The best LLM evaluation framework depends on what you are evaluating. For testing your own application, Promptfoo is the strongest choice if you want a CLI with YAML configs, DeepEval if you want pytest-style tests in Python, Ragas if your focus is retrieval-augmented generation, and Inspect if you are building rigorous, agentic or research-grade evaluations. For benchmarking base models on standard tasks, EleutherAI's lm-evaluation-harness, Hugging Face's lighteval and Stanford's HELM are the reference tools. All of these are free and open source.
The biggest mistake in this space is mixing the two categories. Application evaluation frameworks check whether your prompts, retrieval and agents behave correctly on your data. Model benchmark harnesses score a model on public academic tasks. This guide covers both, clearly separated.
Application evaluation frameworks vs benchmark harnesses
An application evaluation framework runs your own test cases through your own system and scores the results with code checks, LLM judges or reference answers. You use it during development and in CI, to catch regressions when you change a prompt, a model or a retrieval setting.
A model benchmark harness runs a model, usually directly rather than through your application, on standardized tasks like MMLU or GSM8K, with controlled prompting and scoring so results are comparable. You use it to compare models or check a fine-tune, and it is what independent leaderboards rely on. Our LLM benchmarks explainer covers what those tasks measure.
LLM evaluation framework comparison
| Framework | Type | License | Language | Best for |
|---|---|---|---|---|
| Promptfoo | App evaluation and red teaming | MIT | Node.js CLI, YAML | Prompt and model comparisons in CI |
| DeepEval | App evaluation | Apache 2.0 | Python, pytest-style | Unit tests with 50+ metrics |
| Ragas | App evaluation, RAG focus | Apache 2.0 | Python | Retrieval and answer quality metrics |
| Inspect | App and model evaluation | MIT | Python | Agentic and research-grade evals, 200+ prebuilt |
| TruLens | App evaluation and tracing | MIT | Python | Feedback functions on traced apps |
| lm-evaluation-harness | Model benchmark harness | MIT | Python | Standard academic tasks |
| lighteval | Model benchmark harness | MIT | Python | Hugging Face ecosystem |
| HELM | Model benchmark harness | Apache 2.0 | Python | Holistic, multi-metric model reports |
In short: Promptfoo, DeepEval, Ragas and TruLens test applications; lm-evaluation-harness, lighteval and HELM benchmark models; and Inspect spans both, with strong support for agent tasks and a large library of ready-made evaluations.
Application evaluation frameworks
Promptfoo
Promptfoo is a command-line tool where you declare prompts, model providers and test cases with assertions in a YAML file, then run them as a matrix and compare outputs side by side in a web viewer. Assertions range from exact matches and regular expressions to JSON schema checks and LLM-graded rubrics. It also includes a red teaming module, covered in our LLM red teaming tools guide. Getting started takes three commands: promptfoo init to scaffold an example, promptfoo eval to run it, and promptfoo view to open the results. It is MIT licensed and, since March 2026, part of OpenAI, with the project remaining open source.
DeepEval
DeepEval, from Confident AI, brings a pytest feel to LLM testing. You write test cases in Python, attach metrics such as answer relevancy, faithfulness, hallucination or a custom G-Eval rubric, and run them with deepeval test run. It offers more than 50 metrics, each scoring from 0 to 1 against a threshold that defaults to 0.5, and covers RAG, agents and conversations. It is Apache 2.0 licensed, with an optional hosted platform from the same company.
Ragas
Ragas specializes in retrieval-augmented generation, with metrics for faithfulness, context precision, context recall and response relevancy, plus tools for generating synthetic test sets. It is Apache 2.0 licensed; the repository moved to the vibrantlabsai organization, so update old links. Our RAG evaluation metrics guide explains each metric in depth.
Inspect
Inspect was created by the UK AI Security Institute and is MIT licensed (documentation). It structures evaluations as datasets, solvers and scorers, supports tool use, multi-turn dialog, sandboxed agent tasks and model-graded scoring, and ships a collection of more than 200 prebuilt evaluations. You run tasks with the inspect eval command and review transcripts in its log viewer. It is the most rigorous option here and well suited to agent evaluations, at the cost of more setup than Promptfoo or DeepEval.
TruLens and other options
TruLens, now under Snowflake and MIT licensed, instruments your application and runs feedback functions on the traces, including its well-known RAG triad of context relevance, groundedness and answer relevance. Other options worth knowing: Braintrust is a hosted evaluation platform whose autoevals scoring library is open source; Evidently is an Apache 2.0 library for evaluating and monitoring ML and LLM outputs; and OpenAI Evals offers a registry of evaluations, though OpenAI now steers users toward running evals in its dashboard. OpenAI's simple-evals stopped being updated for new models in July 2025, according to its README, and now mainly hosts reference implementations of HealthBench, BrowseComp and SimpleQA.
Observability platforms such as Langfuse, Arize Phoenix and LangSmith also include evaluation features tied to production traces; see our best LLM observability tools roundup.
Model benchmark harnesses
lm-evaluation-harness, from EleutherAI, is the most widely used harness for running models on hundreds of standard tasks (GitHub), and is the engine behind many published results. lighteval, from Hugging Face, plays a similar role with tight integration into the Hugging Face ecosystem. HELM, from Stanford's Center for Research on Foundation Models, emphasizes holistic reporting across accuracy, robustness, fairness and efficiency. OpenCompass is another Apache 2.0 platform with broad benchmark coverage.
Use these when you need comparable numbers on public tasks, for instance to check that a fine-tune did not damage general ability. They will not tell you whether your support bot answers your customers correctly.
Promptfoo vs DeepEval
The Promptfoo vs DeepEval decision is mostly about workflow. Promptfoo is configuration-first: YAML files, a CLI, and a comparison matrix that suits prompt engineering and comparing several models at once, and it works for teams in any language because it calls your app over HTTP or through providers. DeepEval is code-first: Python test functions that sit next to your application code and run in pytest-style CI, with a deeper library of research-backed metrics. JavaScript and polyglot teams tend to prefer Promptfoo; Python teams who think in unit tests tend to prefer DeepEval.
DeepEval vs Ragas
For DeepEval vs Ragas, both cover the core RAG metrics. Ragas is narrower and deeper on retrieval, with strong synthetic test generation. DeepEval is broader, adding agent, conversation, safety and custom rubric metrics plus a test runner. If RAG is your whole problem, Ragas is a focused choice; if RAG is one part of a larger app, DeepEval covers more ground. Many teams use Ragas metrics inside another framework.
How to choose an LLM evaluation framework
- Decide what you are evaluating: an application on your data, or a model on public tasks.
- Match your team's language and workflow: YAML and CLI, or Python tests.
- Check metric coverage: RAG, agents, conversations, safety or custom rubrics.
- Plan for judges: most metrics use an LLM judge, so calibrate them against human labels as explained in our LLM-as-a-judge guide.
- Wire it into CI: run a fixed test set on every change and block merges on regressions.
- Connect to production: pair offline evals with traces and online scores, as described in our playbook on how to evaluate LLM apps.
Pros and cons of open-source evaluation frameworks
Pros: free, transparent metric implementations, run anywhere, no data leaves your infrastructure except calls to your chosen judge model.
Cons: you maintain datasets and infrastructure yourself; judge-based metrics cost API calls and need calibration; collaboration features such as shared dashboards and annotation often live in paid hosted tiers.
Who each framework is for
Prompt engineers and polyglot teams: Promptfoo. Python teams wanting unit tests: DeepEval. RAG-heavy products: Ragas, with TruLens as an alternative. Research, safety and agent evaluation teams: Inspect. Model trainers and evaluators comparing checkpoints: lm-evaluation-harness, lighteval or HELM. If you need to compare model rankings rather than run your own tests, our LLM leaderboards guide explains how the public boards work.
FAQ
What is the best LLM evaluation framework?
For application testing, Promptfoo and DeepEval are the most popular general-purpose choices, Ragas leads for RAG, and Inspect is the most rigorous for agentic and research evaluations. For benchmarking models on standard tasks, lm-evaluation-harness is the reference tool.
What is the difference between Promptfoo and DeepEval?
Promptfoo is a configuration-first CLI that uses YAML files and a side-by-side comparison view, and includes red teaming. DeepEval is a code-first Python framework with pytest-style tests and more than 50 built-in metrics.
What is Inspect AI?
Inspect is an open-source, MIT-licensed framework for large language model evaluations created by the UK AI Security Institute. It supports tool use, multi-turn dialog, agent tasks and model-graded scoring, and includes more than 200 prebuilt evaluations.
What is lm-evaluation-harness used for?
EleutherAI's lm-evaluation-harness runs language models on hundreds of standardized benchmark tasks with consistent prompting and scoring. It is used to compare models and check fine-tunes, not to test applications.
Are LLM evaluation frameworks free?
The frameworks covered here are free and open source under MIT or Apache 2.0 licenses. You still pay for any model API calls, including calls to the LLM judges many metrics rely on, and some vendors sell optional hosted platforms.