Agents
2,269Autonomous AI agents that perform tasks independently
A Python-based logical reasoning system combining rule-based engines with Large Language Models (LLMs) for business automation. Supports deduction and hypothesis testing, partial fact completion, and automated fact retrieval via documents or chat. Features include explainable reasoning, workflow integration, and customizable knowledge bases.
A framework-agnostic UI kit of production-ready components for building AI and LLM chat interfaces.
A multi-agent workflow that resolve tasks using powershell
Python LLM-RAG deep agent using LangChain, LangGraph and LangSmith built on Quart web microframework and served using Hypercorn ASGI and WSGI web server.
FlexiAgent is a simple and easy-to-use framework for creating LLM agents. The agent supports structured output and includes built-in practical agents such as a text2sql agent, allowing for quick deployment in applications.
Prompt generator for LLM agents with interceptors
Adversarial agent loops for verifiable vibe researching. Claude Code plugin. Falsification-first, not production-first.
This is a project aimed at creating an agent that perpetually thinks and develops itself. It can add entries into an SQLite database and change the structure of that databaase. It also has the ability to search the web when it needs to. It utilizes OpenAI function calling and the responses API. All that is needed to run it is an OpenAI API key.
Build knowledge graphs from biomedical literature and query them with an LLM agent
Production-ready frontend with Google login, built using Next.js and integrated with a Cloud Run. This project is part of the GCP AI Agent Starter Kit workshop and provides a clean, customizable interface to connect conversational agents to intelligent backend services. Ideal for developers looking for a practical, reusable starting point
Use a RAG Langchain agent that connects to your supermarket's inventory database and get information about product stocks and locations.
Multi-tier benchmark: Cultural grounding + Triad Engine eliminates LLM hallucination across Claude 4.6, GPT-5.2, Mistral 7B, Gemini 2.5 Pro. Raw 15-58% → 95-100% accuracy on 222 adversarial QA pairs (Ancient Rome 110 CE). Novel topological paradox detection (F1=0.939, zero-shot). Model-agnostic, in production.
这是一个为 AstrBot 设计的 Office 助手插件。它赋予大语言模型（LLM）直接操作文件的能力，支持读取并分析多种格式文件，以及生成 Office 文档和office互转pdf的功能
An intelligent learning assistant system providing personalized learning planning based on user profiles.
🚀 Веб-интерфейс для AI-агента с возможностью выполнения Python-кода, управления файлами и работы с веб-контентом