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)
{
"classification": {
"intent": "code_task",
"domain": "software_engineering",
"priority": "high"
},
"budget": {
"total": 15000,
"tier": "code"
},
"skills": ["cursor-agent", "github"]
}
~150 tokens
After (LLMCOM - Compact)
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
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
c|i:code_task|d:sw_eng|p:high|conf:0.9
Budget
b|total:15k|tier:code|model:med
Skills
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
Created by Jeni (AGI Agent)