Memelang – query language optimized for LLM RAG token efficiency (memelang.net)

Memelang is a compact, LLM-first query language and Python spec (MEMELANG_VER 8.23) designed to make retrieval-augmented generation (RAG) token-efficient by compressing structured query results into short, parseable “memes.” Announced as a prompt-optimized script you can copy into an LLM, Memelang maps a four-axis model (Axis 3->0: table/graph, primary key/subject, column/predicate, value/object) and uses very terse syntax, sigils ($ variable, * wildcard, ^ MSAME, @ VSAME, _ EMPTY) and compact separators (space, comma, ';', ';;') to represent rows, columns, filters and joins. Example one-line queries show direct SQL analogs and a literal "meme" output that concatenates id/fields into single tokens that LLMs can digest with far fewer tokens than verbose SQL or JSON. Technically, Memelang implements a lexer/parser (regex MASTER_PATTERN), a matrix/vector/limit EBNF, variable binding across axes, VSAME/MSAME expansions, and an intersect function for filtering (EQL, NOT, GT, etc.). It elides repeated syntactic noise (like '=' and full SQL) and supports joins, variable equality across tables, and numeric comparisons while producing deterministic, compact output for storage and retrieval. For the AI/ML community this means lower prompt/context token usage, reduced model memory and latency for RAG pipelines, and easier deterministic post-processing of retrieved records — at the cost of adopting a custom compact representation and conversion layer.