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36 repositorios

Awesome GitHub RepositoriesWorkflow Execution Managers

Centralized systems that coordinate task execution and prevent redundant job processing.

Distinct from Centralization Patterns: Distinct from general centralization patterns: focuses on workflow execution and worker coordination.

Explore 36 awesome GitHub repositories matching software engineering & architecture · Workflow Execution Managers. Refine with filters or upvote what's useful.

Awesome Workflow Execution Managers GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • prefecthq/prefectAvatar de PrefectHQ

    PrefectHQ/prefect

    21,640Ver en GitHub↗

    Prefect is a workflow orchestration platform designed to define, schedule, and monitor complex data pipelines as Python code. It functions as a container-native engine that wraps individual tasks in isolated environments, ensuring consistent dependencies and resource allocation across diverse infrastructure. By utilizing a state-machine-based orchestration model, the system tracks execution progress through discrete transitions and persistent event logs to maintain reliable and observable task processing. The platform distinguishes itself through a decoupled worker-API architecture, which sep

    Runs data pipeline tasks as managed custom jobs within cloud provider environments.

    Pythonautomationdatadata-engineering
    Ver en GitHub↗21,640
  • claude-code-best/claude-codeAvatar de claude-code-best

    claude-code-best/claude-code

    20,272Ver en GitHub↗

    Claude Code is a command-line interface and multi-agent orchestration framework designed for autonomous software engineering. It enables AI agents to perform codebase modifications, debugging, and Git workflow management while coordinating multiple specialized agents to decompose and execute complex engineering tasks in parallel. The system distinguishes itself through a high degree of isolation and safety, utilizing Git worktrees to create independent working directories for concurrent agents and implementing a tiered permission system that combines user rules, project policies, and OS-level

    Executes long-running agent tasks asynchronously in the background to keep the main interface operational.

    TypeScript
    Ver en GitHub↗20,272
  • alibaba/page-agentAvatar de alibaba

    alibaba/page-agent

    19,138Ver en GitHub↗

    Page-agent is an LLM browser automation agent and JavaScript in-page GUI controller. It translates natural language instructions into direct browser interface actions to automate web-based tasks and manipulate web page elements through a programmable interface. The system coordinates complex sequences of actions across multiple browser tabs and different websites. It functions as a remote browser control server, providing an interface that allows external clients to operate a browser and manage page interactions. Its capabilities include natural language intent decoding and action mapping, D

    Coordinates complex sequences of browser actions across multiple tabs and different websites.

    TypeScriptagentaiai-agents
    Ver en GitHub↗19,138
  • spotify/luigiAvatar de spotify

    spotify/luigi

    18,676Ver en GitHub↗

    Luigi is a Python framework designed for building and managing complex batch data pipelines. It functions as a workflow orchestration engine that organizes tasks into directed acyclic graphs, ensuring that jobs execute in the correct logical order based on their dependencies. By utilizing a centralized scheduler, the system coordinates task execution across distributed environments, tracks global workflow state, and prevents redundant processing by verifying the existence of output targets before triggering any work. The project distinguishes itself through a robust state-tracking mechanism t

    Coordinates task execution through a central server to track dependencies and prevent concurrent job execution.

    Pythonhadoopluigiorchestration-framework
    Ver en GitHub↗18,676
  • effect-ts/coreAvatar de Effect-TS

    Effect-TS/core

    14,618Ver en GitHub↗

    This project is a functional programming library and toolkit for building production TypeScript applications. It provides a system for managing concurrency, error handling, and resource lifecycles using functional effects. The project distinguishes itself through a comprehensive suite of specialized toolkits, including a dependency injection framework for decoupling service implementations, a workflow orchestrator for coordinating durable processes, and a SQL database toolkit for consistent data operations across multiple dialects. It also implements an OpenTelemetry instrumentation library f

    Manages asynchronous tasks and resource dependencies through a coordinated system to ensure reliable execution.

    TypeScript
    Ver en GitHub↗14,618
  • netflix/conductorAvatar de Netflix

    Netflix/conductor

    12,767Ver en GitHub↗

    Conductor is a distributed workflow manager and microservices orchestration engine. It coordinates long-running business processes across a network of services, using a stateful orchestration model to ensure consistent execution order and reliable state management. The system defines task sequences and conditional business logic using structured JSON configuration files. It manages the execution of complex operations across distributed systems with built-in persistence and failure recovery, tracking the progress of processes through a backend database. The platform provides capabilities for

    Provides a centralized system to coordinate task execution and prevent redundant job processing across a network.

    Java
    Ver en GitHub↗12,767
  • the-pocket/pocketflow-tutorial-codebase-knowledgeAvatar de The-Pocket

    The-Pocket/PocketFlow-Tutorial-Codebase-Knowledge

    12,396Ver en GitHub↗

    This project is a comprehensive suite of AI tools and frameworks, featuring an LLM multi-agent orchestrator, an autonomous agent runtime, and a stateful application framework. It provides the infrastructure to build and manage specialized AI agents capable of coordinating complex tasks through graph-based workflows and shared state. The system is distinguished by its implementation of the Model Context Protocol, allowing for standardized resource discovery and communication between AI clients and servers. It further includes an AI-powered documentation generator designed to analyze source cod

    Coordinates multiple tasks through signatures and primitives to create dependent execution chains.

    Pythoncodinglarge-language-modellarge-language-models
    Ver en GitHub↗12,396
  • boto/boto3Avatar de boto

    boto/boto3

    9,834Ver en GitHub↗

    Boto3 is the AWS SDK for Python, providing a programmatic interface for managing and automating AWS cloud infrastructure and services. It serves as a cloud management API client and resource manager for provisioning, configuring, and scaling virtual servers, databases, and storage. The library enables the implementation of infrastructure-as-code through declarative templates and scripts, allowing for the deployment of identical resource stacks across multiple accounts and geographic regions. It also provides a framework for coordinating distributed workflows, serverless functions, and contain

    AWS coordinates business logic across multiple processes using scheduling, routing, and state management.

    Pythonawsaws-sdkcloud
    Ver en GitHub↗9,834
  • threedotslabs/watermillAvatar de ThreeDotsLabs

    ThreeDotsLabs/watermill

    9,760Ver en GitHub↗

    Watermill is a Go library for building event-driven applications. It provides a framework for implementing event-driven architectures through a consistent interface for message publishing and subscribing, featuring a pub/sub message router and event stream processing pipelines. The library uses interface-based abstractions to decouple message producers and consumers from the underlying message broker. This allows different messaging technologies to be swapped via plugin-based broker integration. It employs a router-driven dispatch system and middleware-based pipelines to manage cross-cutting

    Provides an interface for managing complex patterns such as event sourcing and sagas to coordinate requests.

    Gocqrsevent-drivenevent-sourcing
    Ver en GitHub↗9,760
  • netflix/metaflowAvatar de Netflix

    Netflix/metaflow

    9,764Ver en GitHub↗

    Metaflow is a Python machine learning framework and MLOps workflow orchestrator designed to manage the lifecycle of data pipelines from local prototyping to production. It serves as a distributed compute manager and an experiment tracking system, enabling the creation of reproducible pipelines that transition between development and high-availability production environments. The framework distinguishes itself through an integrated checkpointing system that automatically persists intermediate data artifacts to remote storage, allowing failed runs to be resumed from the last successful step. It

    Coordinates dependent execution chains by linking multiple independent pipelines through an event-driven architecture.

    Pythonagentsaiaws
    Ver en GitHub↗9,764
  • microsoft/ufoAvatar de microsoft

    microsoft/UFO

    9,017Ver en GitHub↗

    UFO is a multi-device task orchestrator and LLM agent orchestration framework designed to decompose natural language requests into executable task graphs. It functions as a cross-platform UI automation tool capable of performing interactions on Windows and mobile devices while routing tasks to distributed agents based on their hardware and software capabilities. The system is distinguished by its RAG-enhanced agent architecture, which integrates external documentation and previous execution traces to improve decision-making. It employs a hybrid UI detection approach that combines computer vis

    Coordinates dependent execution chains and groups of subtasks assigned to specific devices for complex goal achievement.

    Pythonagentautomationcopilot
    Ver en GitHub↗9,017
  • richardknop/machineryAvatar de RichardKnop

    RichardKnop/machinery

    7,956Ver en GitHub↗

    Machinery is a distributed task queue and asynchronous workflow engine. It provides a system for processing heavy workloads outside the main request flow using a network of distributed background workers and a message-based job orchestrator. The project manages complex task lifecycles through sequential chaining, where results are passed between tasks, and parallel coordination, which can trigger callback tasks upon the completion of a group. It supports periodic workflow scheduling for recurring jobs and delayed execution via specific timestamps. The system includes capabilities for result

    Coordinates dependent execution chains and parallel groups of tasks through defined job signatures.

    Goamqpaws-sqsgo
    Ver en GitHub↗7,956
  • alibaba/fish-reduxAvatar de alibaba

    alibaba/fish-redux

    7,275Ver en GitHub↗

    fish-redux is a Flutter application framework and state management library that implements a Redux-based workflow. It provides a centralized state container and uses reducers and actions to maintain a single source of truth, ensuring data consistency across application screens and components. The framework includes a state-to-UI connector that transforms raw state data for display and automatically updates views. It features a component lifecycle manager that triggers logic and state updates based on when UI components mount or unmount. The project covers dependency injection for reducing co

    Coordinates transitions between application pages and logical flows using customizable adapters and state patterns.

    Dartadapteraopcomponent
    Ver en GitHub↗7,275
  • flyteorg/flyteAvatar de flyteorg

    flyteorg/flyte

    7,095Ver en GitHub↗

    Flyte is a Kubernetes-based machine learning orchestrator and containerized pipeline manager designed for coordinating AI workflows and data pipelines. It functions as an engine for defining and executing resilient pipelines, utilizing a data lineage tracker to maintain immutable execution states and ensure reproducible outputs. The platform distinguishes itself by packaging individual tasks into separate containers to ensure dependency isolation and environment consistency. It provides specialized capabilities for machine learning, including the transformation of trained models into scalable

    Provides a centralized system to track, filter, and abort active workflow runs to prevent redundant processing.

    Go
    Ver en GitHub↗7,095
  • mendableai/firecrawl-mcp-serverAvatar de mendableai

    mendableai/firecrawl-mcp-server

    6,602Ver en GitHub↗

    This project is a Model Context Protocol server that connects large language models to web scraping and crawling tools. It functions as a bridge, allowing LLM clients to utilize a web crawling engine and scraping utilities to extract and process web data. The server integrates a markdown web converter that transforms dynamic web pages and PDF documents into clean markdown to optimize consumption by AI models. It also provides a browser automation interface for controlling headless sessions and bypassing access restrictions. The system covers broad capabilities including large-scale website d

    Manages long-running crawl tasks asynchronously via job identifiers and completion polling.

    JavaScript
    Ver en GitHub↗6,602
  • dilame/instagram-private-apiAvatar de dilame

    dilame/instagram-private-api

    6,445Ver en GitHub↗

    This project is a TypeScript-based private API SDK and automation framework for Instagram. It serves as a server-side toolkit for NodeJS that interfaces with undocumented API endpoints to programmatically manage profiles and automate account operations. The library focuses on mimicking authentic user behavior by simulating mobile application network traffic patterns and headers. It uses stateful session management to maintain authentication tokens and employs a sequence-based coordination model to execute complex workflows, such as multi-step media uploads. The framework covers several high-

    Executes predefined series of network requests to complete complex multi-step operations like media uploads.

    TypeScriptinstagraminstagram-apiinstagram-client
    Ver en GitHub↗6,445
  • pujiaxin33/jxcategoryviewAvatar de pujiaxin33

    pujiaxin33/JXCategoryView

    6,165Ver en GitHub↗

    JXCategoryView es un conjunto de componentes de interfaz de usuario para iOS para crear menús de pestañas desplazables y contenedores de paginación. Proporciona un sistema para implementar navegación basada en categorías donde un control segmentado se coordina con un área de contenido para permitir transiciones de sección. El proyecto se centra en la sincronización entre un menú de navegación y un controlador de vista de paginación, asegurando que el desplazamiento a través del contenido actualice la pestaña activa y viceversa. Admite celdas de pestaña altamente personalizables mediante subclases, permitiendo la integración de imágenes, texto enriquecido y distintivos de notificación. La biblioteca incluye capacidades para la carga diferida (lazy-loading) de controladores de vista hijos para gestionar la memoria y el tiempo de inicio. También proporciona lógica para la coordinación de gestos para resolver conflictos entre el menú de navegación y los gestos del sistema, como restringir la navegación de retroceso mediante deslizamiento desde el borde.

    Coordinates synchronization between category views and scroll views to ensure aligned navigation and paging.

    Objective-Ccategorycategoryviewcollectionview
    Ver en GitHub↗6,165
  • mervinpraison/praisonaiAvatar de MervinPraison

    MervinPraison/PraisonAI

    5,592Ver en GitHub↗

    PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allo

    Triggers and manages long-running agent tasks asynchronously, allowing non-blocking execution and later result retrieval.

    Pythonagentsaiai-agent-framework
    Ver en GitHub↗5,592
  • ramotion/expanding-collectionAvatar de Ramotion

    Ramotion/expanding-collection

    5,520Ver en GitHub↗

    Este proyecto es una suite de componentes visuales y modelos de interfaz diseñados para crear transiciones animadas entre vistas previas de cuadrícula condensadas y vistas de detalle expandidas. Proporciona una librería de transición de tarjetas y componentes de interfaz de usuario animados de SwiftUI que permiten una interfaz de tipo peek-and-pop donde las tarjetas de vista previa pequeñas se expanden a contenido de pantalla completa. El sistema implementa un diseño de cuadrícula animado que permite que las colecciones de tarjetas se expandan y contraigan para revelar más información. Estas interacciones están respaldadas por animaciones que transforman celdas de contenido pequeñas en interfaces de pantalla completa para mantener la continuidad visual y el flujo del usuario.

    Provides a coordination layer to manage the logical flow and transitions between grid and detail views.

    Swift
    Ver en GitHub↗5,520
  • inngest/inngestAvatar de inngest

    inngest/inngest

    5,499Ver en GitHub↗

    Inngest is a durable execution framework and event-driven automation engine designed to orchestrate background workflows. It enables developers to build resilient, stateful processes by memoizing function steps, ensuring that long-running tasks can automatically resume from the last successful operation after failures, timeouts, or infrastructure restarts. The platform distinguishes itself through its event-driven architecture, which uses a schema-validated bus to trigger functions and coordinate complex, multi-step logic. It employs an onion-model middleware approach for cross-cutting concer

    Supports complex logic by allowing up to one thousand individual steps within a single function execution.

    Go
    Ver en GitHub↗5,499
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  2. Software Engineering & Architecture
  3. System Internals
  4. Centralization Patterns
  5. Workflow Execution Managers

Explorar subetiquetas

  • Complex Workflow Coordination6 sub-etiquetasSystems for coordinating dependent execution chains and groups of tasks through defined signatures. **Distinct from Workflow Execution Managers:** Focuses on the coordination of dependent task chains rather than just centralized execution management.
  • Managed AI Job Execution3 sub-etiquetasExecuting data pipeline tasks as managed custom jobs within cloud AI environments. **Distinct from Workflow Execution Managers:** Distinct from workflow execution managers: focuses on the integration with cloud-native AI job services.