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The platform for LLM evaluations and AI agent testing
The main features of langwatch/langwatch are: Application Development, Application Services, Generative AI, Inference Optimization, LLM Observability and Evaluation, Model Evaluation and Benchmarking, Model Visualization, Observability and Evaluation.
Projects with overlapping indexed features include: comet-ml/opik — Opik is an observability and evaluation platform designed for generative AI applications and agentic workflows. It… langfuse/langfuse — Langfuse is an open-source observability and evaluation platform designed for language model applications. It provides… helicone/helicone — Helicone is an AI gateway and observability platform designed to intercept, manage, and monitor interactions with… evidentlyai/evidently — Evidently is an AI observability platform and evaluation framework designed to quantify the performance of machine… arize-ai/phoenix — Arize Phoenix is an LLM observability platform and evaluation framework designed to capture execution traces and… latitude-dev/latitude-llm — This project is a self-hosted AI monitoring stack that functions as an LLM observability platform, AI evaluation…
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
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
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
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