Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.

LLM-powered framework for deep document understanding, semantic retrieval, and context-aware answers using RAG paradigm.

KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs. It is used to build logical reasoning and factual Q&A solutions for professional domain knowledge bases. It can effectively overcome the shortcomings of the traditional RAG vector similarity calculation model.

AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation

RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry

Efficient Retrieval Augmentation and Generation Framework

A community-driven collection of RAG (Retrieval-Augmented Generation) frameworks, projects, and resources. Contribute and explore the evolving RAG ecosystem.

Retrieval Augmented Generation (RAG) framework and context engine powered by Pinecone

Multimodal Retrieval-augmented Generation Framework Built by Tongyi Lab, Alibaba Group.

RAGLight is a modular framework for Retrieval-Augmented Generation (RAG). It makes it easy to plug in different LLMs, embeddings, and vector stores, and now includes seamless MCP integration to connect external tools and data sources.

A Python framework for performing information retrieval experiments, building on http://terrier.org/

[EMNLP 2024: Demo Oral] RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation

A PyTorch framework for an image retrieval task including implementation of N-pair Loss (NIPS 2016) and Angular Loss (ICCV 2017).

A-RAG: Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces. State-of-the-art RAG framework with keyword, semantic, and chunk read tools for multi-hop QA.

UMT is a unified and flexible framework which can handle different input modality combinations, and output video moment retrieval and/or highlight detection results.

FlexRAG: A RAG Framework for Information Retrieval and Generation.

A flexible, high-performance framework for large-scale retrieval problems based on TensorFlow.

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+

A framework for fine-tuning retrieval-augmented generation (RAG) systems.

QuantMind is an intelligent knowledge extraction and retrieval framework for quantitative finance.

This repository contains a Retrieval-Augmented Generation (RAG) framework developed in C++ for high performance and scalability, with CUDA support for computational acceleration.

[CVPR 2024] Bridging the Gap: A Unified Video Comprehension Framework for Moment Retrieval and Highlight Detection

Enterprise-grade memory framework for LLMs featuring GPU-optimized inference, vector storage, and automated scaling. Enables hyper-personalized responses through efficient context retrieval and integration.

basic framework for rag(retrieval augment generation)

A modular Python framework implementing the Model Context Protocol (MCP). It features a standardized client-server architecture over StdIO, integrating LLMs with external tools, real-time weather data fetching, and an advanced RAG (Retrieval-Augmented Generation) system.

UBR4CTR is the code for our proposed User Behavior Retrieval for CTR Prediction framework in SIGIR 2020.

This repository contains an implementation of the models introduced in the paper Dialog-based Interactive Image Retrieval. The network is implemented using PyTorch and the rest of the framework is in Python.

Unified Learned Sparse Retrieval Framework

Source codes for paper ”ReACC: A Retrieval-Augmented Code Completion Framework“

A foundation model framework for analyzing multiplexed tissue imaging data across molecular, cellular and tissue scales enabling clinical diagnostics, biological discovery and patient case retrieval.