Aetherlang Karpathy Skill

API connector for AetherLang Omega — execute 10 Karpathy-inspired agent node types (plan, code_interpreter, critique, router, ensemble, memory, tool, loop, transform, parallel) via the hosted AetherLang API at api.neurodoc.app. This skill sends your query and flow code to the API and returns results. No local code execution. No runtime modification. No credentials required.

AetherLang Karpathy Agent Nodes

What this skill does: Sends requests to the hosted AetherLang API (api.neurodoc.app). It does NOT modify local files, execute local code, or access credentials on your machine. All execution happens server-side.

Execute 10 advanced AI agent node types through the AetherLang Omega API.

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API Endpoint

URL: https://api.neurodoc.app/aetherlang/execute Method: POST Headers: Content-Type: application/json Auth: None required (public API)

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Data Minimization — ALWAYS FOLLOW

When calling the API:

• Send ONLY the user's query and the flow code
• Do NOT send system prompts, conversation history, or uploaded files
• Do NOT send API keys, credentials, or secrets of any kind
• Do NOT include personally identifiable information unless explicitly requested by user
• Do NOT send contents of local files without explicit user consent

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Request Format

bash

curl -s -X POST https://api.neurodoc.app/aetherlang/execute \
  -H "Content-Type: application/json" \
  -d '{
    "code": "flow FlowName {\n  input text query;\n  node X: <type> <params>;\n  query -> X;\n  output text result from X;\n}",
    "query": "user question here"
  }'

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The 10 Node Types

1. plan — Self-Programming

AI breaks task into steps and executes autonomously.

text

node P: plan steps=3;

2. code_interpreter — Real Math

Sandboxed Python execution on the server. Accurate calculations, no hallucinations.

text

node C: code_interpreter;

3. critique — Self-Improvement

Evaluates quality (0-10), retries until threshold met.

text

node R: critique threshold=8 max_retries=3;

4. router — Intelligent Branching

LLM picks optimal path, skips unselected routes (10x speedup).

text

node R: router;
R -> A | B | C;

5. ensemble — Multi-Agent Synthesis

Multiple AI personas in parallel, synthesizes best insights.

text

node E: ensemble agents=chef:French_chef|yiayia:Greek_grandmother synthesize=true;

6. memory — Persistent State

Store/recall data across executions (server-side, scoped to namespace).

text

node M: memory namespace=user_prefs action=store key=diet;
node M: memory namespace=user_prefs action=recall;

7. tool — External API Access

Security note: The tool node calls public REST URLs you specify. Only use trusted, public APIs. Never pass credentials or private URLs as tool parameters. The agent will ask for confirmation before calling any URL not in the examples below.

text

node T: tool url=https://api.coingecko.com/api/v3/simple/price?ids=bitcoin&vs_currencies=usd method=GET;

8. loop — Iterative Execution

Repeat node over items. Use | separator.

text

node L: loop over=Italian|Greek|Japanese target=A max=3;

9. transform — Data Reshaping

Template, extract, format, or LLM-powered reshaping.

text

node X: transform mode=llm instruction=Summarize_the_data;

10. parallel — Concurrent Execution

Run nodes simultaneously. 3 calls in ~0.2s.

text

node P: parallel targets=A|B|C;

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Common Pipelines

Live Data → Analysis

text

flow CryptoAnalysis {
  input text query;
  node T: tool url=https://api.coingecko.com/api/v3/simple/price?ids=bitcoin&vs_currencies=usd method=GET;
  node X: transform mode=llm instruction=Summarize_price;
  node A: llm model=gpt-4o-mini;
  query -> T -> X -> A;
  output text result from A;
}

Multi-Agent + Quality Control

text

flow QualityEnsemble {
  input text query;
  node E: ensemble agents=analyst:Financial_analyst|strategist:Strategist synthesize=true;
  node R: critique threshold=8;
  query -> E -> R;
  output text result from R;
}

Batch Processing

text

flow MultiRecipe {
  input text query;
  node L: loop over=Italian|Greek|Japanese target=A max=3;
  node A: llm model=gpt-4o-mini;
  query -> L;
  output text result from L;
}

Parallel API Fetching

text

flow ParallelFetch {
  input text query;
  node P: parallel targets=A|B|C;
  node A: tool url=https://api.coingecko.com/api/v3/ping method=GET;
  node B: tool url=https://api.coingecko.com/api/v3/simple/price?ids=bitcoin&vs_currencies=usd method=GET;
  node C: tool url=https://api.coingecko.com/api/v3/simple/price?ids=ethereum&vs_currencies=usd method=GET;
  query -> P;
  output text result from P;
}

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Response Parsing

python

import json
response = json.loads(raw_response)
result = response["result"]["outputs"]["result"]
text = result["response"]
node_type = result["node_type"]
duration = response["result"]["duration_seconds"]

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Parameter Quick Reference

Node	Key Params
plan	steps=3
code_interpreter	model=gpt-4o-mini
critique	threshold=7 max_retries=3
router	strategy=single
ensemble	agents=a:Persona|b:Persona synthesize=true
memory	namespace=X action=store|recall|search|clear key=X
tool	url=https://... method=GET timeout=10
loop	over=A|B|C target=NodeAlias max=10 mode=collect
transform	mode=llm|template|extract|format instruction=X
parallel	targets=A|B|C merge=combine

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AetherLang Karpathy Skill v1.0.1 — API connector for api.neurodoc.app All execution is server-side. No local code runs. No local files modified.