Memelang: Terse SQL for LLM Generation
A researcher named Bri Holt has published a new query language called Memelang, designed specifically so that large language models can generate accurate database queries with fewer errors and fewer tokens. It replaces verbose SQL syntax with a compact, grid-based grammar that is...
Bri Holt, the researcher behind Memelang, formally introduced the language in a December 18, 2025 arXiv paper titled "Memelang: An Axial Grammar for LLM-Generated Vector-Relational Queries." The project, developed under HOLTWORK LLC, comes with a full Python reference implementation, a GitHub repository at memelang-net/memesql10, a patent application (US20250068615A1), and a companion explainer video on YouTube. The goal is straightforward: stop LLMs from writing broken SQL by giving them a simpler target language to aim .
Why This Matters
The problem Memelang is solving is real, expensive, and largely ignored by the big labs. LLMs hallucinate column names. They reference tables that do not exist. They produce syntactically valid SQL that returns completely wrong data, and the system has no way of knowing. Every team building a text-to-SQL product has dealt with this. The fact that a researcher filed a patent on a purpose-built query language for LLM generation, backed by an academic paper, tells you that prompt engineering alone is not going to fix this, and the industry is starting to accept that.
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The Full Story
The core idea behind Memelang is what Holt calls axial grammar. Instead of writing a SQL query with clauses, keywords, and nested parentheses, Memelang encodes the same information as a sequence of tokens where position in the sequence carries structural meaning. The grammar works on a grid model: each token gets assigned a coordinate covering table, column, and value slots based on rank-specific separator tokens. A single left-to-right parse pass is all you need to reconstruct the full query structure, no backtracking required.
This design choice is directly motivated by how LLMs generate text. Language models produce tokens one at a time, left to right. Verbose SQL requires models to hold a lot of context in mind simultaneously, which is where hallucinations creep in. Memelang's flat, linear structure maps onto the way a model naturally generates output, which means fewer opportunities for the model to introduce structural errors or invent identifiers that do not exist.
The language handles several operations that traditionally require complex SQL clauses, including grouping, aggregation, sorting, and vector similarity search. Memelang encodes all of these as inline tags on value terms. For example, a query asking for the minimum role rating per actor, sorted ascending, looks like this in Memelang: "roles rating :min:asc;actor :grp;". The equivalent SQL would be longer, more fragile to generate, and harder to parse programmatically. The version 10.11 reference implementation, as of the current release, supports operators including cosine similarity, L2 distance, and inner product for vector queries, which puts it squarely in the world of modern RAG pipelines.
Variable binding is another feature worth understanding. Memelang lets you define a variable like $a within a query and reference it later in the same query. The practical example in the source material shows a query that finds costars who appeared alongside both Bruce Willis and Uma Thurman, using a variable binding to exclude the original actors from the result set. That kind of query is notoriously awkward to generate correctly with SQL, and Memelang's explicit variable binding syntax makes the model's job considerably easier.
The language also implements what Holt calls implicit context carry-forward. When parts of a query share the same table or column context, Memelang does not require the model to repeat that context. This is not just a convenience feature. It directly reduces the token count that a model must produce, which cuts inference cost and latency in any production system.
Key Details
- Bri Holt submitted the foundational arXiv paper on December 18, 2025, with paper ID 2512.17967.
- The current implementation is version 10.11, developed under HOLTWORK LLC with a 2026 copyright.
- The patent application US20250068615A1 is pending as of the article's publication date.
- The GitHub repository is hosted at github.com/memelang-net/memesql10.
- The language supports 3 vector similarity operators: cosine similarity, L2 distance, and inner product.
- The syntax covers 7 aggregate functions: min, max, cnt, sum, avg, last, and grp.
- One of the example queries fetches the top 12 movies filtered by vector similarity to "war" with a minimum role rating threshold.
What's Next
The patent pending status suggests HOLTWORK LLC is treating Memelang as a commercial proposition, not just an academic experiment, so watch for licensing announcements or integrations with existing RAG frameworks in 2026. The vector-relational query support positions Memelang to work directly with databases like pgvector or Weaviate, and any team building a hybrid search and relational query system should be testing this. Adoption will likely hinge on whether the Python reference implementation gets packaged into a pip-installable library with clear documentation, which is the basic barrier to entry for developer tooling today.
How This Compares
Tinybird, a managed ClickHouse analytics platform, published its own research comparing which LLMs write the best analytical SQL. Their conclusion acknowledged that reliable SQL generation remains unsolved. That research took the approach of benchmarking existing models against standard SQL, which is effectively trying to fit LLMs into a language designed for humans. Memelang flips the problem entirely by designing the language around what LLMs are naturally good at. That is a fundamentally different bet, and it is a more interesting one.
The broader pattern of using domain-specific languages as intermediate representations for AI output is well established in code generation. Compiler research has done this for decades. What is new here is applying it specifically to query generation for RAG pipelines, where the stakes are high because wrong queries return wrong data silently. The approach documented in a December 23, 2025 article by Sherpa of Data, titled "LLM Patterns for SQL Generation: Teaching AI to Write Queries That Don't Make You Cry," recommended better prompting, schema constraints, and validation layers. Those are all patches on top of SQL. Memelang is a different layer entirely.
IBM Technology published educational content in December 2025 on text-to-SQL capabilities powered by schema-aware LLMs, reflecting how seriously the enterprise data space is taking this problem. But enterprise SQL tooling tends to be conservative, and Memelang's terse syntax will require a cultural adjustment for teams accustomed to reading standard SQL. The language looks foreign on first glance. Once you understand the grid grammar, it reads cleanly, but that learning curve is the real adoption challenge Holt will need to address with tutorials and how-to guides before this lands in production data stacks.
FAQ
Q: What is Memelang and how does it differ from SQL? A: Memelang is a query language designed specifically for large language models to generate. Unlike SQL, which uses verbose clauses and nested syntax, Memelang uses a compact grid-based grammar where token position carries structural meaning. This makes it easier for AI models to produce correct queries because the linear format matches how models generate text, one token at a time from left to right.
Q: Can Memelang query vector databases as well as relational data? A: Yes. Memelang supports 3 vector similarity operators, covering cosine similarity, L2 distance, and inner product. This means a single Memelang query can filter relational data by year or genre while also ranking results by semantic similarity to a text string, which is exactly the kind of hybrid query that modern RAG pipelines need.
Q: Is Memelang free to use and where can developers find it? A: The reference implementation is available on GitHub at memelang-net/memesql10. However, a patent application is pending under HOLTWORK LLC, which means commercial licensing terms may apply depending on how Holt chooses to commercialize the project. Developers interested in evaluating Memelang should check the repository directly for the current license terms.
Memelang is early-stage, but the combination of academic rigor, a working implementation, and a patent filing signals this is not a weekend side project. If the developer community picks it up and builds tooling around it, this could become one of the more practical AI tools for anyone building reliable data agents in 2026. Subscribe to the AI Agents Daily weekly newsletter for daily updates on AI agents, tools, and automation.
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