langwatch/langwatch

• langfuse/langfuse

  langfuse/langfuse

  29,190عرض على GitHub↗

  Langfuse is an open-source observability and evaluation platform designed for language model applications. It provides a centralized system for tracking execution traces, monitoring performance metrics, and managing prompt templates. By capturing hierarchical units of work and telemetry data, the platform enables developers to debug complex application lifecycles and analyze token usage, latency, and model interactions in production environments. The platform distinguishes itself through an integrated evaluation framework that allows for systematic benchmarking and automated scoring of model

  TypeScriptanalyticsautogenevaluation

  عرض على GitHub↗29,190
• comet-ml/opik

  comet-ml/opik

  17,787عرض على GitHub↗

  Opik is an observability and evaluation platform designed for generative AI applications and agentic workflows. It provides a centralized environment for tracing execution flows, managing prompt templates, and monitoring production performance, allowing teams to gain visibility into complex model interactions and tool usage without requiring manual application code changes. The platform distinguishes itself through its integrated approach to the AI development lifecycle, combining distributed trace instrumentation with automated evaluation frameworks. It supports model-as-a-judge scoring, syn

  Pythonevaluationhacktoberfesthacktoberfest2025

  عرض على GitHub↗17,787
• arize-ai/phoenix

  Arize-ai/phoenix

  8,605عرض على GitHub↗

  Arize Phoenix is an LLM observability platform and evaluation framework designed to capture execution traces and monitor large language model applications. It serves as a prompt management system for versioning and testing templates, and as a self-hosted AI operations infrastructure for managing telemetry and experiments. The platform differentiates itself through a specialized embedding visualization tool used to detect data drift and optimize vector search. It provides a comprehensive evaluation suite that utilizes judge-based evaluators and ground-truth datasets to score model outputs, and

  Jupyter Notebookagentsai-monitoringai-observability

  عرض على GitHub↗8,605
• evidentlyai/evidently

  evidentlyai/evidently

  7,137عرض على GitHub↗

  Evidently is an AI observability platform and evaluation framework designed to quantify the performance of machine learning models and large language models. It functions as a monitoring tool for detecting data drift and quality degradation in tabular datasets, while providing a specialized analyzer for the faithfulness and correctness of retrieval augmented generation systems. The project distinguishes itself through an evaluation framework that utilizes judge models and custom rubrics to score language model outputs. It includes tools for iterative prompt optimization and the generation of

  Jupyter Notebookdata-driftdata-qualitydata-science

  عرض على GitHub↗7,137

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