# AuraTrace 🔍
[](https://badge.fury.io/py/auratrace)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/Apache-2.0)
[](https://github.com/psf/black)
[](https://github.com/auratrace/auratrace/actions)
[](https://auratrace.readthedocs.io/)
**AI-powered data lineage and observability tool for Python** that transparently traces data pipelines using pandas, Dask, and PyArrow, capturing lineage, profiling data, detecting quality issues, and providing AI-assisted root cause analysis and visualization.
## ✨ Features
### 🔍 **Automatic Data Lineage**
- Transparent tracing of pandas operations
- Captures dataframe metadata and relationships
- Builds comprehensive DAG of data transformations
- Supports Dask and PyArrow for large-scale processing
### 📊 **Data Profiling & Quality**
- Automatic data profiling and statistics
- PII detection and privacy compliance
- Custom quality rules with YAML configuration
- Real-time quality issue detection and reporting
### ⚡ **Performance Monitoring**
- Execution time tracking for each operation
- Memory usage monitoring and optimization
- Bottleneck identification and suggestions
- Performance regression detection
### 🤖 **AI-Powered Analysis**
- Natural language queries about your data
- AI-assisted root cause analysis
- Performance optimization suggestions
- Automated data quality insights
### 📈 **Visualization & Reporting**
- Interactive lineage graphs
- Performance dashboards
- Quality issue reports
- Pipeline comparison tools
### 🛠️ **Developer-Friendly**
- Simple CLI interface
- Python API for custom integrations
- Comprehensive logging and debugging
- Extensible plugin architecture
## 🚀 Quick Start
### Installation
```bash
pip install auratrace
```
### Basic Usage
```python
# Your existing pandas code works unchanged!
import pandas as pd
# AuraTrace automatically traces these operations
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df_filtered = df[df['A'] > 1]
df_grouped = df.groupby('A').sum()
print("Pipeline completed!")
```
Run with AuraTrace:
```bash
auratrace run your_script.py
```
### CLI Commands
```bash
# Run a pipeline with tracing
auratrace run pipeline.py
# View pipeline results
auratrace view session.json
# Ask AI questions about your data
auratrace ask session.json "What operations were performed?"
# Check data quality
auratrace check session.json
# Compare pipeline versions
auratrace compare session1.json session2.json
# Initialize project configuration
auratrace init
```
## 📖 Documentation
- **[Installation Guide](docs/installation.md)** - Complete setup instructions
- **[Quick Start](docs/quickstart.md)** - Get up and running in 5 minutes
- **[CLI Reference](docs/cli-reference.md)** - Complete command documentation
- **[API Reference](docs/api-reference.md)** - Python API documentation
- **[Examples](docs/examples/)** - Real-world usage examples
## 🏗️ Architecture
AuraTrace consists of several core components:
```
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Tracer │ │ Profiler │ │ Lineage │
│ │ │ │ │ │
│ • Pandas hooks │ │ • Data stats │ │ • DAG building │
│ • Operation │ │ • PII detection │ │ • Graph export │
│ capture │ │ • Schema │ │ • Visualization │
│ • Performance │ │ analysis │ │ • Impact │
│ monitoring │ │ • Quality │ │ analysis │
└─────────────────┘ │ checks │ └─────────────────┘
└─────────────────┘
│
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Quality │ │ Performance │ │ AI Assistant │
│ Engine │ │ Engine │ │ │
│ │ │ │ │ │
│ • Rule engine │ │ • Metrics │ │ • OpenAI │
│ • YAML config │ │ collection │ │ integration │
│ • Issue │ │ • Bottleneck │ │ • Natural │
│ detection │ │ detection │ │ language │
│ • Custom rules │ │ • Optimization │ │ queries │
└─────────────────┘ │ suggestions │ └─────────────────┘
└─────────────────┘
```
## 🔧 Installation Options
### Basic Installation
```bash
pip install auratrace
```
### Full Installation (with AI features)
```bash
pip install auratrace[all]
```
### Development Installation
```bash
git clone https://github.com/auratrace/auratrace.git
cd auratrace
pip install -e .
```
## 🎯 Use Cases
### Data Science Teams
- **Pipeline Debugging**: Quickly identify where data issues originate
- **Performance Optimization**: Find bottlenecks in data processing
- **Quality Assurance**: Ensure data meets quality standards
- **Documentation**: Automatically generate pipeline documentation
### Data Engineering
- **Lineage Tracking**: Understand data dependencies and impact
- **Quality Monitoring**: Set up automated quality checks
- **Performance Tuning**: Optimize large-scale data processing
- **Compliance**: Track PII and sensitive data handling
### Machine Learning
- **Feature Engineering**: Trace feature transformations
- **Model Validation**: Ensure data quality for model training
- **Experiment Tracking**: Compare different data preprocessing approaches
- **Reproducibility**: Maintain complete data lineage
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
### Development Setup
```bash
git clone https://github.com/auratrace/auratrace.git
cd auratrace
pip install -e ".[dev]"
pytest
```
### Ways to Contribute
- 🐛 **Report bugs** - Use [GitHub Issues](https://github.com/auratrace/auratrace/issues)
- 💡 **Suggest features** - Start a [Discussion](https://github.com/auratrace/auratrace/discussions)
- 📝 **Improve docs** - Submit PRs to enhance documentation
- 🔧 **Fix bugs** - Pick up issues labeled "good first issue"
- 🚀 **Add features** - Implement new functionality
## 📊 Project Status
- ✅ **Core tracing engine** - Complete
- ✅ **Data profiling** - Complete
- ✅ **Quality engine** - Complete
- ✅ **Performance monitoring** - Complete
- ✅ **CLI interface** - Complete
- ✅ **AI assistant** - Complete
- ✅ **Documentation** - Complete
- ✅ **Tests** - Complete
- 🔄 **Visualization** - In progress
- 🔄 **Dask integration** - In progress
## 📈 Roadmap
### v1.0 (Current)
- ✅ Core tracing and profiling
- ✅ Quality checks and AI analysis
- ✅ CLI and basic visualization
### v1.1 (Next)
- 🔄 Enhanced visualization
- 🔄 Dask and PyArrow support
- 🔄 Plugin architecture
### v1.2 (Future)
- 🔄 Real-time monitoring
- 🔄 Web dashboard
- 🔄 Enterprise features
## 🆘 Support
### Getting Help
- 📚 **[Documentation](docs/)** - Comprehensive guides and examples
- 💬 **[Discussions](https://github.com/auratrace/auratrace/discussions)** - Ask questions and share ideas
- 🐛 **[Issues](https://github.com/auratrace/auratrace/issues)** - Report bugs and request features
- 📧 **Email** - [support@auratrace.io](mailto:support@auratrace.io)
### Community
- 🌐 **[Website](https://auratrace.io)** - Project homepage
- 📖 **[Blog](https://auratrace.io/blog)** - Latest updates and tutorials
- 🐦 **[Twitter](https://twitter.com/auratrace)** - Follow for updates
- 💼 **[LinkedIn](https://linkedin.com/company/auratrace)** - Professional updates
## 📄 License
This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- **Pandas** - For the excellent data manipulation library
- **OpenAI** - For AI capabilities
- **NetworkX** - For graph operations
- **Rich** - For beautiful terminal output
- **Typer** - For CLI framework
## ⭐ Star History
[](https://star-history.com/#auratrace/auratrace&Date)
---
**Made with ❤️ by the AuraTrace team**
*Empowering data scientists and engineers with transparent, AI-powered observability.*
## 🤖 AI Assistant & LLM Providers (Optional)
AuraTrace's AI features are powered by pluggable LLM providers. You do **not** need to install any AI model or dependency unless you use AI features.
### Supported Providers
- **OpenAI** (e.g., GPT-3.5, GPT-4)
- **Hugging Face** (API or local models)
- **Custom API** (any HTTP endpoint)
- **Local Model** (user-supplied, e.g., transformers)
- **User-supplied Python function**
### Optional Dependencies
- `openai` (for OpenAI models)
- `transformers` (for Hugging Face/local models)
- `requests` (for custom API)
Install only what you need:
```bash
pip install auratrace[openai] # For OpenAI
pip install auratrace[huggingface] # For Hugging Face
pip install auratrace[all] # For all AI features
```
### Configuring the AI Assistant
You can set the provider, model, and API key via environment variables or in code:
```bash
# Example: Use Hugging Face with a specific model
export AURATRACE_LLM_PROVIDER=huggingface
export AURATRACE_LLM_MODEL=mistralai/Mistral-7B-Instruct-v0.2
# Example: Use OpenAI
export AURATRACE_LLM_PROVIDER=openai
export AURATRACE_LLM_API_KEY=sk-...
export AURATRACE_LLM_MODEL=gpt-4
```
Or in Python:
```python
from auratrace.ai import AIAssistant
ai = AIAssistant(provider="huggingface", model="mistralai/Mistral-7B-Instruct-v0.2")
```
### First Use: Model Download/Setup
- If you run an AI command without the required model/dependency, AuraTrace will prompt you to install or configure it.
- You can change the default model/provider at any time.
- If you have your own LLM, you can use it by passing a custom function or API endpoint.
### Example: User-supplied LLM
```python
def my_llm(prompt):
# Your custom logic
return "My LLM response"
ai = AIAssistant(provider="user", custom_generate_fn=my_llm)
```
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"description": "# AuraTrace \ud83d\udd0d\n\n[](https://badge.fury.io/py/auratrace)\n[](https://www.python.org/downloads/)\n[](https://opensource.org/licenses/Apache-2.0)\n[](https://github.com/psf/black)\n[](https://github.com/auratrace/auratrace/actions)\n[](https://auratrace.readthedocs.io/)\n\n**AI-powered data lineage and observability tool for Python** that transparently traces data pipelines using pandas, Dask, and PyArrow, capturing lineage, profiling data, detecting quality issues, and providing AI-assisted root cause analysis and visualization.\n\n## \u2728 Features\n\n### \ud83d\udd0d **Automatic Data Lineage**\n- Transparent tracing of pandas operations\n- Captures dataframe metadata and relationships\n- Builds comprehensive DAG of data transformations\n- Supports Dask and PyArrow for large-scale processing\n\n### \ud83d\udcca **Data Profiling & Quality**\n- Automatic data profiling and statistics\n- PII detection and privacy compliance\n- Custom quality rules with YAML configuration\n- Real-time quality issue detection and reporting\n\n### \u26a1 **Performance Monitoring**\n- Execution time tracking for each operation\n- Memory usage monitoring and optimization\n- Bottleneck identification and suggestions\n- Performance regression detection\n\n### \ud83e\udd16 **AI-Powered Analysis**\n- Natural language queries about your data\n- AI-assisted root cause analysis\n- Performance optimization suggestions\n- Automated data quality insights\n\n### \ud83d\udcc8 **Visualization & Reporting**\n- Interactive lineage graphs\n- Performance dashboards\n- Quality issue reports\n- Pipeline comparison tools\n\n### \ud83d\udee0\ufe0f **Developer-Friendly**\n- Simple CLI interface\n- Python API for custom integrations\n- Comprehensive logging and debugging\n- Extensible plugin architecture\n\n## \ud83d\ude80 Quick Start\n\n### Installation\n\n```bash\npip install auratrace\n```\n\n### Basic Usage\n\n```python\n# Your existing pandas code works unchanged!\nimport pandas as pd\n\n# AuraTrace automatically traces these operations\ndf = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})\ndf_filtered = df[df['A'] > 1]\ndf_grouped = df.groupby('A').sum()\n\nprint(\"Pipeline completed!\")\n```\n\nRun with AuraTrace:\n\n```bash\nauratrace run your_script.py\n```\n\n### CLI Commands\n\n```bash\n# Run a pipeline with tracing\nauratrace run pipeline.py\n\n# View pipeline results\nauratrace view session.json\n\n# Ask AI questions about your data\nauratrace ask session.json \"What operations were performed?\"\n\n# Check data quality\nauratrace check session.json\n\n# Compare pipeline versions\nauratrace compare session1.json session2.json\n\n# Initialize project configuration\nauratrace init\n```\n\n## \ud83d\udcd6 Documentation\n\n- **[Installation Guide](docs/installation.md)** - Complete setup instructions\n- **[Quick Start](docs/quickstart.md)** - Get up and running in 5 minutes\n- **[CLI Reference](docs/cli-reference.md)** - Complete command documentation\n- **[API Reference](docs/api-reference.md)** - Python API documentation\n- **[Examples](docs/examples/)** - Real-world usage examples\n\n## \ud83c\udfd7\ufe0f Architecture\n\nAuraTrace consists of several core components:\n\n```\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Tracer \u2502 \u2502 Profiler \u2502 \u2502 Lineage \u2502\n\u2502 \u2502 \u2502 \u2502 \u2502 \u2502\n\u2502 \u2022 Pandas hooks \u2502 \u2502 \u2022 Data stats \u2502 \u2502 \u2022 DAG building \u2502\n\u2502 \u2022 Operation \u2502 \u2502 \u2022 PII detection \u2502 \u2502 \u2022 Graph export \u2502\n\u2502 capture \u2502 \u2502 \u2022 Schema \u2502 \u2502 \u2022 Visualization \u2502\n\u2502 \u2022 Performance \u2502 \u2502 analysis \u2502 \u2502 \u2022 Impact \u2502\n\u2502 monitoring \u2502 \u2502 \u2022 Quality \u2502 \u2502 analysis \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502 checks \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n \u2502\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Quality \u2502 \u2502 Performance \u2502 \u2502 AI Assistant \u2502\n\u2502 Engine \u2502 \u2502 Engine \u2502 \u2502 \u2502\n\u2502 \u2502 \u2502 \u2502 \u2502 \u2502\n\u2502 \u2022 Rule engine \u2502 \u2502 \u2022 Metrics \u2502 \u2502 \u2022 OpenAI \u2502\n\u2502 \u2022 YAML config \u2502 \u2502 collection \u2502 \u2502 integration \u2502\n\u2502 \u2022 Issue \u2502 \u2502 \u2022 Bottleneck \u2502 \u2502 \u2022 Natural \u2502\n\u2502 detection \u2502 \u2502 detection \u2502 \u2502 language \u2502\n\u2502 \u2022 Custom rules \u2502 \u2502 \u2022 Optimization \u2502 \u2502 queries \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502 suggestions \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n```\n\n## \ud83d\udd27 Installation Options\n\n### Basic Installation\n```bash\npip install auratrace\n```\n\n### Full Installation (with AI features)\n```bash\npip install auratrace[all]\n```\n\n### Development Installation\n```bash\ngit clone https://github.com/auratrace/auratrace.git\ncd auratrace\npip install -e .\n```\n\n## \ud83c\udfaf Use Cases\n\n### Data Science Teams\n- **Pipeline Debugging**: Quickly identify where data issues originate\n- **Performance Optimization**: Find bottlenecks in data processing\n- **Quality Assurance**: Ensure data meets quality standards\n- **Documentation**: Automatically generate pipeline documentation\n\n### Data Engineering\n- **Lineage Tracking**: Understand data dependencies and impact\n- **Quality Monitoring**: Set up automated quality checks\n- **Performance Tuning**: Optimize large-scale data processing\n- **Compliance**: Track PII and sensitive data handling\n\n### Machine Learning\n- **Feature Engineering**: Trace feature transformations\n- **Model Validation**: Ensure data quality for model training\n- **Experiment Tracking**: Compare different data preprocessing approaches\n- **Reproducibility**: Maintain complete data lineage\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n### Development Setup\n\n```bash\ngit clone https://github.com/auratrace/auratrace.git\ncd auratrace\npip install -e \".[dev]\"\npytest\n```\n\n### Ways to Contribute\n\n- \ud83d\udc1b **Report bugs** - Use [GitHub Issues](https://github.com/auratrace/auratrace/issues)\n- \ud83d\udca1 **Suggest features** - Start a [Discussion](https://github.com/auratrace/auratrace/discussions)\n- \ud83d\udcdd **Improve docs** - Submit PRs to enhance documentation\n- \ud83d\udd27 **Fix bugs** - Pick up issues labeled \"good first issue\"\n- \ud83d\ude80 **Add features** - Implement new functionality\n\n## \ud83d\udcca Project Status\n\n- \u2705 **Core tracing engine** - Complete\n- \u2705 **Data profiling** - Complete\n- \u2705 **Quality engine** - Complete\n- \u2705 **Performance monitoring** - Complete\n- \u2705 **CLI interface** - Complete\n- \u2705 **AI assistant** - Complete\n- \u2705 **Documentation** - Complete\n- \u2705 **Tests** - Complete\n- \ud83d\udd04 **Visualization** - In progress\n- \ud83d\udd04 **Dask integration** - In progress\n\n## \ud83d\udcc8 Roadmap\n\n### v1.0 (Current)\n- \u2705 Core tracing and profiling\n- \u2705 Quality checks and AI analysis\n- \u2705 CLI and basic visualization\n\n### v1.1 (Next)\n- \ud83d\udd04 Enhanced visualization\n- \ud83d\udd04 Dask and PyArrow support\n- \ud83d\udd04 Plugin architecture\n\n### v1.2 (Future)\n- \ud83d\udd04 Real-time monitoring\n- \ud83d\udd04 Web dashboard\n- \ud83d\udd04 Enterprise features\n\n## \ud83c\udd98 Support\n\n### Getting Help\n\n- \ud83d\udcda **[Documentation](docs/)** - Comprehensive guides and examples\n- \ud83d\udcac **[Discussions](https://github.com/auratrace/auratrace/discussions)** - Ask questions and share ideas\n- \ud83d\udc1b **[Issues](https://github.com/auratrace/auratrace/issues)** - Report bugs and request features\n- \ud83d\udce7 **Email** - [support@auratrace.io](mailto:support@auratrace.io)\n\n### Community\n\n- \ud83c\udf10 **[Website](https://auratrace.io)** - Project homepage\n- \ud83d\udcd6 **[Blog](https://auratrace.io/blog)** - Latest updates and tutorials\n- \ud83d\udc26 **[Twitter](https://twitter.com/auratrace)** - Follow for updates\n- \ud83d\udcbc **[LinkedIn](https://linkedin.com/company/auratrace)** - Professional updates\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.\n\n## \ud83d\ude4f Acknowledgments\n\n- **Pandas** - For the excellent data manipulation library\n- **OpenAI** - For AI capabilities\n- **NetworkX** - For graph operations\n- **Rich** - For beautiful terminal output\n- **Typer** - For CLI framework\n\n## \u2b50 Star History\n\n[](https://star-history.com/#auratrace/auratrace&Date)\n\n---\n\n**Made with \u2764\ufe0f by the AuraTrace team**\n\n*Empowering data scientists and engineers with transparent, AI-powered observability.* \n\n## \ud83e\udd16 AI Assistant & LLM Providers (Optional)\n\nAuraTrace's AI features are powered by pluggable LLM providers. You do **not** need to install any AI model or dependency unless you use AI features.\n\n### Supported Providers\n- **OpenAI** (e.g., GPT-3.5, GPT-4)\n- **Hugging Face** (API or local models)\n- **Custom API** (any HTTP endpoint)\n- **Local Model** (user-supplied, e.g., transformers)\n- **User-supplied Python function**\n\n### Optional Dependencies\n- `openai` (for OpenAI models)\n- `transformers` (for Hugging Face/local models)\n- `requests` (for custom API)\n\nInstall only what you need:\n\n```bash\npip install auratrace[openai] # For OpenAI\npip install auratrace[huggingface] # For Hugging Face\npip install auratrace[all] # For all AI features\n```\n\n### Configuring the AI Assistant\n\nYou can set the provider, model, and API key via environment variables or in code:\n\n```bash\n# Example: Use Hugging Face with a specific model\nexport AURATRACE_LLM_PROVIDER=huggingface\nexport AURATRACE_LLM_MODEL=mistralai/Mistral-7B-Instruct-v0.2\n\n# Example: Use OpenAI\nexport AURATRACE_LLM_PROVIDER=openai\nexport AURATRACE_LLM_API_KEY=sk-...\nexport AURATRACE_LLM_MODEL=gpt-4\n```\n\nOr in Python:\n\n```python\nfrom auratrace.ai import AIAssistant\nai = AIAssistant(provider=\"huggingface\", model=\"mistralai/Mistral-7B-Instruct-v0.2\")\n```\n\n### First Use: Model Download/Setup\n- If you run an AI command without the required model/dependency, AuraTrace will prompt you to install or configure it.\n- You can change the default model/provider at any time.\n- If you have your own LLM, you can use it by passing a custom function or API endpoint.\n\n### Example: User-supplied LLM\n```python\ndef my_llm(prompt):\n # Your custom logic\n return \"My LLM response\"\nai = AIAssistant(provider=\"user\", custom_generate_fn=my_llm)\n``` ",
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