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 practical Voiceflow review for 2026: what it does well for building conversational AI agents, its pricing model, its limits, and when to consider alternatives.
Read moreHow to add an AI chatbot to your documentation or GitBook in 2026 — native docs AI versus dedicated agents — with grounding, citations, and freshness.
Read moreLLM 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.
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