dagger open source analysis
An open-source runtime for composable workflows. Great for AI agents and CI/CD.
Project overview
⭐ 15032 · Go · Last activity on GitHub: 2025-11-26
GitHub: https://github.com/dagger/dagger
Why it matters for engineering teams
Dagger addresses the complexity of managing composable workflows in modern software development, particularly in environments that rely on continuous integration and deployment. It provides a structured way to define and execute workflows using containers, which helps engineering teams improve consistency and reproducibility across builds and deployments. This open source tool for engineering teams is especially well suited for machine learning and AI engineering roles, where workflows can be intricate and require reliable orchestration. Dagger is mature enough for production use, offering a production ready solution with caching and integration capabilities that support real-world DevOps pipelines. However, it may not be the best choice for teams seeking a simple, lightweight CI/CD tool or those who prefer fully managed cloud services without the need for a self hosted option for workflow orchestration.
When to use this project
Dagger is a strong choice when your team needs a flexible, container-based workflow runtime that can handle complex, composable pipelines, especially in AI or machine learning projects. Teams should consider alternatives if they require minimal setup or prefer a fully managed CI/CD service without the overhead of maintaining a self hosted option.
Team fit and typical use cases
Machine learning and AI engineers benefit most from Dagger as it allows them to build and manage workflows that integrate model training, testing, and deployment in a consistent manner. DevOps engineers also use it to automate and streamline continuous integration and deployment pipelines for containerised applications. Typically, it appears in products that demand reliable orchestration of multi-step workflows, such as AI platforms, data processing systems, and complex microservices environments.
Best suited for
Topics and ecosystem
Activity and freshness
Latest commit on GitHub: 2025-11-26. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.