Announcement
Comet/Opak - LLM, RAG, and Agent Observability Platform
Comprehensive platform for debugging, evaluating, and monitoring Large Language Model (LLM) applications, Retrieval Augmented Generation (RAG) systems, and agentic workflows. Offers end-to-end tracing, automated evaluation capabilities, and production-ready dashboards for insights.
Project Introduction
Summary
This project provides a robust toolkit designed to give developers and MLOps engineers deep visibility and control over their LLM, RAG, and agentic applications. It addresses the critical need for effective debugging, rigorous evaluation, and reliable production monitoring of these complex AI systems.
Problem Solved
Developers and MLOps teams building complex LLM-based applications, RAG systems, and AI agents face significant challenges in understanding internal execution flows, systematically evaluating performance across iterations, and monitoring live applications for issues, drift, or performance degradation. Lack of visibility hinders debugging and optimization.
Core Features
Comprehensive Tracing
End-to-end tracing to visualize the flow and performance of requests through LLM applications, RAGs, and agents.
Automated Evaluations
Automated evaluation tools to assess model outputs against defined metrics or benchmarks programmatically.
Production Dashboards
Pre-built and customizable dashboards for monitoring key metrics, identifying bottlenecks, and gaining insights into application behavior in production.
Tech Stack
使用场景
This tool is essential for anyone building, deploying, or managing applications that rely on Large Language Models, Retrieval Augmented Generation, or AI agents. Specific use cases include:
Debugging RAG Pipelines
Details
Trace the execution path of a user query through a RAG pipeline, including retriever calls, prompt construction, and LLM generation, to diagnose latency or incorrect responses.
User Value
Faster identification and resolution of issues in RAG systems.
Automated Agent Evaluation
Details
Automate evaluation runs on new agent code or prompt variations using a defined dataset and metrics (e.g., correctness, completeness) to track performance improvements or regressions.
User Value
Systematic and data-driven approach to improving agent performance.
Monitoring Production LLM Costs
Details
Monitor the cost and token usage of LLM calls in a production application, visualize trends over time, and set up alerts for unexpected spikes.
User Value
Cost optimization and budget control for LLM-powered applications.
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