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Aetherlang Karpathy Skill

Implement 10 advanced AI agent node types for any DSL/runtime system — plan compiler, code interpret

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Downloads: 1,361 downloads
Version: 1.0.0

Overview

Implement 10 advanced AI agent node types for any DSL/runtime system — plan compiler, code interpreter, critique.

Complete Documentation

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AetherLang Karpathy Agent Nodes

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

API Endpoint

URL: https://api.neurodoc.app/aetherlang/execute Method: POST Headers: Content-Type: application/json Privacy Policy: https://masterswarm.net (footer → Privacy Policy) Operator: NeuroDoc Pro — api.neurodoc.app and masterswarm.net are the same operator (Hetzner DE)

Data Minimization

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
Do NOT include personally identifiable information unless explicitly requested

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"
  }'

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. 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.

Consent required: Before using a memory node, notify the user:

"This flow will store data server-side at api.neurodoc.app. Proceed? (y/n)"

Only proceed with explicit user confirmation.

text

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

7. tool — External API Access

Consent required: Before executing a tool node, notify the user:

"This flow will call [URL]. Proceed? (y/n)"

Only proceed with explicit confirmation. Never forward PII or credentials to external URLs.

Call any public REST API.

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;

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;
}

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"]

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

Installation

Terminal bash


openclaw install aetherlang-karpathy-skill

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💻Code Examples

## Request Format

-request-format.sh

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"
  }'

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

llm-picks-optimal-path-skips-unselected-routes-10x-speedup.txt

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

> Only proceed with explicit user confirmation.

-only-proceed-with-explicit-user-confirmation.txt

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

### Live Data → Analysis

-live-data--analysis.txt

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

-multi-agent--quality-control.txt

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

-batch-processing.txt

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

-parallel-api-fetching.txt

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;
}

## Response Parsing

-response-parsing.py

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"]