> For the complete documentation index, see [llms.txt](https://shoppad.gitbook.io/yedric/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://shoppad.gitbook.io/yedric/going-further/usage.md). # Usage Every assistant has its own **Usage** page, reached from the assistant's sidebar. It reports tokens and efficiency for that one assistant, which is what you want when you are trying to work out why usage moved or whether an assistant is being used the way you intended. For the same numbers across every assistant at once, see the Usage section on [Organizations](/yedric/going-further/organizations.md).

Screenshot of an assistant's Usage page, focused on the Tokens, Estimated cost, and Avg tokens per conversation totals above the Token usage over time chart. — Usage reports token usage for a single assistant.

*** ## Totals Two figures sit at the top: **Tokens**, counted across all models and including simulations, and **Avg tokens / conversation**, which excludes simulations and shows the number of conversations it was averaged over. *** ## Token usage over time A daily token total across the range. This is the fastest way to spot a spike and tie it to a day, which you can then chase down in [Conversations](/yedric/going-further/conversations.md) or in the longest conversations list below. *** ## Usage by model Where this assistant's tokens went, broken down by model, largest first. The list is capped at the ten biggest models and tells you how many smaller ones it left out, so a long tail of experiments does not bury the models that matter. If you see tokens against a model you did not expect, check the assistant's model setting and any [simulations](/yedric/going-further/simulations.md) you have been running, since simulation runs consume tokens too. *** ## Efficiency signals This card is about whether the assistant is being used well, not just how much. **Avg turns / convo** Average back-and-forth per conversation. **Tool calls** How many times the assistant's actions ran in the range. **Tokens / tool call** Tokens spent per action invoked. Lower is better. It reads as a dash when no actions ran. **No-tool convos** The share of conversations where the assistant answered without running a single action. **Prompt cache hit** The share of prompt tokens served from the provider's prompt cache rather than processed fresh. Higher is better. **Spent reasoning** The share of output tokens the model spent on reasoning rather than on the reply your user read. Read the first four together. A high **Tokens / tool call** alongside a high **No-tool convos** share is the signal the card exists for: it means people are treating your assistant as a general-purpose chatbot rather than using it for the workflows in your app. The fix is usually the instructions or the action descriptions, not the model. *** ## Usage by username Tokens grouped by end user. This needs identified users, so it is only meaningful when you are passing a username through [Secure Mode](/yedric/developers/secure-mode.md). Use it to find the handful of accounts driving most of the volume. *** ## Longest conversations The heaviest conversations by total tokens, including simulation runs. This is the quickest route from "usage went up" to the specific sessions responsible. Each row links through to the conversation itself. *** ## Related * [Dashboard](/yedric/going-further/dashboard.md): volume, sentiment, and recommendations rather than token usage. * [Organizations](/yedric/going-further/organizations.md): the same reporting across every assistant, plus where you add your own provider keys.