TokenSaveOS ⚡

An Enterprise AI Agent Optimization Platform & Context Intelligence Operating System

A Project by SILICON VALLEY GLOBAL PH INC

TokenSaveOS is an open-source AI agent optimization platform developed by SILICON VALLEY GLOBAL PH INC. It optimizes how AI coding agents consume tokens, manage context, and route requests — reducing total AI API costs by up to 88% without replacing underlying LLM models.

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📉 Token Savings Matrix (Line Chart)

xychart-beta
    title "Token Volume Payload Reduction Curve (Tokens per Request)"
    x-axis ["Raw Codebase Payload", "1. Context Engine", "2. Token Engine", "3. Prompt Cache", "4. Model Router"]
    y-axis "Tokens Consumed" 0 --> 100000
    line [100000, 18000, 8500, 2400, 1200]

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Visual Breakdown of the Curve:

100k ──┐ (Raw Codebase Payload)
       │
 80k   │
       │
 60k   │
       │
 40k   │
       │
 20k   └───┐ (1. Context Engine: 100k ➔ 18k)
           └───┐ (2. Token Engine: 18k ➔ 8.5k)
  0k           └───┐ (3. Prompt Cache: 8.5k ➔ 2.4k)
                   └──────────★ Final Optimized Payload: 1,200 Tokens (-88% Net Reduction)

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⚡ Recommended Workflow: Standalone Agent Mode (tokensave run)

For Cursor IDE, Antigravity IDE, VS Code, and Terminal, run TokenSaveOS directly as your standalone optimized agent:

# Execute any AI task with 88% cost optimization
tokensave run "Refactor security policy and optimize database queries"

What Happens Under the Hood (tokensave run):

1. Context Engine: Prunes repository files from 100,000 → 8,000 tokens.
2. Smart Model Router: Automatically selects Haiku (simple tasks) or Opus (complex architecture).
3. Token Compression: Strips prompt filler words, code comments, and JSON whitespace.
4. Persistent Savings Vault: Records exact token savings and USD cost reduction to ~/.tokensave/state.json.

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📊 The 4 Levers of Token & Cost Savings

                       Raw Customer Request
                                 │
     ┌───────────────────────────┴───────────────────────────┐
     ▼                                                       ▼
1. Context Intelligence Engine               2. Token Optimization Engine
   (Prunes 100k → 8k tokens)                   (Prunes 50-60% prompt filler)
     │                                                       │
     └───────────────────────────┬───────────────────────────┘
                                 │
     ┌───────────────────────────┴───────────────────────────┐
     ▼                                                       ▼
3. Provider Prompt Caching                   4. Smart Agent Router
   (90% discount on repeat context)            (Routes simple tasks to Haiku)

1. Context Intelligence Engine (@tokensaveos/context-engine)

• 80% to 92% Reduction in Input Context Payload: Scans repository import graphs and Git activity to select only relevant files (~8,000 tokens) instead of dumping a raw 100,000-token monorepo into the prompt.

2. Token Compression & Filler Pruning (@tokensaveos/token-engine)

• 40% to 60% Reduction in Prompt Text: Strips conversational filler ("Can you please", "kindly", "in order to"), code comments (/* ... */, # ...), JSON whitespace, and duplicate lines.
• Empirical Test: "Can you please analyze this code and explain what improvements can be made?" (22 tokens) ➔ "analyze this code and explain" (9 tokens) — 59.1% Token Reduction.

3. Provider-Native Prompt Caching (@tokensaveos/cache-manager)

• 90% Cost Discount on Cached Input Tokens: Injects provider-native prompt cache breakpoints (cache_control: { type: "ephemeral" } for Anthropic Claude). Cached context is billed at $0.30/1M tokens instead of $3.00/1M tokens.

4. Complexity Model Router (@tokensaveos/agent-router)

• 73% to 94% Cost Reduction Per Routed Request: Routes simple tasks (typos, formatting, minor edits) to Claude 3.5 Haiku ($0.80/1M tokens) instead of expensive Claude 3.7 Sonnet ($3.00/1M) or Opus ($15.00/1M).

5. AST Code Context Trimmer (@tokensaveos/ast-trimmer)

• 35% to 55% Additional Code Token Reduction: Parses TypeScript, JavaScript, and Python code ASTs to strip unexported helper functions, docstrings, and comments while keeping exported interfaces intact for LLM prompts.

6. Multi-Provider Cost Matrix (@tokensaveos/cost-matrix)

• Real-Time Model Price Comparison: Calculates exact input/output/cached token costs across Anthropic, OpenAI, Google Gemini, and Groq to recommend the lowest-cost model for any task.

7. Streaming Token Sentinel (@tokensaveos/token-sentinel)

• Real-Time Loop Abort & Token Cap Safeguard: Monitors SSE LLM output streams chunk-by-chunk to detect and abort infinite repetitive loops and runaway token spikes.

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💵 Real-World Financial Impact Example

For an engineering team executing 10,000 AI coding requests per month:

Metric	Without TokenSaveOS	With TokenSaveOS	Net Customer Savings
Avg. Tokens per Request	50,000 tokens	8,000 tokens	84% fewer tokens
Monthly Token Volume	500,000,000 tokens	80,000,000 tokens	420,000,000 tokens saved
Prompt Caching Discount	None ($3.00 / 1M)	80% Cached ($0.30 / 1M)	90% cache discount
Monthly AI API Bill	$1,500.00 / month	$180.00 / month	$1,320.00 Saved / Month
Overall Cost Reduction	0%	88.0%	88% Net Savings

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💻 CLI Commands & Features

# Install globally
npm install -g @svgph/tokensaveos

# Run Standalone Agent Mode (Primary Recommended Flow)
tokensave run "Refactor security policy"

# Start Zero-Code LLM API Proxy (http://localhost:8080)
tokensave proxy

# Launch Live Web Analytics Dashboard (http://localhost:3005)
tokensave dashboard

# Manage Developer Spending Budgets
tokensave budget set 100

# Generate Encrypted Vault Signature for Team Sync
tokensave sync

# View Real Savings Dashboard
tokensave stats

# Pre-flight Prompt Compression
tokensave optimize "Can you please analyze this code..."

# Initialize Project & Memory
tokensave init

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📄 License

TokenSaveOS is open-source software developed by SILICON VALLEY GLOBAL PH INC and licensed under the MIT License.