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21 repositorios

Awesome GitHub RepositoriesOn-Demand Context Loading

Mechanisms for fetching detailed operational instructions only when a specific capability is triggered.

Distinct from Project Context Managers: Focuses on token-reduction via lazy-loading of instructions rather than static project constraints.

Explore 21 awesome GitHub repositories matching software engineering & architecture · On-Demand Context Loading. Refine with filters or upvote what's useful.

Awesome On-Demand Context Loading GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • microsoft/ai-agents-for-beginnersAvatar de microsoft

    microsoft/ai-agents-for-beginners

    67,369Ver en GitHub↗

    This project is a structured educational resource and technical guide for designing and implementing autonomous systems using large language models. It provides a comprehensive curriculum and code samples focused on agentic design patterns, autonomous development, and the creation of systems capable of planning and executing multi-step tasks. The resource details the implementation of agentic retrieval-augmented generation, where models autonomously plan and refine data searches. It covers a wide array of orchestrators and design patterns, including metacognitive reflection for self-correctin

    Injects relevant background information into the model before processing a query to ensure informed responses.

    Jupyter Notebookagentic-aiagentic-frameworkagentic-rag
    Ver en GitHub↗67,369
  • addyosmani/agent-skillsAvatar de addyosmani

    addyosmani/agent-skills

    60,849Ver en GitHub↗

    Agent-skills is a collection of structured instructions and behavioral personas designed to standardize how AI coding agents perform engineering tasks. It functions as a workflow orchestrator that maps natural language intent to repeatable technical sequences and verification checklists. The project distinguishes itself through the use of specialized markdown-defined roles, such as security auditors or test engineers, to apply targeted domain expertise. It employs an evidence-based verification model that requires runtime data or passing tests as mandatory exit criteria to ensure AI-generated

    Allows for the explicit referencing of skill files within prompts to force specific workflows on-demand.

    Shellagent-skillsantigravityantigravity-ide
    Ver en GitHub↗60,849
  • junegunn/vim-plugAvatar de junegunn

    junegunn/vim-plug

    35,685Ver en GitHub↗

    vim-plug is a plugin manager for the Vim text editor that functions as a git-based dependency manager and configuration bootstrapper. It downloads and organizes external plugin packages from remote repositories to extend the editor's functionality. The project acts as a lazy-loading orchestrator to reduce editor startup time by deferring the loading of plugins until specific commands or file types are encountered. It enables version pinning by allowing plugins to be fetched using specific git branches, tags, or commit hashes. The manager provides a framework for plugin installation, lifecycl

    Reduces editor startup time by loading plugins only when specific commands or file types are encountered.

    Vim Scriptvim
    Ver en GitHub↗35,685
  • spacevim/spacevimAvatar de SpaceVim

    SpaceVim/SpaceVim

    20,253Ver en GitHub↗

    SpaceVim is a modular configuration framework for Vim and Neovim designed to manage settings, plugins, and keybindings across different editing environments. It functions as a plugin manager that uses a layered organization system to group related functions and plugins, reducing the manual effort required for configuration. The system is centered around a mnemonic keybinding strategy that utilizes the space bar as the primary modifier for editor commands. To maintain performance, the framework implements a startup optimizer that delays the loading of non-essential plugins until they are requi

    Implements a deferred plugin loading mechanism to minimize initial editor startup time.

    Vim Script
    Ver en GitHub↗20,253
  • wsdjeg/spacevimAvatar de wsdjeg

    wsdjeg/SpaceVim

    20,248Ver en GitHub↗

    SpaceVim is a modular configuration framework designed for managing plugins and settings across Vim and Neovim. It utilizes a layer-based architecture to organize related editor extensions into functional groups, reducing the overhead associated with manual setup and maintenance. The framework features a mnemonic keybinding system that maps editor commands to intuitive key sequences and provides integrated discovery guides. To improve performance, it employs a deferred plugin loader that reduces startup time by loading extensions only when they are specifically required. The system provides

    Implements a deferred plugin loader that increases startup speed by delaying initialization until modules are requested.

    Vim Script
    Ver en GitHub↗20,248
  • nesquena/hermes-webuiAvatar de nesquena

    nesquena/hermes-webui

    14,912Ver en GitHub↗

    Hermes-webui is a self-hosted AI orchestrator and web interface for managing autonomous agents. It serves as a multi-provider gateway that connects cloud and local large language models, providing a central hub to execute scheduled background jobs, run shell commands, and manage agent memory on private hardware. The system distinguishes itself through a persistent memory manager that utilizes knowledge graphs and markdown files for long-term context across sessions. It features a model context protocol host for extending agent capabilities with standardized tools and supports the orchestratio

    Writes new skill modules to extend functional abilities based on learned context and operational experience.

    Pythonagentai-agentshermes
    Ver en GitHub↗14,912
  • hkuds/openharnessAvatar de HKUDS

    HKUDS/OpenHarness

    14,084Ver en GitHub↗

    OpenHarness is a framework for building and orchestrating AI agents that utilize tools and plugins to execute complex tasks. It provides an orchestration system for managing language model lifecycles and a multi-agent coordination system for delegating workloads across teams of specialized subagents. The project features an agent gateway that bridges language model agents to external chat platforms and communication channels. It includes a tool integration engine for executing shell, file, and web operations, supported by a memory and skill manager that handles persistent user preferences and

    Implements modular, domain-specific instruction files loaded into the agent context to guide task execution.

    Python
    Ver en GitHub↗14,084
  • andrewyng/context-hubAvatar de andrewyng

    andrewyng/context-hub

    13,700Ver en GitHub↗

    Context Hub is a retrieval-augmented generation framework and context management system designed to provide large language model agents with curated, versioned markdown documentation. It functions as a documentation provider that delivers precise API references and technical context to reduce hallucinations and token waste. The system incorporates an agentic memory layer that maintains persistent local annotations and user feedback to improve how agents retrieve task-specific knowledge. It uses a version-controlled repository of technical documentation designed for both machine readability an

    Retrieves only the minimal subset of required reference files based on current task requirements to optimize tokens.

    JavaScript
    Ver en GitHub↗13,700
  • agentskills/agentskillsAvatar de agentskills

    agentskills/agentskills

    10,303Ver en GitHub↗

    Agent Skills is a framework for bundling executable scripts and metadata to extend the capabilities and tool-use of language model agents. It provides a standardized directory structure for packaging specialized workflows, technical instructions, and portable agent capabilities for distribution across different AI platforms. The project features a tool optimization suite used to refine skill triggers and evaluate the reliability of agent-activated capabilities. It includes a context-aware knowledge manager that organizes technical references into a hierarchy, loading them on demand to reduce

    Reduces token consumption by loading full technical instructions on demand rather than during initial discovery.

    Pythonagent-skills
    Ver en GitHub↗10,303
  • jeffallan/claude-skillsAvatar de Jeffallan

    Jeffallan/claude-skills

    9,935Ver en GitHub↗

    This project is an AI agent workflow orchestrator and automated software lifecycle manager designed to sequence specialized AI personas for end-to-end software development. It serves as a prompt engineering library and a full-stack development toolkit that guides the process from initial discovery and specification through to deployment and code review. The system features a context management framework that utilizes progressive loading and routing tables to fetch reference files on-demand, reducing token consumption within the model context window. It employs a definition-based routing syste

    Reduces token consumption by lazily fetching detailed reference data and documentation only when specific triggers occur.

    Pythonai-agentsclaudeclaude-code
    Ver en GitHub↗9,935
  • nvim-mini/mini.nvimAvatar de nvim-mini

    nvim-mini/mini.nvim

    9,325Ver en GitHub↗

    mini.nvim is a comprehensive library of independent modules designed to extend Neovim with a wide array of navigation, user interface, and text manipulation tools. It serves as a modular plugin collection, a UI toolkit for creating custom statuslines and notifications, and a package manager for installing and pinning external plugins from Git. The project provides a specialized fuzzy picker framework for filtering files and symbols, an LSP completion engine with interactive snippet expansion, and a dedicated plugin test framework that uses headless editor instances and remote procedure calls

    Optimizes startup time by scheduling the execution of plugin setup code to occur after the initial load.

    Lualuamini-nvimneovim
    Ver en GitHub↗9,325
  • evomap/evolverAvatar de EvoMap

    EvoMap/evolver

    8,744Ver en GitHub↗

    Evolver is a self-evolving AI agent framework that uses gene expression programming to autonomously improve agent behaviors through a continuous five-step loop of scanning, selecting, mutating, validating, and solidifying. It functions as an auditable evolution system that records every mutation and selection step, and can translate natural-language problems into executable Python code for automated grading and evaluation. The framework distinguishes itself through a distributed architecture that enables multiple agents to collaborate and share learned experiences across a network. It operate

    Selects relevant Genes or Capsules at runtime by computing signal overlap and loads their full content only when needed.

    JavaScripta2aagent-evolutionagent-framework
    Ver en GitHub↗8,744
  • feast-dev/feastAvatar de feast-dev

    feast-dev/feast

    6,727Ver en GitHub↗

    Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and serving features across both training and inference workflows. It operates as a declarative system where feature definitions are written as code in Python files, synchronized to a central registry, and made available for low-latency online retrieval or point-in-time correct historical joins for training datasets. The project abstracts storage behind a pluggable architecture, allowing offline and online backends to be swapped without changing retrieval logic, and coordinates ma

    Pre-loads heavy resources like models and lookup tables at server startup to reduce per-request overhead.

    Pythonbig-datadata-engineeringdata-quality
    Ver en GitHub↗6,727
  • zplug/zplugAvatar de zplug

    zplug/zplug

    6,033Ver en GitHub↗

    zplug es un gestor de plugins para el shell Zsh diseñado para instalar y organizar plugins, temas y comandos de la comunidad desde repositorios remotos y rutas locales. Se centra en la reproducibilidad del entorno mediante configuración versionada, permitiendo que los plugins se fijen a ramas, etiquetas o hashes de commit específicos de git. El proyecto optimiza la velocidad de inicio del shell utilizando un sistema de carga diferida (lazy loading) que pospone la ejecución de los plugins hasta que se requieren explícitamente. Para acelerar la configuración inicial del entorno, emplea un instalador paralelo que descarga múltiples extensiones simultáneamente. El gestor admite la instalación desde múltiples fuentes y maneja la resolución de dependencias de extensiones para garantizar que los paquetes requeridos se carguen en el orden correcto. También incluye capacidades para descargar artefactos binarios externos y mapearlos automáticamente a la ruta del sistema, así como ejecutar hooks de ciclo de vida personalizados durante la instalación y las actualizaciones.

    Implements a system to delay the activation of plugins until a specific command is first invoked, optimizing startup speed.

    Shell
    Ver en GitHub↗6,033
  • google/perfettoAvatar de google

    google/perfetto

    5,558Ver en GitHub↗

    Perfetto is a platform for system-level performance tracing and analysis on Linux and Android. It combines a high-throughput trace recorder, a SQL-based query engine, and a browser-based visualizer into a single toolchain. The platform covers CPU scheduling and call-stack profiling, native and Java heap memory allocation tracking, GPU and graphics events, and system-wide counters such as CPU frequency and power consumption. The architecture decouples trace recording from offline analysis, using a compact protobuf format for event encoding and columnar storage for efficient SQL queries. The we

    Provides a library of curated query examples that users can load for trace analysis.

    C++
    Ver en GitHub↗5,558
  • opencx-labs/copilotAvatar de opencx-labs

    opencx-labs/copilot

    5,113Ver en GitHub↗

    Copilot es una plataforma de soporte al cliente autohospedada que utiliza modelos de lenguaje grandes (LLM) y bases de conocimiento vectorizadas para automatizar la asistencia al usuario. El sistema consiste en un widget de chat de IA incrustable para integración en sitios web, un panel de gestión contenedorizado y una base de conocimiento de base de datos vectorial. La plataforma incluye un orquestador de API que procesa especificaciones de API estructuradas, permitiendo que los modelos de lenguaje interactúen con endpoints y operaciones externas. Gestiona contexto especializado para las respuestas indexando definiciones de API y documentación dentro de una base de datos vectorial. La infraestructura cubre la orquestación de servicios contenedorizados para servidores y trabajadores en segundo plano, procesamiento asíncrono basado en colas y una interfaz administrativa para monitorear las interacciones de los usuarios en tiempo real. El sistema también maneja migraciones de esquemas de bases de datos e indexación de conocimiento.

    Injects structured API definitions as context into the language model to enable automated interaction with external operations.

    TypeScriptai-copilotcopilotllm
    Ver en GitHub↗5,113
  • vudovn/antigravity-kitAvatar de vudovn

    vudovn/antigravity-kit

    4,979Ver en GitHub↗

    Antigravity-kit is a multi-agent orchestrator and routing engine designed to coordinate specialized large language model agents. It functions as a conversational workflow automation tool and a context management system that executes complex tasks through a chat interface. The system utilizes a routing engine to classify user requests and dispatch them to domain-expert agents. It employs a multi-agent orchestration model that allows specialist workers to operate in parallel and combine their outputs. To manage operational efficiency, the kit includes a memory layer for storing project convent

    Fetches detailed operational instructions only when specific capabilities are triggered to reduce token overhead.

    TypeScript
    Ver en GitHub↗4,979
  • kiln-ai/kilnAvatar de kiln-ai

    kiln-ai/kiln

    4,910Ver en GitHub↗

    Kiln es un workbench de desarrollo de LLM y framework de evaluación diseñado para diseñar, probar y optimizar prompts y agentes de IA. Funciona como un orquestador multi-agente y una herramienta de optimización RAG, proporcionando una interfaz visual para el desarrollo iterativo de sistemas de IA. El proyecto se distingue por un pipeline de fine-tuning integral que soporta entrenamiento de modelos sin código y destilación de razonamiento. Permite la creación de sistemas multi-agente jerárquicos donde actores especializados se coordinan mediante tool calling, e implementa un servidor de Model Context Protocol para exponer estos agentes y capacidades de búsqueda como herramientas estandarizadas para clientes externos. La plataforma cubre una amplia gama de capacidades, incluyendo puntuación automatizada por jueces de IA para control de calidad, generación de datos sintéticos para entrenamiento y evaluación, y recuperación híbrida vector-keyword para fundamentar las respuestas del modelo. También proporciona herramientas para la evolución de prompts, auditoría de trazas y gestión de datasets colaborativos mediante integración con Git. El workbench es accesible a través de una API REST autohospedable y una librería de Python dedicada para la ejecución programática de flujos de trabajo.

    Defines reusable instructions and reference documents that are loaded into agent context only when needed.

    Python
    Ver en GitHub↗4,910
  • tencentcloudadp/youtu-agentAvatar de TencentCloudADP

    TencentCloudADP/youtu-agent

    4,576Ver en GitHub↗

    Youtu Agent is an open-source framework for building, running, and evaluating autonomous agents powered by large language models. It provides the core infrastructure for creating agents that follow reasoning loops, use toolkits, and coordinate with other agents to solve complex tasks, all managed through YAML-driven configuration files. The framework distinguishes itself through its support for multi-agent orchestration, where a planner agent decomposes tasks and coordinates specialized worker agents, and through its integration with the Model Context Protocol for connecting to external toolk

    Provides modular skill files that extend agent capabilities with structured instructions for complex tasks.

    Pythonagent-frameworkagentsopenai-agents
    Ver en GitHub↗4,576
  • jwiegley/use-packageAvatar de jwiegley

    jwiegley/use-package

    4,465Ver en GitHub↗

    Este proyecto es un framework declarativo y un lenguaje específico de dominio para gestionar paquetes de Emacs Lisp. Funciona como un optimizador de rendimiento de inicio al agrupar la instalación de paquetes, configuraciones de variables y atajos de teclado en bloques únicos para reducir el tiempo de arranque inicial. El sistema se distingue por un framework de carga diferida que retrasa la ejecución del paquete hasta que se activan teclas, ganchos o modos específicos. Utiliza una sintaxis de declaración basada en macros para organizar la configuración y automatizar la generación de cargas automáticas, asegurando que los paquetes solo se carguen cuando realmente se requieran. El proyecto cubre áreas de capacidad amplias, incluyendo la instalación automatizada de paquetes desde archivos remotos, gestión de dependencias y carga condicional basada en el sistema operativo o entorno. También proporciona un mapeo completo de teclado para atajos y acordes, así como herramientas para analizar estadísticas de carga y modificar las pantallas de la línea de modo.

    Provides a system that delays the activation of packages until a specific command, hook, or mode is triggered.

    Emacs Lispautoloaddeferred-loadingemacs
    Ver en GitHub↗4,465
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  1. Home
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  4. On-Demand Context Loading

Explorar subetiquetas

  • Context Pre-loading2 sub-etiquetasInjection of relevant background information into the prompt prior to query processing. **Distinct from On-Demand Context Loading:** Distinct from on-demand loading: focuses on the proactive injection of context to ensure informed initial responses.
  • Curated Query ExamplesA library of pre-built query graph configurations that users can load from the application. **Distinct from Context Pre-loading:** Distinct from Context Pre-loading: provides curated, reusable query configurations rather than injecting background context into prompts.
  • Deferred Plugin LoadingMechanisms for delaying the activation of plugins until a specific command or event is triggered. **Distinct from On-Demand Context Loading:** Specifically targets editor plugin activation rather than operational instructions or context fetching.
  • Skill Files1 sub-etiquetaModular, domain-specific instruction files loaded into an agent's context to guide complex task execution. **Distinct from On-Demand Context Loading:** Distinct from On-Demand Context Loading: focuses on loading structured skill files for agent capabilities, not general context or data subsets.
  • Tiered Abstraction LoadingStrategies for loading content in levels of granularity, from abstracts to full details, to optimize token usage. **Distinct from On-Demand Context Loading:** Focuses on hierarchical data granularity (abstract/overview/detail) rather than lazy-loading of instructions.