agentcloud VS E2B

This is great. we used Qdrant vector DB for an end to end automation for our AI RAG app at https://github.com/rnadigital/agentcloud, but very excited to see you are making a successor. any ETA when it would be ready to use and any quickstart guide? Maybe I can help in writing a blog as well.

AgentCloud is a community-powered open-source project so If you find value in our work and wish to fuel our journey so the best way to show your support is by starring us on GitHub ⭐

⭐️ Support the open-source community, Star Agent Cloud ⭐️

We're open source so the easy way to support us is by giving a star on GitHub ⭐

Talk to your data we're building Agent Cloud which is an open source platform enabling companies to build and deploy private LLM chat apps (like chat GPT), that can enable teams to securely talk to their data.

Agent Cloud is like having your own GPT builder with a bunch extra goodies. The GUI features

1) RAG pipeline which can natively embed 260+ datasources

2) Create Conversational apps (like GPTs)

3) Create Multi Agent process automation apps (crewai)

4) Tools

5) Teams+user permissions. Get started fast with Docker and our install.sh

and it's open source https://github.com/rnadigital/agentcloud

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

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...

Hey folks, we're building an open source runtime for AI agents at E2B.