I built AetherLang, an open-source DSL for orchestrating AI workflows. It started as a side project while building a Greek handwriting OCR tool and grew into its own thing.

What it does:

- A simple DSL for defining AI pipelines (guard → plan → rag → llm chains) - 28 node types (llm, guard, rag, transform, parallel, retry, cache, etc.) - Visual flow designer with SVG rendering and multiple layout algorithms - Step-by-step execution debugger with state inspection at each node - Basic performance profiling (per-node duration, token count, cost estimate) - AI-assisted flow analysis using GPT-4o - BYOK (Bring Your Own Key) - uses your OpenAI API key, no account needed - Bilingual UI (Greek/English)

Example flow:

  flow ResearchPipe {
    input text query;
    node Guard: guard mode="STRICT";
    node Planner: plan steps=5;
    node RAG: rag depth=3;
    node LLM: llm model="gpt-4o", temp=0.2;
    Guard -> Planner -> RAG -> LLM;
    output text result from LLM;
  }

The debugger is the most useful feature — you can step through execution node by node and inspect inputs/outputs at each stage.

Live demo: https://neurodoc.app/aether-nexus-omega-dsl

Background: I'm a cook from Greece with no CS degree. Built this over ~40 days using AI tools (Claude, ChatGPT) for the coding. I designed the architecture and features but needed AI to write the implementation.

Known limitations: - BYOK keys stored in-memory (not persisted yet) - Single server, no horizontal scaling - Early stage — feedback very welcome

Would love to hear what you think, especially about the DSL syntax and the debugging approach.