traceloop/openllmetry

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.

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