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weirdguy

17 karma · joined December 9, 2018

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weirdguy··on Show HN: Kastor – Terraform-style specs for AI agents
UPD: Looked into burr from dagworks deeper, it's actually a good target to support the codegen for, will investigate today and add into the scope of upcoming things.

Thank you for showing those things!

weirdguy··on Show HN: Kastor – Terraform-style specs for AI agents
The codegen side is target-agnostic on purpose, LangGraph is just the first target. Burr (thanks for educating about that piece of tech) could be a good N-one, its state/transition model maps onto the spec real pretty.

Vercel's eve made a similar bet btw (files as source of truth, durable execution built in), except it's a runtime with its own platform underneath. With Kastor I want to stay one layer above and emit code for whatever runtime you pick.

Anyways, would love any kind of help to shape the direction, repo discussions and codebase is open :)

weirdguy··on Show HN: Kastor – Terraform-style specs for AI agents
Good question

For hosted agents only, a Terraform provider probably makes sense.

The reason I didn’t start there is that Kastor has two paths:

1 - build: compile the spec into framework code, e.g. LangGraph

2 - plan/apply for agents hosted on platforms

Terraform is great for the second path, but awkward as the primary tool for the first one. I want the same agent spec to be able to produce code or manage a remote agent.

So I can imagine a Terraform provider being one target later, but I don’t think Terraform itself is the right tool/place for the whole thing.

weirdguy··on Show HN: Kastor – Terraform-style specs for AI agents
I agree with you

don't think the "agent" abstraction is stable enough to standardize the behavior/runtime layer yet.

thing I'm trying to test is smaller: is that possible to standardize the outer contract around agents?

what inputs does it accept? what outputs does it promise? what model does it use? what prompt/template does it depend on? what tools can it call? what target should it be built or applied to?

so here it is intentionally not trying to describe the full control loop or become a runtime. it's more like a source-of-truth layer for the parts that are already showing up everywhere, even if the runtime patterns keep changing underneath.

I might be wrong, but my bet is that the runtime layer will keep changing, while the need for reviewable/diffable agent contracts will not.

weirdguy··on Show HN: Kastor – Terraform-style specs for AI agents
try with www, should work, the apex zone still did not propagate for whatever reason
weirdguy··on Show HN: Kastor – Terraform-style specs for AI agents
thanks — that’s the direction. current version is still just validate + LangGraph codegen, but plan/apply/state is the part I’m trying to get to next
weirdguy··on Show HN: Kastor – Terraform-style specs for AI agents
oh, it's not, just root domain (non-www) without redirect
weirdguy··on Show HN: Kastor – Terraform-style specs for AI agents
hey HN, it's first time posting something here.

I’m building Kastor: Go CLI/declarative language/config for AI agents.

the motivation: agent definitions often end up spread across framework code, prompt files, tool files/mcps, platform UI settings, and env vars. that makes them hard to review, diff, reproduce or move.

Kastor is an attempt to put a source-of-truth layer above that.

right now the working proof of concept is narrow:

- .agent / .tool / .prompt files - HCL parser + validation - dependency/reference checks - LangGraph codegen - runnable weather example - runnable content scheduler example

the long-term direction is Terraform-ish:

- build: compile to framework code - plan/apply: reconcile hosted platform agents - state: track remote resources and detect drift

I’m deliberately not trying to build another agent runtime. the thing I’m trying to validate is whether agents need something closer to IaC: versionable, reviewable, declarative source of truth.

would appreciate any kind of feedback, especially on the language/design.