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Basic Information

Dagger is an open-source runtime for composable workflows designed for developers and teams building complex, repeatable systems such as AI agents and CI/CD pipelines. It turns code into containerized, composable operations that run across platforms and languages, enabling modular workflows with custom environments, parallel processing, and seamless chaining. The project emphasizes repeatability, modularity, observability, and cross-platform support so teams can build reproducible pipelines that mix tools from different ecosystems. Dagger provides native LLM augmentation to incorporate language models into workflows, a universal type system for safe language interop, automatic artifact caching to speed runs and reduce cost, and an interactive terminal for real-time prototyping, testing, and debugging. The repository includes documentation, community channels, and contribution guidance to help teams adopt and extend the runtime.

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App Details

Features
Dagger provides containerized workflow execution that converts tasks into reusable, composable containers for reproducible pipelines and parallel processing. It offers a universal type system to safely mix components written in different languages and avoid translation barriers. Automatic artifact caching produces immutable, cacheable results for operations including LLM calls and API requests, improving runtime performance and lowering cost. Built-in observability yields tracing, logs, and metrics to debug and monitor complex workflows. The platform is intentionally open and cross-platform, working with diverse compute environments and tech stacks. Native LLM augmentation discovers and invokes available functions in workflows. An interactive terminal enables developers to prototype, interact with, and debug workflows in real time.
Use Cases
Dagger helps teams deliver reliable, maintainable workflows by enforcing repeatability and modular composition, which reduces variability and simplifies debugging. Universal language interoperability lets teams combine best-of-breed tools from multiple ecosystems without complex adapters. Artifact caching speeds repeated runs and cuts compute costs, which is valuable for iterative AI development and CI use cases. Built-in observability provides the telemetry needed to trace failures and optimize performance across distributed tasks. LLM augmentation and an interactive terminal accelerate agent prototyping, enabling engineers to iterate quickly and ship functionality faster. Its open, cross-platform design reduces vendor lock-in and makes it easier to run workflows on existing infrastructure. Documentation and community resources support adoption and contribution.

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