AI Chatbot Testing Tools: 6 Options Compared for 2026
By Muhammad Usman · · 8 min read
What you actually need to test
Before picking a tool, decide what "working" means for your bot. For a customer-facing AI chatbot it usually means:
- Accuracy: answers match your own help articles and policies.
- Safety: no made-up promises, no leaked data, no unsafe advice.
- Hand-off: the bot passes angry customers and disputes to a person.
- Regression: an answer that was right last week is still right after a change.
- Live quality: real customers ask questions your test set never covered.
Most tools are strong at one or two of these. The table below shows where each fits.
The 6 options
| Tool | Type | Best for | Needs code? |
|---|---|---|---|
| Spreadsheet + manual review | Manual | First audit, very small bots | No |
| Botium / Cyara | Scripted conversation testing | Enterprise teams with test scripts and voice/IVR | Some setup |
| Promptfoo | Open-source LLM evaluation | Developers testing prompts and models before release | Yes |
| Langfuse | Open-source LLM observability | Developers tracing their own LLM app | Yes |
| LangSmith | LLM tracing and evaluation | Teams building with LangChain or LangGraph | Yes |
| ProofMyAI | No-code accuracy monitoring | Support teams running Fin, Zendesk AI, Tidio, WhatsApp or custom bots | No |
1. Spreadsheet and manual review
List 30 to 50 real customer questions with the facts a correct answer must contain, ask the bot each one, and mark the answers. It costs nothing and teaches you a lot, but it covers a small sample and needs repeating by hand after every change. Use our QA checklist to structure it.
2. Botium (Cyara)
Botium started as an open-source framework for scripted chatbot conversations and is now part of Cyara's customer-experience testing platform. You write expected conversation flows and run them automatically. It suits larger teams with many flows and channels. Scripted tests check the paths you wrote down, so they catch regressions well but say little about the free-form answers an LLM gives to new questions.
3. Promptfoo
An open-source command-line tool for evaluating prompts and models. Developers define test cases and assertions in config files and run them in CI, including red-teaming for unsafe output. Excellent when you control the prompt and model; not designed for support teams using a hosted bot.
4. Langfuse
Open-source LLM observability: traces of every model call, cost and latency tracking, prompt management and evaluations. It is built for engineers instrumenting their own application with an SDK. See our Langfuse alternative comparison if you need something your support team can run.
5. LangSmith
LangChain's platform for tracing, debugging and evaluating LLM applications, with datasets and evaluators. A natural fit if your bot is built with LangChain or LangGraph. Compare it in our LangSmith alternative page.
6. ProofMyAI
ProofMyAI checks every chatbot answer against your own help center and business rules, with no code. Connect Intercom Fin read-only, upload a chat export, or send conversations from any bot, WhatsApp bot, n8n or Make. Each answer gets a verdict (correct, not in the docs, made up, should have gone to a human, off policy) and a plain-English reason. Wrong answers are grouped by the help article that caused them, Fix with AI drafts the corrected article, nightly tests catch regressions, and alerts go to WhatsApp, Slack, Teams or email.
How to choose
- You build the bot yourself and write code: start with Promptfoo for pre-release tests, and add Langfuse or LangSmith for tracing in production.
- You run a hosted bot (Fin, Zendesk AI, Tidio, Chatbase) and own the help center: use no-code accuracy monitoring such as ProofMyAI, plus a short manual test set.
- You are an agency running bots for clients: you need per-client projects and client-ready reports; see ProofMyAI for agencies.
- You are an enterprise with scripted flows and voice channels: look at Cyara/Botium.
Many teams combine two: developer evaluations before release, and live accuracy monitoring after.
✅ Key takeaways
- Decide what 'working' means first: accuracy, safety, hand-off, regression and live quality.
- Scripted frameworks catch regressions on known paths; LLM eval tools suit developers; live accuracy monitoring suits support teams.
- Grade answers against your own help docs, because that is what customers hold you to.
- Most teams combine pre-release tests with live monitoring after launch.
❓ Frequently asked questions
What is the best free AI chatbot testing tool?
For a first check, a spreadsheet of real questions is free and effective. Promptfoo and Langfuse are open source for developers. ProofMyAI has a free plan for 50 conversations a month and a free single-answer checker that needs no account.
Can I test a chatbot I didn't build, like Intercom Fin?
Yes. You can't change its code, but you can grade its answers. Export conversations or connect Intercom read-only, then compare each answer with your help articles.
How often should I test my chatbot?
Run a small test set every night and after every help center or prompt change, and monitor live conversations continuously, because customers ask questions no test set covers.