MOL – A programming language where pipelines trace themselves
Hi HN, 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") This auto-prints a trace table with each step's execution time and output type. Elixir and F# have |> but neither auto-traces. Other features: - 12 built-in domain types (Document, Chunk, Embedding, VectorStore, Thought, Memory, Node) - Guard assertions: `guard answer.confidence > 0.5 : "Too low"` -…
In plain words
MOL is a domain-specific language for building AI pipelines that automatically generates execution traces showing timing, types, and data at each step without requiring logging or print debugging. Developers write pipelines using a pipe operator syntax, and the language outputs trace tables for easy inspection. It includes twelve built-in types for AI workflows like Document and Embedding, guard assertions for validation, and a standard library of over ninety functions. MOL transpiles to Python and JavaScript, is available on PyPI, and offers an online playground for immediate experimentation.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN, 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") This auto-prints a trace table with each step's execution time and output type. Elixir and F# have |> but neither auto-traces. 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. """
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