diff --git a/apps/docs/brain.mdx b/apps/docs/brain.mdx index fef251f87..63d7e0fae 100644 --- a/apps/docs/brain.mdx +++ b/apps/docs/brain.mdx @@ -117,30 +117,26 @@ become canonical memory. **Settings → Brain** is the deployment-wide view of the memory, for admins. It answers three questions in order. -**Is it working?** Summary tiles carry the corpus size, the task memories -recorded, and the sources connected; the status section reports the Brain's -endpoint, whether recall is semantic or keyword-only, and which provider is -serving its embeddings and synthesis. A Brain that is running but has no -provider key is called out as needing attention rather than shown as healthy: -without one it can only match keywords, so recall would look real while -missing everything semantic. A configuration section shows the models the -Brain runs: the synthesis model (changeable through `R_BRAIN_MODEL`, applied -immediately) and the embedding model, which is fixed when the Brain is created -because it sizes the vector store. +**Is it working?** Summary tiles carry the corpus size and the task memories +recorded; the status section reports whether recall is semantic or keyword-only +and which provider is serving its embeddings and synthesis. A Brain that is +running but has no provider key is called out as needing attention rather than +shown as healthy: without one it can only match keywords, so recall would look +real while missing everything semantic. A configuration section shows the +models the Brain runs: the synthesis model (changeable through `R_BRAIN_MODEL`, +applied immediately) and the embedding model, which is fixed when the Brain is +created because it sizes the vector store. **What has it learned?** A composition bar breaks the corpus down by what each page came from, such as task memories, pull requests, Slack, meetings, and people, followed by a chart of the pages written over the last 30 days and the pages written most recently. **Browse memory** opens the corpus page -by page, searchable and filterable by source, so an admin can read exactly -what the Brain stored. The Brain lists its newest pages first and answers -with a bounded window, so on a large deployment this is labelled as a recent -sample rather than presented as a total. - -**Where does it learn from?** Each source shows whether its integration is -connected, when it was last read, and how far its one-time history sweep has -got. A source with nothing connected upstream says so, which distinguishes -"nothing collected yet" from "nothing to collect from". +by page, searchable and filterable by source. Large corpora load in pages, so +admins can search and inspect older memory as well as the newest pages. + +**Where does it learn from?** Connected sources are listed with their current +collection state, when they were last read, and how far their one-time history +sweep has got. Sources without a connected upstream integration are omitted. The page ends with the task-memory queue: how many completed tasks have been recorded, how many are waiting, and how many failed. Two actions live there. diff --git a/apps/docs/cost-analytics.mdx b/apps/docs/cost-analytics.mdx index bc31aa14a..9c661a0ee 100644 --- a/apps/docs/cost-analytics.mdx +++ b/apps/docs/cost-analytics.mdx @@ -5,9 +5,9 @@ description: Review Roomote inference spend by task type, environment, provider, --- Cost Analytics helps deployment teams understand how Roomote uses inference -across tasks and automations. It reports the cost of recorded model usage in -US dollars, so you can spot the environments, models, and work types that -drive spend. +across tasks, Fast-mode conversations, and automations. It reports the cost of +recorded model usage in US dollars, so you can spot the environments, models, +and work types that drive spend. Any signed-in deployment user can open **Analytics** from the dashboard, then select **Costs**. @@ -18,6 +18,7 @@ The Costs view includes summary cards, a chart, and a detailed breakdown of recorded inference usage. Use it to answer questions such as: - which task types account for the most spend +- how much non-task inference, including Fast mode, contributes to spend - whether a particular environment or model is driving costs - how usage differs between people and automations - how a provider's cost changes over time @@ -25,6 +26,8 @@ recorded inference usage. Use it to answer questions such as: The view starts grouped by **Task Type**. You can instead group or filter the data by user, environment, provider, or model. Choose a time range before comparing periods so the chart and breakdown use the same window. +Fast-mode inference that runs outside a launched task appears as **Non-task +inference** in the task-type breakdown. ## Exporting data diff --git a/apps/docs/models.mdx b/apps/docs/models.mdx index 6d4bed63b..c78143378 100644 --- a/apps/docs/models.mdx +++ b/apps/docs/models.mdx @@ -157,6 +157,8 @@ ANTHROPIC_API_KEY=... You can also split model roles with env vars: ```sh +R_ORCHESTRATION_MODEL=openrouter/google/gemini-3.7-flash +R_ORCHESTRATION_MODEL_REASONING_EFFORT=low R_SMALL_MODEL=openrouter/openai/gpt-4.1-mini R_VISION_MODEL=openrouter/openai/gpt-5.6-sol R_CODE_REVIEW_MODEL=openrouter/openai/gpt-5.6-sol @@ -229,9 +231,10 @@ where you want more speed, quality, or cost control. | Explore model | Repository search and focused codebase exploration | Fast tool use, low latency, and reliable source-grounded investigation | | Advisor model | Planning turns and advisor consultations inside longer coding tasks | Deliberate reasoning, decomposition, and ability to keep constraints in mind | -You do not need a separate model for every role. Many teams start with one -strong default model, then add a cheaper helper model or a stronger review -model after they can see real task usage. +You do not need a separate model for every role. Set a specialized role to +**Same as coding model** to inherit the default. Many teams start with one +strong default model, then split out a faster orchestration or helper model, or +a stronger review model, after they can see real usage. ## Reasoning settings