Dagger was something I looked into two or so years ago before they got consumed by the LLM and AI agent hype, and while the promise of being able to run the exact CI workflows locally seemed excellent, it seemed that there's basically no way be a Dagger user without buying into their Dagger Cloud product.
I ended up opting for CUE and GitHub Actions, and I'm glad I did as it made everything much, much simpler.
> If you’ve been active in the Dagger community, this news will come as no surprise. Since we released multi-language support, we have seen a steep decline in usage of our original CUE configuration syntax, and have made it clear that feature parity with newer SDKs would not be a priority.
That is, of course, a self-fulfilling prophecy (or, perhaps, a self-inflicted wound). As soon as Dagger's "multi-language support" came out (actually a bit before), the CUE SDK was rendered abandonware. Development only happened on the new backend, and CUE support was never ported over to the new one.
Dagger founder here. We moved away from CUE because the number one complaint from our early users was having to learn CUE. The number two complaint was bugs in the language that we diligently escalated upstream, but would never get fixed, including crippling memory leaks.
We shipped multi-language support because we had no choice. It was a major engineering effort that we hadn't originally planned for, but it was painfully obvious that remaining a CUE-only platform was suicide.
Can you explain/link to why you can't really use this without their cloud product? I'm not seeing anything at a glance, and this looks useful for a project of mine, but I don't want to be trapped by limitations that I only find out about after putting in weeks of work
Overall I like Dagger conceptually, but I wish they'd start focusing more on API stability and documentation (tbf it's not v1.0). v0.19 broke our Dockerfile builds and I don't feel like figuring out the new syntax atm. Having to commit dev time to the upgrade treadmill to keep CI/CD working was not the dream.
re: the cloud specifically see these GitHub issues:
Basically if you want consistently fast cached builds it's a PITA and/or not possible without the cloud product, depending on how you set things up. We do run it self-hosted though, YMMV.
One thing that I liked about switching from a Docker-based solution like Dagger to Nix is that it relaxed the infrastructure requirements to getting good caching properties.
We used Dagger, and later Nix, mostly to implement various kinds of security scans on our codebases using a mix of open-source tools and clients for proprietary ones that my employer purchases. We've been using Nix for years now, and still haven't set up any of our own binary cache. But we still have mostly-cached builds thanks to the public NixOS binary cache, and we hit that relatively sparingly because we run those jobs on bare metal in self-hosted CI runners. Each scan job typically finishes in less than 15 seconds once the cache is warm, and takes up to 3 minutes when the local cache is cold (in case we build a custom dependency).
Some time in the next quarter or two I'll finish our containerization effort for this so that all the jobs on a runner will share a /nix/store and Nix daemon socket bind-mounted from the host, so we can have relatively safe "multi-tenant" runners where all jobs run under different users in rootless Podman containers while still sharing a global cache for all Nix-provided dependencies. Then we get a bit more isolation and free cleanup for all our jobs but we can still keep our pipelines running fast.
We only have a few thousand codebases, so a few big CI boxes should be fine, but if we ever want to autoscale down, it should be possible to convert such EC2 boxes into Kubernetes nodes, which would be a fun learning project for me. Maybe we could get wider sharing that way and stand up fewer runner VMs.
Somewhere on my backlog is experimenting with Cachix, so we should get per-derivation caching as well, which is finer-grained than Docker's layers.
Hi, I'm the founder of Dagger. It's not true that you can't use Dagger without our cloud offering. At the moment our only commercial product is observability for your Dagger pipelines. It's based on standard otel telemetry emitted by our open source engine. It's completely optional.
If you have questions about Dagger, I encourage you to join our Discord server, we will be happy to answer them!
When I got started it was much more difficult as you had to do a lot of manual work to get things started, and you really had to believe the promises that CUE offered (which I did...), but nowadays they've made so many steps in the right direction that getting something going is far quicker!
Maybe it’s just me, but these sample workflows don’t look less complicated, just another kind of complex? If you’re already heavily using CUE in your project this lateral complexity shift might make sense, but I don’t see why I would start using it…
To some extent yes. If all you have is 2 GitHub Actions YAML files you are not going to reap massive benefits.
I'm a big fan of CUE myself. The benefits compound as you need to output more and more artifacts (= YAML config). Think of several k8s manifests, several GitHub Actions files, e.g. for building across several combinations of OSes, settings, etc.
CUE strikes a really nice balance between being primarily data description and not a Turing-complete language (e.g. cdk8s can get arbitrarily complex and abstract), reducing boilerplate (having you spell out the common bits once only, and each non-commit bit once only) and being type-safe (validation at build/export time, with native import of Go types, JSON schema and more).
They recently added an LSP which helps close the gap to other ecosystems. For example, cdk8s being TS means it naturally has fantastic IDE support, which CUE has been lacking in. CUE error messages can also be very verbose and unhelpful.
At work, we generate a couple thousand lines of k8s YAML from ~0.1x of that in CUE. The CUE is commented liberally, and validation imported from native k8s types, and sprinkled in where needed otherwise (e.g. we know for our application the FOO setting needs to be between 5 and 10). The generated YAML is clean, without any indentation and quoting worries. We also generate YAML-in-YAML, i.e. our application takes YAML config, which itself is in an outer k8s YAML ConfigMap. YAML-in-YAML is normally an enormous pain and easy to get wrong. In CUE it's just `yaml.Marshal`.
You get a lot of benefit for a comparatively simple mental model: all your CUE files form just one large document, and for export to YAML it's merged. Any conflicting values and any missing values fail the export. That's it. The mental model of e.g. cdk8s is massively more complex and has unbounded potential for abstraction footguns (being TypeScript). Not to mention CUE is Go and shipped as a single binary; the CUE v0.15.0 you use today will still compile and work 10 years from now.
You can start very simple, with CUE looking not unlike JSON, and add CUE-specific bits from there. You can always rip out the CUE and just keep the generated YAML, or replace CUE with e.g. cdk8s. It's not a one-way door.
The cherry on top are CUE scripts/tasks. In our case we use a CUE script to split the one-large-document (10s of thousands of lines) into separate files, according to some criteria. This is all defined in CUE as well, meaning I can write ~40 lines of CUE (this has a bit of a learning curve) instead of ~200 lines of cursed, buggy bash.
What I don't get is why would someone code in the terrible GitHub actions dsl which only runs on GitHub actions and nowhere else when there are so many other options that run perfectly fine if you just run it from GitHub actions.
I thought Dagger had/has a lot of potential to be "AWS-CDK for CI pipelines".
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".
I loved the original promise of Dagger and it’s still 90% great.
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.
You can filter input directories before they are loaded, to avoid this. There's no reason you shouldn't be able to get precise cache invalidation in a large monorepo. If you want, DM me on the Dagger Discord and I'll help you out!
Hi, I'm the founder of Dagger. I can't speak to the negativity, but if you're looking for a way to make your CI more portable, I recommend joining our Discord and asking our community directly about the pros and cons of using Dagger. Even if you don't end up using it, there are a lot of people there who are passionate about CI and can recommend other alternatives, in a more constructive and pragmatic way than you are getting here.
I used the old CUE-based version when it came out, and was really excited about it. I liked it, and enjoyed working with CUE, but the API was clunky and incomplete.
Then they completely abandoned not just the CUE frontend, but CUE altogether (while strenuously denying that they were doing so) for a GraphQL-based rewrite that focused on letting people use popular general-purpose languages to construct their workflows. The initial rollout of this was not feature complete and only supported imperative languages (Python and TypeScript, IIRC), which I didn't like.
Instead of porting everything over to all their new interfaces, I hopped off the train and rewrote all of our portable pipeline scripts in Nix, via Devenv. At the time, I'd never used Devenv before, but getting the work done that time still took maybe a tenth of the time or less. More than anything else, this was due to not having to fuck around with the additional overhead Docker entails (fussing with mount points, passing files from one stage to another, rebuilding images, setting up VMs... all of it). I got the reproducibility without the extra bullshit, and got to work with interfaces that have proven much more stable.
I still think there's a place for something like Dagger, focused just on CI, perhaps even still using Docker as a deployment/distribution strategy. But I no longer trust Dagger to execute on that. I think a proper external DSL (probably special-purposw but still Turing-complete, e.g., Nickel) is the right fit for this domain, and that it should support multiple means of achieving repeatability rather than just Docker (e.g., Nix on bare metal and Podman, to start). An option to work on bare metal via reproducible environment management tools like Nix, Guix, or Spack is a valuable alternative to burdensome approaches like containers.
I haven't looked at Dagger in several months, but the other big piece that is missing for portable CI workflows is a library that abstracts over popular CI platforms so you can easily configure pull/merge request pipelines without worrying about the implementation details like what environment variables each platform exposes to indicate source and target branch.
Idk anything about all the AI horseshit; I was off the Dagger bandwagon before they took that turn. I don't know if it's serious or a nominal play to court investors. But that kind of pivot is another reason not to build core infra on top of the work of startups imo. If the product is 70% of what you want, you have no way of knowing whether filling that 30% gap is something the maintainers will suddenly pivot away from, even if their current direction looks aligned with yours.
I'd recommend considering tools in this space only if (a) they're already close to 100% of what you need and (b) they're open-source. Maybe you can relax (a) if it's really easy to extend the codebase (I find this to be true for Devenv's Nix modules, for example.)
Do you have any examples of your Devenv workflow you can share? I took a look at Dagger and really like the concept, but I'm trying to figure out the limitations/why there's so much negativity in this thread.
I currently manage my development environments via NixOS and Devenv, so if I could just keep that and achieve the same functionality, that sounds good to me.
If I understand correctly, this is essentially a more composable way to write Dockerfiles? That alone is a very welcome improvement. They would do themselves a big favor if they were more clear on that in their marketing, instead of boasting around the bush with all kinds of other terminology and claims of redefining foundations.
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've tried it, but there's too much "sorry not in the open-source edition, please buy the entreprise edition" stuff all around, which makes it quite unusable
A lot of the comments here feel like they're disappointed that this is a "Docker with unnecessary LLM crap thrown in" when I think what they're really going for is more "LLM workflows with a higher degree of observability and sanity".
I think a more interesting point of comparison is the Claude Code Github Action, Co-Pilot code reviews, etc.
I might be getting old but the videos are too fast for me to understand. Why can't they just put the full command text and the output of it instead of a video.
Works fine for us for glueing a bunch of CI steps together which would've been a pile of bash otherwise. Works well with depot.dev caches. We don't use it for anything AI either.
I'd say it's somewhere in between. Sure it's marketed in the CI space, but to me the selling point of Dagger is not so much "write your GitHub workflows/GitLab CI in JavaScript" but "local exec, sandboxing, determinism, and fine-grained (remote) caching for mere mortals". So comparing it to Bazel/Buck2 is reasonable.
I wouldn't because build systems like Bazel are declarative and dagger is imperative. I accidentally created a build system in dagger and saw the difference; the code based way was highly branched, and thus unwieldy. I think you would want to call bazel from dagger to handle the build step.
But that dependency order is usually just one big blob of ”COPY src/ . + RUN make”, within that block you have none of the benefits. Bazel/Buck has much finer awareness down to every individual file.
Out of curiosity, would it be feasible to take a big cmake project and generate thousands of compile rules into dagger and use it as a substitute for make with sandboxing? I’ve never seen builkit used with such many nodes, how would it fare?
I use it to do builds in our monorepo. We got onboard before the LLM trash features. The base design is ok but there's things I'd do different today if I knew the build stuff would fade away for the LLM push.
I was interested in the beginning for CI/CD, but then they tried to take a kind of "AI-oriented" view in order to ride the AI wave, and the value prop of their tool was completely muddied up...
This looks interesting but I’m trying to understand it in more layman’s terms. Is it more about providing abstractions for llms to work within to do things?
I've never tried it. My first impression based purely on reading the homepage is it adds complexity to something I can already do with a Dockerfile and bash. What can it do that I can't already do more simply?
It does a pretty good job of caching and that does help speed up builds. I also run all of my end to end tests from it because I can coordinate secrets and clusters of containers through it.
I think it started as some kind of CI/CD tools, then they jumped on the AI hype and they started to use it to make it possible to run agents in containers easily... perhaps to do automated actions on CI/CD pipelines that use agents (eg try to solve some minor bugs automatically when you push on a branch, 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?
Dagger was something I looked into two or so years ago before they got consumed by the LLM and AI agent hype, and while the promise of being able to run the exact CI workflows locally seemed excellent, it seemed that there's basically no way be a Dagger user without buying into their Dagger Cloud product.
I ended up opting for CUE and GitHub Actions, and I'm glad I did as it made everything much, much simpler.
dagger was originally CUE-based, but there was not enough demand so it was dropped. https://dagger.io/blog/ending-cue-support
> If you’ve been active in the Dagger community, this news will come as no surprise. Since we released multi-language support, we have seen a steep decline in usage of our original CUE configuration syntax, and have made it clear that feature parity with newer SDKs would not be a priority.
That is, of course, a self-fulfilling prophecy (or, perhaps, a self-inflicted wound). As soon as Dagger's "multi-language support" came out (actually a bit before), the CUE SDK was rendered abandonware. Development only happened on the new backend, and CUE support was never ported over to the new one.
Dagger founder here. We moved away from CUE because the number one complaint from our early users was having to learn CUE. The number two complaint was bugs in the language that we diligently escalated upstream, but would never get fixed, including crippling memory leaks.
We shipped multi-language support because we had no choice. It was a major engineering effort that we hadn't originally planned for, but it was painfully obvious that remaining a CUE-only platform was suicide.
Do you have examples of your CUE and Github Actions setup?
I see someone else asked below:
https://news.ycombinator.com/item?id=46262846
Can you explain/link to why you can't really use this without their cloud product? I'm not seeing anything at a glance, and this looks useful for a project of mine, but I don't want to be trapped by limitations that I only find out about after putting in weeks of work
Overall I like Dagger conceptually, but I wish they'd start focusing more on API stability and documentation (tbf it's not v1.0). v0.19 broke our Dockerfile builds and I don't feel like figuring out the new syntax atm. Having to commit dev time to the upgrade treadmill to keep CI/CD working was not the dream.
re: the cloud specifically see these GitHub issues:
https://github.com/dagger/dagger/issues/6486
https://github.com/dagger/dagger/issues/8004
Basically if you want consistently fast cached builds it's a PITA and/or not possible without the cloud product, depending on how you set things up. We do run it self-hosted though, YMMV.
One thing that I liked about switching from a Docker-based solution like Dagger to Nix is that it relaxed the infrastructure requirements to getting good caching properties.
We used Dagger, and later Nix, mostly to implement various kinds of security scans on our codebases using a mix of open-source tools and clients for proprietary ones that my employer purchases. We've been using Nix for years now, and still haven't set up any of our own binary cache. But we still have mostly-cached builds thanks to the public NixOS binary cache, and we hit that relatively sparingly because we run those jobs on bare metal in self-hosted CI runners. Each scan job typically finishes in less than 15 seconds once the cache is warm, and takes up to 3 minutes when the local cache is cold (in case we build a custom dependency).
Some time in the next quarter or two I'll finish our containerization effort for this so that all the jobs on a runner will share a /nix/store and Nix daemon socket bind-mounted from the host, so we can have relatively safe "multi-tenant" runners where all jobs run under different users in rootless Podman containers while still sharing a global cache for all Nix-provided dependencies. Then we get a bit more isolation and free cleanup for all our jobs but we can still keep our pipelines running fast.
We only have a few thousand codebases, so a few big CI boxes should be fine, but if we ever want to autoscale down, it should be possible to convert such EC2 boxes into Kubernetes nodes, which would be a fun learning project for me. Maybe we could get wider sharing that way and stand up fewer runner VMs.
Somewhere on my backlog is experimenting with Cachix, so we should get per-derivation caching as well, which is finer-grained than Docker's layers.
Hi, I'm the founder of Dagger. It's not true that you can't use Dagger without our cloud offering. At the moment our only commercial product is observability for your Dagger pipelines. It's based on standard otel telemetry emitted by our open source engine. It's completely optional.
If you have questions about Dagger, I encourage you to join our Discord server, we will be happy to answer them!
Same: the promise of defining CI/CD in code is good, but the implementation didn't make sense to me even before the LLM stuff
Same - we began the migration to Dagger but then switched to just Docker-In-Docker and custom scripts which run vendor-agnostic
Do you have any more details on using Cue with GHA? I've also looked at Dagger and been quite disappointed with it (and their terrible documentation).
When I got started it was much more difficult as you had to do a lot of manual work to get things started, and you really had to believe the promises that CUE offered (which I did...), but nowadays they've made so many steps in the right direction that getting something going is far quicker!
Here are a few links to whet your appetite:
- https://cue.dev/docs/getting-started-with-github-actions-cue...
- https://cue.dev/docs/drying-up-github-actions-workflows/
- https://cue.dev/docs/spotting-errors-earlier-github-actions-...
Definitely read through the CUE documentation (https://cuelang.org/docs/), watch their YouTube videos (https://www.youtube.com/@cuelang/videos), and join the community Slack channel (https://cuelang.org/community/). I've gotten a lot of help in the Slack from both enthusiastic community members and from the developers themselves whenever I've gotten stuck.
Maybe it’s just me, but these sample workflows don’t look less complicated, just another kind of complex? If you’re already heavily using CUE in your project this lateral complexity shift might make sense, but I don’t see why I would start using it…
> just another kind of complex?
To some extent yes. If all you have is 2 GitHub Actions YAML files you are not going to reap massive benefits.
I'm a big fan of CUE myself. The benefits compound as you need to output more and more artifacts (= YAML config). Think of several k8s manifests, several GitHub Actions files, e.g. for building across several combinations of OSes, settings, etc.
CUE strikes a really nice balance between being primarily data description and not a Turing-complete language (e.g. cdk8s can get arbitrarily complex and abstract), reducing boilerplate (having you spell out the common bits once only, and each non-commit bit once only) and being type-safe (validation at build/export time, with native import of Go types, JSON schema and more).
They recently added an LSP which helps close the gap to other ecosystems. For example, cdk8s being TS means it naturally has fantastic IDE support, which CUE has been lacking in. CUE error messages can also be very verbose and unhelpful.
At work, we generate a couple thousand lines of k8s YAML from ~0.1x of that in CUE. The CUE is commented liberally, and validation imported from native k8s types, and sprinkled in where needed otherwise (e.g. we know for our application the FOO setting needs to be between 5 and 10). The generated YAML is clean, without any indentation and quoting worries. We also generate YAML-in-YAML, i.e. our application takes YAML config, which itself is in an outer k8s YAML ConfigMap. YAML-in-YAML is normally an enormous pain and easy to get wrong. In CUE it's just `yaml.Marshal`.
You get a lot of benefit for a comparatively simple mental model: all your CUE files form just one large document, and for export to YAML it's merged. Any conflicting values and any missing values fail the export. That's it. The mental model of e.g. cdk8s is massively more complex and has unbounded potential for abstraction footguns (being TypeScript). Not to mention CUE is Go and shipped as a single binary; the CUE v0.15.0 you use today will still compile and work 10 years from now.
You can start very simple, with CUE looking not unlike JSON, and add CUE-specific bits from there. You can always rip out the CUE and just keep the generated YAML, or replace CUE with e.g. cdk8s. It's not a one-way door.
The cherry on top are CUE scripts/tasks. In our case we use a CUE script to split the one-large-document (10s of thousands of lines) into separate files, according to some criteria. This is all defined in CUE as well, meaning I can write ~40 lines of CUE (this has a bit of a learning curve) instead of ~200 lines of cursed, buggy bash.
What I don't get is why would someone code in the terrible GitHub actions dsl which only runs on GitHub actions and nowhere else when there are so many other options that run perfectly fine if you just run it from GitHub actions.
I thought Dagger had/has a lot of potential to be "AWS-CDK for CI pipelines".
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".
They have SDKs in many languages, not just Go. I use the python one. And they use code, not a DSL.
And for those who may not know the founder of Dagger is the same guy who founded Docker - Solomon Hykes.
I loved the original promise of Dagger and it’s still 90% great.
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.
You can filter input directories before they are loaded, to avoid this. There's no reason you shouldn't be able to get precise cache invalidation in a large monorepo. If you want, DM me on the Dagger Discord and I'll help you out!
Wow, the comments are generally negative. That’s sad. I was about to create a POC to present to my team.
What else could be used to abstract away your CICD from the launcher (Jenkins, Argo Workflows, GitHub Actions, etc.)?
Hi, I'm the founder of Dagger. I can't speak to the negativity, but if you're looking for a way to make your CI more portable, I recommend joining our Discord and asking our community directly about the pros and cons of using Dagger. Even if you don't end up using it, there are a lot of people there who are passionate about CI and can recommend other alternatives, in a more constructive and pragmatic way than you are getting here.
The title says "dev environments". Can dagger be used for creatin dev environments, like devcontainers or https://www.jetify.com/docs/devbox/quickstart ?
Anybody using it extensively? It doesn't seem to have made the splash I expected it to at launch
I used the old CUE-based version when it came out, and was really excited about it. I liked it, and enjoyed working with CUE, but the API was clunky and incomplete.
Then they completely abandoned not just the CUE frontend, but CUE altogether (while strenuously denying that they were doing so) for a GraphQL-based rewrite that focused on letting people use popular general-purpose languages to construct their workflows. The initial rollout of this was not feature complete and only supported imperative languages (Python and TypeScript, IIRC), which I didn't like.
Instead of porting everything over to all their new interfaces, I hopped off the train and rewrote all of our portable pipeline scripts in Nix, via Devenv. At the time, I'd never used Devenv before, but getting the work done that time still took maybe a tenth of the time or less. More than anything else, this was due to not having to fuck around with the additional overhead Docker entails (fussing with mount points, passing files from one stage to another, rebuilding images, setting up VMs... all of it). I got the reproducibility without the extra bullshit, and got to work with interfaces that have proven much more stable.
I still think there's a place for something like Dagger, focused just on CI, perhaps even still using Docker as a deployment/distribution strategy. But I no longer trust Dagger to execute on that. I think a proper external DSL (probably special-purposw but still Turing-complete, e.g., Nickel) is the right fit for this domain, and that it should support multiple means of achieving repeatability rather than just Docker (e.g., Nix on bare metal and Podman, to start). An option to work on bare metal via reproducible environment management tools like Nix, Guix, or Spack is a valuable alternative to burdensome approaches like containers.
I haven't looked at Dagger in several months, but the other big piece that is missing for portable CI workflows is a library that abstracts over popular CI platforms so you can easily configure pull/merge request pipelines without worrying about the implementation details like what environment variables each platform exposes to indicate source and target branch.
Idk anything about all the AI horseshit; I was off the Dagger bandwagon before they took that turn. I don't know if it's serious or a nominal play to court investors. But that kind of pivot is another reason not to build core infra on top of the work of startups imo. If the product is 70% of what you want, you have no way of knowing whether filling that 30% gap is something the maintainers will suddenly pivot away from, even if their current direction looks aligned with yours.
I'd recommend considering tools in this space only if (a) they're already close to 100% of what you need and (b) they're open-source. Maybe you can relax (a) if it's really easy to extend the codebase (I find this to be true for Devenv's Nix modules, for example.)
Do you have any examples of your Devenv workflow you can share? I took a look at Dagger and really like the concept, but I'm trying to figure out the limitations/why there's so much negativity in this thread.
I currently manage my development environments via NixOS and Devenv, so if I could just keep that and achieve the same functionality, that sounds good to me.
This seemed cool as it looked like a new CI/CD tool or IaC system.
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.
Dagger is already a very popular thing -- a DI framework
It's also a type of blade, been around for hundreds of years!
/s I've never heard of Dagger the DI framework but I have heard of this Dagger. Names will overlap sometimes and it's not a big deal.
Yeah I initially thought of this coming from android
If I understand correctly, this is essentially a more composable way to write Dockerfiles? That alone is a very welcome improvement. They would do themselves a big favor if they were more clear on that in their marketing, instead of boasting around the bush with all kinds of other terminology and claims of redefining foundations.
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?
Curious if anyone in the thread has / is using windmill?
They don't seem to have jumped for AI hype (yet?)...
https://www.windmill.dev/
have used it, and i do like it, but the licensing situation is not great. It open source but its not free software by any means.
I've tried it, but there's too much "sorry not in the open-source edition, please buy the entreprise edition" stuff all around, which makes it quite unusable
A lot of the comments here feel like they're disappointed that this is a "Docker with unnecessary LLM crap thrown in" when I think what they're really going for is more "LLM workflows with a higher degree of observability and sanity".
I think a more interesting point of comparison is the Claude Code Github Action, Co-Pilot code reviews, etc.
I might be getting old but the videos are too fast for me to understand. Why can't they just put the full command text and the output of it instead of a video.
Anybody used it?
Without the LLM bits, this is basically like Bazel or buck2, right?
Works fine for us for glueing a bunch of CI steps together which would've been a pile of bash otherwise. Works well with depot.dev caches. We don't use it for anything AI either.
Founder of Depot here! Glad to hear it’s working for you all. Always happy to help expand things or make things better if you ever have ideas.
No, it's code based CI/CD not a build system.
I'd say it's somewhere in between. Sure it's marketed in the CI space, but to me the selling point of Dagger is not so much "write your GitHub workflows/GitLab CI in JavaScript" but "local exec, sandboxing, determinism, and fine-grained (remote) caching for mere mortals". So comparing it to Bazel/Buck2 is reasonable.
I wouldn't because build systems like Bazel are declarative and dagger is imperative. I accidentally created a build system in dagger and saw the difference; the code based way was highly branched, and thus unwieldy. I think you would want to call bazel from dagger to handle the build step.
Dagger uses BuildKit. all your “imperative” code does it assemble a graph that’s gonna be executed by BuildKit in dependency order.
But that dependency order is usually just one big blob of ”COPY src/ . + RUN make”, within that block you have none of the benefits. Bazel/Buck has much finer awareness down to every individual file.
Out of curiosity, would it be feasible to take a big cmake project and generate thousands of compile rules into dagger and use it as a substitute for make with sandboxing? I’ve never seen builkit used with such many nodes, how would it fare?
Dagger is a declarative DAG engine. So, yes, you can do that.
There is an overhead per container launched so it would probably not be worth it.
Yes, but that does not help you build declaratively in dagger, does it? I would like to see an example of how to do so, if you have one.
Its not as fine grained as bazel/buck. That doesnt necessarily matter.
Yeah, it's more like a .NET Aspire
I use it to do builds in our monorepo. We got onboard before the LLM trash features. The base design is ok but there's things I'd do different today if I knew the build stuff would fade away for the LLM push.
I was interested in the beginning for CI/CD, but then they tried to take a kind of "AI-oriented" view in order to ride the AI wave, and the value prop of their tool was completely muddied up...
Same, the marketing was even worse a few months ago.
This looks interesting but I’m trying to understand it in more layman’s terms. Is it more about providing abstractions for llms to work within to do things?
It’s actually a CI/CD as code tool, where some pieces can be LLM agents.
But, the marketing heavily focuses on LLM stuff to the point of making everyone confused.
I've never tried it. My first impression based purely on reading the homepage is it adds complexity to something I can already do with a Dockerfile and bash. What can it do that I can't already do more simply?
It does a pretty good job of caching and that does help speed up builds. I also run all of my end to end tests from it because I can coordinate secrets and clusters of containers through it.
Instead of YAML workflows you write code, and there is an analog of the Github Actions Marketplace (the Daggerverse).
why do i need this when we got docker? anyone mind explaining?
I think it started as some kind of CI/CD tools, then they jumped on the AI hype and they started to use it to make it possible to run agents in containers easily... perhaps to do automated actions on CI/CD pipelines that use agents (eg try to solve some minor bugs automatically when you push on a branch, 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?