Awesome GitHub RepositoriesRetrieval Augmentation

This project provides a comprehensive framework for building, training, and managing autonomous agents. It enables the construction of systems that utilize language models to plan, manage memory, and execute multi-step tasks through iterative reasoning loops and tool-based actions. The framework distinguishes itself by offering specialized capabilities for interacting with graphical user interfaces and legacy software, allowing agents to perceive visual elements and perform actions like a human user. It supports complex, cross-application workflows through graph-based orchestration and provid

Quivr is a framework for building retrieval-augmented generation pipelines that connect large language models to custom knowledge bases. It serves as a generative AI integration layer that abstracts the process of transforming diverse document sources into searchable context for AI responses. The project orchestrates the end-to-end flow between document ingestion, vector storage management, and model provider interfaces. It features a vector-store-agnostic retrieval system and a modular API layer that allows for flexible switching between different generative model providers. The system cove

This project is a feature-rich Go client library designed for interacting with Redis. It serves as a comprehensive interface for managing remote data stores, enabling developers to execute standard database commands, handle complex data structures, and perform asynchronous operations within Go applications. The library distinguishes itself through its support for advanced Redis capabilities, including connection pooling, pipelining, and transactional integrity. It provides specialized primitives for managing distributed clusters, including automated topology updates and request routing to sha

RAG-Anything is a retrieval-augmented generation framework designed to index diverse document formats and perform semantic search using local machine learning models. It functions as a local multimodal data processor, extracting and organizing information from various file types into a unified knowledge base to facilitate private document analysis. The system distinguishes itself through its high-throughput ingestion engine, which processes large batches of documents into searchable vector embeddings. By executing machine learning models directly on local hardware, the framework ensures that

LangChain.js is a framework for building, executing, and monitoring stateful agentic applications. It provides an orchestration engine that models workflows as directed graphs, allowing developers to connect language models, data sources, and external tools into modular, multi-step processes. The platform distinguishes itself through its focus on stateful execution and human-in-the-loop control. It manages agent lifecycles by persisting execution state across threads, enabling fault tolerance and the ability to pause workflows at designated breakpoints for manual review or modification. This

This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

OpenMetadata is an enterprise data catalog, metadata platform, and governance suite that functions as a knowledge graph for data assets. It serves as an AI-ready metadata layer, providing governed context and organizational memory to large language model agents via the Model Context Protocol. The platform distinguishes itself by capturing institutional knowledge, linking conversations, decisions, and remediation notes directly to data assets to preserve tribal knowledge. It integrates AI agents to automate metadata governance, such as suggesting descriptions and identifying sensitive data thr

DataHub is a metadata management platform designed to unify technical, operational, and business context across diverse data ecosystems. By utilizing a graph-based metadata model and an event-driven ingestion architecture, it creates a centralized source of truth that maps complex data relationships, lineage, and ownership. This foundational framework enables organizations to maintain a synchronized view of their data landscape, supporting both human-led discovery and automated data operations. The platform distinguishes itself through its focus on grounding artificial intelligence and autono

Sn1per is a vulnerability management platform and penetration testing orchestrator designed to automate reconnaissance, vulnerability scanning, and exploit verification. It functions as a dockerized security toolkit that coordinates multiple tools into a unified automated pipeline to identify security flaws across network and web assets. The platform features an attack surface manager for discovering internet-facing assets through OSINT, DNS enumeration, and certificate transparency. It distinguishes itself with an AI-powered security analyzer that uses large language models to summarize scan

OpenChatKit is a training and inference toolkit for large language models. It provides a comprehensive set of tools for managing the model lifecycle, including a fine-tuning pipeline, a model weight converter, and a command-line interface for interacting with conversational agents. The toolkit features a framework for retrieval augmented generation, allowing models to incorporate relevant context from external vector indices. It also includes utilities for converting trained model checkpoints into formats compatible with standard inference libraries. The project covers conversational AI trai

Superagent is a framework for AI assistant orchestration and agent security. It provides the tools to build intelligent assistants that integrate external APIs and maintain conversation memory to automate complex tasks. The project focuses on AI agent security through adversarial testing, red teaming, and the detection of prompt injections and malicious tool calls. It includes automated vulnerability patching, which scans codebases and configurations for security flaws and generates pull requests with fixes. The platform supports retrieval augmented generation by connecting language models t

Wenda es una plataforma de infraestructura y gateway autohospedada para desplegar modelos de lenguaje dentro de redes internas para garantizar la privacidad y seguridad de los datos. Funciona como un hub centralizado y API gateway que unifica la comunicación entre varios ejecutores de modelos offline y proveedores de servicios online a través de una única interfaz. La plataforma incluye un orquestador de flujo de trabajo que utiliza scripts personalizados y llamadas a API para automatizar flujos de conversación complejos y configuraciones de modelos. También incorpora un sistema de recuperación que aumenta las respuestas del modelo con conocimiento externo recuperado de bases de datos vectoriales y motores de búsqueda. El sistema gestiona el estado y la memoria conversacional persistiendo el historial de diálogo en una base de datos para mantener el contexto a través de las sesiones de usuario. Utiliza un enfoque de integración modular para permitir la adición de nuevos proveedores de modelos sin modificar la aplicación principal.

This is a Go backend template that structures a web service into domain, usecase, controller, and repository layers with strict dependency inversion. It provides a foundation for building maintainable and testable REST APIs by separating business logic from transport and data access concerns. The project implements JWT-based authentication, issuing access and refresh tokens for user signup, login, and protected endpoint access. It uses the Gin HTTP framework to build a Docker-packaged REST API with public and private route groups, request validation, and middleware-based authentication. Depen

Este proyecto es un recurso de aprendizaje integral y un conjunto de demostraciones centradas en la integración, despliegue y ajuste fino de modelos de lenguaje de gran tamaño. Proporciona contenido educativo y guías prácticas para trabajar con modelos de inteligencia artificial. El recurso incluye tutoriales y cursos específicos sobre la adaptación de modelos pre-entrenados a conjuntos de datos especializados utilizando técnicas de ajuste fino eficientes en parámetros. También proporciona contenido instructivo para ejecutar modelos cuantizados en hardware de consumo y construir pipelines de generación aumentada por recuperación (RAG) utilizando bases de datos vectoriales e indexación de documentos. El proyecto cubre una amplia gama de desarrollo de aplicaciones de IA, incluyendo la integración de API de modelos de lenguaje de gran tamaño para conversaciones en streaming, la creación de interfaces de usuario basadas en web y la implementación de pipelines RAG. La ejecución se admite mediante cuadernos basados en la nube preconfigurados en Kaggle y Colab para proporcionar acceso a GPU sin requerir instalaciones de hardware local.

This project provides a search service designed to retrieve and rerank web content for use in large language model applications. It functions as a retrieval augmented search engine that processes natural language queries to fetch contextually relevant information from external web sources. The system distinguishes itself through a combination of semantic retrieval and precision-focused reranking. It converts user queries into high-dimensional embeddings to perform similarity searches across indexed collections, then refines these results by passing candidate pairs through a secondary model to