# cmdrdata-gemini
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[](https://codecov.io/gh/cmdrdata-ai/cmdrdata-gemini)
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[](https://pypi.org/project/cmdrdata-gemini/)
[](https://opensource.org/licenses/MIT)
**The standard for AI customer intelligence - track every Gemini call by customer, feature, or any dimension**
Join hundreds of companies making customer-level AI tracking the default. One line of code to add complete visibility into your AI operations. Free during beta.
## 📊 Complete AI Intelligence Layer
`cmdrdata-gemini` is the missing analytics layer for your AI-powered application:
### **Track Everything That Matters**
- **Customer Intelligence** - Know exactly which customers use what features
- **Metadata Everything** - Tag usage by feature, experiment, team, region, or any dimension
- **Usage Patterns** - Understand how your AI is actually being used
- **Real-time Analytics** - Instant visibility into your AI operations
### **Built for Modern AI Apps**
- **One-line integration** - Drop-in replacement for Google GenAI SDK
- **Zero latency overhead** - Async tracking never blocks your API calls
- **Unlimited custom fields** - Track any metadata that matters to your business
- **Privacy first** - Your data never touches our servers (optional self-hosting)
### **What You Can Track**
- **Token usage** by customer, feature, experiment, or any dimension
- **Model usage** patterns (Gemini 1.5 Flash, Gemini 1.5 Pro, etc.)
- **Customer behavior** - Who uses what, when, and how much
- **Custom metadata** - Unlimited fields for your specific needs
- **Performance metrics** - Latency, errors, success rates by segment
### 💎 Advanced Analytics with Custom Metadata
Track arbitrary metadata with each API call to enable sophisticated analytics:
```python
# Example: AI-powered content generation with feature tracking
response = client.models.generate_content(
model="gemini-1.5-pro",
contents="Write a comprehensive guide about renewable energy...",
customer_id="customer-123",
# Custom metadata for analytics
custom_metadata={
"feature": "content_generation",
"experiment_group": "gemini_pro_test",
"content_type": "technical_guide",
"user_segment": "enterprise",
"session_id": "sess_xyz789"
}
)
# Example: AI tutoring platform with learning analytics
response = client.models.generate_content(
model="gemini-1.5-flash",
contents=complex_physics_problem,
customer_id="customer-456",
custom_metadata={
"use_case": "educational_tutoring",
"subject": "physics",
"interaction_count": 5,
"learning_path": "advanced_physics",
"engagement_score": "high"
}
)
# Example: Multi-modal analysis with usage patterns
response = client.models.generate_content(
model="gemini-1.5-pro-vision",
contents=[image_data, "Analyze this image"],
customer_id="customer-789",
custom_metadata={
"modality": "vision_text",
"workflow": "image_analysis",
"api_version": "v2",
"client_platform": "web",
"feature_flag": "vision_enabled"
}
)
```
**Intelligence Use Cases:**
- **Feature adoption**: Track which AI features customers actually use
- **A/B testing**: Compare model performance across experiment groups
- **Learning analytics**: Understand educational engagement patterns
- **Multi-modal insights**: Analyze usage across different modalities
- **Platform optimization**: Identify performance bottlenecks by platform
- **Product development**: Data-driven feature prioritization
## 🛡️ Production Ready
**Extremely robust and reliable** - Built for production environments with:
- **Resilient Tracking:** Gemini calls succeed even if tracking fails.
- **Non-blocking I/O:** Fire-and-forget tracking never slows down your application.
- **Automatic Retries:** Failed tracking attempts are automatically retried with exponential backoff.
- **Thread-Safe Context:** Safely track usage across multi-threaded and async applications.
- **Enterprise Security:** API key sanitization and input validation.
## 🚀 Quick Start
### Installation
```bash
pip install cmdrdata-gemini
```
### Basic Usage
```python
# Before
from google import genai
client = genai.Client(api_key="your-gemini-key")
# After - same API, automatic tracking!
import cmdrdata_gemini
client = cmdrdata_gemini.TrackedGemini(
api_key="your-gemini-key",
cmdrdata_api_key="your-cmdrdata-key"
)
# Same API as regular Google Gen AI client
response = client.models.generate_content(
model="gemini-1.5-flash",
contents="Explain how AI works"
)
print(response.text)
# Usage automatically tracked to cmdrdata backend!
```
### Async Support
```python
import cmdrdata_gemini
async def main():
client = cmdrdata_gemini.AsyncTrackedGemini(
api_key="your-gemini-key",
cmdrdata_api_key="your-cmdrdata-key"
)
response = await client.models.generate_content(
model="gemini-1.5-flash",
contents="Hello, Gemini!"
)
print(response.text)
# Async usage tracking included!
```
## 🎯 Customer Context Management
### Automatic Customer Tracking
```python
from cmdrdata_gemini.context import customer_context
# Set customer context for automatic tracking
with customer_context("customer-123"):
response = client.models.generate_content(
model="gemini-1.5-flash",
contents="Help me code"
)
# Automatically tracked for customer-123!
# Or pass customer_id directly
response = client.models.generate_content(
model="gemini-1.5-flash",
contents="Hello",
customer_id="customer-456" # Direct customer ID
)
```
### Manual Context Management
```python
from cmdrdata_gemini.context import set_customer_context, clear_customer_context
# Set context for current thread
set_customer_context("customer-789")
response = client.models.generate_content(...) # Tracked for customer-789
# Clear context
clear_customer_context()
```
## ⚙️ Configuration
### Environment Variables
```bash
# Optional: Set via environment variables
export GEMINI_API_KEY="your-gemini-key"
export CMDRDATA_API_KEY="your-cmdrdata-key"
export CMDRDATA_ENDPOINT="https://api.cmdrdata.ai/api/events" # Optional
```
```python
# Then use without passing keys
client = cmdrdata_gemini.TrackedGemini()
```
### Custom Configuration
```python
client = cmdrdata_gemini.TrackedGemini(
api_key="your-gemini-key",
cmdrdata_api_key="your-cmdrdata-key",
cmdrdata_endpoint="https://your-custom-endpoint.com/api/events",
track_usage=True, # Enable/disable tracking
timeout=30, # Custom timeout
max_retries=3 # Custom retry logic
)
```
## 🔒 Security & Privacy
### Automatic Data Sanitization
- **API keys automatically redacted** from logs
- **Sensitive data sanitized** before transmission
- **Input validation** prevents injection attacks
- **Secure defaults** for all configuration
### What Gets Tracked
```python
# Tracked data (anonymized):
{
"customer_id": "customer-123",
"model": "gemini-1.5-flash",
"input_tokens": 25,
"output_tokens": 150,
"total_tokens": 175,
"provider": "google",
"timestamp": "2025-01-15T10:30:00Z",
"metadata": {
"response_id": "resp_abc123",
"model_version": "001",
"finish_reason": "STOP",
"safety_ratings": null
}
}
```
**Note**: Message content is never tracked - only metadata and token counts.
## 📊 Monitoring & Performance
### Built-in Performance Monitoring
```python
# Get performance statistics
stats = client.get_performance_stats()
print(f"Average response time: {stats['api_calls']['avg']}ms")
print(f"Total API calls: {stats['api_calls']['count']}")
```
### Health Monitoring
```python
# Check tracking system health
tracker = client.get_usage_tracker()
health = tracker.get_health_status()
print(f"Tracking healthy: {health['healthy']}")
```
## 🛠️ Advanced Usage
### Token Counting
```python
# Count tokens without generating content (also tracked)
token_count = client.models.count_tokens(
model="gemini-1.5-flash",
contents="How many tokens is this?"
)
print(f"Token count: {token_count.total_tokens}")
```
### Disable Tracking for Specific Calls
```python
# Disable tracking for sensitive operations
response = client.models.generate_content(
model="gemini-1.5-flash",
contents="Private query",
track_usage=False # This call won't be tracked
)
```
### Error Handling
```python
from cmdrdata_gemini.exceptions import CMDRDataError, TrackingError
try:
client = cmdrdata_gemini.TrackedGemini(
api_key="invalid-key",
cmdrdata_api_key="invalid-cmdrdata-key"
)
except CMDRDataError as e:
print(f"Configuration error: {e}")
# Handle configuration issues
```
### Integration with Existing Error Handling
```python
# All original Google Gen AI exceptions work the same way
try:
response = client.models.generate_content(...)
except Exception as e: # Google Gen AI exceptions
print(f"Google Gen AI error: {e}")
# Your existing error handling works unchanged
```
## 🔧 Development
### Requirements
- Python 3.9+
- google-genai>=0.1.0
### Installation for Development
```bash
git clone https://github.com/cmdrdata-ai/cmdrdata-gemini.git
cd cmdrdata-gemini
pip install -e .[dev]
```
### Running Tests
```bash
# Run all tests
pytest
# Run with coverage
pytest --cov=cmdrdata_gemini
# Run specific test categories
pytest -m unit # Unit tests only
pytest -m integration # Integration tests only
```
### Code Quality
```bash
# Format code
black cmdrdata_gemini/
isort cmdrdata_gemini/
# Type checking
mypy cmdrdata_gemini/
# Security scanning
safety check
```
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
### Development Workflow
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Make your changes
4. Add tests for your changes
5. Ensure all tests pass (`pytest`)
6. Format your code (`black . && isort .`)
7. Commit your changes (`git commit -m 'Add amazing feature'`)
8. Push to the branch (`git push origin feature/amazing-feature`)
9. Open a Pull Request
## 📜 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🆘 Support
- **Documentation**: [https://docs.cmdrdata.ai/gemini](https://docs.cmdrdata.ai/gemini)
- **Issues**: [GitHub Issues](https://github.com/cmdrdata-ai/cmdrdata-gemini/issues)
- **Support**: [spot@cmdrdata.ai](mailto:spot@cmdrdata.ai)
## 🔗 Related Projects
- **[cmdrdata-openai](https://github.com/cmdrdata-ai/cmdrdata-openai)** - Usage tracking for OpenAI
- **[cmdrdata-anthropic](https://github.com/cmdrdata-ai/cmdrdata-anthropic)** - Usage tracking for Anthropic Claude
- **[CMDR Data Platform](https://www.cmdrdata.ai)** - Complete LLM usage analytics
## 📈 Changelog
See [CHANGELOG.md](CHANGELOG.md) for a complete list of changes and version history.
---
**Built with ❤️ by the CMDR Data team**
*Become the Google Analytics of your AI - understand everything, optimize everything.*
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"description": "# cmdrdata-gemini\n\n[](https://github.com/cmdrdata-ai/cmdrdata-gemini/actions)\n[](https://codecov.io/gh/cmdrdata-ai/cmdrdata-gemini)\n[](https://badge.fury.io/py/cmdrdata-gemini)\n[](https://pypi.org/project/cmdrdata-gemini/)\n[](https://opensource.org/licenses/MIT)\n\n**The standard for AI customer intelligence - track every Gemini call by customer, feature, or any dimension**\n\nJoin hundreds of companies making customer-level AI tracking the default. One line of code to add complete visibility into your AI operations. Free during beta.\n\n## \ud83d\udcca Complete AI Intelligence Layer\n\n`cmdrdata-gemini` is the missing analytics layer for your AI-powered application:\n\n### **Track Everything That Matters**\n- **Customer Intelligence** - Know exactly which customers use what features\n- **Metadata Everything** - Tag usage by feature, experiment, team, region, or any dimension\n- **Usage Patterns** - Understand how your AI is actually being used\n- **Real-time Analytics** - Instant visibility into your AI operations\n\n### **Built for Modern AI Apps**\n- **One-line integration** - Drop-in replacement for Google GenAI SDK\n- **Zero latency overhead** - Async tracking never blocks your API calls \n- **Unlimited custom fields** - Track any metadata that matters to your business\n- **Privacy first** - Your data never touches our servers (optional self-hosting)\n\n### **What You Can Track**\n- **Token usage** by customer, feature, experiment, or any dimension\n- **Model usage** patterns (Gemini 1.5 Flash, Gemini 1.5 Pro, etc.)\n- **Customer behavior** - Who uses what, when, and how much\n- **Custom metadata** - Unlimited fields for your specific needs\n- **Performance metrics** - Latency, errors, success rates by segment\n\n### \ud83d\udc8e Advanced Analytics with Custom Metadata\n\nTrack arbitrary metadata with each API call to enable sophisticated analytics:\n\n```python\n# Example: AI-powered content generation with feature tracking\nresponse = client.models.generate_content(\n model=\"gemini-1.5-pro\",\n contents=\"Write a comprehensive guide about renewable energy...\",\n customer_id=\"customer-123\",\n # Custom metadata for analytics\n custom_metadata={\n \"feature\": \"content_generation\",\n \"experiment_group\": \"gemini_pro_test\",\n \"content_type\": \"technical_guide\",\n \"user_segment\": \"enterprise\",\n \"session_id\": \"sess_xyz789\"\n }\n)\n\n# Example: AI tutoring platform with learning analytics\nresponse = client.models.generate_content(\n model=\"gemini-1.5-flash\",\n contents=complex_physics_problem,\n customer_id=\"customer-456\",\n custom_metadata={\n \"use_case\": \"educational_tutoring\",\n \"subject\": \"physics\",\n \"interaction_count\": 5,\n \"learning_path\": \"advanced_physics\",\n \"engagement_score\": \"high\"\n }\n)\n\n# Example: Multi-modal analysis with usage patterns\nresponse = client.models.generate_content(\n model=\"gemini-1.5-pro-vision\",\n contents=[image_data, \"Analyze this image\"],\n customer_id=\"customer-789\",\n custom_metadata={\n \"modality\": \"vision_text\",\n \"workflow\": \"image_analysis\",\n \"api_version\": \"v2\",\n \"client_platform\": \"web\",\n \"feature_flag\": \"vision_enabled\"\n }\n)\n```\n\n**Intelligence Use Cases:**\n- **Feature adoption**: Track which AI features customers actually use\n- **A/B testing**: Compare model performance across experiment groups\n- **Learning analytics**: Understand educational engagement patterns\n- **Multi-modal insights**: Analyze usage across different modalities\n- **Platform optimization**: Identify performance bottlenecks by platform\n- **Product development**: Data-driven feature prioritization\n\n## \ud83d\udee1\ufe0f Production Ready\n\n**Extremely robust and reliable** - Built for production environments with:\n\n- **Resilient Tracking:** Gemini calls succeed even if tracking fails.\n- **Non-blocking I/O:** Fire-and-forget tracking never slows down your application.\n- **Automatic Retries:** Failed tracking attempts are automatically retried with exponential backoff.\n- **Thread-Safe Context:** Safely track usage across multi-threaded and async applications.\n- **Enterprise Security:** API key sanitization and input validation.\n\n## \ud83d\ude80 Quick Start\n\n### Installation\n\n```bash\npip install cmdrdata-gemini\n```\n\n### Basic Usage\n\n```python\n# Before\nfrom google import genai\nclient = genai.Client(api_key=\"your-gemini-key\")\n\n# After - same API, automatic tracking!\nimport cmdrdata_gemini\nclient = cmdrdata_gemini.TrackedGemini(\n api_key=\"your-gemini-key\",\n cmdrdata_api_key=\"your-cmdrdata-key\"\n)\n\n# Same API as regular Google Gen AI client\nresponse = client.models.generate_content(\n model=\"gemini-1.5-flash\",\n contents=\"Explain how AI works\"\n)\n\nprint(response.text)\n# Usage automatically tracked to cmdrdata backend!\n```\n\n### Async Support\n\n```python\nimport cmdrdata_gemini\n\nasync def main():\n client = cmdrdata_gemini.AsyncTrackedGemini(\n api_key=\"your-gemini-key\",\n cmdrdata_api_key=\"your-cmdrdata-key\"\n )\n\n response = await client.models.generate_content(\n model=\"gemini-1.5-flash\",\n contents=\"Hello, Gemini!\"\n )\n\n print(response.text)\n # Async usage tracking included!\n```\n\n## \ud83c\udfaf Customer Context Management\n\n### Automatic Customer Tracking\n\n```python\nfrom cmdrdata_gemini.context import customer_context\n\n# Set customer context for automatic tracking\nwith customer_context(\"customer-123\"):\n response = client.models.generate_content(\n model=\"gemini-1.5-flash\",\n contents=\"Help me code\"\n )\n # Automatically tracked for customer-123!\n\n# Or pass customer_id directly\nresponse = client.models.generate_content(\n model=\"gemini-1.5-flash\",\n contents=\"Hello\",\n customer_id=\"customer-456\" # Direct customer ID\n)\n```\n\n### Manual Context Management\n\n```python\nfrom cmdrdata_gemini.context import set_customer_context, clear_customer_context\n\n# Set context for current thread\nset_customer_context(\"customer-789\")\n\nresponse = client.models.generate_content(...) # Tracked for customer-789\n\n# Clear context\nclear_customer_context()\n```\n\n## \u2699\ufe0f Configuration\n\n### Environment Variables\n\n```bash\n# Optional: Set via environment variables\nexport GEMINI_API_KEY=\"your-gemini-key\"\nexport CMDRDATA_API_KEY=\"your-cmdrdata-key\"\nexport CMDRDATA_ENDPOINT=\"https://api.cmdrdata.ai/api/events\" # Optional\n```\n\n```python\n# Then use without passing keys\nclient = cmdrdata_gemini.TrackedGemini()\n```\n\n### Custom Configuration\n\n```python\nclient = cmdrdata_gemini.TrackedGemini(\n api_key=\"your-gemini-key\",\n cmdrdata_api_key=\"your-cmdrdata-key\",\n cmdrdata_endpoint=\"https://your-custom-endpoint.com/api/events\",\n track_usage=True, # Enable/disable tracking\n timeout=30, # Custom timeout\n max_retries=3 # Custom retry logic\n)\n```\n\n## \ud83d\udd12 Security & Privacy\n\n### Automatic Data Sanitization\n\n- **API keys automatically redacted** from logs\n- **Sensitive data sanitized** before transmission\n- **Input validation** prevents injection attacks\n- **Secure defaults** for all configuration\n\n### What Gets Tracked\n\n```python\n# Tracked data (anonymized):\n{\n \"customer_id\": \"customer-123\",\n \"model\": \"gemini-1.5-flash\",\n \"input_tokens\": 25,\n \"output_tokens\": 150,\n \"total_tokens\": 175,\n \"provider\": \"google\",\n \"timestamp\": \"2025-01-15T10:30:00Z\",\n \"metadata\": {\n \"response_id\": \"resp_abc123\",\n \"model_version\": \"001\",\n \"finish_reason\": \"STOP\",\n \"safety_ratings\": null\n }\n}\n```\n\n**Note**: Message content is never tracked - only metadata and token counts.\n\n## \ud83d\udcca Monitoring & Performance\n\n### Built-in Performance Monitoring\n\n```python\n# Get performance statistics\nstats = client.get_performance_stats()\nprint(f\"Average response time: {stats['api_calls']['avg']}ms\")\nprint(f\"Total API calls: {stats['api_calls']['count']}\")\n```\n\n### Health Monitoring\n\n```python\n# Check tracking system health\ntracker = client.get_usage_tracker()\nhealth = tracker.get_health_status()\nprint(f\"Tracking healthy: {health['healthy']}\")\n```\n\n## \ud83d\udee0\ufe0f Advanced Usage\n\n### Token Counting\n\n```python\n# Count tokens without generating content (also tracked)\ntoken_count = client.models.count_tokens(\n model=\"gemini-1.5-flash\",\n contents=\"How many tokens is this?\"\n)\nprint(f\"Token count: {token_count.total_tokens}\")\n```\n\n### Disable Tracking for Specific Calls\n\n```python\n# Disable tracking for sensitive operations\nresponse = client.models.generate_content(\n model=\"gemini-1.5-flash\",\n contents=\"Private query\",\n track_usage=False # This call won't be tracked\n)\n```\n\n### Error Handling\n\n```python\nfrom cmdrdata_gemini.exceptions import CMDRDataError, TrackingError\n\ntry:\n client = cmdrdata_gemini.TrackedGemini(\n api_key=\"invalid-key\",\n cmdrdata_api_key=\"invalid-cmdrdata-key\"\n )\nexcept CMDRDataError as e:\n print(f\"Configuration error: {e}\")\n # Handle configuration issues\n```\n\n### Integration with Existing Error Handling\n\n```python\n# All original Google Gen AI exceptions work the same way\ntry:\n response = client.models.generate_content(...)\nexcept Exception as e: # Google Gen AI exceptions\n print(f\"Google Gen AI error: {e}\")\n # Your existing error handling works unchanged\n```\n\n## \ud83d\udd27 Development\n\n### Requirements\n\n- Python 3.9+\n- google-genai>=0.1.0\n\n### Installation for Development\n\n```bash\ngit clone https://github.com/cmdrdata-ai/cmdrdata-gemini.git\ncd cmdrdata-gemini\npip install -e .[dev]\n```\n\n### Running Tests\n\n```bash\n# Run all tests\npytest\n\n# Run with coverage\npytest --cov=cmdrdata_gemini\n\n# Run specific test categories\npytest -m unit # Unit tests only\npytest -m integration # Integration tests only\n```\n\n### Code Quality\n\n```bash\n# Format code\nblack cmdrdata_gemini/\nisort cmdrdata_gemini/\n\n# Type checking\nmypy cmdrdata_gemini/\n\n# Security scanning\nsafety check\n```\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n### Development Workflow\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\n3. Make your changes\n4. Add tests for your changes\n5. Ensure all tests pass (`pytest`)\n6. Format your code (`black . && isort .`)\n7. Commit your changes (`git commit -m 'Add amazing feature'`)\n8. Push to the branch (`git push origin feature/amazing-feature`)\n9. Open a Pull Request\n\n## \ud83d\udcdc License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## \ud83c\udd98 Support\n\n- **Documentation**: [https://docs.cmdrdata.ai/gemini](https://docs.cmdrdata.ai/gemini)\n- **Issues**: [GitHub Issues](https://github.com/cmdrdata-ai/cmdrdata-gemini/issues)\n- **Support**: [spot@cmdrdata.ai](mailto:spot@cmdrdata.ai)\n\n## \ud83d\udd17 Related Projects\n\n- **[cmdrdata-openai](https://github.com/cmdrdata-ai/cmdrdata-openai)** - Usage tracking for OpenAI\n- **[cmdrdata-anthropic](https://github.com/cmdrdata-ai/cmdrdata-anthropic)** - Usage tracking for Anthropic Claude\n- **[CMDR Data Platform](https://www.cmdrdata.ai)** - Complete LLM usage analytics\n\n## \ud83d\udcc8 Changelog\n\nSee [CHANGELOG.md](CHANGELOG.md) for a complete list of changes and version history.\n\n---\n\n**Built with \u2764\ufe0f by the CMDR Data team**\n\n*Become the Google Analytics of your AI - understand everything, optimize everything.*\n",
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