Best AI Agent Evaluation Tools
2026 Market Guide
A criteria-based guide to the tools for evaluating AI agents, offline and in production, maintained by Prefactor, which builds one of them, with every claim about the other tools checked against their own sites.
How we compared: 7 criteria
- Agent-level evaluation: full trajectories and tool use, not just single LLM outputs
- Offline evaluation against a golden dataset for CI gating
- Continuous online scoring of live production traffic
- Who can write and tune evaluations: engineers only, or product managers and subject matter experts too
- A mix of rule-based checks, LLM-as-a-judge, and human review
- Framework coverage: LangChain, LangGraph, CrewAI, and others
- Open source / self-hosting option
What matters when evaluating agents in production?
- Evaluating every run. A test set before release shows how the agent handles known cases. Scoring each completed run in production shows how it handles real inputs, which is where most of the useful signal is.
- Judging against the outcome the business wants. Generic metrics such as faithfulness or toxicity say whether an answer is well formed. Criteria drawn from what the business counts as a good outcome say whether the agent did the job.
- Who can write and tune evaluations. The people who know what good looks like are often product managers and subject matter experts. Check whether they can write and adjust evaluations themselves, or whether every change goes through an engineer.
- How results lead to changes in the agent. An evaluation earns its place when it changes the agent. Look at how results reach the people who own the agent, and whether a change can be checked against the same evaluations afterwards.
Agent evaluation tools compared
Updated October 2026. Each value was checked against the tool's own site, and the sources are linked on every row. Prefactor publishes this page and is listed alphabetically with the rest.
| Tool | Best for | How it evaluates | Deployment | Pricing model | Sources |
|---|---|---|---|---|---|
| Arize AX | Improving agents in production (Arize is now part of Dynatrace) | LLM-as-judge, agent-as-judge and code evaluators label production traces continuously; datasets and experiments compare changes; human annotation calibrates scores. | Cloud; self-hosted on Enterprise | Free tier, then monthly plan metered on spans, ingestion and retention | Pricing · Docs |
| Arize Phoenix | Local-first tracing, evaluation and experiments you run yourself | LLM-based response and retrieval evals and code evaluators, run over versioned datasets and experiments. | Self-hosted (Elastic License 2.0, source-available) | Free; Arize AX is the paid hosted option | GitHub · Pricing |
| Braintrust | Experiments and evals for teams improving AI products | Scorers (built-in, LLM-as-judge or custom code) run on datasets as experiments, in CI and on production logs. | Cloud; self-hosted data plane on Enterprise | Free tier, then platform fee plus usage; unlimited users | Pricing · Evaluate docs · Self-hosting |
| DeepEval (Confident AI) | Pytest-style evals in CI, with a hosted platform on top | 50+ metrics, including LLM-as-judge, agent, RAG and multi-turn, run as unit tests; Confident AI adds online evals on traces and human annotation. | DeepEval runs locally (Apache 2.0); Confident AI is cloud, on-premises on Enterprise | DeepEval free; Confident AI free tier, then flat plans plus trace usage | GitHub · Pricing |
| Galileo (Splunk Agent Observability) | Evaluating and monitoring enterprise AI apps and agents, now within Splunk | LLM-as-judge, small Luna evaluation models and code-based metrics, applied in experiments and to production logs. | Cloud (Splunk Observability Cloud) or self-hosted | Free edition, then annual pricing based on span volume | Metrics docs · Acquisition · Pricing |
| Langfuse | Open source tracing and evals you can self-host (now part of ClickHouse) | LLM-as-judge, code evaluators, user feedback and manual labelling on traces, plus datasets for experiments. | Cloud or self-hosted (MIT, apart from licensed add-ons) | Free to self-host; cloud free tier, then monthly plan plus usage units | Pricing · Self-hosting · GitHub |
| LangSmith | Teams building agents, especially on LangChain or LangGraph | Offline experiments on datasets plus online evaluation of production traces, using LLM-as-judge, code rules, pairwise comparison and human review. | Cloud (US or EU); hybrid or self-hosted on Enterprise | Free single-seat plan, then per seat plus pay-as-you-go traces | Pricing · Evaluation docs |
| Opik (Comet) | Open source LLM and agent observability with evaluation | LLM-as-judge and heuristic metrics on datasets, online evaluation rules on production traces, feedback scores and a PyTest integration for CI. | Cloud or self-hosted (Apache 2.0) | Free to self-host; cloud free tier, then flat monthly plans metered on spans | Pricing · GitHub |
| Prefactor | Evaluating every agent run against the company's own knowledge and context | Judges score each completed run against criteria your team writes, or built-in ones such as correctness and hallucination, and record a pass or fail verdict with an explanation on the run. Product managers and subject matter experts can write and tune criteria in the web app, not only engineers. Runtime policies, approval holds and a kill switch can act on what the judges find. | Cloud; self-hosted on Enterprise. Judges can also run in your own infrastructure | Free tier, then usage-based plans metered on spans | Evals docs · Pricing |
| Ragas | Moving from spot checks to systematic LLM and RAG evaluation in code | LLM-based and traditional metrics, custom LLM-judged metrics and synthetic test set generation, run as experiments in code. | Python library you run yourself (Apache 2.0) | Free; no published paid tier | Docs · GitHub |
| W&B Weave | Tracing and evaluating AI agents (Weights & Biases is now part of CoreWeave) | Evaluation pipelines with LLM-judge and custom scorers over datasets; monitors score production traces automatically. | Cloud (CoreWeave-hosted); dedicated or private hosting on Enterprise | Free tier, then plan with seat caps plus ingestion overage | Pricing · Monitors docs · GitHub |
Prefactor
Evaluation against the company's own knowledge and contextClosed sourceBest for Evaluating every agent run against what the business counts as a good outcome
Records every run of your agents through native SDKs or OpenTelemetry, and judges score each completed run against criteria drawn from the company's own knowledge and context, or built-in criteria such as correctness and hallucination. Each verdict is recorded on the run as a pass or fail with an explanation. Criteria are written in the web app, so product managers and subject matter experts can write and tune evaluations, not only engineers. Judges run on your own LLM provider, or in your own infrastructure. It can also act on what the judges find, with runtime policies, approval holds and a kill switch for a run, an agent or a team.
Visit Prefactor →Arize Phoenix
Source-available eval & tracingPartial OSSBest for Teams who want self-hosted evals tied to traces
Source-available observability and evaluation (Elastic License 2.0) built on OpenInference/OpenTelemetry; Arize is part of Dynatrace since 1 October 2026. Run evals locally over traced runs, with Arize AX for production scale.
Visit Arize Phoenix →Braintrust
Commercial evaluation platformClosed sourceBest for Eval-driven development and experimentation
An evaluation and experimentation platform with eval harnesses, scoring functions, datasets, and a playground. Strong on the offline experimentation and regression-testing workflow.
Visit Braintrust →DeepEval
Open-source eval libraryOpen sourceBest for Developers who want pytest-style evals in CI
An open-source LLM and agent evaluation framework with a large metric library (faithfulness, relevancy, tool-use correctness) that runs like unit tests, paired with the hosted Confident AI platform for tracking.
Visit DeepEval →LangSmith
Commercial eval & experimentsClosed sourceBest for Teams building on LangChain or LangGraph
LangChain's platform for datasets, offline experiments, and online evaluation, with the tightest integration into the LangChain/LangGraph ecosystem.
Visit LangSmith →Ragas
Open-source RAG/agent evalOpen sourceBest for RAG-heavy pipelines
An open-source framework focused on evaluating RAG and agent pipelines with largely reference-free metrics; widely used and integrates with most tracing tools.
Visit Ragas →Frequently asked questions
How do I choose an agent evaluation tool?
Start from where your gap is. If you want evals as code in CI, an open-source library like DeepEval or Ragas drops into your test suite. If you're on LangChain, LangSmith's datasets and experiments are the path of least resistance. If you want a dedicated experimentation platform, Braintrust is built for that.
After offline evals, most teams add scoring of production runs, and then need the results to reach the people who can change the agent. Look at who writes the evaluations and what happens when one fails. Prefactor focuses on that production side: evaluating every run against the company's own knowledge and context, with criteria product managers and subject matter experts can tune, plus runtime policies, approval holds and a kill switch for a run, an agent or a team to contain what the evaluations catch. Many teams pair an offline harness with production evaluation rather than choosing one.
What is the difference between agent evaluation and LLM evaluation?
LLM evaluation scores a model in isolation: accuracy on a benchmark or a fixed dataset at dev time. Agent evaluation scores the whole agent: did it complete the multi-step task, select the right tools, stay grounded, and do it at acceptable cost, on real inputs.
A state-of-the-art model can still be a broken agent, which is why agent evaluation looks at full trajectories rather than single completions. See our guide to what agent evaluation is for the full explanation.
Do I need both offline and online evaluation?
Usually yes. Offline evals against a golden dataset gate regressions before you ship: they confirm the agent works on known cases. Online evals score live runs: they catch the failures and drift a fixed test set never anticipated, since agents can change behaviour with no code change when a model or tool shifts.
The strongest programmes feed failed production runs back into the offline dataset, so the two reinforce each other. And the most complete add runtime controls on top, so a detected failure can be contained, not just logged, which is where Prefactor focuses.
Are open-source agent evaluation tools good enough?
For offline evaluation, yes: DeepEval, Ragas, and Arize Phoenix cover metrics and trajectory checks well and keep your data in your own environment. The trade-offs are the operational cost of running them and, often, lighter continuous production scoring than commercial platforms.
A common pattern is an open-source library for CI gating plus a platform that scores every production run.
See how Prefactor evaluates every agent run against your company's knowledge and context
Prefactor evaluates agents using your company's own knowledge of what good looks like, so you know they're doing the job and where they need to improve. It records every run, judges score each one against the criteria your team sets, and runtime policies act on what they find.
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