Margarita
Programming language for Agents using Markdown-ish syntax
Details
- External ID
- 48756596
- Source
- HN
- Company
- —
- Product
- Margarita
- Website domain
- margarita.run
- Launched
- July 2, 2026
- Cohort
- —
- Upvotes
- 7
- Upvotes percentile
- 0.3972520908004779
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
On my list of build it from scratch has always been to build a programming language. So with the help of AI I was able to get it done! Why did I build it? At work I've seen two major problems with our ai workflows/ skills libraries. There is a lack of determinism when your whole workflow is a markdown file of 100 steps, and markdown skill libraries lack composability. Meaning we violate things like DRY in the all the md files in the skills library.I built Margarita to allow for markdown and logical operators to exist together, which means you can bring in determinism through code structures when it makes sense, and fall back to llm dynamic code when that makes sense. As an added bonus allows for composable prompts ala React which solve my other gripe with skills libraries being a mash of text everywhere.Overall I've been getting pretty luke warm responses from Reddit, so I'll probably just shelve it, but it was a blast to make. Got to build code agents for pretty much every llm provider and built my own harness. I would recommend doing that it's a great learning experience.https://www.margarita.run https://github.com/Banyango/margarita
Enrichment
- Theme
- ai coding agents and tooling
- Vertical
- Horizontal
- Function
- Dev tools
- Audience
- Developer
- AI stance
- AI-native
- Project type
- Commercial product
- Normalized one-liner
- programming language for ai agents
- Manually corrected
- False
Could you build this?
Partial A basic markdown-like prompt orchestrator can be scripted with AI, but building a robust custom domain-specific language compiler, runtime, and execution engine requires formal language parsing knowledge.
What it would actually take: The architecture involves an AST parser (using tools like Tree-sitter or Chevrotain), a state machine runtime to manage agent loop execution, context window token budgeting, and tool invocation hooks. The hard part is building deterministic grammar parsing, error recovery, and robust state rollbacks without brittle regex evaluation.
Discussion
4 comments analyzed.
Competitors mentioned: Python with coding agents, Claude (for HTML generation)
Concerns raised: Unclear if agent is following instructions or being influenced by comments, Developers prefer Python directly over new DSL, Non-developers intimidated by Python-ish syntax, No training data examples for new language in LLMs, Unclear target audience value proposition
Feature requests: Clearer documentation on instruction-following behavior
Competitors
Other products that read as similar to this one — 183 launches clear the similarity bar, closest 8 shown.
Attention rank: #122 of 184 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 243 days after the earliest competitor.
- Remy, an AI agent that compiles annotated Markdown into full-stack apps · hn · 2026-04-13 · 5 upvotes · similarity 0.48
- Skillscript · hn · 2026-07-12 · 18 upvotes · similarity 0.43
- Marky · hn · 2026-04-16 · 75 upvotes · similarity 0.42
- Sigil · hn · 2026-04-05 · 5 upvotes · similarity 0.41
- Skills · hn · 2026-02-20 · 6 upvotes · similarity 0.41
- Try Benzi · hn · 2026-08-08 · 11 upvotes · similarity 0.40
- Libretto · hn · 2026-04-15 · 134 upvotes · similarity 0.40
- Learning Rust by writing a Markdown to HTML compiler · hn · 2026-07-29 · 25 upvotes · similarity 0.40
Other launches for this product
- No other launches for this product.
Same idea, different domain
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