dagger open source analysis
An open-source runtime for composable workflows. Great for AI agents and CI/CD.
Project overview
⭐ 14975 · Go · Last activity on GitHub: 2025-11-15
GitHub: https://github.com/dagger/dagger
Why it matters for engineering teams
Dagger addresses the complexity of managing and automating workflows in software engineering, particularly where integration and deployment pipelines intersect with AI agents and containerised environments. It provides a composable runtime that simplifies the orchestration of tasks across CI/CD systems, enabling teams to build reliable, repeatable processes. This open source tool for engineering teams is especially suited for machine learning and AI engineering roles that require flexible workflow management integrated with container technologies. The project is mature enough for production use, offering stability and extensibility in demanding environments. However, it may not be the best fit for teams seeking a lightweight or fully managed CI/CD solution, as it requires some setup and maintenance as a self hosted option.
When to use this project
Dagger is a strong choice when your team needs a production ready solution for complex, composable workflows that integrate AI agents and container workflows. Teams should consider alternatives if they prefer fully managed CI/CD platforms or have simpler pipeline requirements that do not benefit from a custom runtime.
Team fit and typical use cases
Machine learning and AI engineering teams benefit most from Dagger, using it to orchestrate workflows that combine containerised environments with automated deployment and testing. DevOps engineers also find value in its ability to integrate with existing CI/CD pipelines. It is commonly used in products requiring continuous integration and deployment of AI models and microservices, where flexibility and control over workflow composition are essential.
Best suited for
Topics and ecosystem
Activity and freshness
Latest commit on GitHub: 2025-11-15. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.