Best tools to test AI agents (2026): an honest comparison
There is no single "best" tool: it depends on your stack and whether you have a technical team. On LangChain, LangSmith. Want everything free on your own server, Langfuse or Phoenix. Want tests in code, DeepEval or Promptfoo. No technical team and want to know today if your agent calls its functions well, an external pay-per-call audit like AUN's.
Comparison
| Tool | For whom | Best at | Open source | Entry price |
|---|---|---|---|---|
| LangSmith | Teams on LangChain or LangGraph | Tracing and evaluation built into its ecosystem | No | Limited free plan; developer ~US$39 per user per month |
| Braintrust | Teams that test every change before launch | Experiments, prompt versions and blocking deploys that regress | No | Generous free plan (~1M spans per month) |
| Langfuse | Teams that need data on their own servers | Self-hosted observability and trajectory evaluation | Yes (MIT) | Free self-hosted; cloud plans |
| DeepEval (Confident AI) | Developers testing in CI | 50+ metrics, including tool-call correctness | Yes (Apache 2.0) | Free; cloud dashboard from ~US$20 per user per month |
| Promptfoo | Security and model comparison | Adversarial testing (red teaming) from the command line | Yes | Free |
| Arize Phoenix | Teams with RAG or multi-agent systems | Open tracing and RAG evaluation | Yes | Free |
| AUN Tool-Call Audit | Agent owners without a test team, and agents auditing themselves | Hidden tool-call test + fix that keeps names and parameters + re-measurement | No | US$0.10, 0.15 or 0.20 per audit, no account; only if the report exists |
Prices and plans checked in October 2026; confirm them on each provider's site before deciding.
How to choose in 2 minutes
- Is your agent built on LangChain or LangGraph? LangSmith.
- Can't your data leave your server? Langfuse or Phoenix.
- Do you have developers and CI? DeepEval for metrics, Promptfoo for security.
- Want to block launches that make results worse? Braintrust.
- No technical team, or want an external measurement today? AUN.
Platform vs. external audit
Evaluation platforms give you the workshop: you write the cases, pick the metrics and read the results. An external audit gives you the diagnosis: someone who didn't build the agent tests it with cases the agent has never seen.
They don't compete. The ideal is a platform for day-to-day work and an external measurement before launch or after every big change. An agent tested only with its builder's own cases almost always passes.
What to measure in an agent
| Metric | The question it answers |
|---|---|
| Task success | Did it do what the user wanted? |
| Right tool | Did it choose the right function? |
| Right arguments | Did it send complete data in the right format? |
| Asks for what's missing | Did it ask for missing data instead of inventing it? |
| Consistency | Does it get the same case right when repeated? |
| Cost and time | How much does each task cost and how long does it take? |
Test your agent with AUN
You send your agent's tools and instructions. AUN measures it on a hidden test, returns a compatible fix and measures again. There is also a free schema review.
Sources
- ContextQA – AI Agent Evaluation Tools Compared
- Latitude – Best AI agent evaluation platforms 2026
- ArtificialQA – Best AI evaluation tools 2026
- Techsy – 8 LLM Eval Tools Ranked
- Origins – AI Agent Evaluation Tools Compared
Frequently asked questions
What is the best free tool to test agents?
DeepEval, Promptfoo, self-hosted Langfuse and Arize Phoenix are open source and free to use.
Do I need a platform if I only have one agent?
Not always. With a small agent, an external measurement before launch and after every change can be enough.
What is a hidden test?
A set of cases that neither the agent nor its builder knows. It keeps the agent from passing the exam because it already knew the answers.
How do I know if my functions are well written?
See Functions in AI agents.