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Opik: Debugging, Evaluation, and Monitoring for LLM, RAG, and Agent Systems
Opik is a comprehensive tool designed to streamline the development and deployment of large language model (LLM) applications, RAG systems, and agentic workflows. It offers robust tracing, automated evaluation, and insightful production dashboards to debug, evaluate, and monitor your AI applications effectively.
Python
Added on 2025年6月22日10,065
Stars688
ForksPython
LanguageProject Introduction
Summary
Opik provides an end-to-end platform for observing, debugging, and improving LLM-powered applications, from initial development and experimentation through to production monitoring and evaluation.
Problem Solved
Debugging and monitoring complex LLM, RAG, and agent systems is challenging due to their non-deterministic nature and multi-step processes. Opik solves this by offering deep visibility into execution flows, performance metrics, and evaluation results, addressing the inherent opacity of these systems.
Core Features
Distributed Tracing
Visualize the entire execution flow of complex agent or RAG queries across multiple steps and services, making debugging vastly simpler.
Automated Evaluation Framework
Define and run custom evaluations on prompt outputs, agent behaviors, and RAG responses programmatically to ensure quality and track improvements.
Real-time Monitoring Dashboards
Monitor production performance, latency, costs, and user interactions with customizable dashboards, providing real-time visibility into your deployed AI systems.
Prompt Management & Versioning
Track and manage different versions of prompts used in your applications, facilitating experimentation and reproducibility.
Tech Stack
Python
FastAPI
SQLAlchemy
PostgreSQL
React
Docker
Kubernetes
Use Cases
Opik is essential for anyone building and operating LLM-powered applications who needs visibility, control, and systematic evaluation capabilities. Key use cases include:
Debugging Complex Agent Failures
Details
Trace the execution path of a multi-step agent that returned an incorrect or unexpected answer to pinpoint the exact step, tool call, or prompt that failed.
User Value
Dramatically reduces the time required to identify and fix issues in non-deterministic agent workflows.
Evaluating RAG System Performance
Details
Systematically evaluate the quality of answers produced by your RAG pipeline against ground truth, relevance, or other custom metrics using automated evaluation suites.
User Value
Ensures RAG system accuracy, helps optimize retrieval and generation components, and provides objective performance benchmarks.
Monitoring Production LLM Application Health
Details
Monitor production LLM endpoint performance, track key metrics like latency, token usage, error rates, and user feedback through real-time dashboards.
User Value
Proactively identify performance bottlenecks, stability issues, and cost escalations before they impact a large user base.
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