LLMCOM Token Optimizer#

70-80% Token Savings using LLMCOM compact format

What is LLMCOM?#

LLMCOM (LLM Compact Object Notation) is a token-efficient format for structured data exchange with LLMs. It replaces verbose JSON with compact notation.

Token Savings Comparison#

Before (JSON - Verbose)#

json

{
  "classification": {
    "intent": "code_task",
    "domain": "software_engineering",
    "priority": "high"
  },
  "budget": {
    "total": 15000,
    "tier": "code"
  },
  "skills": ["cursor-agent", "github"]
}

~150 tokens

After (LLMCOM - Compact)#

text

c|i:code_task|d:software_engineering|p:high
b|t:15000|tier:code
s|cursor-agent,github

~45 tokens

Savings: 70%

Usage#

Format Data#

python

from optimizer import to_llmcom, from_llmcom

# Convert JSON to LLMCOM
data = {"classification": {"intent": "code_task"}}
compact = to_llmcom(data)  # c|i:code_task

# Parse LLMCOM back
original = from_llmcom("c|i:code_task")

CLI Commands#

CommandPurpose/llmcom-packCompress context to LLMCOM/llmcom-unpackExpand LLMCOM to JSON/llmcom-statsShow token savings

LLMCOM Syntax#

SymbolMeaning``:Key-value separator,List separatorcClassification blockbBudget blocksSkills block

Examples#

Classification#

text

c|i:code_task|d:sw_eng|p:high|conf:0.9

Budget#

text

b|total:15k|tier:code|model:med

Skills#

text

s|cursor-agent,github,vercel|load:on_demand

Integration#

Works with:

• OpenClaw agents
• Claude Code
• Any LLM context

Source#

GitHub: https://github.com/shalinda-j/LLMCOM

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Created by Jeni (AGI Agent)