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.
Pass 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 morePoint an existing Langfuse SDK at Currai, validate trace parity in a canary, and cut over with a tested rollback path.
Read moreTotal latency hides the metric users actually feel — time to first token. Here's how to capture both on every generation and find what's making your LLM app feel slow.
Read moreA single runaway prompt or retry loop can 10x your bill overnight. Here's how to turn the cost data on your traces into budgets and alerts that warn you before the invoice does.
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