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# Introduction
Whenever you have a new idea for a large language model (LLM) application, you need to evaluate it properly to understand its performance. Without evaluation, it is difficult to determine how well the application functions. However, the plethora of benchmarks, metrics and tools – often each with their own scripts – can make managing this process extremely difficult. Fortunately, open source developers and companies continue to release new frameworks to help with this challenge.
Although there are many options, my personal favorites are shared in this article LLM Assessment Platform. Additionally, a “gold cache” full of LLM assessment resources is attached at the end.
# 1. Deepwal
Dipole There is an open source framework specifically for evaluating LLM outputs. It’s easy to use and works much like Piste. You write test cases for your indicators and expected results, and Depaual computes a variety of metrics. It includes more than 30 built-in metrics (accuracy, consistency, relevance, deception check, etc.) that work on single-turn and multi-turn LLM tasks. You can also create custom metrics using a native LLMS or natural language processing (NLP) model.
It also allows you to generate synthetic datasets. It works with any LLM application (chatbots, retrieval-related generation (RAG) pipelines, agents, etc.) to help you benchmark and validate model behavior. Another useful feature is the ability to perform security scanning of your LLM applications for security threats. This is effective for quickly finding problems such as drift or model errors.
# 2. Aries (axe and phoenix)
arise offers both a freemium platform (ArizX) and an open source counterpart, Ariz-PhoenixLLM for Observation and Evaluation. Phoenix is ​​completely open source and self-hosted. You can log every model call, use built-in or custom evaluators, version control indicators, and group output to quickly find failures. It’s production-ready with async workers, scalable storage, and Open Limit (OTEL)-first integrations. This makes it easy to plug assessment results into your analytics pipelines. It is ideal for teams that want to work in a fully controlled or regulated environment.
ArizX offers a community edition of its product with many features, with a paid upgrade available for teams running at scale to LLM. It uses the same trace system as Phoenix but brings enterprise features such as SOC2 compliance, role-based access, bring your own key (BYOK) encryption, and over-the-air deployment. X also includes Alex, an AI assistant that analyzes traces, clusters failures, and drafts follow-up diagnostics so your team can work faster as part of the free product. You get dashboards, monitors and alerts in one place. Both tools make it easy to see where agents break down, allow you to create datasets and experiments, and optimize without having to juggle multiple tools.
# 3. Opaque
Opaque (by Comet) is an open source LLM assessment platform built for end-to-end testing of AI applications. It allows you to track the detailed marks of each LLM call, interpret them and view the results in a dashboard. You can experiment with automated LLM-judge metrics (for facts, toxicity, etc.), indicators, and inject parameters for safety (such as rejecting 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 to continuously improve and secure your LLM pipelines.
# 4. Langfuse
langfuse Another open source LLM is an engineering platform that focuses on observation and assessment. It automatically captures everything that happens during an LLM call (input, output, API calls, etc.) to provide complete traceability. It also provides features like centralized prompt versioning and an instant playground where you can quickly iterate over inputs and parameters.
On the evaluation side, Langfuss supports flexible workflows: you can use judges’ measurements as LLM, 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. This works well for teams that want full visibility into both the developer user experience (UX) (playground, prompt editor) and deployed LLM applications.
# 5. Use of language model assessment
Using language model assessment (by Eleutherai) is a classic open source benchmark framework. It bundles dozens of standard LLM benchmarks (more than 60 tasks like Big Bench, Massive Multitask Language Understanding (MMLU), Helswag, etc.) into one library. It supports models loaded through APIs like Face Transformers, GPT-Nukes, Megatron DeepSpeed, VLLM Inference Engine, and even OpenAI or TextSynth.
It is identified by the Hugging Face OpenLLM leaderboard, so it is used in the research community and cited by hundreds of papers. It’s not specifically for “app-centric” diagnostics (like tracking an agent). Rather, it provides reproducibility metrics across many tasks so you can measure how well the model performs against published baselines.
# Wrapping (and sleeping bags)
Each tool here has its own strengths. Deepval is good if you want to run tests locally and check security issues. Aries gives you deep visibility for self-hosted setups with Phoenix and X for enterprise scale. OPIC is great for end-to-end testing and optimizing agent workflows. Langfoss makes tracing and managing gestures easy. Finally, the use of LM assessment is perfect for benchmarking in many standard academic tasks.
To make things even easier, LLM Assessment The Repository by Andrey Loptenko gathers all the important LLM assessment tools, datasets, benchmarks and resources in one place. If you want a single center to test, evaluate and improve your models, this is it.
Kanwal Mehreen is a machine learning engineer and technical writer with a deep passion for data science and the intersection of AI with medicine. He co-authored the eBook “Maximizing Productivity with ChatGPT.” As a 2022 Google Generation Scholar for APAC, she champions diversity and academic excellence. He has also been recognized as a Teradata Diversity in Tech Scholar, a MITACS GlobalLink Research Scholar, and a Harvard Wicked Scholar. Kanwal is a passionate advocate for change, having founded the Fame Code to empower women in stem fields.