LLM Evaluation Frameworks: Head-to-Head Comparison

As teams work on complex AI agents and expand what LLM-powered applications can achieve, a variety of LLM evaluation frameworks are emerging to help developers track, analyze, and improve how those applications perform. Certain core functions are becoming standard, but the truth is that two tools may look similar on the surface while providing very different results under the hood.

If you’re comparing LLM evaluation frameworks, you’ll want to do your own research and testing to confirm the best option for your application and use case. Still, it’s helpful to have some benchmarks and key feature comparisons as a starting point.

In this guest post originally published by the Trilogy AI Center of Excellence, Leonardo Gonzalez benchmarks many of today’s leading LLM evaluation frameworks, directly comparing their core features and capabilities, performance and reliability at scale, developer experience, and more.

Overview of Leading LLM Evaluation Frameworks

A wide range of frameworks and tools are available for evaluating Large Language Model (LLM) applications. Each offers unique features to help developers test prompts, measure model outputs, and monitor performance. Below is an overview of the notable LLM evaluation alternatives, along with their key features:

Promptfoo – A popular open-source toolkit for prompt testing and evaluation. It allows easy A/B testing of prompts and LLM outputs via simple YAML or CLI configurations, and even supports LLM-as-a-judge evaluations. It’s widely adopted (over 51,000 developers) and requires no complex setup (no cloud dependencies or SDK required). Promptfoo is especially useful for quick prompt iterations and automated “red-teaming” (e.g. checking for injections or toxic content) in a development workflow.

DeepEval – An open-source LLM evaluation framework (from Confident AI) designed to integrate into Python testing workflows. DeepEval is described as “Pytest for LLMs,” providing a simple, unit-test-like interface to validate model outputs. Developers can define custom metrics or use built-in ones to assess criteria like correctness or relevance. It’s favored for its ease of use and its ability to systematically unit test prompts and LLM-based functions.

MLflow LLM Evaluate – An extension of the MLflow platform that adds LLM model evaluation capabilities. It offers a modular way to run evaluations as part of ML pipelines, with support for common tasks like question-answering and RAG (Retrieval-Augmented Generation) evaluations out-of-the-box. This allows teams already using MLflow for experiment tracking to incorporate LLM evaluation alongside other ML metrics.

RAGAs – A framework purpose-built for evaluating RAG pipelines (LLM applications with retrieval). RAGAs computes five core metrics – Faithfulness, Contextual Relevancy, Answer Relevancy, Contextual Recall, and Contextual Precision – which together form an overall RAG score. It integrates recent research on retrieval evaluation. However, while RAGAs makes RAG-specific evaluation straightforward, its metrics are somewhat opaque (not self-explanatory), which can make debugging tricky when a score is low. It’s best suited for teams focused on QA systems or chatbots that rely heavily on document retrieval.

Deepchecks (LLM) – An open-source tool originally for ML model validation that now includes LLM evaluation modules. Deepchecks is geared more toward evaluating the LLM model itself rather than full application logic. It provides rich visualization dashboards to inspect model outputs, detect distribution shifts, and catch anomalies. This emphasis on UI and charts makes it easier to visualize evaluation results, though the setup is more complex and comes with a steeper learning curve.

LangSmith – An evaluation and observability platform introduced by the LangChain team. LangSmith offers tools to log and analyze LLM interactions, and it includes specialized evaluation capabilities for tasks such as bias detection and safety testing. It’s a powerful option if you are building chain-of-thought workflows with LangChain. However, LangSmith is a managed (hosted) service rather than pure open-source. It excels in tracking complex prompt sequences and ensuring responses meet certain safety or quality standards.

TruLens – An open-source library focused on qualitative analysis of LLM responses. TruLens works by injecting feedback functions that run after each LLM call to analyze the result. These feedback functions (often powered by an LLM or custom rules) automatically evaluate the original response—flagging issues like factuality or coherence. TruLens provides a framework to define such evaluators and gather their feedback, helping to interpret and improve model outputs. It’s primarily a Python library and is often used to monitor aspects such as bias, toxicity, or accuracy in real time during development.

Arize Phoenix – Open-sourced by Arize AI, Phoenix is an observability tool tailored for LLM applications. It logs LLM traces (multi-step interactions) and provides analytics to debug and improve LLM-driven workflows. Phoenix comes with a limited but useful built-in evaluation suite focused on Q&A accuracy, hallucination detection, and toxicity. This makes it handy for spotting these specific issues in model outputs—especially in Retrieval-Augmented Generation use cases. However, Phoenix does not include prompt management features (for example, you cannot version or centrally manage your prompts in its interface), so it is best utilized alongside broader platforms or in combination with other evaluation tools.

Langfuse – An open-source LLM engineering platform that covers tracing, evaluation, prompt management, and analytics in one system. Langfuse enables developers to instrument their LLM apps to log each step (spans of a chain or agent), and then review those traces in a dashboard. It supports custom evaluations and LLM-as-a-judge scoring on outputs (including running evaluations on production data for monitoring). A notable feature of Langfuse is its prompt management UI: you can store prompt templates, version them, and test changes easily, which helps standardize prompts across your team. It also tracks usage metrics and user feedback, making it a full-stack observability solution. Langfuse is known to be easy to self-host and is considered battle-tested for production use.

Comet Opik – An open-source end-to-end LLM evaluation and monitoring platform from Comet. Opik provides a suite of observability tools to track, evaluate, test, and monitor LLM applications across their development and production lifecycle. It logs complete traces and spans of prompt workflows, supports automated metrics (including complex ones like factual correctness via an LLM judge), and lets you compare performance across different prompt or model versions.

Each of these tools addresses LLM evaluation from a slightly different angle – some focus on automated scoring and metrics, others on prompt experimentation, and still others on production monitoring. Next, we’ll take a closer look at three standout options – Opik, Langfuse, and Phoenix – to see how they compare in depth.

Expanded Research: Comparing Opik, Langfuse, and Phoenix

Among the many LLM evaluation frameworks, Opik, Langfuse, and Phoenix often rise to the top due to their comprehensive feature sets and active development. Here we conduct an in-depth comparison of these three, focusing on critical factors like performance speed, functionality, usability, and unique offerings. We also highlight why Opik emerges as the leader based on benchmark data and capabilities.

Performance Benchmark: Speed of Logging and Evaluation

In LLMOps, speed matters. Fast logging and evaluation feedback loops mean you can iterate on prompts or models more quickly. A recent benchmark test measured how quickly each framework could log LLM traces and produce evaluation results:

• Opik: Completed logging of traces and spans in 23.10 seconds, with evaluation results available in just 0.34 seconds thereafter (total time ~23.44 seconds). This means Opik processed interactions and delivered metrics almost instantly after logging—a remarkably fast turnaround.
• Phoenix: Took about 41 seconds to log traces, and then evaluation results appeared after 128.59 seconds (a combined time of roughly 169.60 seconds), making it about 7 times slower than Opik.
• Langfuse: Logging took about 119.67 seconds, with results ready after 207.49 seconds, totaling approximately 327.15 seconds—around 14 times slower than Opik’s end-to-end evaluation time.

In a development scenario, Opik’s superior speed offers a clear edge, enabling rapid prompt tweaking and model tuning.

Feature Set and Functionality

All three platforms cover the fundamentals of LLM observability and evaluation, but there are notable differences in breadth and depth of features:

Tracing and Logging:

All three tools capture detailed traces of an LLM application, including logging prompts, responses, and metadata. Phoenix and Langfuse were originally positioned as observability solutions, while Opik emphasizes comprehensive tracing (even capturing nested calls in complex workflows). Both Langfuse and Opik support distributed tracing and external integrations for non-LLM steps.

Automated Evaluations:

Opik and Langfuse provide flexible evaluation setups—you can define custom metrics or use pre-built ones (including LLM-based evaluators for subjective criteria such as factual correctness or toxicity). Phoenix, however, offers only three fixed evaluation metrics (Correctness, Hallucination, Toxicity), which may require extension if additional criteria are needed.

Prompt Management: 

Both Opik and Langfuse recognize the importance of managing prompts.

Opik’s Prompt Library allows teams to centralize and version prompt templates, synchronizing prompt definitions from code (using an Opik.Prompt object) to ensure consistency.

Langfuse similarly includes prompt management within its UI.
In contrast, Phoenix lacks built-in prompt management, meaning teams must manage prompt versions separately.

Prompt Playground / Testing UI:

Opik’s interactive Prompt Playground lets users quickly test different prompt configurations—inputting system, user, and assistant messages, adjusting parameters like temperature, swapping models, and even batch testing against datasets. Langfuse offers a similar playground feature for testing and logging runs, while Phoenix does not provide an interactive prompt tester in its open-source version.

Integration and Extensibility:

All three tools integrate with common LLM libraries and endpoints, providing Python SDKs and callbacks for frameworks like LangChain or LlamaIndex. Opik further integrates with universal API wrappers (e.g., LiteLLM) to automatically log calls made to multiple LLM providers.

Dashboards and Analytics:

Each platform provides a web interface for reviewing evaluation results and traces. Both Opik and Langfuse offer polished dashboards with capabilities for filtering, comparing experiment runs, and drilling into usage analytics. Phoenix’s UI is more narrowly focused on troubleshooting evaluation issues, particularly in RAG scenarios.

Usability and Unique Offerings

Opik’s Developer-Friendly Design:

Opik is designed to be non-intrusive—rather than acting as a proxy for LLM calls, it logs interactions via decorators or callbacks, ensuring virtually zero latency impact. This ease of integration, along with features like the Prompt Playground and a centralized Prompt Library, makes it a strong candidate for both development and production scenarios.

Langfuse and Phoenix:

While Langfuse offers robust production monitoring and comprehensive analytics, its setup may be more complex for new users. Phoenix, on the other hand, is streamlined for quick debugging of specific issues (such as hallucinations or toxicity) but does not scale as well for broader evaluation needs.

Unique Capabilities:

Opik brings LLM unit testing integration into the fold, letting you define test cases that assert specific output conditions—providing a regression testing framework for prompts.

Its combination of human feedback (through manual annotations) with automated metrics creates a feedback loop that continuously refines evaluation criteria.

Langfuse emphasizes dataset integration and continual evaluation, ideal for tracking performance drift over time, while Phoenix specializes in RAG-focused troubleshooting by correlating retrieval failures with generation errors.

Opik: UI Functionality and Detailed Capabilities

A standout strength of Opik lies in its extensive UI features and robust SDK capabilities. Here’s a closer look at what Opik offers:

UI functionality includes:

Datasets: Manage and version evaluation datasets, ensuring consistency in the test data used across experiments.

Experiments: Track every evaluation run as an experiment, enabling side-by-side comparisons and performance trending over time.

Prompt Library: Centrally store, version, and organize your prompt templates. This helps standardize prompts across your team and simplifies rollback when a new variant underperforms.

Prompt Playground: An interactive interface that lets you experiment with prompt configurations in real time—adjusting system, user, and assistant messages; tweaking parameters; and testing on sample datasets.

SDK Capabilities:

Evaluate Prompts: Score and compare prompt outputs using built-in or custom metrics, ensuring each prompt meets performance expectations.

Evaluate LLM Apps: Assess entire LLM applications, verifying that the integrated system performs reliably under production conditions

Manage Prompts in Code: Integrate prompt management directly into your codebase using Opik’s Python SDK, facilitating seamless development workflows.

Pytest Integration: Incorporate prompt evaluation into your existing CI/CD pipelines with straightforward Pytest integration.

Production Monitoring: Monitor LLM applications in real time to ensure continuous performance and quality, even after deployment.

Customized Scoring Rules: Define and apply custom scoring rules to tailor evaluations to specific use cases, providing granular insight into model behavior.

Conclusion and Recommendation

After surveying the landscape and examining the top options, Opik stands out as the preferred LLM evaluation framework. It demonstrated dramatically faster performance in benchmarks, offers a rich feature set (including comprehensive tracing, automated and custom evaluations, and robust prompt management via both a UI and code integration), and is built with developer usability in mind.

Opik’s extensive UI functionalities—from managing datasets and tracking experiments to a centralized prompt library and interactive prompt playground—empower teams to standardize their evaluation workflows. Coupled with capabilities such as prompt evaluation, LLM application assessment, and integration with testing frameworks and production monitoring, Opik creates a seamless environment for both development and production.

We recommend Opik for teams seeking a reliable, efficient, and comprehensive evaluation framework. Its speed can save countless hours during large evaluation runs, while its rich set of features enables consistent prompt testing, detailed metrics tracking, and immediate feedback through an interactive playground. Furthermore, when paired with a production deployment and observability platform like PortKey, the synergy ensures that your LLM not only performs well during evaluation but also continues to excel in real-world usage.

Opik’s design, which minimizes integration overhead and maximizes developer control, positions it as the ideal tool for continuous improvement in LLM performance. By leveraging Opik’s powerful UI and robust SDK capabilities, you can confidently test, ship, and scale your LLM-powered applications to meet both performance standards and user needs.

Here is a GitHub repository with a POC for model evaluation and prompt evaluation, as well as a corresponding demo video.