LLM Evaluation Tools (2026): 10 Options for Testing AI Output

Evaluating an LLM app is how you move from vibes to metrics, a phrase one tool here uses on its own home page. You build a set of test inputs, run your prompts, model or RAG pipeline against them, and score each output with a metric or with another model acting as judge. Run that on every release and you see the moment a change makes things worse. Stress-testing for jailbreaks and misuse is covered by AI red teaming tools.

This list covers evaluation only. The ten tools below are listed alphabetically and in no particular order, and every description comes from each vendor's own website, read as of Oct 2026. If you need tracing and live monitoring first, my guide to LLM observability tools covers that side.

LLM eval libraries and platforms at a glance

I checked the free plan column on each vendor's own website, as of Oct 2026. Yes means the page names a free plan, a free version or an open source edition. Trial only means it names a time-limited free trial. Not stated means the page I read named neither, so check the vendor's own site before you sign up.

Tool	Type	Good for	Free plan
Arize AI	Observability and eval platform	Managed platform plus Phoenix open source	Yes
Braintrust	Agent observability and evals	Turning production patterns into evals	Not stated
Confident AI	Evaluation and observability platform	DeepEval framework plus a hosted team platform	Yes
Evidently AI	Open source eval framework	LLMs, RAG, agents and ML models together	Yes
Galileo	Observability and eval engineering	Evals that become production guardrails	Not stated
Langfuse	Open source evals and observability	One loop from traces to experiments	Yes
Opik	Open source observability and evals (Comet)	Logging and scoring every agent step	Yes
Promptfoo	AI testing and red teaming	Prompt, model and RAG pipeline tests	Yes
Ragas	Open source RAG eval library	Component-wise RAG metrics and synthetic data	Yes
TruLens	Open source evals and tracing	Agent tool selection and plan checks	Yes

Ten LLM evaluation tools for prompts, RAG and agents

Arize AI

Best for: Teams wanting a managed AI engineering platform, with Phoenix as the open source option for observability and evals they run themselves.

Braintrust

Best for: Agent builders who want to discover patterns in production, turn them into evals and improve quality with every release.

Confident AI

Best for: Engineers, product managers and QA sharing one eval platform built on the open source DeepEval framework, with eval coverage running in CI.

Evidently AI

Best for: Evaluating LLMs, RAG apps, AI agents and classic ML models in one open source framework, backed by free courses on LLM evals.

Galileo

Best for: Turning offline evals into production guardrails, with metrics auto-tuned from live feedback so scores fit your own environment.

Langfuse

Best for: Developers who want tracing, datasets, experiments and evaluation in one open source loop, now backed by ClickHouse.

Opik

Best for: Comet users wanting open source observability and evals that log every step an agent takes, from user input to context retrieval.

Promptfoo

Best for: Testing prompts, models and RAG pipelines, with red teaming and code scanning available from the same open source project.

Ragas

Best for: RAG apps, scoring context relevance, recall and precision component by component, and generating synthetic test data when you have little of your own.

TruLens

Best for: Agent teams checking tool selection and plan adherence, with OpenTelemetry-native tracing and evals for complete multi-turn conversations.

Evals and observability have merged, and two names changed hands

Here's what I noticed reading the vendor pages. The Langfuse home page now carries a banner announcing it joins ClickHouse, and the Humanloop home page reads Humanloop joins Anthropic. Neither is a reason to avoid a tool, but it's worth asking who sets the roadmap before you build a test suite on it.

The other shift is in the page titles. Arize, Galileo, Confident AI and Braintrust now describe themselves as observability and evaluation platforms in one, so the old split between an eval tool and a monitoring tool has mostly gone. The practical choice left is whether you run an open source library yourself or sign up to a hosted platform.

Open source eval library or hosted platform?

• Open source library. You install it in your own code, often with a single pip install, and run evals in a notebook or in CI. It's the route for engineers who want full control and no data leaving their stack. Eight of the ten tools here name an open source edition or framework on their own site.
• Hosted platform. You get datasets, experiments, human annotation and dashboards that product managers and QA can use without writing code, a use case one vendor here lists on its home page.
• Both. Several vendors run an open source core with a hosted product on top, so you can start in code and move to the platform once more people need to see results.

What to score before an LLM app ships

1. RAG quality. Context relevance, recall and precision, plus whether the answer stays faithful to what was retrieved.
2. Agent behaviour. Did it pick the right tool, follow its plan and finish the task? Tool selection and plan adherence are now standard metrics.
3. Multi-turn conversations. Single prompts miss what happens five messages in, and some tools now simulate users and whole conversations before you ship.
4. Safety. Jailbreaks and adversarial prompts belong in the suite too, even if a separate team owns red teaming.
5. Judge accuracy. If a model grades your outputs, check its scores against human labels on a sample first.

Evals feed straight into the rules your app enforces live. My AI agent guardrails checklist covers what to put in place once the tests pass.

Do you make an LLM evaluation tool? Tool makers can ask to be considered for a featured place on this list.

My plain advice on choosing LLM evaluation tools

Start with the open source route on one real feature. Write 30 to 50 test cases from real user questions, pick three metrics that match what can go wrong, and run them in CI on every prompt change. Move to a hosted platform when product managers, QA or domain experts need to review results without opening a notebook.

And don't confuse a passing eval suite with a healthy product. Evals sit inside a bigger machine learning stack, from training to deployment, which my guide to MLOps tools covers.

Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.

Frequently asked questions

What is LLM evaluation?

It's the process of scoring a language model app's outputs against test cases, using automatic metrics, human review or another model acting as a judge, so you measure quality instead of guessing.

What's the difference between LLM evaluation and LLM observability?

Evaluation tests outputs against a dataset, usually before release. Observability traces and monitors what happens in production. Most platforms on this list now do both, but the jobs are still different.

Are there free open source LLM evaluation tools?

Yes. Most tools on this list name an open source framework or edition on their own site, shown in the free plan column. You run those yourself.

What is LLM as a judge?

It's using one language model to grade another model's answers against criteria you set, such as accuracy or tone. It's fast, but check its grades against human labels before you trust them.

How many test cases do I need to start?

Fewer than you'd think. A few dozen real examples that cover your main use cases and known failures beat hundreds of synthetic ones nobody looks at.