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