traceloop/openllmetry
Openllmetry
OpenLLMetry is an OpenTelemetry-based observability framework and instrumentation library for generative AI applications. It provides toolsets for tracing and monitoring large language model workflows, capturing telemetry from model providers, agent frameworks, and vector databases using standardized semantic conventions.
The project distinguishes itself by providing a specialized evaluation and experimentation suite that associates user feedback and prompt version hashes with specific execution traces. It includes a system for tracking model reasoning paths and enforcing security guardrails on model inputs and outputs.
The framework covers broad capability areas including token usage monitoring for cost management, vector store performance tracking, and the capture of nested AI workloads through span-based hierarchies. It also implements data privacy management to suppress sensitive content from telemetry payloads before exporting data to external monitoring platforms.
Features
- OpenTelemetry Standard Integrations - Captures traces and metrics for AI providers and frameworks using vendor-neutral OpenTelemetry standards.
- AI Observability Tracing - Provides specialized systems for capturing and analyzing execution traces and performance of AI applications.
- Semantic Convention Standardizers - Translates provider-specific API responses into standardized data formats for consistent cross-vendor observability.
- LLM Observability - Implements an observability framework using OpenTelemetry to track traces, metrics, and spans for LLMs.
- LLM Tracing Systems - Captures prompt versions, token usage, and reasoning paths to debug and optimize AI workflows.
- Execution Span Hierarchies - Organizes nested AI workloads and agentic tasks into a tree of spans to visualize execution flow.
- AI Instrumentation Libraries - Provides a toolkit for generating observability data from generative AI frameworks, model providers, and vector databases.
- Prompt and Agent Versioning - Tracks prompt iterations using version hashes and timestamps to correlate changes with model performance.
- Observability Instrumentation - Provides instrumentation and tracing hooks specifically designed for observability within AI and ML pipelines.
- AI Guardrails - Provides a security layer for inspecting and filtering AI requests to enforce safety and usage policies.
- AI Security and Governance - Manages data privacy and enforces safety guardrails on model inputs and outputs.
- Prompt Experimentation - Provides a framework for conducting comparative experiments on prompt strategies and model configurations.
- LLM Cost Management - Monitors and optimizes token consumption across different model providers to control operational expenses.
- LLM Evaluation Frameworks - Runs comparative model experiments and associates user feedback with specific execution traces.
- Reasoning Path Recording - Records reasoning attributes and logic paths for models utilizing chain-of-thought or complex processing.
- User Feedback Collection - Gathers explicit user ratings and associates them with specific execution traces to evaluate output quality.
- Performance Monitoring - Measures retrieval efficiency and query latency for vector stores to improve retrieval augmented generation.
- AI Prompt Engineering - Enables the comparison of model outputs across different prompt versions and configurations to optimize quality.
- AI Application Debugging - Traces execution paths and reasoning logic within agent frameworks to diagnose failures in multi-step tasks.
- Data Privacy Controls - Ensures sensitive information is excluded from telemetry payloads before transmission to ensure privacy compliance.
- Telemetry Data Suppression - Implements mechanisms to suppress sensitive content from telemetry payloads to ensure privacy and security compliance.
- Dynamic Function Interception - Injects monitoring logic around AI library calls to automatically record inputs, outputs, and token usage.
- Automatic Tracing Instrumentation - Provides lightweight instrumentation for capturing execution details and performance metrics in AI agent frameworks.
- Vector - Captures query performance and retrieval metrics from vector databases to identify latency bottlenecks.
- Telemetry Exporters - Pushes internal performance metrics and traces to third-party observability platforms using standardized exporters.
- Workload Tracing - Tracks calls to generative AI models and vector databases to monitor behavior across different providers.
- OpenTelemetry Exporters - Provides pluggable export pipelines that comply with OpenTelemetry standards for routing telemetry data.
- Telemetry Exporters - Forwards captured system metrics and trace data to external storage or analysis platforms.
- Token Consumption Trackers - Extracts and aggregates token usage data from model responses to monitor operational costs.
- Model Evaluation and Benchmarking - Performance monitoring and execution tracing for LLM applications.
- Observability and Evaluation - Open-source observability for LLM applications based on OpenTelemetry.
- Observability and Tracing - OpenTelemetry-based observability for LLM apps.
- Observability and Evaluation - OpenTelemetry-based observability for LLM and agent workflows.
- Testing and Observability - Observability for LLM apps based on OpenTelemetry.
