The Best Agent Evaluation Tools — 2026 Market Guide
A vendor-led, criteria-based guide to the tools for evaluating AI agents — offline and in production — maintained by Prefactor and refreshed monthly, candid about where Prefactor leads and where others fit.
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
- Enforcement — acting on results at runtime (guardrails, policy, controls), not only scoring
- 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
| Tool | Category | Best for | Open source |
|---|---|---|---|
| Prefactor | Continuous agent eval & risk management in production | Scoring agents on live traffic, tracking quality per agent, and managing risk in production | No |
| Arize Phoenix | Open-source eval & tracing | Teams who want open-source evals tied to traces | Yes |
| Braintrust | Commercial evaluation platform | Eval-driven development and experimentation | No |
| DeepEval | Open-source eval library | Developers who want pytest-style evals in CI | Yes |
| LangSmith | Commercial eval & experiments | Teams building on LangChain or LangGraph | No |
| Ragas | Open-source RAG/agent eval | RAG-heavy pipelines | Yes |
Prefactor
Continuous agent eval & risk management in productionRuns evals on a sample of production traffic on top of the traces you already collect — tracking agent quality and cost per agent across sessions, sub-agents, and tool calls — and then enforces: runtime guardrails, policy, and controls that contain what the evals catch, rather than only scoring it. Across any framework via native SDKs or OpenTelemetry.
Visit Prefactor →Arize Phoenix
Open-source eval & tracing Open sourceBest for Teams who want open-source evals tied to traces
Open-source observability and evaluation built on OpenInference/OpenTelemetry. Run evals locally over traced runs, with Arize AX for production scale.
Phoenix runs evals you operate yourself, tied to traces. Prefactor runs them continuously on production traffic, tracks quality per agent, and adds runtime risk management to act on what evals find — across frameworks via native SDKs and OTel.
Braintrust
Commercial evaluation platform Closed 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.
Braintrust is strong on the offline, pre-ship half — experiments and regression testing. Prefactor focuses on the online half — scoring live traffic, catching drift, and managing risk at runtime — and complements an offline harness like Braintrust.
DeepEval
Open-source eval library Open 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.
DeepEval is open-source, pytest-style evals for CI gating. Prefactor adds the production side — continuous scoring per agent plus runtime risk management to act on failures — on top of the traces you already collect, and pairs well with a CI library like DeepEval.
LangSmith
Commercial eval & experiments Closed 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.
LangSmith is the path of least resistance on LangChain. Prefactor is framework-agnostic and centres on continuous production evaluation, per-agent cost and quality, and runtime risk management across any stack.
Ragas
Open-source RAG/agent eval Open 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.
Ragas provides open-source metrics for RAG and agent pipelines. Prefactor uses metrics like these but runs them continuously in production, per agent, and adds runtime risk management to act on failures rather than only scoring them.
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.
The gap most teams hit after offline evals is continuous scoring of live traffic — and then acting on what it finds. Knowing the agent regressed isn't the same as containing it. Prefactor focuses on that production half: continuous evaluation plus runtime risk management. Many teams pair an offline harness with an online scorer 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 a sample of live traffic — they catch the failures and drift a fixed test set never anticipated, since agents degrade with no code change when a model or tool shifts.
The strongest programmes feed failed production sessions back into the offline dataset, so the two reinforce each other. And the most complete add runtime risk management 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 and runtime risk controls than commercial platforms.
A common pattern is an open-source library for CI gating plus a commercial or specialised layer for online scoring and risk management at scale.
See how Prefactor evaluates and manages the risk of your agents on live production traffic
Prefactor helps teams observe, evaluate, and improve their AI agents in production — across every framework and provider.
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