openLLMtelemetry
Universal Observability and Guardrails for LLMs
openLLMtelemetry is a package built on top of OpenTelemetry instrumentation and provides both observability and guardrails for LLMs (Large Language Models).
The guardrail feature is enabled when you have the WhyLabs' Guardrails APIs deployed.
Key Features
- Observability: Monitor and analyze the performance and behavior of your LLM applications.
- Guardrails: Implement safety measures and control mechanisms for LLM outputs.
- Flexible Integration: Choose between code-based and zero-code solutions.
- OpenTelemetry Compatible: Built on top of industry-standard tracing technology.
Integration Options
Using openLLMtelemetry, you can instrument your LLM applications in two primary ways:
Code-based solutions via APIs and SDKs
- Direct integration into your application code
- Fine-grained control over instrumentation
- Customizable to fit specific use cases
Zero-code solutions
- Quick and easy setup without modifying application code
- Ideal for rapid prototyping or minimal-effort integration
- Suitable for teams with limited development resources
The integration is minimal, built on top of OpenTelemetry tracing. You can choose to export the traces to WhyLabs, or to your existing tracing stack.
Guardrails Integration
Once you have our Guardrails APIs deployment up and running, you can seamlessly integrate with our policy via openLLMtelemetry. This allows you to:
- Enforce content policies
- Implement safety checks
- Control LLM output based on predefined rules
Comprehensive Tracing and Data Flow Analysis
OpenLLMTelemetry's foundation on OpenTelemetry tracing provides significant benefits beyond LLM guardrailing. It allows users to trace data flow through various parts of their systems, enabling:
End-to-End Visibility: Track requests from ingestion to response in a distributed systems, including all intermediate processing steps. This includes traditional applications as well as novel LLM/GenAI agents and workflows.
Quality Assurance:
- Identify bottlenecks and data quality issues in data processing pipelines.
- Monitor the quality of inputs and outputs at each stage of your system.
- Detect anomalies or unexpected behaviors in real-time.
Security Enhancement:
- Trace sensitive data movement through your system.
- Identify potential security vulnerabilities or unauthorized access points.
- Monitor compliance with data handling policies and regulations.
Performance Optimization:
- Analyze latency and resource utilization across different components.
- Identify areas for optimization or scaling.
Error Tracing and Debugging:
- Quickly pinpoint the source of errors or unexpected outputs.
- Understand the context and data state at each step of processing.
AI/ML Model Lifecycle Management:
- Track model versions and their performance in production.
- Monitor feature engineering processes and data transformations.
Data Lineage:
- Understand the origin and transformations of data used in LLM inputs.
- Ensure data quality and relevance throughout the pipeline.
Getting Started
To start using openLLMtelemetry in your project:
- Install the package:
pip install openllmtelemetry
Choose your integration method (code-based or zero-code).
Configure your tracing export destination (WhyLabs or your existing tracing stack).
Implement guardrails using the Guardrails API integration.
For detailed documentation and examples, visit the openLLMtelemetry GitHub repository.
Conclusion
openLLMtelemetry provides a powerful toolkit for managing and monitoring your LLM applications. By combining observability and guardrails, it helps ensure the safe and efficient operation of AI-powered systems in production environments. The flexibility and extensibility of the package make it suitable for a wide range of use cases, from small-scale research projects to large-scale enterprise applications.
If you have any questions or feature requests, please reach out to us through the GitHub repository.
