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Deep research agent built with Neuron PHP AI framewokrk

A self-hosted, secure code execution sandbox for LLM agents deployed on your cloud infrastructure using SkyPilot. Built on llm-sandbox for multi-language code execution.

A sleek, customizable interface for managing LLMs with responsive design and easy agent personalization.

Build & connect smart devices using AI. Vento is your playground for building with AI, code and hardware — no limits, just creation.🚀🚀

UnOfficial Gradio Repo for ICML 2024 paper "Executable Code Actions Elicit Better LLM Agents" by Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji.

Cactus AI framework plugin for UE5 to run local LLMs at runtime. https://github.com/cactus-compute/cactus

Conversation support for Home Assistant using HuggingChat.

Data Dialogue enables natural language querying of databases by integrating LLMs with SQL databases.

A lightweight, agent-style framework for fact-checking atomic claims using iterative retrieval and verification. Reduces LLM and search cost while maintaining strong factuality performance.

Agent Interface Discovery

Build your resume one and only once. An experimental project to use chatGPT multi-agent system to tailor content in your resume for any jobs.

Remote Agent Invocation Layer

The MathGPT is a web application built with Streamlit that allows users to input mathematical problems and receive solutions. The app supports both text and LaTeX input, making it versatile for various mathematical expressions.

Local AI Agent Runtime

In this solution accelerator, we demonstrate how generative AI, retrieval augmented generation (RAG) and multi stage reasoning can be used to better navigate through the complexities of regulatory filings, bringing more transparency for companies to disclose their societal and environmental impacts.

Embodied-OS：面向具身智能的分层运行时——Driver·Capability·Skill·Brain 全栈参考实现

This sample application demonstrates the practical implementation and usage patterns of the LLM Agents library.

It automatically describes images in PDF files and generates questions from these descriptions. With its advanced RAG structure, it directs these questions directly to PDF text content, providing comprehensive information extraction and analysis.

This repository contains sample code demonstrating how to implement a verified semantic cache using Amazon Bedrock Knowledge Bases to prevent hallucinations in Large Language Model (LLM) responses while improving latency and reducing costs.

HumanStudy-Bench: Towards AI Agent Design for Participant Simulation