code-interpreter VS awesome-ai-agents

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.

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

E2B | https://e2b.dev | San Francisco, CA | Full-time | In-person

[E2B](https://e2b.dev) is building a secure open-source runtime that will power next billion of AI apps & agents.

We found an early traction with making it easy for developers to add [code interpreting](https://github.com/e2b-dev/code-interpreter) to their AI apps with our SDK built on top of our [agentic runtime](https://github.com/e2b-dev/e2b). We have paying customers from seed to enterprise companies.

We're hiring:

- Frontend/Product Engineer

- Infrastructure Engineer

Check the roles here https://e2b.dev/careers

We will show how to build a code interpreter with Llama 3 on Groq, and powered by open-source Code Interpreter SDK by E2B. The E2B Code Interpreter SDK quickly creates a secure cloud sandbox powered by Firecracker. Inside this sandbox is a running Jupyter server that the LLM can use.

Hi, 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].

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 don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that on users. We're really just a boring code execution layer that sats at the bottom that we're building specifically for the future software that will be building another software. We work with any LLM. Here's how we added code interpreter to Claude [5].

Our long-term plan is to build an automated AWS for AI apps and agents.

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/blob/main/examples/c...

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 don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that on users. We're really just a boring code execution layer that sats at the bottom that we're building specifically 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.

Happy to answer any questions and hear feedback!

[0] https://e2b.dev/

# TODO: Get your Groq AI API key from https://console.groq.com/ GROQ_API_KEY = "" # TODO: Get your E2B API key from https://e2b.dev/docs E2B_API_KEY = "" # Or use 8b version # MODEL_NAME = "llama3-8b-8192" MODEL_NAME = "llama3-70b-8192" SYSTEM_PROMPT = """you are a python data scientist. you are given tasks to complete and you run python code to solve them. - the python code runs in jupyter notebook. - every time you call `execute_python` tool, the python code is executed in a separate cell. it's okay to multiple calls to `execute_python`. - display visualizations using matplotlib or any other visualization library directly in the notebook. don't worry about saving the visualizations to a file. - you have access to the internet and can make api requests. - you also have access to the filesystem and can read/write files. - you can install any pip package (if it exists) if you need to but the usual packages for data analysis are already preinstalled. - you can run any python code you want, everything is running in a secure sandbox environment""" tools = [ { "type": "function", "function": { "name": "execute_python", "description": "Execute python code in a Jupyter notebook cell and returns any result, stdout, stderr, display_data, and error.", "parameters": { "type": "object", "properties": { "code": { "type": "string", "description": "The python code to execute in a single cell.", } }, "required": ["code"], }, }, } ]

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].

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 don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that on users. We're really just a boring code execution layer that sats at the bottom that we're building specifically for the future software that will be building another software. We work with any LLM. Here's how we added code interpreter to the new Llama-3 models [5].

Our long-term plan is to build an automated AWS for AI apps and agents.

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/blob/main/examples/l...

github.com/awesome-ai-agents

Github: https://github.com/e2b-dev/awesome-ai-agents and https://github.com/EmbraceAGI/Awesome-AGI