llm-observability

🪢 Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23

Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.

Next-generation AI Agent Optimization Platform: Cozeloop addresses challenges in AI agent development by providing full-lifecycle management capabilities from development, debugging, and evaluation to monitoring.

🧊 Open source LLM observability platform. One line of code to monitor, evaluate, and experiment. YC W23 🍓

Open Source TypeScript AI Agent Framework

The open-source LLMOps platform: prompt playground, prompt management, LLM evaluation, and LLM observability all in one place.

Laminar - open-source all-in-one platform for engineering AI products. Create data flywheel for your AI app. Traces, Evals, Datasets, Labels. YC S24.

The open source post-building layer for agents. Our environment data and evals power agent post-training (RL, SFT) and monitoring.

Build, Improve Performance, and Productionize your LLM Application with an Integrated Framework

Modular, open source LLMOps stack that separates concerns: LiteLLM unifies LLM APIs, manages routing and cost controls, and ensures high-availability, while Langfuse focuses on detailed observability, prompt versioning, and performance evaluations.

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.

React components for visualizing traces from AI agents

🪢 Auto-generated Java Client for Langfuse API

The reliability layer between your code and LLM providers.

Streamlit-based chatbot leveraging Ollama via LangChain and PostHog-LLM for advanced logging and monitoring

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.

AI Observability Platform – Advanced monitoring and analytics for LLM applications with intelligent gateway. 40+ AI-specific metrics, real-time insights, OpenAI-compatible.

The Modelmetry JS/TS SDK allows developers to easily integrate Modelmetry’s advanced guardrails and monitoring capabilities into their LLM-powered applications.