Blog
Highlights from the Currai blog: the posts worth reading first.
A complete, code-first guide to turning OpenAI Realtime voice conversations and text-to-speech generations into readable Currai User Stories and correctly nested traces.
A step-by-step guide to importing completed Retell AI calls into Currai with signed call-ended webhooks—without adding capture code to the live voice agent.
A step-by-step guide to importing completed Vapi calls into Currai with an authenticated webhook—without adding capture code or sharing a Vapi private API key.
Browse implementation notes, observability guides, product decisions, and workflow ideas by topic.
LLM observability captures every prompt, completion, token, and tool call so you can explain what your model did and debug it faster.
Read moreLogs tell you a line ran. Traces tell you what the model saw, said, and cost across the whole request. Here's why LLM apps need tracing, not more print statements.
Read moreLLM apps fail in ways your APM never sees. We built Currai so you can watch every prompt, token, and tool call the way you already watch latency and errors.
Read moreA RAG answer that takes four seconds could be slow retrieval, a fat prompt, or the model itself. Nested traces tell you which one — here's how to find the bottleneck.
Read morePass token usage on every generation and Currai turns it into cost — rolled up per trace, model, user, and day. Stop guessing what an LLM feature costs.
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