#java #cloud #cloudstack #iaas #infrastructure #kubernetes #kvm #libvirt #orchestration #python #virtual_machine #virtualization #vmware #vsphere #xcp_ng #xenserver

https://github.com/apache/cloudstack

#typescript #agent #ai #anthropic #backend_as_a_service #chatbot #gemini #genai #gpt #gpt_4 #llama3 #llm #llmops #nextjs #openai #orchestration #python #rag #workflow #workflows

Dify is an open-source platform for developing AI applications, especially those using Large Language Models (LLMs). It offers a user-friendly interface to build and test AI workflows, integrate various LLMs, and manage models. Key features include a visual workflow builder, comprehensive model support (including GPT, Mistral, and more), a prompt IDE for crafting and testing prompts, RAG pipeline capabilities for document ingestion and retrieval, and agent capabilities with pre-built tools like Google Search and DALL·E.

Using Dify, you can quickly move from prototyping to production with features like observability to monitor application performance and backend-as-a-service for easy integration into your business logic. You can deploy Dify via their cloud service or self-host it in your environment. This makes it highly versatile and beneficial for developers looking to leverage AI efficiently in their projects.

https://github.com/langgenius/dify

#go #docker #docker_compose #go #golang #orchestration

Docker Compose is a tool that helps you run multiple containers for your application easily. You define how your containers should be set up using a Compose file, and then you can start your entire application with just one command: `docker compose up`. This makes it simple to manage and run complex applications. You can get Docker Compose through Docker Desktop on Windows and macOS, or download it manually for Linux. Using Docker Compose saves time and effort by automating the setup and execution of your multi-container applications.

https://github.com/docker/compose

#java #airflow #azkaban #cloud_native #data_pipelines #job_scheduler #orchestration #powerful_data_pipelines #task_scheduler #workflow #workflow_orchestration #workflow_schedule

Apache DolphinScheduler is a powerful tool for managing data workflows. It makes it easy to create and manage complex tasks with a user-friendly interface and low-code options. You can deploy it in several ways, including standalone, cluster, Docker, and Kubernetes, making it flexible for different environments. It's highly reliable, scalable, and performs much faster than other platforms, supporting millions of tasks daily. The tool also offers features like versioning, state control of workflows, multi-tenancy support, and permission control. This helps you manage your data pipelines efficiently and reliably, saving time and effort.

https://github.com/apache/dolphinscheduler

#python #analytics #dagster #data_engineering #data_integration #data_orchestrator #data_pipelines #data_science #etl #metadata #mlops #orchestration #python #scheduler #workflow #workflow_automation

Dagster is a tool that helps you manage and automate your data workflows. You can define your data assets, like tables or machine learning models, using Python functions. Dagster then runs these functions at the right time and keeps your data up-to-date. It offers features like integrated lineage and observability, making it easier to track and manage your data. This tool is useful for every stage of data development, from local testing to production, and it integrates well with other popular data tools. Using Dagster, you can build reusable components, spot data quality issues early, and scale your data pipelines efficiently. This makes your work more productive and helps maintain control over complex data systems.

https://github.com/dagster-io/dagster

#python #airflow #apache #apache_airflow #automation #dag #data_engineering #data_integration #data_orchestrator #data_pipelines #data_science #elt #etl #machine_learning #mlops #orchestration #python #scheduler #workflow #workflow_engine #workflow_orchestration

Apache Airflow is a tool that helps you manage and automate workflows. You can write your workflows as code, making them easier to maintain, version, test, and collaborate on. Airflow lets you schedule tasks and monitor their progress through a user-friendly interface. It supports dynamic pipeline generation, is highly extensible, and scalable, allowing you to define your own operators and executors.

Using Airflow benefits you by making your workflows more organized, efficient, and reliable. It simplifies the process of managing complex tasks and provides clear visualizations of your workflow's performance, helping you identify and troubleshoot issues quickly. This makes it easier to manage data processing and other automated tasks effectively.

https://github.com/apache/airflow

#python #automation #data #data_engineering #data_ops #data_science #infrastructure #ml_ops #observability #orchestration #pipeline #prefect #python #workflow #workflow_engine

Prefect is a tool that helps you automate and manage data workflows in Python. It makes it easy to turn your scripts into reliable and flexible workflows that can handle unexpected changes. With Prefect, you can schedule tasks, retry failed operations, and monitor your workflows. You can install it using `pip install -U prefect` and start creating workflows with just a few lines of code. This helps data teams work more efficiently, reduce errors, and save time. You can also use Prefect Cloud for more advanced features and support.

https://github.com/PrefectHQ/prefect

#python #cloud_native #cncf #deep_learning #docker #fastapi #framework #generative_ai #grpc #jaeger #kubernetes #llmops #machine_learning #microservice #mlops #multimodal #neural_search #opentelemetry #orchestration #pipeline #prometheus

Jina-serve is a tool that helps you build and deploy AI services easily. It supports major machine learning frameworks and allows you to scale your services from local development to production quickly. You can use it to create AI services that communicate via gRPC, HTTP, and WebSockets. It has features like built-in Docker integration, one-click cloud deployment, and support for Kubernetes and Docker Compose, making it easy to manage and scale your AI applications. This makes it simpler for you to focus on the core logic of your AI projects without worrying about the technical details of deployment and scaling.

https://github.com/jina-ai/serve

#rust #beginner_friendly #featured #finance #hacktoberfest #high_performance #open_source #orchestration #payments #postgresql #redis #restful_api #rust #sdk #works_with_react

Hyperswitch is an open-source payments platform that helps businesses manage payments easily. It uses a single API to access various payment methods and features, making it simple to integrate different payment flows like cards, wallets, and bank transfers. The platform includes a backend for seamless payment processing, an SDK for unified payment experiences across web and mobile, and a Control Center for managing payments without coding. You can try Hyperswitch by setting it up locally or deploying it on cloud services like AWS, GCP, or Azure. This platform is designed to be reliable, secure, and customizable, allowing businesses to own and tailor their payment stack according to their needs.

https://github.com/juspay/hyperswitch

#typescript #cloud_native #dashboard #debugging #devops #headlamp #k8s #kinvolk #kubernetes #kubernetes_dashboard #kubernetes_debugging #kubernetes_monitoring #kubernetes_ui #orchestration #plugins

Headlamp is a user-friendly tool for managing Kubernetes. It provides a graphical interface that makes it easier for people to use Kubernetes without needing to write complex commands. This tool is extensible, meaning you can add plugins to customize it for your needs. It works with multiple clusters and shows resources like pods and deployments. Headlamp also respects user permissions, so you can only perform actions you are allowed to do. This helps make Kubernetes more accessible to a wider range of users.

https://github.com/kubernetes-sigs/headlamp

#java #automation #data_orchestration #devops #high_availability #infrastructure_as_code #java #low_code #lowcode #orchestration #pipeline #pipeline_as_code #workflow

Kestra is an open-source platform that helps manage complex workflows easily. It uses a simple YAML code to define workflows, which can be automated based on schedules or real-time events. Kestra supports many plugins, allowing integration with various data sources and tools. This makes it easy to automate tasks like data processing and infrastructure management. The platform is scalable, fault-tolerant, and offers real-time monitoring, making it beneficial for teams handling large data pipelines and complex workflows. It simplifies workflow management, reduces errors, and boosts efficiency.

https://github.com/kestra-io/kestra

#typescript #12_factor #12_factor_agents #agents #ai #context_window #framework #llms #memory #orchestration #prompt_engineering #rag

The 12-Factor Agents are a set of proven principles to build reliable, scalable, and maintainable AI applications powered by large language models (LLMs). They help you combine the creativity of AI with the stability of traditional software by managing prompts, context, tool calls, error handling, and human collaboration effectively. Instead of relying solely on complex frameworks, you can apply these modular concepts to improve your existing products quickly and reach high-quality AI performance for real users. This approach makes AI software easier to develop, debug, and scale, ensuring it works well in production environments[1][3][5].

https://github.com/humanlayer/12-factor-agents

#java #distributed_systems #durable_execution #grpc #java #javascript #microservice_orchestration #orchestration_engine #orchestrator #reactjs #spring_boot #workflow_automation #workflow_engine #workflow_management #workflows

Conductor is an open-source tool that helps you manage and automate complex workflows involving many microservices and systems. It makes your workflows flexible, reliable, and scalable by handling retries, errors, and monitoring automatically. You can define workflows as code in JSON, use various task types, and manage workflows dynamically without tightly coupling services. It offers an easy-to-use web interface and supports multiple databases like Redis and MySQL. This helps you build, run, and monitor workflows efficiently, saving time and reducing errors in managing distributed applications. It also has SDKs for Java, Python, JavaScript, Go, and C# to integrate easily with your projects.

https://github.com/conductor-oss/conductor

#python #agent_framework #agentic_ai #agents #ai #dotnet #multi_agent #orchestration #python #sdk #workflows

Microsoft Agent Framework is an open-source toolkit that helps you build and manage AI agents and multi-agent workflows using Python or .NET. It combines the best features of previous Microsoft AI projects to let you create simple chatbots or complex workflows where multiple agents work together. It supports many AI models, connects easily to external tools and APIs, and runs anywhere—on cloud or on-premises. The framework also includes features like human review, workflow checkpointing, and monitoring to make your AI applications reliable and adaptable. This means you can build powerful, flexible AI solutions faster and with less code.

https://github.com/microsoft/agent-framework

#python #agents #ai_agents #anthropic #anthropic_claude #automation #claude #claude_code #claude_code_cli #claude_code_commands #claude_code_plugin #claude_code_plugins #claude_code_subagents #claude_skills #claudecode #claudecode_config #claudecode_subagents #orchestration #sub_agents #subagents #workflows

Claude Code Plugins provide a comprehensive system of 63 focused plugins containing 85 specialized agents, 47 skills, and 44 development tools organized for intelligent automation across software development. You install only what you need, keeping token usage minimal while accessing domain experts in architecture, languages, infrastructure, quality, and operations. Each plugin loads independently with its own agents and commands, letting you compose multiple plugins for complex workflows. This granular design means faster, cleaner sessions with progressive disclosure—knowledge loads only when activated. The benefit is significant productivity gains: you get expert-level assistance tailored to your specific task without unnecessary overhead, enabling your entire team to work more efficiently on development, infrastructure, security, and automation challenges.

https://github.com/wshobson/agents

#go #containers #deployment #devops #docker #docker_compose #golang #hacktoberfest #kubernetes #orchestration #self_hosted

Uncloud lets you run and manage web apps across multiple servers (cloud, home, or bare metal) as easily as using Docker Compose, but with production features like zero-downtime updates, automatic HTTPS, and cross-machine scaling. It connects your machines into a secure, private network without needing a central control server, so there’s less to manage and no single point of failure. You keep full control of your infrastructure and data, avoid vendor lock-in, and get a simple, cloud-like experience without the complexity of Kubernetes.

https://github.com/psviderski/uncloud