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. """