feat(dashboard): LLM stats answer who/what spends tokens — member scope chips and spend breakdowns

The stats section was designed single-user: four global charts, no
attribution. Request metadata rows now carry the session's source and
the frame's agent_id/depth (additive ensure_column), denormalized at log
time by the LoggingModel from the owner's pool — off the turn's hot
path, degrading to NULLs, never to a lost row.

The dashboard gains member scope chips filtering every chart, and a
breakdown row splitting the range's billed tokens by member, kind
(chat / sub-agents / cron / system agents / channels), agent, model and
provider — a sub-agent's spend is attributed to the sub-agent itself.
Rows predating the columns group under 'older data'.

CHANGELOG.md

@@ -10,6 +10,13 @@ release PR may merge — and a section is closed at the commit that bumps it.
 
 ### Added
 
+- The dashboard's **LLM stats** section answers "who and what is spending tokens", not
+  just "how much": a row of member chips filters every chart to one person (or the whole
+  instance, as before), and a new breakdown row splits the billed tokens of the selected
+  range **by member, by kind** (chat / sub-agents / scheduled tasks / system agents /
+  channels), **by agent, by model and by provider** — a sub-agent's spend is attributed to
+  the sub-agent itself. Request metadata rows now carry the session's source and the
+  frame's agent/depth, denormalized at log time; older rows group under "Older data".
 - The file viewer opens **word-processor documents** (`.docx`, `.doc`, `.odt`, `.rtf`):
   when LibreOffice is installed on the server they are converted to PDF and shown as the
   document, live-reloading when the file changes, exactly like a compiled `.tex`. A

crates/skald-core/src/db/llm_requests/mod.rs

@@ -6,7 +6,8 @@
 //! of the traffic is known — `user_id` is what the UI filters on).
 //! Payloads (request/response bodies + headers) live in `llm_request_payloads`
 //! in the owner bucket (`{userid}.db`), correlated by `request_id`.
-//! Rows are retained for `llm.request_log.retention_days` days (default 14).
+//! Rows are retained for `llm.requests_log.cleanup_rows_after` days (the
+//! shipped `default.config.yaml` sets 90; unset = kept forever).
 
 use anyhow::Result;
 use sqlx::SqlitePool;
@@ -31,6 +32,13 @@ pub struct LlmRequestRow {
     pub cache_read_tokens:     Option<i64>,
     /// Tokens written into the provider's prompt cache (Anthropic only).
     pub cache_creation_tokens: Option<i64>,
+    /// Denormalized attribution (see `db::mod`): the session's `source`, the
+    /// frame's `agent_id` (the sub-agent's own for a child frame) and `depth`
+    /// (0 = main agent, >0 = sub-agent). Resolved by the LoggingModel from the
+    /// owner's pool; `None` when it was unavailable.
+    pub source:                Option<String>,
+    pub agent_id:              Option<String>,
+    pub depth:                 Option<i64>,
 }
 
 // ── Writes ────────────────────────────────────────────────────────────────────
@@ -40,8 +48,9 @@ pub async fn insert(pool: &SqlitePool, row: LlmRequestRow) -> Result<i64> {
         "INSERT INTO llm_requests (
             request_id, user_id, session_id, stack_id, model_name,
             error_text, input_tokens, output_tokens, duration_ms,
-            cache_read_tokens, cache_creation_tokens
+            cache_read_tokens, cache_creation_tokens,
-         ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+            source, agent_id, depth
+         ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
          RETURNING id",
     )
     .bind(&row.request_id)
@@ -55,6 +64,9 @@ pub async fn insert(pool: &SqlitePool, row: LlmRequestRow) -> Result<i64> {
     .bind(row.duration_ms)
     .bind(row.cache_read_tokens)
     .bind(row.cache_creation_tokens)
+    .bind(&row.source)
+    .bind(&row.agent_id)
+    .bind(row.depth)
     .fetch_one(pool)
     .await?;
 

crates/skald-core/src/db/mod.rs

@@ -421,6 +421,16 @@ pub(crate) async fn create_registry_tables(pool: &SqlitePool) -> Result<()> {
     .execute(pool)
     .await?;
 
+    // Attribution columns for the multi-user stats (who/what consumed): the
+    // session's `source`, the frame's `agent_id` and its `depth` (0 = the
+    // conversation's main agent, >0 = a sub-agent). They live in the owner
+    // bucket, so the LoggingModel denormalizes them onto the row at insert
+    // time — a registry query cannot join an encrypted per-user file. NULL on
+    // rows predating the columns and when the owner's pool was not available.
+    ensure_column(pool, "llm_requests", "source", "TEXT").await?;
+    ensure_column(pool, "llm_requests", "agent_id", "TEXT").await?;
+    ensure_column(pool, "llm_requests", "depth", "INTEGER").await?;
+
     // User directory + auth material. Read before every login, so it lives in
     // the registry — which means it must never hold anything that derives a
     // user's key: `database_password` is the DEK sealed under a key derived

crates/skald-core/src/llm/logging.rs

@@ -5,7 +5,8 @@
 //!
 //! * a **metadata-only** row in `llm_requests` (`system.db`) — cost, tokens,
 //!   timing, plus the correlation the UI filters on (`user_id`, `session_id`,
-//!   `stack_id`);
+//!   `stack_id`) and the denormalized attribution the dashboard breaks down on
+//!   (`source`, `agent_id`, `depth`);
 //! * the **payload** (request/response bodies + headers) in
 //!   `llm_request_payloads` in the caller's own database, keyed by the same
 //!   `request_id`.
@@ -90,6 +91,90 @@ impl LoggingModel {
     }
 }
 
+/// Resolves the denormalized attribution columns from the owner's pool: the
+/// session's `source`, and the frame's `agent_id`/`depth`. The frame row
+/// carries the sub-agent's own id for a child frame, so it wins over the
+/// session's agent; the root frame carries the session's main agent. Runs
+/// inside the spawned insert task — never on the turn's hot path — and any
+/// failure degrades to NULLs, never to a lost metadata row.
+async fn resolve_attribution(
+    pool:       &SqlitePool,
+    session_id: i64,
+    stack_id:   Option<i64>,
+) -> (Option<String>, Option<String>, Option<i64>) {
+    let session = sqlx::query_as::<_, (String, String)>(
+        "SELECT source, agent_id FROM chat_sessions WHERE id = ?",
+    )
+    .bind(session_id)
+    .fetch_optional(pool)
+    .await
+    .ok()
+    .flatten();
+
+    let frame = match stack_id {
+        Some(id) => sqlx::query_as::<_, (i64, String)>(
+            "SELECT depth, agent_id FROM chat_sessions_stack WHERE id = ?",
+        )
+        .bind(id)
+        .fetch_optional(pool)
+        .await
+        .ok()
+        .flatten(),
+        None => None,
+    };
+
+    let source = session.as_ref().map(|(s, _)| s.clone());
+    let agent_id = frame
+        .as_ref()
+        .map(|(_, a)| a.clone())
+        .or_else(|| session.as_ref().map(|(_, a)| a.clone()));
+    let depth = frame.as_ref().map(|(d, _)| *d);
+    (source, agent_id, depth)
+}
+
+#[allow(clippy::too_many_arguments)]
+fn spawn_insert(
+    registry:   Arc<SqlitePool>,
+    owner:      Option<Arc<SqlitePool>>,
+    request_id: Option<String>,
+    user_id:    Option<String>,
+    session_id: Option<i64>,
+    stack_id:   Option<i64>,
+    model_name: String,
+    error_text: Option<String>,
+    duration_ms: i64,
+    // (input, output, cache_read, cache_creation) — all None on HTTP failure.
+    usage:      (Option<i64>, Option<i64>, Option<i64>, Option<i64>),
+) {
+    tokio::spawn(async move {
+        // Attribution needs the owner's pool (chat_sessions lives there); a
+        // locked owner still gets the metadata row, only without attribution.
+        let (source, agent_id, depth) = match (owner.as_deref(), session_id) {
+            (Some(p), Some(sid)) => resolve_attribution(p, sid, stack_id).await,
+            _ => (None, None, None),
+        };
+        let (input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens) = usage;
+        if let Err(e) = llm_requests::insert(&registry, llm_requests::LlmRequestRow {
+            request_id,
+            user_id,
+            session_id,
+            stack_id,
+            model_name,
+            error_text,
+            input_tokens,
+            output_tokens,
+            duration_ms,
+            cache_read_tokens,
+            cache_creation_tokens,
+            source,
+            agent_id,
+            depth,
+        }).await {
+            warn!(error = %e, "llm_requests: failed to insert log row");
+        }
+    });
+}
+
 #[async_trait]
 impl Model for LoggingModel {
     async fn complete(
@@ -108,11 +193,12 @@ impl Model for LoggingModel {
         let request_id = Some(req.request_id.clone());
         let model_name = self.model_name.clone();
         let pool       = Arc::clone(&self.registry);
+        let owner      = self.target.payloads.clone();
 
         match &result {
             Ok(resp) => {
                 let usage = resp.usage();
-                let (input_tokens, output_tokens, cache_read, cache_write) = (
+                let usage = (
                     usage.input_tokens.map(|n| n as i64),
                     usage.output_tokens.map(|n| n as i64),
                     usage.cache_read.map(|n| n as i64),
@@ -121,23 +207,8 @@ impl Model for LoggingModel {
                 if let Some(raw) = resp.raw() {
                     self.spawn_payload(&req.request_id, raw);
                 }
-                tokio::spawn(async move {
+                spawn_insert(pool, owner, request_id, user_id, session_id, stack_id,
-                    if let Err(e) = llm_requests::insert(&pool, llm_requests::LlmRequestRow {
+                             model_name, None, duration_ms, usage);
-                        request_id,
-                        user_id,
-                        session_id,
-                        stack_id,
-                        model_name,
-                        error_text:            None,
-                        input_tokens,
-                        output_tokens,
-                        duration_ms,
-                        cache_read_tokens:     cache_read,
-                        cache_creation_tokens: cache_write,
-                    }).await {
-                        warn!(error = %e, "llm_requests: failed to insert log row");
-                    }
-                });
             }
             Err(e) => {
                 // Only an HTTP failure carries a body (a provider 400 is exactly
@@ -145,24 +216,9 @@ impl Model for LoggingModel {
                 if let Some(raw) = e.raw.as_ref() {
                     self.spawn_payload(&req.request_id, raw);
                 }
-                let error_text = e.to_string();
+                spawn_insert(pool, owner, request_id, user_id, session_id, stack_id,
-                tokio::spawn(async move {
+                             model_name, Some(e.to_string()), duration_ms,
-                    if let Err(log_err) = llm_requests::insert(&pool, llm_requests::LlmRequestRow {
+                             (None, None, None, None));
-                        request_id,
-                        user_id,
-                        session_id,
-                        stack_id,
-                        model_name,
-                        error_text:            Some(error_text),
-                        input_tokens:          None,
-                        output_tokens:         None,
-                        duration_ms,
-                        cache_read_tokens:     None,
-                        cache_creation_tokens: None,
-                    }).await {
-                        warn!(error = %log_err, "llm_requests: failed to insert error log row");
-                    }
-                });
             }
         }
 
@@ -240,6 +296,14 @@ mod tests {
         let path = temp_db_path("llmlog-ok");
         let pool = Arc::new(crate::db::init_system_pool(&path).await.unwrap());
 
+        // The attribution the row should carry: session 42 is a cron session
+        // whose agent is "assistant", frame 7 is a child frame of the
+        // "researcher" sub-agent — the frame's agent wins over the session's.
+        sqlx::query("INSERT INTO chat_sessions (id, source, agent_id) VALUES (42, 'cron', 'assistant')")
+            .execute(&*pool).await.unwrap();
+        sqlx::query("INSERT INTO chat_sessions_stack (id, session_id, agent_id, depth) VALUES (7, 42, 'researcher', 1)")
+            .execute(&*pool).await.unwrap();
+
         let mut resp = ModelResponse::message("hi");
         *resp.usage_mut() = Usage {
             input_tokens:  Some(11),
@@ -270,6 +334,13 @@ mod tests {
         assert_eq!(model_name, "gpt-test");
         assert_eq!((input, output), (Some(11), Some(7)));
 
+        let (source, agent_id, depth): (Option<String>, Option<String>, Option<i64>) =
+            sqlx::query_as("SELECT source, agent_id, depth FROM llm_requests WHERE request_id = 'req-1'")
+                .fetch_one(&*pool).await.unwrap();
+        assert_eq!(source.as_deref(), Some("cron"), "the dashboard breaks down on source");
+        assert_eq!(agent_id.as_deref(), Some("researcher"), "the frame's agent wins (sub-agent)");
+        assert_eq!(depth, Some(1));
+
         wait_for(&pool, "SELECT COUNT(*) FROM llm_request_payloads").await;
         let body: String = sqlx::query_scalar(
             "SELECT request_json FROM llm_request_payloads WHERE request_id = 'req-1'")

dev-docs/frontend.md

@@ -52,7 +52,7 @@ Two independent things have to be true, and both were violated at some point:
 | `copilot-render.js` | (helpers) | `renderMsg`, `renderTool`, `renderDiff`, etc. — shared by copilot and chat-page |
 | `sidebar.js` | `<app-sidebar>` | Nav sidebar; role-driven (`ui_mode`); inbox badge is **live** — the chat WS forwards the inbox lifecycle events (`approval_requested/resolved`, `clarification_*`, `elicitation_*`) regardless of `source`, `chat-session.js` re-dispatches them as the `inbox-changed` window event, and the sidebar (+ `agent-inbox.js`) refreshes on it; a 60 s poll remains as fallback |
 | `topbar.js` | `<app-topbar>` | Top nav bar; per-user avatar color hashed from the username |
-| `dashboard-page.js` | `<dashboard-page>` | `#dashboard` — status hero, LLM stats charts, pending inbox, quick guide |
+| `dashboard-page.js` | `<dashboard-page>` | `#dashboard` — status hero, LLM stats (member-scope chips + spend breakdowns by member/kind/agent/model/provider over `llm_requests` attribution columns), pending inbox, quick guide |
 | `shared/file-viewer-base.js` | `FileViewerBase` (base) | Shared file-viewer engine (fetch, kind detection, markdown/PDF/SVG/LaTeX/word-docs, watcher, `_renderBody`); driven by `_show`/`_hide`. Extended by desktop + mobile |
 | `file-viewer-page.js` | `<file-viewer-page>` | Desktop file viewer: `FileViewerBase` + hash routing via `window.openFile(path)` → `#file_viewer?path=...` |
 | `shared/file-viewer-mobile.js` | `<mobile-file-viewer-page>` | Mobile file viewer: `FileViewerBase` + prop-driven (`visible`/`path`), full-screen with back button |

dev-docs/llm-stack.md

@@ -8,7 +8,7 @@
 
 ## The client layer (`crates/skald-core/src/llm/`)
 
-LLM client abstraction (OpenAI-compat, Anthropic, Ollama…). OpenAI-compatible provider *types* are runtime data, not code: `providers/declared.rs` loads `providers.yaml` at boot (see Config in [../CLAUDE.md](../CLAUDE.md)); only non-OpenAI-compatible or bespoke providers (anthropic, ollama, openai, openrouter) stay native. **Retriability** (`Model::is_retriable`, `agent-loop`) keys on the real HTTP status carried by `ModelError { status }`, **not** a substring of the message — a model id/token count containing "404"/"401" cannot mis-classify; 401/403/404/422 don't retry, 400/429/5xx/network do. **Request logging** is the `logging.rs::LoggingModel` decorator, attached by the *caller's* `ModelSelector` (`loop_adapters/selector.rs::SkaldSelector::with_log`) — never by `LlmManager`, which builds one shared client per model and cannot know whose traffic it serves. The decorator's `RequestLogTarget` carries the owner: metadata → `llm_requests` in the registry (`user_id`, the column the UI filters on), payload bodies/headers → `llm_request_payloads` in that user's own encrypted DB, keyed by `request_id`; session + frame come from the request's own `conversation`/`frame`, so kernel rounds, sub-agent frames and compaction summaries are all attributed with no extra plumbing (`ModelRequest::log` is unused here)
+LLM client abstraction (OpenAI-compat, Anthropic, Ollama…). OpenAI-compatible provider *types* are runtime data, not code: `providers/declared.rs` loads `providers.yaml` at boot (see Config in [../CLAUDE.md](../CLAUDE.md)); only non-OpenAI-compatible or bespoke providers (anthropic, ollama, openai, openrouter) stay native. **Retriability** (`Model::is_retriable`, `agent-loop`) keys on the real HTTP status carried by `ModelError { status }`, **not** a substring of the message — a model id/token count containing "404"/"401" cannot mis-classify; 401/403/404/422 don't retry, 400/429/5xx/network do. **Request logging** is the `logging.rs::LoggingModel` decorator, attached by the *caller's* `ModelSelector` (`loop_adapters/selector.rs::SkaldSelector::with_log`) — never by `LlmManager`, which builds one shared client per model and cannot know whose traffic it serves. The decorator's `RequestLogTarget` carries the owner: metadata → `llm_requests` in the registry (`user_id`, the column the UI filters on), payload bodies/headers → `llm_request_payloads` in that user's own encrypted DB, keyed by `request_id`; session + frame come from the request's own `conversation`/`frame`, so kernel rounds, sub-agent frames and compaction summaries are all attributed with no extra plumbing (`ModelRequest::log` is unused here). The metadata row also carries **denormalized attribution** for the dashboard's spend breakdowns — `source`, `agent_id`, `depth` — resolved by the spawned insert task from the owner's pool (`chat_sessions` + `chat_sessions_stack`; the frame row supplies the sub-agent's own id for child frames). That resolution is the only read the log path ever does, stays off the turn's hot path, and any failure degrades to NULLs — a missing attribution must never cost the row. Rows pre-dating the columns read as the stats' "older data" bucket.
 
 ## `providers.yaml` — two traps in the model metadata
 

docs/dashboard.md

@@ -15,17 +15,27 @@ The status reflects the **whole instance**, not one person's account: there is o
 
 ## LLM usage stats
 
-Four charts with a range switch (last hour / 24 hours / 7 days / 30 days):
+The section answers two questions: *how much is the instance being used?* and *who or what is spending the tokens?* A range switch (last hour / 24 hours / 7 days / 30 days) applies to everything on it, and — on an instance with more than one member — a row of member chips filters **all** the charts to one person; **Everyone** is the whole instance, as before.
+
+Three charts show the trend over the range:
 
 - **Requests** — how many LLM calls per minute, hour or day.
 - **Tokens** — the metered volume, split into input (split again into cached and non-cached) and output. The tooltip shows the cache-hit percentage: repeated context that was *cached* costs less and answers faster, so a high hit rate is good news, not a sign something is stuck.
 - **Avg latency** — how long a model call took on average.
-- **Models** — the top models by requests in the range.
+
+Below them, **How the spend splits** breaks the range's billed tokens down into bars:
+
+- **By member** — who consumed what (hidden while a member chip is selected: it would be a single bar).
+- **By kind** — direct chats vs **sub-agents** (specialists the assistant delegates to) vs **scheduled tasks** and the other background system agents. Data from before this breakdown existed groups under *Older data*.
+- **By agent** — which assistant or specialist consumed the most.
+- **By model** and **By provider** — where the money actually goes.
+
+A bar's tooltip shows the request count and the input/output/cached split.
 
 Three honest answers to give with a straight face:
 
-- **These numbers are everyone's, together.** The charts aggregate the whole instance; there is no per-person breakdown on this page.
+- **They record how much, when, by whom and on which model — never what was said.** The content of a request lives in the requester's own encrypted space; the charts read only counters.
-- **They record how much, when and which model — never what was said.** The content of a request lives in the requester's own encrypted space; the charts read only counters.
+- **The per-member chips are a consumption view, not a surveillance tool.** They show token counts, not conversations.
 - **Empty is normal on a new instance.** "No LLM requests in the selected range" means exactly that: nothing has run in that window.
 
 ## Pending
@@ -41,12 +51,12 @@ The same cards as the [Inbox](inbox.md) — approvals, questions and sign-in pro
 
 - **Not a monitor.** Nothing here alerts anyone; it shows the present state to whoever is looking.
 - **Not where models are configured.** Adding providers and models, and their priority order, is the admin's Models and Providers pages.
-- **Not per-person.** No page on the instance shows "who used how much" — deliberately; usage is shared, like the models.
+- **Not a message log.** The per-member and per-kind charts show volumes of tokens, never what anyone asked or was answered.
 
 ## Common questions
 
 - *"It says Degraded — should I worry?"* — it means the model checks are not all passing. Individual chats may still work on a fallback model; if it persists, the admin checks the provider (its key, its quota) on the Models/Providers pages.
-- *"Why are the bars so high at odd hours?"* — scheduled background work (system agents, cron tasks) uses the same models. The Tasks page and the system-agents page show what ran when.
+- *"Why are the bars so high at odd hours?"* — scheduled background work (system agents, cron tasks) uses the same models. The **By kind** chart shows exactly how much of the spend is background work versus direct chats; the Tasks page and the system-agents page show what ran when.
 - *"What is a token?"* — the unit LLM providers meter and bill by, roughly a word fragment. Input is what was sent (long history = more input; caching repeats cheaply), output is what was written back.
 - *"Why doesn't my child see this page?"* — their role uses the simple interface: chat, inbox and projects only.
 - *"Does the dashboard show what people asked?"* — no. Only counts, timings and model names; never content.

docs/index.md

@@ -12,7 +12,7 @@ This index will grow over time. Right now it covers the chat window, the inbox,
 | --- | --- |
 | [chat.md](chat.md) | The chat window: full-page vs docked, the tab bar and what lands where, the composer's controls, the slash commands, and what happens while an answer is being written |
 | [inbox.md](inbox.md) | The Inbox: the three kinds of pending request, why background work asks here rather than in the chat, answering one (and the time-limited approvals), and why an unanswered card means a stopped job |
-| [dashboard.md](dashboard.md) | The Dashboard: the instance status line, the LLM usage charts (everyone's together, counts never content), the pending-inbox section, and what the no-models banner means |
+| [dashboard.md](dashboard.md) | The Dashboard: the instance status line, the LLM usage charts (filterable per member, with spend broken down by member, kind, agent, model and provider — counts never content), the pending-inbox section, and what the no-models banner means |
 | [security-groups.md](security-groups.md) | Security groups: allow / ask / deny per tool, the shield in the chat, what the default group already permits, and how an admin edits the rules |
 | [memory.md](memory.md) | Private and shared memory: what goes where, the indexes and history log, why some shared facts can't be changed on request |
 | [agents.md](agents.md) | Agents: the three kinds (chat, task, system), which one you are talking to and why, the specialist agents the assistant delegates to, how the model is chosen, and adding a custom agent |

src/frontend/api/stats.rs

@@ -22,6 +22,9 @@ pub enum StatsRange {
 #[derive(Deserialize)]
 pub struct StatsQuery {
     pub range: Option<StatsRange>,
+    /// Scope filter: a `user_id` restricts every series and breakdown to that
+    /// member; absent = the whole instance.
+    pub user:  Option<String>,
 }
 
 #[derive(Serialize)]
@@ -34,18 +37,40 @@ pub struct DailyStats {
     pub avg_duration_ms:   f64,
 }
 
+/// One row of a "how the spend splits" chart (by user, kind, agent, model,
+/// provider). Bars are drawn on `total_tokens` — tokens are what the provider
+/// bills — with the rest in the tooltip.
 #[derive(Serialize)]
-pub struct ModelStats {
+pub struct BreakdownRow {
-    pub model_name: String,
+    pub key:               String,
-    pub requests:   i64,
+    pub requests:          i64,
+    pub input_tokens:      i64,
+    pub output_tokens:     i64,
+    pub cache_read_tokens: i64,
+    pub total_tokens:      i64,
+}
+
+/// A selectable member for the scope chips (id + display label only).
+#[derive(Serialize)]
+pub struct MemberEntry {
+    pub id:    String,
+    pub label: String,
 }
 
 #[derive(Serialize)]
 pub struct LlmStatsResponse {
-    pub daily:  Vec<DailyStats>,
+    pub daily:       Vec<DailyStats>,
-    pub models: Vec<ModelStats>,
+    pub members:     Vec<MemberEntry>,
+    pub by_user:     Vec<BreakdownRow>,
+    pub by_kind:     Vec<BreakdownRow>,
+    pub by_agent:    Vec<BreakdownRow>,
+    pub by_model:    Vec<BreakdownRow>,
+    pub by_provider: Vec<BreakdownRow>,
 }
 
+// Every query shares the same two filters: the time window and the optional
+// user scope (`? IS NULL OR user_id = ?`).
+
 const SQL_DAILY_HOUR: &str =
     "SELECT strftime('%H:%M', created_at, 'localtime')        AS day,
             COUNT(*)                                          AS requests,
@@ -55,6 +80,7 @@ const SQL_DAILY_HOUR: &str =
             AVG(duration_ms)                                  AS avg_duration_ms
      FROM llm_requests
      WHERE created_at >= datetime('now', ?)
+       AND (? IS NULL OR user_id = ?)
      GROUP BY strftime('%H:%M', created_at, 'localtime')
      ORDER BY day ASC";
 
@@ -67,6 +93,7 @@ const SQL_DAILY_HOUR_BUCKET: &str =
             AVG(duration_ms)                                  AS avg_duration_ms
      FROM llm_requests
      WHERE created_at >= datetime('now', ?)
+       AND (? IS NULL OR user_id = ?)
      GROUP BY strftime('%m-%d %H:00', created_at, 'localtime')
      ORDER BY day ASC";
 
@@ -79,22 +106,121 @@ const SQL_DAILY_DATE: &str =
             AVG(duration_ms)                                  AS avg_duration_ms
      FROM llm_requests
      WHERE created_at >= datetime('now', ?)
+       AND (? IS NULL OR user_id = ?)
      GROUP BY DATE(created_at, 'localtime')
      ORDER BY day ASC";
 
-const SQL_MODELS: &str =
+// The breakdown projections. The SELECT tail is identical for all of them —
-    "SELECT model_name, COUNT(*) AS requests
+// counts + token sums, grouped and ordered by billed volume.
+
+const SQL_BY_USER: &str =
+    "SELECT COALESCE(user_id, '') AS key,
+            COUNT(*)                              AS requests,
+            COALESCE(SUM(input_tokens),      0)   AS input_tokens,
+            COALESCE(SUM(output_tokens),     0)   AS output_tokens,
+            COALESCE(SUM(cache_read_tokens), 0)   AS cache_read_tokens,
+            COALESCE(SUM(input_tokens), 0)
+          + COALESCE(SUM(output_tokens), 0)       AS total_tokens
      FROM llm_requests
      WHERE created_at >= datetime('now', ?)
-     GROUP BY model_name
+       AND (? IS NULL OR user_id = ?)
-     ORDER BY requests DESC
+     GROUP BY user_id ORDER BY total_tokens DESC";
-     LIMIT 6";
+
+// "What consumed it": a child frame (depth > 0) is sub-agent work regardless
+// of the session's source; otherwise the source itself (web / mobile /
+// telegram / cron / a system agent's name). NULL = rows predating the column.
+const SQL_BY_KIND: &str =
+    "SELECT CASE WHEN depth > 0 THEN 'sub-agent'
+                 WHEN source IS NULL OR source = '' THEN 'unknown'
+                 ELSE source END AS key,
+            COUNT(*)                              AS requests,
+            COALESCE(SUM(input_tokens),      0)   AS input_tokens,
+            COALESCE(SUM(output_tokens),     0)   AS output_tokens,
+            COALESCE(SUM(cache_read_tokens), 0)   AS cache_read_tokens,
+            COALESCE(SUM(input_tokens), 0)
+          + COALESCE(SUM(output_tokens), 0)       AS total_tokens
+     FROM llm_requests
+     WHERE created_at >= datetime('now', ?)
+       AND (? IS NULL OR user_id = ?)
+     GROUP BY key ORDER BY total_tokens DESC";
+
+const SQL_BY_AGENT: &str =
+    "SELECT COALESCE(NULLIF(agent_id, ''), 'unknown') AS key,
+            COUNT(*)                              AS requests,
+            COALESCE(SUM(input_tokens),      0)   AS input_tokens,
+            COALESCE(SUM(output_tokens),     0)   AS output_tokens,
+            COALESCE(SUM(cache_read_tokens), 0)   AS cache_read_tokens,
+            COALESCE(SUM(input_tokens), 0)
+          + COALESCE(SUM(output_tokens), 0)       AS total_tokens
+     FROM llm_requests
+     WHERE created_at >= datetime('now', ?)
+       AND (? IS NULL OR user_id = ?)
+     GROUP BY agent_id ORDER BY total_tokens DESC LIMIT 10";
+
+const SQL_BY_MODEL: &str =
+    "SELECT model_name AS key,
+            COUNT(*)                              AS requests,
+            COALESCE(SUM(input_tokens),      0)   AS input_tokens,
+            COALESCE(SUM(output_tokens),     0)   AS output_tokens,
+            COALESCE(SUM(cache_read_tokens), 0)   AS cache_read_tokens,
+            COALESCE(SUM(input_tokens), 0)
+          + COALESCE(SUM(output_tokens), 0)       AS total_tokens
+     FROM llm_requests
+     WHERE created_at >= datetime('now', ?)
+       AND (? IS NULL OR user_id = ?)
+     GROUP BY model_name ORDER BY total_tokens DESC LIMIT 10";
+
+// Provider is resolved through the model row. No `removed_at` filter on
+// purpose: models are soft-deleted precisely so telemetry keeps resolving.
+const SQL_BY_PROVIDER: &str =
+    "SELECT COALESCE(p.name, 'other') AS key,
+            COUNT(*)                                AS requests,
+            COALESCE(SUM(r.input_tokens),      0)   AS input_tokens,
+            COALESCE(SUM(r.output_tokens),     0)   AS output_tokens,
+            COALESCE(SUM(r.cache_read_tokens), 0)   AS cache_read_tokens,
+            COALESCE(SUM(r.input_tokens), 0)
+          + COALESCE(SUM(r.output_tokens), 0)       AS total_tokens
+     FROM llm_requests r
+     LEFT JOIN llm_models    m ON m.name = r.model_name
+     LEFT JOIN llm_providers p ON p.id   = m.provider_id
+     WHERE r.created_at >= datetime('now', ?)
+       AND (? IS NULL OR r.user_id = ?)
+     GROUP BY p.name ORDER BY total_tokens DESC";
+
+const SQL_MEMBERS: &str =
+    "SELECT id, COALESCE(NULLIF(display_name, ''), username) AS label
+     FROM users
+     WHERE active = 1
+     ORDER BY label COLLATE NOCASE ASC";
+
+type DailyRow     = (String, i64, i64, i64, i64, f64);
+type BreakdownSql = (String, i64, i64, i64, i64, i64);
+
+async fn run_breakdown(
+    pool: &sqlx::SqlitePool,
+    sql: &'static str,
+    window: &str,
+    user: Option<&str>,
+) -> Result<Vec<BreakdownRow>, sqlx::Error> {
+    let rows = sqlx::query_as::<_, BreakdownSql>(sql)
+        .bind(window)
+        .bind(user).bind(user)
+        .fetch_all(pool)
+        .await?;
+    Ok(rows
+        .into_iter()
+        .map(|(key, requests, input_tokens, output_tokens, cache_read_tokens, total_tokens)| {
+            BreakdownRow { key, requests, input_tokens, output_tokens, cache_read_tokens, total_tokens }
+        })
+        .collect())
+}
 
 pub async fn llm_stats(
     State(skald): State<Arc<Skald>>,
     Query(params): Query<StatsQuery>,
 ) -> Result<impl IntoResponse, ApiError> {
     let range = params.range.unwrap_or_default();
+    let user  = params.user.filter(|u| !u.is_empty());
 
     let (window, daily_sql) = match range {
         StatsRange::Hour  => ("-60 minutes", SQL_DAILY_HOUR),
@@ -103,9 +229,12 @@ pub async fn llm_stats(
         StatsRange::Month => ("-30 days",    SQL_DAILY_DATE),
     };
 
-    let daily = sqlx::query_as::<_, (String, i64, i64, i64, i64, f64)>(daily_sql)
+    let db = &**skald.db();
+
+    let daily = sqlx::query_as::<_, DailyRow>(daily_sql)
         .bind(window)
-        .fetch_all(&**skald.db())
+        .bind(user.as_deref()).bind(user.as_deref())
+        .fetch_all(db)
         .await?
         .into_iter()
         .map(|(day, requests, input_tokens, output_tokens, cache_read_tokens, avg_duration_ms)| {
@@ -113,13 +242,20 @@ pub async fn llm_stats(
         })
         .collect::<Vec<_>>();
 
-    let models = sqlx::query_as::<_, (String, i64)>(SQL_MODELS)
+    let members = sqlx::query_as::<_, (String, String)>(SQL_MEMBERS)
-        .bind(window)
+        .fetch_all(db)
-        .fetch_all(&**skald.db())
         .await?
         .into_iter()
-        .map(|(model_name, requests)| ModelStats { model_name, requests })
+        .map(|(id, label)| MemberEntry { id, label })
         .collect::<Vec<_>>();
 
-    Ok(Json(LlmStatsResponse { daily, models }))
+    let (by_user, by_kind, by_agent, by_model, by_provider) = tokio::try_join!(
+        run_breakdown(db, SQL_BY_USER,     window, user.as_deref()),
+        run_breakdown(db, SQL_BY_KIND,     window, user.as_deref()),
+        run_breakdown(db, SQL_BY_AGENT,    window, user.as_deref()),
+        run_breakdown(db, SQL_BY_MODEL,    window, user.as_deref()),
+        run_breakdown(db, SQL_BY_PROVIDER, window, user.as_deref()),
+    )?;
+
+    Ok(Json(LlmStatsResponse { daily, members, by_user, by_kind, by_agent, by_model, by_provider }))
 }

web/components/dashboard-page.js

@@ -3,6 +3,38 @@ import { LightElement }  from '../lib/base.js';
 import { t }             from '../lib/i18n.js';
 import { InboxMixin }    from '../lib/inbox-mixin.js';
 
+// Shared palette for the breakdown bars (cycled when there are more rows).
+const PALETTE = ['#3b82f6', '#10b981', '#f59e0b', '#8b5cf6', '#ef4444', '#06b6d4'];
+
+// The `kind` values that have a proper label (the server sends the session's
+// source, or 'sub-agent' for child frames); anything else renders as-is, so a
+// new source needs no frontend change to appear.
+const KIND_LABELS = {
+  'web':                 'dashboard.stats.kind.web',
+  'mobile':              'dashboard.stats.kind.mobile',
+  'telegram':            'dashboard.stats.kind.telegram',
+  'cron':                'dashboard.stats.kind.cron',
+  'sub-agent':           'dashboard.stats.kind.sub_agent',
+  'event-triage':        'dashboard.stats.kind.event_triage',
+  'memory-lint':         'dashboard.stats.kind.memory_lint',
+  'conversation-review': 'dashboard.stats.kind.conversation_review',
+  'unknown':             'dashboard.stats.kind.unknown',
+};
+
+function fmtTok(n) {
+  if (n == null) return '0';
+  if (n >= 1e6) return (n / 1e6).toFixed(1) + 'M';
+  if (n >= 1e3) return (n / 1e3).toFixed(1) + 'k';
+  return String(n);
+}
+
+function kindLabel(key) {
+  const i18nKey = KIND_LABELS[key];
+  if (!i18nKey) return key;
+  const label = t(i18nKey);
+  return label === i18nKey ? key : label;
+}
+
 export class DashboardPage extends InboxMixin(LightElement) {
 
   static get properties() {
@@ -13,6 +45,7 @@ export class DashboardPage extends InboxMixin(LightElement) {
       _plugins:      { state: true },
       _stats:        { state: true },
       _statsRange:   { state: true },
+      _statsUser:    { state: true },
     };
   }
 
@@ -24,6 +57,9 @@ export class DashboardPage extends InboxMixin(LightElement) {
     this._pollTimer    = null;
     this._stats          = null;   // null = loading
     this._statsRange     = 'week';
+    this._statsUser      = '';     // '' = whole instance
+    this._members        = [];     // cached out of the stats response: the chips
+                                   // must survive a reload that filters them away
     this._chartInstances = {};
     this._statsTimer     = null;
   }
@@ -100,11 +136,16 @@ export class DashboardPage extends InboxMixin(LightElement) {
 
   async _loadStats() {
     try {
-      const res = await fetch(`/api/stats/llm?range=${this._statsRange}`);
+      const params = new URLSearchParams({ range: this._statsRange });
+      if (this._statsUser) params.set('user', this._statsUser);
+      const res = await fetch(`/api/stats/llm?${params}`);
       if (!res.ok) throw new Error(`HTTP ${res.status}`);
       this._stats = await res.json();
+      if (Array.isArray(this._stats.members) && this._stats.members.length) {
+        this._members = this._stats.members;
+      }
     } catch {
-      this._stats = { daily: [], models: [] };
+      this._stats = { daily: [], by_user: [], by_kind: [], by_agent: [], by_model: [], by_provider: [] };
     }
   }
 
@@ -115,6 +156,13 @@ export class DashboardPage extends InboxMixin(LightElement) {
     await this._loadStats();
   }
 
+  async _setUser(user) {
+    if (user === this._statsUser) return;
+    this._statsUser = user;
+    this._stats = null;
+    await this._loadStats();
+  }
+
   get _honchoActive() {
     return this._plugins?.some(p => p.id === 'honcho' && p.enabled && p.running) ?? false;
   }
@@ -161,6 +209,39 @@ export class DashboardPage extends InboxMixin(LightElement) {
       .replace(/-\d{8}$/, '');
   }
 
+  _memberLabel(key) {
+    if (!key) return t('dashboard.stats.user_unknown');
+    const m = this._members.find(m => m.id === key);
+    return m ? m.label : key;
+  }
+
+  // The breakdown cards to show for the current data, in display order. Both
+  // the render and the chart init consume this, so they can never disagree
+  // about which canvases exist. The per-member card is pointless — and empty
+  // by construction — when the scope is already a single member.
+  get _breakdownSpecs() {
+    const s = this._stats;
+    if (!s) return [];
+    return [
+      !this._statsUser && { id: 'chart-by-user',     title: t('dashboard.stats.by_user'),     rows: s.by_user ?? [] },
+      { id: 'chart-by-kind',     title: t('dashboard.stats.by_kind'),     rows: s.by_kind ?? [] },
+      { id: 'chart-by-agent',    title: t('dashboard.stats.by_agent'),    rows: s.by_agent ?? [] },
+      { id: 'chart-by-model',    title: t('dashboard.stats.by_model'),    rows: s.by_model ?? [] },
+      { id: 'chart-by-provider', title: t('dashboard.stats.by_provider'), rows: s.by_provider ?? [] },
+    ]
+      .filter(Boolean)
+      .filter(c => c.rows.length > 0)
+      .map(c => ({
+        ...c,
+        labelFn: c.id === 'chart-by-user'  ? k => this._memberLabel(k)
+               : c.id === 'chart-by-kind'  ? k => kindLabel(k)
+               : c.id === 'chart-by-model' ? k => this._shortModelName(k)
+               : k => k,
+        // The axis may carry a shortened label; the tooltip shows the key itself.
+        titleFn: c.id === 'chart-by-model' ? k => k : null,
+      }));
+  }
+
   get _periodLabel() {
     return { hour: t('dashboard.stats.per_min'), day: t('dashboard.stats.per_hour'), week: t('dashboard.stats.per_day'), month: t('dashboard.stats.per_day') }[this._statsRange] ?? t('dashboard.stats.per_day');
   }
@@ -212,7 +293,6 @@ export class DashboardPage extends InboxMixin(LightElement) {
     const cache  = filled.map(d => d.cache_read_tokens);
     // null for empty slots so the latency line doesn't touch zero where there were no requests
     const lat    = filled.map(d => d.requests > 0 ? Math.round(d.avg_duration_ms) : null);
-    const models = this._stats.models;
 
     const axisDefaults = () => ({
       ticks:  { color: textColor, font: { size: 11 } },
@@ -322,38 +402,76 @@ export class DashboardPage extends InboxMixin(LightElement) {
       options: baseOpts(),
     });
 
-    // Models — always horizontal bar
+    // Breakdowns — "how the spend splits": horizontal bars on billed tokens,
-    const c4 = get('chart-models');
+    // requests and the input/output/cache split in the tooltip.
-    if (c4) this._chartInstances.models = new Chart(c4, {
+    for (const spec of this._breakdownSpecs) {
-      type: 'bar',
+      const c = get(spec.id);
-      data: {
+      if (!c) continue;
-        labels:   models.map(m => this._shortModelName(m.model_name)),
+      this._chartInstances[spec.id] = new Chart(c, {
-        datasets: [{
+        type: 'bar',
-          data:            models.map(m => m.requests),
+        data: {
-          backgroundColor: ['#3b82f6','#10b981','#f59e0b','#8b5cf6','#ef4444','#06b6d4'],
+          labels:   spec.rows.map(r => spec.labelFn(r.key)),
-          borderRadius:    4,
+          datasets: [{
-          borderSkipped:   false,
+            data:            spec.rows.map(r => r.total_tokens),
-        }],
+            backgroundColor: spec.rows.map((_, i) => PALETTE[i % PALETTE.length]),
-      },
+            borderRadius:    4,
-      options: {
+            borderSkipped:   false,
-        ...baseOpts(),
+          }],
-        indexAxis: 'y',
-        scales: {
-          x: { ...axisDefaults(), beginAtZero: true },
-          y: { ...axisDefaults(), ticks: { color: textColor, font: { size: 10 } } },
         },
-      },
+        options: {
-    });
+          ...baseOpts({
+            tooltip: {
+              callbacks: {
+                title: items => {
+                  const r = spec.rows[items[0]?.dataIndex];
+                  return r ? (spec.titleFn ?? spec.labelFn)(r.key) : '';
+                },
+                label: item => {
+                  const r = spec.rows[item.dataIndex];
+                  if (!r) return '';
+                  return [
+                    `${fmtTok(r.total_tokens)} tokens · ${r.requests} ${t('dashboard.stats.tip.requests')}`,
+                    `${t('dashboard.stats.chart.input')}: ${fmtTok(r.input_tokens)} · ${t('dashboard.stats.chart.output')}: ${fmtTok(r.output_tokens)} · ${t('dashboard.stats.chart.cached')}: ${fmtTok(r.cache_read_tokens)}`,
+                  ];
+                },
+              },
+            },
+          }),
+          indexAxis: 'y',
+          scales: {
+            x: { ...axisDefaults(), beginAtZero: true,
+                 ticks: { color: textColor, font: { size: 10 }, callback: v => fmtTok(v) } },
+            y: { ...axisDefaults(), ticks: { color: textColor, font: { size: 10 } } },
+          },
+        },
+      });
+    }
   }
 
   // ── Render ────────────────────────────────────────────────────────────────
 
+  _renderScopeChips() {
+    if (this._members.length < 2) return nothing;
+    return html`
+      <div class="home-stats-scope">
+        <button class="home-stats-range-btn ${this._statsUser === '' ? 'active' : ''}"
+                @click=${() => this._setUser('')}>${t('dashboard.stats.scope.all')}</button>
+        ${this._members.map(m => html`
+          <button class="home-stats-range-btn ${this._statsUser === m.id ? 'active' : ''}"
+                  @click=${() => this._setUser(m.id)}>${m.label}</button>
+        `)}
+      </div>
+    `;
+  }
+
   _renderStats() {
     if (this._stats === null) {
       return html`<div class="home-stats-loading"><i class="bi bi-hourglass-split"></i> ${t('dashboard.stats.loading')}</div>`;
     }
 
-    const empty = this._stats.daily.length === 0 && this._stats.models.length === 0;
+    const empty = this._stats.daily.length === 0
+      && !(this._stats.by_user ?? []).length
+      && !(this._stats.by_model ?? []).length;
     if (empty) {
       return html`
         <div class="home-stats-empty">
@@ -363,6 +481,8 @@ export class DashboardPage extends InboxMixin(LightElement) {
       `;
     }
 
+    const breakdown = this._breakdownSpecs;
+
     return html`
       <div class="home-stats-grid">
         <div class="home-stat-card">
@@ -377,11 +497,20 @@ export class DashboardPage extends InboxMixin(LightElement) {
           <div class="home-stat-card-title">${t('dashboard.stats.latency')}</div>
           <div class="home-stat-canvas-wrap"><canvas id="chart-latency"></canvas></div>
         </div>
-        <div class="home-stat-card">
-          <div class="home-stat-card-title">${t('dashboard.stats.models')}</div>
-          <div class="home-stat-canvas-wrap"><canvas id="chart-models"></canvas></div>
-        </div>
       </div>
+      ${breakdown.length ? html`
+        <div class="home-stats-sub">${t('dashboard.stats.sub.breakdown')}</div>
+        <div class="home-stats-grid">
+          ${breakdown.map(c => html`
+            <div class="home-stat-card">
+              <div class="home-stat-card-title">${c.title}</div>
+              <div class="home-stat-canvas-wrap" style="height:${Math.max(140, c.rows.length * 30)}px">
+                <canvas id=${c.id}></canvas>
+              </div>
+            </div>
+          `)}
+        </div>
+      ` : nothing}
     `;
   }
 
@@ -437,6 +566,7 @@ export class DashboardPage extends InboxMixin(LightElement) {
             `)}
           </div>
         </div>
+        ${this._renderScopeChips()}
         ${this._renderStats()}
 
         <!-- ── Pending inbox ── -->

web/css/home.css

@@ -312,6 +312,24 @@ dashboard-page {
   color: #fff;
 }
 
+/* Scope chips (member filter) — same pill look as the range buttons. */
+.home-stats-scope {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 6px;
+  margin: -0.25rem 0 1rem;
+}
+
+/* Subsection label above the breakdown grid. */
+.home-stats-sub {
+  font-size: 0.78rem;
+  font-weight: 600;
+  color: var(--bs-secondary-color);
+  text-transform: uppercase;
+  letter-spacing: 0.04em;
+  margin: 0.25rem 0 0.75rem;
+}
+
 .home-stats-loading,
 .home-stats-empty {
   display: flex;
@@ -325,7 +343,7 @@ dashboard-page {
 
 .home-stats-grid {
   display: grid;
-  grid-template-columns: 1fr 1fr;
+  grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
   gap: 1rem;
   margin-bottom: 2rem;
 }

web/i18n/en.js

@@ -419,7 +419,6 @@ export default {
   'dashboard.stats.requests': 'Requests {per}',
   'dashboard.stats.tokens':   'Tokens {per}',
   'dashboard.stats.latency':  'Avg latency (ms)',
-  'dashboard.stats.models':   'Models',
   'dashboard.stats.per_min':  '/ min',
   'dashboard.stats.per_hour': '/ hour',
   'dashboard.stats.per_day':  '/ day',
@@ -435,6 +434,26 @@ export default {
   'dashboard.stats.chart.non_cached': 'Non-cached',
   'dashboard.stats.chart.cache_hit':  'Cache hit: {pct}%',
 
+  'dashboard.stats.scope.all':      'Everyone',
+  'dashboard.stats.sub.breakdown':  'How the spend splits in the selected range',
+  'dashboard.stats.by_user':        'By member',
+  'dashboard.stats.by_kind':        'By kind',
+  'dashboard.stats.by_agent':       'By agent',
+  'dashboard.stats.by_model':       'By model',
+  'dashboard.stats.by_provider':    'By provider',
+  'dashboard.stats.user_unknown':   'Unknown',
+  'dashboard.stats.tip.requests':   'requests',
+
+  'dashboard.stats.kind.web':                 'Chat',
+  'dashboard.stats.kind.mobile':              'Mobile',
+  'dashboard.stats.kind.telegram':            'Telegram',
+  'dashboard.stats.kind.cron':                'Scheduled tasks',
+  'dashboard.stats.kind.sub_agent':           'Sub-agents',
+  'dashboard.stats.kind.event_triage':        'Event triage',
+  'dashboard.stats.kind.memory_lint':         'Memory lint',
+  'dashboard.stats.kind.conversation_review': 'Conversation review',
+  'dashboard.stats.kind.unknown':             'Older data',
+
   'dashboard.hero.subtitle':  'Your AI command centre — research, code, plan, and orchestrate. All in one place.',
 
   'dashboard.banner.no_models.title':  'No LLM models configured.',

web/i18n/fr.js

@@ -416,7 +416,6 @@ export default {
   'dashboard.stats.requests': 'Requêtes {per}',
   'dashboard.stats.tokens':   'Tokens {per}',
   'dashboard.stats.latency':  'Latence moyenne (ms)',
-  'dashboard.stats.models':   'Modèles',
   'dashboard.stats.per_min':  '/ min',
   'dashboard.stats.per_hour': '/ heure',
   'dashboard.stats.per_day':  '/ jour',
@@ -432,6 +431,26 @@ export default {
   'dashboard.stats.chart.non_cached': 'Non en cache',
   'dashboard.stats.chart.cache_hit':  'Cache hit : {pct}%',
 
+  'dashboard.stats.scope.all':      'Tous',
+  'dashboard.stats.sub.breakdown':  'Répartition des dépenses sur la période sélectionnée',
+  'dashboard.stats.by_user':        'Par membre',
+  'dashboard.stats.by_kind':        'Par type',
+  'dashboard.stats.by_agent':       'Par agent',
+  'dashboard.stats.by_model':       'Par modèle',
+  'dashboard.stats.by_provider':    'Par fournisseur',
+  'dashboard.stats.user_unknown':   'Inconnu',
+  'dashboard.stats.tip.requests':   'requêtes',
+
+  'dashboard.stats.kind.web':                 'Chat',
+  'dashboard.stats.kind.mobile':              'Mobile',
+  'dashboard.stats.kind.telegram':            'Telegram',
+  'dashboard.stats.kind.cron':                'Tâches planifiées',
+  'dashboard.stats.kind.sub_agent':           'Sous-agents',
+  'dashboard.stats.kind.event_triage':        'Triage des événements',
+  'dashboard.stats.kind.memory_lint':         'Lint mémoire',
+  'dashboard.stats.kind.conversation_review': 'Revue des conversations',
+  'dashboard.stats.kind.unknown':             'Données anciennes',
+
   'dashboard.hero.subtitle':  'Votre centre de commande IA — recherche, code, planification et orchestration. Tout en un seul endroit.',
 
   'dashboard.banner.no_models.title':  'Aucun modèle LLM configuré.',

web/i18n/it.js

@@ -416,7 +416,6 @@ export default {
   'dashboard.stats.requests': 'Richieste {per}',
   'dashboard.stats.tokens':   'Token {per}',
   'dashboard.stats.latency':  'Latenza media (ms)',
-  'dashboard.stats.models':   'Modelli',
   'dashboard.stats.per_min':  '/ min',
   'dashboard.stats.per_hour': '/ h',
   'dashboard.stats.per_day':  '/ giorno',
@@ -432,6 +431,26 @@ export default {
   'dashboard.stats.chart.non_cached': 'Non in cache',
   'dashboard.stats.chart.cache_hit':  'Cache hit: {pct}%',
 
+  'dashboard.stats.scope.all':      'Tutti',
+  'dashboard.stats.sub.breakdown':  'Come si distribuisce la spesa nel periodo selezionato',
+  'dashboard.stats.by_user':        'Per membro',
+  'dashboard.stats.by_kind':        'Per tipo',
+  'dashboard.stats.by_agent':       'Per agente',
+  'dashboard.stats.by_model':       'Per modello',
+  'dashboard.stats.by_provider':    'Per provider',
+  'dashboard.stats.user_unknown':   'Sconosciuto',
+  'dashboard.stats.tip.requests':   'richieste',
+
+  'dashboard.stats.kind.web':                 'Chat',
+  'dashboard.stats.kind.mobile':              'Mobile',
+  'dashboard.stats.kind.telegram':            'Telegram',
+  'dashboard.stats.kind.cron':                'Attività pianificate',
+  'dashboard.stats.kind.sub_agent':           'Sub-agent',
+  'dashboard.stats.kind.event_triage':        'Triage eventi',
+  'dashboard.stats.kind.memory_lint':         'Lint memoria',
+  'dashboard.stats.kind.conversation_review': 'Revisione conversazioni',
+  'dashboard.stats.kind.unknown':             'Dati precedenti',
+
   'dashboard.hero.subtitle':  'Il tuo centro di comando AI — ricerca, codice, pianificazione e orchestrazione. Tutto in un unico posto.',
 
   'dashboard.banner.no_models.title':  'Nessun modello LLM configurato.',