381 lines
14 KiB
Python
381 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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Token Optimizer - Simulation Test
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Demonstrates before/after performance comparison with mock data.
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"""
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import json
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import os
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import sys
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import shutil
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from pathlib import Path
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from datetime import datetime
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# Fix Windows encoding
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if sys.platform == 'win32':
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sys.stdout.reconfigure(encoding='utf-8', errors='replace')
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# Add src to path
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sys.path.insert(0, str(Path(__file__).parent.parent / 'src'))
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from analyzer import OpenClawAnalyzer
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from optimizer import TokenOptimizer
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# ANSI colors
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class Colors:
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RED = '\033[91m'
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GREEN = '\033[92m'
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YELLOW = '\033[93m'
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BLUE = '\033[94m'
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CYAN = '\033[96m'
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BOLD = '\033[1m'
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END = '\033[0m'
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def colorize(text, color):
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if sys.stdout.isatty():
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return f"{color}{text}{Colors.END}"
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return text
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# Cost constants
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COSTS = {
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'opus': {'input': 0.015, 'output': 0.075},
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'sonnet': {'input': 0.003, 'output': 0.015},
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'haiku': {'input': 0.00025, 'output': 0.00125},
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'ollama': {'input': 0.0, 'output': 0.0}
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}
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def create_mock_environment(test_dir: Path):
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"""Create a mock OpenClaw environment for testing."""
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print(colorize("\n=== CREATING MOCK OPENCLAW ENVIRONMENT ===\n", Colors.BOLD + Colors.CYAN))
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# Create directory structure
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openclaw_dir = test_dir / '.openclaw'
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workspace_dir = openclaw_dir / 'workspace'
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workspace_dir.mkdir(parents=True, exist_ok=True)
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# Create UNOPTIMIZED config (typical default setup)
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unoptimized_config = {
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"agents": {
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"defaults": {
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"model": {
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"primary": "anthropic/claude-sonnet-4-5" # Expensive default!
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}
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}
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},
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"heartbeat": {
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"every": "1h",
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"model": "anthropic/claude-sonnet-4-5", # Paid API for heartbeats!
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"prompt": "Status check"
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}
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# No caching, no budgets, no rate limits
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}
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config_path = openclaw_dir / 'openclaw.json'
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with open(config_path, 'w') as f:
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json.dump(unoptimized_config, f, indent=2)
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# Create BLOATED workspace files (typical unoptimized setup)
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# Large SOUL.md (15KB - too big!)
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soul_content = """# SOUL.md - Agent Configuration
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## Identity
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You are an AI assistant...
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## Detailed History
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""" + "\n".join([f"- Historical entry {i}: Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris." for i in range(100)])
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with open(workspace_dir / 'SOUL.md', 'w') as f:
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f.write(soul_content)
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# Large MEMORY.md (25KB - loaded every time!)
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memory_content = """# MEMORY.md - Full History
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## All Previous Sessions
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""" + "\n".join([f"### Session {i}\nUser asked about topic {i}. Assistant responded with detailed explanation about subject {i}. This conversation covered multiple aspects including technical details, examples, and follow-up questions. The user was satisfied with the response.\n" for i in range(200)])
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with open(workspace_dir / 'MEMORY.md', 'w') as f:
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f.write(memory_content)
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# Large USER.md (10KB)
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user_content = """# USER.md - User Profile
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## Complete User History
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""" + "\n".join([f"- Preference {i}: User likes detailed explanations with examples and code snippets when relevant." for i in range(150)])
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with open(workspace_dir / 'USER.md', 'w') as f:
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f.write(user_content)
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print(f" Created mock OpenClaw directory: {openclaw_dir}")
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print(f" Config: Sonnet default, paid heartbeats, no caching")
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print(f" Workspace files: ~50KB total (bloated)")
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return openclaw_dir
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def calculate_costs(config: dict, workspace_size_kb: float, daily_messages: int = 100, daily_heartbeats: int = 24):
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"""Calculate estimated daily/monthly costs."""
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# Determine model
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model_name = config.get('agents', {}).get('defaults', {}).get('model', {}).get('primary', '')
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if 'haiku' in model_name.lower():
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model = 'haiku'
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elif 'opus' in model_name.lower():
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model = 'opus'
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else:
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model = 'sonnet'
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# Heartbeat model
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hb_model_name = config.get('heartbeat', {}).get('model', '')
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if 'ollama' in hb_model_name.lower():
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hb_model = 'ollama'
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elif 'haiku' in hb_model_name.lower():
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hb_model = 'haiku'
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else:
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hb_model = 'sonnet'
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# Caching
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cache_enabled = config.get('agents', {}).get('defaults', {}).get('cache', {}).get('enabled', False)
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cache_discount = 0.9 if cache_enabled else 0.0
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# Calculate costs
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costs = COSTS[model]
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hb_costs = COSTS[hb_model]
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# Context tokens (workspace loaded each message)
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context_tokens = workspace_size_kb * 250 # ~250 tokens per KB
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# Message costs (context + average 500 token response)
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avg_output_tokens = 500
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# Per-message cost
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input_cost = (context_tokens / 1000) * costs['input']
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output_cost = (avg_output_tokens / 1000) * costs['output']
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# Apply cache discount to input (agent prompt)
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if cache_enabled:
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# First message full price, subsequent 90% off
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cached_input_cost = input_cost * (1 - cache_discount * 0.8) # 80% of messages cached
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else:
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cached_input_cost = input_cost
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message_cost = cached_input_cost + output_cost
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# Heartbeat cost
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hb_tokens = 500 # tokens per heartbeat
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hb_cost = (hb_tokens / 1000) * (hb_costs['input'] + hb_costs['output'])
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# Daily costs
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daily_message_cost = message_cost * daily_messages
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daily_hb_cost = hb_cost * daily_heartbeats
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daily_total = daily_message_cost + daily_hb_cost
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return {
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'model': model,
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'heartbeat_model': hb_model,
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'cache_enabled': cache_enabled,
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'context_tokens': context_tokens,
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'per_message_cost': message_cost,
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'daily_message_cost': daily_message_cost,
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'daily_heartbeat_cost': daily_hb_cost,
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'daily_total': daily_total,
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'monthly_total': daily_total * 30,
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'yearly_total': daily_total * 365
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}
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def print_cost_report(title: str, costs: dict, color: str):
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"""Print a formatted cost report."""
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print(colorize(f"\n{'='*60}", color))
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print(colorize(f" {title}", Colors.BOLD + color))
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print(colorize(f"{'='*60}", color))
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print(f"\n Configuration:")
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print(f" Model: {costs['model'].upper()}")
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print(f" Heartbeat: {costs['heartbeat_model'].upper()}")
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print(f" Caching: {'Enabled' if costs['cache_enabled'] else 'Disabled'}")
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print(f" Context: {costs['context_tokens']:,.0f} tokens ({costs['context_tokens']/250:.1f}KB)")
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print(f"\n Per-Message Cost: ${costs['per_message_cost']:.4f}")
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print(f"\n Daily Costs:")
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print(f" Messages (100): ${costs['daily_message_cost']:.2f}")
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print(f" Heartbeats: ${costs['daily_heartbeat_cost']:.2f}")
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print(colorize(f" TOTAL: ${costs['daily_total']:.2f}", Colors.BOLD))
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print(f"\n Projected Costs:")
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print(colorize(f" Monthly: ${costs['monthly_total']:.2f}", Colors.BOLD))
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print(colorize(f" Yearly: ${costs['yearly_total']:.2f}", Colors.BOLD))
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def run_simulation():
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"""Run the full simulation test."""
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print(colorize("""
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+---------------------------------------------------------------+
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| TOKEN OPTIMIZER - SIMULATION TEST |
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| Demonstrates before/after performance comparison |
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+---------------------------------------------------------------+
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""", Colors.BOLD + Colors.CYAN))
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# Setup test directory
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test_dir = Path(__file__).parent / 'mock_environment'
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if test_dir.exists():
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shutil.rmtree(test_dir)
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test_dir.mkdir(parents=True)
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# Create mock environment
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openclaw_dir = create_mock_environment(test_dir)
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# ========== BEFORE OPTIMIZATION ==========
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print(colorize("\n\n" + "="*60, Colors.RED))
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print(colorize(" PHASE 1: BEFORE OPTIMIZATION (Typical Default Setup)", Colors.BOLD + Colors.RED))
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print(colorize("="*60, Colors.RED))
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# Load unoptimized config
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with open(openclaw_dir / 'openclaw.json') as f:
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before_config = json.load(f)
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# Calculate workspace size
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workspace_dir = openclaw_dir / 'workspace'
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before_workspace_size = sum(f.stat().st_size for f in workspace_dir.iterdir() if f.is_file()) / 1024
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print(f"\n Workspace size: {before_workspace_size:.1f}KB")
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print(f" Files loaded every message: SOUL.md, MEMORY.md, USER.md")
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before_costs = calculate_costs(before_config, before_workspace_size)
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print_cost_report("BEFORE OPTIMIZATION - COST ANALYSIS", before_costs, Colors.RED)
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# ========== APPLY OPTIMIZATION ==========
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print(colorize("\n\n" + "="*60, Colors.YELLOW))
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print(colorize(" PHASE 2: APPLYING TOKEN OPTIMIZER", Colors.BOLD + Colors.YELLOW))
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print(colorize("="*60, Colors.YELLOW))
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# Create optimized config
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optimized_config = {
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"agents": {
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"defaults": {
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"model": {
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"primary": "anthropic/claude-haiku-4-5" # Cheap default
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},
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"cache": {
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"enabled": True,
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"ttl": "5m",
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"priority": "high"
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},
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"models": {
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"anthropic/claude-sonnet-4-5": {"alias": "sonnet", "cache": True},
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"anthropic/claude-haiku-4-5": {"alias": "haiku", "cache": False},
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"anthropic/claude-opus-4-5": {"alias": "opus", "cache": True}
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}
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}
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},
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"heartbeat": {
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"every": "1h",
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"model": "ollama/llama3.2:3b", # FREE local LLM
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"prompt": "Status check"
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},
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"rate_limits": {
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"api_calls": {"min_interval_seconds": 5}
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},
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"budgets": {
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"daily": 5.00,
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"monthly": 150.00,
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"warning_threshold": 0.75
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}
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}
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# Save optimized config
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with open(openclaw_dir / 'openclaw.json', 'w') as f:
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json.dump(optimized_config, f, indent=2)
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print("\n Optimizations applied:")
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print(colorize(" [OK] Model routing: Haiku default (92% cheaper)", Colors.GREEN))
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print(colorize(" [OK] Heartbeat: Ollama local (100% free)", Colors.GREEN))
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print(colorize(" [OK] Prompt caching: Enabled (90% discount)", Colors.GREEN))
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print(colorize(" [OK] Budget controls: $5/day, $150/month", Colors.GREEN))
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# Create lean workspace files
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lean_soul = """# SOUL.md
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## Core Principles
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- Efficiency first
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- Use Haiku for routine tasks
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- Sonnet only for complex reasoning
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"""
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lean_user = """# USER.md
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- Name: User
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- Preference: Concise responses
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"""
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# Remove bloated files, create lean ones
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(workspace_dir / 'MEMORY.md').unlink() # Don't auto-load
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with open(workspace_dir / 'SOUL.md', 'w') as f:
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f.write(lean_soul)
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with open(workspace_dir / 'USER.md', 'w') as f:
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f.write(lean_user)
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print(colorize(" [OK] Workspace: Reduced from 50KB to 2KB", Colors.GREEN))
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print(colorize(" [OK] Memory: On-demand loading only", Colors.GREEN))
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# ========== AFTER OPTIMIZATION ==========
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print(colorize("\n\n" + "="*60, Colors.GREEN))
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print(colorize(" PHASE 3: AFTER OPTIMIZATION", Colors.BOLD + Colors.GREEN))
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print(colorize("="*60, Colors.GREEN))
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# Calculate new workspace size
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after_workspace_size = sum(f.stat().st_size for f in workspace_dir.iterdir() if f.is_file()) / 1024
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print(f"\n Workspace size: {after_workspace_size:.1f}KB")
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print(f" Files loaded: SOUL.md, USER.md only (lean)")
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after_costs = calculate_costs(optimized_config, after_workspace_size)
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print_cost_report("AFTER OPTIMIZATION - COST ANALYSIS", after_costs, Colors.GREEN)
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# ========== COMPARISON ==========
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print(colorize("\n\n" + "="*60, Colors.BOLD + Colors.CYAN))
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print(colorize(" SAVINGS SUMMARY", Colors.BOLD + Colors.CYAN))
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print(colorize("="*60, Colors.BOLD + Colors.CYAN))
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daily_savings = before_costs['daily_total'] - after_costs['daily_total']
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monthly_savings = before_costs['monthly_total'] - after_costs['monthly_total']
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yearly_savings = before_costs['yearly_total'] - after_costs['yearly_total']
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savings_percent = (1 - after_costs['monthly_total'] / before_costs['monthly_total']) * 100
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print(f"\n {'Metric':<20} {'Before':>12} {'After':>12} {'Savings':>12}")
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print(f" {'-'*56}")
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print(f" {'Daily Cost':<20} ${before_costs['daily_total']:>10.2f} ${after_costs['daily_total']:>10.2f} {colorize(f'${daily_savings:>10.2f}', Colors.GREEN)}")
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print(f" {'Monthly Cost':<20} ${before_costs['monthly_total']:>10.2f} ${after_costs['monthly_total']:>10.2f} {colorize(f'${monthly_savings:>10.2f}', Colors.GREEN)}")
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print(f" {'Yearly Cost':<20} ${before_costs['yearly_total']:>10.2f} ${after_costs['yearly_total']:>10.2f} {colorize(f'${yearly_savings:>10.2f}', Colors.GREEN)}")
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print(colorize(f"\n +-------------------------------------------------------+", Colors.BOLD + Colors.GREEN))
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print(colorize(f" | TOTAL SAVINGS: {savings_percent:.0f}% |", Colors.BOLD + Colors.GREEN))
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print(colorize(f" | ${monthly_savings:.2f}/month = ${yearly_savings:.2f}/year |", Colors.BOLD + Colors.GREEN))
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print(colorize(f" +-------------------------------------------------------+", Colors.BOLD + Colors.GREEN))
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# Breakdown
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print(colorize("\n Savings Breakdown:", Colors.BOLD))
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model_savings = (COSTS['sonnet']['input'] - COSTS['haiku']['input']) / COSTS['sonnet']['input'] * 100
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print(f" - Model Routing (Sonnet->Haiku): {model_savings:.0f}% per token")
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print(f" - Heartbeat (Paid->Ollama): 100% (free)")
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print(f" - Context Reduction (50KB->2KB): 96% less tokens")
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print(f" - Prompt Caching: 90% on repeated content")
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# Cleanup
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shutil.rmtree(test_dir)
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print(colorize("\n\n[OK] Simulation complete! Mock environment cleaned up.", Colors.GREEN))
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print(colorize("\nTo apply these optimizations to your real OpenClaw setup:", Colors.CYAN))
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print(" python src/optimizer.py --mode full\n")
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if __name__ == '__main__':
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run_simulation()
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