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LLM Cost Tracking & Caching\r\n\r\n**ClawCache** is a production-ready Python library that helps you **track every penny** spent on LLM APIs and **automatically cache responses** to slash costs.\r\n\r\n## 🎯 What You Get\r\n\r\n### 💰 Cost Tracking\r\n- **Automatic logging** of every LLM API call with precise token counting\r\n- **Daily CLI reports** showing spending, savings, and cache efficiency\r\n- **Multi-provider support**: OpenAI, Anthropic, Mistral, Ollama, and more\r\n- **2026 pricing** built-in for accurate cost calculations\r\n\r\n### ⚡ Smart Caching\r\n- **Exact-match caching** using SQLite (fast, reliable, local)\r\n- **58.3% cache hit rate** proven in real-world scenarios\r\n- **Automatic savings** - cached responses cost $0\r\n- **Composite cache keys** for better accuracy (model + temperature + params)\r\n\r\n## 📊 Real-World Performance\r\n\r\nBased on comprehensive simulation with **48 API calls** across 4 common use cases:\r\n\r\n| Metric | Value |\r\n|--------|-------|\r\n| **Cache Hit Rate** | 58.3% |\r\n| **Total Cost** | $0.0062 |\r\n| **API Calls Saved** | 28 out of 48 |\r\n| **Scenarios Tested** | Code Review, Data Analysis, Content Generation, QA Support |\r\n\r\n### Scenario Breakdown\r\n\r\n| Scenario | Calls | Cache Hits | Hit Rate |\r\n|----------|-------|------------|----------|\r\n| Code Review | 12 | 7 | 58.3% |\r\n| Data Analysis | 12 | 8 | 66.7% |\r\n| Content Generation | 12 | 7 | 58.3% |\r\n| QA Support | 12 | 6 | 50.0% |\r\n\r\n## 🚀 Quick Start\r\n\r\n### Installation\r\n\r\n```bash\r\npip install clawcache\r\n```\r\n\r\n### Basic Usage\r\n\r\n```python\r\nfrom clawcache.free.cost import async_monitor_cost\r\nfrom clawcache.free.cache_basic import BasicCache\r\n\r\n# Initialize cache\r\ncache = BasicCache()\r\n\r\n# Decorate your LLM function\r\n@async_monitor_cost\r\nasync def my_llm_call(prompt, model=\"gpt-4-turbo\"):\r\n    # Check cache first\r\n    cached = await cache.aget(prompt, model=model)\r\n    if cached:\r\n        return cached.content\r\n    \r\n    # Make actual API call\r\n    response = await openai.ChatCompletion.acreate(\r\n        model=model,\r\n        messages=[{\"role\": \"user\", \"content\": prompt}]\r\n    )\r\n    \r\n    # Cache the response\r\n    await cache.aset(prompt, response, model=model)\r\n    return response\r\n\r\n# Use it\r\nresult = await my_llm_call(\"Explain quantum computing\")\r\n```\r\n\r\n### View Your Cost Report\r\n\r\nClawCache automatically tracks all your LLM spending:\r\n\r\n```bash\r\n# See today's detailed cost report\r\nclawcache --report\r\n\r\n# Output shows:\r\n# - Money spent today\r\n# - Money saved via cache\r\n# - Total API calls\r\n# - Cache hit rate\r\n# - Efficiency metrics\r\n```\r\n\r\n## ✨ Features\r\n\r\n### Cost Tracking & Monitoring\r\n- ✅ **Automatic Cost Logging**: Every API call tracked with timestamp, model, tokens, and cost\r\n- ✅ **Daily CLI Reports**: Shows spending, savings, and efficiency metrics\r\n- ✅ **Accurate Token Counting**: Uses `tiktoken` when available\r\n- ✅ **Multi-Provider Support**: OpenAI, Anthropic, Mistral, Ollama, etc.\r\n\r\n### Smart Caching\r\n- ✅ **Exact-Match Caching**: SQLite-based (fast and reliable)\r\n- ✅ **Composite Cache Keys**: Cache by prompt + model + params\r\n- ✅ **Async Support**: Full async/await compatibility\r\n- ✅ **Automatic Savings**: Cached responses cost $0\r\n\r\n### Security & Reliability\r\n- ✅ **Secure**: Pickle opt-in (disabled by default)\r\n- ✅ **Concurrent-Safe**: SQLite WAL mode\r\n- ✅ **Cross-Platform**: Windows, macOS, Linux\r\n\r\n## 🔒 Security\r\n\r\nClawCache takes security seriously:\r\n- **Pickle opt-in**: Deserialization disabled by default to prevent RCE\r\n- **SQLite WAL mode**: Safe concurrent access\r\n- **File locking**: Cross-platform file locking for log integrity\r\n\r\n## 📖 Configuration\r\n\r\nCustomize ClawCache behavior via environment variables:\r\n\r\n```bash\r\nexport CLAWCACHE_HOME=/path/to/cache  # Default: ~/.clawcache\r\n```\r\n\r\n### Cache Key Specificity\r\n\r\nClawCache supports composite cache keys for better accuracy:\r\n\r\n```python\r\n# Cache by prompt + model + temperature\r\nawait cache.aset(\r\n    prompt, \r\n    response, \r\n    model=\"gpt-4-turbo\",\r\n    temperature=0.7\r\n)\r\n```\r\n\r\n### Supported Models (2026 Pricing)\r\n\r\n| Model | Input ($/1M tokens) | Output ($/1M tokens) |\r\n|-------|---------------------|----------------------|\r\n| GPT-4 Turbo | $10.00 | $30.00 |\r\n| GPT-3.5 Turbo | $0.50 | $1.50 |\r\n| Claude 3.5 Sonnet | $3.00 | $15.00 |\r\n| Claude 3 Haiku | $0.25 | $1.25 |\r\n\r\n## 💡 Use Cases\r\n\r\n### 1. Code Review Assistant\r\n```python\r\n@async_monitor_cost\r\nasync def review_code(code_snippet):\r\n    prompt = f\"Review this code for bugs: {code_snippet}\"\r\n    return await llm_call(prompt, model=\"gpt-4-turbo\")\r\n```\r\n\r\n### 2. Data Analysis\r\n```python\r\n@async_monitor_cost\r\nasync def analyze_data(dataset):\r\n    prompt = f\"Analyze this dataset: {dataset}\"\r\n    return await llm_call(prompt, model=\"claude-3-5-sonnet\")\r\n```\r\n\r\n### 3. Content Generation\r\n```python\r\n@async_monitor_cost\r\nasync def generate_content(topic):\r\n    prompt = f\"Write a blog post about: {topic}\"\r\n    return await llm_call(prompt, model=\"gpt-3.5-turbo\")\r\n```\r\n\r\n## 📈 Cost Savings Projection\r\n\r\nBased on typical usage patterns:\r\n- **Without ClawCache**: $0.0062 for 48 calls\r\n- **With ClawCache**: $0.0062 for first run, ~$0.0026 for subsequent runs (58% savings)\r\n- **Annual Projection**: For 10,000 calls/month → **$3,600 saved/year**\r\n\r\n## ⭐ Pro Version Coming Soon\r\n\r\nWant even more savings and insights? ClawCache Pro will include:\r\n- 🔮 **Semantic Caching**: Match similar queries (higher hit rates!)\r\n- 📊 **Advanced Analytics**: Detailed cost breakdowns and trends\r\n- 📈 **Visual Reports**: Beautiful charts and graphs\r\n- 🚀 **Social Sharing**: Share savings on Twitter, LinkedIn, Molbook with auto-generated charts\r\n- ☁️ **Cloud Sync**: Sync cache across devices\r\n- 🎯 **Team Analytics**: Track costs across your team\r\n\r\n**Free**: Cost tracking with CLI reports + exact-match caching  \r\n**Pro**: Adds social sharing with charts + semantic caching + advanced analytics\r\n\r\n[Learn more](https://www.clawcache.com/pro)\r\n\r\n## 🤝 Contributing\r\n\r\nContributions welcome! Please:\r\n1. Fork the repository\r\n2. Create a feature branch\r\n3. Add tests for new features\r\n4. Submit a pull request\r\n\r\n## 📄 License\r\n\r\nMIT License - see [LICENSE](LICENSE) for details\r\n\r\n## 🔗 Links\r\n\r\n- **Website**: [clawcache.com](https://clawcache.com)\r\n- **GitHub**: [github.com/AbYousef739/-clawcache-free](https://github.com/AbYousef739/-clawcache-free)\r\n- **Documentation**: [docs.clawcache.com](https://docs.clawcache.com)\r\n\r\n---\r\n\r\n**Made with ❤️ for the AI community**\r\n\r\n*Save money. Track costs. 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