Agents
The Semantic Layer
for every data app
Connect data silos, drive consistent metrics, and power your AI and analytics with context.
for every data app
Connect data silos, drive consistent metrics, and power your AI and analytics with context.
Cube — Semantic Layer for Building Data Applications
📖 Introduction
ReactAgent is an experimental autonomous agent that uses GPT-4 language model to generate and compose React components from user stories. It is built with React, TailwindCSS, Typescript, Radix UI, Shandcn UI, and OpenAI API.
🚀 Features
ReactAgent is an experimental autonomous agent that uses GPT-4 language model to generate and compose React components from user stories. It is built with React, TailwindCSS, Typescript, Radix UI, Shandcn UI, and OpenAI API.
🚀 Features
- Generate React Components from user stories
- Compose React Components from existing components
- Use a
eylonmiz • GitHub - eylonmiz/react-agent: The open-source React.js Autonomous LLM Agent
The problem I've found with all of them is it is difficult to get consistent solid results that you would want to build around. Even listening to podcasts this seems to be a theme with the people at the forefront of MAS LLMs.
r/AI_Agents - Reddit
Sandbox for AI Apps & Agents
Secure sandboxed cloud environments made for AI agents and AI apps
Secure sandboxed cloud environments made for AI agents and AI apps
Sandbox for AI Apps & Agents
In an Agentic UX, an AI agent pulls weight across 3 categories: cognitive, creative, and logistical.
- Cognitive weight — the built-in agent pulls analysis and decision-making weight
- Creative weight — the built-in agent takes on visualization and media creation weight
- Logistical weight — the built-in agent takes tasks on operational and workflow
The agentic era of UX
🤖 Create perpetual chatbots with self-editing memory!
🗃️ Chat with your data - talk to your SQL database or your local files!
📄 You can also talk to docs - for example ask about LlamaIndex!
🗃️ Chat with your data - talk to your SQL database or your local files!
📄 You can also talk to docs - for example ask about LlamaIndex!
cpacker • GitHub - cpacker/MemGPT: Teaching LLMs memory management for unbounded context 📚🦙
The LLM doesn’t call the tool directly (yet), but it does pass back to the application what functions should be called — and with which parameters. And, now, OpenAI lets multiple function calls be “invoked” at once.
But, this idea is not just about GPT. The open source world is moving towards this model as well.
This Is The Future…It’s Just Not Here... See more
But, this idea is not just about GPT. The open source world is moving towards this model as well.
This Is The Future…It’s Just Not Here... See more
Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]
ht - headless terminal
ht (short for headless terminal ) is a command line program that wraps an arbitrary other binary (e.g. bash , vim , etc.) with a VT100 style terminal interface--i.e. a pseudoterminal client (PTY) plus terminal server--and allows easy programmatic access to the input and output of that terminal (via JSON over stdin/stdout). ht... See more
ht (short for headless terminal ) is a command line program that wraps an arbitrary other binary (e.g. bash , vim , etc.) with a VT100 style terminal interface--i.e. a pseudoterminal client (PTY) plus terminal server--and allows easy programmatic access to the input and output of that terminal (via JSON over stdin/stdout). ht... See more
GitHub - andyk/ht: headless terminal - wrap any binary with a terminal interface for easy programmatic access.
Large Language Model State Machine (llmstatemachine)
Introduction
llmstatemachine is a library for creating agents with GPT-based language models and state machine logic.
Introduction
llmstatemachine is a library for creating agents with GPT-based language models and state machine logic.
- Chat History as Memory : Leverages large context window models, making chat history the primary source of agent memory.
- Custom Python Functions with JSON Generation : Allows the