Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
The open-source LLMOps platform: prompt playground, prompt management, LLM evaluation, and LLM observability all in one place.
The open source post-building layer for agents. Our environment data and evals power agent post-training (RL, SFT) and monitoring.
A powerful AI observability framework that provides comprehensive insights into agent interactions across platforms, enabling developers to monitor, analyze, and optimize AI-driven applications with minimal integration effort.
A comprehensive solution for monitoring your AI models in production
Open-source observability for your LLM application.
A Python package for tracking and analyzing LLM usage across different models and applications. It is primarily designed as a library for integration into development process of LLM-based agentic workflow tooling, providing robust tracking capabilities.
Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like AI Agent, LLM and tools tracing, debugging multi-agentic system, self-hosted dashboards and advanced analytics with timeline and execution graph view.
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