The original FMPL (“of Accardi”) came out of UC Berkeley’s Experimental Computing Facility around 1992 — a MUD server language in the LambdaMOO / ColdMUD tradition, with an interpreter written by Jon Blow. Those systems shared a distinctive shape: a live image of prototype-based objects, edited from inside, serving many users at once, persisting across restarts. The program wasn’t a file you ran; it was a world you inhabited.
-This FMPL is Norman Nunley’s — a descendant of the original, not a restoration of it. The surviving link to the 1992 language is an EBNF grammar Nunley extracted from its sources back in the late 1990s; decades later, that grammar became the seed of this project. The syntax is only lightly similar to the original, and everything beyond the grammar — the streaming model, the PEG grammar system, the indexed-RPN VM, the metacircular bootstrap — is new design. The MUD lineage is first-hand, too: Nunley co-wrote cool++, a C++ rewrite of Stephen White’s CoolMUD (White also created MOO, the server LambdaMOO grew from).
+This FMPL is Norman Nunley’s — a descendant of the original, not a restoration of it. The surviving link to the 1992 language is an EBNF grammar Nunley extracted from its sources back in the late 1990s; decades later, that grammar became the seed of this project. The syntax is only lightly similar to the original, and everything beyond the grammar — the streaming model, the PEG grammar system, the indexed-RPN VM, the metacircular bootstrap — is new design. The MUD lineage is first-hand, too: Nunley was one of the developers on cool++/ColdMUD and ColdStore.
The project bets that the MUD shape is the right shape for AI agents. A modern agent system wants exactly what a MUD had: long-lived stateful objects, many concurrent actors (now LLMs as well as humans), capability-scoped access between them, and durable state that survives a crash mid-conversation. FMPL adds the modern half: streaming as a first-class concern, OMeta-style PEG grammars as the universal tool for parsing any stream — text, bytes, or structured values — and an async runtime for tool calls.
One more thing worth saying plainly, because it shapes the codebase: most of this implementation was written by an autonomous LLM agent loop that Nunley set up and steered, iterating against a roadmap for months. It was recently rehabilitated for public release by hand — the agent harness sidelined to an archive branch (~30,000 lines), a build that works on a fresh clone, CI, and a documentation pass in which every claim in the tutorial was re-verified against a live REPL. The result is unusually honest about itself: the test suite encodes where the language is and, via 74 deliberately-ignored tests, where it is going.