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9 Repos

Awesome GitHub RepositoriesManaged AI Job Execution

Executing 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.

Explore 9 awesome GitHub repositories matching software engineering & architecture · Managed AI Job Execution. Refine with filters or upvote what's useful.

Awesome Managed AI Job Execution GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • prefecthq/prefectAvatar von PrefectHQ

    PrefectHQ/prefect

    21,640Auf GitHub ansehen↗

    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
    Auf GitHub ansehen↗21,640
  • claude-code-best/claude-codeAvatar von claude-code-best

    claude-code-best/claude-code

    20,272Auf GitHub ansehen↗

    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
    Auf GitHub ansehen↗20,272
  • mendableai/firecrawl-mcp-serverAvatar von mendableai

    mendableai/firecrawl-mcp-server

    6,602Auf GitHub ansehen↗

    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
    Auf GitHub ansehen↗6,602
  • mervinpraison/praisonaiAvatar von MervinPraison

    MervinPraison/PraisonAI

    5,592Auf GitHub ansehen↗

    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
    Auf GitHub ansehen↗5,592
  • volcano-sh/volcanoAvatar von volcano-sh

    volcano-sh/volcano

    5,337Auf GitHub ansehen↗

    Volcano is a Kubernetes-native batch scheduler specialized for AI, machine learning, and high-performance computing workloads. It provides gang scheduling to atomically allocate resources for all tasks of a distributed job, preventing deadlocks from partial allocation, and supports hierarchical queue management for multi-tenant resource isolation with configurable quotas, borrowing, and preemption. Topology-aware placement optimizes communication-intensive workloads by modeling network hierarchy to minimize cross-switch latency. Volcano differentiates itself with automated orchestration of di

    Provides workflow examples and relaxed validation for running MPI jobs in workflow automation systems.

    Goaibatch-systemsbigdata
    Auf GitHub ansehen↗5,337
  • stevearc/conform.nvimAvatar von stevearc

    stevearc/conform.nvim

    4,925Auf GitHub ansehen↗

    conform.nvim is a Neovim plugin that formats code buffers using external formatters while preserving editor state such as cursor position, folds, and viewport. It supports embedded code block formatting, applying language-specific formatting rules to code blocks inside Markdown and similar documents. The plugin runs formatters asynchronously via Neovim's job control, keeping the editor responsive during formatting operations. It offers a configurable formatter selection algorithm that picks a formatter based on filetype, buffer-local variables, or custom predicate functions. Users can define

    Asynchronously runs external formatters via Neovim job control for non-blocking editor operation.

    Luaneovimneovim-pluginnvim
    Auf GitHub ansehen↗4,925
  • david-patrick-chuks/riona-ai-agentAvatar von David-patrick-chuks

    David-patrick-chuks/Riona-AI-Agent

    4,223Auf GitHub ansehen↗

    Riona-AI-Agent ist ein LLM-Automatisierungsagent und Browser-Automatisierungsframework, das darauf ausgelegt ist, komplexe Workflows auszuführen, auf benutzerdefinierten Daten zu trainieren und KI-Inhalte zu generieren. Es fungiert als Social-Media-Automatisierungstool zur Planung von Inhalten, Verwaltung mehrerer Profile und Automatisierung von Engagement-Aktionen über soziale Plattformen hinweg. Das System enthält ein zentrales Monitoring-Dashboard zur Verfolgung des Live-Status, der Laufzeitzusammenfassungen und Aktivitätsprotokolle von KI-Agenten. Es nutzt Modelle des maschinellen Lernens, um visuelle Sicherheitsabfragen während automatisierter Browsing-Sitzungen zu identifizieren und zu umgehen. Die Fähigkeiten des Agenten decken die Social-Media-Automatisierung ab, einschließlich der Automatisierung repetitiver Interaktionen und der Planung von Beiträgen. Es unterstützt das Training von KI-Agenten durch die Aufnahme von Weblinks, Audio und Dokumenten, um die Wissensbasis und das Verhalten des Agenten anzupassen. Das Framework ist mit Node.js und TypeScript erstellt.

    Manages and triggers long-running AI agent tasks asynchronously to handle complex operational logic.

    HTML
    Auf GitHub ansehen↗4,223
  • polyaxon/polyaxonAvatar von polyaxon

    polyaxon/polyaxon

    3,707Auf GitHub ansehen↗

    Polyaxon is a Kubernetes-native machine learning orchestration platform and MLOps pipeline orchestrator. It serves as a control plane for managing distributed deep learning workloads, automated machine learning pipelines, and experiment tracking. The platform distinguishes itself through specialized services for distributed training management, including MPI-based coordination for PyTorch and TensorFlow. It provides an automated hyperparameter optimization service utilizing Bayesian, random, and grid search algorithms, alongside managed interactive AI workspaces for launching Jupyter notebook

    Deploys and manages interactive environments, including notebooks and visualization tools, as managed services.

    MDX
    Auf GitHub ansehen↗3,707
  • langchain-ai/langchain-mcp-adaptersAvatar von langchain-ai

    langchain-ai/langchain-mcp-adapters

    3,366Auf GitHub ansehen↗

    This project provides a translation layer and set of adapters designed to bridge AI agents with the Model Context Protocol. It functions as an integration layer that allows agents to operate as protocol-compliant servers and enables the conversion of protocol-based tools into formats compatible with agent frameworks and logic graphs. The adapters facilitate tool interoperability by wrapping external protocol tools for use within agent workflows and exposing internal agent capabilities to any client implementing the Model Context Protocol. This creates a communication bridge that supports inte

    Provides the ability to trigger and manage long-running agent tasks asynchronously without blocking the caller.

    Pythonlangchainlanggraphmcp
    Auf GitHub ansehen↗3,366
  1. Home
  2. Software Engineering & Architecture
  3. System Internals
  4. Centralization Patterns
  5. Workflow Execution Managers
  6. Managed AI Job Execution

Unter-Tags erkunden

  • Asynchronous Agent Job ExecutionTriggering and managing long-running agent tasks asynchronously without blocking the caller. **Distinct from Managed AI Job Execution:** Distinct from Managed AI Job Execution: focuses on triggering and managing agent tasks asynchronously, not cloud-native data pipeline jobs.
  • MPI Job WorkflowsExecutes MPI jobs within workflow automation systems with relaxed validation for single-master configurations. **Distinct from Managed AI Job Execution:** More specific than Managed AI Job Execution: focuses on MPI-specific job execution in workflows, not general cloud AI task execution.
  • Managed Workspace ServicesProvisioning of interactive AI development environments as fully managed services. **Distinct from Managed AI Job Execution:** Distinct from Managed AI Job Execution by focusing on the persistent interactive environment rather than a discrete task execution.