK [3]. These use cases were often centered around AI data analysis so code interpreter-like behavior made sense
The way our code interpret SDK works is by spawning an E2B sandbox with Jupyter Server. We then communicate with this Jupyter server through Jupyter Kernel messaging protocol [4]. We work with an LLM and AI framework. We have different examples on how to use the SDK with Llama models, Anthropic models, LangChain, LangGraph, and more in our cookbook [5].
We don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that to our users. We're really just a boring code execution layer that sits at the bottom. We're building for the future software that will be building another software.
Our long-term plan is to build an automated AWS for AI apps and agents where AI can build and deploy its own software while giving developers powerful observability into what's happening inside our sandboxes. With everything being open-source.
Happy to answer any questions and hear feedback!
[0] https://e2b.dev
[1] https://github.com/e2b-dev
[2] https://github.com/firecracker-microvm/firecracker
[3] https://e2b.dev/docs
[4] https://jupyter-client.readthedocs.io/en/latest/messaging.ht...
[5] https://github.com/e2b-dev/e2b-cookbook