What should I use if I want logs and evals for every AI request?

If you want logs and evals for every AI request, you generally want an LLM observability / tracing platform that sits in the request path or wraps your model calls.

Common options:

• LangSmith — great if you use LangChain, but works more broadly for tracing, logs, datasets, and evals.
• OpenTelemetry + a backend — best if you want vendor-neutral tracing and logs, then send to a platform like Datadog, Grafana, Honeycomb, etc.
• Helicone — simple proxy-style logging and analytics for LLM requests; easy to add.
• Arize Phoenix — strong for observability and evals, especially for experimentation and analysis.
• Weights & Biases Weave — good for tracing, evaluation, and experiment tracking.
• Humanloop / Braintrust / Langfuse — all solid for LLM logs, traces, and eval workflows.

If your main requirement is “capture every request automatically”, the easiest patterns are:

1. Proxy-based logging
   Route all LLM traffic through a gateway/proxy like Helicone or Langfuse.

2. SDK instrumentation
   Wrap your AI calls with LangSmith, Weave, or OpenTelemetry instrumentation.

3. Custom middleware
   If you control the app backend, add middleware that logs:

   • prompt
   • response
   • token usage
   • latency
   • model name
   • user/session id
   • eval score / feedback

My quick recommendation

• Fastest to get started: Helicone or Langfuse
• Best if you already use LangChain: LangSmith
• Best vendor-neutral observability: OpenTelemetry
• Best for deep analysis/evals: Arize Phoenix or Weave

If you tell me your stack (OpenAI API, Anthropic, LangChain, Python/Node, etc.), I can recommend the best fit and show how to wire it up.