diff --git a/app/src/pages/Conversations.tsx b/app/src/pages/Conversations.tsx
index bb794e441..11f82be29 100644
--- a/app/src/pages/Conversations.tsx
+++ b/app/src/pages/Conversations.tsx
@@ -33,6 +33,9 @@ import {
   isTauri,
   openhumanAutocompleteAccept,
   openhumanAutocompleteCurrent,
+  openhumanLocalAiAnalyzeSentiment,
+  openhumanLocalAiShouldSendGif,
+  openhumanLocalAiTenorSearch,
   openhumanVoiceStatus,
   openhumanVoiceTranscribeBytes,
   openhumanVoiceTts,
@@ -138,11 +141,21 @@ const Conversations = () => {
     Record<string, ToolTimelineEntry[]>
   >({});
   const rustChat = useRustChat();
+  const defaultChannelType = useAppSelector(
+    state => state.channelConnections?.defaultMessagingChannel ?? 'web'
+  );
   const [reactionPickerMsgId, setReactionPickerMsgId] = useState<string | null>(null);
   const pendingReactionRef = useRef<
     Map<string, { msgId: string; content: string; threadId: string }>
   >(new Map());
 
+  /** Message counter for GIF cadence — check every ~7 messages. */
+  const gifCadenceCountRef = useRef(0);
+  const GIF_CADENCE_MESSAGES = 7;
+  /** Timestamp (ms) of last sentiment analysis — run roughly every hour. */
+  const lastSentimentAtRef = useRef(0);
+  const SENTIMENT_INTERVAL_MS = 60 * 60 * 1000; // 1 hour
+
   const selectedThreadIdRef = useRef(selectedThreadId);
   useEffect(() => {
     selectedThreadIdRef.current = selectedThreadId;
@@ -431,6 +444,13 @@ const Conversations = () => {
             );
           }
         }
+
+        // Fire-and-forget: GIF decision + sentiment analysis (cadence-based)
+        const pendingMsg = pendingReactionRef.current.get(event.thread_id);
+        if (pendingMsg) {
+          maybeCheckGif(pendingMsg.content, pendingMsg.threadId);
+          maybeSentimentAnalysis(pendingMsg.content);
+        }
         pendingReactionRef.current.delete(event.thread_id);
 
         // Only add the response bubble if Rust didn't already deliver it
@@ -495,6 +515,73 @@ const Conversations = () => {
     // eslint-disable-next-line react-hooks/exhaustive-deps
   }, [rustChat, socketStatus]);
 
+  /**
+   * Fire-and-forget: periodically check if a GIF response is appropriate
+   * (every ~GIF_CADENCE_MESSAGES messages). If the model says yes, search
+   * Tenor and dispatch the top result as a gif-type message.
+   */
+  const maybeCheckGif = (messageContent: string, threadId: string) => {
+    if (!isTauri()) return;
+
+    gifCadenceCountRef.current += 1;
+    if (gifCadenceCountRef.current < GIF_CADENCE_MESSAGES) return;
+    gifCadenceCountRef.current = 0;
+
+    console.debug('[conversations:gif] cadence reached, evaluating gif decision');
+
+    void openhumanLocalAiShouldSendGif(messageContent, defaultChannelType)
+      .then(async response => {
+        const decision = response.result;
+        if (!decision?.should_send_gif || !decision.search_query) return;
+
+        console.debug('[conversations:gif] searching tenor for:', decision.search_query);
+        const tenorResponse = await openhumanLocalAiTenorSearch(decision.search_query, 5);
+        const results = tenorResponse.result?.results;
+        if (!results || results.length === 0) return;
+
+        // Pick a random GIF from top results
+        const picked = results[Math.floor(Math.random() * Math.min(results.length, 3))];
+        const gifUrl =
+          picked.media?.mediumgif?.url || picked.media?.gif?.url || picked.media?.tinygif?.url;
+        if (!gifUrl) return;
+
+        console.debug('[conversations:gif] sending gif:', picked.title || picked.id);
+        dispatch(addInferenceResponse({ content: gifUrl, threadId }));
+      })
+      .catch(err => {
+        console.debug('[conversations:gif] failed:', err);
+      });
+  };
+
+  /**
+   * Fire-and-forget: periodically analyze user sentiment (~every hour).
+   * Stores the result in debug logs for now.
+   */
+  const maybeSentimentAnalysis = (messageContent: string) => {
+    if (!isTauri()) return;
+
+    const now = Date.now();
+    if (now - lastSentimentAtRef.current < SENTIMENT_INTERVAL_MS) return;
+    lastSentimentAtRef.current = now;
+
+    console.debug('[conversations:sentiment] interval reached, analyzing sentiment');
+
+    void openhumanLocalAiAnalyzeSentiment(messageContent)
+      .then(response => {
+        const sentiment = response.result;
+        if (!sentiment) return;
+        console.debug(
+          '[conversations:sentiment] result:',
+          sentiment.emotion,
+          sentiment.valence,
+          `(${sentiment.confidence})`
+        );
+      })
+      .catch(err => {
+        console.debug('[conversations:sentiment] failed:', err);
+      });
+  };
+
   const handleSendMessage = async (text?: string) => {
     const normalized = text ?? inputValue;
     const trimmed = normalized.trim();
diff --git a/app/src/utils/tauriCommands.ts b/app/src/utils/tauriCommands.ts
index 78cc4b73f..35765281d 100644
--- a/app/src/utils/tauriCommands.ts
+++ b/app/src/utils/tauriCommands.ts
@@ -1613,6 +1613,89 @@ export async function openhumanLocalAiShouldReact(
   });
 }
 
+// --- Sentiment analysis (local model) ---
+
+export interface SentimentResult {
+  emotion: string;
+  valence: string;
+  confidence: number;
+}
+
+/**
+ * Classify the emotion and sentiment of a user message via the local model.
+ * Designed to be called periodically (~every hour), not on every message.
+ */
+export async function openhumanLocalAiAnalyzeSentiment(
+  message: string
+): Promise<CommandResponse<SentimentResult>> {
+  return await callCoreRpc<CommandResponse<SentimentResult>>({
+    method: 'openhuman.local_ai_analyze_sentiment',
+    params: { message },
+  });
+}
+
+// --- GIF decision (local model) + Tenor search ---
+
+export interface GifDecision {
+  should_send_gif: boolean;
+  search_query: string | null;
+}
+
+export interface TenorMediaFormat {
+  url: string;
+  dims: [number, number];
+  size: number;
+  duration?: number;
+}
+
+export interface TenorGifResult {
+  id: string;
+  title: string;
+  contentDescription: string;
+  url: string;
+  media: {
+    gif?: TenorMediaFormat;
+    tinygif?: TenorMediaFormat;
+    mediumgif?: TenorMediaFormat;
+    mp4?: TenorMediaFormat;
+    tinymp4?: TenorMediaFormat;
+  };
+  created: number;
+}
+
+export interface TenorSearchResult {
+  results: TenorGifResult[];
+  next: string;
+}
+
+/**
+ * Ask the local model whether a GIF response is appropriate for this message.
+ * Designed to be called every ~5-10 messages, not on every message.
+ */
+export async function openhumanLocalAiShouldSendGif(
+  message: string,
+  channelType: string
+): Promise<CommandResponse<GifDecision>> {
+  return await callCoreRpc<CommandResponse<GifDecision>>({
+    method: 'openhuman.local_ai_should_send_gif',
+    params: { message, channel_type: channelType },
+  });
+}
+
+/**
+ * Search for GIFs via the backend Tenor proxy.
+ * Requires a valid session (charges against user budget).
+ */
+export async function openhumanLocalAiTenorSearch(
+  query: string,
+  limit?: number
+): Promise<CommandResponse<TenorSearchResult>> {
+  return await callCoreRpc<CommandResponse<TenorSearchResult>>({
+    method: 'openhuman.local_ai_tenor_search',
+    params: { query, limit },
+  });
+}
+
 export async function openhumanLocalAiAssetsStatus(): Promise<
   CommandResponse<LocalAiAssetsStatus>
 > {
diff --git a/src/api/rest.rs b/src/api/rest.rs
index a5e468e97..fcafdc61e 100644
--- a/src/api/rest.rs
+++ b/src/api/rest.rs
@@ -525,6 +525,28 @@ impl BackendOAuthClient {
         .await
     }
 
+    /// `POST /agent-integrations/tenor/search` — Search for GIFs via Tenor.
+    pub async fn search_tenor_gifs(
+        &self,
+        bearer_jwt: &str,
+        query: &str,
+        limit: Option<u32>,
+    ) -> Result<Value> {
+        anyhow::ensure!(!query.trim().is_empty(), "query is required");
+        let body = serde_json::json!({
+            "query": query.trim(),
+            "limit": limit.unwrap_or(5),
+            "contentFilter": "medium",
+        });
+        self.authed_json(
+            bearer_jwt,
+            Method::POST,
+            "agent-integrations/tenor/search",
+            Some(body),
+        )
+        .await
+    }
+
     /// `POST /channels/:channel/threads` — Create a thread in a channel.
     pub async fn create_channel_thread(
         &self,
diff --git a/src/openhuman/local_ai/gif_decision.rs b/src/openhuman/local_ai/gif_decision.rs
new file mode 100644
index 000000000..5b6e58a2c
--- /dev/null
+++ b/src/openhuman/local_ai/gif_decision.rs
@@ -0,0 +1,262 @@
+//! GIF decision via local AI model + Tenor search via the backend API.
+
+use serde_json::Value;
+
+use crate::api::config::effective_api_url;
+use crate::api::jwt::get_session_token;
+use crate::api::rest::BackendOAuthClient;
+use crate::openhuman::config::Config;
+use crate::openhuman::local_ai;
+use crate::rpc::RpcOutcome;
+
+// ---------------------------------------------------------------------------
+// GIF decision — local model decides whether a GIF response is appropriate
+// ---------------------------------------------------------------------------
+
+/// Result of the GIF-decision prompt.
+#[derive(Debug, serde::Serialize)]
+pub struct GifDecision {
+    /// Whether the model thinks sending a GIF is appropriate right now.
+    pub should_send_gif: bool,
+    /// Tenor search query (only meaningful when `should_send_gif` is true).
+    pub search_query: Option<String>,
+}
+
+/// Ask the local model whether the assistant should respond with a GIF,
+/// based on channel type and message content. Designed to be called every
+/// ~5-10 messages, not on every message. Lightweight: ~12 output tokens.
+pub async fn local_ai_should_send_gif(
+    config: &Config,
+    message: &str,
+    channel_type: &str,
+) -> Result<RpcOutcome<GifDecision>, String> {
+    tracing::debug!(
+        channel_type,
+        msg_len = message.len(),
+        "[local_ai:gif] evaluating gif decision"
+    );
+
+    if message.trim().is_empty() {
+        return Ok(RpcOutcome::single_log(
+            GifDecision {
+                should_send_gif: false,
+                search_query: None,
+            },
+            "empty message — no gif",
+        ));
+    }
+
+    let service = local_ai::global(config);
+    let status = service.status();
+    if !matches!(status.state.as_str(), "ready") {
+        tracing::debug!("[local_ai:gif] local model not ready, skipping");
+        return Ok(RpcOutcome::single_log(
+            GifDecision {
+                should_send_gif: false,
+                search_query: None,
+            },
+            "local model not ready",
+        ));
+    }
+
+    let prompt = format!(
+        "You decide whether an AI assistant should respond with a GIF.\n\
+         GIFs are appropriate for: humor, celebration, empathy, reactions to exciting news, \
+         casual banter in friendly channels.\n\
+         GIFs are NOT appropriate for: technical questions, serious topics, first messages, \
+         professional channels (slack, email), or when the user seems upset or frustrated.\n\n\
+         Channel: {channel_type}\nUser message: {message}\n\n\
+         Reply with EXACTLY one line:\n\
+         NONE (no GIF) OR a 2-4 word Tenor search query for a fitting GIF."
+    );
+
+    let output = service.prompt(config, &prompt, Some(12), true).await;
+
+    let decision = match output {
+        Ok(raw) => {
+            let trimmed = raw.trim();
+            tracing::debug!(
+                response = %trimmed,
+                "[local_ai:gif] model response"
+            );
+            parse_gif_response(trimmed)
+        }
+        Err(e) => {
+            tracing::debug!(error = %e, "[local_ai:gif] inference failed, skipping");
+            GifDecision {
+                should_send_gif: false,
+                search_query: None,
+            }
+        }
+    };
+
+    tracing::debug!(
+        should_send = decision.should_send_gif,
+        query = ?decision.search_query,
+        "[local_ai:gif] decision"
+    );
+    Ok(RpcOutcome::single_log(decision, "gif decision completed"))
+}
+
+/// Parse the model's response into a `GifDecision`.
+fn parse_gif_response(text: &str) -> GifDecision {
+    let trimmed = text.trim();
+
+    if trimmed.is_empty()
+        || trimmed.eq_ignore_ascii_case("NONE")
+        || trimmed.eq_ignore_ascii_case("no gif")
+    {
+        return GifDecision {
+            should_send_gif: false,
+            search_query: None,
+        };
+    }
+
+    // The model should return a short search query. Sanity-check length:
+    // reject anything too long (probably the model rambled) or too short.
+    let word_count = trimmed.split_whitespace().count();
+    if word_count > 8 || trimmed.len() > 80 {
+        tracing::debug!(
+            words = word_count,
+            len = trimmed.len(),
+            "[local_ai:gif] response too long, treating as NONE"
+        );
+        return GifDecision {
+            should_send_gif: false,
+            search_query: None,
+        };
+    }
+
+    GifDecision {
+        should_send_gif: true,
+        search_query: Some(trimmed.to_string()),
+    }
+}
+
+// ---------------------------------------------------------------------------
+// Tenor search — proxy through the backend API
+// ---------------------------------------------------------------------------
+
+/// A single GIF result from Tenor.
+#[derive(Debug, serde::Serialize, serde::Deserialize)]
+#[serde(rename_all = "camelCase")]
+pub struct TenorGifResult {
+    pub id: String,
+    pub title: String,
+    #[serde(default)]
+    pub content_description: String,
+    pub url: String,
+    #[serde(default)]
+    pub media: Value,
+    #[serde(default)]
+    pub created: i64,
+}
+
+/// Wrapper for the Tenor search response.
+#[derive(Debug, serde::Serialize, serde::Deserialize)]
+pub struct TenorSearchResult {
+    pub results: Vec<TenorGifResult>,
+    #[serde(default)]
+    pub next: String,
+}
+
+/// Search for GIFs via the backend's Tenor proxy endpoint.
+/// Requires a valid session JWT (the backend charges against user budget).
+pub async fn tenor_search(
+    config: &Config,
+    query: &str,
+    limit: Option<u32>,
+) -> Result<RpcOutcome<TenorSearchResult>, String> {
+    tracing::debug!(
+        query,
+        limit = ?limit,
+        "[local_ai:gif] searching tenor"
+    );
+
+    if query.trim().is_empty() {
+        return Err("query is required".to_string());
+    }
+
+    let api_url = effective_api_url(&config.api_url);
+    let jwt = get_session_token(config)?
+        .ok_or_else(|| "session JWT required; complete login first".to_string())?;
+
+    let client = BackendOAuthClient::new(&api_url).map_err(|e| e.to_string())?;
+    let raw = client
+        .search_tenor_gifs(&jwt, query, limit)
+        .await
+        .map_err(|e| format!("tenor search failed: {e}"))?;
+
+    tracing::debug!(
+        result_keys = ?raw.as_object().map(|o| o.keys().collect::<Vec<_>>()),
+        "[local_ai:gif] tenor search response received"
+    );
+
+    // The backend wraps results in { success, data: { results, next, costUsd } }.
+    // Extract the inner data.
+    let data = raw.get("data").cloned().unwrap_or_else(|| raw.clone());
+
+    let result: TenorSearchResult = serde_json::from_value(data).map_err(|e| {
+        tracing::debug!(error = %e, "[local_ai:gif] failed to parse tenor response");
+        format!("parse tenor response: {e}")
+    })?;
+
+    tracing::debug!(
+        count = result.results.len(),
+        "[local_ai:gif] tenor returned {} results",
+        result.results.len()
+    );
+
+    Ok(RpcOutcome::single_log(result, "tenor search completed"))
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn parse_none_response() {
+        let d = parse_gif_response("NONE");
+        assert!(!d.should_send_gif);
+        assert!(d.search_query.is_none());
+    }
+
+    #[test]
+    fn parse_none_case_insensitive() {
+        let d = parse_gif_response("none");
+        assert!(!d.should_send_gif);
+    }
+
+    #[test]
+    fn parse_empty_response() {
+        let d = parse_gif_response("");
+        assert!(!d.should_send_gif);
+    }
+
+    #[test]
+    fn parse_valid_query() {
+        let d = parse_gif_response("happy dance celebration");
+        assert!(d.should_send_gif);
+        assert_eq!(d.search_query.as_deref(), Some("happy dance celebration"));
+    }
+
+    #[test]
+    fn parse_short_query() {
+        let d = parse_gif_response("thumbs up");
+        assert!(d.should_send_gif);
+        assert_eq!(d.search_query.as_deref(), Some("thumbs up"));
+    }
+
+    #[test]
+    fn parse_too_long_response() {
+        let long = "this is a very long response that the model should not have generated because it rambled on and on";
+        let d = parse_gif_response(long);
+        assert!(!d.should_send_gif);
+    }
+
+    #[test]
+    fn parse_no_gif_variant() {
+        let d = parse_gif_response("no gif");
+        assert!(!d.should_send_gif);
+    }
+}
diff --git a/src/openhuman/local_ai/mod.rs b/src/openhuman/local_ai/mod.rs
index b18b6d729..77b9f98ce 100644
--- a/src/openhuman/local_ai/mod.rs
+++ b/src/openhuman/local_ai/mod.rs
@@ -2,9 +2,11 @@
 
 mod core;
 pub mod device;
+pub mod gif_decision;
 pub mod ops;
 pub mod presets;
 mod schemas;
+pub mod sentiment;
 
 mod install;
 pub(crate) mod model_ids;
@@ -16,6 +18,7 @@ mod types;
 
 pub use core::*;
 pub use device::DeviceProfile;
+pub use gif_decision::{GifDecision, TenorGifResult, TenorSearchResult};
 pub use ops as rpc;
 pub use ops::*;
 pub use presets::{ModelPreset, ModelTier};
@@ -23,6 +26,7 @@ pub use schemas::{
     all_controller_schemas as all_local_ai_controller_schemas,
     all_registered_controllers as all_local_ai_registered_controllers,
 };
+pub use sentiment::SentimentResult;
 pub(crate) use service::whisper_engine;
 pub use service::LocalAiService;
 pub use types::{
diff --git a/src/openhuman/local_ai/schemas.rs b/src/openhuman/local_ai/schemas.rs
index 385049abd..09e2f1568 100644
--- a/src/openhuman/local_ai/schemas.rs
+++ b/src/openhuman/local_ai/schemas.rs
@@ -117,6 +117,23 @@ struct LocalAiShouldReactParams {
     channel_type: String,
 }
 
+#[derive(Debug, Deserialize)]
+struct LocalAiAnalyzeSentimentParams {
+    message: String,
+}
+
+#[derive(Debug, Deserialize)]
+struct LocalAiShouldSendGifParams {
+    message: String,
+    channel_type: String,
+}
+
+#[derive(Debug, Deserialize)]
+struct LocalAiTenorSearchParams {
+    query: String,
+    limit: Option<u32>,
+}
+
 pub fn all_controller_schemas() -> Vec<ControllerSchema> {
     vec![
         schemas("agent_chat"),
@@ -145,6 +162,9 @@ pub fn all_controller_schemas() -> Vec<ControllerSchema> {
         schemas("local_ai_diagnostics"),
         schemas("local_ai_chat"),
         schemas("local_ai_should_react"),
+        schemas("local_ai_analyze_sentiment"),
+        schemas("local_ai_should_send_gif"),
+        schemas("local_ai_tenor_search"),
     ]
 }
 
@@ -254,6 +274,18 @@ pub fn all_registered_controllers() -> Vec<RegisteredController> {
             schema: schemas("local_ai_should_react"),
             handler: handle_local_ai_should_react,
         },
+        RegisteredController {
+            schema: schemas("local_ai_analyze_sentiment"),
+            handler: handle_local_ai_analyze_sentiment,
+        },
+        RegisteredController {
+            schema: schemas("local_ai_should_send_gif"),
+            handler: handle_local_ai_should_send_gif,
+        },
+        RegisteredController {
+            schema: schemas("local_ai_tenor_search"),
+            handler: handle_local_ai_tenor_search,
+        },
     ]
 }
 
@@ -509,6 +541,35 @@ pub fn schemas(function: &str) -> ControllerSchema {
             ],
             outputs: vec![json_output("decision", "Reaction decision: {should_react, emoji}.")],
         },
+        "local_ai_analyze_sentiment" => ControllerSchema {
+            namespace: "local_ai",
+            function: "analyze_sentiment",
+            description: "Classify the emotion and sentiment of a user message. Returns emotion label, valence, and confidence.",
+            inputs: vec![
+                required_string("message", "User message content to analyze."),
+            ],
+            outputs: vec![json_output("sentiment", "Sentiment result: {emotion, valence, confidence}.")],
+        },
+        "local_ai_should_send_gif" => ControllerSchema {
+            namespace: "local_ai",
+            function: "should_send_gif",
+            description: "Ask the local model whether a GIF response is appropriate, and if so return a Tenor search query.",
+            inputs: vec![
+                required_string("message", "User message content to evaluate."),
+                required_string("channel_type", "Channel type: web, telegram, discord, slack, etc."),
+            ],
+            outputs: vec![json_output("decision", "GIF decision: {should_send_gif, search_query}.")],
+        },
+        "local_ai_tenor_search" => ControllerSchema {
+            namespace: "local_ai",
+            function: "tenor_search",
+            description: "Search for GIFs via the backend Tenor proxy. Requires a valid session.",
+            inputs: vec![
+                required_string("query", "Tenor search query."),
+                optional_u64("limit", "Max results to return (default 5, max 50)."),
+            ],
+            outputs: vec![json_output("result", "Tenor search result: {results, next}.")],
+        },
         _ => ControllerSchema {
             namespace: "local_ai",
             function: "unknown",
@@ -896,6 +957,43 @@ fn handle_local_ai_should_react(params: Map<String, Value>) -> ControllerFuture
     })
 }
 
+fn handle_local_ai_analyze_sentiment(params: Map<String, Value>) -> ControllerFuture {
+    Box::pin(async move {
+        let p = deserialize_params::<LocalAiAnalyzeSentimentParams>(params)?;
+        let config = config_rpc::load_config_with_timeout().await?;
+        to_json(
+            crate::openhuman::local_ai::sentiment::local_ai_analyze_sentiment(&config, &p.message)
+                .await?,
+        )
+    })
+}
+
+fn handle_local_ai_should_send_gif(params: Map<String, Value>) -> ControllerFuture {
+    Box::pin(async move {
+        let p = deserialize_params::<LocalAiShouldSendGifParams>(params)?;
+        let config = config_rpc::load_config_with_timeout().await?;
+        to_json(
+            crate::openhuman::local_ai::gif_decision::local_ai_should_send_gif(
+                &config,
+                &p.message,
+                &p.channel_type,
+            )
+            .await?,
+        )
+    })
+}
+
+fn handle_local_ai_tenor_search(params: Map<String, Value>) -> ControllerFuture {
+    Box::pin(async move {
+        let p = deserialize_params::<LocalAiTenorSearchParams>(params)?;
+        let config = config_rpc::load_config_with_timeout().await?;
+        to_json(
+            crate::openhuman::local_ai::gif_decision::tenor_search(&config, &p.query, p.limit)
+                .await?,
+        )
+    })
+}
+
 fn handle_local_ai_chat(params: Map<String, Value>) -> ControllerFuture {
     Box::pin(async move {
         let p = deserialize_params::<LocalAiChatParams>(params)?;
diff --git a/src/openhuman/local_ai/sentiment.rs b/src/openhuman/local_ai/sentiment.rs
new file mode 100644
index 000000000..4c3531b31
--- /dev/null
+++ b/src/openhuman/local_ai/sentiment.rs
@@ -0,0 +1,200 @@
+//! Emotion / sentiment analysis via the bundled local AI model.
+
+use crate::openhuman::config::Config;
+use crate::openhuman::local_ai;
+use crate::rpc::RpcOutcome;
+
+/// Result of sentiment / emotion analysis on a user message.
+#[derive(Debug, serde::Serialize)]
+pub struct SentimentResult {
+    /// Primary emotion label.
+    /// One of: joy, sadness, anger, surprise, fear, disgust, neutral.
+    pub emotion: String,
+    /// Overall valence: positive, negative, or neutral.
+    pub valence: String,
+    /// Model's self-reported confidence (0.0–1.0).
+    pub confidence: f32,
+}
+
+impl SentimentResult {
+    /// Safe default when analysis is skipped or parsing fails.
+    fn neutral() -> Self {
+        Self {
+            emotion: "neutral".to_string(),
+            valence: "neutral".to_string(),
+            confidence: 1.0,
+        }
+    }
+}
+
+/// Known emotion labels the model is expected to produce.
+const VALID_EMOTIONS: &[&str] = &[
+    "joy", "sadness", "anger", "surprise", "fear", "disgust", "neutral",
+];
+
+/// Known valence labels.
+const VALID_VALENCES: &[&str] = &["positive", "negative", "neutral"];
+
+/// Ask the local model to classify the emotion and sentiment of a user
+/// message. Designed to be called periodically (e.g. every hour), not on
+/// every single message. Lightweight: ~8 output tokens, fire-and-forget safe.
+pub async fn local_ai_analyze_sentiment(
+    config: &Config,
+    message: &str,
+) -> Result<RpcOutcome<SentimentResult>, String> {
+    tracing::debug!(
+        msg_len = message.len(),
+        "[local_ai:sentiment] evaluating sentiment"
+    );
+
+    if message.trim().is_empty() {
+        return Ok(RpcOutcome::single_log(
+            SentimentResult::neutral(),
+            "empty message — neutral sentiment",
+        ));
+    }
+
+    let service = local_ai::global(config);
+    let status = service.status();
+    if !matches!(status.state.as_str(), "ready") {
+        tracing::debug!("[local_ai:sentiment] local model not ready, returning neutral");
+        return Ok(RpcOutcome::single_log(
+            SentimentResult::neutral(),
+            "local model not ready",
+        ));
+    }
+
+    let prompt = format!(
+        "Classify the emotion and sentiment of this user message.\n\
+         Reply with EXACTLY three words separated by spaces:\n\
+         EMOTION VALENCE CONFIDENCE\n\
+         Where EMOTION is one of: joy, sadness, anger, surprise, fear, disgust, neutral\n\
+         VALENCE is one of: positive, negative, neutral\n\
+         CONFIDENCE is a number from 0.0 to 1.0\n\n\
+         User message: {message}"
+    );
+
+    let output = service.prompt(config, &prompt, Some(8), true).await;
+
+    let result = match output {
+        Ok(raw) => {
+            let trimmed = raw.trim().to_lowercase();
+            tracing::debug!(
+                raw = %trimmed,
+                "[local_ai:sentiment] model response"
+            );
+            parse_sentiment_response(&trimmed)
+        }
+        Err(e) => {
+            tracing::debug!(error = %e, "[local_ai:sentiment] inference failed, returning neutral");
+            SentimentResult::neutral()
+        }
+    };
+
+    tracing::debug!(
+        emotion = %result.emotion,
+        valence = %result.valence,
+        confidence = result.confidence,
+        "[local_ai:sentiment] analysis complete"
+    );
+    Ok(RpcOutcome::single_log(
+        result,
+        "sentiment analysis completed",
+    ))
+}
+
+/// Parse the model's 3-word response into a `SentimentResult`.
+/// Falls back to neutral on any parsing error.
+fn parse_sentiment_response(text: &str) -> SentimentResult {
+    let parts: Vec<&str> = text.split_whitespace().collect();
+    if parts.len() < 3 {
+        tracing::debug!(
+            parts = parts.len(),
+            "[local_ai:sentiment] unexpected token count, falling back to neutral"
+        );
+        return SentimentResult::neutral();
+    }
+
+    let emotion = parts[0].to_string();
+    let valence = parts[1].to_string();
+    let confidence: f32 = parts[2].parse().unwrap_or(0.5);
+
+    // Validate labels, fall back to neutral for garbage
+    let emotion = if VALID_EMOTIONS.contains(&emotion.as_str()) {
+        emotion
+    } else {
+        tracing::debug!(raw = %emotion, "[local_ai:sentiment] unknown emotion label, defaulting to neutral");
+        "neutral".to_string()
+    };
+
+    let valence = if VALID_VALENCES.contains(&valence.as_str()) {
+        valence
+    } else {
+        tracing::debug!(raw = %valence, "[local_ai:sentiment] unknown valence label, defaulting to neutral");
+        "neutral".to_string()
+    };
+
+    let confidence = confidence.clamp(0.0, 1.0);
+
+    SentimentResult {
+        emotion,
+        valence,
+        confidence,
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn parse_valid_response() {
+        let r = parse_sentiment_response("joy positive 0.9");
+        assert_eq!(r.emotion, "joy");
+        assert_eq!(r.valence, "positive");
+        assert!((r.confidence - 0.9).abs() < 0.01);
+    }
+
+    #[test]
+    fn parse_valid_negative() {
+        let r = parse_sentiment_response("anger negative 0.75");
+        assert_eq!(r.emotion, "anger");
+        assert_eq!(r.valence, "negative");
+        assert!((r.confidence - 0.75).abs() < 0.01);
+    }
+
+    #[test]
+    fn parse_unknown_emotion_falls_back() {
+        let r = parse_sentiment_response("excited positive 0.8");
+        assert_eq!(r.emotion, "neutral");
+        assert_eq!(r.valence, "positive");
+    }
+
+    #[test]
+    fn parse_too_few_tokens() {
+        let r = parse_sentiment_response("joy");
+        assert_eq!(r.emotion, "neutral");
+        assert_eq!(r.valence, "neutral");
+    }
+
+    #[test]
+    fn parse_bad_confidence() {
+        let r = parse_sentiment_response("sadness negative abc");
+        assert_eq!(r.emotion, "sadness");
+        assert_eq!(r.valence, "negative");
+        assert!((r.confidence - 0.5).abs() < 0.01);
+    }
+
+    #[test]
+    fn parse_clamps_confidence() {
+        let r = parse_sentiment_response("joy positive 2.5");
+        assert!((r.confidence - 1.0).abs() < 0.01);
+    }
+
+    #[test]
+    fn parse_empty_returns_neutral() {
+        let r = parse_sentiment_response("");
+        assert_eq!(r.emotion, "neutral");
+        assert_eq!(r.valence, "neutral");
+    }
+}