openai-multi-client
E2B
| openai-multi-client | E2B | |
|---|---|---|
| 1 | 35 | |
| 96 | 6,164 | |
| - | 3.9% | |
| 5.1 | 9.9 | |
| 4 months ago | 6 days ago | |
| Python | TypeScript | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
openai-multi-client
E2B
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Ask HN: Who is hiring? (May 2024)
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
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Show HN: Add AI code interpreter to any LLM via SDK
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...
- Open Source Python Code Interpreter for Any LLM
- Show HN: Open-Source Infrastructure for AI Code Interpreters
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We're building cloud runtime for AI agents and gradually open-sourcing everything
Hey folks, we're building an open source runtime for AI agents at E2B.
- Show HN: Run LLM-generated code in sandboxed envs
- Sandboxed cloud environments for AI agents & apps with a single line of code
- We're building a cloud for AI agents & AI apps, It's free and we're gradually open-sourcing the infra. Would love to hear your feedback!
- [P] We're building a cloud for AI agents & AI apps, It's free and we're gradually open-sourcing the infra. Would love to hear your feedback!
What are some alternatives?
llmflows - LLMFlows - Simple, Explicit and Transparent LLM Apps
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/Auto-GPT]
RasaGPT - 💬 RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. Built w/ Rasa, FastAPI, Langchain, LlamaIndex, SQLModel, pgvector, ngrok, telegram
chatgpt-shell - ChatGPT and DALL-E Emacs shells + Org babel 🦄 + a shell maker for other providers
summarizepaper - An AI-powered arXiv paper summarization website with a virtual assistant for answering questions.
IncognitoPilot - An AI code interpreter for sensitive data, powered by GPT-4 or Code Llama / Llama 2.
magentic - Seamlessly integrate LLMs as Python functions
Selefra - The open-source policy-as-code software that provides analysis for Multi-Cloud and SaaS environments, you can get insight with natural language (powered by OpenAI).
gpt4-openai-api - Python package that provides (unofficial) API access to the GPT-4 through chat.openai.com. Works with langchain. Supports browsing, DALL-E 3, plugins, continuing generation.
JARVIS - JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
chatgpt-api - ChatGPT HTTP API Client and CLI
telegram-chatgpt-concierge-bot - Interact with OpenAI's ChatGPT via Telegram and Voice.