4 repositorios
Systems that dynamically provide relevant information and tools to enhance the operational context of AI agents.
Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Context Engineering. Refine with filters or upvote what's useful.
LangChain is an orchestration framework designed for building, managing, and deploying applications powered by large language models. It provides a unified integration layer that normalizes disparate model provider APIs into a consistent set of primitives, enabling developers to build complex, multi-step AI workflows that manage state, memory, and tool execution. The project distinguishes itself through a durable execution runtime that maintains persistent state across long-running processes by checkpointing progress to external storage. It models agent workflows as directed graphs, allowing
Augments operational context by dynamically injecting relevant data and tool access into agent prompts.
This project is an autonomous software development assistant and project management tool that utilizes a multi-agent orchestrator to automate complex workflows. It functions as an agentic framework designed to research, plan, execute, and verify software development tasks by coordinating specialized agents that manage context windows and system performance. The system distinguishes itself through a structured, interview-based requirement engineering phase that clarifies project objectives before initiating automated work. It employs atomic task decomposition to break goals into independent un
Maintains project-specific documentation and state files to provide high-quality context for automated operations.
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
Organizes project information into a hierarchy of global rules, architecture specs, and transient outputs to optimize agent context.
Este proyecto es una colección de bases de conocimientos estandarizadas y modelos de competencia que definen metodologías profesionales para profesionales de gestión de productos y agentes de inteligencia artificial. Proporciona un framework estructurado de habilidades profesionales y conocimientos para asegurar un nivel consistente de calidad de salida a través del descubrimiento de productos, estrategia y alineación de partes interesadas. El repositorio se centra en frameworks especializados para la gestión de productos con modelos de lenguaje grandes, incluyendo directrices para evaluar la preparación para la inteligencia artificial, ingeniería de contexto y la orquestación de flujos de trabajo multi-agente. Utiliza estructuración de conocimiento basada en markdown para guiar a los agentes de IA en la producción de entregables profesionales y análisis estratégicos en lugar de salidas genéricas. El proyecto cubre una amplia gama de capacidades de gestión de productos, incluyendo análisis de métricas de negocio para la salud operativa, descubrimiento de clientes y validación de hipótesis, y planificación estratégica de roadmaps utilizando modelos de priorización. También incluye frameworks para la autoría de documentos de requisitos de producto e historias de usuario, mapeo de influencia de partes interesadas y coaching ejecutivo para transiciones de liderazgo.
Implements systems for organizing domain knowledge and operational constraints into prompts to guide AI agent orchestration.