Opik
Track, evaluate, and optimize your open-source LLM applications, RAG systems, and agentic workflows with ease.
Overview
Opik is an open-source platform designed to streamline the evaluation, debugging, and monitoring of Large Language Model (LLM) applications. It provides a comprehensive suite of tools that enable developers to track traces and spans, define and compute custom evaluation metrics, and score LLM outputs. This allows for easy comparison of performance across different application versions and iterations, ensuring continuous improvement and optimization. A core strength of Opik lies in its ability to automate prompt engineering and agent optimization. It offers four powerful optimizers—Few-shot Bayesian, MIPRO, evolutionary, and LLM-powered MetaPrompt—to help users achieve elite system prompts and freeze them into reusable, production-ready assets. Furthermore, Opik integrates robust AI guardrails to maximize trust and safety by screening user inputs and LLM outputs, detecting and redacting sensitive information like PII, competitor mentions, or off-topic discussions, using either its built-in models or third-party libraries.
Pricing
Free
- Unlimited team members
- 25k spans per month
- 60-day data retention
- LLM tracing
- Datasets and experiments
- LLM-as-a-judge metrics
Pro
- Unlimited team members
- 100k spans per month
- 60-day data retention
- Everything in the Free plan
- Options to upgrade and customize number of monthly spans
- Options to upgrade and customize data retention periods
Pros & Cons
Pros
- Comprehensive platform for LLM lifecycle management from development to production
- Automated prompt optimization significantly reduces manual tuning efforts
- Robust guardrails enhance trust and safety for LLM applications
- Open-source nature allows for transparency and community contributions
- Detailed tracing and evaluation metrics provide deep insights into LLM behavior
- Supports various LLM application types including RAG systems and agentic workflows
Cons
- Requires technical expertise to set up and integrate effectively
- Steep learning curve for new users unfamiliar with LLM evaluation concepts
- Reliance on open-source models might require more self-management compared to proprietary solutions
- Performance and scalability could depend on the underlying infrastructure provided by the user
Use Cases
Reviews & Ratings
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Best For
- Debugging and optimizing LLM responses in development
- Benchmarking different LLM models or application versions
- Ensuring safety and compliance of LLM outputs in production
- Automating prompt engineering for complex AI agents
- Monitoring the performance and behavior of RAG systems
- Iterative improvement of LLM-powered features based on evaluation metrics
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