Cache-Augmented Generation: A Simple, Efficient Alternative to RAG

An open-source, AI-powered application using Agentic CAG to chat with any public GitHub repository or developer profile, offering deep code analysis, visual architecture maps and security audits

A Demo of Cache-Augmented Generation (CAG) in an LLM

Kusto and Log Analytics MCP server help you execute a KQL (Kusto Query Language) query within an AI prompt, analyze, and visualize the data.

A local LLM chatbot with CAG / Context Caching for PDF input files

AI-Suite - n8n, Open WebUI, OpenCode, Llama.cpp/Ollama, Flowise, Langfuse, MCP Gateway and more!

computer aided governance map and process

RAG framework that combines structured knowledge representation using hypergraphs with efficient response generation via semantic caching.

Integrate Anyparser's powerful content extraction capabilities with LangChain for enhanced AI workflows. This integration package enables seamless use of Anyparser's document processing and data extraction features within your LangChain applications.

This repository demonstrates Cache-Augmented Generation (CAG) using the Mistral-7B model.

AI-powered agent is designed to compare the performance of these two cutting-edge approaches, providing insights into their strengths, weaknesses, and real-world applications.

Python framework for building AI Agents -> CLI & Quart-SSE UIs, Toolsmith CAG | RAG plug-ins, full Assistant-API workflows via the OpenAI SDK (OpenAI / Azure today). Support for DeepSeek & Qwen is pending Assistant endpoint access.

Your AI-Powered Intelligent Search Assistant for Insurance Documents

Supercharge your AI workflows by combining Anyparser’s advanced content extraction with Crew AI. With this integration, you can effortlessly leverage Anyparser’s document processing and data extraction tools within your Crew AI applications.

AI-Powered Git Repository Assistant. (An AI tool to answer questions about any GitHub repo)

Voice and text powered Agentic AI orchestration platform

An LLM-powered augmented generation suite leveraging LangChain, Ollama, and vector databases to enhance response quality through caching, contextual memory, and retrieval-based methods. This collection of Jupyter notebooks showcases modular techniques for building intelligent, memory-efficient generative systems with real-time semantic awareness.

AI-Powered Language Learning Platform with Spaced Repetition System, Interactive Roadmap & Smart Vocabulary Management | Flutter + FastAPI + PostgreSQL

A cost-optimization case study comparing RAG and CAG architectures, showcasing efficient API usage and local CPU deployment with Google Gemini and Llama 3.2 via Ollama.

educational overview and technical walkthrough of three key techniques used to enhance the capabilities of Large Language Models (LLMs): Retrieval-Augmented Generation (RAG), Cache-Augmented Generation (CAG), and Fine-Tuning