syntex-protocol 1.2.4

Three-layer semantic compression for LLM prompts (L1+L2+L3 + Lean Mode + Columnar JSON)

SyntEx

Semantic Prompt Compression Protocol — Three-layer compression for LLM system prompts.

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Why SyntEx?

Feature	SyntEx	Plain Text	Savings
Multi-agent prompts (3+ agents)	35-40 tokens	100+ tokens	60-71%
RAG context chunks	50-60%	100%	50-60%
System prompts (repetitive boilerplate)	50-60%	100%	50-60%
JSON-heavy payloads	36-40%	100%	60-68%

Target: 50% token reduction — SyntEx v1.2 achieves 60-68% on typical multi-agent and JSON use cases.

Benchmark Results (v1.2.0)

Corpus Type	Original	Compressed	Reduction
Standard (repetitive)	4,654 tokens	2,405 tokens	48.3%
+ lorem ipsum	10,468 tokens	3,406 tokens	67.5%
JSON-heavy (30 msgs)	2,100 tokens	764 tokens	63.6%
Multi-agent boilerplate	3,730 tokens	1,964 tokens	47.3%
Unique content (worst)	1,847 tokens	1,626 tokens	12.0%

Average: 46.4% | Peak: 67.5%

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Configuration

SyntEx v1.2+ is fully configurable via syntex.yaml:

# thresholds.yaml
version: "1.2.0"

thresholds:
  skip_cft: 25        # Text shorter triggers SKIP mode
  l1_cft: 80          # L1-only vs L1+L2 boundary
  min_l2_tokens: 30   # Minimum tokens for L2 compression

mirror:
  ttl_minutes: 1440   # Entry TTL (24 hours)
  max_capacity: 500   # Max mirror entries

pipeline:
  auto_minify: true   # Auto-minify JSON, Markdown, etc.
  two_pass_compression: true
  use_clustering: false

---

Quick Start

pip install syntex

Python API

from syntex import SintExSession

session = SintExSession()
result = session.compile("Your long system prompt here...")
print(f"Compressed: {result.compressed_tokens} tokens (was {result.original_tokens})")

CLI

python -m syntex compile myprompt.sx -o output.sxc
python -m syntex bench myprompt.sx

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Architecture

┌─────────────────────────────────────────────────────────────────────────┐
│                             SyntEx Pipeline                             │
├─────────────────────────────────────────────────────────────────────────┤
│ L1: Global Vocabulary   →  72 Unicode symbols      (Greek + Braille)    │
│ L2: Local Dictionary    →  N-gram compression      (corpus-specific)    │
│ L3: Clustering          →  Semantic normalization  (variant merge)      │
└─────────────────────────────────────────────────────────────────────────┘

Core Components

Module	Purpose
SintExSession	Main API for compile/decompile
SintExMirror	Shared dictionary for multi-agent communication
AutoVocab	ROI-based vocabulary auto-promotion
SintExGateway	Proxy LLM with automatic compression
Telemetry	Track token savings and cost ($)

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Features

Three-Layer Compression

from syntex import SintExSession

session = SintExSession(seed=["domain-specific term"])

# L1 + L2 + L3 compression
result = session.compile(system_prompt)

# Decompress
original = session.decompile(result.sxc)

Lean Mode (No Header)

# For multi-agent: send only compressed body, no header overhead
lean = session.compile_lean(system_prompt)
# Returns: body only, 2-3 token savings vs full format

Multi-Agent Communication (SintExMirror)

from syntex import SintExMirror

mirror = SintExMirror()

# Agent 1: Add candidates, build shared dictionary
candidates = ["Act as a senior architect", "Design scalable systems"]
mirror.add_candidates(candidates)
mirror.build_dict()

# Agent 2: Use same dictionary
compressed = mirror.compress(agent1_prompt, lean_mode=True)
decompressed = mirror.decompress(compressed)

AutoVocab (Automatic ROI-Based Promotion)

from syntex import AutoVocab

av = AutoVocab(threshold_roi=2.0)
av.feed(agent_interactions)
av.promote_top(max_promotions=5)
# Auto-promotes phrases with best token savings

Gateway (LLM Proxy)

from syntex import SintExGateway

gateway = SintExGateway(
    api_key="sk-...",
    model="gpt-4",
    base_url="https://api.openai.com/v1"
)

# Compression automatic
response = gateway.chat.completions.create(
    messages=[{"role": "system", "content": long_prompt}]
)

Telemetry

from syntex import Telemetry

t = Telemetry()
t.track("prompt_1", original_tokens=500, compressed_tokens=200)
t.report()
# Shows: savings %, dollars saved, ROI

---

CLI

syntex compile <file.sx>      # Compile .sx → .sxc
syntex decompile <file.sxc>   # Decompile → text
syntex bench <file.sx>        # Benchmark compression
syntex tokens <file>          # Count tokens
syntex version                # Show version

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Format

Input .sx

$role=Act as a senior software architect

@system_architect
- $role
- Design scalable systems
- Consider trade-offs

@system_reviewer
- $role
- Review designs critically

Output .sxc

[SXC]
[DICT]
$a=Act as a senior software architect
[/DICT]
[BODY]
@system_architect
- $a
- Design scalable systems
...
[/BODY]
[/SXC]

Lean Mode (no header)

[DICT]
$a=Act as a senior software architect
[/DICT]
[BODY]
@system_architect
- $a
...
[/BODY]

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Changelog

v1.2.0 (current)

• Config-Driven: All magic numbers extracted to syntex.yaml
• Columnar JSON: Automatic transformation of repetitive JSON to columnar format
• Data Optimizations: Integer scaling, delta timestamps, enum encoding
• JSON Compression: 0.2% → 63.6% on JSON-heavy payloads
• Config Tests: Full test coverage for configuration system

v1.1.0

• SintExMirror: Multi-agent shared dictionary with consensus
• Lean Mode: Compression without header (2-3 token savings)
• AutoVocab: ROI-based automatic vocabulary promotion
• Thread-Safety: Locking and snapshot for concurrent ops
• Graceful Fallbacks: Auto-recovery on decompression failure

v1.0

• Full L1+L2+L3 pipeline
• 72 global vocabulary entries (Greek + Braille)
• Telemetry tracking

v0.9.x

• Initial releases with dual-layer compression

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Enterprise: Solve the Rate Limit Problem

The Problem: Every LLM provider (OpenAI, Anthropic, Google) has strict rate limits. Companies running multi-agent systems burn through tokens faster than they can scale.

The Solution: SyntEx reduces token usage by 50-70% automatically.

Metric	Before SyntEx	After SyntEx
10-agents prompts	~8,000 tokens	~3,200 tokens
API calls/day (budget)	50,000	20,000
Monthly cost (est. $10/1M)	$500	$200
Rate limit risk	High	Eliminated

Enterprise Features

• SintExGateway: Drop-in proxy that compresses automatically
• Telemetry: Track savings in dollars, not just tokens
• SLA Guarantee: Zero data loss (100% round-trip integrity)
• On-premise option: Your data never leaves your infrastructure

Quick Integration

from syntex import SintExGateway

gateway = SintExGateway(api_key="sk-...")
gateway.chat.completions.create(
    messages=[{"role": "system", "content": large_prompt}]
)
# Automatically compressed → 50-70% fewer tokens

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License

MIT — See LICENSE and LEGAL_NOTICE.md