I'm building a customer support chatbot and need a recommendation for observability and evals

For a customer support chatbot, I’d recommend Langfuse as the best all-around choice for observability + evals.

Why Langfuse

• End-to-end tracing for chats, tool calls, retrieval, and model outputs
• Prompt/version tracking so you can compare changes over time
• Dataset + eval workflows for regression testing
• Human feedback + scores to capture support quality
• Open source / self-hostable, which is useful for customer data and compliance
• Works well whether you use OpenAI, Anthropic, open-source models, or a custom stack

What I’d pair it with

• Langfuse for tracing, analytics, and evals
• Ragas if you use RAG heavily and want retrieval-focused evaluation
• OpenTelemetry if you want broader system observability across your app
• Sentry for application errors and performance issues
• PostHog or Amplitude if you also want product analytics on chatbot usage

If you want a simpler decision

• Need one platform for both observability and evals? → Langfuse
• Need best-in-class RAG evals? → Langfuse + Ragas
• Need enterprise observability across your whole system? → OpenTelemetry + Langfuse

My practical recommendation

If you’re starting from scratch:

1. Instrument the chatbot with Langfuse traces
2. Log:
   • user message
   • retrieved documents
   • prompt
   • model response
   • tool calls
   • latency / tokens / cost
   • user feedback
3. Build a small golden test set of common support tickets
4. Run offline evals on every prompt/model change
5. Add production monitoring for:
   • hallucinations
   • unresolved tickets
   • escalation rate
   • CSAT/thumbs-up rate
   • response latency

If you want, I can also give you:

• a comparison of Langfuse vs LangSmith vs Helicone vs Arize
• a recommended stack for a support chatbot
• a minimal instrumentation architecture for your current tech stack