Monte Carlo Vector Database Observability

Open-source observability for GenAI and LLM applications based on OpenTelemetry, providing AI-aware instrumentation for vector databases, LLM frameworks, and model providers.

An open-source LLM observability and evaluation platform that provides comprehensive tracking, monitoring, and evaluation capabilities for large language model applications. Designed for production AI systems with focus on debugging and performance optimization.

Open-source observability layer designed to help developers monitor and understand how their applications interact with large language models. Acts as a lightweight proxy between applications and LLM providers.

An AI observability and evaluation platform that helps monitor and evaluate LLM outputs, RAG pipelines, and data quality, with tools for detecting hallucinations and measuring retrieval quality.

Comprehensive observability solution for vector databases through Zilliz Cloud integration, providing metrics for QPS, latency, slow queries, and failure rates alongside full stack monitoring.

Production-grade observability and evaluation platform for LLM applications from LangChain, providing tracing, debugging, prompt evaluation, and performance monitoring for reliable LLM workflows in development and production.