fix linter

.claude/settings.local.json

@@ -0,0 +1,13 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(cargo check:*)",
+      "Bash(cargo clippy:*)",
+      "Bash(cargo test:*)",
+      "Bash(cargo fmt:*)",
+      "Bash(grep:*)",
+      "Bash(env:*)",
+      "Skill(ship)"
+    ]
+  }
+}

src/agent/dispatcher.rs

@@ -17,6 +17,16 @@ use crate::error::Error;
 use crate::llm::{ChatMessage, Reasoning, ReasoningContext, RespondResult};
 use crate::tools::redact_params;
 
+/// Represents image generation sentinel data in tool output.
+#[derive(serde::Deserialize)]
+struct ImageGeneratedSentinel<'a> {
+    #[serde(rename = "type")]
+    ty: &'a str,
+    data: &'a str,
+    media_type: &'a str,
+    path: &'a str,
+}
+
 /// Result of the agentic loop execution.
 pub(super) enum AgenticLoopResult {
     /// Completed with a response.
@@ -640,6 +650,28 @@ impl Agent {
                                             &message.metadata,
                                         )
                                         .await;
+
+                                    // Check for image_generated sentinel and emit SSE event
+                                    if let Ok(sentinel) =
+                                        serde_json::from_str::<ImageGeneratedSentinel>(output)
+                                        && sentinel.ty == "image_generated"
+                                    {
+                                        let data_url = format!(
+                                            "data:{};base64,{}",
+                                            sentinel.media_type, sentinel.data
+                                        );
+                                        let _ = self
+                                            .channels
+                                            .send_status(
+                                                &message.channel,
+                                                StatusUpdate::ImageGenerated {
+                                                    data_url,
+                                                    path: sentinel.path.to_string(),
+                                                },
+                                                &message.metadata,
+                                            )
+                                            .await;
+                                    }
                                 }
 
                                 // Record result in thread

src/agent/session.rs

@@ -16,7 +16,7 @@ use chrono::{DateTime, Utc};
 use serde::{Deserialize, Serialize};
 use uuid::Uuid;
 
-use crate::llm::{ChatMessage, ToolCall};
+use crate::llm::{ChatMessage, ImageAttachment, ToolCall};
 
 /// A session containing one or more threads.
 #[derive(Debug, Clone, Serialize, Deserialize)]
@@ -250,6 +250,22 @@ impl Thread {
         &mut self.turns[turn_number]
     }
 
+    /// Start a new turn with user input and image attachments.
+    pub fn start_turn_with_images(
+        &mut self,
+        user_input: impl Into<String>,
+        images: Vec<ImageAttachment>,
+    ) -> &mut Turn {
+        let turn_number = self.turns.len();
+        let mut turn = Turn::new(turn_number, user_input);
+        turn.images = images;
+        self.turns.push(turn);
+        self.state = ThreadState::Processing;
+        self.updated_at = Utc::now();
+        // turn_number was len() before push, so it's a valid index after push
+        &mut self.turns[turn_number]
+    }
+
     /// Complete the current turn with a response.
     pub fn complete_turn(&mut self, response: impl Into<String>) {
         if let Some(turn) = self.turns.last_mut() {
@@ -320,7 +336,14 @@ impl Thread {
     pub fn messages(&self) -> Vec<ChatMessage> {
         let mut messages = Vec::new();
         for turn in &self.turns {
-            messages.push(ChatMessage::user(&turn.user_input));
+            if turn.images.is_empty() {
+                messages.push(ChatMessage::user(&turn.user_input));
+            } else {
+                messages.push(ChatMessage::user_with_images(
+                    &turn.user_input,
+                    turn.images.clone(),
+                ));
+            }
             if let Some(ref response) = turn.response {
                 messages.push(ChatMessage::assistant(response));
             }
@@ -407,6 +430,9 @@ pub struct Turn {
     pub completed_at: Option<DateTime<Utc>>,
     /// Error message (if failed).
     pub error: Option<String>,
+    /// Images attached to this turn's user input.
+    #[serde(default, skip_serializing_if = "Vec::is_empty")]
+    pub images: Vec<ImageAttachment>,
 }
 
 impl Turn {
@@ -421,6 +447,7 @@ impl Turn {
             started_at: Utc::now(),
             completed_at: None,
             error: None,
+            images: Vec::new(),
         }
     }
 

src/agent/thread_ops.rs

@@ -264,7 +264,11 @@ impl Agent {
                 .threads
                 .get_mut(&thread_id)
                 .ok_or_else(|| Error::from(crate::error::JobError::NotFound { id: thread_id }))?;
-            thread.start_turn(content);
+            if message.images.is_empty() {
+                thread.start_turn(content);
+            } else {
+                thread.start_turn_with_images(content, message.images.clone());
+            }
             thread.messages()
         };
 

src/app.rs

@@ -376,6 +376,46 @@ impl AppBuilder {
             }
             let ws = Arc::new(ws);
             tools.register_memory_tools(Arc::clone(&ws));
+
+            // Register image tools if image generation models are available
+            match llm.list_models().await {
+                Ok(models) => {
+                    if let Some(image_model) =
+                        crate::llm::image_models::suggest_image_model(&models)
+                    {
+                        tools.register_image_tools(self.config.llm.nearai.clone(), Arc::clone(&ws));
+                        tracing::info!(
+                            "Image generation tools registered (model: {})",
+                            image_model
+                        );
+                    } else {
+                        tracing::debug!(
+                            "No image generation models detected in available models: {:?}",
+                            models
+                        );
+                    }
+
+                    // Register vision analysis tool if vision models are available
+                    if let Some(vision_model) =
+                        crate::llm::vision_models::suggest_vision_model(&models)
+                    {
+                        tools.register_vision_tools(Arc::clone(&ws));
+                        tracing::info!(
+                            "Image analysis tool registered (vision model: {})",
+                            vision_model
+                        );
+                    } else {
+                        tracing::debug!("No vision-capable models detected in available models");
+                    }
+                }
+                Err(e) => {
+                    tracing::warn!(
+                        "Failed to list available models for image tool registration: {}",
+                        e
+                    );
+                }
+            }
+
             Some(ws)
         } else {
             None

src/channels/channel.rs

@@ -9,6 +9,7 @@ use futures::Stream;
 use uuid::Uuid;
 
 use crate::error::ChannelError;
+use crate::llm::ImageAttachment;
 
 /// A message received from an external channel.
 #[derive(Debug, Clone)]
@@ -29,6 +30,8 @@ pub struct IncomingMessage {
     pub received_at: DateTime<Utc>,
     /// Channel-specific metadata.
     pub metadata: serde_json::Value,
+    /// Images attached to this message.
+    pub images: Vec<ImageAttachment>,
 }
 
 impl IncomingMessage {
@@ -47,6 +50,7 @@ impl IncomingMessage {
             thread_id: None,
             received_at: Utc::now(),
             metadata: serde_json::Value::Null,
+            images: Vec::new(),
         }
     }
 
@@ -67,6 +71,12 @@ impl IncomingMessage {
         self.user_name = Some(name.into());
         self
     }
+
+    /// Attach image attachments.
+    pub fn with_images(mut self, images: Vec<ImageAttachment>) -> Self {
+        self.images = images;
+        self
+    }
 }
 
 /// Stream of incoming messages.
@@ -163,6 +173,8 @@ pub enum StatusUpdate {
         success: bool,
         message: String,
     },
+    /// An image was generated or edited by a tool.
+    ImageGenerated { data_url: String, path: String },
 }
 
 impl StatusUpdate {

src/channels/repl.rs

@@ -585,6 +585,9 @@ impl Channel for ReplChannel {
                     eprintln!("\x1b[31m  {extension_name}: {message}\x1b[0m");
                 }
             }
+            StatusUpdate::ImageGenerated { path, .. } => {
+                eprintln!("  \x1b[36m[image]\x1b[0m {path}");
+            }
         }
         Ok(())
     }

src/channels/wasm/wrapper.rs

@@ -2591,6 +2591,11 @@ fn status_to_wit(status: &StatusUpdate, metadata: &serde_json::Value) -> wit_cha
             ),
             metadata_json,
         },
+        StatusUpdate::ImageGenerated { path, .. } => wit_channel::StatusUpdate {
+            status: wit_channel::StatusType::Status,
+            message: format!("Image generated: {}", path),
+            metadata_json,
+        },
     }
 }
 

src/channels/web/mod.rs

@@ -369,6 +369,19 @@ impl Channel for GatewayChannel {
                 success,
                 message,
             },
+            StatusUpdate::ImageGenerated { data_url, path } => {
+                tracing::debug!(
+                    path = %path,
+                    data_url_len = data_url.len(),
+                    thread_id = ?thread_id,
+                    "Broadcasting ImageGenerated SSE event"
+                );
+                SseEvent::ImageGenerated {
+                    data_url,
+                    path,
+                    thread_id,
+                }
+            }
         };
 
         self.state.sse.broadcast(event);

src/channels/web/openai_compat.rs

@@ -247,6 +247,7 @@ pub fn convert_messages(messages: &[OpenAiMessage]) -> Result<Vec<ChatMessage>,
                     tool_call_id: None,
                     name: m.name.clone(),
                     tool_calls: None,
+                    images: Vec::new(),
                 }),
             }
         })

src/channels/web/server.rs

@@ -43,6 +43,7 @@ use crate::channels::web::types::*;
 use crate::channels::web::util::{build_turns_from_db_messages, truncate_preview};
 use crate::db::Database;
 use crate::extensions::ExtensionManager;
+use crate::llm::ImageAttachment;
 use crate::orchestrator::job_manager::ContainerJobManager;
 use crate::tools::ToolRegistry;
 use crate::workspace::Workspace;
@@ -626,6 +627,17 @@ async fn chat_send_handler(
         msg = msg.with_metadata(serde_json::json!({"thread_id": thread_id}));
     }
 
+    // Convert image data to ImageAttachment
+    let images: Vec<ImageAttachment> = req
+        .images
+        .into_iter()
+        .map(|img| ImageAttachment {
+            media_type: img.media_type,
+            data: img.data,
+        })
+        .collect();
+    msg = msg.with_images(images);
+
     let msg_id = msg.id;
     tracing::debug!(
         "[chat_send_handler] Created message id={}, content={:?}",
@@ -951,18 +963,25 @@ async fn chat_history_handler(
                 tool_calls: t
                     .tool_calls
                     .iter()
-                    .map(|tc| ToolCallInfo {
-                        name: tc.name.clone(),
-                        has_result: tc.result.is_some(),
-                        has_error: tc.error.is_some(),
-                        result_preview: tc.result.as_ref().map(|r| {
-                            let s = match r {
-                                serde_json::Value::String(s) => s.clone(),
-                                other => other.to_string(),
-                            };
-                            truncate_preview(&s, 500)
-                        }),
-                        error: tc.error.clone(),
+                    .map(|tc| {
+                        // Image tools need full results (large base64 data), don't truncate
+                        let limit = match tc.name.as_str() {
+                            "image_generate" | "image_edit" | "image_analyze" => usize::MAX,
+                            _ => 500,
+                        };
+                        ToolCallInfo {
+                            name: tc.name.clone(),
+                            has_result: tc.result.is_some(),
+                            has_error: tc.error.is_some(),
+                            result_preview: tc.result.as_ref().map(|r| {
+                                let s = match r {
+                                    serde_json::Value::String(s) => s.clone(),
+                                    other => other.to_string(),
+                                };
+                                truncate_preview(&s, limit)
+                            }),
+                            error: tc.error.clone(),
+                        }
                     })
                     .collect(),
             })

src/channels/web/sse.rs

@@ -55,6 +55,10 @@ impl SseManager {
 
     /// Broadcast an event to all connected clients.
     pub fn broadcast(&self, event: SseEvent) {
+        // Log image events for debugging
+        if matches!(&event, SseEvent::ImageGenerated { .. }) {
+            tracing::debug!("Broadcasting image_generated SSE event to all connected clients");
+        }
         // Ignore send errors (no receivers is fine)
         let _ = self.tx.send(event);
     }
@@ -143,6 +147,7 @@ impl SseManager {
                     SseEvent::JobResult { .. } => "job_result",
                     SseEvent::Heartbeat => "heartbeat",
                     SseEvent::ExtensionStatus { .. } => "extension_status",
+                    SseEvent::ImageGenerated { .. } => "image_generated",
                 };
                 Ok(Event::default().event(event_type).data(data))
             });

src/channels/web/static/app.js

@@ -41,6 +41,9 @@ const SLASH_COMMANDS = [
 let _slashSelected = -1;
 let _slashMatches = [];
 
+// --- Image Attachments ---
+let stagedImages = []; // Array of { media_type, data, previewUrl }
+
 // --- Tool Activity State ---
 let _activeGroup = null;
 let _activeToolCards = {};
@@ -113,6 +116,78 @@ document.getElementById('token-input').addEventListener('keydown', (e) => {
   }
 })();
 
+// --- Image Attachment Handlers ---
+
+// Handle file picker selection
+document.getElementById('image-input').addEventListener('change', (e) => {
+  const files = e.target.files;
+  if (files) {
+    for (let file of files) {
+      if (file.type.startsWith('image/')) {
+        const reader = new FileReader();
+        reader.onload = (evt) => {
+          const base64Data = evt.target.result.split(',')[1]; // Remove data URL prefix
+          stagedImages.push({
+            media_type: file.type,
+            data: base64Data,
+            previewUrl: evt.target.result,
+          });
+          renderImagePreviews();
+        };
+        reader.readAsDataURL(file);
+      }
+    }
+  }
+  // Reset file input so the same file can be selected again
+  e.target.value = '';
+});
+
+// Handle paste event
+document.getElementById('chat-input').addEventListener('paste', (e) => {
+  const items = e.clipboardData.items;
+  for (let item of items) {
+    if (item.type.startsWith('image/')) {
+      e.preventDefault();
+      const file = item.getAsFile();
+      const reader = new FileReader();
+      reader.onload = (evt) => {
+        const base64Data = evt.target.result.split(',')[1];
+        stagedImages.push({
+          media_type: item.type,
+          data: base64Data,
+          previewUrl: evt.target.result,
+        });
+        renderImagePreviews();
+      };
+      reader.readAsDataURL(file);
+    }
+  }
+});
+
+function renderImagePreviews() {
+  const strip = document.getElementById('image-preview-strip');
+  if (stagedImages.length === 0) {
+    strip.style.display = 'none';
+    return;
+  }
+  strip.style.display = 'flex';
+  strip.innerHTML = '';
+  stagedImages.forEach((img, idx) => {
+    const container = document.createElement('div');
+    container.className = 'image-preview';
+    container.innerHTML = `
+      <img src="${img.previewUrl}" alt="Preview">
+      <button class="image-preview-remove" onclick="removeImage(${idx})" title="Remove">×</button>
+    `;
+    strip.appendChild(container);
+  });
+}
+
+function removeImage(idx) {
+  stagedImages.splice(idx, 1);
+  renderImagePreviews();
+}
+
 // --- API helper ---
 
 function apiFetch(path, options) {
@@ -315,6 +390,17 @@ function connectSSE() {
     setToolCardOutput(data.name, data.preview);
   });
 
+  eventSource.addEventListener('image_generated', (e) => {
+    const data = JSON.parse(e.data);
+    console.log('Received image_generated event:', { thread_id: data.thread_id, path: data.path, data_url_len: data.data_url ? data.data_url.length : 0 });
+    if (!isCurrentThread(data.thread_id)) {
+      console.log('Image event ignored: not current thread', { currentThreadId, eventThreadId: data.thread_id });
+      return;
+    }
+    console.log('Adding generated image to chat');
+    addGeneratedImage(data.data_url, data.path);
+  });
+
   eventSource.addEventListener('stream_chunk', (e) => {
     const data = JSON.parse(e.data);
     if (!isCurrentThread(data.thread_id)) return;
@@ -430,19 +516,28 @@ function sendMessage() {
     return;
   }
   const content = input.value.trim();
-  if (!content) return;
+  if (!content && stagedImages.length === 0) return;
 
   addMessage('user', content);
   input.value = '';
   autoResizeTextarea(input);
   input.focus();
 
+  const images = stagedImages.map(img => ({
+    media_type: img.media_type,
+    data: img.data,
+  }));
+
   apiFetch('/api/chat/send', {
     method: 'POST',
-    body: { content, thread_id: currentThreadId || undefined },
+    body: { content, thread_id: currentThreadId || undefined, images },
   }).catch((err) => {
     addMessage('system', 'Failed to send: ' + err.message);
   });
+
+  // Clear staged images after sending
+  stagedImages = [];
+  renderImagePreviews();
 }
 
 function enableChatInput() {
@@ -858,6 +953,30 @@ function finalizeActivityGroup() {
   _activeToolCards = {};
 }
 
+function addGeneratedImage(dataUrl, path) {
+  const container = document.getElementById('chat-messages');
+  console.log('addGeneratedImage called', { dataUrl_len: dataUrl ? dataUrl.length : 0, path });
+  const card = document.createElement('div');
+  card.className = 'generated-image-card';
+
+  const img = document.createElement('img');
+  img.src = dataUrl;
+  img.alt = 'Generated image';
+  img.className = 'generated-image';
+  img.onerror = () => console.error('Failed to load image from data URL:', dataUrl.substring(0, 100));
+  img.onload = () => console.log('Image loaded successfully from data URL');
+
+  const pathLabel = document.createElement('div');
+  pathLabel.className = 'generated-image-path';
+  pathLabel.textContent = 'Saved to: ' + path;
+
+  card.appendChild(img);
+  card.appendChild(pathLabel);
+  container.appendChild(card);
+  console.log('Image card appended to DOM');
+  container.scrollTop = container.scrollHeight;
+}
+
 function showApproval(data) {
   const container = document.getElementById('chat-messages');
   const card = document.createElement('div');
@@ -1223,10 +1342,33 @@ function createToolCallsSummaryElement(toolCalls) {
     item.appendChild(nameSpan);
 
     if (tc.result_preview) {
-      const preview = document.createElement('div');
-      preview.className = 'tool-call-preview';
-      preview.textContent = tc.result_preview;
-      item.appendChild(preview);
+      // Check if this is an image result
+      try {
+        const parsed = JSON.parse(tc.result_preview);
+        if (parsed.type === 'image_generated' && parsed.data && parsed.media_type) {
+          const dataUrl = `data:${parsed.media_type};base64,${parsed.data}`;
+          const imgDiv = document.createElement('div');
+          imgDiv.className = 'generated-image-card';
+          const img = document.createElement('img');
+          img.src = dataUrl;
+          img.alt = 'Generated image';
+          img.className = 'generated-image';
+          imgDiv.appendChild(img);
+          item.appendChild(imgDiv);
+        } else {
+          // Regular text result
+          const preview = document.createElement('div');
+          preview.className = 'tool-call-preview';
+          preview.textContent = tc.result_preview;
+          item.appendChild(preview);
+        }
+      } catch {
+        // Not JSON, display as text
+        const preview = document.createElement('div');
+        preview.className = 'tool-call-preview';
+        preview.textContent = tc.result_preview;
+        item.appendChild(preview);
+      }
     }
     if (tc.error) {
       const errDiv = document.createElement('div');

src/channels/web/static/index.html

@@ -129,7 +129,10 @@
       <div class="chat-container">
         <div class="chat-messages" id="chat-messages"></div>
         <div id="slash-autocomplete" class="slash-autocomplete" style="display:none"></div>
+        <div class="image-preview-strip" id="image-preview-strip" style="display:none;"></div>
         <div class="chat-input">
+          <input type="file" id="image-input" accept="image/*" multiple style="display:none">
+          <button id="attach-btn" class="attach-btn" title="Attach image" onclick="document.getElementById('image-input').click()">📎</button>
           <textarea id="chat-input" placeholder="Message or / for commands..." rows="1"></textarea>
           <button id="send-btn" onclick="sendMessage()">Send</button>
         </div>

src/channels/web/static/style.css

@@ -1093,6 +1093,37 @@ body {
   font-style: italic;
 }
 
+/* Generated image card */
+.generated-image-card {
+  align-self: flex-start;
+  width: 50%;
+  background: var(--bg-secondary);
+  border: 1px solid var(--border);
+  border-radius: var(--radius-lg);
+  overflow: hidden;
+  margin: 8px 0;
+  box-shadow: var(--shadow);
+  display: flex;
+  flex-direction: column;
+  flex-shrink: 0;
+}
+
+.generated-image {
+  display: block;
+  width: 100%;
+  border-radius: var(--radius-lg);
+  object-fit: contain;
+}
+
+.generated-image-path {
+  padding: 8px 12px;
+  font-size: 12px;
+  color: var(--text-secondary);
+  background: var(--bg-tertiary);
+  border-top: 1px solid var(--border);
+  word-break: break-all;
+}
+
 /* Tool calls summary (persisted between user/assistant messages) */
 .tool-calls-summary {
   background: var(--bg-secondary);
@@ -1325,6 +1356,73 @@ body {
   cursor: not-allowed;
 }
 
+.attach-btn {
+  padding: 8px 12px;
+  background: transparent;
+  color: var(--text-secondary);
+  border: 1px solid var(--border);
+  border-radius: var(--radius);
+  cursor: pointer;
+  font-size: 16px;
+  transition: all 0.2s;
+}
+
+.attach-btn:hover {
+  background: var(--bg);
+  color: var(--text);
+  border-color: var(--accent);
+}
+
+.image-preview-strip {
+  display: flex;
+  padding: 12px 16px 0 16px;
+  gap: 12px;
+  background: var(--bg-secondary);
+  overflow-x: auto;
+  border-top: 1px solid var(--border);
+}
+
+.image-preview {
+  position: relative;
+  width: 80px;
+  height: 80px;
+  flex-shrink: 0;
+  border-radius: var(--radius);
+  overflow: hidden;
+  background: var(--bg);
+  border: 1px solid var(--border);
+}
+
+.image-preview img {
+  width: 100%;
+  height: 100%;
+  object-fit: cover;
+}
+
+.image-preview-remove {
+  position: absolute;
+  top: -1px;
+  right: -1px;
+  width: 24px;
+  height: 24px;
+  padding: 0;
+  background: rgba(0, 0, 0, 0.6);
+  color: white;
+  border: none;
+  border-radius: 0;
+  font-size: 18px;
+  font-weight: bold;
+  cursor: pointer;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  transition: background 0.2s;
+}
+
+.image-preview-remove:hover {
+  background: rgba(0, 0, 0, 0.8);
+}
+
 /* Memory Tab */
 .memory-container {
   flex: 1;

src/channels/web/types.rs

@@ -5,10 +5,18 @@ use uuid::Uuid;
 
 // --- Chat ---
 
+#[derive(Debug, Clone, Deserialize, Serialize)]
+pub struct ImageData {
+    pub media_type: String,
+    pub data: String, // base64-encoded
+}
+
 #[derive(Debug, Deserialize)]
 pub struct SendMessageRequest {
     pub content: String,
     pub thread_id: Option<String>,
+    #[serde(default)]
+    pub images: Vec<ImageData>,
 }
 
 #[derive(Debug, Serialize)]
@@ -225,6 +233,17 @@ pub enum SseEvent {
         #[serde(skip_serializing_if = "Option::is_none")]
         message: Option<String>,
     },
+
+    /// An image was generated or edited.
+    #[serde(rename = "image_generated")]
+    ImageGenerated {
+        /// Base64 data URL: "data:image/png;base64,..."
+        data_url: String,
+        /// Workspace path where the image is saved.
+        path: String,
+        #[serde(skip_serializing_if = "Option::is_none")]
+        thread_id: Option<String>,
+    },
 }
 
 // --- Memory ---
@@ -606,6 +625,8 @@ pub enum WsClientMessage {
     Message {
         content: String,
         thread_id: Option<String>,
+        #[serde(default)]
+        images: Vec<ImageData>,
     },
     /// Approve or deny a pending tool execution.
     #[serde(rename = "approval")]
@@ -673,6 +694,7 @@ impl WsServerMessage {
             SseEvent::JobStatus { .. } => "job_status",
             SseEvent::JobResult { .. } => "job_result",
             SseEvent::ExtensionStatus { .. } => "extension_status",
+            SseEvent::ImageGenerated { .. } => "image_generated",
         };
         let data = serde_json::to_value(event).unwrap_or(serde_json::Value::Null);
         WsServerMessage::Event {
@@ -791,9 +813,14 @@ mod tests {
         let json = r#"{"type":"message","content":"hello","thread_id":"t1"}"#;
         let msg: WsClientMessage = serde_json::from_str(json).unwrap();
         match msg {
-            WsClientMessage::Message { content, thread_id } => {
+            WsClientMessage::Message {
+                content,
+                thread_id,
+                images,
+            } => {
                 assert_eq!(content, "hello");
                 assert_eq!(thread_id.as_deref(), Some("t1"));
+                assert!(images.is_empty());
             }
             _ => panic!("Expected Message variant"),
         }
@@ -804,9 +831,14 @@ mod tests {
         let json = r#"{"type":"message","content":"hi"}"#;
         let msg: WsClientMessage = serde_json::from_str(json).unwrap();
         match msg {
-            WsClientMessage::Message { content, thread_id } => {
+            WsClientMessage::Message {
+                content,
+                thread_id,
+                images,
+            } => {
                 assert_eq!(content, "hi");
                 assert!(thread_id.is_none());
+                assert!(images.is_empty());
             }
             _ => panic!("Expected Message variant"),
         }

src/channels/web/ws.rs

@@ -22,6 +22,7 @@ use crate::agent::submission::Submission;
 use crate::channels::IncomingMessage;
 use crate::channels::web::server::GatewayState;
 use crate::channels::web::types::{WsClientMessage, WsServerMessage};
+use crate::llm::ImageAttachment;
 
 /// Tracks active WebSocket connections.
 pub struct WsConnectionTracker {
@@ -156,12 +157,26 @@ async fn handle_client_message(
     direct_tx: &mpsc::Sender<WsServerMessage>,
 ) {
     match msg {
-        WsClientMessage::Message { content, thread_id } => {
+        WsClientMessage::Message {
+            content,
+            thread_id,
+            images,
+        } => {
             let mut incoming = IncomingMessage::new("gateway", user_id, &content);
             if let Some(ref tid) = thread_id {
                 incoming = incoming.with_thread(tid);
             }
 
+            // Convert image data to ImageAttachment
+            let image_attachments: Vec<ImageAttachment> = images
+                .into_iter()
+                .map(|img| ImageAttachment {
+                    media_type: img.media_type,
+                    data: img.data,
+                })
+                .collect();
+            incoming = incoming.with_images(image_attachments);
+
             let tx_guard = state.msg_tx.read().await;
             if let Some(ref tx) = *tx_guard {
                 if tx.send(incoming).await.is_err() {
@@ -349,6 +364,7 @@ mod tests {
             WsClientMessage::Message {
                 content: "hello agent".to_string(),
                 thread_id: Some("t1".to_string()),
+                images: vec![],
             },
             &state,
             "user1",
@@ -373,6 +389,7 @@ mod tests {
             WsClientMessage::Message {
                 content: "hello".to_string(),
                 thread_id: None,
+                images: vec![],
             },
             &state,
             "user1",

src/llm/image_models.rs

@@ -0,0 +1,139 @@
+//! Detection of image generation models across inference providers.
+
+/// Check if a model name indicates image generation capability.
+///
+/// Detects models like:
+/// - FLUX (Black Forest Labs): `flux`, `flux.2`, `flux-pro`, etc.
+/// - DALL-E (OpenAI): `dall-e-2`, `dall-e-3`, etc.
+/// - Stable Diffusion: `stable-diffusion`, `sdxl`, etc.
+/// - Imagen (Google): `imagen`, `imagen-2`, etc.
+/// - Other generation models
+pub fn is_image_generation_model(model: &str) -> bool {
+    let model_lower = model.to_lowercase();
+
+    // FLUX models
+    if model_lower.contains("flux") {
+        return true;
+    }
+
+    // DALL-E models
+    if model_lower.contains("dall-e") || model_lower.contains("dalle") {
+        return true;
+    }
+
+    // Stable Diffusion models
+    if model_lower.contains("stable-diffusion")
+        || model_lower.contains("sdxl")
+        || model_lower.contains("stability")
+    {
+        return true;
+    }
+
+    // Imagen models
+    if model_lower.contains("imagen") {
+        return true;
+    }
+
+    // Midjourney (if exposed via API)
+    if model_lower.contains("midjourney") {
+        return true;
+    }
+
+    // Replicate FLUX via API
+    if model_lower.contains("black-forest-labs") || model_lower.contains("lucataco") {
+        return true;
+    }
+
+    false
+}
+
+/// Check if any model in a list is an image generation model.
+pub fn has_image_generation_model(models: &[String]) -> bool {
+    models.iter().any(|m| is_image_generation_model(m))
+}
+
+/// Suggest the best image generation model from available models.
+///
+/// Priority: FLUX > DALL-E > others
+pub fn suggest_image_model(models: &[String]) -> Option<String> {
+    // Prefer FLUX
+    if let Some(flux) = models.iter().find(|m| m.to_lowercase().contains("flux")) {
+        return Some(flux.clone());
+    }
+
+    // Then DALL-E
+    if let Some(dalle) = models
+        .iter()
+        .find(|m| m.to_lowercase().contains("dall-e") || m.to_lowercase().contains("dalle"))
+    {
+        return Some(dalle.clone());
+    }
+
+    // Then any other image model
+    models
+        .iter()
+        .find(|m| is_image_generation_model(m))
+        .cloned()
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_flux_detection() {
+        assert!(is_image_generation_model(
+            "black-forest-labs/FLUX.2-klein-4B"
+        ));
+        assert!(is_image_generation_model("flux"));
+        assert!(is_image_generation_model("flux-pro"));
+    }
+
+    #[test]
+    fn test_dalle_detection() {
+        assert!(is_image_generation_model("dall-e-3"));
+        assert!(is_image_generation_model("dall-e-2"));
+        assert!(is_image_generation_model("dalle-3"));
+    }
+
+    #[test]
+    fn test_stable_diffusion_detection() {
+        assert!(is_image_generation_model("stable-diffusion-3"));
+        assert!(is_image_generation_model("sdxl"));
+    }
+
+    #[test]
+    fn test_imagen_detection() {
+        assert!(is_image_generation_model("imagen"));
+        assert!(is_image_generation_model("imagen-3"));
+    }
+
+    #[test]
+    fn test_non_image_models() {
+        assert!(!is_image_generation_model("claude-3-5-sonnet"));
+        assert!(!is_image_generation_model("gpt-4"));
+        assert!(!is_image_generation_model("gemini-pro"));
+    }
+
+    #[test]
+    fn test_suggest_image_model() {
+        let models = vec![
+            "gpt-4".to_string(),
+            "black-forest-labs/FLUX.2-klein-4B".to_string(),
+            "dall-e-3".to_string(),
+        ];
+
+        // Should prefer FLUX
+        assert_eq!(
+            suggest_image_model(&models),
+            Some("black-forest-labs/FLUX.2-klein-4B".to_string())
+        );
+    }
+
+    #[test]
+    fn test_suggest_dalle_when_no_flux() {
+        let models = vec!["gpt-4".to_string(), "dall-e-3".to_string()];
+
+        assert_eq!(suggest_image_model(&models), Some("dall-e-3".to_string()));
+    }
+}

src/llm/mod.rs

@@ -10,6 +10,7 @@
 pub mod circuit_breaker;
 pub mod costs;
 pub mod failover;
+pub mod image_models;
 mod nearai_chat;
 mod provider;
 mod reasoning;
@@ -20,13 +21,15 @@ pub mod retry;
 mod rig_adapter;
 pub mod session;
 pub mod smart_routing;
+pub mod vision_models;
 
 pub use circuit_breaker::{CircuitBreakerConfig, CircuitBreakerProvider};
 pub use failover::{CooldownConfig, FailoverProvider};
 pub use nearai_chat::{ModelInfo, NearAiChatProvider};
 pub use provider::{
-    ChatMessage, CompletionRequest, CompletionResponse, FinishReason, LlmProvider, ModelMetadata,
-    Role, ToolCall, ToolCompletionRequest, ToolCompletionResponse, ToolDefinition, ToolResult,
+    ChatMessage, CompletionRequest, CompletionResponse, FinishReason, ImageAttachment, LlmProvider,
+    ModelMetadata, Role, ToolCall, ToolCompletionRequest, ToolCompletionResponse, ToolDefinition,
+    ToolResult,
 };
 pub use reasoning::{
     ActionPlan, Reasoning, ReasoningContext, RespondOutput, RespondResult, SILENT_REPLY_TOKEN,

src/llm/nearai_chat.rs

@@ -671,7 +671,7 @@ struct ChatCompletionRequest {
 struct ChatCompletionMessage {
     role: String,
     #[serde(skip_serializing_if = "Option::is_none")]
-    content: Option<String>,
+    content: Option<serde_json::Value>,
     #[serde(skip_serializing_if = "Option::is_none")]
     tool_call_id: Option<String>,
     #[serde(skip_serializing_if = "Option::is_none")]
@@ -839,10 +839,15 @@ fn flatten_tool_messages(messages: Vec<ChatCompletionMessage>) -> Vec<ChatComple
             if let (true, Some(calls)) = (msg.role == "assistant", &msg.tool_calls) {
                 // Convert assistant tool_calls into descriptive text
                 let mut parts: Vec<String> = Vec::new();
-                if let Some(ref text) = msg.content
-                    && !text.is_empty()
-                {
-                    parts.push(text.clone());
+                if let Some(content) = &msg.content {
+                    // Extract string from JSON value
+                    let text = match content {
+                        serde_json::Value::String(s) => s.as_str(),
+                        _ => "",
+                    };
+                    if !text.is_empty() {
+                        parts.push(text.to_string());
+                    }
                 }
                 for tc in calls {
                     parts.push(format!(
@@ -852,7 +857,7 @@ fn flatten_tool_messages(messages: Vec<ChatCompletionMessage>) -> Vec<ChatComple
                 }
                 ChatCompletionMessage {
                     role: "assistant".to_string(),
-                    content: Some(parts.join("\n")),
+                    content: Some(serde_json::json!(parts.join("\n"))),
 
                     tool_call_id: None,
                     name: None,
@@ -861,10 +866,16 @@ fn flatten_tool_messages(messages: Vec<ChatCompletionMessage>) -> Vec<ChatComple
             } else if msg.role == "tool" {
                 // Convert tool result into a user message
                 let tool_name = msg.name.as_deref().unwrap_or("unknown");
-                let result = msg.content.as_deref().unwrap_or("");
+                let result = match &msg.content {
+                    Some(serde_json::Value::String(s)) => s.as_str(),
+                    _ => "",
+                };
                 ChatCompletionMessage {
                     role: "user".to_string(),
-                    content: Some(format!("[Tool `{}` returned: {}]", tool_name, result)),
+                    content: Some(serde_json::json!(format!(
+                        "[Tool `{}` returned: {}]",
+                        tool_name, result
+                    ))),
 
                     tool_call_id: None,
                     name: None,
@@ -902,8 +913,23 @@ impl From<ChatMessage> for ChatCompletionMessage {
 
         let content = if role == "assistant" && tool_calls.is_some() && msg.content.is_empty() {
             None
+        } else if !msg.images.is_empty() && role == "user" {
+            // User message with images: create a content array with text and image parts
+            let mut parts = vec![serde_json::json!({
+                "type": "text",
+                "text": msg.content
+            })];
+            for img in msg.images {
+                parts.push(serde_json::json!({
+                    "type": "image_url",
+                    "image_url": {
+                        "url": format!("data:{};base64,{}", img.media_type, img.data)
+                    }
+                }));
+            }
+            Some(serde_json::Value::Array(parts))
         } else {
-            Some(msg.content)
+            Some(serde_json::json!(msg.content))
         };
 
         Self {
@@ -1068,7 +1094,7 @@ mod tests {
         let msg = ChatMessage::user("Hello");
         let chat_msg: ChatCompletionMessage = msg.into();
         assert_eq!(chat_msg.role, "user");
-        assert_eq!(chat_msg.content, Some("Hello".to_string()));
+        assert_eq!(chat_msg.content, Some(serde_json::json!("Hello")));
     }
 
     #[test]
@@ -1142,14 +1168,14 @@ mod tests {
         let messages = vec![
             ChatCompletionMessage {
                 role: "system".to_string(),
-                content: Some("You are helpful.".to_string()),
+                content: Some(serde_json::json!("You are helpful.")),
                 tool_call_id: None,
                 name: None,
                 tool_calls: None,
             },
             ChatCompletionMessage {
                 role: "user".to_string(),
-                content: Some("Hello".to_string()),
+                content: Some(serde_json::json!("Hello")),
                 tool_call_id: None,
                 name: None,
                 tool_calls: None,
@@ -1166,7 +1192,7 @@ mod tests {
         let messages = vec![
             ChatCompletionMessage {
                 role: "user".to_string(),
-                content: Some("test".to_string()),
+                content: Some(serde_json::json!("test")),
                 tool_call_id: None,
                 name: None,
                 tool_calls: None,
@@ -1187,7 +1213,7 @@ mod tests {
             },
             ChatCompletionMessage {
                 role: "tool".to_string(),
-                content: Some("hi".to_string()),
+                content: Some(serde_json::json!("hi")),
                 tool_call_id: Some("call_1".to_string()),
                 name: Some("echo".to_string()),
                 tool_calls: None,
@@ -1200,24 +1226,28 @@ mod tests {
         // Assistant tool_calls → plain assistant text
         assert_eq!(result[1].role, "assistant");
         assert!(result[1].tool_calls.is_none());
-        assert!(
-            result[1]
-                .content
-                .as_ref()
-                .unwrap()
-                .contains("[Called tool `echo`")
-        );
+        if let Some(content) = &result[1].content {
+            if let serde_json::Value::String(s) = content {
+                assert!(s.contains("[Called tool `echo`"));
+            } else {
+                panic!("Content should be a string");
+            }
+        } else {
+            panic!("Content should be present");
+        }
 
         // Tool result → user message
         assert_eq!(result[2].role, "user");
         assert!(result[2].tool_call_id.is_none());
-        assert!(
-            result[2]
-                .content
-                .as_ref()
-                .unwrap()
-                .contains("[Tool `echo` returned: hi]")
-        );
+        if let Some(content) = &result[2].content {
+            if let serde_json::Value::String(s) = content {
+                assert!(s.contains("[Tool `echo` returned: hi]"));
+            } else {
+                panic!("Content should be a string");
+            }
+        } else {
+            panic!("Content should be present");
+        }
     }
 
     #[test]
@@ -1225,7 +1255,7 @@ mod tests {
         let messages = vec![
             ChatCompletionMessage {
                 role: "assistant".to_string(),
-                content: Some("Let me check that.".to_string()),
+                content: Some(serde_json::json!("Let me check that.")),
                 tool_call_id: None,
                 name: None,
                 tool_calls: Some(vec![ChatCompletionToolCall {
@@ -1239,7 +1269,7 @@ mod tests {
             },
             ChatCompletionMessage {
                 role: "tool".to_string(),
-                content: Some("found it".to_string()),
+                content: Some(serde_json::json!("found it")),
                 tool_call_id: Some("call_1".to_string()),
                 name: Some("search".to_string()),
                 tool_calls: None,
@@ -1247,9 +1277,16 @@ mod tests {
         ];
 
         let result = flatten_tool_messages(messages);
-        let text = result[0].content.as_ref().unwrap();
-        assert!(text.starts_with("Let me check that."));
-        assert!(text.contains("[Called tool `search`"));
+        if let Some(content) = result[0].content.as_ref() {
+            if let serde_json::Value::String(text) = content {
+                assert!(text.starts_with("Let me check that."));
+                assert!(text.contains("[Called tool `search`"));
+            } else {
+                panic!("Content should be a string");
+            }
+        } else {
+            panic!("Content should be present");
+        }
     }
 
     #[test]

src/llm/provider.rs

@@ -16,6 +16,15 @@ pub enum Role {
     Tool,
 }
 
+/// An image attachment for user messages.
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ImageAttachment {
+    /// MIME type (e.g., "image/jpeg", "image/png", "image/gif", "image/webp")
+    pub media_type: String,
+    /// Base64-encoded image data (without data URL prefix)
+    pub data: String,
+}
+
 /// A message in a conversation.
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct ChatMessage {
@@ -31,6 +40,9 @@ pub struct ChatMessage {
     /// to appear on the assistant message preceding tool result messages).
     #[serde(skip_serializing_if = "Option::is_none")]
     pub tool_calls: Option<Vec<ToolCall>>,
+    /// Images attached to user messages.
+    #[serde(default, skip_serializing_if = "Vec::is_empty")]
+    pub images: Vec<ImageAttachment>,
 }
 
 impl ChatMessage {
@@ -42,6 +54,7 @@ impl ChatMessage {
             tool_call_id: None,
             name: None,
             tool_calls: None,
+            images: Vec::new(),
         }
     }
 
@@ -53,6 +66,19 @@ impl ChatMessage {
             tool_call_id: None,
             name: None,
             tool_calls: None,
+            images: Vec::new(),
+        }
+    }
+
+    /// Create a user message with image attachments.
+    pub fn user_with_images(content: impl Into<String>, images: Vec<ImageAttachment>) -> Self {
+        Self {
+            role: Role::User,
+            content: content.into(),
+            tool_call_id: None,
+            name: None,
+            tool_calls: None,
+            images,
         }
     }
 
@@ -64,6 +90,7 @@ impl ChatMessage {
             tool_call_id: None,
             name: None,
             tool_calls: None,
+            images: Vec::new(),
         }
     }
 
@@ -82,6 +109,7 @@ impl ChatMessage {
             } else {
                 Some(tool_calls)
             },
+            images: Vec::new(),
         }
     }
 
@@ -97,6 +125,7 @@ impl ChatMessage {
             tool_call_id: Some(tool_call_id.into()),
             name: Some(name.into()),
             tool_calls: None,
+            images: Vec::new(),
         }
     }
 }

src/llm/rig_adapter.rs

@@ -10,8 +10,8 @@ use rig::completion::{
     ToolDefinition as RigToolDefinition, Usage as RigUsage,
 };
 use rig::message::{
-    Message as RigMessage, ToolChoice as RigToolChoice, ToolFunction, ToolResult as RigToolResult,
-    ToolResultContent, UserContent,
+    DocumentSourceKind, Image, ImageMediaType, Message as RigMessage, ToolChoice as RigToolChoice,
+    ToolFunction, ToolResult as RigToolResult, ToolResultContent, UserContent,
 };
 use rust_decimal::Decimal;
 use serde::Serialize;
@@ -230,7 +230,33 @@ fn convert_messages(messages: &[ChatMessage]) -> (Option<String>, Vec<RigMessage
                 }
             }
             crate::llm::Role::User => {
-                history.push(RigMessage::user(&msg.content));
+                if msg.images.is_empty() {
+                    history.push(RigMessage::user(&msg.content));
+                } else {
+                    // User message with images: create multi-part content
+                    let mut parts: Vec<UserContent> = vec![UserContent::text(&msg.content)];
+                    for img in &msg.images {
+                        let media_type = match img.media_type.to_lowercase().as_str() {
+                            "image/jpeg" => ImageMediaType::JPEG,
+                            "image/png" => ImageMediaType::PNG,
+                            "image/gif" => ImageMediaType::GIF,
+                            "image/webp" => ImageMediaType::WEBP,
+                            _ => ImageMediaType::JPEG,
+                        };
+                        parts.push(UserContent::Image(Image {
+                            data: DocumentSourceKind::Base64(img.data.clone()),
+                            media_type: Some(media_type),
+                            detail: None,
+                            additional_params: Default::default(),
+                        }));
+                    }
+                    if let Ok(many) = OneOrMany::many(parts) {
+                        history.push(RigMessage::User { content: many });
+                    } else {
+                        // Fallback to text only
+                        history.push(RigMessage::user(&msg.content));
+                    }
+                }
             }
             crate::llm::Role::Assistant => {
                 if let Some(ref tool_calls) = msg.tool_calls {
@@ -635,6 +661,7 @@ mod tests {
             tool_call_id: None,
             name: Some("search".to_string()),
             tool_calls: None,
+            images: vec![],
         }];
         let (_preamble, history) = convert_messages(&messages);
         match &history[0] {
@@ -784,6 +811,7 @@ mod tests {
             tool_call_id: None,
             name: Some("search".to_string()),
             tool_calls: None,
+            images: vec![],
         };
         let messages = vec![assistant_msg, tool_result_msg];
         let (_preamble, history) = convert_messages(&messages);

src/llm/vision_models.rs

@@ -0,0 +1,160 @@
+//! Detection of vision-capable models across inference providers.
+
+/// Check if a model name indicates vision capability.
+///
+/// Detects models like:
+/// - Claude (Anthropic): `claude-opus`, `claude-sonnet`, etc.
+/// - GPT (OpenAI): `gpt-4-vision`, `gpt-4-turbo`, `gpt-4o`, etc.
+/// - Gemini (Google): `gemini-pro-vision`, `gemini-2.0-flash`, etc.
+/// - Llama (Meta): `llama-2-vision`, etc.
+/// - Other vision-capable models
+pub fn is_vision_model(model: &str) -> bool {
+    let model_lower = model.to_lowercase();
+
+    // Claude models (Anthropic)
+    if model_lower.contains("claude") {
+        return true;
+    }
+
+    // GPT-4 models with vision support
+    if (model_lower.contains("gpt-4")
+        || model_lower.contains("gpt-4o")
+        || model_lower.contains("gpt-4-turbo")
+        || model_lower.contains("gpt-4-vision"))
+        && !model_lower.contains("gpt-4-mini")
+    {
+        return true;
+    }
+
+    // Gemini models
+    if model_lower.contains("gemini") {
+        return true;
+    }
+
+    // Llava and other vision models
+    if model_lower.contains("llava")
+        || model_lower.contains("vision")
+        || model_lower.contains("multimodal")
+    {
+        return true;
+    }
+
+    false
+}
+
+/// Check if any model in a list is a vision-capable model.
+pub fn has_vision_model(models: &[String]) -> bool {
+    models.iter().any(|m| is_vision_model(m))
+}
+
+/// Suggest the best vision model from available models.
+///
+/// Priority: Claude > GPT-4 > Gemini > others
+pub fn suggest_vision_model(models: &[String]) -> Option<String> {
+    // Prefer Claude
+    if let Some(claude) = models.iter().find(|m| m.to_lowercase().contains("claude")) {
+        return Some(claude.clone());
+    }
+
+    // Then GPT-4
+    if let Some(gpt4) = models
+        .iter()
+        .find(|m| m.to_lowercase().contains("gpt-4") && !m.to_lowercase().contains("gpt-4-mini"))
+    {
+        return Some(gpt4.clone());
+    }
+
+    // Then Gemini
+    if let Some(gemini) = models.iter().find(|m| m.to_lowercase().contains("gemini")) {
+        return Some(gemini.clone());
+    }
+
+    // Then any other vision model
+    models.iter().find(|m| is_vision_model(m)).cloned()
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_claude_detection() {
+        assert!(is_vision_model("claude-opus-4-20250514"));
+        assert!(is_vision_model("claude-sonnet-4-20250514"));
+        assert!(is_vision_model("claude-haiku-3-5-sonnet"));
+    }
+
+    #[test]
+    fn test_gpt4_detection() {
+        assert!(is_vision_model("gpt-4-turbo"));
+        assert!(is_vision_model("gpt-4o"));
+        assert!(is_vision_model("gpt-4-vision"));
+        assert!(is_vision_model("gpt-4-32k"));
+    }
+
+    #[test]
+    fn test_gpt4_mini_not_vision() {
+        assert!(!is_vision_model("gpt-4-mini"));
+    }
+
+    #[test]
+    fn test_gemini_detection() {
+        assert!(is_vision_model("gemini-pro-vision"));
+        assert!(is_vision_model("gemini-2.0-flash"));
+        assert!(is_vision_model("gemini-1.5-pro"));
+    }
+
+    #[test]
+    fn test_llava_detection() {
+        assert!(is_vision_model("llava-1.6"));
+        assert!(is_vision_model("llava-v1-7b"));
+    }
+
+    #[test]
+    fn test_multimodal_detection() {
+        assert!(is_vision_model("my-multimodal-model"));
+        assert!(is_vision_model("custom-vision-model"));
+    }
+
+    #[test]
+    fn test_non_vision_models() {
+        assert!(!is_vision_model("text-davinci-3"));
+        assert!(!is_vision_model("llama-2-7b"));
+        assert!(!is_vision_model("mistral-7b"));
+    }
+
+    #[test]
+    fn test_suggest_vision_model() {
+        let models = vec![
+            "gpt-4-turbo".to_string(),
+            "claude-opus-4-20250514".to_string(),
+            "gemini-2.0-flash".to_string(),
+        ];
+
+        // Should prefer Claude
+        assert_eq!(
+            suggest_vision_model(&models),
+            Some("claude-opus-4-20250514".to_string())
+        );
+    }
+
+    #[test]
+    fn test_suggest_gpt4_when_no_claude() {
+        let models = vec!["gpt-4-turbo".to_string(), "gemini-2.0-flash".to_string()];
+
+        assert_eq!(
+            suggest_vision_model(&models),
+            Some("gpt-4-turbo".to_string())
+        );
+    }
+
+    #[test]
+    fn test_suggest_gemini_when_no_claude_or_gpt4() {
+        let models = vec!["gemini-2.0-flash".to_string(), "text-davinci-3".to_string()];
+
+        assert_eq!(
+            suggest_vision_model(&models),
+            Some("gemini-2.0-flash".to_string())
+        );
+    }
+}

src/tools/builtin/image_analyze.rs

@@ -0,0 +1,236 @@
+//! Image analysis tool for vision-capable LLMs.
+//!
+//! Reads images from the workspace and prepares them for vision analysis.
+//! The LLM can then analyze the image content based on the user's query.
+
+use std::sync::Arc;
+
+use async_trait::async_trait;
+use serde_json::{Value, json};
+
+use crate::context::JobContext;
+use crate::tools::tool::{ApprovalRequirement, Tool, ToolError, ToolOutput};
+use crate::workspace::Workspace;
+
+/// Tool for analyzing images using a vision-capable LLM.
+pub struct ImageAnalyzeTool {
+    workspace: Arc<Workspace>,
+}
+
+impl ImageAnalyzeTool {
+    /// Create a new image analysis tool.
+    pub fn new(workspace: Arc<Workspace>) -> Self {
+        Self { workspace }
+    }
+
+    /// Infer media type from file extension.
+    fn infer_media_type(path: &str) -> &'static str {
+        let lower_path = path.to_lowercase();
+        if lower_path.ends_with(".png") || lower_path.ends_with(".b64") {
+            "image/png"
+        } else if lower_path.ends_with(".jpg") || lower_path.ends_with(".jpeg") {
+            "image/jpeg"
+        } else if lower_path.ends_with(".gif") {
+            "image/gif"
+        } else if lower_path.ends_with(".webp") {
+            "image/webp"
+        } else {
+            "image/png" // Default to PNG
+        }
+    }
+}
+
+#[async_trait]
+impl Tool for ImageAnalyzeTool {
+    fn name(&self) -> &str {
+        "image_analyze"
+    }
+
+    fn description(&self) -> &str {
+        "Analyze an image using the LLM's vision capabilities. Provide the workspace path to the image and a question or prompt about what you want to know about the image."
+    }
+
+    fn parameters_schema(&self) -> Value {
+        json!({
+            "type": "object",
+            "properties": {
+                "path": {
+                    "type": "string",
+                    "description": "Workspace path to the image (e.g., 'images/generated/abc123.b64')"
+                },
+                "query": {
+                    "type": "string",
+                    "description": "What do you want to know about the image? (e.g., 'describe the objects in this image', 'is there text in this image?')"
+                }
+            },
+            "required": ["path", "query"]
+        })
+    }
+
+    async fn execute(&self, params: Value, _ctx: &JobContext) -> Result<ToolOutput, ToolError> {
+        let start = std::time::Instant::now();
+
+        // Parse parameters
+        let path = params
+            .get("path")
+            .and_then(|v| v.as_str())
+            .ok_or_else(|| {
+                ToolError::InvalidParameters("Missing or invalid 'path' parameter".to_string())
+            })?
+            .to_string();
+
+        let query = params
+            .get("query")
+            .and_then(|v| v.as_str())
+            .ok_or_else(|| {
+                ToolError::InvalidParameters("Missing or invalid 'query' parameter".to_string())
+            })?
+            .to_string();
+
+        if query.is_empty() {
+            return Err(ToolError::InvalidParameters(
+                "Query cannot be empty".to_string(),
+            ));
+        }
+
+        // Read image from workspace
+        let doc = self.workspace.read(&path).await.map_err(|e| {
+            ToolError::ExecutionFailed(format!("Failed to read image from workspace: {}", e))
+        })?;
+
+        // Infer media type from path
+        let media_type = Self::infer_media_type(&path).to_string();
+
+        // Return the image data and query so the agent can include the image in its vision analysis
+        Ok(ToolOutput::success(
+            json!({
+                "type": "image_analysis_ready",
+                "path": path,
+                "query": query,
+                "data": doc.content,
+                "media_type": media_type,
+                "instruction": format!("The user wants you to analyze this image with the following query: {}", query)
+            }),
+            start.elapsed(),
+        ))
+    }
+
+    fn requires_approval(&self, _params: &Value) -> ApprovalRequirement {
+        // Image analysis is read-only, no approval needed
+        ApprovalRequirement::Never
+    }
+
+    fn sensitive_params(&self) -> &[&str] {
+        &[]
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use serde_json::json;
+
+    #[test]
+    fn test_infer_media_type_png() {
+        assert_eq!(
+            ImageAnalyzeTool::infer_media_type("images/test.png"),
+            "image/png"
+        );
+        assert_eq!(
+            ImageAnalyzeTool::infer_media_type("images/test.b64"),
+            "image/png"
+        );
+    }
+
+    #[test]
+    fn test_infer_media_type_jpeg() {
+        assert_eq!(
+            ImageAnalyzeTool::infer_media_type("images/test.jpg"),
+            "image/jpeg"
+        );
+        assert_eq!(
+            ImageAnalyzeTool::infer_media_type("images/test.jpeg"),
+            "image/jpeg"
+        );
+    }
+
+    #[test]
+    fn test_infer_media_type_gif() {
+        assert_eq!(
+            ImageAnalyzeTool::infer_media_type("images/test.gif"),
+            "image/gif"
+        );
+    }
+
+    #[test]
+    fn test_infer_media_type_webp() {
+        assert_eq!(
+            ImageAnalyzeTool::infer_media_type("images/test.webp"),
+            "image/webp"
+        );
+    }
+
+    #[test]
+    fn test_infer_media_type_default() {
+        assert_eq!(
+            ImageAnalyzeTool::infer_media_type("images/test.unknown"),
+            "image/png"
+        );
+    }
+
+    #[test]
+    fn test_parameters_schema_required_fields() {
+        let schema = json!({
+            "type": "object",
+            "properties": {
+                "path": {
+                    "type": "string",
+                    "description": "Workspace path to the image (e.g., 'images/generated/abc123.b64')"
+                },
+                "query": {
+                    "type": "string",
+                    "description": "What do you want to know about the image? (e.g., 'describe the objects in this image', 'is there text in this image?')"
+                }
+            },
+            "required": ["path", "query"]
+        });
+
+        assert_eq!(schema["type"], "object");
+        assert!(schema["properties"]["path"].is_object());
+        assert!(schema["properties"]["query"].is_object());
+        assert_eq!(schema["required"], json!(["path", "query"]));
+    }
+
+    #[test]
+    fn test_infer_media_type_uppercase_extension_defaults() {
+        // Uppercase extensions are now case-insensitively matched
+        assert_eq!(
+            ImageAnalyzeTool::infer_media_type("images/test.PNG"),
+            "image/png"
+        );
+        assert_eq!(
+            ImageAnalyzeTool::infer_media_type("images/test.JPG"),
+            "image/jpeg"
+        );
+    }
+
+    #[test]
+    fn test_infer_media_type_nested_path() {
+        assert_eq!(
+            ImageAnalyzeTool::infer_media_type("images/generated/2024-03-06/deep/nested/image.png"),
+            "image/png"
+        );
+    }
+
+    #[test]
+    fn test_infer_media_type_multiple_dots() {
+        assert_eq!(
+            ImageAnalyzeTool::infer_media_type("images/my.test.image.png"),
+            "image/png"
+        );
+        assert_eq!(
+            ImageAnalyzeTool::infer_media_type("images/file.backup.jpg"),
+            "image/jpeg"
+        );
+    }
+}

src/tools/builtin/image_edit.rs

@@ -0,0 +1,231 @@
+//! Image editing tool using NEAR AI cloud-api (FLUX model).
+
+use std::sync::Arc;
+use std::time::Duration;
+
+use async_trait::async_trait;
+use base64::Engine;
+use secrecy::ExposeSecret;
+use serde_json::{Value, json};
+use uuid::Uuid;
+
+use crate::config::NearAiConfig;
+use crate::context::JobContext;
+use crate::tools::tool::{ApprovalRequirement, Tool, ToolError, ToolOutput, ToolRateLimitConfig};
+use crate::workspace::Workspace;
+
+/// Tool for editing existing images using NEAR AI cloud-api (FLUX).
+pub struct ImageEditTool {
+    config: NearAiConfig,
+    client: reqwest::Client,
+    workspace: Arc<Workspace>,
+}
+
+impl ImageEditTool {
+    /// Create a new image editing tool.
+    pub fn new(config: NearAiConfig, workspace: Arc<Workspace>) -> Self {
+        Self {
+            config,
+            client: reqwest::Client::new(),
+            workspace,
+        }
+    }
+}
+
+#[async_trait]
+impl Tool for ImageEditTool {
+    fn name(&self) -> &str {
+        "image_edit"
+    }
+
+    fn description(&self) -> &str {
+        "Edit an existing image using NEAR AI cloud-api (FLUX) by providing the workspace path and a description of changes. \
+         Returns the edited image saved to the workspace."
+    }
+
+    fn parameters_schema(&self) -> Value {
+        json!({
+            "type": "object",
+            "properties": {
+                "path": {
+                    "type": "string",
+                    "description": "Workspace path to the source image (e.g., 'images/generated/abc123.b64')"
+                },
+                "prompt": {
+                    "type": "string",
+                    "description": "Description of the edits to apply (max 4000 characters)"
+                },
+                "size": {
+                    "type": "string",
+                    "enum": ["1024x1024", "1792x1024", "1024x1792"],
+                    "description": "Image dimensions. Default: 1024x1024"
+                }
+            },
+            "required": ["path", "prompt"]
+        })
+    }
+
+    async fn execute(&self, params: Value, _ctx: &JobContext) -> Result<ToolOutput, ToolError> {
+        let start = std::time::Instant::now();
+
+        // Parse parameters
+        let path = params
+            .get("path")
+            .and_then(|v| v.as_str())
+            .ok_or_else(|| {
+                ToolError::InvalidParameters("Missing or invalid 'path' parameter".to_string())
+            })?
+            .to_string();
+
+        let prompt = params
+            .get("prompt")
+            .and_then(|v| v.as_str())
+            .ok_or_else(|| {
+                ToolError::InvalidParameters("Missing or invalid 'prompt' parameter".to_string())
+            })?
+            .to_string();
+
+        if prompt.is_empty() {
+            return Err(ToolError::InvalidParameters(
+                "Prompt cannot be empty".to_string(),
+            ));
+        }
+
+        if prompt.len() > 4000 {
+            return Err(ToolError::InvalidParameters(format!(
+                "Prompt exceeds 4000 character limit (got {})",
+                prompt.len()
+            )));
+        }
+
+        let size = params
+            .get("size")
+            .and_then(|v| v.as_str())
+            .unwrap_or("1024x1024");
+
+        // Read base64 image data from workspace
+        let doc = self.workspace.read(&path).await.map_err(|e| {
+            ToolError::ExecutionFailed(format!("Failed to read image from workspace: {}", e))
+        })?;
+
+        // Decode base64 to bytes
+        let image_bytes = base64::engine::general_purpose::STANDARD
+            .decode(&doc.content)
+            .map_err(|e| {
+                ToolError::ExecutionFailed(format!("Failed to decode base64 image data: {}", e))
+            })?;
+
+        // Build multipart form
+        let form = reqwest::multipart::Form::new()
+            .text("model", "black-forest-labs/FLUX.2-klein-4B")
+            .part(
+                "image",
+                reqwest::multipart::Part::bytes(image_bytes).file_name("image.png"),
+            )
+            .text("prompt", prompt.clone())
+            .text("n", "1")
+            .text("size", size.to_string())
+            .text("response_format", "b64_json");
+
+        // Call NEAR AI cloud-api edit endpoint
+        let endpoint = format!(
+            "{}/v1/images/edits",
+            self.config.base_url.trim_end_matches('/')
+        );
+
+        let auth_header = if let Some(api_key) = &self.config.api_key {
+            format!("Bearer {}", api_key.expose_secret())
+        } else {
+            "Bearer ".to_string()
+        };
+
+        let response = self
+            .client
+            .post(&endpoint)
+            .header("Authorization", auth_header)
+            .multipart(form)
+            .timeout(Duration::from_secs(120))
+            .send()
+            .await
+            .map_err(|e| ToolError::ExternalService(format!("NEAR AI image edit failed: {}", e)))?;
+
+        if !response.status().is_success() {
+            let status = response.status();
+            let error_text = response
+                .text()
+                .await
+                .unwrap_or_else(|_| "Unknown error".to_string());
+            return Err(ToolError::ExternalService(format!(
+                "NEAR AI image edit error ({}): {}",
+                status, error_text
+            )));
+        }
+
+        let response_json: Value = response.json().await.map_err(|e| {
+            ToolError::ExternalService(format!("Failed to parse NEAR AI response: {}", e))
+        })?;
+
+        // Extract base64 edited image data
+        let edited_base64 = response_json
+            .get("data")
+            .and_then(|d| d.get(0))
+            .and_then(|item| item.get("b64_json"))
+            .and_then(|v| v.as_str())
+            .ok_or_else(|| {
+                ToolError::ExternalService(
+                    "Invalid NEAR AI response structure: missing base64 data".to_string(),
+                )
+            })?
+            .to_string();
+
+        // Generate unique filename for edited image
+        let edit_id = Uuid::new_v4().to_string();
+        let filename = format!("images/generated/{}_edit.png", edit_id);
+
+        // Store edited image to workspace
+        let edit_path = format!("images/generated/{}_edit.b64", edit_id);
+        self.workspace
+            .write(&edit_path, &edited_base64)
+            .await
+            .map_err(|e| {
+                ToolError::ExecutionFailed(format!(
+                    "Failed to save edited image to workspace: {}",
+                    e
+                ))
+            })?;
+
+        // Return sentinel JSON for agent_loop to detect and emit SSE event
+        Ok(ToolOutput::success(
+            json!({
+                "type": "image_generated",
+                "path": edit_path,
+                "data": edited_base64,
+                "media_type": "image/png",
+                "prompt": prompt,
+                "size": size,
+                "filename": filename,
+                "source_path": path
+            }),
+            start.elapsed(),
+        ))
+    }
+
+    fn requires_approval(&self, _params: &Value) -> ApprovalRequirement {
+        // Image editing is read-only on external state
+        ApprovalRequirement::Never
+    }
+
+    fn rate_limit_config(&self) -> Option<ToolRateLimitConfig> {
+        // DALL-E is expensive; rate limit aggressively
+        Some(ToolRateLimitConfig::new(6, 30))
+    }
+
+    fn sensitive_params(&self) -> &[&str] {
+        &[]
+    }
+
+    fn execution_timeout(&self) -> std::time::Duration {
+        // Image editing can take 2+ minutes on the NEAR AI cloud-api
+        std::time::Duration::from_secs(180)
+    }
+}

src/tools/builtin/image_gen.rs

@@ -0,0 +1,203 @@
+//! Image generation tool using NEAR AI cloud-api (FLUX model).
+
+use std::sync::Arc;
+use std::time::Duration;
+
+use async_trait::async_trait;
+use secrecy::ExposeSecret;
+use serde_json::{Value, json};
+use uuid::Uuid;
+
+use crate::config::NearAiConfig;
+use crate::context::JobContext;
+use crate::tools::tool::{ApprovalRequirement, Tool, ToolError, ToolOutput, ToolRateLimitConfig};
+use crate::workspace::Workspace;
+
+/// Tool for generating images from text prompts using NEAR AI cloud-api (FLUX).
+pub struct ImageGenerateTool {
+    config: NearAiConfig,
+    client: reqwest::Client,
+    workspace: Arc<Workspace>,
+}
+
+impl ImageGenerateTool {
+    /// Create a new image generation tool.
+    pub fn new(config: NearAiConfig, workspace: Arc<Workspace>) -> Self {
+        Self {
+            config,
+            client: reqwest::Client::new(),
+            workspace,
+        }
+    }
+}
+
+#[async_trait]
+impl Tool for ImageGenerateTool {
+    fn name(&self) -> &str {
+        "image_generate"
+    }
+
+    fn description(&self) -> &str {
+        "Generate an image from a text prompt using NEAR AI cloud-api (FLUX.2-klein-4B). \
+         Returns the generated image saved to the workspace."
+    }
+
+    fn parameters_schema(&self) -> Value {
+        json!({
+            "type": "object",
+            "properties": {
+                "prompt": {
+                    "type": "string",
+                    "description": "Detailed text description of the image to generate (max 4000 characters)"
+                },
+                "size": {
+                    "type": "string",
+                    "enum": ["1024x1024", "1792x1024", "1024x1792"],
+                    "description": "Image dimensions. Default: 1024x1024"
+                }
+            },
+            "required": ["prompt"]
+        })
+    }
+
+    async fn execute(&self, params: Value, _ctx: &JobContext) -> Result<ToolOutput, ToolError> {
+        let start = std::time::Instant::now();
+
+        // Parse parameters
+        let prompt = params
+            .get("prompt")
+            .and_then(|v| v.as_str())
+            .ok_or_else(|| {
+                ToolError::InvalidParameters("Missing or invalid 'prompt' parameter".to_string())
+            })?
+            .to_string();
+
+        if prompt.is_empty() {
+            return Err(ToolError::InvalidParameters(
+                "Prompt cannot be empty".to_string(),
+            ));
+        }
+
+        if prompt.len() > 4000 {
+            return Err(ToolError::InvalidParameters(format!(
+                "Prompt exceeds 4000 character limit (got {})",
+                prompt.len()
+            )));
+        }
+
+        let size = params
+            .get("size")
+            .and_then(|v| v.as_str())
+            .unwrap_or("1024x1024");
+
+        // Call NEAR AI cloud-api for image generation (FLUX model)
+        let request_body = json!({
+            "model": "black-forest-labs/FLUX.2-klein-4B",
+            "prompt": prompt,
+            "n": 1,
+            "size": size,
+            "response_format": "b64_json"
+        });
+
+        let endpoint = format!(
+            "{}/v1/images/generations",
+            self.config.base_url.trim_end_matches('/')
+        );
+
+        let auth_header = if let Some(api_key) = &self.config.api_key {
+            format!("Bearer {}", api_key.expose_secret())
+        } else {
+            // Fallback: use default NEAR AI cloud-api without explicit key
+            // (expects auth via environment or other mechanism)
+            "Bearer ".to_string()
+        };
+
+        let response = self
+            .client
+            .post(&endpoint)
+            .header("Authorization", auth_header)
+            .json(&request_body)
+            .timeout(Duration::from_secs(120))
+            .send()
+            .await
+            .map_err(|e| {
+                ToolError::ExternalService(format!("NEAR AI image generation failed: {}", e))
+            })?;
+
+        if !response.status().is_success() {
+            let status = response.status();
+            let error_text = response
+                .text()
+                .await
+                .unwrap_or_else(|_| "Unknown error".to_string());
+            return Err(ToolError::ExternalService(format!(
+                "NEAR AI image generation error ({}): {}",
+                status, error_text
+            )));
+        }
+
+        let response_json: Value = response.json().await.map_err(|e| {
+            ToolError::ExternalService(format!("Failed to parse NEAR AI response: {}", e))
+        })?;
+
+        // Extract base64 image data
+        let base64_data = response_json
+            .get("data")
+            .and_then(|d| d.get(0))
+            .and_then(|item| item.get("b64_json"))
+            .and_then(|v| v.as_str())
+            .ok_or_else(|| {
+                ToolError::ExternalService(
+                    "Invalid NEAR AI response structure: missing base64 data".to_string(),
+                )
+            })?
+            .to_string();
+
+        // Generate unique filename
+        let image_id = Uuid::new_v4().to_string();
+        let filename = format!("images/generated/{}.png", image_id);
+
+        // Store the image file (with extension) containing the base64 data
+        let image_path = format!("images/generated/{}.b64", image_id);
+        self.workspace
+            .write(&image_path, &base64_data)
+            .await
+            .map_err(|e| {
+                ToolError::ExecutionFailed(format!("Failed to save image to workspace: {}", e))
+            })?;
+
+        // Return sentinel JSON for agent_loop to detect and emit SSE event
+        Ok(ToolOutput::success(
+            json!({
+                "type": "image_generated",
+                "path": image_path,
+                "data": base64_data,
+                "media_type": "image/png",
+                "prompt": prompt,
+                "size": size,
+                "filename": filename
+            }),
+            start.elapsed(),
+        ))
+    }
+
+    fn requires_approval(&self, _params: &Value) -> ApprovalRequirement {
+        // Image generation from a prompt is read-only on external state
+        // so no approval needed
+        ApprovalRequirement::Never
+    }
+
+    fn rate_limit_config(&self) -> Option<ToolRateLimitConfig> {
+        // DALL-E is expensive; rate limit aggressively
+        Some(ToolRateLimitConfig::new(6, 30))
+    }
+
+    fn sensitive_params(&self) -> &[&str] {
+        &[]
+    }
+
+    fn execution_timeout(&self) -> std::time::Duration {
+        // Image generation can take 2+ minutes on the NEAR AI cloud-api
+        std::time::Duration::from_secs(180)
+    }
+}

src/tools/builtin/mod.rs

@@ -4,6 +4,9 @@ mod echo;
 pub mod extension_tools;
 mod file;
 mod http;
+mod image_analyze;
+mod image_edit;
+mod image_gen;
 mod job;
 mod json;
 mod memory;
@@ -23,6 +26,9 @@ pub use extension_tools::{
 };
 pub use file::{ApplyPatchTool, ListDirTool, ReadFileTool, WriteFileTool};
 pub use http::HttpTool;
+pub use image_analyze::ImageAnalyzeTool;
+pub use image_edit::ImageEditTool;
+pub use image_gen::ImageGenerateTool;
 pub use job::{
     CancelJobTool, CreateJobTool, JobEventsTool, JobPromptTool, JobStatusTool, ListJobsTool,
     PromptQueue, SchedulerSlot,

src/tools/registry.rs

@@ -17,11 +17,11 @@ use crate::skills::registry::SkillRegistry;
 use crate::tools::builder::{BuildSoftwareTool, BuilderConfig, LlmSoftwareBuilder};
 use crate::tools::builtin::{
     ApplyPatchTool, CancelJobTool, CreateJobTool, EchoTool, ExtensionInfoTool, HttpTool,
-    JobEventsTool, JobPromptTool, JobStatusTool, JsonTool, ListDirTool, ListJobsTool,
-    MemoryReadTool, MemorySearchTool, MemoryTreeTool, MemoryWriteTool, PromptQueue, ReadFileTool,
-    ShellTool, SkillInstallTool, SkillListTool, SkillRemoveTool, SkillSearchTool, TimeTool,
-    ToolActivateTool, ToolAuthTool, ToolInstallTool, ToolListTool, ToolRemoveTool, ToolSearchTool,
-    WriteFileTool,
+    ImageEditTool, ImageGenerateTool, JobEventsTool, JobPromptTool, JobStatusTool, JsonTool,
+    ListDirTool, ListJobsTool, MemoryReadTool, MemorySearchTool, MemoryTreeTool, MemoryWriteTool,
+    PromptQueue, ReadFileTool, ShellTool, SkillInstallTool, SkillListTool, SkillRemoveTool,
+    SkillSearchTool, TimeTool, ToolActivateTool, ToolAuthTool, ToolInstallTool, ToolListTool,
+    ToolRemoveTool, ToolSearchTool, WriteFileTool,
 };
 use crate::tools::rate_limiter::RateLimiter;
 use crate::tools::tool::{Tool, ToolDomain};
@@ -71,6 +71,9 @@ const PROTECTED_TOOL_NAMES: &[&str] = &[
     "message",
     "web_fetch",
     "restart",
+    "image_generate",
+    "image_edit",
+    "image_analyze",
 ];
 
 /// Registry of available tools.
@@ -302,6 +305,34 @@ impl ToolRegistry {
         tracing::info!("Registered 4 memory tools");
     }
 
+    /// Register image generation tools with NEAR AI config and workspace.
+    ///
+    /// Image tools require NEAR AI cloud-api access and workspace for storing generated images.
+    pub fn register_image_tools(
+        &self,
+        config: crate::config::NearAiConfig,
+        workspace: Arc<Workspace>,
+    ) {
+        self.register_sync(Arc::new(ImageGenerateTool::new(
+            config.clone(),
+            Arc::clone(&workspace),
+        )));
+        self.register_sync(Arc::new(ImageEditTool::new(config, workspace)));
+
+        tracing::info!("Registered 2 image tools (NEAR AI FLUX)");
+    }
+
+    /// Register image analysis tool with workspace access.
+    ///
+    /// Vision tool allows analyzing images using the LLM's vision capabilities.
+    pub fn register_vision_tools(&self, workspace: Arc<Workspace>) {
+        self.register_sync(Arc::new(crate::tools::builtin::ImageAnalyzeTool::new(
+            workspace,
+        )));
+
+        tracing::info!("Registered 1 vision tool (image analysis)");
+    }
+
     /// Register job management tools.
     ///
     /// Job tools allow the LLM to create, list, check status, and cancel jobs.

tests/e2e_routine_heartbeat.rs

@@ -203,6 +203,7 @@ mod tests {
             thread_id: None,
             received_at: Utc::now(),
             metadata: serde_json::json!({}),
+            images: vec![],
         };
         let fired = engine.check_event_triggers(&matching_msg).await;
         assert!(
@@ -223,6 +224,7 @@ mod tests {
             thread_id: None,
             received_at: Utc::now(),
             metadata: serde_json::json!({}),
+            images: vec![],
         };
         let fired_neg = engine.check_event_triggers(&non_matching_msg).await;
         assert_eq!(fired_neg, 0, "Expected 0 routines fired on non-match");
@@ -286,6 +288,7 @@ mod tests {
             thread_id: None,
             received_at: Utc::now(),
             metadata: serde_json::json!({}),
+            images: vec![],
         };
         let fired1 = engine.check_event_triggers(&msg).await;
         assert!(fired1 >= 1, "First fire should work");