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# Introduction
Whenever you have a new idea for a large language model (LLM) application, you must evaluate it properly to understand its performance. Without evaluation, it is difficult to determine how well the application functions. However, the abundance of benchmarks, metrics, and tools — often each with its own scripts — can make managing the process extremely difficult. Fortunately, open-source developers and companies continue to release new frameworks to assist with this challenge.
While there are many options, this article shares my personal favorite LLM evaluation platforms. Additionally, a “gold repository” packed with resources for LLM evaluation is linked at the end.
# 1. DeepEval
DeepEval is an open-source framework specifically for testing LLM outputs. It is simple to use and works much like Pytest. You write test cases for your prompts and expected outputs, and DeepEval computes a variety of metrics. It includes over 30 built-in metrics (correctness, consistency, relevancy, hallucination checks, etc.) that work on single-turn and multi-turn LLM tasks. You can also build custom metrics using LLMs or natural language processing (NLP) models running locally.
It also allows you to generate synthetic datasets. It works with any LLM application (chatbots, retrieval-augmented generation (RAG) pipelines, agents, etc.) to help you benchmark and validate model behavior. Another useful feature is the ability to perform safety scanning of your LLM applications for security vulnerabilities. It is effective for quickly spotting issues like prompt drift or model errors.
# 2. Arize (AX & Phoenix)
Arize offers both a freemium platform (Arize AX) and an open-source counterpart, Arize-Phoenix, for LLM observability and evaluation. Phoenix is fully open-source and self-hosted. You can log every model call, run built-in or custom evaluators, version-control prompts, and group outputs to spot failures quickly. It is production-ready with async workers, scalable storage, and OpenTelemetry (OTel)-first integrations. This makes it easy to plug evaluation results into your analytics pipelines. It is ideal for teams that want full control or work in regulated environments.
Arize AX offers a community edition of its product with many of the same features, with paid upgrades available for teams running LLMs at scale. It uses the same trace system as Phoenix but adds enterprise features like SOC 2 compliance, role-based access, bring your own key (BYOK) encryption, and air-gapped deployment. AX also includes Alyx, an AI assistant that analyzes traces, clusters failures, and drafts follow-up evaluations so your team can act fast as part of the free product. You get dashboards, monitors, and alerts all in one place. Both tools make it easier to see where agents break, allow you to create datasets and experiments, and improve without juggling multiple tools.
# 3. Opik
Opik (by Comet) is an open-source LLM evaluation platform built for end-to-end testing of AI applications. It lets you log detailed traces of every LLM call, annotate them, and visualize results in a dashboard. You can run automated LLM-judge metrics (for factuality, toxicity, etc.), experiment with prompts, and inject guardrails for safety (like redacting personally identifiable information (PII) or blocking unwanted topics). It also integrates with continuous integration and continuous delivery (CI/CD) pipelines so you can add tests to catch problems every time you deploy. It is a comprehensive toolkit for continuously improving and securing your LLM pipelines.
# 4. Langfuse
Langfuse is another open-source LLM engineering platform focused on observability and evaluation. It automatically captures everything that happens during an LLM call (inputs, outputs, API calls, etc.) to provide full traceability. It also provides features like centralized prompt versioning and a prompt playground where you can quickly iterate on inputs and parameters.
On the evaluation side, Langfuse supports flexible workflows: you can use LLM-as-judge metrics, collect human annotations, run benchmarks with custom test sets, and track results across different app versions. It even has dashboards for production monitoring and lets you run A/B experiments. It works well for teams that want both developer user experience (UX) (playground, prompt editor) and full visibility into deployed LLM applications.
# 5. Language Model Evaluation Harness
Language Model Evaluation Harness (by EleutherAI) is a classic open-source benchmark framework. It bundles dozens of standard LLM benchmarks (over 60 tasks like Big-Bench, Massive Multitask Language Understanding (MMLU), HellaSwag, etc.) into one library. It supports models loaded via Hugging Face Transformers, GPT-NeoX, Megatron-DeepSpeed, the vLLM inference engine, and even APIs like OpenAI or TextSynth.
It underlies the Hugging Face Open LLM Leaderboard, so it is used in the research community and cited by hundreds of papers. It is not specifically for “app-centric” evaluation (like tracing an agent); rather, it provides reproducible metrics across many tasks so you can measure how good a model is against published baselines.
# Wrapping Up (and a Gold Repository)
Every tool here has its strengths. DeepEval is good if you want to run tests locally and check for safety issues. Arize gives you deep visibility with Phoenix for self-hosted setups and AX for enterprise scale. Opik is great for end-to-end testing and improving agent workflows. Langfuse makes tracing and managing prompts simple. Lastly, the LM Evaluation Harness is perfect for benchmarking across a lot of standard academic tasks.
To make things even easier, the LLM Evaluation repository by Andrei Lopatenko collects all the main LLM evaluation tools, datasets, benchmarks, and resources in one place. If you want a single hub to test, evaluate, and improve your models, this is it.
Kanwal Mehreen is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She’s also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.
