curraiLLM observability
LLM observability for every prompt, token, and tool call
Currai captures the full path of an LLM request so engineering teams can debug failures, measure quality, and understand production cost without stitching together logs by hand.
See what the model saw and did
Traditional observability can tell you a request was slow or failed. Currai shows the prompt, completion, tool calls, retriever spans, tokens, cost, latency, user, session, tags, and metadata behind that request.
Each trace becomes a replayable timeline. You can inspect a single model call, follow a nested agent run, or group a multi-turn conversation by session and user.
- Trace prompts, completions, spans, tools, and retrieval steps.
- Roll up token usage and cost by trace, model, user, and day.
- Filter production traces by environment, tag, metadata, user, and session.
Improve quality from production evidence
Currai connects observability to the improvement loop: run evals on real outputs, compare prompt versions, and watch cost and latency move as your app changes.
The goal is not just collecting traces. The goal is knowing why an LLM app behaved the way it did and which change made it better.
