Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.

280+ free n8n automation templates — ready-to-use workflows for Gmail, Telegram, Slack, Discord, WhatsApp, Google Drive, Notion, OpenAI, and more. AI agents, RAG chatbots, email automation, social media, DevOps, and document processing. The largest open-source n8n template collection.

Test your prompts, agents, and RAGs. Red teaming/pentesting/vulnerability scanning for AI. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and CI/CD integration. Used by OpenAI and Anthropic.

All-in-one LLM CLI tool featuring Shell Assistant, Chat-REPL, RAG, AI Tools & Agents, with access to OpenAI, Claude, Gemini, Ollama, Groq, and more.

🤖 MLE-Agent: Your intelligent companion for seamless AI engineering and research. 🔍 Integrate with arxiv and paper with code to provide better code/research plans 🧰 OpenAI, Anthropic, Gemini, Ollama, etc supported. :fireworks: Code RAG

A modular and comprehensive solution to deploy a Multi-LLM and Multi-RAG powered chatbot (Amazon Bedrock, Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere, Mistral) using AWS CDK on AWS

A Solution Accelerator for the RAG pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences. This includes most common requirements and best practices.

Sharing the learning along the way we been gathering to enable Azure OpenAI at enterprise scale in a secure manner. GPT-RAG core is a Retrieval-Augmented Generation pattern running in Azure, using Azure Cognitive Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.

RAG based tool for indexing and searching PDF text data using OpenAI API and FAISS (Facebook AI Similarity Search) index, designed for rapid information retrieval and superior search accuracy.

This repository provides programs to build Retrieval Augmented Generation (RAG) code for Generative AI with LlamaIndex, Deep Lake, and Pinecone leveraging the power of OpenAI and Hugging Face models for generation and evaluation.

IIMS delivers a modern full-stack solution via Spring Boot 3.5 (Java 17) and Vue 3 (TypeScript), featuring RBAC/JWT authentication, Milvus RAG, Spring AI (Ollama/DeepSeek/OpenAI), MyBatis Plus, MySQL, Redis, MinIO, Element Plus, Pinia, Vditor, Mermaid, plus dedicated EAS and DMS modules.

🔥 Instantly create AI chatbots for any website with RAG-powered search, streaming responses, and OpenAI-compatible API endpoints

A simple example implementation of the VoiceRAG pattern to power interactive voice generative AI experiences using RAG with Azure AI Search and Azure OpenAI's gpt-4o-realtime-preview model.

A RAG app to ask questions about rows in a database table. Deployable on Azure Container Apps with PostgreSQL Flexible Server.

Advanced Retrieval-Augmented Generation (RAG) through practical notebooks, using the power of the Langchain, OpenAI GPTs ,META LLAMA3 ,Agents.

A curated collection of resources for 🌌 Azure OpenAI, 🦙 LLMs (+RAG, Agents).

一款JavaSDK用于快速接入AI大模型应用，整合多平台大模型，如OpenAi、智谱Zhipu(ChatGLM)、深度求索DeepSeek、月之暗面Moonshot(Kimi)、腾讯混元Hunyuan、零一万物(01)等等，提供统一的输入输出(对齐OpenAi)消除差异化，优化函数调用(Tool Call)，优化RAG调用、支持向量数据库(Pinecone)、内置联网增强，并且支持JDK1.8，为用户提供快速整合AI的能力。

The Open Assistant API is a ready-to-use, open-source, self-hosted agent/gpts orchestration creation framework, supporting customized extensions for LLM, RAG, function call, and tools capabilities. It also supports seamless integration with the openai/langchain sdk.

Your Trusty Memory-enabled AI Companion - Simple RAG chatbot optimized for local LLMs | 12 Languages Supported | OpenAI API Compatible

Tools for evaluation of RAG Chat Apps using Azure AI Evaluate SDK and OpenAI

A copilot sample that uses python to ground the copilot responses in company data.

在本项目中模拟健康档案私有知识库构建和检索全流程，通过一份代码实现了同时支持多种大模型（如OpenAI、阿里通义千问等）的RAG（检索增强生成）功能:(1)离线步骤:文档加载->文档切分->向量化->灌入向量数据库；在线步骤:获取用户问题->用户问题向量化->检索向量数据库->将检索结果和用户问题填入prompt模版->用最终的prompt调用LLM->由LLM生成回复

The Rule-based Retrieval package is a Python package that enables you to create and manage Retrieval Augmented Generation (RAG) applications with advanced filtering capabilities. It seamlessly integrates with OpenAI for text generation and Pinecone or Milvus for efficient vector database management.

💼法律AI助手，法律RAG，通过全部200+本法律手册📖、网页搜索内容💻结合LLM回答你的问题，并且给出相应的法规和网站，基于⚡️ langchain，Gradio，openai，chroma，duckduckgo-search

The AI Operating System for Delphi. 100% native framework with RAG 2.0 for knowledge retrieval, autonomous agents with semantic memory, visual workflow orchestration, and universal LLM connector. Supports OpenAI, Claude, Gemini, Ollama, and more. Enterprise-grade AI for Delphi 10.3+

Simple Pinecone + OpenAI framework to do RAG on movie reviews (or anything else)

Create your own ChatGPT with Retrieval-Augmented-Generation workshop

🔍 Deep RAG: Advanced Retrieval-Augmented Generation system that goes beyond vector search. Enables AI to perform multi-hop reasoning, negation queries, numerical comparisons & global aggregation on your knowledge base. Supports OpenAI, Anthropic, Google Gemini. Python FastAPI + React UI

Open-source document chat platform with semantic search, RAG (Retrieval Augmented Generation), and multi-provider AI support (OpenRouter, OpenAI, ImageRouter).

A Blazor Web App and Minimal API for performing RAG (Retrieval Augmented Generation) and vector search using the native VECTOR type in Azure SQL Database and Azure OpenAI.