I built MOL, a domain-specific language for AI pipelines. The main idea: the pipe operator |> automatically generates execution traces — showing timing, types, and data at each step. No logging, no print debugging.
Example:
let index be doc |> chunk(512) |> embed("model-v1") |> store("kb")
Other features: - 12 built-in domain types (Document, Chunk, Embedding, VectorStore, Thought, Memory, Node) - Guard assertions: `guard answer.confidence > 0.5 : "Too low"` - 90+ stdlib functions - Transpiles to Python and JavaScript - LALR parser using Lark
The interpreter is written in Python (~3,500 lines). 68 tests passing. On PyPI: `pip install mol-lang`.
Online playground (no install needed): http://135.235.138.217:8000
We're building this as part of IntraMind, a cognitive computing platform at CruxLabx. """
Of the languages listed, Elixir, Python and Rust can all achieve this combination. Elixir has a pipe operator built-in, and Python and Rust have operator overloading, so you could overload the bitwise | operator (or any other operator you want) to act as a pipeline operator. And Rust and Elixir have macros, and Python has decorators, which can be used to automatically add logging/tracing to functions.
It's not automatic for all functions, though having to be explicit/selective about what is logged/traced is generally considered a good thing. It's rare that real-world software wants to log/trace literally everything, since it's not only costly (and slow) but also a PII risk.
Using dbg/2 [1]:
# In dbg_pipes.exs
__ENV__.file
|> String.split("/", trim: true)
|> List.last()
|> File.exists?()
|> dbg()
[dbg_pipes.exs:5: (file)]
__ENV__.file #=> "/home/myuser/dbg_pipes.exs"
|> String.split("/", trim: true) #=> ["home", "myuser", "dbg_pipes.exs"]
|> List.last() #=> "dbg_pipes.exs"
|> File.exists?() #=> true
1. Debugging - dbg/2
I had a custom lark grammar I thought was cool to do something similar, but after a while I just discarded it and went back to straight python, and found it was faster my an order of magnitude.