babyagi
awesome-ai-agents
babyagi | awesome-ai-agents | |
---|---|---|
33 | 15 | |
19,239 | 6,757 | |
- | 17.6% | |
5.5 | 9.3 | |
13 days ago | 4 days ago | |
Python | ||
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.
babyagi
-
AGI has, in some sense, been achieved: Tell me why I am wrong
Define agency. Does AutoGPT or BabyAGI fit the definition?
-
Overview: AI Assembly Architectures
BabyAGI: github.com/yoheinakajima/babyagi
-
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
- Weaviate as Vector Database in BabyAGI
- BabyAGI
-
What innovations/discoveries have come out because/since the release of LLMS since the gain of popularity in the last 5ish months?
People also have been trying to build multi-agent and task-planning systems. MS research in Asia seems to produce decent results with Task Matrix and HuggingGPT. Similar things have been tried in the form of Auto-GPT and BabyAGI , but both projects are setting their goal so high that they may not achieve the at all, and they are likely to see a complete rework when multi-modal solutions become widespread.
-
Palantir in the world of Generative AI
Joke's on you, /u/ILoveThisPlace is actually just a bot responding using the BabyAGI script, we've all been had!
-
autogpt-like framework?
BabyAGI AI-Powered Task Management for OpenAI + Pinecone or Llama.cpp
-
What’s with the fear?
Yes, we haven't seen anything like that yet. But we do see the people trying to build these things (see AutoGPT, babyagi, ChaosGPT, etc) today, and with the last few years of advancement in LLMs they now have the fundamental building blocks to succeed in the near term (say the next 5 years) rather than in some imaginary far future.
-
Could an AI learn things or discover things humans have not been able to understand or not discovered yet?
You should check out some of the projects that combine LangChain with LLMs to automate this process like BabyAGI (https://github.com/yoheinakajima/babyagi) and AutoGPT (https://github.com/Significant-Gravitas/Auto-GPT). They were originally designed around ChatGPT models but have expanded to include llamacpp as an alternative. These provide your language models with the ability to save long term memory, a goal-oriented task list and extra functionality like surfing the web and, in some cases, creating and modifying files on disk.
awesome-ai-agents
-
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
- We made a list of popular open-source AI agents
What are some alternatives?
AutoGPT - AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/AutoGPT]
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/Auto-GPT]
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
JARVIS - JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
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
AgentGPT - 🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.
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
bondai
AGiXT - AGiXT is a dynamic AI Agent Automation Platform that seamlessly orchestrates instruction management and complex task execution across diverse AI providers. Combining adaptive memory, smart features, and a versatile plugin system, AGiXT delivers efficient and comprehensive AI solutions.
tinyllm - Develop, evaluate and monitor LLM applications at scale