It serves a place where a dockerfile is not enough, and CI workflows are too difficult to debug or reason about.
I do have some current problems with it though:
1. I don't care at all about the LLM agent workflows, I get that it is possible, but the same people that chose dagger for what it was, is not the same audience that runs agents like that. I can't choose dagger currently, because I don't know if they align with my interests as an engineer solving a specific problems for where I work (delivering software, not running agents).
2. I advocated for modules before it was a thing, but I never implemented it. It is too much magic, I want to write code, not a DSL that looks like code, dagger is already special in that regard, to modules takes it a step too far. You can't find the code in their docs anymore, but dagger can be written with just a .go, .py or .rs file. Simply take in dagger as a dependency and build your workflow.
3. Too complex to operate, dagger doesn't have runners currently, and it is difficult to run a setup in production for CI yourself, without running it in the actions themselves, which can be disastrous for build times, as dagger often leads you into using quite a few images, so having a cache is a must.
Dagger needs to choose and execute; not having runners, even when we we're willing to throw money at them was a mistake IMO. Love the tool, the team, the vision but it is too distracted, magical and impatient to pick up at the moment.
I ended up opting for CUE and GitHub Actions, and I'm glad I did as it made everything much, much simpler.
I.e. declaratively setup a web of CI / deployment tasks, based on docker, with a code-first DSL, instead of the morass of copy-pasted (and yes orbs) CircleCI yaml files we have strewn about our internals repos.
But their DSL for defining your pipelines is ... golang? Like who would pick golang as "a friendly language for setting up configs".
The underlying tech is technically language-agnostic, just as aws-cdk's is (you can share cdk constructs across TypeScript/Python), but it's rooted in golang as the originating/first-class language, so imo will never hit aws-cdk levels of ergonomics.
That technical nit aside, I love the idea; ran a few examples of it a year or so ago and was really impressed with the speed; just couldn't wrap my around "how can I make this look like cdk".
What else could be used to abstract away your CICD from the launcher (Jenkins, Argo Workflows, GitHub Actions, etc.)?
But one flaw (IMO) that it can’t export artifacts and import into other steps without breaking the cache.
Eg if you provide monorepo as input, and then on some step narrow your build to one specific dir, then even when files change outside of that dir then caching still is invalidated.
Which makes it extremely verbose and maintenance nightmare to keep multiple narrow inputs and keep all those paths up to date.
Then... it wasn't. The more I read the less I ever want to see this again. The LLM train has got to end at some point.
They don't seem to have jumped for AI hype (yet?)...
Without the LLM bits, this is basically like Bazel or buck2, right?
If I already have a Dockerfile that doesn’t need composition, how does this help me vs being a small cosmetic improvement over ”docker build” command line?
I think a more interesting point of comparison is the Claude Code Github Action, Co-Pilot code reviews, etc.
Although I'm not sure if that's so much a value-added? It's not so hard to just create a container and launch an agent in it.
The whole interesting thing was to use actual programming languages for Docker build, which I think was what they initially tried to do, but now it's a bit incomprehensible... I guess conceptually Dagger relates to Dockerfile a bit like Pulumi related to Terraform?