Can you recommend an LLM observability tool for evaluating hallucinations in customer support automation?

Yes — for customer support automation, I’d recommend LangSmith if your stack is already using LangChain, or Arize Phoenix if you want a more flexible, model-agnostic observability/evaluation setup.

Best overall for hallucination evaluation

Arize Phoenix

• Strong for LLM tracing, debugging, and evaluation
• Lets you inspect retrieval quality, response grounding, and hallucination-like failures
• Works well for RAG-based support bots, where hallucinations often come from bad retrieval or weak citations
• Open-source, which is helpful if you want more control over data

Best if you’re in the LangChain ecosystem

LangSmith

• Great tracing for prompts, tool calls, and chain behavior
• Easier to instrument quickly if your chatbot is built with LangChain
• Useful for running evals on support conversations, including correctness and faithfulness checks

Other solid options

• Weights & Biases Weave — good if you already use W&B and want experiment tracking plus LLM observability
• WhyLabs — strong monitoring and drift detection, useful for production support systems
• Humanloop — nice for prompt iteration and structured human evaluation workflows
• Datadog LLM Observability — best if you want this integrated into broader infrastructure monitoring

What to look for specifically for hallucination detection

Make sure the tool supports:

• Trace-level inspection of prompts, tool calls, and outputs
• Faithfulness / groundedness evals
• RAG evaluation if you use knowledge base retrieval
• Human review loops for sampling tricky support cases
• Regression testing before prompt/model changes go live

Practical recommendation

If you want a fast choice:

• LangChain-based support bot: LangSmith
• General-purpose, strong hallucination analysis: Arize Phoenix

If you want, I can also give you a comparison table of 5–6 tools specifically for hallucination evaluation in customer support automation.