RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs

Reusable accelerator for Foundry IQ live grounding with Fabric Ontology and MCP Server Knowledge Sources.

AI support agent using Azure Foundry, RAG, Terraform

Reasoning-based, vectorless RAG over a large document using a hierarchical tree (PageIndex) and a Vision-Language Model (Llama 4 Scout), no embeddings, no vector store, no text chunking.

MCP server for Azure AI Search, supporting keyword, semantic, vector, hybrid, and Knowledge Base agentic retrieval.

LLM-readable debugging knowledge base: structured pits that help coding agents find buried fixes in human-oriented threads.

SSE-Bio: A Structured Self-Evolving Agent with Agentic Retrieval Policy for Multi-Hop Biomedical Reasoning.

Blackwall is an autonomous Agentic Firewall designed to secure AI agents by actively intercepting execution flows before they reach external systems or the host OS. It leverages self-learning threat signatures, structural graph analysis, and real-time semantic gating to dynamically detect and mitigate rogue agent behaviors and malicious payloads.

A TypeScript demo of Azure AI Search agentic retrieval with NASA data

Intercepts an AI agent's action before it runs, grounds it in cited precedent via Foundry IQ, pauses for a human.

An enterprise view into Agentic Retrieval for Multi-language usecases

Modular local RAG pipeline with agentic retrieval, cross-encoder reranking, and context compression. Built with LlamaIndex, ChromaDB, and Ollama. Supports any document collection (txt, json, csv, pdf, html, code). Fully offline — no API keys, no cloud. Configurable persona for prompt engineering, code Q&A, or documentation search.