Llmcom Token Optimizer

Token-efficient context format using LLMCOM specification - reduces token usage by 70-80% through compact object notation.

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

Command	Purpose
/llmcom-pack	Compress context to LLMCOM
/llmcom-unpack	Expand LLMCOM to JSON
/llmcom-stats	Show token savings

LLMCOM Syntax

Symbol	Meaning
`	`
:	Key-value separator
,	List separator
c	Classification block
b	Budget block
s	Skills 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)