ClawCache Free - LLM Cost Tracking & Caching

Smart LLM cost tracking and caching for Python

ClawCache is a production-ready Python library that helps you track every penny spent on LLM APIs and automatically cache responses to slash costs.

🎯 What You Get

💰 Cost Tracking

• Automatic logging of every LLM API call with precise token counting
• Daily CLI reports showing spending, savings, and cache efficiency
• Multi-provider support: OpenAI, Anthropic, Mistral, Ollama, and more
• 2026 pricing built-in for accurate cost calculations

⚡ Smart Caching

• Exact-match caching using SQLite (fast, reliable, local)
• 58.3% cache hit rate proven in real-world scenarios
• Automatic savings - cached responses cost $0
• Composite cache keys for better accuracy (model + temperature + params)

📊 Real-World Performance

Based on comprehensive simulation with 48 API calls across 4 common use cases:

Metric	Value
Cache Hit Rate	58.3%
Total Cost	$0.0062
API Calls Saved	28 out of 48
Scenarios Tested	Code Review, Data Analysis, Content Generation, QA Support

Scenario Breakdown

Scenario	Calls	Cache Hits	Hit Rate
Code Review	12	7	58.3%
Data Analysis	12	8	66.7%
Content Generation	12	7	58.3%
QA Support	12	6	50.0%

🚀 Quick Start

Installation

bash

pip install clawcache

Basic Usage

python

from clawcache.free.cost import async_monitor_cost
from clawcache.free.cache_basic import BasicCache

# Initialize cache
cache = BasicCache()

# Decorate your LLM function
@async_monitor_cost
async def my_llm_call(prompt, model="gpt-4-turbo"):
    # Check cache first
    cached = await cache.aget(prompt, model=model)
    if cached:
        return cached.content
    
    # Make actual API call
    response = await openai.ChatCompletion.acreate(
        model=model,
        messages=[{"role": "user", "content": prompt}]
    )
    
    # Cache the response
    await cache.aset(prompt, response, model=model)
    return response

# Use it
result = await my_llm_call("Explain quantum computing")

View Your Cost Report

ClawCache automatically tracks all your LLM spending:

bash

# See today's detailed cost report
clawcache --report

# Output shows:
# - Money spent today
# - Money saved via cache
# - Total API calls
# - Cache hit rate
# - Efficiency metrics

✨ Features

Cost Tracking & Monitoring

• ✅ Automatic Cost Logging: Every API call tracked with timestamp, model, tokens, and cost
• ✅ Daily CLI Reports: Shows spending, savings, and efficiency metrics
• ✅ Accurate Token Counting: Uses tiktoken when available
• ✅ Multi-Provider Support: OpenAI, Anthropic, Mistral, Ollama, etc.

Smart Caching

• ✅ Exact-Match Caching: SQLite-based (fast and reliable)
• ✅ Composite Cache Keys: Cache by prompt + model + params
• ✅ Async Support: Full async/await compatibility
• ✅ Automatic Savings: Cached responses cost $0

Security & Reliability

• ✅ Secure: Pickle opt-in (disabled by default)
• ✅ Concurrent-Safe: SQLite WAL mode
• ✅ Cross-Platform: Windows, macOS, Linux

🔒 Security

ClawCache takes security seriously:

• Pickle opt-in: Deserialization disabled by default to prevent RCE
• SQLite WAL mode: Safe concurrent access
• File locking: Cross-platform file locking for log integrity

📖 Configuration

Customize ClawCache behavior via environment variables:

bash

export CLAWCACHE_HOME=/path/to/cache  # Default: ~/.clawcache

Cache Key Specificity

ClawCache supports composite cache keys for better accuracy:

python

# Cache by prompt + model + temperature
await cache.aset(
    prompt, 
    response, 
    model="gpt-4-turbo",
    temperature=0.7
)

Supported Models (2026 Pricing)

Model	Input ($/1M tokens)	Output ($/1M tokens)
GPT-4 Turbo	$10.00	$30.00
GPT-3.5 Turbo	$0.50	$1.50
Claude 3.5 Sonnet	$3.00	$15.00
Claude 3 Haiku	$0.25	$1.25

💡 Use Cases

1. Code Review Assistant

python

@async_monitor_cost
async def review_code(code_snippet):
    prompt = f"Review this code for bugs: {code_snippet}"
    return await llm_call(prompt, model="gpt-4-turbo")

2. Data Analysis

python

@async_monitor_cost
async def analyze_data(dataset):
    prompt = f"Analyze this dataset: {dataset}"
    return await llm_call(prompt, model="claude-3-5-sonnet")

3. Content Generation

python

@async_monitor_cost
async def generate_content(topic):
    prompt = f"Write a blog post about: {topic}"
    return await llm_call(prompt, model="gpt-3.5-turbo")

📈 Cost Savings Projection

Based on typical usage patterns:

• Without ClawCache: $0.0062 for 48 calls
• With ClawCache: $0.0062 for first run, ~$0.0026 for subsequent runs (58% savings)
• Annual Projection: For 10,000 calls/month → $3,600 saved/year

⭐ Pro Version Coming Soon

Want even more savings and insights? ClawCache Pro will include:

• 🔮 Semantic Caching: Match similar queries (higher hit rates!)
• 📊 Advanced Analytics: Detailed cost breakdowns and trends
• 📈 Visual Reports: Beautiful charts and graphs
• 🚀 Social Sharing: Share savings on Twitter, LinkedIn, Molbook with auto-generated charts
• ☁️ Cloud Sync: Sync cache across devices
• 🎯 Team Analytics: Track costs across your team

Free: Cost tracking with CLI reports + exact-match caching
Pro: Adds social sharing with charts + semantic caching + advanced analytics

Learn more

🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new features
4. Submit a pull request

📄 License

MIT License - see LICENSE for details

🔗 Links

• Website: clawcache.com
• GitHub: github.com/AbYousef739/-clawcache-free
• Documentation: docs.clawcache.com

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