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8 repositorios

Awesome GitHub RepositoriesAI Agent Deployments

Packaging and deploying multi-modal AI agents to Kubernetes clusters using Helm charts.

Distinct from Kubernetes Deployments: Distinct from Kubernetes Deployments: focuses on deploying AI agents specifically, not general applications.

Explore 8 awesome GitHub repositories matching devops & infrastructure · AI Agent Deployments. Refine with filters or upvote what's useful.

Awesome AI Agent Deployments GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • yeasy/docker_practiceAvatar de yeasy

    yeasy/docker_practice

    26,111Ver en GitHub↗

    This project is a Docker educational resource and a collection of practical examples designed for learning containerization technologies. It serves as a guide for understanding container fundamentals, including the creation and management of custom images and the use of registries. The repository provides specialized references for container security hardening, such as managing kernel privileges and implementing supply chain security. It also includes tutorials for multi-container orchestration and a DevOps guide focused on CI/CD automation and image optimization. The material covers a broad

    Illustrates how to package and deploy AI agents and native systems into containerized clusters.

    Gobookcloud-computingcontainer
    Ver en GitHub↗26,111
  • crestalnetwork/intentkitAvatar de crestalnetwork

    crestalnetwork/intentkit

    6,502Ver en GitHub↗

    IntentKit is an open-source platform for deploying and managing a collaborative team of AI agents that can work together to complete complex tasks. It provides a self-hosted agent orchestrator that coordinates multiple agents through a modular pipeline of entrypoints, orchestration, and storage, all running as containerized services using Docker Compose or Swarm for production-grade deployment. The platform distinguishes itself by offering a plugin-based system for extending agent capabilities without modifying the core codebase, along with built-in integrations for connecting agents to socia

    Deploys a self-hosted cluster of collaborative AI agents that work together to complete complex tasks.

    Pythonagent-frameworkagenticai
    Ver en GitHub↗6,502
  • deis/deisAvatar de deis

    deis/deis

    5,999Ver en GitHub↗

    Deis is an open-source, self-hosted Platform-as-a-Service that deploys and manages containerized applications on a CoreOS cluster using a Heroku-inspired git push workflow. It accepts application code via git push, automatically builds a Docker image, and runs it as a container on the cluster, with systemd and etcd providing service discovery and configuration management. The platform provides a developer experience modeled after Heroku, with a command-line interface for creating, scaling, configuring, and monitoring applications. It hosts a private Git remote per application that triggers th

    Mentions AI model deployment on Kubernetes but is not a primary capability of this PaaS.

    Pythoncontainerscoreosdeis-paas
    Ver en GitHub↗5,999
  • getstream/vision-agentsAvatar de GetStream

    GetStream/Vision-Agents

    6,029Ver en GitHub↗

    Ships a Helm chart for deploying multi-modal AI agents to any Kubernetes cluster.

    Pythonagentic-aiagentsai
    Ver en GitHub↗6,029
  • creativetimofficial/material-kitAvatar de creativetimofficial

    creativetimofficial/material-kit

    5,926Ver en GitHub↗

    Material Kit is an open-source UI component library that provides pre-styled Material Design elements for building responsive web interfaces with Bootstrap 5. It offers a collection of reusable components like buttons, inputs, navbars, cards, and modals that follow Google's Material Design guidelines, along with a 12-column flexbox grid system for fluid layouts that adapt to any screen size. The kit distinguishes itself by including pre-built page sections such as headers, feature blocks, pricing tables, and footers that can be combined into complete page layouts, reducing the time needed to

    Mentions deploying autonomous AI agents, but this is not a capability of the repository.

    HTMLbootstrapbootstrap-material-designbootstrap5
    Ver en GitHub↗5,926
  • coleam00/ottomator-agentsAvatar de coleam00

    coleam00/ottomator-agents

    5,359Ver en GitHub↗

    Ottomator-agents is a framework for building and deploying autonomous AI agents using structured workflow files and source code. It serves as a declarative deployment tool and workflow orchestrator that translates static configuration files into executable sequences of AI agent tasks and logic flows. The system utilizes manifest-driven instantiation and template-driven deployment to create functional agent identities by populating source code templates with user-specified parameters. It incorporates a modular skill system that equips agents with discrete, reusable source code units and toolse

    Sets up and runs functional AI agents using pre-defined source code and structured workflow configurations.

    Python
    Ver en GitHub↗5,359
  • panaversity/learn-agentic-aiAvatar de panaversity

    panaversity/learn-agentic-ai

    3,908Ver en GitHub↗

    This project is an educational curriculum and architectural framework for building autonomous AI agents and multi-agent systems. It provides a structured learning path focused on the development of independent software components capable of planning, executing tasks, and utilizing external tools to achieve high-level goals. The framework emphasizes multi-agent system orchestration through distributed architectures where specialized agents collaborate using standardized communication protocols. It details specific design patterns such as dual-memory systems for maintaining short-term plans and

    Packaging and deploying multi-modal AI agents to Kubernetes clusters to ensure efficient scaling.

    Jupyter Notebooka2aagentic-aidapr
    Ver en GitHub↗3,908
  • archestra-ai/archestraAvatar de archestra-ai

    archestra-ai/archestra

    3,570Ver en GitHub↗

    Archestra is a platform for enterprise AI agent deployment and Model Context Protocol orchestration. It provides a centralized system for configuring specialized agents with specific system prompts and toolsets, and managing the deployment of Model Context Protocol servers that provide large language models with external tools and data sources. The system features an AI agent gateway that exposes configured agents as networked services for external clients and integrated development environments. It incorporates a security suite that provides deterministic guardrails to prevent prompt injecti

    Provides a platform for packaging and deploying specialized AI agents to Kubernetes clusters using Helm charts.

    TypeScripta2aa2a-mcpacp
    Ver en GitHub↗3,570
  1. Home
  2. DevOps & Infrastructure
  3. Kubernetes Deployments
  4. AI Agent Deployments

Explorar subetiquetas

  • Agent Cluster LaunchersStarting the API server and supporting services to begin managing a team of AI agents. **Distinct from AI Agent Deployments:** Distinct from AI Agent Deployments: focuses on the launch action for a multi-agent cluster, not general packaging.
  • Collaborative Agent ClustersDeployment of self-hosted clusters of AI agents that work together and call each other to complete tasks. **Distinct from AI Agent Deployments:** Distinct from AI Agent Deployments: focuses on multi-agent collaborative clusters, not single-agent deployments.
  • Configuration-Driven DeploymentsDeployment of AI agents based on structured workflow configurations and source templates. **Distinct from AI Agent Deployments:** Focuses on the configuration-driven aspect of agent deployment rather than Kubernetes/Helm specifics.