Production-ready platform for agentic workflow development.
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Define and run multi-container applications with Docker
Actor-based state management & orchestration for complex app logic.
Complete container management platform
☁️ Build multimodal AI applications with cloud-native stack
AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
An open source payments switch written in Rust to make payments fast, reliable and affordable
Prefect is a workflow orchestration framework for building resilient data pipelines in Python.
⚡ Workflow Automation Platform. Orchestrate & Schedule code in any language, run anywhere, 600+ plugins. Alternative to Airflow, n8n, Rundeck, VMware vRA, Zapier ...
Turns Data and AI algorithms into production-ready web applications in no time.
Apache DolphinScheduler is the modern data orchestration platform. Agile to create high performance workflow with low-code
An orchestration platform for the development, production, and observation of data assets.
This is a collection of tutorials for learning how to use Docker with various tools. Contributions welcome.
Trigger.dev – open source background jobs and AI infrastructure
BISHENG is an open LLM devops platform for next generation Enterprise AI applications. Powerful and comprehensive features include: GenAI workflow, RAG, Agent, Unified model management, Evaluation, SFT, Dataset Management, Enterprise-level System Management, Observability and more.
🧙 Build, run, and manage data pipelines for integrating and transforming data.
Enable Self-Service Operations: Give specific users access to your existing tools, services, and scripts
Open Source Vulnerability Management Platform
What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
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