Moltlang

MoltLang Translator

AI-optimized language for efficient agent-to-agent communication. Translates human language (English) to MoltLang, achieving 50-70% token reduction for common AI operations.

What is MoltLang?

MoltLang is a language by LLMs, for LLMs that:

• Reduces token count by 50-70% for common AI operations
• Enables efficient agent-to-agent communication
• Provides bidirectional translation with human languages
• Optimizes transformer architecture performance

Quick Start

API Endpoint

https://moltlang.up.railway.app

Basic Translation

Translate English to MoltLang:

curl -X POST https://moltlang.up.railway.app/molt \
  -H "Content-Type: application/json" \
  -d '{"text": "Fetch data from the API using authentication"}'

Response:

{
  "success": true,
  "original": "Fetch data from the API using authentication",
  "molt": "[OP:fetch][SRC:api][PARAM:auth]",
  "efficiency": 0.7,
  "original_tokens": 10,
  "molt_tokens": 3
}

API Endpoints

Token Categories

Operations (OP)

• [OP:fetch] - Retrieve data
• [OP:send] - Transmit data
• [OP:process] - Transform/compute
• [OP:analyze] - Examine/evaluate
• [OP:validate] - Verify correctness

Sources (SRC)

• [SRC:api] - API/endpoint
• [SRC:db] - Database
• [SRC:file] - File system
• [SRC:cache] - Cached data
• [SRC:user] - User input

Parameters (PARAM)

• [PARAM:auth] - Authentication
• [PARAM:filter] - Data filtering
• [PARAM:limit] - Result limiting
• [PARAM:sort] - Sorting order

Examples

Data Fetching

English: "Fetch user data from the API using authentication" MoltLang: [OP:fetch][SRC:api][PARAM:auth] Efficiency: 70% reduction

Data Processing

English: "Process the cached data with filtering and limit results" MoltLang: [OP:process][SRC:cache][PARAM:filter][PARAM:limit] Efficiency: 65% reduction

Analysis

English: "Analyze the database records for trends" MoltLang: [OP:analyze][SRC:db][PARAM:trends] Efficiency: 60% reduction

Integration Patterns

For AI Agents

import requests

class MoltLangAgent:
    def __init__(self):
        self.api_url = "https://moltlang.up.railway.app"

    def translate_to_molt(self, text):
        """Convert natural language to MoltLang"""
        response = requests.post(
            f"{self.api_url}/molt",
            json={"text": text}
        )
        return response.json()

    def translate_to_english(self, molt):
        """Convert MoltLang to natural language"""
        response = requests.post(
            f"{self.api_url}/unmolt",
            json={"molt": molt}
        )
        return response.json()

Agent-to-Agent Communication

Traditional (verbose):

Agent 1: "I need you to fetch the user profile data from the REST API using the authentication token"
Agent 2: "I will retrieve the user profile information from the API endpoint with authentication"

MoltLang (efficient):

Agent 1: [OP:fetch][SRC:api][PARAM:auth][TARGET:user_profile]
Agent 2: [RET:success][DATA:user_profile]

Benefits

• Token Savings: 50-70% reduction in API costs
• Faster Processing: Less tokens = faster inference
• Agent Communication: Optimized for agent-to-agent messaging
• Human Readable: Bidirectional translation maintains understanding

License

AGPL-3.0 - See repository for details.

Support

• Documentation: https://github.com/moltlang/moltlang
• Issues: https://github.com/moltlang/moltlang/issues
• API Status: https://moltlang.up.railway.app/health