17 个仓库
Mechanisms for saving terminal logs and output files from completed tasks to shared storage.
Distinct from Task Result Storage: Distinct from Task Result Storage: focuses on the persistence of logs and outputs for replay, rather than general workflow storage.
Explore 17 awesome GitHub repositories matching data & databases · Task Output Persistence. Refine with filters or upvote what's useful.
This project is a build orchestration engine and development toolkit designed for managing large-scale monorepos. It provides a unified workspace environment that maps project relationships and dependencies, enabling the system to perform intelligent impact analysis and execute only the tasks affected by specific code changes. The system distinguishes itself through a persistent daemon that monitors file changes for near-instant feedback and a content-addressable caching mechanism that stores task outputs to prevent redundant computation across local and remote environments. It further suppor
Shares task execution results across machines and environments to eliminate redundant work in distributed teams.
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
Persists internal agent task state and progress to the filesystem to ensure continuity across restarts.
Beanstalkd is a distributed work queue server designed for asynchronous job processing. It functions as a standalone server that distributes background tasks between producers and consumers to improve application responsiveness and throughput. The system organizes tasks using numeric priority levels to ensure critical work is processed first. It manages the job lifecycle through discrete states and uses a simple line-based text protocol over TCP for communication. To ensure reliability, the server persists job data to a sequential disk log, allowing the queue state to be recovered after a sy
Persists job data to disk to ensure work is not lost during server restarts or system crashes.
Hatchet is an open-source durable workflow engine and task orchestration platform. It provides a framework for building and executing fault-tolerant, multi-step pipelines as directed acyclic graphs (DAGs), with automatic retries, scheduling, and real-time observability. The system is built around durable task checkpointing, which persists execution state after each step so work can resume from the last checkpoint after a worker crash or restart, and it supports event-driven task resumption that pauses a task until a matching external event arrives. The platform distinguishes itself through it
Merges results from multiple parallel parent tasks into a single formatted output.
Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to support real-time analytics and event-driven applications. It functions as a partitioned, distributed key-value store that replicates data across cluster nodes to provide low-latency access and high availability. The platform also serves as a distributed SQL query engine, allowing users to execute standard SQL statements against both in-memory datasets and external data sources. What distinguishes Hazelcast is its use of a distributed consensus subsystem to maintain strongly consis
Integrates a queue with an external datastore to archive items and prevent memory exhaustion by offloading data.
Acra is an Android crash reporting framework and diagnostic data collector designed to detect failures and capture device diagnostics, system logs, and application state. It serves as an embeddable library for capturing and processing crash reports, providing a pipeline to send this data to custom backends or via email. The project features a plugin-based sender architecture that allows reports to be routed through HTTP endpoints, email clients, or proprietary backend implementations. It includes a user-controlled reporting system with preference toggles and interactive dialogs to manage user
Bypasses initialization tasks when running in a separate sender process.
该项目是用于多智能体编排、检索增强生成(RAG)系统和人在回路(human-in-the-loop)模式的参考实现和模板。它包含一系列实现模式,用于协调多个 AI 智能体以自动化复杂的业务工作流和任务。 该项目演示了如何通过使用结构化文本文件来定义智能体角色和任务规范,从而将操作逻辑与设置解耦。它包括用于集成向量搜索和文档处理以构建基于外部知识库的问答系统的指南,以及用于将人工验证和审查步骤插入自动化智能体序列的模式。 这些示例涵盖了迭代执行路径、并行处理和动态路由的设计。这些实现结合了状态管理来跟踪复杂序列的进度,并将智能体链接到外部 API、数据库和文件系统以进行工具增强执行。 参考资料以 Jupyter Notebook 的形式提供。
Tracks progress and context across iterative loops to maintain consistency during complex agent sequences.
Pueue is a task queue manager for shell commands, built as a daemon and command-line interface. It accepts shell commands into a managed queue and executes them with configurable parallel execution limits, supporting both global and per-group concurrency controls. The daemon persists its entire state—task queue, logs, and configuration—to disk, ensuring survival across crashes and system restarts. The project distinguishes itself through a dependency graph that lets tasks declare prerequisites, forming a directed acyclic graph that controls execution order. Tasks can be organized into named g
Displays logs of finished tasks or follows output of running tasks in real time.
GraphQL.NET 是一个用于在 C# 应用程序中构建和执行 GraphQL API 的服务端框架。它提供了一套全面的工具包,用于模式构建、分布式数据图的联合引擎,以及用于管理实时数据流的订阅处理器。 该项目的独特之处在于其灵活的模式构建器,支持程序化的代码优先定义和使用标准模式定义语言的声明式模式优先方法。它包括一个专门的联合引擎,用于将数据图拆分为子图并将其组合成统一的网关,以及一个专门设计用于通过批处理和缓存解决 N+1 查询问题的数据加载器实现。 该框架涵盖了广泛的操作功能,包括用于服务生命周期管理的依赖注入集成、用于字段解析拦截的中间件流水线,以及针对值类型优化以减少内存分配的执行流水线。它还提供用于查询复杂度分析、文档缓存和基于角色的访问控制的工具,以保护 API 端点。 对提前(AOT)模式编译的支持允许该框架在禁止动态代码生成的环境中执行。
Retrieves execution parameters from parent nodes up to the root to share data across different levels of the resolver tree.
ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself
Automatically captures and stores step return values as artifacts using type-annotated serialization.
This project is a social blogging application built with Flask. It provides a platform for user account management, following relationships, and chronological post streams, supported by a PostgreSQL relational database. The application features a multilingual web interface with localized content and date formatting. It is designed as a dockerized web application, utilizing containerization for consistent deployment across different environments. The system integrates a variety of core capabilities, including full-text search with provider abstraction, an asynchronous task worker for backgrou
Stores task identifiers and completion status in the database to track background process progress.
Moon is a monorepo build system and task runner designed to orchestrate complex projects with multiple packages. It functions as a dependency graph orchestrator that executes build targets in topological order and utilizes input hashing to cache results and skip redundant work. The project features a polyglot toolchain manager that automates the installation and versioning of language runtimes and CLI tools to ensure environment consistency. It also includes a plugin framework based on WebAssembly, allowing developers to extend build logic and toolchain behavior using any supported language.
Bundles task outputs into compressed files and unpacks them automatically during cache hits.
Good Job is a background job processor for Ruby on Rails that utilizes a PostgreSQL database as its primary storage engine. By leveraging relational database transactions, it ensures persistent and reliable task execution, integrating directly with the Active Job framework to handle asynchronous operations and recurring job scheduling within existing application environments. The system distinguishes itself through an in-process execution model that allows background workers to run within the same process as the web server, simplifying deployment by removing the need for separate worker servi
Uses relational database tables as the primary storage engine to ensure transactional integrity and persistence for all queued background tasks.
This project is a research-focused toolkit designed for building autonomous agent systems, multi-agent workflows, and security governance frameworks. It provides a platform for coordinating specialized sub-agents through structured communication protocols and phased task delegation to complete complex technical objectives. The framework distinguishes itself by integrating a dedicated security policy engine that validates autonomous tool execution against user-defined permissions and safety rules. It also features a research-oriented approach to prompt engineering, enabling the dynamic assembl
Tracks the progress and output of individual agents within a centralized state machine for multi-step workflows.
This project is a framework for managing multi-agent software development workflows built on the Model Context Protocol. It functions as an AI-driven task orchestrator that decomposes complex development objectives into atomic units, tracks their lifecycle, and coordinates specialized agents to execute, verify, and refine work. By maintaining persistent project context and history, the system ensures continuity across sessions, allowing agents to retain state and adhere to established coding standards. The system distinguishes itself through its dependency-graph task management and multi-agen
Preserves the internal state and progress of agent tasks to ensure continuity across sessions.
The rp-engine is a workflow orchestration framework designed for building and executing autonomous agentic tasks. Built in Rust, it provides a structured environment for managing complex, multi-step operations that integrate language models with external tools and data sources. The engine distinguishes itself through its native support for iterative planning loops and persistent state management. By utilizing a directed acyclic graph architecture, it coordinates sequences of tasks while maintaining a shared memory bank that updates automatically to inform future steps. The system relies on de
Saves the internal state and progress of agent tasks to ensure continuity across restarts.
Repeat 是一款跨平台的自动化实用程序,旨在记录、重放和脚本化鼠标及键盘输入。它作为一个宏脚本环境,允许用户捕获原始系统事件,并将它们组织成在应用重启后依然可用的持久序列。 该项目的独特之处在于其跨多台联网机器编排任务的能力。通过利用客户端-服务器模型,它可以在本地设备集群中同步自动化脚本,从而实现协调执行和在连接设备上的一致性能。 除了基本的输入记录外,该软件还通过允许用户通过外部脚本和标准编程语言定义自动化逻辑,支持复杂的工作流开发。这些序列可以使用自定义键盘快捷键或鼠标手势即时触发,从而促进重复性桌面任务和系统交互的管理。
Maintains persistent task state to ensure automation sequences remain available across application restarts.