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.
A 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.
Read moreOne conversation is many traces. Pass a session id to stitch every turn into one thread, and a user id to slice cost, latency, and volume by the people using your app.
Read moreTrace, span, generation — three nouns that cover everything an LLM app does. Understand the data model and your instrumentation stops being guesswork.
Read morePoint an existing Langfuse SDK at Currai, validate trace parity in a canary, and cut over with a tested rollback path.
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