awesome-ai-agents
Voyager
awesome-ai-agents | Voyager | |
---|---|---|
16 | 53 | |
6,903 | 5,184 | |
19.3% | 2.1% | |
9.3 | 4.7 | |
11 days ago | about 1 month ago | |
JavaScript | ||
- | MIT License |
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.
awesome-ai-agents
- Open-source secure sandboxes for AI code execution
-
Llama 3 with Function Calling and Code Interpreter
# 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"], }, }, } ]
-
Show HN: Open-source SDK for creating custom code interpreters with any LLM
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...
- List of open-source AI assistants
- List of AI Agents
-
Overview: AI Assembly Architectures
github.com/awesome-ai-agents
-
List of Awesome AI Agents like AutoGPT and BabyAGI / Many open-source Agents with code included!
Github: https://github.com/e2b-dev/awesome-ai-agents and https://github.com/EmbraceAGI/Awesome-AGI
- Show HN: A list of open-source AI agents
- We're building a list of open-source AI agents. How has been your experience with them?
- We made a comprehensive list of popular AI agents out there
Voyager
-
Google Launches Gemini, Its "Most Powerful" AI Model to Date
Source: Conversation with Bing, 12/10/2023 (1) Wes Roth - YouTube. https://www.youtube.com/@WesRoth. (2) I've set most of my videos to Public again - Community. https://community.openai.com/t/ive-set-most-of-my-videos-to-public-again/24535. (3) AI Updates: Meta Develops Mind-Reading AI System, OpenAI’s Q* Is Here .... https://www.windermeresun.com/2023/11/20/ai-updates-meta-develops-mind-reading-ai-system-openais-q-is-here-how-economy-will-work-after-agi/. (4) David Shapiro. https://www.daveshap.io/. (5) undefined. https://natural20.com/. (6) undefined. https://arxiv.org/abs/2305.16291. (7) undefined. https://twitter.com/DrJimFan/status/1. (8) undefined. https://voyager.minedojo.org/. (9) undefined. https://minedojo.org/. (10) undefined. https://www.youtube.com/@DavidShapiroAutomator/videos.
- Is there any game that allow us to interact with it by python?
-
A Coder Considers the Waning Days of the Craft
> AI cannot sustain itself trained on AI work.
This isn’t true. You can train LLMs entirely on synthetic data and get strong results. [0]
> If new languages, engines etc pop up it cannot synthesize new forms of coding without that code having existed in the first place.
You can describe the semantics to a LLM, have it generate code, tell it what went wrong (i.e. with compiler feedback), and then train on that. For an example of this workflow in a different context, see [1].
> And most importantly, it cannot fundamentally rationalize about what code does or how it functions.
Most competent LLMs can trivially describe what some code does and speculate on the reasoning behind it.
I don’t disagree that they’re flawed and imperfect, but I also do not think this is an unassailable state of affairs. They’re only going to get better from here.
[0]: https://arxiv.org/abs/2309.05463
[1]: https://voyager.minedojo.org/
-
AutoGen: Enable Next-Gen Large Language Model Applications
In a way it is the same thing, agents are mostly an abstraction that make it easier to know what’s going on.
I think of agents more or less as python classes with a mixture of natural language and code functions. You design them to do something with information they produce, and to interface with other agents or “tools” in some way.
But all the agents can be the same language model under the hood, they are frames used to build different kinds of contexts.
And yes I think the idea is that emergent behaviour can be useful. This comes to mind
https://github.com/MineDojo/Voyager
But I think we are still a small ways off from being really smart about agents. My opinion is that we haven’t quite figured out what we are doing yet.
-
Open/Local LLM support for MineDojo/Voyager
This k8s application deploys an instance of Voyager along with a Fabric Minecraft server with required fabric mods. It assumes you have a local deployment of a Large Language Model (LLM) with 4K-8K token context length with a compatible OpenAI API, including embeddings support.
- Voyager – Minecraft Embodied Agent with Large Language Models
-
List of Awesome AI Agents like AutoGPT and BabyAGI / Many open-source Agents with code included!
In my opinion the most interesting Agents: Auto-GPT Github: https://github.com/Significant-Gravitas/Auto-GPT BabyAGI Github: https://github.com/yoheinakajima/babyagi Voyager Github: https://github.com/MineDojo/Voyager / Paper: https://arxiv.org/abs/2305.16291 I would also add: ChemCrow: Augmenting large-language models with chemistry tools Github: https://github.com/ur-whitelab/chemcrow-public/ Paper: https://arxiv.org/abs/2304.05376
-
[D] - Are there any AI benchmarks that involve successful longterm problem solving when running as autonomous agents (like in autogpt)? How do we compare the effectiveness of models as agents?
Does this beat the voyager? I read about it and wondered what if we add a skill library to langchain/llamaindex agents. It could be the same vector store for storing static data but after each task is performed, the agent will evaluate and archive the recipe of steps to perform a new task. Next time when the agent is asked to perform a task, it can just look at the library to retrieve a recipe. Unlike traditional fine tuning, you dont update the model parameters, these recipes are much more interpretable and can be manually edited/inserted by humans. There may also be an automatic way to convert wikihow articles or youtube tutorials into recipes.
-
GPT-4 was set free in Minecraft, here's what happened next...
Source. P.S. If you love geeking over AI updates, I have this free newsletter you might want to check out. Thank you!
Source.
What are some alternatives?
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/AutoGPT]
GITM - Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
tree-of-thought-llm - [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
AgentVerse - 🤖 AgentVerse 🪐 is designed to facilitate the deployment of multiple LLM-based agents in various applications, which primarily provides two frameworks: task-solving and simulation
mineflayer - Create Minecraft bots with a powerful, stable, and high level JavaScript API.
bondai
llm-awq - [MLSys 2024] AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
EdgeChains - EdgeChains.js Typescript/Javascript production-friendly Generative AI. Based on Jsonnet. Works anywhere that Webassembly does. Prompts live declaratively & "outside code in config". Kubernetes & edge friendly. Compatible with OpenAI GPT, Gemini, Llama2, Anthropic, Mistral and others
gorilla - Gorilla: An API store for LLMs
malmo - Project Malmo is a platform for Artificial Intelligence experimentation and research built on top of Minecraft. We aim to inspire a new generation of research into challenging new problems presented by this unique environment. --- For installation instructions, scroll down to *Getting Started* below, or visit the project page for more information:
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ