chore: update clone traffic data [skip ci]

* fix(server): preserve grounded agent stream content

* test(server): cover active grounded stream path

* fix(server): retain agent stream bridge

.github/clone-stats/badge.json

@@ -1,7 +1,7 @@
 {
   "schemaVersion": 1,
   "label": "Git Clones",
-  "message": "181,942",
+  "message": "190,252",
   "color": "green",
   "namedLogo": "git"
 }

.github/clone-stats/clone-data.json

@@ -1,6 +1,6 @@
 {
-  "total_clones": 181942,
-  "last_updated": "2026-08-05T08:31:05Z",
+  "total_clones": 190252,
+  "last_updated": "2026-08-14T07:19:51Z",
   "daily": {
     "2026-03-27": 2189,
     "2026-03-28": 1874,
@@ -132,6 +132,15 @@
     "2026-08-01": 567,
     "2026-08-02": 1248,
     "2026-08-03": 724,
-    "2026-08-04": 708
+    "2026-08-04": 708,
+    "2026-08-05": 647,
+    "2026-08-06": 604,
+    "2026-08-07": 624,
+    "2026-08-08": 706,
+    "2026-08-09": 1076,
+    "2026-08-10": 1060,
+    "2026-08-11": 2182,
+    "2026-08-12": 641,
+    "2026-08-13": 770
   }
 }

.github/workflows/desktop.yml

@@ -121,6 +121,104 @@ jobs:
           # latest release tag (#526).
           fetch-depth: 0
 
+      # Validate Apple credentials BEFORE the expensive work. Notarization is
+      # the very last thing `tauri-action` does, so a bad credential or a
+      # lapsed account agreement previously surfaced ~10 minutes in — after the
+      # Rust toolchain, npm install, two Ollama sidecar downloads and a
+      # universal cargo build — as a single opaque line:
+      #
+      #   failed to bundle project: failed codesign application: failed to
+      #   notarize app: Error: HTTP status code: 403. ...
+      #
+      # `notarytool history` is a read-only call (it submits nothing) that
+      # exercises the identical auth path, so every credential/account failure
+      # mode reaches us here first, in seconds, with the specific cause named.
+      # `xcrun` is preinstalled on macOS runners, hence placement before the
+      # toolchain steps rather than next to "Configure Apple signing".
+      - name: Preflight Apple notarization credentials
+        if: matrix.platform == 'macos-14'
+        env:
+          CERT: ${{ secrets.APPLE_CERTIFICATE }}
+          A_ID: ${{ secrets.APPLE_ID }}
+          A_PASS: ${{ secrets.APPLE_PASSWORD }}
+          A_TEAM: ${{ secrets.APPLE_TEAM_ID }}
+        shell: bash
+        run: |
+          set -uo pipefail
+
+          # Mirror the skip logic in "Configure Apple signing": without a
+          # certificate the build is unsigned and never notarizes, so there is
+          # nothing to preflight. Tag builds still hard-fail there.
+          if [ -z "$CERT" ]; then
+            echo "No Apple certificate configured; skipping notarization preflight."
+            exit 0
+          fi
+
+          missing=""
+          [ -z "$A_ID" ]   && missing="$missing APPLE_ID"
+          [ -z "$A_PASS" ] && missing="$missing APPLE_PASSWORD"
+          [ -z "$A_TEAM" ] && missing="$missing APPLE_TEAM_ID"
+          if [ -n "$missing" ]; then
+            echo "::error::APPLE_CERTIFICATE is set but notarization secrets are missing:$missing"
+            echo "::error::Signing would succeed and notarization would then fail. Set them or clear APPLE_CERTIFICATE."
+            exit 1
+          fi
+
+          # Retry only to absorb transient network faults. Credential and
+          # account errors are deterministic, so we classify and exit on the
+          # first definitive answer rather than retrying into the same wall.
+          attempt=1
+          while [ "$attempt" -le 3 ]; do
+            out=$(xcrun notarytool history \
+              --apple-id "$A_ID" \
+              --team-id "$A_TEAM" \
+              --password "$A_PASS" \
+              --output-format json 2>&1)
+            rc=$?
+
+            if [ $rc -eq 0 ]; then
+              echo "Apple notarization preflight OK — credentials valid, team reachable, agreements in effect."
+              exit 0
+            fi
+
+            case "$out" in
+              *"Invalid credentials"*|*"401"*)
+                echo "::error::Apple notarization preflight failed: invalid credentials (HTTP 401)."
+                echo "::error::APPLE_PASSWORD must be an app-specific password from appleid.apple.com,"
+                echo "::error::generated while signed in as the SAME Apple ID as APPLE_ID. A regular"
+                echo "::error::Apple ID password will not work, and a password minted under a different"
+                echo "::error::Apple ID authenticates as that other account."
+                exit 1
+                ;;
+              *"Invalid or inaccessible developer team ID"*)
+                echo "::error::Apple notarization preflight failed: APPLE_ID is not a member of team APPLE_TEAM_ID (HTTP 403)."
+                echo "::error::The Team ID must match the signing certificate. Read it from the cert's"
+                echo "::error::subject, where it appears as: Developer ID Application: NAME (TEAMID)."
+                echo "::error::If you belong to several teams, confirm APPLE_ID is a member of this one."
+                exit 1
+                ;;
+              *"required agreement"*|*"agreement"*)
+                echo "::error::Apple notarization preflight failed: the team has no in-effect agreement (HTTP 403)."
+                echo "::error::Apple reissues the Developer Program License Agreement periodically and"
+                echo "::error::notarization is refused until it is accepted. ONLY THE ACCOUNT HOLDER can"
+                echo "::error::accept it — team Admins cannot. Sign in to the account that owns this team:"
+                echo "::error::  1. https://developer.apple.com/account -> review any pending agreement"
+                echo "::error::  2. App Store Connect -> Business -> accept anything pending there too"
+                echo "::error::Certificates stay valid while this is outstanding, so signing still works."
+                exit 1
+                ;;
+            esac
+
+            echo "Preflight attempt ${attempt}/3 failed with a non-credential error."
+            echo "$out" | tail -5
+            attempt=$((attempt + 1))
+            [ "$attempt" -le 3 ] && sleep 10
+          done
+
+          echo "::error::Apple notarization preflight failed after 3 attempts. Last output:"
+          echo "$out" | tail -20
+          exit 1
+
       - name: Install system dependencies (Linux)
         if: matrix.platform == 'ubuntu-22.04'
         run: |

docs/user-guide/evaluations.md

@@ -31,6 +31,16 @@ uv sync --extra dev --extra eval-wandb     # Weights & Biases run tracking
 uv sync --extra dev --extra eval-sheets    # Google Sheets results export
 ```
 
+TauBench additionally requires Python 3.12 or newer and the upstream `tau2`
+package. Install the pinned revision explicitly before running that benchmark:
+
+```bash
+uv pip install "tau2 @ git+https://github.com/sierra-research/tau2-bench.git@fc0055dc4e0a316c3f83133267fbd6faaa770992"
+```
+
+OpenJarvis does not install third-party packages automatically when an
+evaluation is imported or run.
+
 !!! note "Python version requirement"
     Python 3.10 requires the `tomli` package for TOML config parsing. `openjarvis` declares it as a conditional dependency, so it is installed automatically.
 

frontend/src/App.tsx

@@ -31,7 +31,6 @@ export default function App() {
   const prevModelRef = useRef<string>('');
   const setModels = useAppStore((s) => s.setModels);
   const setModelsLoading = useAppStore((s) => s.setModelsLoading);
-  const setSelectedModel = useAppStore((s) => s.setSelectedModel);
   const selectedModel = useAppStore((s) => s.selectedModel);
   const setServerInfo = useAppStore((s) => s.setServerInfo);
   const setSavings = useAppStore((s) => s.setSavings);
@@ -70,7 +69,6 @@ export default function App() {
     fetchModels()
       .then((m) => {
         setModels(m);
-        if (!selectedModel && m.length > 0) setSelectedModel(m[0].id);
       })
       .catch(() => setModels([]))
       .finally(() => setModelsLoading(false));

frontend/src/components/Chat/ChatArea.tsx

@@ -15,6 +15,7 @@ function getGreeting(): string {
 }
 
 export function ChatArea() {
+  const activeId = useAppStore((s) => s.activeId);
   const messages = useAppStore((s) => s.messages);
   const streamState = useAppStore((s) => s.streamState);
   const systemPanelOpen = useAppStore((s) => s.systemPanelOpen);
@@ -24,6 +25,8 @@ export function ChatArea() {
   const shouldAutoScroll = useRef(true);
   const wasStreaming = useRef(false);
   const lastScrollTop = useRef(0);
+  const isCurrentChatStreaming = streamState.isStreaming && streamState.conversationId === activeId;
+  const currentStreamContent = isCurrentChatStreaming ? streamState.content : '';
 
   // Check if any data sources are connected
   const [hasConnectedSources, setHasConnectedSources] = useState<boolean | null>(null);
@@ -38,14 +41,14 @@ export function ChatArea() {
   useEffect(() => {
     // Sending a message always pins the view to the bottom, even if the
     // user had scrolled up to read earlier messages.
-    if (streamState.isStreaming && !wasStreaming.current) {
+    if (isCurrentChatStreaming && !wasStreaming.current) {
       shouldAutoScroll.current = true;
     }
-    wasStreaming.current = streamState.isStreaming;
+    wasStreaming.current = isCurrentChatStreaming;
     if (shouldAutoScroll.current && listRef.current) {
       listRef.current.scrollTop = listRef.current.scrollHeight;
     }
-  }, [messages, streamState.content, streamState.isStreaming]);
+  }, [messages, currentStreamContent, isCurrentChatStreaming]);
 
   const handleScroll = () => {
     if (!listRef.current) return;
@@ -66,7 +69,7 @@ export function ChatArea() {
     }
   };
 
-  const isEmpty = messages.length === 0 && !streamState.isStreaming;
+  const isEmpty = messages.length === 0 && !isCurrentChatStreaming;
 
   const PanelIcon = systemPanelOpen ? PanelRightClose : PanelRightOpen;
 
@@ -174,12 +177,12 @@ export function ChatArea() {
                 <MessageBubble
                   key={msg.id}
                   message={msg}
-                  isLive={isLastAssistant && streamState.isStreaming}
+                  isLive={isLastAssistant && isCurrentChatStreaming}
                 />
               );
             })}
             {(() => {
-              if (!streamState.isStreaming || streamState.content !== '') return null;
+              if (!isCurrentChatStreaming || streamState.content !== '') return null;
               // For research messages the ResearchTimeline handles its own
               // pre-content loading state — suppress the generic dots.
               const last = messages[messages.length - 1];

frontend/src/components/Chat/InputArea.tsx

@@ -5,6 +5,7 @@ import { useAppStore, generateId } from '../../lib/store';
 import { streamChat, streamResearch } from '../../lib/sse';
 import { fetchSavings, getBase } from '../../lib/api';
 import { listConnectors, getSyncStatus } from '../../lib/connectors-api';
+import { serializeToolCallArguments } from '../../lib/tool-call';
 import { MicButton } from './MicButton';
 import { useSpeech } from '../../hooks/useSpeech';
 import type {
@@ -96,6 +97,7 @@ export function InputArea() {
   const deepResearch = useAppStore((s) => s.deepResearch);
   const setDeepResearch = useAppStore((s) => s.setDeepResearch);
   const corpusSync = useResearchCorpusSync(deepResearch);
+  const isCurrentChatStreaming = streamState.isStreaming && streamState.conversationId === activeId;
 
   const {
     state: speechState,
@@ -226,6 +228,7 @@ export function InputArea() {
     let ttftMs: number | undefined;
 
     setStreamState({
+      conversationId: convId,
       isStreaming: true,
       phase: deepResearch ? 'Researching...' : 'Generating...',
       elapsedMs: 0,
@@ -387,7 +390,7 @@ export function InputArea() {
             const tc: ToolCallInfo = {
               id: generateId(),
               tool: data.tool,
-              arguments: data.arguments || '',
+              arguments: serializeToolCallArguments(data.arguments),
               status: 'running',
             };
             toolCalls.push(tc);
@@ -398,7 +401,7 @@ export function InputArea() {
             updateLastAssistant(convId, accumulatedContent, [...toolCalls]);
             useAppStore.getState().addLogEntry({
               timestamp: Date.now(), level: 'info', category: 'tool',
-              message: `Calling ${data.tool}(${data.arguments || ''})`,
+              message: `Calling ${data.tool}(${serializeToolCallArguments(data.arguments)})`,
             });
           } catch {}
         } else if (eventName === 'tool_call_end') {
@@ -466,7 +469,10 @@ export function InputArea() {
       }
       const totalMs = Date.now() - startTime;
       const _CLOUD_PREFIXES = ['gpt-', 'o1-', 'o3-', 'o4-', 'claude-', 'gemini-', 'openrouter/', 'MiniMax-', 'chatgpt-'];
-      const engineLabel = _CLOUD_PREFIXES.some(p => selectedModel.startsWith(p)) ? 'cloud' : 'ollama';
+      const selectedOwner = useAppStore.getState().models.find((m) => m.id === selectedModel)?.owned_by;
+      const engineLabel = selectedOwner === 'litellm'
+        ? 'litellm'
+        : _CLOUD_PREFIXES.some(p => selectedModel.startsWith(p)) ? 'cloud' : 'ollama';
       const telemetry: MessageTelemetry = {
         engine: engineLabel,
         model_id: selectedModel,
@@ -599,7 +605,7 @@ export function InputArea() {
           style={{ color: 'var(--color-text)', maxHeight: '200px' }}
           disabled={streamState.isStreaming || modelLoading}
         />
-        {streamState.isStreaming ? (
+        {isCurrentChatStreaming ? (
           <button
             onClick={stopStreaming}
             className="p-2 rounded-xl transition-colors shrink-0 cursor-pointer"
@@ -618,7 +624,7 @@ export function InputArea() {
             />
             <button
               onClick={sendMessage}
-              disabled={!input.trim() || modelLoading || !selectedModel}
+              disabled={streamState.isStreaming || !input.trim() || modelLoading || !selectedModel}
               title={selectedModel ? 'Send message' : 'Pick a model first (⌘K)'}
               className="p-2 rounded-xl transition-colors shrink-0 cursor-pointer disabled:opacity-30 disabled:cursor-default"
               style={{

frontend/src/components/Chat/ToolCallCard.tsx

@@ -1,6 +1,7 @@
 import { useState } from 'react';
 import { ChevronDown, ChevronRight, Loader2, CheckCircle2, XCircle } from 'lucide-react';
 import type { ToolCallInfo } from '../../types';
+import { serializeToolCallArguments } from '../../lib/tool-call';
 
 interface Props {
   toolCall: ToolCallInfo;
@@ -35,7 +36,10 @@ export function ToolCallCard({ toolCall }: Props) {
   const [expanded, setExpanded] = useState(false);
   const config = statusConfig[toolCall.status];
   const StatusIcon = config.icon;
-  const preview = previewArgs(toolCall.arguments);
+  // Persisted conversations may contain the pre-fix object payload despite
+  // the TypeScript contract, so normalize again at the final render boundary.
+  const argumentsText = serializeToolCallArguments(toolCall.arguments);
+  const preview = previewArgs(argumentsText);
 
   return (
     <div
@@ -95,7 +99,7 @@ export function ToolCallCard({ toolCall }: Props) {
           className="px-2.5 pb-2 pt-0.5"
           style={{ borderTop: '1px solid var(--color-border-subtle, var(--color-border))' }}
         >
-          {toolCall.arguments && (
+          {argumentsText && (
             <div className="mt-1.5">
               <div
                 style={{
@@ -120,7 +124,7 @@ export function ToolCallCard({ toolCall }: Props) {
                   wordBreak: 'break-all',
                 }}
               >
-                {formatJson(toolCall.arguments)}
+                {formatJson(argumentsText)}
               </pre>
             </div>
           )}

frontend/src/components/CommandPalette.tsx

@@ -143,7 +143,7 @@ export function CommandPalette() {
     }
   }, [pullSuccess]);
 
-  const handleSelect = async (modelId: string) => {
+  const handleSelect = async (modelId: string, owner?: string) => {
     const previousModel = selectedModel;
     setSelectedModel(modelId);
     setCommandPaletteOpen(false);
@@ -153,7 +153,7 @@ export function CommandPalette() {
       setModelLoading(true);
       addLogEntry({ timestamp: Date.now(), level: 'info', category: 'model', message: `Switching to ${modelId}...` });
       try {
-        await preloadModel(modelId);
+        await preloadModel(modelId, owner);
         addLogEntry({ timestamp: Date.now(), level: 'info', category: 'model', message: `${modelId} loaded` });
       } catch (e: any) {
         addLogEntry({ timestamp: Date.now(), level: 'error', category: 'model', message: `Failed to load ${modelId}: ${e.message}` });
@@ -255,7 +255,8 @@ export function CommandPalette() {
       setSelectedIdx((i) => Math.max(i - 1, 0));
     } else if (e.key === 'Enter' && tab === 'installed' && filtered.length > 0) {
       e.preventDefault();
-      handleSelect((filtered[selectedIdx] as any).id);
+      const model = filtered[selectedIdx] as (typeof models)[number];
+      handleSelect(model.id, model.owned_by);
     }
   };
 
@@ -365,11 +366,15 @@ export function CommandPalette() {
                     onMouseEnter={() => setSelectedIdx(idx)}
                   >
                     <button
-                      onClick={() => handleSelect(model.id)}
+                      onClick={() => handleSelect(model.id, model.owned_by)}
                       className="flex items-center gap-3 flex-1 min-w-0 text-left cursor-pointer"
                       style={{ background: 'none', border: 'none', padding: 0 }}
                     >
-                      <Cpu size={16} style={{ color: isActive ? 'var(--color-accent)' : 'var(--color-text-tertiary)' }} />
+                      {model.owned_by === 'litellm' ? (
+                        <Cloud size={16} style={{ color: isActive ? 'var(--color-accent)' : 'var(--color-text-tertiary)' }} />
+                      ) : (
+                        <Cpu size={16} style={{ color: isActive ? 'var(--color-accent)' : 'var(--color-text-tertiary)' }} />
+                      )}
                       <div className="flex-1 min-w-0">
                         <div className="text-sm truncate" style={{ color: isActive ? 'var(--color-accent)' : 'var(--color-text)', fontWeight: isActive ? 500 : 400 }}>
                           {model.id}
@@ -381,17 +386,19 @@ export function CommandPalette() {
                         </span>
                       )}
                     </button>
-                    <button
-                      onClick={() => handleDelete(model.id)}
-                      disabled={isDeleting}
-                      className="p-1 rounded transition-colors cursor-pointer"
-                      style={{ color: 'var(--color-text-tertiary)', opacity: 0 }}
-                      title="Delete model"
-                      onMouseEnter={(e) => { e.currentTarget.style.opacity = '1'; e.currentTarget.style.color = 'var(--color-error)'; }}
-                      onMouseLeave={(e) => { e.currentTarget.style.opacity = '0'; e.currentTarget.style.color = 'var(--color-text-tertiary)'; }}
-                    >
-                      {isDeleting ? <Loader2 size={14} className="animate-spin" /> : <Trash2 size={14} />}
-                    </button>
+                    {model.owned_by !== 'litellm' && (
+                      <button
+                        onClick={() => handleDelete(model.id)}
+                        disabled={isDeleting}
+                        className="p-1 rounded transition-colors cursor-pointer"
+                        style={{ color: 'var(--color-text-tertiary)', opacity: 0 }}
+                        title="Delete model"
+                        onMouseEnter={(e) => { e.currentTarget.style.opacity = '1'; e.currentTarget.style.color = 'var(--color-error)'; }}
+                        onMouseLeave={(e) => { e.currentTarget.style.opacity = '0'; e.currentTarget.style.color = 'var(--color-text-tertiary)'; }}
+                      >
+                        {isDeleting ? <Loader2 size={14} className="animate-spin" /> : <Trash2 size={14} />}
+                      </button>
+                    )}
                   </div>
                 );
               })

frontend/src/components/SetupScreen.tsx

@@ -7,6 +7,7 @@ import {
   type SetupStatus,
 } from '../lib/api';
 import { useAppStore } from '../lib/store';
+import { isEmbedOnlyModel } from '../lib/model-capabilities';
 
 const STEPS = [
   { key: 'ollama_ready', label: 'Inference Engine', icon: Cpu, detail: 'Starting Ollama...' },
@@ -91,12 +92,14 @@ export function SetupScreen({ onReady }: { onReady: () => void }) {
           fetchRecommendedModel().catch(() => ({ model: '', reason: '' })),
         ]);
         const store = useAppStore.getState();
+        const hadSelection = !!store.selectedModel;
         store.setModels(models);
         store.setModelsLoading(false);
-        const recommended = rec.model && models.some((m) => m.id === rec.model)
+        const chatModels = models.filter((m) => !isEmbedOnlyModel(m.id));
+        const recommended = rec.model && chatModels.some((m) => m.id === rec.model)
           ? rec.model
-          : models[0]?.id || '';
-        if (recommended && !store.selectedModel) {
+          : chatModels[0]?.id || '';
+        if (recommended && !hadSelection) {
           store.setSelectedModel(recommended);
         }
       } catch {

frontend/src/components/Sidebar/ConversationList.tsx

@@ -22,6 +22,9 @@ export function ConversationList({ searchQuery }: Props) {
   const navigate = useNavigate();
   const conversations = useAppStore((s) => s.conversations);
   const activeId = useAppStore((s) => s.activeId);
+  const streamingConversationId = useAppStore((s) =>
+    s.streamState.isStreaming ? s.streamState.conversationId : null,
+  );
   const selectConversation = useAppStore((s) => s.selectConversation);
   const deleteConversation = useAppStore((s) => s.deleteConversation);
 
@@ -43,6 +46,7 @@ export function ConversationList({ searchQuery }: Props) {
     <div className="flex flex-col gap-0.5 py-1">
       {filtered.map((conv) => {
         const isActive = conv.id === activeId;
+        const isStreaming = conv.id === streamingConversationId;
         return (
           <div
             key={conv.id}
@@ -82,11 +86,18 @@ export function ConversationList({ searchQuery }: Props) {
                 e.stopPropagation();
                 deleteConversation(conv.id);
               }}
-              className="p-1.5 mr-1 rounded opacity-0 group-hover:opacity-100 transition-opacity cursor-pointer"
+              disabled={isStreaming}
+              className="p-1.5 mr-1 rounded opacity-0 group-hover:opacity-100 transition-opacity cursor-pointer disabled:cursor-not-allowed disabled:opacity-30"
               style={{ color: 'var(--color-text-tertiary)' }}
-              onMouseEnter={(e) => (e.currentTarget.style.color = 'var(--color-error)')}
+              onMouseEnter={(e) => {
+                if (!isStreaming) e.currentTarget.style.color = 'var(--color-error)';
+              }}
               onMouseLeave={(e) => (e.currentTarget.style.color = 'var(--color-text-tertiary)')}
-              title="Delete conversation"
+              title={
+                isStreaming
+                  ? 'Stop generating before deleting this conversation'
+                  : 'Delete conversation'
+              }
             >
               <Trash2 size={14} />
             </button>

frontend/src/lib/api.ts

@@ -1,5 +1,6 @@
 import type { ModelInfo, SavingsData, ServerInfo } from '../types';
 import { SUPABASE_ANON_KEY, SUPABASE_URL } from './supabase';
+import { serializeToolCallArguments } from './tool-call';
 
 // ---------------------------------------------------------------------------
 // Supabase config
@@ -218,9 +219,9 @@ export async function deleteModel(modelName: string): Promise<void> {
 
 const _CLOUD_PREFIXES = ['gpt-', 'o1-', 'o3-', 'o4-', 'claude-', 'gemini-', 'openrouter/'];
 
-export async function preloadModel(modelName: string): Promise<void> {
+export async function preloadModel(modelName: string, owner?: string): Promise<void> {
   // Cloud models don't need Ollama preloading
-  if (_CLOUD_PREFIXES.some(p => modelName.startsWith(p))) {
+  if (owner === 'litellm' || _CLOUD_PREFIXES.some(p => modelName.startsWith(p))) {
     return;
   }
   // Trigger Ollama to load the model into memory (empty prompt, no generation).
@@ -741,7 +742,7 @@ export async function sendAgentMessage(
               const parsed = JSON.parse(data);
               callbacks?.onToolCallStart?.({
                 tool: parsed.tool,
-                arguments: parsed.arguments ?? '',
+                arguments: serializeToolCallArguments(parsed.arguments),
               });
             } catch {
               /* skip */

frontend/src/lib/model-capabilities.test.ts

@@ -0,0 +1,19 @@
+import { describe, expect, it } from 'vitest';
+
+import { isEmbedOnlyModel } from './model-capabilities';
+
+describe('isEmbedOnlyModel', () => {
+  it.each([
+    'nomic-embed-text',
+    'mxbai-embed-large',
+    'text-embedding-3-small',
+    'all-minilm:latest',
+    'hf.co/BAAI/bge-m3:latest',
+  ])('classifies %s as embedding-only', (modelId) => {
+    expect(isEmbedOnlyModel(modelId)).toBe(true);
+  });
+
+  it.each(['qwen3.5:4b', 'codegemma:7b'])('keeps %s available for chat', (modelId) => {
+    expect(isEmbedOnlyModel(modelId)).toBe(false);
+  });
+});

frontend/src/lib/model-capabilities.ts

@@ -0,0 +1,22 @@
+const EMBEDDING_MODEL_PREFIXES = [
+  'all-minilm',
+  'bge-',
+  'bge_',
+  'e5-',
+  'e5_',
+  'gte-',
+  'gte_',
+  'jina-embeddings',
+  'nomic-bert',
+  'sentence-transformers',
+];
+
+export function isEmbedOnlyModel(modelId: string): boolean {
+  const name = (modelId || '').trim().toLowerCase();
+  const leaf = name.slice(name.lastIndexOf('/') + 1).split(':')[0];
+  return (
+    leaf.includes('embed') ||
+    leaf.includes('minilm') ||
+    EMBEDDING_MODEL_PREFIXES.some((prefix) => leaf.startsWith(prefix))
+  );
+}

frontend/src/lib/store.models.test.ts

@@ -0,0 +1,64 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+
+import type { ModelInfo } from '../types';
+
+class MemoryStorage {
+  private store = new Map<string, string>();
+
+  getItem(key: string): string | null {
+    return this.store.get(key) ?? null;
+  }
+
+  setItem(key: string, value: string): void {
+    this.store.set(key, String(value));
+  }
+}
+
+const model = (id: string): ModelInfo => ({
+  id,
+  object: 'model',
+  created: 0,
+  owned_by: 'openjarvis',
+});
+
+beforeEach(() => {
+  vi.resetModules();
+  (globalThis as unknown as { localStorage: MemoryStorage }).localStorage =
+    new MemoryStorage();
+});
+
+afterEach(() => {
+  (globalThis as unknown as { localStorage?: MemoryStorage }).localStorage =
+    undefined;
+});
+
+describe('setModels', () => {
+  it('does not select an embedding-only model', async () => {
+    const { useAppStore } = await import('./store');
+
+    useAppStore.getState().setModels([model('nomic-embed-text')]);
+
+    expect(useAppStore.getState().selectedModel).toBe('');
+  });
+
+  it('clears a missing selection when no chat fallback exists', async () => {
+    const { useAppStore } = await import('./store');
+    useAppStore.getState().setSelectedModel('deleted-chat-model');
+
+    useAppStore.getState().setModels([model('nomic-embed-text')]);
+
+    expect(useAppStore.getState().selectedModel).toBe('');
+  });
+
+  it('replaces an embedding selection with an available chat model', async () => {
+    const { useAppStore } = await import('./store');
+    useAppStore.getState().setSelectedModel('all-minilm:latest');
+
+    useAppStore.getState().setModels([
+      model('all-minilm:latest'),
+      model('qwen3.5:4b'),
+    ]);
+
+    expect(useAppStore.getState().selectedModel).toBe('qwen3.5:4b');
+  });
+});

frontend/src/lib/store.stream-ownership.test.ts

@@ -0,0 +1,82 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+
+class MemoryStorage {
+  private store = new Map<string, string>();
+
+  getItem(key: string): string | null {
+    return this.store.get(key) ?? null;
+  }
+
+  setItem(key: string, value: string): void {
+    this.store.set(key, String(value));
+  }
+
+  removeItem(key: string): void {
+    this.store.delete(key);
+  }
+}
+
+beforeEach(() => {
+  vi.resetModules();
+  (globalThis as unknown as { localStorage: MemoryStorage }).localStorage =
+    new MemoryStorage();
+});
+
+afterEach(() => {
+  (globalThis as unknown as { localStorage?: MemoryStorage }).localStorage =
+    undefined;
+});
+
+async function freshStore() {
+  return (await import('./store')).useAppStore;
+}
+
+describe('conversation stream ownership', () => {
+  it('persists background stream updates without replacing the active messages', async () => {
+    const store = await freshStore();
+    const sourceId = store.getState().createConversation('test-model');
+    store.getState().addMessage(sourceId, {
+      id: 'assistant',
+      role: 'assistant',
+      content: '',
+      timestamp: 1,
+    });
+
+    const activeId = store.getState().createConversation('test-model');
+    store.getState().setStreamState({
+      conversationId: sourceId,
+      isStreaming: true,
+      content: 'streamed response',
+    });
+    store.getState().updateLastAssistant(sourceId, 'streamed response');
+
+    expect(store.getState().activeId).toBe(activeId);
+    expect(store.getState().messages).toEqual([]);
+
+    store.getState().selectConversation(sourceId);
+    expect(store.getState().messages).toHaveLength(1);
+    expect(store.getState().messages[0].content).toBe('streamed response');
+  });
+
+  it('keeps the stream-owning conversation until generation stops', async () => {
+    const store = await freshStore();
+    const sourceId = store.getState().createConversation('test-model');
+    const activeId = store.getState().createConversation('test-model');
+    store.getState().setStreamState({
+      conversationId: sourceId,
+      isStreaming: true,
+    });
+
+    store.getState().deleteConversation(sourceId);
+    expect(
+      store.getState().conversations.map((conversation) => conversation.id),
+    ).toContain(sourceId);
+    expect(store.getState().activeId).toBe(activeId);
+
+    store.getState().resetStream();
+    store.getState().deleteConversation(sourceId);
+    expect(
+      store.getState().conversations.map((conversation) => conversation.id),
+    ).not.toContain(sourceId);
+  });
+});

frontend/src/lib/store.tool-call-repair.test.ts

@@ -0,0 +1,122 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+
+const CONVERSATIONS_KEY = 'openjarvis-conversations';
+
+class MemoryStorage {
+  private store = new Map<string, string>();
+
+  getItem(key: string): string | null {
+    return this.store.get(key) ?? null;
+  }
+
+  setItem(key: string, value: string): void {
+    this.store.set(key, String(value));
+  }
+
+  removeItem(key: string): void {
+    this.store.delete(key);
+  }
+}
+
+beforeEach(() => {
+  vi.resetModules();
+  (globalThis as unknown as { localStorage: MemoryStorage }).localStorage =
+    new MemoryStorage();
+});
+
+afterEach(() => {
+  (globalThis as unknown as { localStorage?: MemoryStorage }).localStorage =
+    undefined;
+});
+
+describe('persisted tool calls', () => {
+  it('repairs parsed argument objects while loading conversations', async () => {
+    localStorage.setItem(
+      CONVERSATIONS_KEY,
+      JSON.stringify({
+        version: 1,
+        activeId: 'conversation-1',
+        conversations: {
+          'conversation-1': {
+            id: 'conversation-1',
+            title: 'Broken chat',
+            createdAt: 1,
+            updatedAt: 1,
+            model: 'test-model',
+            messages: [
+              {
+                id: 'assistant-1',
+                role: 'assistant',
+                content: '',
+                timestamp: 1,
+                toolCalls: [
+                  {
+                    id: 'call-1',
+                    tool: 'web_search',
+                    arguments: { query: 'python' },
+                    status: 'success',
+                  },
+                ],
+              },
+            ],
+          },
+        },
+      }),
+    );
+
+    const { useAppStore } = await import('./store');
+
+    expect(useAppStore.getState().messages[0].toolCalls?.[0].arguments).toBe(
+      '{"query":"python"}',
+    );
+    const repaired = JSON.parse(localStorage.getItem(CONVERSATIONS_KEY) ?? '{}');
+    expect(
+      repaired.conversations['conversation-1'].messages[0].toolCalls[0].arguments,
+    ).toBe('{"query":"python"}');
+  });
+
+  it('keeps repaired conversations in memory when writeback fails', async () => {
+    localStorage.setItem(
+      CONVERSATIONS_KEY,
+      JSON.stringify({
+        version: 1,
+        activeId: 'conversation-1',
+        conversations: {
+          'conversation-1': {
+            id: 'conversation-1',
+            title: 'Readable chat',
+            createdAt: 1,
+            updatedAt: 1,
+            model: 'test-model',
+            messages: [
+              {
+                id: 'assistant-1',
+                role: 'assistant',
+                content: '',
+                timestamp: 1,
+                toolCalls: [
+                  {
+                    id: 'call-1',
+                    tool: 'web_search',
+                    arguments: { query: 'python' },
+                    status: 'success',
+                  },
+                ],
+              },
+            ],
+          },
+        },
+      }),
+    );
+    vi.spyOn(localStorage, 'setItem').mockImplementation(() => {
+      throw new DOMException('Storage quota exceeded', 'QuotaExceededError');
+    });
+
+    const { useAppStore } = await import('./store');
+
+    expect(useAppStore.getState().messages).toHaveLength(1);
+    expect(useAppStore.getState().messages[0].toolCalls?.[0].arguments).toBe(
+      '{"query":"python"}',
+    );
+  });
+});

frontend/src/lib/store.ts

@@ -15,6 +15,8 @@ import type {
   TokenUsage,
 } from '../types';
 import type { ManagedAgent } from './api';
+import { isEmbedOnlyModel } from './model-capabilities';
+import { serializeToolCallArguments } from './tool-call';
 
 export interface CachedConnector {
   connector_id: string;
@@ -54,7 +56,30 @@ function loadConversations(): ConversationStore {
     const raw = localStorage.getItem(CONVERSATIONS_KEY);
     if (!raw) return { version: 1, conversations: {}, activeId: null };
     const parsed = JSON.parse(raw);
-    if (parsed.version === 1) return parsed;
+    if (parsed.version === 1) {
+      let repaired = false;
+      for (const conversation of Object.values(parsed.conversations ?? {}) as Conversation[]) {
+        for (const message of conversation.messages ?? []) {
+          for (const toolCall of message.toolCalls ?? []) {
+            const argumentsText = serializeToolCallArguments(toolCall.arguments);
+            if (argumentsText !== toolCall.arguments) {
+              toolCall.arguments = argumentsText;
+              repaired = true;
+            }
+          }
+        }
+      }
+      if (repaired) {
+        try {
+          localStorage.setItem(CONVERSATIONS_KEY, JSON.stringify(parsed));
+        } catch {
+          // Keep the repaired conversations usable in memory when storage is
+          // read-only or full. A failed best-effort writeback must not make
+          // otherwise readable conversation history disappear from the UI.
+        }
+      }
+      return parsed;
+    }
     return { version: 1, conversations: {}, activeId: null };
   } catch {
     return { version: 1, conversations: {}, activeId: null };
@@ -110,6 +135,7 @@ function saveSettings(settings: Settings): void {
 // ── Store ─────────────────────────────────────────────────────────────
 
 const INITIAL_STREAM: StreamState = {
+  conversationId: null,
   isStreaming: false,
   phase: '',
   elapsedMs: 0,
@@ -351,6 +377,9 @@ export const useAppStore = create<AppState>((set, get) => {
     },
 
     deleteConversation: (id: string) => {
+      const streamState = get().streamState;
+      if (streamState.isStreaming && streamState.conversationId === id) return;
+
       const store = loadConversations();
       delete store.conversations[id];
       if (store.activeId === id) {
@@ -393,12 +422,14 @@ export const useAppStore = create<AppState>((set, get) => {
           (message.content.length > 50 ? '...' : '');
       }
       saveConversations(store);
-      set({
-        messages: [...conv.messages],
-        conversations: Object.values(store.conversations).sort(
-          (a, b) => b.updatedAt - a.updatedAt,
-        ),
-      });
+      const conversations = Object.values(store.conversations).sort(
+        (a, b) => b.updatedAt - a.updatedAt,
+      );
+      if (get().activeId === conversationId) {
+        set({ messages: [...conv.messages], conversations });
+      } else {
+        set({ conversations });
+      }
     },
 
     updateLastAssistant: (
@@ -425,7 +456,9 @@ export const useAppStore = create<AppState>((set, get) => {
         if (researchSources) lastMsg.researchSources = researchSources;
         conv.updatedAt = Date.now();
         saveConversations(store);
-        set({ messages: [...conv.messages] });
+        if (get().activeId === conversationId) {
+          set({ messages: [...conv.messages] });
+        }
       }
     },
 
@@ -444,11 +477,36 @@ export const useAppStore = create<AppState>((set, get) => {
     // ── Models & server ────────────────────────────────────────────
 
     setModels: (models: ModelInfo[]) =>
-      set((state) =>
-        !state.selectedModel && models.length > 0
-          ? { models, selectedModel: models[0].id }
-          : { models },
-      ),
+      set((state) => {
+        // Ollama returns embed-only models (e.g. nomic-embed-text) in the
+        // same list as chat models. Auto-picking models[0] selected the
+        // embedder and every chat failed with HTTP 400 "does not support
+        // chat". Prefer a real chat model for selection / fallback.
+        const chatModels = models.filter((m) => !isEmbedOnlyModel(m.id));
+        const preferred =
+          (state.settings.defaultModel &&
+            chatModels.some((m) => m.id === state.settings.defaultModel) &&
+            state.settings.defaultModel) ||
+          chatModels[0]?.id ||
+          models.find((m) => !isEmbedOnlyModel(m.id))?.id ||
+          '';
+
+        const currentIsBad =
+          !!state.selectedModel && isEmbedOnlyModel(state.selectedModel);
+        const currentMissing =
+          !!state.selectedModel &&
+          !models.some((m) => m.id === state.selectedModel);
+
+        if (!state.selectedModel || currentIsBad || currentMissing) {
+          // Prefer a real chat model. If none exist, clear a bad/missing
+          // selection rather than keeping an embed-only id that 400s on chat.
+          return {
+            models,
+            selectedModel: preferred,
+          };
+        }
+        return { models };
+      }),
     setModelsLoading: (loading: boolean) => set({ modelsLoading: loading }),
     setSelectedModel: (model: string) => set({ selectedModel: model }),
     setServerInfo: (info: ServerInfo | null) => set({ serverInfo: info }),

frontend/src/lib/tool-call.test.ts

@@ -0,0 +1,22 @@
+import { describe, expect, it } from 'vitest';
+
+import { serializeToolCallArguments } from './tool-call';
+
+describe('serializeToolCallArguments', () => {
+  it('preserves JSON strings', () => {
+    expect(serializeToolCallArguments('{"query":"python"}')).toBe(
+      '{"query":"python"}',
+    );
+  });
+
+  it('serializes parsed argument objects', () => {
+    expect(serializeToolCallArguments({ query: 'python' })).toBe(
+      '{"query":"python"}',
+    );
+  });
+
+  it('uses an empty string for missing arguments', () => {
+    expect(serializeToolCallArguments(null)).toBe('');
+    expect(serializeToolCallArguments(undefined)).toBe('');
+  });
+});

frontend/src/lib/tool-call.ts

@@ -0,0 +1,11 @@
+/** Convert tool-call arguments from API or persisted data into display-safe text. */
+export function serializeToolCallArguments(value: unknown): string {
+  if (typeof value === 'string') return value;
+  if (value == null) return '';
+
+  try {
+    return JSON.stringify(value) ?? String(value);
+  } catch {
+    return String(value);
+  }
+}

frontend/src/pages/AgentsPage.tsx

@@ -574,7 +574,7 @@ function ToolsPicker({
           </div>
           {/* Live description strip */}
           <div
-            className="flex items-center gap-2 px-2.5 py-1.5"
+            className="flex items-start gap-2 px-2.5 py-1.5"
             style={{
               borderTop: '1px solid var(--color-border)',
               background: 'var(--color-bg)',
@@ -608,10 +608,11 @@ function ToolsPicker({
               </span>
             )}
             <span
-              className="truncate"
+              className="min-w-0 whitespace-normal break-words"
               style={{
                 flex: 1,
                 color: 'var(--color-text-tertiary)',
+                lineHeight: 1.4,
               }}
             >
               {hovered ? `— ${hint}` : hint}

frontend/src/types/index.ts

@@ -147,6 +147,7 @@ export interface ConversationStore {
 // --- Stream State ---
 
 export interface StreamState {
+  conversationId: string | null;
   isStreaming: boolean;
   phase: string;
   elapsedMs: number;

src/openjarvis/agents/_stubs.py

@@ -57,6 +57,10 @@ class BaseAgent(ABC):
 
     agent_id: str
     accepts_tools: bool = False
+    # Plain conversational agents may opt into the managed runtime's generic
+    # function-calling loop.  Specialized agents keep their own execution
+    # class even when process-wide MCP tools are available.
+    supports_managed_tool_fallback: bool = False
 
     def __init__(
         self,
@@ -151,6 +155,9 @@ class BaseAgent(ABC):
         conversation messages, and finally the user input.
         """
         messages: list[Message] = []
+        context_messages = (
+            list(context.conversation.messages) if context is not None else []
+        )
         # Check if the context already supplies a system message
         _context_has_system = (
             context
@@ -172,9 +179,28 @@ class BaseAgent(ABC):
             except Exception:
                 effective_system_prompt = None
         if effective_system_prompt:
+            context_system_text = "\n\n".join(
+                message.text
+                for message in context_messages
+                if message.role == Role.SYSTEM
+                and message.metadata.get("memory_context")
+                and message.text
+            )
+            if context_system_text:
+                effective_system_prompt = (
+                    f"{effective_system_prompt}\n\n{context_system_text}"
+                )
+                context_messages = [
+                    message
+                    for message in context_messages
+                    if not (
+                        message.role == Role.SYSTEM
+                        and message.metadata.get("memory_context")
+                    )
+                ]
             messages.append(Message(role=Role.SYSTEM, content=effective_system_prompt))
-        if context and context.conversation.messages:
-            messages.extend(context.conversation.messages)
+        if context_messages:
+            messages.extend(context_messages)
         messages.append(Message(role=Role.USER, content=input))
         return messages
 

src/openjarvis/agents/executor.py

@@ -2,7 +2,9 @@
 
 from __future__ import annotations
 
+import json
 import logging
+import threading
 import time
 from typing import TYPE_CHECKING, Any
 
@@ -14,6 +16,7 @@ from openjarvis.agents.errors import (
     classify_error,
     retry_delay,
 )
+from openjarvis.agents.tool_resolver import resolve_agent_tools
 from openjarvis.core.events import EventBus, EventType
 
 if TYPE_CHECKING:
@@ -33,6 +36,32 @@ _MAX_RETRIES = 3
 _AGENT_TICK_DEFAULT_MODEL = "gemma4:31b"
 
 
+def _tool_calls_for_storage(result: AgentResult) -> list[dict[str, Any]] | None:
+    """Convert executor tool results to the managed-message storage contract."""
+
+    calls: list[dict[str, Any]] = []
+    for tool_result in result.tool_results:
+        metadata = getattr(tool_result, "metadata", {}) or {}
+        arguments = metadata.get("arguments", "")
+        if not isinstance(arguments, str):
+            try:
+                arguments = json.dumps(arguments, sort_keys=True)
+            except (TypeError, ValueError):
+                arguments = json.dumps(str(arguments))
+        calls.append(
+            {
+                "tool": getattr(tool_result, "tool_name", ""),
+                "arguments": arguments,
+                "result": getattr(tool_result, "content", "") or "",
+                "success": bool(getattr(tool_result, "success", False)),
+                # SSE and the frontend persist/display latency in milliseconds.
+                "latency": float(getattr(tool_result, "latency_seconds", 0.0) or 0.0)
+                * 1000.0,
+            }
+        )
+    return calls or None
+
+
 class AgentExecutor:
     """Executes a single tick for a managed agent.
 
@@ -51,6 +80,7 @@ class AgentExecutor:
         self._manager = manager
         self._bus = event_bus
         self._trace_store = trace_store
+        self._toolkit_local = threading.local()
 
     def set_system(self, system: Any) -> None:
         """Deferred system injection — called after JarvisSystem is constructed."""
@@ -63,27 +93,6 @@ class AgentExecutor:
         except Exception:
             pass  # Non-critical
 
-    def _inject_tool_deps(self, tool: Any) -> None:
-        """Inject runtime dependencies into a tool instance.
-
-        Mirrors SystemBuilder._inject_tool_deps (system.py:920-945)
-        but uses the lightweight system's references.
-        """
-        if self._system is None:
-            return
-        name = getattr(getattr(tool, "spec", None), "name", "")
-        if name == "llm":
-            if hasattr(tool, "_engine"):
-                tool._engine = self._system.engine
-            if hasattr(tool, "_model"):
-                tool._model = self._system.model
-        elif name == "retrieval" or name.startswith("memory_"):
-            if hasattr(tool, "_backend"):
-                tool._backend = getattr(self._system, "memory_backend", None)
-        elif name.startswith("channel_"):
-            if hasattr(tool, "_channel"):
-                tool._channel = getattr(self._system, "channel_backend", None)
-
     def run_ephemeral(
         self,
         agent_type: str,
@@ -248,7 +257,20 @@ class AgentExecutor:
         raise last_error or FatalError("max retries exhausted")
 
     def _invoke_agent(self, agent: dict) -> AgentResult:
-        """Invoke the actual agent run. Tests mock this method."""
+        """Invoke one agent while owning every resource its resolver opens."""
+
+        previous = getattr(self._toolkit_local, "current", None)
+        self._toolkit_local.current = None
+        try:
+            return self._invoke_agent_impl(agent)
+        finally:
+            current = getattr(self._toolkit_local, "current", None)
+            if current is not None:
+                current.close()
+            self._toolkit_local.current = previous
+
+    def _invoke_agent_impl(self, agent: dict) -> AgentResult:
+        """Implementation split out so the wrapper owns resolver lifetime."""
         from openjarvis.agents import AgentRegistry
 
         agent_type = agent.get("agent_type", "monitor_operative")
@@ -257,6 +279,10 @@ class AgentExecutor:
             raise FatalError(f"Unknown agent type: {agent_type}")
 
         config = agent.get("config", {})
+        agent_accepts_tools = bool(getattr(agent_cls, "accepts_tools", False))
+        supports_tool_fallback = bool(
+            getattr(agent_cls, "supports_managed_tool_fallback", False)
+        )
 
         # Resolve engine + model from JarvisSystem
         engine = self._system.engine if self._system else None
@@ -300,64 +326,88 @@ class AgentExecutor:
             except Exception:
                 pass  # Fall back to configured model
 
-        # Resolve tools from config via ToolRegistry
-        tool_names = config.get("tools", [])
-        if isinstance(tool_names, str):
-            tool_names = [t.strip() for t in tool_names.split(",") if t.strip()]
+        mcp_tools: list[Any] = []
+        mcp_clients: list[Any] = []
+        if (
+            config.get("mcp_tools", True) is not False
+            and self._system is not None
+            and (agent_accepts_tools or supports_tool_fallback)
+        ):
+            provider = getattr(
+                self._system,
+                "get_managed_agent_mcp_tools",
+                None,
+            )
+            if callable(provider):
+                try:
+                    mcp_tools, mcp_clients = provider()
+                except Exception as exc:
+                    logger.warning("Managed-agent MCP discovery failed: %s", exc)
+            else:
+                mcp_tools = list(getattr(self._system, "mcp_tools", []) or [])
+                mcp_clients = list(getattr(self._system, "_mcp_clients", []) or [])
 
-        tool_instances: list[Any] = []
-        if tool_names:
-            try:
-                from openjarvis.server.agent_manager_routes import (
-                    _ensure_registries_populated,
-                )
+            if not mcp_tools:
+                try:
+                    from openjarvis.tools.mcp_adapter import MCPToolAdapter
 
-                _ensure_registries_populated()
-            except ImportError:
-                pass
-            from openjarvis.core.registry import ToolRegistry
+                    pool = (
+                        getattr(
+                            getattr(self._system, "tool_executor", None),
+                            "_tools",
+                            {},
+                        )
+                        or {}
+                    )
+                    mcp_tools = [
+                        tool
+                        for tool in pool.values()
+                        if isinstance(tool, MCPToolAdapter)
+                    ]
+                except Exception:
+                    mcp_tools = []
 
-            for tname in tool_names:
-                if ToolRegistry.contains(tname):
-                    try:
-                        tool_cls = ToolRegistry.get(tname)
-                        tool = tool_cls()
-                        self._inject_tool_deps(tool)
-                        tool_instances.append(tool)
-                    except Exception:
-                        logger.warning("Failed to instantiate tool %s", tname)
+        resolved_toolkit = resolve_agent_tools(
+            agent,
+            engine=engine,
+            model=model,
+            memory_backend=getattr(self._system, "memory_backend", None),
+            channel_backend=getattr(self._system, "channel_backend", None),
+            mcp_tools=mcp_tools,
+            mcp_clients=mcp_clients,
+            knowledge_db_path=getattr(self._system, "knowledge_db_path", None),
+        )
+        self._toolkit_local.current = resolved_toolkit
+        tool_instances = resolved_toolkit.instances
+        logger.info(
+            "Agent %s: resolved %d tools (%s)",
+            agent["name"],
+            len(tool_instances),
+            ", ".join(resolved_toolkit.by_name) or "none",
+        )
 
-            # Pull tools already discovered by SystemBuilder (e.g. external MCP
-            # adapters) that aren't in the static ToolRegistry. Without this,
-            # agents declaring MCP-discovered tools in their template would
-            # silently fall back to natives only.
-            if (
-                self._system is not None
-                and getattr(self._system, "tool_executor", None) is not None
-            ):
-                mcp_pool = getattr(self._system.tool_executor, "_tools", {}) or {}
-                existing = {t.spec.name for t in tool_instances}
-                for tname in tool_names:
-                    if tname in existing:
-                        continue
-                    pooled = mcp_pool.get(tname)
-                    if pooled is not None:
-                        tool_instances.append(pooled)
+        execution_agent_cls = agent_cls
+        if tool_instances and not agent_accepts_tools and supports_tool_fallback:
+            # Managed SSE already runs configured tools through a native
+            # function-calling loop regardless of the selected class. Use the
+            # same capability for immediate/scheduled ticks instead of
+            # silently discarding the resolved toolkit for SimpleAgent and
+            # other explicitly compatible non-tool classes.
+            from openjarvis.agents.orchestrator import OrchestratorAgent
 
-            if tool_instances:
-                logger.info(
-                    "Agent %s: resolved %d/%d tools",
-                    agent["name"],
-                    len(tool_instances),
-                    len(tool_names),
-                )
+            execution_agent_cls = OrchestratorAgent
+            logger.info(
+                "Agent %s: %s does not accept tools; using %s for this "
+                "tool-enabled tick",
+                agent["name"],
+                agent_cls.__name__,
+                execution_agent_cls.__name__,
+            )
 
         # Construct agent instance
         agent_kwargs: dict[str, Any] = {}
         sys_prompt = config.get("system_prompt")
-        if sys_prompt is not None:
-            agent_kwargs["system_prompt"] = sys_prompt
-        if getattr(agent_cls, "accepts_tools", False) and tool_instances:
+        if getattr(execution_agent_cls, "accepts_tools", False) and tool_instances:
             agent_kwargs["tools"] = tool_instances
         # Hand the agent our EventBus so its ToolExecutor can publish
         # TOOL_CALL_START/END — without this, ToolExecutor's ``self._bus``
@@ -379,7 +429,7 @@ class AgentExecutor:
         # recall / persistence paths.
         import inspect
 
-        init_sig = inspect.signature(agent_cls.__init__)
+        init_sig = inspect.signature(execution_agent_cls.__init__)
         accepts_var_kw = any(
             p.kind == inspect.Parameter.VAR_KEYWORD
             for p in init_sig.parameters.values()
@@ -388,6 +438,16 @@ class AgentExecutor:
         def _accepts(name: str) -> bool:
             return accepts_var_kw or name in init_sig.parameters
 
+        # Unsupported kwargs used to trigger the broad TypeError fallback
+        # below, which retried with a bare constructor and silently discarded
+        # valid prompt/state wiring. Filter by the selected class's signature
+        # before construction instead.
+        if sys_prompt is not None and _accepts("system_prompt"):
+            agent_kwargs["system_prompt"] = sys_prompt
+        agent_kwargs = {
+            name: value for name, value in agent_kwargs.items() if _accepts(name)
+        }
+
         state_kwargs: dict[str, Any] = {}
         if _accepts("operator_id"):
             state_kwargs["operator_id"] = agent["id"]
@@ -404,23 +464,49 @@ class AgentExecutor:
             # agents, mirroring the one-shot `jarvis ask` path so they no
             # longer apply to CLI calls only (#376).
             cfg = getattr(self._system, "config", None)
-            if cfg is not None and _accepts("prompt_builder"):
+            if _accepts("prompt_builder") and (
+                cfg is not None or sys_prompt is not None
+            ):
                 from openjarvis.prompt.builder import SystemPromptBuilder
 
                 state_kwargs["prompt_builder"] = SystemPromptBuilder(
-                    agent_template=getattr(cfg.agent, "default_system_prompt", "")
-                    or "",
-                    memory_files_config=cfg.memory_files,
-                    system_prompt_config=cfg.system_prompt,
+                    agent_template=(
+                        sys_prompt
+                        if sys_prompt is not None
+                        else getattr(
+                            getattr(cfg, "agent", None),
+                            "default_system_prompt",
+                            "",
+                        )
+                        or ""
+                    ),
+                    memory_files_config=getattr(cfg, "memory_files", None),
+                    system_prompt_config=getattr(cfg, "system_prompt", None),
                 )
 
         try:
-            agent_instance = agent_cls(engine, model, **agent_kwargs, **state_kwargs)
-        except TypeError:
             try:
-                agent_instance = agent_cls(engine, model, **agent_kwargs)
+                agent_instance = execution_agent_cls(
+                    engine,
+                    model,
+                    **agent_kwargs,
+                    **state_kwargs,
+                )
             except TypeError:
-                agent_instance = agent_cls(engine, model)
+                try:
+                    agent_instance = execution_agent_cls(
+                        engine,
+                        model,
+                        **agent_kwargs,
+                    )
+                except TypeError:
+                    agent_instance = execution_agent_cls(engine, model)
+        except Exception:
+            resolved_toolkit.close()
+            raise
+
+        if resolved_toolkit.mcp_clients:
+            agent_instance._mcp_clients = resolved_toolkit.mcp_clients
 
         # Inject the managed-agent UUID into the agent's ToolExecutor so
         # emitted TOOL_CALL_START/END events carry it; the trace subscriber
@@ -436,7 +522,7 @@ class AgentExecutor:
             agent["name"],
             len(tool_instances),
             ", ".join(t.spec.name for t in tool_instances) or "none",
-            agent_cls.__name__,
+            execution_agent_cls.__name__,
         )
 
         # Build input from instruction + summary_memory + pending messages.
@@ -551,21 +637,24 @@ class AgentExecutor:
             len(input_text),
         )
         _t0 = time.time()
-        result = agent_instance.run(input_text, context=agent_ctx)
-
-        # Retry once if the model returned empty content (common with
-        # Qwen3.5 thinking mode consuming all tokens).
-        if not (result.content or "").strip():
-            self._set_activity(
-                agent["id"],
-                "Retrying (empty response)...",
-            )
-            logger.warning(
-                "Agent %s: empty content, retrying once",
-                agent["name"],
-            )
+        try:
             result = agent_instance.run(input_text, context=agent_ctx)
 
+            # Retry once if the model returned empty content (common with
+            # Qwen3.5 thinking mode consuming all tokens).
+            if not (result.content or "").strip():
+                self._set_activity(
+                    agent["id"],
+                    "Retrying (empty response)...",
+                )
+                logger.warning(
+                    "Agent %s: empty content, retrying once",
+                    agent["name"],
+                )
+                result = agent_instance.run(input_text, context=agent_ctx)
+        finally:
+            resolved_toolkit.close()
+
         _elapsed = time.time() - _t0
         logger.info(
             "Agent %s: agent.run() completed in %.1fs, "
@@ -655,7 +744,11 @@ class AgentExecutor:
                 # message keeps the complete report. The old [:2000] slices
                 # double-truncated and cut findings off mid-sentence.
                 self._manager.update_summary_memory(agent_id, result.content)
-                self._manager.store_agent_response(agent_id, result.content)
+                self._manager.store_agent_response(
+                    agent_id,
+                    result.content,
+                    tool_calls=_tool_calls_for_storage(result),
+                )
 
             # Budget enforcement (post-tick check)
             agent_data = self._manager.get_agent(agent_id)

src/openjarvis/agents/orchestrator.py

@@ -57,6 +57,7 @@ class OrchestratorAgent(ToolUsingAgent):
         max_tokens: Optional[int] = None,
         mode: str = "function_calling",
         system_prompt: Optional[str] = None,
+        prompt_builder: Optional[Any] = None,
         parallel_tools: bool = True,
         interactive: bool = False,
         confirm_callback=None,
@@ -71,6 +72,7 @@ class OrchestratorAgent(ToolUsingAgent):
             max_tokens=max_tokens,
             interactive=interactive,
             confirm_callback=confirm_callback,
+            prompt_builder=prompt_builder,
         )
         self._mode = mode
         self._system_prompt = system_prompt
@@ -214,7 +216,11 @@ class OrchestratorAgent(ToolUsingAgent):
         self._emit_turn_start(input)
 
         # Build initial messages
-        messages = self._build_messages(input, context)
+        messages = self._build_messages(
+            input,
+            context,
+            system_prompt=self._system_prompt,
+        )
 
         # Get OpenAI-format tool definitions
         openai_tools = self._executor.get_openai_tools() if self._tools else []

src/openjarvis/agents/proactive_agent.py

@@ -37,6 +37,7 @@ called from your app startup:
 from __future__ import annotations
 
 import json
+import logging
 from datetime import datetime
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Set
@@ -56,6 +57,15 @@ from openjarvis.tools.approval_store import (
 )
 from openjarvis.tools.proactive_tools import get_store
 
+logger = logging.getLogger(__name__)
+
+_PROACTIVE_CRON_PROMPT = (
+    "Run the proactive agent: collect overnight data, execute approved actions, "
+    "notify pending approvals."
+)
+_PROACTIVE_TASK_KEY = "proactive-daily"
+_PROACTIVE_TASK_KEY_FIELD = "openjarvis_task_key"
+
 _SYSTEM_PROMPT = """You are a proactive personal assistant agent. You have already collected
 data from the user's connected sources (email, messages, calendar). Your job is to:
 
@@ -252,14 +262,31 @@ def _build_notification_channel(channel_spec: str) -> Optional[Any]:
 
         if ChannelRegistry.contains(channel_type):
             channel_cls = ChannelRegistry.get(channel_type)
-            instance = channel_cls()
+            # Load credentials from config so the channel uses bot_token from
+            # config.toml rather than falling back to a bare env var.
             try:
-                instance.connect()
+                from openjarvis.core.config import load_config
+                from openjarvis.system._channel_kwargs import build_channel_kwargs
+
+                _cfg = load_config()
+                _kwargs = build_channel_kwargs(_cfg.channel, channel_type)
             except Exception:
-                pass
+                _kwargs = {}
+            instance = channel_cls(**_kwargs)
+            # Telegram.send() is self-contained, while connect() starts a
+            # getUpdates loop.  A second loop for the same bot token conflicts
+            # with the server's main listener.  Other channel implementations
+            # may initialize resources required by send() in connect(), so keep
+            # their established lifecycle intact.
+            if channel_type != "telegram":
+                instance.connect()
             return instance
     except Exception:
-        pass
+        logger.warning(
+            "Failed to build proactive notification channel %s",
+            channel_type,
+            exc_info=True,
+        )
 
     return None
 
@@ -299,6 +326,7 @@ class ProactiveAgent(ToolUsingAgent):
             self._notification_channel_id
         )
         self._notification_channel = notification_channel
+        self._notification_destination = self._notification_channel_id.partition(":")[2]
 
         from openjarvis.tools.channel_tools import ChannelSendTool
         from openjarvis.tools.digest_collect import DigestCollectTool
@@ -484,13 +512,13 @@ class ProactiveAgent(ToolUsingAgent):
         # --- Step 5: Build and send notification ---
         notification = self._build_notification(executed_results, pending_actions)
 
-        if notification and self._notification_channel_id:
+        if notification and self._notification_destination:
             send_call = ToolCall(
                 id="proactive-notify-1",
                 name="channel_send",
                 arguments=json.dumps(
                     {
-                        "channel": self._notification_channel_id,
+                        "channel": self._notification_destination,
                         "content": notification,
                     }
                 ),
@@ -592,15 +620,74 @@ def register_cron(
         hours_back = hours_back or 24
         timezone = timezone or "America/Los_Angeles"
 
+    metadata = {
+        "notification_channel_id": notification_channel_id,
+        "hours_back": hours_back,
+        "timezone": timezone,
+        _PROACTIVE_TASK_KEY_FIELD: _PROACTIVE_TASK_KEY,
+    }
+
+    # Match the stable key for tasks created by this version and the historical
+    # agent+prompt signature so existing installations are migrated on startup.
+    existing = [
+        task
+        for task in scheduler.list_tasks()
+        if task.status in {"active", "paused"}
+        and task.agent == "proactive"
+        and (
+            task.metadata.get(_PROACTIVE_TASK_KEY_FIELD) == _PROACTIVE_TASK_KEY
+            or (task.prompt == _PROACTIVE_CRON_PROMPT and task.schedule_type == "cron")
+        )
+    ]
+
+    # A scheduler pause is an explicit user choice and must survive restart.
+    # Keep one deterministically and remove any active or paused duplicates.
+    paused = [task for task in existing if task.status == "paused"]
+    if paused:
+        keep = min(paused, key=lambda task: task.id)
+        _cancel_proactive_duplicates(scheduler, existing, keep=keep)
+        return keep
+
+    matching = [
+        task
+        for task in existing
+        if task.prompt == _PROACTIVE_CRON_PROMPT
+        and task.schedule_type == "cron"
+        and task.schedule_value == cron_expr
+        and task.context_mode == "isolated"
+        and task.metadata == metadata
+    ]
+    if matching:
+        keep = min(matching, key=lambda task: task.id)
+        _cancel_proactive_duplicates(scheduler, existing, keep=keep)
+        return keep
+
+    # Configuration changed. Replace stale active tasks so the schedule and
+    # notification settings from config.toml take effect on this startup.
+    _cancel_proactive_duplicates(scheduler, existing)
+
     return scheduler.create_task(
-        prompt="Run the proactive agent: collect overnight data, execute approved actions, notify pending approvals.",
+        prompt=_PROACTIVE_CRON_PROMPT,
         schedule_type="cron",
         schedule_value=cron_expr,
         agent="proactive",
         context_mode="isolated",
-        metadata={
-            "notification_channel_id": notification_channel_id,
-            "hours_back": hours_back,
-            "timezone": timezone,
-        },
+        metadata=metadata,
     )
+
+
+def _cancel_proactive_duplicates(
+    scheduler: Any, tasks: List[Any], *, keep: Optional[Any] = None
+) -> None:
+    """Cancel managed proactive tasks other than *keep*."""
+    for task in tasks:
+        if keep is not None and task.id == keep.id:
+            continue
+        try:
+            scheduler.cancel_task(task.id)
+        except Exception:
+            logger.warning(
+                "Failed to cancel duplicate proactive task %s",
+                task.id,
+                exc_info=True,
+            )

src/openjarvis/agents/scheduler.py

@@ -123,15 +123,35 @@ class AgentScheduler:
         self._thread.start()
         logger.info("Agent scheduler started")
 
-    def stop(self) -> None:
-        """Stop the scheduler background thread."""
+    def request_stop(self) -> None:
+        """Prevent new scheduled ticks without waiting for the worker."""
+
         self._stop_event.set()
         if self._bus:
             self._bus.unsubscribe(EventType.AGENT_TICK_END, self._on_tick_event)
-        if self._thread is not None:
-            self._thread.join(timeout=10)
+
+    def wait_stopped(self, timeout: float = 10.0) -> bool:
+        """Wait for an active tick to finish, retaining live thread state."""
+
+        thread = self._thread
+        if thread is None:
+            return True
+        if thread is threading.current_thread():
+            return False
+        thread.join(timeout=timeout)
+        if thread.is_alive():
+            logger.warning("Agent scheduler did not stop within %.1fs", timeout)
+            return False
+        if self._thread is thread:
             self._thread = None
-        logger.info("Agent scheduler stopped")
+        return True
+
+    def stop(self, timeout: float = 10.0) -> None:
+        """Stop dispatching and wait for the scheduler worker."""
+
+        self.request_stop()
+        if self.wait_stopped(timeout=timeout):
+            logger.info("Agent scheduler stopped")
 
     def _loop(self) -> None:
         """Main scheduler loop."""
@@ -160,6 +180,8 @@ class AgentScheduler:
             ]
 
         for agent_id, info in due:
+            if self._stop_event.is_set():
+                break
             agent = self._manager.get_agent(agent_id)
             if agent is None or agent["status"] in (
                 "paused",

src/openjarvis/agents/simple.py

@@ -13,6 +13,7 @@ class SimpleAgent(BaseAgent):
     """Single-turn agent: query -> model -> response.  No tool calling."""
 
     agent_id = "simple"
+    supports_managed_tool_fallback = True
 
     def run(
         self,

src/openjarvis/agents/tool_resolver.py

@@ -0,0 +1,502 @@
+"""Canonical managed-agent tool resolution.
+
+Managed agents can run through streaming HTTP, immediate/scheduled ticks, or
+the persistent-agent CLI.  Those paths must bind the same live tool instances:
+agent-type grants first, then configured native tools, then MCP adapters.
+"""
+
+from __future__ import annotations
+
+import importlib
+import logging
+import sys
+import weakref
+from dataclasses import dataclass, field, replace
+from pathlib import Path
+from typing import Any, Iterable, Mapping
+
+logger = logging.getLogger(__name__)
+
+BROWSER_SUB_TOOLS = (
+    "browser_navigate",
+    "browser_click",
+    "browser_type",
+    "browser_screenshot",
+    "browser_extract",
+    "browser_axtree",
+)
+
+_MEMORY_TOOLS = frozenset(
+    {"retrieval", "memory_store", "memory_search", "memory_index", "memory_retrieve"}
+)
+_CHANNEL_TOOLS = frozenset({"channel_send", "channel_list", "channel_status"})
+
+
+class _SpecOverrideTool:
+    """Delegate execution while exposing an agent-configured OpenAI schema."""
+
+    def __init__(self, wrapped: Any, advertised_spec: dict[str, Any]) -> None:
+        self._wrapped = wrapped
+        self._advertised_spec = advertised_spec
+
+    @property
+    def spec(self) -> Any:
+        base = self._wrapped.spec
+        function = self._advertised_spec.get("function", {})
+        return replace(
+            base,
+            name=function.get("name", base.name),
+            description=function.get("description", base.description),
+            parameters=function.get("parameters", base.parameters),
+        )
+
+    def execute(self, **params: Any) -> Any:
+        return self._wrapped.execute(**params)
+
+    def to_openai_function(self) -> dict[str, Any]:
+        return self._advertised_spec
+
+    def __getattr__(self, name: str) -> Any:
+        return getattr(self._wrapped, name)
+
+
+def _tool_name(tool: Any) -> str:
+    try:
+        return str(tool.spec.name)
+    except Exception:
+        return ""
+
+
+def _spec_name(spec: Mapping[str, Any]) -> str:
+    function = spec.get("function")
+    if not isinstance(function, Mapping):
+        return ""
+    name = function.get("name")
+    return str(name) if name else ""
+
+
+def _openai_spec(tool: Any) -> dict[str, Any]:
+    to_openai_function = getattr(tool, "to_openai_function", None)
+    if callable(to_openai_function):
+        try:
+            advertised = to_openai_function()
+        except Exception:
+            logger.debug(
+                "Failed to build advertised schema for tool %r; falling back "
+                "to its ToolSpec",
+                _tool_name(tool),
+                exc_info=True,
+            )
+        else:
+            if isinstance(advertised, Mapping) and _spec_name(advertised):
+                return dict(advertised)
+            logger.debug(
+                "Tool %r returned an invalid advertised schema; falling back "
+                "to its ToolSpec",
+                _tool_name(tool),
+            )
+
+    spec = tool.spec
+    return {
+        "type": "function",
+        "function": {
+            "name": spec.name,
+            "description": spec.description,
+            "parameters": spec.parameters,
+        },
+    }
+
+
+def _close_resources(resources: tuple[Any, ...]) -> None:
+    for resource in reversed(resources):
+        close = getattr(resource, "close", None)
+        if callable(close):
+            try:
+                close()
+            except Exception:
+                logger.debug("Failed to close resolved tool resource", exc_info=True)
+
+
+@dataclass
+class ResolvedAgentTools:
+    """One resolved toolkit, with views for agent loops and raw streaming."""
+
+    instances: list[Any] = field(default_factory=list)
+    extra_specs: list[dict[str, Any]] = field(default_factory=list)
+    advertised_specs: list[dict[str, Any]] = field(default_factory=list)
+    mcp_clients: list[Any] = field(default_factory=list)
+    owned_resources: list[Any] = field(default_factory=list, repr=False)
+    _closed: bool = field(default=False, init=False, repr=False)
+    _finalizer: weakref.finalize = field(init=False, repr=False)
+
+    def __post_init__(self) -> None:
+        # This fallback covers exceptions anywhere after resolution, including
+        # before an executor/response installs its normal explicit cleanup.
+        self._finalizer = weakref.finalize(
+            self,
+            _close_resources,
+            tuple(self.owned_resources),
+        )
+
+    @property
+    def by_name(self) -> dict[str, Any]:
+        return {name: tool for tool in self.instances if (name := _tool_name(tool))}
+
+    @property
+    def openai_specs(self) -> list[dict[str, Any]]:
+        specs: list[dict[str, Any]] = []
+        seen: set[str] = set()
+        advertised = self.advertised_specs
+        if not advertised:
+            advertised = [*map(_openai_spec, self.instances), *self.extra_specs]
+        for spec in advertised:
+            name = _spec_name(spec)
+            if name and name in seen:
+                continue
+            specs.append(spec)
+            if name:
+                seen.add(name)
+        return specs
+
+    def close(self) -> None:
+        """Close request-local resources without touching shared MCP clients."""
+
+        if self._closed:
+            return
+        self._closed = True
+        self._finalizer()
+
+    def __enter__(self) -> ResolvedAgentTools:
+        return self
+
+    def __exit__(self, *exc_info: object) -> None:
+        self.close()
+
+
+def ensure_registries_populated() -> None:
+    """Populate tool/channel registries, including after tests clear them."""
+
+    from openjarvis.core.registry import ChannelRegistry, ToolRegistry
+
+    try:
+        import openjarvis.channels  # noqa: F401
+    except Exception:
+        pass
+
+    try:
+        import openjarvis.tools  # noqa: F401
+    except Exception:
+        pass
+
+    browser_modules = ("openjarvis.tools.browser", "openjarvis.tools.browser_axtree")
+    for module_name in browser_modules:
+        try:
+            importlib.import_module(module_name)
+        except Exception:
+            pass
+
+    if not ChannelRegistry.keys():
+        for module_name in list(sys.modules):
+            if module_name.startswith(
+                "openjarvis.channels."
+            ) and not module_name.endswith("_stubs"):
+                try:
+                    importlib.reload(sys.modules[module_name])
+                except Exception:
+                    pass
+
+    if not ToolRegistry.keys():
+        for module_name in list(sys.modules):
+            if (
+                module_name.startswith("openjarvis.tools.")
+                and not module_name.endswith("_stubs")
+                and not module_name.endswith("agent_tools")
+            ):
+                try:
+                    importlib.reload(sys.modules[module_name])
+                except Exception:
+                    pass
+
+    if not any(ToolRegistry.contains(name) for name in BROWSER_SUB_TOOLS):
+        for module_name in browser_modules:
+            module = sys.modules.get(module_name)
+            if module is not None:
+                try:
+                    importlib.reload(module)
+                except Exception:
+                    pass
+
+
+def instantiate_registered_tool(
+    tool_cls: Any,
+    name: str,
+    *,
+    engine: Any,
+    model: str,
+    memory_backend: Any = None,
+    channel_backend: Any = None,
+) -> Any:
+    """Instantiate a registry tool with its runtime dependencies."""
+
+    if name in _MEMORY_TOOLS:
+        if memory_backend is None:
+            logger.warning(
+                "Memory tool %r instantiated without a backend — calls will "
+                "return no results.",
+                name,
+            )
+        return tool_cls(backend=memory_backend)
+    if name in _CHANNEL_TOOLS:
+        if channel_backend is None:
+            logger.warning(
+                "Channel tool %r instantiated without a channel — calls will "
+                "fail with 'No channel backend configured'.",
+                name,
+            )
+        return tool_cls(channel=channel_backend)
+    if name == "llm":
+        return tool_cls(engine=engine, model=model)
+    return tool_cls()
+
+
+def build_deep_research_tools(
+    engine: Any,
+    model: str,
+    knowledge_db_path: str | Path | None = None,
+) -> list[Any]:
+    """Construct the live knowledge tools granted to ``deep_research``."""
+
+    if not knowledge_db_path:
+        from openjarvis.core.config import DEFAULT_CONFIG_DIR
+
+        knowledge_db_path = DEFAULT_CONFIG_DIR / "knowledge.db"
+
+    path = Path(knowledge_db_path)
+    if not path.exists():
+        return []
+
+    from openjarvis.connectors.retriever import TwoStageRetriever
+    from openjarvis.connectors.store import KnowledgeStore
+    from openjarvis.tools.knowledge_search import KnowledgeSearchTool
+    from openjarvis.tools.knowledge_sql import KnowledgeSQLTool
+    from openjarvis.tools.scan_chunks import ScanChunksTool
+    from openjarvis.tools.think import ThinkTool
+
+    store = KnowledgeStore(str(path))
+    try:
+        retriever = TwoStageRetriever(store)
+        return [
+            KnowledgeSearchTool(retriever=retriever),
+            KnowledgeSQLTool(store=store),
+            ScanChunksTool(store=store, engine=engine, model=model),
+            ThinkTool(),
+        ]
+    except Exception:
+        store.close()
+        raise
+
+
+def _normalized_tool_config(tool_config: Any) -> list[Any]:
+    if not tool_config:
+        return []
+    if isinstance(tool_config, str):
+        return [part.strip() for part in tool_config.split(",") if part.strip()]
+    if isinstance(tool_config, Mapping):
+        return [dict(tool_config)]
+    try:
+        return list(tool_config)
+    except TypeError:
+        return []
+
+
+def resolve_agent_tools(
+    agent_record: Mapping[str, Any],
+    *,
+    engine: Any,
+    model: str,
+    memory_backend: Any = None,
+    channel_backend: Any = None,
+    mcp_tools: Iterable[Any] = (),
+    mcp_clients: Iterable[Any] = (),
+    knowledge_db_path: str | Path | None = None,
+) -> ResolvedAgentTools:
+    """Resolve the effective live toolkit for a managed agent.
+
+    Resolution is stable and first-wins: agent-type grants take precedence
+    over configured registry tools, which take precedence over MCP adapters.
+    ``config["mcp_tools"] = false`` excludes MCP adapters from this agent;
+    process-wide runtimes may still own connections used by other agents.
+    """
+
+    ensure_registries_populated()
+    from openjarvis.core.registry import ChannelRegistry, ToolRegistry
+
+    config = agent_record.get("config") or {}
+    if not isinstance(config, Mapping):
+        config = {}
+
+    instances: list[Any] = []
+    extra_specs: list[dict[str, Any]] = []
+    advertised_specs: list[dict[str, Any]] = []
+    owned_resources: list[Any] = []
+    seen: set[str] = set()
+
+    def add_instance(
+        tool: Any,
+        *,
+        advertised_spec: dict[str, Any] | None = None,
+    ) -> None:
+        name = _tool_name(tool)
+        if not name or name in seen:
+            return
+        instances.append(tool)
+        advertised_specs.append(advertised_spec or _openai_spec(tool))
+        seen.add(name)
+
+    use_mcp = config.get("mcp_tools", True) is not False
+    mcp_tool_list = list(mcp_tools) if use_mcp else []
+    mcp_by_name: dict[str, Any] = {}
+    for tool in mcp_tool_list:
+        name = _tool_name(tool)
+        if name and name not in mcp_by_name:
+            mcp_by_name[name] = tool
+
+    if agent_record.get("agent_type") == "deep_research":
+        granted_tools = build_deep_research_tools(
+            engine=engine,
+            model=model,
+            knowledge_db_path=knowledge_db_path,
+        )
+        owned_ids: set[int] = set()
+        for tool in granted_tools:
+            resource = getattr(tool, "_store", None)
+            if (
+                resource is not None
+                and callable(getattr(resource, "close", None))
+                and id(resource) not in owned_ids
+            ):
+                owned_resources.append(resource)
+                owned_ids.add(id(resource))
+            add_instance(tool)
+
+    for entry in _normalized_tool_config(config.get("tools")):
+        if isinstance(entry, Mapping):
+            raw_spec = entry if isinstance(entry, dict) else dict(entry)
+            name = _spec_name(raw_spec)
+            if name and name in seen:
+                continue
+
+            backing_tool = None
+            if name and not ChannelRegistry.contains(name):
+                if ToolRegistry.contains(name):
+                    try:
+                        backing_tool = instantiate_registered_tool(
+                            ToolRegistry.get(name),
+                            name,
+                            engine=engine,
+                            model=model,
+                            memory_backend=memory_backend,
+                            channel_backend=channel_backend,
+                        )
+                    except Exception as exc:
+                        logger.warning(
+                            "Could not instantiate tool '%s' (%s) — "
+                            "advertising its custom spec without execution",
+                            name,
+                            exc,
+                        )
+                elif name in mcp_by_name:
+                    backing_tool = mcp_by_name[name]
+
+            if backing_tool is not None:
+                add_instance(
+                    _SpecOverrideTool(backing_tool, raw_spec),
+                    advertised_spec=raw_spec,
+                )
+            else:
+                logger.warning(
+                    "Custom tool spec '%s' has no registered or MCP execution "
+                    "backend — dropping",
+                    name or "<unnamed>",
+                )
+            continue
+        if not isinstance(entry, str):
+            continue
+
+        names = BROWSER_SUB_TOOLS if entry == "browser" else (entry,)
+        for name in names:
+            if name in seen:
+                continue
+            if ChannelRegistry.contains(name):
+                continue
+            if not ToolRegistry.contains(name):
+                logger.warning(
+                    "Tool '%s' referenced in agent config but not in ToolRegistry",
+                    name,
+                )
+                continue
+            try:
+                add_instance(
+                    instantiate_registered_tool(
+                        ToolRegistry.get(name),
+                        name,
+                        engine=engine,
+                        model=model,
+                        memory_backend=memory_backend,
+                        channel_backend=channel_backend,
+                    )
+                )
+            except Exception as exc:
+                logger.warning(
+                    "Could not instantiate tool '%s' (%s) — dropping", name, exc
+                )
+
+    if use_mcp:
+        for tool in mcp_tool_list:
+            add_instance(tool)
+
+    return ResolvedAgentTools(
+        instances=instances,
+        extra_specs=extra_specs,
+        advertised_specs=advertised_specs,
+        mcp_clients=list(mcp_clients) if use_mcp else [],
+        owned_resources=owned_resources,
+    )
+
+
+def resolve_tool_specs(tool_config: Any) -> list[dict[str, Any]]:
+    """Compatibility view for callers that only need configured specs."""
+
+    specs: list[dict[str, Any]] = []
+    seen: set[str] = set()
+    for entry in _normalized_tool_config(tool_config):
+        if isinstance(entry, dict):
+            specs.append(entry)
+            name = _spec_name(entry)
+            if name:
+                seen.add(name)
+            continue
+        resolved = resolve_agent_tools(
+            {"config": {"tools": [entry]}},
+            engine=None,
+            model="",
+        )
+        for spec in resolved.openai_specs:
+            name = _spec_name(spec)
+            if name and name in seen:
+                continue
+            specs.append(spec)
+            if name:
+                seen.add(name)
+    return specs
+
+
+__all__ = [
+    "BROWSER_SUB_TOOLS",
+    "ResolvedAgentTools",
+    "build_deep_research_tools",
+    "ensure_registries_populated",
+    "instantiate_registered_tool",
+    "resolve_agent_tools",
+    "resolve_tool_specs",
+]

src/openjarvis/cli/_install_detect.py

@@ -11,7 +11,9 @@ Three install paths are supported today:
 - **Editable git checkout** (``uv sync`` / ``pip install -e .`` from a
   cloned repo). The package's ``__file__`` is inside a working tree
   with a ``.git`` directory at the repo root. Upgrade with
-  ``git pull && uv sync`` from the checkout.
+  ``git pull && uv sync --inexact`` from the checkout. ``--inexact`` is
+  important here: a bare ``uv sync`` removes packages installed by extras or
+  dependency groups that are not part of the base project.
 
 We detect by inspecting ``openjarvis.__file__``. If we can't tell with
 confidence we fall back to the PyPI command — that's the most common
@@ -68,7 +70,7 @@ def detect_install() -> InstallInfo:
         if (candidate / ".git").exists() and (candidate / "pyproject.toml").exists():
             return InstallInfo(
                 kind="editable-git",
-                upgrade_command=f"cd {candidate} && git pull && uv sync",
+                upgrade_command=(f"cd {candidate} && git pull && uv sync --inexact"),
                 repo_root=candidate,
             )
         if candidate.parent == candidate:

src/openjarvis/cli/ask.py

@@ -248,6 +248,17 @@ def _get_memory_backend(config):
         return None
 
 
+def _get_memory_facts(config):
+    """Load facts captured by the automatic memory service."""
+    try:
+        from openjarvis.memory import load_configured_facts
+
+        return load_configured_facts(config)
+    except Exception as exc:
+        logger.debug("Automatic memory facts unavailable (optional): %s", exc)
+        return []
+
+
 _MEMORY_TOOLS = frozenset(
     {"retrieval", "memory_store", "memory_search", "memory_index", "memory_retrieve"}
 )
@@ -416,7 +427,8 @@ def _run_agent(
             from openjarvis.tools.storage.context import ContextConfig, inject_context
 
             backend = _get_memory_backend(config)
-            if backend is not None:
+            facts = _get_memory_facts(config)
+            if backend is not None or facts:
                 ctx_cfg = ContextConfig(
                     top_k=config.memory.context_top_k,
                     min_score=config.memory.context_min_score,
@@ -427,6 +439,7 @@ def _run_agent(
                     [],
                     backend,
                     config=ctx_cfg,
+                    facts=facts,
                 )
                 for msg in context_messages:
                     ctx.conversation.add(msg)
@@ -963,7 +976,8 @@ def ask(
             )
 
             backend = _get_memory_backend(config)
-            if backend is not None:
+            facts = _get_memory_facts(config)
+            if backend is not None or facts:
                 ctx_cfg = ContextConfig(
                     top_k=config.memory.context_top_k,
                     min_score=config.memory.context_min_score,
@@ -974,6 +988,7 @@ def ask(
                     messages,
                     backend,
                     config=ctx_cfg,
+                    facts=facts,
                 )
         except Exception as exc:
             logger.debug("Failed to inject memory context: %s", exc)

src/openjarvis/cli/chat_cmd.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import logging
 import sys
 from typing import List, Optional
 
@@ -15,6 +16,8 @@ from openjarvis.core.events import EventBus
 from openjarvis.core.types import Message, Role
 from openjarvis.memory import publish_completed_exchange
 
+logger = logging.getLogger(__name__)
+
 
 def _read_input(prompt: str = "You> ") -> Optional[str]:
     """Read user input with graceful EOF handling."""
@@ -194,6 +197,15 @@ def chat(
         console.print(f"[yellow]Memory service unavailable: {exc}[/yellow]")
         memory_service = None
 
+    # The document backend and automatic fact store are separate persistence
+    # mechanisms. Context injection combines both at read time so facts from
+    # previous sessions are immediately available without a manual index step.
+    memory_backend = None
+    if config.agent.context_from_memory:
+        from openjarvis.cli.ask import _get_memory_backend
+
+        memory_backend = _get_memory_backend(config)
+
     # Conversation state
     if not system_prompt:
         from openjarvis.prompt.builder import SystemPromptBuilder
@@ -262,15 +274,55 @@ def chat(
         # Add user message
         history.append(Message(role=Role.USER, content=user_input))
 
-        # Generate response
+        generation_history = history
+        agent_context_message = None
+        if config.agent.context_from_memory:
+            try:
+                from openjarvis.memory import load_configured_facts
+                from openjarvis.tools.storage.context import (
+                    ContextConfig,
+                    inject_context,
+                )
+
+                if memory_service is not None and hasattr(memory_service, "list_facts"):
+                    facts = memory_service.list_facts()
+                else:
+                    facts = load_configured_facts(config)
+                ctx_cfg = ContextConfig(
+                    top_k=config.memory.context_top_k,
+                    min_score=config.memory.context_min_score,
+                    max_context_tokens=config.memory.context_max_tokens,
+                )
+                context_messages = inject_context(
+                    user_input,
+                    [] if agent is not None else history,
+                    memory_backend,
+                    config=ctx_cfg,
+                    facts=facts,
+                )
+                if agent is not None:
+                    if context_messages:
+                        agent_context_message = context_messages[0]
+                else:
+                    generation_history = context_messages
+            except Exception:
+                logger.debug("Failed to inject memory context", exc_info=True)
+
+        # Generate response even when optional memory context is unavailable.
         try:
             if agent is not None:
-                response = agent.run(user_input)
+                agent_context = None
+                if agent_context_message is not None:
+                    from openjarvis.agents._stubs import AgentContext
+
+                    agent_context = AgentContext()
+                    agent_context.conversation.add(agent_context_message)
+                response = agent.run(user_input, context=agent_context)
                 content = (
                     response.content if hasattr(response, "content") else str(response)
                 )
             else:
-                result = engine.generate(history, model=model)
+                result = engine.generate(generation_history, model=model)
                 content = (
                     result.get("content", "")
                     if isinstance(result, dict)

src/openjarvis/cli/config_cmd.py

@@ -158,7 +158,7 @@ def _show_toml_config(console: Console, config_path: Path) -> None:
     console.print(f"[dim]Loading config from: {config_path}[/dim]")
 
     if config_path.exists():
-        config_content = config_path.read_text()
+        config_content = config_path.read_text(encoding="utf-8")
         syntax = Syntax(config_content, "toml", theme="monokai", line_numbers=True)
         console.print(Panel(syntax, title="Config File", border_style="cyan"))
     else:
@@ -170,7 +170,7 @@ def _show_json_config(console: Console, config_path: Path) -> None:
     console.print(f"[dim]Loading config from: {config_path}[/dim]")
 
     if config_path.exists():
-        config_content = config_path.read_text()
+        config_content = config_path.read_text(encoding="utf-8")
 
         try:
             import tomllib  # Python 3.11+
@@ -375,7 +375,7 @@ def set_config(key: str, value: str) -> None:
         os.environ.get("OPENJARVIS_CONFIG", DEFAULT_CONFIG_DIR / "config.toml")
     )
     if config_path.exists():
-        doc = tomlkit.parse(config_path.read_text())
+        doc = tomlkit.parse(config_path.read_text(encoding="utf-8"))
     else:
         doc = tomlkit.document()
         config_path.parent.mkdir(parents=True, exist_ok=True)
@@ -390,7 +390,7 @@ def set_config(key: str, value: str) -> None:
     current[parts[-1]] = typed_value
 
     # Write back
-    config_path.write_text(tomlkit.dumps(doc))
+    config_path.write_text(tomlkit.dumps(doc), encoding="utf-8")
 
     console.print(f"[green]Set[/green] {key} = {value!r}")
 

src/openjarvis/cli/daemon_cmd.py

@@ -17,18 +17,64 @@ _PID_FILE = DEFAULT_CONFIG_DIR / "server.pid"
 _LOG_FILE = DEFAULT_CONFIG_DIR / "server.log"
 
 
+def _pid_alive(pid: int) -> bool:
+    """Return whether *pid* identifies a running process without signaling it."""
+    if pid <= 0:
+        return False
+
+    if os.name == "nt":
+        import ctypes
+        from ctypes import wintypes
+
+        error_invalid_parameter = 87
+        synchronize = 0x00100000
+        wait_object_0 = 0x00000000
+
+        kernel32 = ctypes.WinDLL("kernel32", use_last_error=True)
+        kernel32.OpenProcess.argtypes = [wintypes.DWORD, wintypes.BOOL, wintypes.DWORD]
+        kernel32.OpenProcess.restype = wintypes.HANDLE
+        kernel32.WaitForSingleObject.argtypes = [wintypes.HANDLE, wintypes.DWORD]
+        kernel32.WaitForSingleObject.restype = wintypes.DWORD
+        kernel32.CloseHandle.argtypes = [wintypes.HANDLE]
+        kernel32.CloseHandle.restype = wintypes.BOOL
+
+        handle = kernel32.OpenProcess(synchronize, False, pid)
+        if not handle:
+            # OpenProcess reports ERROR_INVALID_PARAMETER when the PID does not
+            # exist. For access-denied and other inconclusive failures, retain
+            # the PID file rather than declaring a potentially live daemon dead.
+            return ctypes.get_last_error() != error_invalid_parameter
+
+        try:
+            wait_result = kernel32.WaitForSingleObject(handle, 0)
+            # WAIT_OBJECT_0 proves the process exited. WAIT_TIMEOUT proves it
+            # is live; unexpected failures are inconclusive, so retain the PID.
+            return wait_result != wait_object_0
+        finally:
+            kernel32.CloseHandle(handle)
+
+    try:
+        os.kill(pid, 0)
+    except ProcessLookupError:
+        return False
+    except PermissionError:
+        return True
+    return True
+
+
 def _read_pid() -> int | None:
     """Read PID from pid file, return None if not found or stale."""
     if not _PID_FILE.exists():
         return None
     try:
         pid = int(_PID_FILE.read_text().strip())
-        # Check if process is still running
-        os.kill(pid, 0)
-        return pid
-    except (ValueError, OSError):
+    except (OSError, ValueError):
         _PID_FILE.unlink(missing_ok=True)
         return None
+    if not _pid_alive(pid):
+        _PID_FILE.unlink(missing_ok=True)
+        return None
+    return pid
 
 
 def _write_pid(pid: int) -> None:
@@ -127,14 +173,13 @@ def stop() -> None:
         # Wait up to 10 seconds for graceful shutdown
         for _ in range(20):
             time.sleep(0.5)
-            try:
-                os.kill(pid, 0)
-            except OSError:
+            if not _pid_alive(pid):
                 break
         else:
-            # Force kill if still running
+            # SIGKILL is POSIX-only. On Windows SIGTERM already maps to
+            # TerminateProcess, so repeating it is the available escalation.
             try:
-                os.kill(pid, signal.SIGKILL)
+                os.kill(pid, getattr(signal, "SIGKILL", signal.SIGTERM))
             except OSError:
                 pass
     except OSError:

src/openjarvis/cli/init_cmd.py

@@ -344,7 +344,9 @@ def init(
             console.print(f"  Looked in: {examples_dir}")
             raise SystemExit(1)
         DEFAULT_CONFIG_DIR.mkdir(parents=True, exist_ok=True)
-        DEFAULT_CONFIG_PATH.write_text(preset_path.read_text())
+        DEFAULT_CONFIG_PATH.write_text(
+            preset_path.read_text(encoding="utf-8"), encoding="utf-8"
+        )
         console.print(
             f"[green]Preset '{preset}' installed to {DEFAULT_CONFIG_PATH}[/green]"
         )

src/openjarvis/cli/self_update_cmd.py

@@ -4,7 +4,9 @@ Runs the right upgrade command for how the user installed OpenJarvis:
 
 - PyPI installs get ``pip install --upgrade openjarvis``.
 - uv-tool installs get ``uv tool upgrade openjarvis``.
-- Editable git checkouts get ``git pull && uv sync`` in the checkout.
+- Editable git checkouts get ``git pull && uv sync --inexact`` in the checkout.
+  The inexact sync preserves packages previously installed through extras and
+  dependency groups.
 
 The detection logic is shared with the post-command "new version
 available" hint in ``_version_check.py`` so both surfaces stay in sync.

src/openjarvis/cli/serve.py

@@ -25,6 +25,30 @@ from openjarvis.intelligence import (
 
 logger = logging.getLogger(__name__)
 
+_DEFAULT_TOOLS = frozenset({"think", "calculator", "web_search"})
+
+
+def _resolve_allowed_tools(config: object) -> tuple[set[str], bool]:
+    """Return configured tool names and whether the selection was explicit.
+
+    ``tools.enabled`` is the canonical setting used by ``SystemBuilder`` and
+    the interactive CLI.  ``agent.tools`` remains as a backward-compatible
+    fallback, followed by the server's default tool set when neither is set.
+    """
+    configured = config.tools.enabled or config.agent.tools
+    if not configured:
+        return set(_DEFAULT_TOOLS), False
+
+    if isinstance(configured, list):
+        allowed = {
+            tool.strip()
+            for tool in configured
+            if isinstance(tool, str) and tool.strip()
+        }
+    else:
+        allowed = {tool.strip() for tool in configured.split(",") if tool.strip()}
+    return allowed, True
+
 
 def _unique_model_ids(model_ids: list[str]) -> list[str]:
     """Return model ids in first-seen order without duplicates."""
@@ -96,7 +120,7 @@ def _resolve_server_model(
     "--agent",
     "agent_name",
     default=None,
-    help="Agent for non-streaming requests (simple, orchestrator, react, openhands).",
+    help="Agent for chat requests (simple, orchestrator, react, openhands).",
 )
 @click.pass_context
 def serve(
@@ -279,6 +303,15 @@ def serve(
     # (which would re-discover the engine, re-resolve tools, re-open the channel,
     # etc.). See the scheduler block near the bottom of this function (#263).
     resolved_tools: list = []
+    managed_mcp_tools: list = []
+    mcp_clients: list = []
+    try:
+        from openjarvis.mcp.loader import load_mcp_tools_from_config
+
+        managed_mcp_tools, mcp_clients = load_mcp_tools_from_config(config.tools.mcp)
+    except Exception as exc:
+        logger.warning("Managed-agent MCP tools failed to load: %s", exc)
+
     if agent_key:
         try:
             import openjarvis.agents  # noqa: F401
@@ -290,32 +323,13 @@ def serve(
                 if sec.capability_policy is not None:
                     agent_kwargs["capability_policy"] = sec.capability_policy
 
-                # MCP transports persisted on the agent at the bottom of
-                # this block — initialise here so the reference is valid
-                # even when accepts_tools is False (#461).
-                mcp_clients: list = []
-
                 # Load tools for agents that support them
                 if getattr(agent_cls, "accepts_tools", False):
                     import openjarvis.tools  # noqa: F401  # trigger registration
                     from openjarvis.core.registry import ToolRegistry
                     from openjarvis.tools._stubs import BaseTool
 
-                    _DEFAULT_TOOLS = {"think", "calculator", "web_search"}
-                    configured = config.agent.tools
-                    if configured:
-                        if isinstance(configured, list):
-                            allowed = {
-                                t.strip()
-                                for t in configured
-                                if isinstance(t, str) and t.strip()
-                            }
-                        else:
-                            allowed = {
-                                t.strip() for t in configured.split(",") if t.strip()
-                            }
-                    else:
-                        allowed = _DEFAULT_TOOLS
+                    allowed, tools_configured = _resolve_allowed_tools(config)
 
                     tools = []
                     for name in ToolRegistry.keys():
@@ -331,12 +345,13 @@ def serve(
 
                     # MCP server tools from config.tools.mcp.servers
                     # (#461 — these were silently dropped).
-                    from openjarvis.mcp.loader import load_mcp_tools_from_config
-
-                    mcp_tools, mcp_clients = load_mcp_tools_from_config(
-                        config.tools.mcp,
-                        allowed_names=allowed if configured else None,
-                    )
+                    mcp_tools = managed_mcp_tools
+                    if tools_configured:
+                        mcp_tools = [
+                            tool
+                            for tool in managed_mcp_tools
+                            if tool.spec.name in allowed
+                        ]
                     if mcp_tools:
                         existing = {t.spec.name for t in tools}
                         for t in mcp_tools:
@@ -389,10 +404,6 @@ def serve(
         channel_agent = config.channel.default_agent or agent_key or "simple"
 
         _channel_tools: list = []
-        # MCP transports persisted at function scope (= server-process
-        # lifetime); see the comment near the channel-MCP-load block
-        # below. Initialise here so it's always bound. #461.
-        _channel_mcp_clients: list = []
         if channel_agent:
             try:
                 import openjarvis.agents
@@ -405,23 +416,7 @@ def serve(
                         from openjarvis.core.registry import ToolRegistry
                         from openjarvis.tools._stubs import BaseTool
 
-                        _DEFAULT_TOOLS = {"think", "calculator", "web_search"}
-                        configured = config.agent.tools
-                        if configured:
-                            if isinstance(configured, list):
-                                _allowed = {
-                                    t.strip()
-                                    for t in configured
-                                    if isinstance(t, str) and t.strip()
-                                }
-                            else:
-                                _allowed = {
-                                    t.strip()
-                                    for t in configured.split(",")
-                                    if t.strip()
-                                }
-                        else:
-                            _allowed = _DEFAULT_TOOLS
+                        _allowed, _tools_configured = _resolve_allowed_tools(config)
 
                         for _tname in ToolRegistry.keys():
                             if _tname not in _allowed:
@@ -432,29 +427,23 @@ def serve(
                             elif isinstance(_tcls, BaseTool):
                                 _channel_tools.append(_tcls)
 
-                        # MCP tools for the channel agent too (#461).
-                        from openjarvis.mcp.loader import (
-                            load_mcp_tools_from_config,
-                        )
-
-                        _ch_mcp_tools, _ch_mcp_clients = load_mcp_tools_from_config(
-                            config.tools.mcp,
-                            allowed_names=_allowed if configured else None,
-                        )
+                        # Reuse the process-owned MCP pool so channels do not
+                        # open a second transport to every configured server.
+                        _ch_mcp_tools = managed_mcp_tools
+                        if _tools_configured:
+                            _ch_mcp_tools = [
+                                tool
+                                for tool in managed_mcp_tools
+                                if tool.spec.name in _allowed
+                            ]
                         if _ch_mcp_tools:
                             _existing = {t.spec.name for t in _channel_tools}
                             for t in _ch_mcp_tools:
                                 if t.spec.name not in _existing:
                                     _channel_tools.append(t)
                                     _existing.add(t.spec.name)
-                        # Hold a reference at module / function scope —
-                        # the channel agent is constructed inside
-                        # JarvisSystem below; we extend its lifetime by
-                        # keeping the list bound here.
-                        _channel_mcp_clients = _ch_mcp_clients
             except Exception as exc:
                 logger.warning("Channel tools failed to load: %s", exc)
-                _channel_mcp_clients = []
 
         _wire_system = JarvisSystem(
             config=config,
@@ -464,6 +453,8 @@ def serve(
             model=model_name,
             agent_name=channel_agent,
             tools=_channel_tools,
+            mcp_tools=managed_mcp_tools,
+            _mcp_clients=mcp_clients,
         )
         _wire_system.wire_channel(channel_bridge)
 
@@ -481,23 +472,24 @@ def serve(
     # Create app
     from openjarvis.server.app import create_app
 
-    # Set up memory backend for context injection. Built before the scheduler
-    # block so the executor's JarvisSystem can reference it (#263).
+    # Set up the memory backend for storage tools, API routes, and optional
+    # prompt-context injection. ``context_from_memory`` controls only the last
+    # of those, so disabling it must not leave explicit memory_* tools with a
+    # null backend. Built before the scheduler so AgentExecutor can reuse it.
     memory_backend = None
-    if config.agent.context_from_memory:
-        try:
-            import openjarvis.tools.storage  # noqa: F401
-            from openjarvis.core.registry import MemoryRegistry
+    try:
+        import openjarvis.tools.storage  # noqa: F401
+        from openjarvis.core.registry import MemoryRegistry
 
-            mem_key = config.memory.default_backend
-            if MemoryRegistry.contains(mem_key):
-                memory_backend = MemoryRegistry.create(
-                    mem_key,
-                    db_path=config.memory.db_path,
-                )
-                console.print("  Memory:    [cyan]active[/cyan]")
-        except Exception as exc:
-            logger.debug("Memory backend init failed: %s", exc)
+        mem_key = config.memory.default_backend
+        if MemoryRegistry.contains(mem_key):
+            memory_backend = MemoryRegistry.create(
+                mem_key,
+                db_path=config.memory.db_path,
+            )
+            console.print("  Memory:    [cyan]active[/cyan]")
+    except Exception as exc:
+        logger.debug("Memory backend init failed: %s", exc)
 
     # Automatic long-term memory service (background fact extraction).
     memory_service = None
@@ -592,6 +584,7 @@ def serve(
                 agent=agent,
                 agent_name=agent_key or "",
                 tools=resolved_tools,
+                mcp_tools=managed_mcp_tools,
                 tool_executor=_sched_tool_executor,
                 memory_backend=memory_backend,
                 telemetry_store=telem_store,
@@ -600,6 +593,7 @@ def serve(
                 capability_policy=sec.capability_policy,
                 agent_manager=agent_manager,
                 agent_executor=executor,
+                _mcp_clients=mcp_clients,
             )
             executor.set_system(system)
 
@@ -691,10 +685,13 @@ def serve(
         channel_bridge=channel_bridge,
         config=config,
         memory_backend=memory_backend,
+        own_memory_backend=memory_backend is not None,
         memory_service=memory_service,
         speech_backend=speech_backend,
         agent_manager=agent_manager,
         agent_scheduler=agent_scheduler,
+        mcp_tools=managed_mcp_tools,
+        mcp_clients=mcp_clients,
         api_key=api_key,
         webhook_config=webhook_config,
         cors_origins=config.server.cors_origins,
@@ -723,6 +720,4 @@ def serve(
             "authenticated requests to your instance."
         )
 
-    import uvicorn
-
     uvicorn.run(app, host=bind_host, port=bind_port, log_level="info")

src/openjarvis/engine/_discovery.py

@@ -35,6 +35,12 @@ def _make_engine(key: str, config: JarvisConfig) -> InferenceEngine:
     """Instantiate a registered engine with the appropriate config host."""
     cls = EngineRegistry.get(key)
 
+    # LiteLLM cannot enumerate every model supported by every provider.  Its
+    # list_models() contract therefore advertises the configured default
+    # model, which must be supplied when discovery constructs the engine.
+    if key == "litellm":
+        return cls(default_model=config.intelligence.default_model or None)
+
     # gemma_cpp: pass config fields instead of host
     if key == "gemma_cpp":
         cfg = config.engine.gemma_cpp

src/openjarvis/engine/cloud.py

@@ -9,6 +9,7 @@ import json
 import logging
 import os
 import time
+import uuid
 from collections.abc import AsyncIterator, Sequence
 from typing import Any, Dict, List, Tuple
 
@@ -1305,6 +1306,160 @@ class CloudEngine(InferenceEngine):
             if chunk.text:
                 yield chunk.text
 
+    async def _stream_full_google(
+        self,
+        messages: Sequence[Message],
+        *,
+        model: str,
+        temperature: float,
+        max_tokens: int,
+        **kwargs: Any,
+    ) -> AsyncIterator[StreamChunk]:
+        """Stream Google text and function-call parts as full chunks."""
+        if self._google_client is None:
+            raise EngineConnectionError("Google client not available")
+
+        system_text = ""
+        contents: List[Dict[str, Any]] = []
+        for message in messages:
+            if message.role.value == "system":
+                system_text = message.content
+            elif message.role.value == "tool":
+                function_response = {
+                    "function_response": {
+                        "name": message.name or "unknown",
+                        "response": {"result": message.content},
+                    }
+                }
+                if (
+                    contents
+                    and contents[-1]["role"] == "user"
+                    and contents[-1]["parts"]
+                    and "function_response" in contents[-1]["parts"][-1]
+                ):
+                    contents[-1]["parts"].append(function_response)
+                else:
+                    contents.append({"role": "user", "parts": [function_response]})
+            elif message.role.value == "assistant" and message.tool_calls:
+                parts: List[Dict[str, Any]] = []
+                if message.content:
+                    parts.append({"text": message.content})
+                for tool_call in message.tool_calls:
+                    args = tool_call.arguments
+                    if isinstance(args, str):
+                        try:
+                            args = json.loads(args)
+                        except (json.JSONDecodeError, TypeError):
+                            args = {"input": args}
+                    function_call_part: Dict[str, Any] = {
+                        "function_call": {
+                            "name": tool_call.name,
+                            "args": args if isinstance(args, dict) else {},
+                        }
+                    }
+                    signature = self._thought_sigs.get(tool_call.id)
+                    if signature is not None:
+                        function_call_part["thought_signature"] = signature
+                    parts.append(function_call_part)
+                contents.append({"role": "model", "parts": parts})
+            elif message.role.value == "assistant":
+                contents.append({"role": "model", "parts": [{"text": message.content}]})
+            else:
+                contents.append({"role": "user", "parts": [{"text": message.content}]})
+
+        from google.genai import types as genai_types
+
+        config = genai_types.GenerateContentConfig(
+            temperature=temperature,
+            max_output_tokens=max_tokens,
+        )
+        if system_text:
+            config.system_instruction = system_text
+
+        tools = kwargs.pop("tools", None)
+        if tools:
+            config.tools = [{"function_declarations": _convert_tools_to_google(tools)}]
+
+        tool_call_count = 0
+        stream_id = uuid.uuid4().hex
+        final_usage: Dict[str, Any] | None = None
+        for chunk in self._google_client.models.generate_content_stream(
+            model=model,
+            contents=contents,
+            config=config,
+        ):
+            usage_metadata = getattr(chunk, "usage_metadata", None)
+            if usage_metadata is not None:
+                prompt_tokens = getattr(usage_metadata, "prompt_token_count", 0) or 0
+                completion_tokens = (
+                    getattr(usage_metadata, "candidates_token_count", 0) or 0
+                )
+                final_usage = {
+                    "prompt_tokens": prompt_tokens,
+                    "completion_tokens": completion_tokens,
+                    "total_tokens": prompt_tokens + completion_tokens,
+                }
+
+            candidates = getattr(chunk, "candidates", None)
+            parts = []
+            if candidates:
+                parts = getattr(candidates[0].content, "parts", []) or []
+
+            if parts:
+                text_found = False
+                calls: List[Dict[str, Any]] = []
+                for part in parts:
+                    text = getattr(part, "text", None)
+                    if text:
+                        text_found = True
+                        yield StreamChunk(content=text)
+
+                    function_call = getattr(part, "function_call", None)
+                    if function_call:
+                        name = getattr(function_call, "name", "")
+                        raw_args = getattr(function_call, "args", {})
+                        args = dict(raw_args) if hasattr(raw_args, "items") else {}
+                        # Gemini emits complete function-call parts, so each part is
+                        # a distinct invocation. The same function may legitimately
+                        # be called more than once in a parallel response.
+                        tool_index = tool_call_count
+                        # The engine is shared across server requests, and saved
+                        # thought signatures are keyed by tool-call ID. Include a
+                        # per-stream nonce so concurrent conversations cannot
+                        # overwrite each other's signatures.
+                        tool_id = f"google_{stream_id}_{tool_index}"
+                        tool_call_count += 1
+                        tool_call = {
+                            "index": tool_index,
+                            "id": tool_id,
+                            "type": "function",
+                            "function": {
+                                "name": name,
+                                "arguments": json.dumps(args),
+                            },
+                        }
+                        calls.append(tool_call)
+                        signature = getattr(part, "thought_signature", None)
+                        if signature is not None:
+                            tool_call["thought_signature"] = signature
+                            self._thought_sigs[tool_id] = signature
+                if calls:
+                    yield StreamChunk(tool_calls=calls)
+                if text_found:
+                    continue
+
+            try:
+                text = chunk.text
+            except (AttributeError, ValueError):
+                text = None
+            if text:
+                yield StreamChunk(content=text)
+
+        yield StreamChunk(
+            finish_reason="tool_calls" if tool_call_count else "stop",
+            usage=final_usage,
+        )
+
     async def _stream_openrouter(
         self,
         messages: Sequence[Message],
@@ -1600,7 +1755,7 @@ class CloudEngine(InferenceEngine):
             async for chunk in self._stream_full_anthropic(messages, **kw):
                 yield chunk
         elif _is_google_model(model):
-            async for chunk in super().stream_full(messages, **kw):
+            async for chunk in self._stream_full_google(messages, **kw):
                 yield chunk
         else:
             async for chunk in self._stream_full_openai(messages, **kw):

src/openjarvis/engine/multi.py

@@ -26,16 +26,19 @@ class MultiEngine(InferenceEngine):
     def __init__(self, engines: list[tuple[str, InferenceEngine]]) -> None:
         self._engines = engines
         self._model_map: Dict[str, InferenceEngine] = {}
+        self._model_key_map: Dict[str, str] = {}
         self._refresh_map()
 
     def _refresh_map(self) -> None:
         self._model_map.clear()
-        for _key, engine in self._engines:
+        self._model_key_map.clear()
+        for key, engine in self._engines:
             try:
                 for model_id in engine.list_models():
                     self._model_map[model_id] = engine
+                    self._model_key_map[model_id] = key
             except Exception as exc:
-                logger.debug("Failed to list models for %s: %s", _key, exc)
+                logger.debug("Failed to list models for %s: %s", key, exc)
 
     _CLOUD_PREFIXES = ("gpt-", "o1-", "o3-", "o4-", "claude-", "gemini-", "openrouter/")
 
@@ -117,6 +120,14 @@ class MultiEngine(InferenceEngine):
         self._refresh_map()
         return list(self._model_map.keys())
 
+    def engine_key_for(self, model: str) -> str | None:
+        """Return the registry key of the engine advertising *model*."""
+        key = self._model_key_map.get(model)
+        if key is not None:
+            return key
+        self._refresh_map()
+        return self._model_key_map.get(model)
+
     def health(self) -> bool:
         return any(engine.health() for _key, engine in self._engines)
 

src/openjarvis/evals/datasets/taubench.py

@@ -8,13 +8,12 @@ Reference: https://github.com/sierra-research/tau2-bench
 
 from __future__ import annotations
 
+import json
 import logging
 import os
-import subprocess
-import sys
+from importlib import metadata
 from typing import Iterable, List, Optional
 
-from openjarvis.core.paths import get_cache_dir
 from openjarvis.evals.core.dataset import DatasetProvider
 from openjarvis.evals.core.splits import apply_split
 from openjarvis.evals.core.types import EvalRecord
@@ -22,48 +21,50 @@ from openjarvis.evals.core.types import EvalRecord
 LOGGER = logging.getLogger(__name__)
 
 TAU2_REPO = "https://github.com/sierra-research/tau2-bench.git"
-CACHE_DIR = get_cache_dir() / "tau2-bench"
+# v1.0.1. Keep the full commit SHA here (rather than a movable tag) so every
+# TauBench setup uses the same third-party code.
+TAU2_REVISION = "fc0055dc4e0a316c3f83133267fbd6faaa770992"
+TAU2_INSTALL_SPEC = f"tau2 @ git+{TAU2_REPO}@{TAU2_REVISION}"
 
 DOMAINS = ("airline", "retail", "telecom")
 
 
 def _ensure_tau2() -> None:
-    """Ensure tau2 package is importable; install from cache if needed."""
+    """Ensure the explicitly installed, pinned tau2 package is importable."""
+    try:
+        distribution = metadata.distribution("tau2")
+    except metadata.PackageNotFoundError as exc:
+        raise ImportError(
+            "TauBench requires tau2, which OpenJarvis does not install at "
+            "runtime. Install the pinned dependency explicitly (Python >=3.12): "
+            f'uv pip install "{TAU2_INSTALL_SPEC}"'
+        ) from exc
+
+    try:
+        direct_url_text = distribution.read_text("direct_url.json")
+        direct_url = json.loads(direct_url_text or "")
+        vcs_info = direct_url.get("vcs_info", {})
+        installed_repo = direct_url.get("url")
+        installed_revision = vcs_info.get("commit_id")
+    except (json.JSONDecodeError, AttributeError):
+        installed_repo = None
+        installed_revision = None
+
+    if installed_repo != TAU2_REPO or installed_revision != TAU2_REVISION:
+        raise ImportError(
+            "The installed tau2 package does not match OpenJarvis's pinned "
+            "source revision. Reinstall it explicitly (Python >=3.12): "
+            f'uv pip install --force-reinstall "{TAU2_INSTALL_SPEC}"'
+        )
+
     try:
         import tau2  # noqa: F401
-    except ImportError:
-        # Clone and install from source
-        if not CACHE_DIR.exists():
-            LOGGER.info("Cloning tau2-bench from %s ...", TAU2_REPO)
-            CACHE_DIR.parent.mkdir(parents=True, exist_ok=True)
-            subprocess.run(
-                ["git", "clone", "--depth", "1", TAU2_REPO, str(CACHE_DIR)],
-                check=True,
-                capture_output=True,
-            )
-        LOGGER.info("Installing tau2-bench ...")
-        # Try `python -m pip` first; fall back to `uv pip` for uv-managed venvs
-        # which don't ship pip by default.
-        try:
-            subprocess.run(
-                [sys.executable, "-m", "pip", "install", "-e", str(CACHE_DIR)],
-                check=True,
-                capture_output=True,
-            )
-        except (subprocess.CalledProcessError, FileNotFoundError):
-            subprocess.run(
-                [
-                    "uv",
-                    "pip",
-                    "install",
-                    "--python",
-                    sys.executable,
-                    "-e",
-                    str(CACHE_DIR),
-                ],
-                check=True,
-                capture_output=True,
-            )
+    except ImportError as exc:
+        raise ImportError(
+            "The pinned tau2 package is installed but cannot be imported. "
+            "Reinstall it explicitly (Python >=3.12): "
+            f'uv pip install --force-reinstall "{TAU2_INSTALL_SPEC}"'
+        ) from exc
 
 
 class TauBenchDataset(DatasetProvider):

src/openjarvis/mcp/client.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import itertools
+import threading
 from typing import Any, Dict, List
 
 from openjarvis.mcp.protocol import MCPError, MCPRequest, MCPResponse
@@ -24,18 +25,31 @@ class MCPClient:
         self._initialized = False
         self._capabilities: Dict[str, Any] = {}
         self._id_counter = itertools.count(1)
+        # A client may be shared by server, scheduled, and channel agents.
+        # Keep each transport request/response exchange atomic so stdio
+        # readers cannot consume another thread's JSON-RPC response.
+        self._request_lock = threading.RLock()
+        # Closing must not wait for ``_request_lock``: transport.close() is
+        # what interrupts a request that is blocked in a transport read.
+        # An event lets queued requests fail before touching that transport,
+        # while this separate lock keeps close itself idempotent.
+        self._closed = threading.Event()
+        self._transport_closed = threading.Event()
+        self._close_lock = threading.Lock()
 
     def _next_id(self) -> int:
         return next(self._id_counter)
 
     def _send(self, method: str, params: Dict[str, Any] | None = None) -> MCPResponse:
         """Send a request and check for errors."""
-        request = MCPRequest(
-            method=method,
-            params=params or {},
-            id=self._next_id(),
-        )
-        response = self._transport.send(request)
+        with self._request_lock:
+            self._raise_if_closed()
+            request = MCPRequest(
+                method=method,
+                params=params or {},
+                id=self._next_id(),
+            )
+            response = self._transport.send(request)
         if response.error is not None:
             raise MCPError(
                 code=response.error.get("code", -1),
@@ -44,6 +58,10 @@ class MCPClient:
             )
         return response
 
+    def _raise_if_closed(self) -> None:
+        if self._closed.is_set():
+            raise RuntimeError("MCP client is closed")
+
     def initialize(self) -> Dict[str, Any]:
         """Perform the MCP initialize handshake.
 
@@ -75,7 +93,9 @@ class MCPClient:
             params=params or {},
             id=None,  # None → no id field in JSON (notification)
         )
-        self._transport.send_notification(request)
+        with self._request_lock:
+            self._raise_if_closed()
+            self._transport.send_notification(request)
 
     def list_tools(self) -> List[ToolSpec]:
         """Discover available tools from the server.
@@ -114,7 +134,15 @@ class MCPClient:
 
     def close(self) -> None:
         """Close the transport connection."""
-        self._transport.close()
+        # Do not acquire _request_lock here. A transport request can be stuck
+        # waiting for a server response, and closing the underlying transport
+        # is the mechanism that unblocks it.
+        with self._close_lock:
+            if self._transport_closed.is_set():
+                return
+            self._closed.set()
+            self._transport.close()
+            self._transport_closed.set()
 
     def __enter__(self) -> MCPClient:
         return self

src/openjarvis/memory/__init__.py

@@ -19,6 +19,7 @@ from openjarvis.memory.store import (
     FactStore,
     LocalFactStore,
     create_fact_store,
+    load_configured_facts,
 )
 
 __all__ = [
@@ -29,5 +30,6 @@ __all__ = [
     "MemoryService",
     "build_memory_service",
     "create_fact_store",
+    "load_configured_facts",
     "publish_completed_exchange",
 ]

src/openjarvis/memory/store.py

@@ -16,7 +16,7 @@ import time
 from abc import ABC, abstractmethod
 from dataclasses import asdict, dataclass
 from pathlib import Path
-from typing import Iterable, List
+from typing import Any, Iterable, List
 
 from openjarvis.core.paths import get_config_dir
 from openjarvis.core.registry import FactStoreRegistry
@@ -205,4 +205,30 @@ def create_fact_store(
     return FactStoreRegistry.create(key, path, max_facts=max_facts)
 
 
-__all__ = ["Fact", "FactStore", "LocalFactStore", "create_fact_store"]
+def load_configured_facts(config: Any) -> List[Fact]:
+    """Load automatic-memory facts from *config* when the service is enabled.
+
+    Context injection is also used by short-lived commands such as
+    ``jarvis ask``, where no :class:`MemoryService` instance exists.  This
+    helper gives those callers the same configured fact-store view without
+    coupling them to the service lifecycle.
+    """
+    memory = getattr(config, "memory", None)
+    if memory is None or not getattr(memory, "enabled", False):
+        return []
+
+    store = create_fact_store(
+        getattr(memory, "backend", "local"),
+        path=getattr(memory, "facts_path", None),
+        max_facts=getattr(memory, "max_facts", 1000),
+    )
+    return store.list()
+
+
+__all__ = [
+    "Fact",
+    "FactStore",
+    "LocalFactStore",
+    "create_fact_store",
+    "load_configured_facts",
+]

src/openjarvis/sdk.py

@@ -522,14 +522,15 @@ class Jarvis:
         # Context injection
         if context and self._config.agent.context_from_memory:
             try:
-                from openjarvis.cli.ask import _get_memory_backend
+                from openjarvis.cli.ask import _get_memory_backend, _get_memory_facts
                 from openjarvis.tools.storage.context import (
                     ContextConfig,
                     inject_context,
                 )
 
                 backend = _get_memory_backend(self._config)
-                if backend is not None:
+                facts = _get_memory_facts(self._config)
+                if backend is not None or facts:
                     ctx_cfg = ContextConfig(
                         top_k=self._config.memory.context_top_k,
                         min_score=self._config.memory.context_min_score,
@@ -540,6 +541,7 @@ class Jarvis:
                         [],
                         backend,
                         config=ctx_cfg,
+                        facts=facts,
                     )
                     for msg in context_messages:
                         ctx.conversation.add(msg)
@@ -570,17 +572,24 @@ class Jarvis:
     ) -> List[Message]:
         """Inject memory context into messages."""
         try:
-            from openjarvis.cli.ask import _get_memory_backend
+            from openjarvis.cli.ask import _get_memory_backend, _get_memory_facts
             from openjarvis.tools.storage.context import ContextConfig, inject_context
 
             backend = _get_memory_backend(self._config)
-            if backend is not None:
+            facts = _get_memory_facts(self._config)
+            if backend is not None or facts:
                 ctx_cfg = ContextConfig(
                     top_k=self._config.memory.context_top_k,
                     min_score=self._config.memory.context_min_score,
                     max_context_tokens=self._config.memory.context_max_tokens,
                 )
-                return inject_context(query, messages, backend, config=ctx_cfg)
+                return inject_context(
+                    query,
+                    messages,
+                    backend,
+                    config=ctx_cfg,
+                    facts=facts,
+                )
         except Exception as exc:
             logger.warning("Failed to inject memory context: %s", exc)
         return messages

src/openjarvis/server/app.py

@@ -4,6 +4,7 @@ from __future__ import annotations
 
 import logging
 import pathlib
+import threading
 import time
 
 from fastapi import FastAPI
@@ -21,6 +22,8 @@ from openjarvis.server.routes import router
 from openjarvis.server.upload_router import router as upload_router
 
 logger = logging.getLogger(__name__)
+_MANAGED_SHUTDOWN_GRACE_SECONDS = 0.25
+_MANAGED_SHUTDOWN_DRAIN_SECONDS = 10.0
 
 
 def _restore_sendblue_bindings(app: FastAPI) -> None:
@@ -151,10 +154,13 @@ def create_app(
     channel_bridge=None,
     config=None,
     memory_backend=None,
+    own_memory_backend: bool = False,
     memory_service=None,
     speech_backend=None,
     agent_manager=None,
     agent_scheduler=None,
+    mcp_tools=None,
+    mcp_clients=None,
     api_key: str = "",
     webhook_config: dict | None = None,
     cors_origins: list[str] | None = None,
@@ -221,16 +227,129 @@ def create_app(
     )
     app.state.channel_bridge = channel_bridge
     app.state.config = config
+    app.state._memory_backend_lock = threading.Lock()
     app.state.memory_backend = memory_backend
+    app.state._owns_memory_backend = bool(own_memory_backend)
     app.state.memory_service = memory_service
     app.state.speech_backend = speech_backend
     app.state.agent_manager = agent_manager
     app.state.agent_scheduler = agent_scheduler
+    app.state.mcp_tools = list(mcp_tools or [])
+    app.state._mcp_discovery_lock = threading.Lock()
+    app.state._mcp_clients_lock = threading.Lock()
+    app.state._mcp_clients = list(mcp_clients or [])
+    app.state._managed_worker_lock = threading.Lock()
+    app.state._managed_workers: set[threading.Thread] = set()
+    app.state._managed_runtime_stopping = False
     app.state.session_start = time.time()
     # Exposed so WebSocket handlers can authenticate the handshake (the HTTP
     # AuthMiddleware never sees WS upgrade requests). Empty = auth disabled.
     app.state.api_key = api_key
 
+    @app.on_event("shutdown")
+    async def _shutdown_managed_runtime() -> None:
+        # Quiesce every producer before touching the shared MCP pool. Route
+        # workers are registered under this lock, so none can slip in after
+        # the snapshot. The scheduler has a two-phase stop because closing an
+        # MCP transport may be what releases an in-flight tick.
+        with app.state._managed_worker_lock:
+            app.state._managed_runtime_stopping = True
+            managed_workers = list(app.state._managed_workers)
+
+        # Stop external listener threads before draining ticks or closing the
+        # shared MCP pool. Channel callbacks are wired to that same pool by
+        # ``serve`` and otherwise could race teardown or survive app restart.
+        channel_bridge = getattr(app.state, "channel_bridge", None)
+        disconnect_channels = getattr(channel_bridge, "disconnect", None)
+        if callable(disconnect_channels):
+            try:
+                disconnect_channels()
+            except Exception:
+                logger.debug("Channel bridge shutdown failed", exc_info=True)
+
+        def _join_workers(timeout: float) -> None:
+            deadline = time.monotonic() + timeout
+            for thread in managed_workers:
+                remaining = deadline - time.monotonic()
+                if remaining <= 0:
+                    break
+                thread.join(timeout=remaining)
+
+        scheduler = getattr(app.state, "agent_scheduler", None)
+        scheduler_wait = None
+        scheduler_drained = True
+        if scheduler is not None:
+            try:
+                request_stop = getattr(scheduler, "request_stop", None)
+                wait_stopped = getattr(scheduler, "wait_stopped", None)
+                if callable(request_stop) and callable(wait_stopped):
+                    request_stop()
+                    scheduler_wait = wait_stopped
+                    scheduler_drained = bool(
+                        wait_stopped(timeout=_MANAGED_SHUTDOWN_GRACE_SECONDS)
+                    )
+                else:
+                    scheduler.stop()
+                    scheduler_drained = not bool(
+                        getattr(scheduler, "is_running", False)
+                    )
+            except Exception:
+                scheduler_drained = False
+                logger.debug("Agent scheduler shutdown failed", exc_info=True)
+
+        # Give normal work a brief chance to finish before cancellation.
+        _join_workers(timeout=_MANAGED_SHUTDOWN_GRACE_SECONDS)
+        with app.state._mcp_clients_lock:
+            mcp_clients_to_close = list(app.state._mcp_clients)
+        for client in mcp_clients_to_close:
+            try:
+                client.close()
+            except Exception:
+                logger.debug("MCP client shutdown failed", exc_info=True)
+
+        # Transport closure interrupts blocked MCP reads. Drain the workers a
+        # second time so shutdown does not return while they still own runtime
+        # state. Any stragglers can no longer issue transport requests because
+        # MCPClient marks itself closed before closing its transport.
+        if scheduler_wait is not None:
+            try:
+                scheduler_drained = bool(
+                    scheduler_wait(timeout=_MANAGED_SHUTDOWN_DRAIN_SECONDS)
+                )
+            except Exception:
+                scheduler_drained = False
+                logger.debug("Agent scheduler drain failed", exc_info=True)
+        _join_workers(timeout=_MANAGED_SHUTDOWN_DRAIN_SECONDS)
+        alive = [thread.name for thread in managed_workers if thread.is_alive()]
+        if alive:
+            logger.warning("Managed workers did not stop during shutdown: %s", alive)
+
+        # A backend created by ``serve`` or lazily by a managed route belongs
+        # to this app process. Close it only after every tracked consumer has
+        # been drained; injected/borrowed backends remain the caller's concern.
+        owned_memory_backend = None
+        runtime_drained = scheduler_drained and not alive
+        if runtime_drained:
+            with app.state._memory_backend_lock:
+                if app.state._owns_memory_backend:
+                    owned_memory_backend = app.state.memory_backend
+                    app.state.memory_backend = None
+                    app.state._owns_memory_backend = False
+        else:
+            # A live worker may itself hold _memory_backend_lock while opening
+            # the backend. Respect the bounded shutdown deadline: do not wait
+            # on that lock or mutate ownership until every consumer is gone.
+            logger.warning(
+                "Skipping memory backend cleanup because managed runtime "
+                "consumers did not stop"
+            )
+        close_memory = getattr(owned_memory_backend, "close", None)
+        if callable(close_memory):
+            try:
+                close_memory()
+            except Exception:
+                logger.debug("Memory backend shutdown failed", exc_info=True)
+
     # Wire up trace store if traces are enabled.
     #
     # We deliberately do NOT subscribe the trace store to the bus. The chat

src/openjarvis/server/model_capabilities.py

@@ -0,0 +1,34 @@
+"""Model capability helpers shared by server model-selection routes."""
+
+_EMBEDDING_MODEL_PREFIXES = (
+    "all-minilm",
+    "bge-",
+    "bge_",
+    "e5-",
+    "e5_",
+    "gte-",
+    "gte_",
+    "jina-embeddings",
+    "nomic-bert",
+    "sentence-transformers",
+)
+
+
+def is_embed_only_model(model_name: str) -> bool:
+    """Return whether a model identifier denotes a non-chat embedder.
+
+    Ollama does not expose capabilities through its model-list response, so
+    model selection needs a conservative name-based guard.  Most embedding
+    models contain ``embed``; the explicit prefixes cover common families
+    such as MiniLM, BGE, E5, and GTE whose names do not.
+    """
+    name = (model_name or "").strip().lower()
+    leaf = name.rsplit("/", 1)[-1].split(":", 1)[0]
+    return (
+        "embed" in leaf
+        or "minilm" in leaf
+        or leaf.startswith(_EMBEDDING_MODEL_PREFIXES)
+    )
+
+
+__all__ = ["is_embed_only_model"]

src/openjarvis/server/routes.py

@@ -11,7 +11,8 @@ from fastapi import APIRouter, HTTPException, Request
 from fastapi.responses import StreamingResponse
 
 from openjarvis.core.paths import get_config_dir
-from openjarvis.core.types import Message, Role
+from openjarvis.core.types import Message, Role, ToolCall
+from openjarvis.server.model_capabilities import is_embed_only_model
 from openjarvis.server.models import (
     ChatCompletionChunk,
     ChatCompletionRequest,
@@ -39,6 +40,15 @@ def _to_messages(chat_messages) -> list[Message]:
                 role=role,
                 content=m.content or "",
                 name=m.name,
+                tool_calls=[
+                    ToolCall(
+                        id=tool_call.get("id", ""),
+                        name=tool_call.get("function", {}).get("name", ""),
+                        arguments=tool_call.get("function", {}).get("arguments", "{}"),
+                    )
+                    for tool_call in (m.tool_calls or [])
+                ]
+                or None,
                 tool_call_id=m.tool_call_id,
             )
         )
@@ -113,13 +123,15 @@ async def chat_completions(request_body: ChatCompletionRequest, request: Request
     memory_backend = getattr(request.app.state, "memory_backend", None)
     if (
         config is not None
-        and memory_backend is not None
         and config.agent.context_from_memory
         and request_body.messages
     ):
         try:
             from openjarvis.tools.storage.context import ContextConfig, inject_context
 
+            memory_service = getattr(request.app.state, "memory_service", None)
+            facts = memory_service.list_facts() if memory_service is not None else []
+
             # Extract query from the last user message
             query_text = ""
             for m in reversed(request_body.messages):
@@ -129,6 +141,7 @@ async def chat_completions(request_body: ChatCompletionRequest, request: Request
 
             if query_text:
                 messages = _to_messages(request_body.messages)
+                messages = _ensure_identity_prompt(messages, config)
                 ctx_cfg = ContextConfig(
                     top_k=config.memory.context_top_k,
                     min_score=config.memory.context_min_score,
@@ -139,22 +152,35 @@ async def chat_completions(request_body: ChatCompletionRequest, request: Request
                     messages,
                     memory_backend,
                     config=ctx_cfg,
+                    facts=facts,
                 )
-                # Rebuild request messages from enriched Message objects
-                if len(enriched) > len(messages):
-                    from openjarvis.server.models import ChatMessage
+                # Rebuild after identity/context merging so downstream engine
+                # adapters always receive exactly one system message.
+                from openjarvis.server.models import ChatMessage
 
-                    new_msgs = []
-                    for msg in enriched:
-                        new_msgs.append(
-                            ChatMessage(
-                                role=msg.role.value,
-                                content=msg.content,
-                                name=msg.name,
-                                tool_call_id=getattr(msg, "tool_call_id", None),
-                            )
+                new_msgs = []
+                for msg in enriched:
+                    new_msgs.append(
+                        ChatMessage(
+                            role=msg.role.value,
+                            content=msg.content,
+                            name=msg.name,
+                            tool_calls=[
+                                {
+                                    "id": tool_call.id,
+                                    "type": "function",
+                                    "function": {
+                                        "name": tool_call.name,
+                                        "arguments": tool_call.arguments,
+                                    },
+                                }
+                                for tool_call in (msg.tool_calls or [])
+                            ]
+                            or None,
+                            tool_call_id=getattr(msg, "tool_call_id", None),
                         )
-                    request_body.messages = new_msgs
+                    )
+                request_body.messages = new_msgs
         except Exception:
             logging.getLogger("openjarvis.server").debug(
                 "Memory context injection failed",
@@ -199,12 +225,14 @@ async def chat_completions(request_body: ChatCompletionRequest, request: Request
         # When the client passes `tools`, stream the model's raw
         # OpenAI-compat function-calling decision directly from the engine
         # (bypassing the agent) — the streaming mirror of the non-streaming
-        # #454 fix.  Routing tools through the agent stream bridge ignored
-        # `request_body.tools`, ran the agent's own tool loop, and
-        # word-split generic filler content into fake token deltas, so the
-        # caller's tool_calls were dropped entirely (the streaming analog of
-        # #414).  For plain chat (no tools), stream token-by-token directly
-        # from the engine for true real-time output.
+        # #454 fix. Routing client-supplied tools through a server-side agent
+        # would execute the agent's different tool set and drop the raw tool
+        # call the caller expects (#414).
+        #
+        # Without client-supplied tools, keep streaming requests on the
+        # configured server agent so its server-side tool loop is available
+        # to the desktop UI and other stream:true clients (#735). Fall back to
+        # direct token streaming when no tool-bearing agent is configured.
         if request_body.tools:
             return await _handle_stream_tools(
                 engine,
@@ -215,6 +243,16 @@ async def chat_completions(request_body: ChatCompletionRequest, request: Request
                 bus=getattr(request.app.state, "bus", None),
                 memory_service=getattr(request.app.state, "memory_service", None),
             )
+        if agent is not None and getattr(agent, "_tools", None):
+            return await _handle_agent_stream(
+                agent,
+                model,
+                request_body,
+                complexity_info,
+                trace_store=getattr(request.app.state, "trace_store", None),
+                bus=getattr(request.app.state, "bus", None),
+                memory_service=getattr(request.app.state, "memory_service", None),
+            )
         return await _handle_stream(
             engine,
             model,
@@ -336,6 +374,34 @@ def _remember_exchange(
     )
 
 
+def _engine_key_for_model(engine: Any, model: str) -> str | None:
+    """Resolve the engine that advertised *model* through wrapper layers."""
+    from openjarvis.engine.multi import MultiEngine
+    from openjarvis.security.guardrails import GuardrailsEngine
+    from openjarvis.telemetry.instrumented_engine import InstrumentedEngine
+
+    current = engine
+    while current is not None:
+        if isinstance(current, MultiEngine):
+            return current.engine_key_for(model)
+        if isinstance(current, InstrumentedEngine):
+            current = current._inner
+            continue
+        if isinstance(current, GuardrailsEngine):
+            current = current._engine
+            continue
+        engine_id = getattr(current, "engine_id", None)
+        return engine_id if isinstance(engine_id, str) else None
+    return None
+
+
+def _uses_direct_cloud_router(engine: Any, model: str) -> bool:
+    """Whether *model* should bypass the configured engine for direct cloud."""
+    from openjarvis.server.cloud_router import is_cloud_model
+
+    return is_cloud_model(model) and _engine_key_for_model(engine, model) != "litellm"
+
+
 def _handle_direct(
     engine,
     model: str,
@@ -518,6 +584,114 @@ def _handle_agent(
     )
 
 
+async def _handle_agent_stream(
+    agent,
+    model: str,
+    req: ChatCompletionRequest,
+    complexity_info=None,
+    *,
+    trace_store=None,
+    bus=None,
+    memory_service=None,
+):
+    """Run the configured agent and return its result as an SSE response.
+
+    Agents own the tool-execution loop, which is synchronous today.  Run that
+    loop in a worker thread and stream its final answer once complete.  This
+    keeps ``stream:true`` clients (including the desktop UI) on the same agent
+    and configured toolkit as non-streaming requests instead of bypassing the
+    agent and silently dropping server-side tools.
+
+    Requests that explicitly supply OpenAI ``tools`` continue to use
+    ``_handle_stream_tools`` so their raw tool-call deltas are preserved.
+    """
+    chunk_id = f"chatcmpl-{uuid.uuid4().hex[:12]}"
+    query_text = ""
+    for message in reversed(req.messages):
+        if message.role == "user" and message.content:
+            query_text = message.content
+            break
+
+    async def generate():
+        first_chunk = ChatCompletionChunk(
+            id=chunk_id,
+            model=model,
+            choices=[StreamChoice(delta=DeltaMessage(role="assistant"))],
+        )
+        yield f"data: {first_chunk.model_dump_json()}\n\n"
+
+        try:
+            response = await asyncio.to_thread(
+                _handle_agent,
+                agent,
+                model,
+                req,
+                complexity_info,
+                trace_store=trace_store,
+                bus=bus,
+            )
+        except Exception as exc:
+            logging.getLogger("openjarvis.server").error(
+                "Agent stream error: %s",
+                exc,
+                exc_info=True,
+            )
+            error_chunk = ChatCompletionChunk(
+                id=chunk_id,
+                model=model,
+                choices=[
+                    StreamChoice(
+                        delta=DeltaMessage(
+                            content=f"Sorry, an error occurred: {exc}",
+                        ),
+                        finish_reason="stop",
+                    )
+                ],
+            )
+            yield f"data: {error_chunk.model_dump_json()}\n\n"
+            yield "data: [DONE]\n\n"
+            return
+
+        content = _response_content(response)
+        if content:
+            content_chunk = ChatCompletionChunk(
+                id=chunk_id,
+                model=model,
+                choices=[StreamChoice(delta=DeltaMessage(content=content))],
+            )
+            yield f"data: {content_chunk.model_dump_json()}\n\n"
+
+        import json as _json
+
+        finish_chunk = ChatCompletionChunk(
+            id=chunk_id,
+            model=model,
+            choices=[
+                StreamChoice(delta=DeltaMessage(), finish_reason="stop"),
+            ],
+        )
+        finish_data = _json.loads(finish_chunk.model_dump_json())
+        finish_data["usage"] = response.usage.model_dump()
+        if complexity_info is not None:
+            finish_data["complexity"] = complexity_info.model_dump()
+        yield f"data: {_json.dumps(finish_data)}\n\n"
+
+        _record_completed_exchange(
+            memory_service,
+            query_text,
+            content,
+            bus=bus,
+            source="server.chat.stream",
+        )
+        yield "data: [DONE]\n\n"
+
+    return StreamingResponse(
+        generate(),
+        media_type="text/event-stream",
+        headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
+    )
+
+
 async def _handle_stream_tools(
     engine,
     model: str,
@@ -541,12 +715,13 @@ async def _handle_stream_tools(
     tool_calls) — identical to the prior plain-stream behaviour, so this never
     regresses non-tool-capable engines.
     """
-    from openjarvis.server.cloud_router import is_cloud_model
-
     messages = _to_messages(req.messages)
     messages = _ensure_identity_prompt(messages, app_config)
     chunk_id = f"chatcmpl-{uuid.uuid4().hex[:12]}"
-    use_cloud = is_cloud_model(model)
+    use_cloud = _uses_direct_cloud_router(engine, model)
+    telemetry_engine = (
+        "cloud" if use_cloud else (_engine_key_for_model(engine, model) or "ollama")
+    )
     query_text = ""
     for _m in reversed(req.messages):
         if _m.role == "user" and _m.content:
@@ -626,7 +801,7 @@ async def _handle_stream_tools(
         # Tag the finish chunk with the engine label, matching _handle_stream
         # so UI/telemetry consumers see the same field on the tools path.
         finish_dict.setdefault("telemetry", {})
-        finish_dict["telemetry"]["engine"] = "cloud" if use_cloud else "ollama"
+        finish_dict["telemetry"]["engine"] = telemetry_engine
         if complexity_info is not None:
             finish_dict["complexity"] = complexity_info.model_dump()
         yield f"data: {_json.dumps(finish_dict)}\n\n"
@@ -660,19 +835,14 @@ async def _handle_stream(
 ):
     """Stream response using SSE format.
 
-    This path streams straight from the engine, bypassing the agent /
+    This no-agent fallback streams straight from the engine, bypassing the
     ``TraceCollector``. When *trace_store* is set we accumulate the streamed
     tokens and record a minimal ``Trace`` once the stream completes
-    successfully — otherwise streamed chats (the desktop GUI's main path)
-    would never populate ``traces.db``.
+    successfully.
     """
     import time
 
-    from openjarvis.server.cloud_router import (
-        is_cloud_model,
-        stream_cloud,
-        stream_local,
-    )
+    from openjarvis.server.cloud_router import stream_cloud, stream_local
 
     messages = _to_messages(req.messages)
     messages = _ensure_identity_prompt(messages, app_config)
@@ -687,7 +857,10 @@ async def _handle_stream(
 
     # Route directly to the right backend — bypasses engine routing entirely
     # so broken MultiEngine state can never misdirect requests.
-    use_cloud = is_cloud_model(model)
+    use_cloud = _uses_direct_cloud_router(engine, model)
+    telemetry_engine = (
+        "cloud" if use_cloud else (_engine_key_for_model(engine, model) or "ollama")
+    )
 
     async def generate():
         started_at = time.time()
@@ -792,7 +965,7 @@ async def _handle_stream(
                 query=query_text,
                 result=full_content,
                 model=model,
-                engine="cloud" if use_cloud else "ollama",
+                engine=telemetry_engine,
                 started_at=started_at,
                 ended_at=time.time(),
             )
@@ -825,7 +998,7 @@ async def _handle_stream(
         # We use the routing decision (use_cloud) directly rather than
         # unwrapping the engine chain, which can be in a broken state.
         finish_dict.setdefault("telemetry", {})
-        finish_dict["telemetry"]["engine"] = "cloud" if use_cloud else "ollama"
+        finish_dict["telemetry"]["engine"] = telemetry_engine
 
         if complexity_info is not None:
             finish_dict["complexity"] = complexity_info.model_dump()
@@ -842,24 +1015,45 @@ async def _handle_stream(
 
 @router.get("/v1/models")
 async def list_models(request: Request) -> ModelListResponse:
-    """List locally installed models (Ollama).
+    """List selectable engine models for the installed-model picker.
 
-    Cloud models are not included here — they live in the Cloud Models tab
-    of the UI and are selected there, not from this endpoint.
+    Direct cloud models live in the Cloud Models tab. Models advertised by a
+    configured LiteLLM engine remain here because LiteLLM owns their routing
+    and may use provider-qualified IDs that resemble OpenRouter IDs.
     """
     from openjarvis.server.cloud_router import is_cloud_model, list_local_models
 
     # Prefer engine.list_models() so mock engines work in tests.
-    # Filter out any cloud model IDs that may appear via MultiEngine.
+    # Filter out direct-cloud model IDs that may appear via MultiEngine, but
+    # retain provider-qualified IDs owned by the configured LiteLLM engine.
     # Fall back to direct Ollama query only when the engine returns nothing.
     engine = request.app.state.engine
     all_ids = await asyncio.to_thread(engine.list_models)
-    model_ids = [m for m in all_ids if not is_cloud_model(m)]
+    model_ids = [
+        m
+        for m in all_ids
+        if not is_cloud_model(m) or _engine_key_for_model(engine, m) == "litellm"
+    ]
     if not model_ids:
         model_ids = await list_local_models()
 
+    # Keep embed-only models out of the chat model picker. They still work for
+    # memory/retrieval via the embedder path; putting them in /v1/models made
+    # the UI auto-select nomic-embed-text and fail every generation with 400.
+    model_ids = [m for m in model_ids if not is_embed_only_model(m)]
+
     return ModelListResponse(
-        data=[ModelObject(id=mid) for mid in model_ids],
+        data=[
+            ModelObject(
+                id=mid,
+                owned_by=(
+                    "litellm"
+                    if _engine_key_for_model(engine, mid) == "litellm"
+                    else "openjarvis"
+                ),
+            )
+            for mid in model_ids
+        ],
     )
 
 

src/openjarvis/server/stream_bridge.py

@@ -110,6 +110,12 @@ class AgentStreamBridge:
 
     def _format_named_event(self, name: str, data: dict) -> str:
         """Format an SSE event with an explicit ``event:`` field."""
+        if name == "tool_call_start" and not isinstance(data.get("arguments"), str):
+            # The in-process event bus uses parsed arguments for trace/eval
+            # consumers, while the web SSE contract expects their JSON text.
+            # Copy before normalizing so other subscribers keep the object.
+            data = dict(data)
+            data["arguments"] = json.dumps(data.get("arguments"))
         return f"event: {name}\ndata: {json.dumps(data)}\n\n"
 
     def _run_agent(self) -> object:
@@ -240,62 +246,15 @@ class AgentStreamBridge:
                     {"results": tool_results_data},
                 )
 
-            # Stream content using real LLM token streaming via
-            # engine.stream_full() when the engine is available.
+            # ``agent.run()`` already produced the authoritative, grounded
+            # response.  Do not call the engine again here: a second inference
+            # would not have the agent's system prompt, tool transcript, or
+            # other internal context and could therefore contradict the
+            # result reported by the agent events.  Replay the final content
+            # in chunks so the OpenAI-compatible streaming response stays
+            # consistent with the completed agent run.
             content = agent_result.content or ""
-            engine = getattr(self._agent, "_engine", None)
-            used_real_streaming = False
-
-            if engine is not None and hasattr(engine, "stream_full") and content:
-                # Re-stream using the engine for real token delivery.
-                # Build the same messages the agent used for its final turn.
-                try:
-                    from openjarvis.core.types import Message as MsgType
-                    from openjarvis.core.types import Role as RoleType
-
-                    replay_messages = []
-                    for m in self._request.messages:
-                        role = (
-                            RoleType(m.role)
-                            if m.role in {r.value for r in RoleType}
-                            else RoleType.USER
-                        )
-                        replay_messages.append(
-                            MsgType(
-                                role=role,
-                                content=m.content or "",
-                                name=m.name,
-                                tool_call_id=m.tool_call_id,
-                            )
-                        )
-
-                    async for sc in engine.stream_full(
-                        replay_messages,
-                        model=self._model,
-                    ):
-                        if sc.content:
-                            chunk = ChatCompletionChunk(
-                                id=self._chunk_id,
-                                model=self._model,
-                                choices=[
-                                    StreamChoice(
-                                        delta=DeltaMessage(content=sc.content),
-                                    )
-                                ],
-                            )
-                            yield f"data: {chunk.model_dump_json()}\n\n"
-                    used_real_streaming = True
-                except Exception as stream_exc:
-                    import logging as _logging
-
-                    _logger = _logging.getLogger("openjarvis.server")
-                    _logger.warning(
-                        "Real streaming failed, falling back to word replay: %s",
-                        stream_exc,
-                    )
-
-            # Fallback: word-by-word replay if real streaming was not used
-            if not used_real_streaming and content:
+            if content:
                 words = content.split(" ")
                 for i, word in enumerate(words):
                     token = word if i == 0 else " " + word

src/openjarvis/server/ws_bridge.py

@@ -79,14 +79,41 @@ def create_ws_router(event_bus: EventBus) -> Any:
         queue: asyncio.Queue = asyncio.Queue(maxsize=100)
         loop = asyncio.get_running_loop()
         clients[websocket] = (queue, loop)
+        recv: asyncio.Task | None = None
+        payload: asyncio.Task | None = None
+        disconnected = False
         try:
+            recv = asyncio.create_task(websocket.receive())
+            payload = asyncio.create_task(queue.get())
             while True:
-                payload = await queue.get()
-                await websocket.send_json(payload)
+                done, _ = await asyncio.wait(
+                    {recv, payload}, return_when=asyncio.FIRST_COMPLETED
+                )
+                if recv in done:
+                    # Starlette surfaces a disconnect message only when the app
+                    # reads from the socket. Without this receive, the handler
+                    # can stay parked on queue.get() after the client leaves.
+                    message = await recv
+                    if message.get("type") == "websocket.disconnect":
+                        disconnected = True
+                        break
+                    recv = asyncio.create_task(websocket.receive())
+                if payload in done:
+                    await websocket.send_json(payload.result())
+                    payload = asyncio.create_task(queue.get())
         except WebSocketDisconnect:
-            pass
+            disconnected = True
         finally:
             clients.pop(websocket, None)
+            pending = [task for task in (recv, payload) if task is not None]
+            for task in pending:
+                task.cancel()
+            cleanup = asyncio.gather(*pending, return_exceptions=True)
+            try:
+                await asyncio.shield(cleanup)
+            except asyncio.CancelledError:
+                if not disconnected:
+                    raise
 
     return router
 

src/openjarvis/system/builder.py

@@ -48,6 +48,7 @@ class SystemBuilder:
         self._sessions: Optional[bool] = None
         self._speech: Optional[bool] = None
         self._mcp_clients: List = []
+        self._mcp_tools: List[BaseTool] = []
 
     def engine(self, key: str) -> SystemBuilder:
         self._engine_key = key
@@ -113,6 +114,33 @@ class SystemBuilder:
 
     def build(self) -> JarvisSystem:
         """Construct a fully wired JarvisSystem."""
+        # Discovery state belongs to one build only.  Once a system is
+        # returned, that system owns the clients and adapters captured below;
+        # retaining them here would make a reused builder hand closed clients
+        # from an earlier system to the next one.
+        self._clear_mcp_discovery_state(close_clients=True)
+        try:
+            system = self._build()
+        except BaseException:
+            # No system took ownership, so release any clients opened before
+            # the build failed.
+            self._clear_mcp_discovery_state(close_clients=True)
+            raise
+        self._clear_mcp_discovery_state(close_clients=False)
+        return system
+
+    def _clear_mcp_discovery_state(self, *, close_clients: bool) -> None:
+        if close_clients:
+            for client in getattr(self, "_mcp_clients", []):
+                try:
+                    client.close()
+                except Exception:
+                    logger.debug("Error closing unowned MCP client", exc_info=True)
+        self._mcp_clients = []
+        self._mcp_tools = []
+
+    def _build(self) -> JarvisSystem:
+        """Build one system using fresh, build-local MCP discovery state."""
         config = self._config
         bus = self._bus or get_event_bus()
 
@@ -291,6 +319,7 @@ class SystemBuilder:
             model=model,
             agent_name=agent_name,
             tools=tool_list,
+            mcp_tools=list(self._mcp_tools),
             tool_executor=tool_executor,
             memory_backend=memory_backend,
             channel_backend=channel_backend,
@@ -440,7 +469,7 @@ class SystemBuilder:
         else:
             tools = []
 
-        if config.tools.mcp.servers:
+        if config.tools.mcp.enabled and config.tools.mcp.servers:
             try:
                 import json
 
@@ -449,6 +478,7 @@ class SystemBuilder:
                     for server_cfg in server_list:
                         try:
                             external_tools = self._discover_external_mcp(server_cfg)
+                            self._mcp_tools.extend(external_tools)
                             if tool_names:
                                 external_tools = [
                                     t

src/openjarvis/system/core.py

@@ -86,6 +86,9 @@ class JarvisSystem:
     skill_manager: Optional[SkillManager] = None
     _learning_orchestrator: Optional[LearningOrchestrator] = None
     _mcp_clients: List[MCPClient] = field(default_factory=list)
+    # Keep newly added fields after every pre-existing positional field so
+    # older positional JarvisSystem(...) calls retain their original meaning.
+    mcp_tools: List[BaseTool] = field(default_factory=list)
 
     @property
     def security(self) -> SecurityContext:

src/openjarvis/system/orchestrator.py

@@ -40,8 +40,9 @@ class QueryOrchestrator:
 
         messages = [Message(role=Role.USER, content=query)]
 
-        if context and s.memory_backend and s.config.agent.context_from_memory:
+        if context and s.config.agent.context_from_memory:
             try:
+                from openjarvis.memory import load_configured_facts
                 from openjarvis.tools.storage.context import (
                     ContextConfig,
                     inject_context,
@@ -52,11 +53,13 @@ class QueryOrchestrator:
                     min_score=s.config.memory.context_min_score,
                     max_context_tokens=s.config.memory.context_max_tokens,
                 )
+                facts = load_configured_facts(s.config)
                 messages = inject_context(
                     query,
                     messages,
                     s.memory_backend,
                     config=ctx_cfg,
+                    facts=facts,
                 )
             except Exception as exc:
                 logger.warning("Failed to inject memory context: %s", exc)

src/openjarvis/telemetry/instrumented_engine.py

@@ -212,6 +212,7 @@ class InstrumentedEngine(InferenceEngine):
             completion_tokens=completion_tokens,
             total_tokens=prompt_tok + completion_tokens,
             latency_seconds=latency,
+            cost_usd=result.get("cost_usd", 0.0),
             ttft=ttft,
             throughput_tok_per_sec=throughput,
             energy_per_output_token_joules=energy_per_output_token,

src/openjarvis/tools/__init__.py

@@ -142,4 +142,14 @@ try:
 except ImportError:
     pass
 
+try:
+    import openjarvis.tools.scan_chunks  # noqa: F401
+except ImportError:
+    pass
+
+try:
+    import openjarvis.tools.knowledge_sql  # noqa: F401
+except ImportError:
+    pass
+
 __all__ = ["BaseTool", "ToolExecutor", "ToolSpec"]

src/openjarvis/tools/storage/context.py

@@ -2,13 +2,16 @@
 
 from __future__ import annotations
 
-from dataclasses import dataclass
-from typing import List, Optional
+from dataclasses import dataclass, replace
+from typing import TYPE_CHECKING, List, Optional, Sequence
 
 from openjarvis.core.events import EventType, get_event_bus
 from openjarvis.core.types import Message, Role
 from openjarvis.tools.storage._stubs import MemoryBackend, RetrievalResult
 
+if TYPE_CHECKING:
+    from openjarvis.memory.store import Fact
+
 
 @dataclass(slots=True)
 class ContextConfig:
@@ -46,28 +49,75 @@ def format_context(results: List[RetrievalResult]) -> str:
 
 def build_context_message(
     results: List[RetrievalResult],
+    facts: Sequence[Fact] = (),
 ) -> Message:
     """Create a system message with formatted context."""
-    context_text = format_context(results)
-    content = (
-        "The following context was retrieved from the knowledge"
-        " base. Use it to inform your response, citing sources"
-        " where applicable:\n\n" + context_text
+    sections = []
+    if facts:
+        fact_text = "\n".join(f"- {fact.text}" for fact in facts)
+        sections.append(
+            "The following durable facts were remembered from prior "
+            "conversations. Use them when relevant to the user's request:\n\n"
+            + fact_text
+        )
+    if results:
+        sections.append(
+            "The following context was retrieved from the knowledge"
+            " base. Use it to inform your response, citing sources"
+            " where applicable:\n\n" + format_context(results)
+        )
+    content = "\n\n".join(sections)
+    return Message(
+        role=Role.SYSTEM,
+        content=content,
+        metadata={"memory_context": True},
     )
-    return Message(role=Role.SYSTEM, content=content)
+
+
+def _merge_context_message(
+    messages: List[Message],
+    context_message: Message,
+) -> List[Message]:
+    """Return a copy with context folded into the existing system prompt."""
+    system_messages = [message for message in messages if message.role == Role.SYSTEM]
+    if not system_messages:
+        return [context_message, *messages]
+
+    content = "\n\n".join(
+        part
+        for part in (
+            *(message.text for message in system_messages),
+            context_message.text,
+        )
+        if part
+    )
+    combined = replace(system_messages[0], content=content)
+    merged: List[Message] = []
+    inserted = False
+    for message in messages:
+        if message.role == Role.SYSTEM:
+            if not inserted:
+                merged.append(combined)
+                inserted = True
+            continue
+        merged.append(message)
+    return merged
 
 
 def inject_context(
     query: str,
     messages: List[Message],
-    backend: MemoryBackend,
+    backend: Optional[MemoryBackend],
     *,
     config: Optional[ContextConfig] = None,
+    facts: Sequence[Fact] = (),
 ) -> List[Message]:
     """Retrieve relevant context and prepend it to *messages*.
 
     Returns a **new** list — the original list is not mutated.
-    If no results pass the score threshold, returns the original
+    Automatic-memory facts are included independently of the retrieval
+    backend, so persisted facts remain recallable even when the document
+    store is empty. If no facts or results are available, returns the original
     messages unchanged.
 
     Parameters
@@ -77,33 +127,55 @@ def inject_context(
     messages:
         The existing message list.
     backend:
-        The memory backend to search.
+        The memory backend to search, or ``None`` when only facts are available.
     config:
         Context injection settings (uses defaults if ``None``).
+    facts:
+        Durable facts captured by the automatic memory service.
     """
     cfg = config or ContextConfig()
     if not cfg.enabled:
         return messages
 
-    results = backend.retrieve(query, top_k=cfg.top_k)
+    results = backend.retrieve(query, top_k=cfg.top_k) if backend is not None else []
 
     # Filter by minimum score
     results = [r for r in results if r.score >= cfg.min_score]
 
-    if not results:
-        return messages
-
-    # Truncate to max_context_tokens
-    truncated: List[RetrievalResult] = []
+    # When both sources have data, cap facts at half the total budget so they
+    # cannot starve query-specific document retrieval. Unused fact budget is
+    # still available to documents. Newest facts win within the fact budget.
+    fact_budget = cfg.max_context_tokens
+    if results:
+        fact_budget //= 2
+    selected_facts: List[Fact] = []
     total_tokens = 0
+    for fact in reversed(facts):
+        tokens = _count_tokens(fact.text)
+        if total_tokens + tokens > fact_budget:
+            continue
+        selected_facts.append(fact)
+        total_tokens += tokens
+
+    # Fill the remaining context budget with retrieved documents.
+    truncated: List[RetrievalResult] = []
     for r in results:
         tokens = _count_tokens(r.content)
+        if total_tokens + tokens > cfg.max_context_tokens:
+            # A large top result should not disappear solely because facts
+            # consumed their reserved share. Prefer that result when it fits
+            # the total budget on its own.
+            if not truncated and selected_facts and tokens <= cfg.max_context_tokens:
+                selected_facts = []
+                total_tokens = 0
+            else:
+                break
         if total_tokens + tokens > cfg.max_context_tokens:
             break
         truncated.append(r)
         total_tokens += tokens
 
-    if not truncated:
+    if not selected_facts and not truncated:
         return messages
 
     # Publish event
@@ -114,13 +186,14 @@ def inject_context(
             "context_injection": True,
             "query": query,
             "num_results": len(truncated),
+            "num_facts": len(selected_facts),
             "total_tokens": total_tokens,
         },
     )
 
     # Build context message and prepend
-    ctx_msg = build_context_message(truncated)
-    return [ctx_msg] + list(messages)
+    ctx_msg = build_context_message(truncated, selected_facts)
+    return _merge_context_message(messages, ctx_msg)
 
 
 __all__ = [

tests/agents/test_base_agent.py

@@ -205,6 +205,40 @@ class TestBuildMessages:
         assert messages[1].content == "prev"
         assert messages[2].content == "new"
 
+    def test_prompt_builder_merges_context_system_message(self):
+        engine = MagicMock()
+        prompt_builder = MagicMock()
+        prompt_builder.build.return_value = "You are OpenJarvis."
+        agent = _ConcreteAgent(engine, "m", prompt_builder=prompt_builder)
+        conv = Conversation()
+        conv.add(
+            Message(
+                role=Role.SYSTEM,
+                content="Remember: user likes jazz.",
+                metadata={"memory_context": True},
+            )
+        )
+        ctx = AgentContext(conversation=conv)
+
+        messages = agent._build_messages("new", ctx)
+
+        system_messages = [m for m in messages if m.role == Role.SYSTEM]
+        assert len(system_messages) == 1
+        assert "You are OpenJarvis." in system_messages[0].content
+        assert "user likes jazz" in system_messages[0].content
+
+    def test_prompt_builder_preserves_caller_system_context(self):
+        engine = MagicMock()
+        prompt_builder = MagicMock()
+        prompt_builder.build.return_value = "Agent instructions."
+        agent = _ConcreteAgent(engine, "m", prompt_builder=prompt_builder)
+        conv = Conversation()
+        conv.add(Message(role=Role.SYSTEM, content="You are helpful."))
+
+        messages = agent._build_messages("new", AgentContext(conversation=conv))
+
+        assert any(message.content == "You are helpful." for message in messages)
+
 
 class TestGenerate:
     def test_delegates_to_engine(self):

tests/agents/test_executor_tool_persistence.py

@@ -0,0 +1,127 @@
+"""Regression tests for managed-agent tool-call persistence."""
+
+from __future__ import annotations
+
+import json
+
+import pytest
+
+from openjarvis.agents._stubs import AgentResult
+from openjarvis.agents.executor import AgentExecutor, _tool_calls_for_storage
+from openjarvis.agents.manager import AgentManager
+from openjarvis.core.events import EventBus
+from openjarvis.core.types import ToolResult
+
+
+def test_tool_results_are_serialized_for_managed_messages() -> None:
+    result = AgentResult(
+        content="Finished",
+        tool_results=[
+            ToolResult(
+                tool_name="knowledge_search",
+                content="Found the requested note",
+                success=True,
+                latency_seconds=0.42,
+                metadata={
+                    "arguments": {
+                        "query": "financial independence",
+                        "limit": 3,
+                    }
+                },
+            ),
+            ToolResult(
+                tool_name="shell_exec",
+                content="Permission denied",
+                success=False,
+                latency_seconds=1.25,
+                metadata={"arguments": '{"command":"whoami"}'},
+            ),
+        ],
+    )
+
+    calls = _tool_calls_for_storage(result)
+
+    assert calls is not None
+    assert len(calls) == 2
+
+    knowledge_call = calls[0]
+    assert knowledge_call["tool"] == "knowledge_search"
+    assert isinstance(knowledge_call["arguments"], str)
+    assert json.loads(knowledge_call["arguments"]) == {
+        "query": "financial independence",
+        "limit": 3,
+    }
+    assert knowledge_call["result"] == "Found the requested note"
+    assert knowledge_call["success"] is True
+    assert knowledge_call["latency"] == pytest.approx(420.0)
+
+    failed_call = calls[1]
+    assert failed_call["arguments"] == '{"command":"whoami"}'
+    assert failed_call["result"] == "Permission denied"
+    assert failed_call["success"] is False
+    assert failed_call["latency"] == pytest.approx(1250.0)
+
+
+def test_no_tool_results_serialize_as_none() -> None:
+    assert _tool_calls_for_storage(AgentResult(content="Plain response")) is None
+
+
+def test_finalize_tick_persists_tool_calls_round_trip(tmp_path) -> None:
+    manager = AgentManager(str(tmp_path / "agents.db"))
+    try:
+        agent = manager.create_agent("researcher")
+        manager.start_tick(agent["id"])
+        result = AgentResult(
+            content="Answer grounded in the knowledge base",
+            tool_results=[
+                ToolResult(
+                    tool_name="knowledge_search",
+                    content="Matching source text",
+                    success=True,
+                    latency_seconds=0.007,
+                    metadata={"arguments": {"query": "grounded answer"}},
+                )
+            ],
+        )
+
+        executor = AgentExecutor(manager, EventBus())
+        executor._finalize_tick(
+            agent["id"],
+            result,
+            error=None,
+            duration=0.01,
+        )
+
+        messages = manager.list_messages(agent["id"])
+        assert len(messages) == 1
+        stored = messages[0]
+        assert stored["content"] == result.content
+        assert stored["direction"] == "agent_to_user"
+        assert stored["tool_calls"] == _tool_calls_for_storage(result)
+        assert isinstance(stored["tool_calls"][0]["arguments"], str)
+        assert json.loads(stored["tool_calls"][0]["arguments"]) == {
+            "query": "grounded answer"
+        }
+        assert stored["tool_calls"][0]["latency"] == pytest.approx(7.0)
+    finally:
+        manager.close()
+
+
+def test_finalize_tick_without_tools_stores_null_tool_calls(tmp_path) -> None:
+    manager = AgentManager(str(tmp_path / "agents.db"))
+    try:
+        agent = manager.create_agent("plain-agent")
+        manager.start_tick(agent["id"])
+
+        executor = AgentExecutor(manager, EventBus())
+        executor._finalize_tick(
+            agent["id"],
+            AgentResult(content="No tools needed"),
+            error=None,
+            duration=0.01,
+        )
+
+        stored = manager.list_messages(agent["id"])[0]
+        assert stored["tool_calls"] is None
+    finally:
+        manager.close()

tests/agents/test_executor_tools.py

@@ -2,13 +2,91 @@
 
 from __future__ import annotations
 
+import gc
+import sqlite3
+from types import SimpleNamespace
+from unittest.mock import MagicMock
+
+import pytest
+
+from openjarvis.agents._stubs import AgentResult
 from openjarvis.agents.executor import AgentExecutor
 from openjarvis.agents.manager import AgentManager
+from openjarvis.agents.tool_resolver import ResolvedAgentTools
+from openjarvis.connectors.store import KnowledgeStore
+from openjarvis.core.config import MemoryFilesConfig, SystemPromptConfig
 from openjarvis.core.events import EventBus
+from openjarvis.core.registry import AgentRegistry, ToolRegistry
+from openjarvis.core.types import Role, ToolResult
+from openjarvis.tools._stubs import BaseTool, ToolSpec
 from tests.agents.fake_engine import FakeEngine
 from tests.agents.scenario_harness import FakeSystem
 
 
+class _CapturingToolAgent:
+    """Minimal agent that exposes the toolkit received by AgentExecutor."""
+
+    accepts_tools = True
+    captured_tools = []
+    captured_search_result = None
+
+    def __init__(self, engine, model, *, tools=None, **kwargs):
+        self.engine = engine
+        self.model = model
+        type(self).captured_tools = list(tools or [])
+
+    def run(self, input_text, context=None):
+        tools_by_name = {tool.spec.name: tool for tool in self.captured_tools}
+        search = tools_by_name.get("knowledge_search")
+        if search is not None:
+            type(self).captured_search_result = search.execute(
+                query="EXECUTOR_RESOLVER_SENTINEL"
+            )
+        return AgentResult(content="captured")
+
+
+class _NonToolAgent:
+    """Agent class whose run method must not swallow a configured toolkit."""
+
+    accepts_tools = False
+    supports_managed_tool_fallback = True
+    runs = 0
+
+    def __init__(self, engine, model, **kwargs):
+        pass
+
+    def run(self, input_text, context=None):
+        type(self).runs += 1
+        raise AssertionError("non-tool agent should use the managed tool loop")
+
+
+class _SpecializedNonToolAgent:
+    """Non-tool agent that must retain its specialized execution path."""
+
+    accepts_tools = False
+    runs = 0
+
+    def __init__(self, engine, model, **kwargs):
+        pass
+
+    def run(self, input_text, context=None):
+        type(self).runs += 1
+        return AgentResult(content="specialized response")
+
+
+class _ExecutorProbeTool(BaseTool):
+    tool_id = "executor_probe"
+    calls = 0
+
+    @property
+    def spec(self) -> ToolSpec:
+        return ToolSpec(name=self.tool_id, description="Executor parity probe")
+
+    def execute(self, **params) -> ToolResult:
+        type(self).calls += 1
+        return ToolResult(tool_name=self.tool_id, content="probe-result")
+
+
 def _register_agent():
     """Re-register MonitorOperativeAgent (cleared by autouse fixture)."""
     from openjarvis.agents.monitor_operative import MonitorOperativeAgent
@@ -102,3 +180,396 @@ def test_executor_handles_string_tools(tmp_path):
     result_agent = mgr.get_agent(agent["id"])
     assert result_agent["status"] == "idle"
     mgr.close()
+
+
+def test_executor_uses_tool_loop_for_non_tool_agent_with_configured_tools(tmp_path):
+    """Immediate/scheduled ticks match SSE instead of discarding tools."""
+
+    AgentRegistry.register_value("non_tool_probe", _NonToolAgent)
+    ToolRegistry.register_value(_ExecutorProbeTool.tool_id, _ExecutorProbeTool)
+    _NonToolAgent.runs = 0
+    _ExecutorProbeTool.calls = 0
+
+    engine = FakeEngine(
+        [
+            {
+                "tool_calls": [
+                    {
+                        "id": "call-executor-probe",
+                        "name": _ExecutorProbeTool.tool_id,
+                        "arguments": "{}",
+                    }
+                ]
+            },
+            {"content": "tool-backed final response"},
+        ]
+    )
+    system = FakeSystem(engine=engine)
+    system.config = SimpleNamespace(
+        agent=SimpleNamespace(default_system_prompt="GLOBAL_DEFAULT"),
+        memory_files=MemoryFilesConfig(persona_name="none"),
+        system_prompt=SystemPromptConfig(),
+    )
+    manager = AgentManager(db_path=str(tmp_path / "agents.db"))
+    agent = manager.create_agent(
+        "non-tool with tools",
+        agent_type="non_tool_probe",
+        config={
+            "model": "test-model",
+            "tools": [_ExecutorProbeTool.tool_id],
+            "instruction": "Use the probe.",
+            "system_prompt": "NON_TOOL_SYSTEM_SENTINEL",
+        },
+    )
+
+    try:
+        AgentExecutor(manager, EventBus(), system=system).execute_tick(agent["id"])
+
+        assert _NonToolAgent.runs == 0
+        assert _ExecutorProbeTool.calls == 1
+        assert engine.call_count == 2
+        assert any(
+            message.role is Role.SYSTEM
+            and message.content == "NON_TOOL_SYSTEM_SENTINEL"
+            for message in engine.last_messages or []
+        )
+        refreshed = manager.get_agent(agent["id"])
+        assert refreshed["status"] == "idle"
+        assert refreshed["total_runs"] == 1
+        responses = [
+            message
+            for message in manager.list_messages(agent["id"])
+            if message["direction"] == "agent_to_user"
+        ]
+        assert responses[-1]["content"] == "tool-backed final response"
+        assert responses[-1]["tool_calls"][0]["tool"] == "executor_probe"
+    finally:
+        manager.close()
+
+
+def test_simple_agent_uses_global_mcp_tools_without_native_tool_config(tmp_path):
+    """Fallback-compatible simple agents preserve SSE/global-MCP parity."""
+
+    from openjarvis.agents.simple import SimpleAgent
+
+    AgentRegistry.register_value("simple", SimpleAgent)
+    _ExecutorProbeTool.calls = 0
+    provider = MagicMock(return_value=([_ExecutorProbeTool()], []))
+    engine = FakeEngine(
+        [
+            {
+                "tool_calls": [
+                    {
+                        "id": "call-global-mcp-probe",
+                        "name": _ExecutorProbeTool.tool_id,
+                        "arguments": "{}",
+                    }
+                ]
+            },
+            {"content": "global MCP response"},
+        ]
+    )
+    system = SimpleNamespace(
+        engine=engine,
+        model="test-model",
+        config=None,
+        memory_backend=None,
+        channel_backend=None,
+        session_store=None,
+        knowledge_db_path=None,
+        get_managed_agent_mcp_tools=provider,
+    )
+    manager = AgentManager(db_path=str(tmp_path / "agents.db"))
+    agent = manager.create_agent(
+        "simple global MCP",
+        agent_type="simple",
+        config={"model": "test-model", "instruction": "Use MCP."},
+    )
+
+    try:
+        AgentExecutor(manager, EventBus(), system=system).execute_tick(agent["id"])
+
+        provider.assert_called_once_with()
+        assert _ExecutorProbeTool.calls == 1
+        assert engine.call_count == 2
+        responses = [
+            message
+            for message in manager.list_messages(agent["id"])
+            if message["direction"] == "agent_to_user"
+        ]
+        assert responses[-1]["content"] == "global MCP response"
+    finally:
+        manager.close()
+
+
+def test_simple_agent_without_tools_keeps_its_custom_system_prompt(tmp_path):
+    """Signature filtering must not discard prompt-builder state on retry."""
+
+    from openjarvis.agents.simple import SimpleAgent
+
+    AgentRegistry.register_value("simple", SimpleAgent)
+    engine = FakeEngine([{"content": "custom prompt response"}])
+    system = FakeSystem(engine=engine)
+    system.config = SimpleNamespace(
+        agent=SimpleNamespace(default_system_prompt="GLOBAL_DEFAULT"),
+        memory_files=MemoryFilesConfig(persona_name="none"),
+        system_prompt=SystemPromptConfig(),
+    )
+    manager = AgentManager(db_path=str(tmp_path / "agents.db"))
+    agent = manager.create_agent(
+        "simple custom prompt",
+        agent_type="simple",
+        config={
+            "model": "test-model",
+            "instruction": "Answer directly.",
+            "system_prompt": "SIMPLE_CUSTOM_SYSTEM_SENTINEL",
+            "mcp_tools": False,
+        },
+    )
+
+    try:
+        AgentExecutor(manager, EventBus(), system=system).execute_tick(agent["id"])
+
+        assert engine.call_count == 1
+        assert any(
+            message.role is Role.SYSTEM
+            and message.content == "SIMPLE_CUSTOM_SYSTEM_SENTINEL"
+            for message in engine.last_messages or []
+        )
+    finally:
+        manager.close()
+
+
+def test_specialized_non_tool_agent_is_not_replaced_by_generic_tool_loop(tmp_path):
+    """Configured/global tools never replace a non-opted-in agent class."""
+
+    AgentRegistry.register_value("specialized_non_tool", _SpecializedNonToolAgent)
+    ToolRegistry.register_value(_ExecutorProbeTool.tool_id, _ExecutorProbeTool)
+    _SpecializedNonToolAgent.runs = 0
+    _ExecutorProbeTool.calls = 0
+    provider = MagicMock(return_value=([_ExecutorProbeTool()], []))
+    system = SimpleNamespace(
+        engine=FakeEngine([{"content": "unused"}]),
+        model="test-model",
+        config=None,
+        memory_backend=None,
+        channel_backend=None,
+        session_store=None,
+        knowledge_db_path=None,
+        get_managed_agent_mcp_tools=provider,
+    )
+    manager = AgentManager(db_path=str(tmp_path / "agents.db"))
+    agent = manager.create_agent(
+        "specialized with configured tool",
+        agent_type="specialized_non_tool",
+        config={
+            "model": "test-model",
+            "instruction": "Keep the specialized path.",
+            "tools": [_ExecutorProbeTool.tool_id],
+        },
+    )
+
+    try:
+        AgentExecutor(manager, EventBus(), system=system).execute_tick(agent["id"])
+
+        provider.assert_not_called()
+        assert _SpecializedNonToolAgent.runs == 1
+        assert _ExecutorProbeTool.calls == 0
+        responses = [
+            message
+            for message in manager.list_messages(agent["id"])
+            if message["direction"] == "agent_to_user"
+        ]
+        assert responses[-1]["content"] == "specialized response"
+    finally:
+        manager.close()
+
+
+def test_executor_grants_deep_research_live_knowledge_tools(tmp_path):
+    """Immediate ticks receive the same live Deep Research grant as SSE."""
+
+    AgentRegistry.register_value("deep_research", _CapturingToolAgent)
+    _CapturingToolAgent.captured_tools = []
+    _CapturingToolAgent.captured_search_result = None
+
+    knowledge_db_path = tmp_path / "knowledge.db"
+    with KnowledgeStore(db_path=knowledge_db_path) as store:
+        store.store(
+            "The EXECUTOR_RESOLVER_SENTINEL decision was approved.",
+            source="test",
+            doc_type="note",
+        )
+
+    manager = AgentManager(db_path=str(tmp_path / "agents.db"))
+    agent = manager.create_agent(
+        "researcher",
+        agent_type="deep_research",
+        config={
+            "model": "agent-selected-model",
+            # These duplicate two agent-type grants and must not replace them.
+            "tools": ["knowledge_search", "think"],
+            "instruction": "Find the sentinel.",
+        },
+    )
+    manager.send_message(agent["id"], "Search the knowledge base.", mode="immediate")
+
+    system = SimpleNamespace(
+        engine=FakeEngine([{"content": "unused"}]),
+        model="system-model",
+        memory_backend=None,
+        channel_backend=None,
+        tool_executor=None,
+        _mcp_clients=[],
+        knowledge_db_path=knowledge_db_path,
+        config=None,
+        session_store=None,
+    )
+    executor = AgentExecutor(manager=manager, event_bus=EventBus(), system=system)
+
+    try:
+        executor.execute_tick(agent["id"])
+
+        tools_by_name = {
+            tool.spec.name: tool for tool in _CapturingToolAgent.captured_tools
+        }
+        assert set(tools_by_name) == {
+            "knowledge_search",
+            "knowledge_sql",
+            "scan_chunks",
+            "think",
+        }
+        result = _CapturingToolAgent.captured_search_result
+        assert result is not None
+        assert result.success is True
+        assert "EXECUTOR_RESOLVER_SENTINEL" in result.content
+        assert tools_by_name["scan_chunks"]._model == "agent-selected-model"
+        assert manager.get_agent(agent["id"])["status"] == "idle"
+        with pytest.raises(sqlite3.ProgrammingError):
+            tools_by_name["knowledge_sql"]._store._conn.execute("SELECT 1")
+    finally:
+        manager.close()
+
+
+def test_executor_mcp_opt_out_does_not_call_lazy_provider(tmp_path):
+    """An opted-out tick must not trigger request-local MCP discovery."""
+
+    AgentRegistry.register_value("capturing", _CapturingToolAgent)
+    provider = MagicMock(side_effect=AssertionError("MCP discovery must stay lazy"))
+    system = SimpleNamespace(
+        engine=FakeEngine([{"content": "unused"}]),
+        model="system-model",
+        memory_backend=None,
+        channel_backend=None,
+        tool_executor=None,
+        _mcp_clients=[],
+        config=None,
+        session_store=None,
+        get_managed_agent_mcp_tools=provider,
+    )
+    manager = AgentManager(db_path=str(tmp_path / "agents.db"))
+    agent = manager.create_agent(
+        "no-mcp",
+        agent_type="capturing",
+        config={"model": "test-model", "mcp_tools": False},
+    )
+
+    try:
+        AgentExecutor(manager, EventBus(), system=system).execute_tick(agent["id"])
+        provider.assert_not_called()
+        assert manager.get_agent(agent["id"])["status"] == "idle"
+    finally:
+        manager.close()
+
+
+def test_executor_preserves_custom_dict_tool_schema(tmp_path):
+    """Executor-based agents see the same custom schema advertised by SSE."""
+
+    from openjarvis.tools.think import ThinkTool
+
+    AgentRegistry.register_value("capturing", _CapturingToolAgent)
+    ToolRegistry.register_value("think", ThinkTool)
+    _CapturingToolAgent.captured_tools = []
+    custom_spec = {
+        "type": "function",
+        "function": {
+            "name": "think",
+            "description": "Agent-specific thinking schema",
+            "parameters": {
+                "type": "object",
+                "properties": {"thought": {"type": "string"}},
+                "required": ["thought"],
+            },
+        },
+    }
+    system = SimpleNamespace(
+        engine=FakeEngine([{"content": "unused"}]),
+        model="test-model",
+        memory_backend=None,
+        channel_backend=None,
+        tool_executor=None,
+        mcp_tools=[],
+        _mcp_clients=[],
+        config=None,
+        session_store=None,
+    )
+    manager = AgentManager(db_path=str(tmp_path / "agents.db"))
+    agent = manager.create_agent(
+        "custom-schema",
+        agent_type="capturing",
+        config={"model": "test-model", "tools": [custom_spec]},
+    )
+
+    try:
+        AgentExecutor(manager, EventBus(), system=system).execute_tick(agent["id"])
+        assert len(_CapturingToolAgent.captured_tools) == 1
+        configured_tool = _CapturingToolAgent.captured_tools[0]
+        assert configured_tool.to_openai_function() == custom_spec
+        assert configured_tool.spec.description == "Agent-specific thinking schema"
+        assert configured_tool.execute(thought="same instance").success is True
+    finally:
+        manager.close()
+
+
+def test_executor_closes_resolver_resources_when_pre_run_setup_fails(
+    tmp_path,
+    monkeypatch,
+):
+    """The resolver finalizer covers failures before agent.run is reached."""
+
+    AgentRegistry.register_value("capturing", _CapturingToolAgent)
+    resource = MagicMock()
+
+    def _resolve(*args, **kwargs):
+        return ResolvedAgentTools(owned_resources=[resource])
+
+    monkeypatch.setattr("openjarvis.agents.executor.resolve_agent_tools", _resolve)
+    system = SimpleNamespace(
+        engine=FakeEngine([{"content": "unused"}]),
+        model="test-model",
+        memory_backend=None,
+        channel_backend=None,
+        tool_executor=None,
+        mcp_tools=[],
+        _mcp_clients=[],
+        config=None,
+        session_store=None,
+    )
+    manager = AgentManager(db_path=str(tmp_path / "agents.db"))
+    agent = manager.create_agent(
+        "cleanup",
+        agent_type="capturing",
+        config={"model": "test-model"},
+    )
+    monkeypatch.setattr(
+        manager,
+        "get_pending_messages",
+        MagicMock(side_effect=RuntimeError("pre-run setup failed")),
+    )
+
+    try:
+        with pytest.raises(RuntimeError, match="pre-run setup failed"):
+            AgentExecutor(manager, EventBus(), system=system)._invoke_agent(agent)
+        gc.collect()
+        resource.close.assert_called_once_with()
+    finally:
+        manager.close()

tests/agents/test_proactive_agent.py

@@ -0,0 +1,131 @@
+"""Regression tests for proactive scheduling and notification setup."""
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from openjarvis.agents.proactive_agent import (
+    _PROACTIVE_CRON_PROMPT,
+    _build_notification_channel,
+    register_cron,
+)
+from openjarvis.core.registry import ChannelRegistry
+from openjarvis.scheduler.scheduler import TaskScheduler
+from openjarvis.scheduler.store import SchedulerStore
+
+
+@pytest.fixture()
+def scheduler(tmp_path):
+    store = SchedulerStore(tmp_path / "scheduler.db")
+    scheduler = TaskScheduler(store)
+    yield scheduler
+    scheduler.stop()
+    store.close()
+
+
+def _register(scheduler, *, schedule="0 5 * * *", channel="telegram:123"):
+    return register_cron(
+        scheduler,
+        notification_channel_id=channel,
+        cron_expr=schedule,
+        hours_back=24,
+        timezone="UTC",
+    )
+
+
+class TestRegisterCron:
+    def test_reuses_exact_task_and_cancels_duplicates(self, scheduler):
+        first = _register(scheduler)
+        duplicate = scheduler.create_task(
+            _PROACTIVE_CRON_PROMPT,
+            "cron",
+            "0 5 * * *",
+            agent="proactive",
+            metadata=first.metadata,
+        )
+
+        returned = _register(scheduler)
+
+        assert returned.id in {first.id, duplicate.id}
+        assert [task.id for task in scheduler.list_tasks(status="active")] == [
+            returned.id
+        ]
+        cancelled_id = scheduler.list_tasks(status="cancelled")[0].id
+        assert cancelled_id == ({first.id, duplicate.id} - {returned.id}).pop()
+
+    def test_replaces_task_when_configuration_changes(self, scheduler):
+        old = _register(scheduler, schedule="0 5 * * *", channel="telegram:old")
+
+        new = _register(scheduler, schedule="0 7 * * *", channel="telegram:new")
+
+        assert new.id != old.id
+        assert new.schedule_value == "0 7 * * *"
+        assert new.metadata["notification_channel_id"] == "telegram:new"
+        assert scheduler.list_tasks(status="cancelled")[0].id == old.id
+
+    def test_preserves_pause_across_restart(self, scheduler):
+        paused = _register(scheduler)
+        scheduler.pause_task(paused.id)
+
+        returned = _register(scheduler, schedule="0 7 * * *")
+
+        assert returned.id == paused.id
+        assert returned.status == "paused"
+        assert scheduler.list_tasks(status="active") == []
+
+    def test_migrates_legacy_tasks_without_stable_key(self, scheduler):
+        legacy = scheduler.create_task(
+            _PROACTIVE_CRON_PROMPT,
+            "cron",
+            "0 5 * * *",
+            agent="proactive",
+            metadata={
+                "notification_channel_id": "telegram:123",
+                "hours_back": 24,
+                "timezone": "UTC",
+            },
+        )
+
+        current = _register(scheduler)
+
+        assert current.id != legacy.id
+        assert current.metadata["openjarvis_task_key"] == "proactive-daily"
+        assert scheduler.list_tasks(status="cancelled")[0].id == legacy.id
+
+
+class TestNotificationChannel:
+    def test_telegram_is_configured_without_starting_polling(self):
+        class FakeTelegram:
+            def __init__(self, **kwargs):
+                self.kwargs = kwargs
+                self.connect = MagicMock()
+
+        config = MagicMock()
+        with (
+            patch.object(ChannelRegistry, "contains", return_value=True),
+            patch.object(ChannelRegistry, "get", return_value=FakeTelegram),
+            patch("openjarvis.core.config.load_config", return_value=config),
+            patch(
+                "openjarvis.system._channel_kwargs.build_channel_kwargs",
+                return_value={"bot_token": "configured-token"},
+            ),
+        ):
+            channel = _build_notification_channel("telegram:123")
+
+        assert channel.kwargs == {"bot_token": "configured-token"}
+        channel.connect.assert_not_called()
+
+    def test_non_telegram_channel_keeps_connect_lifecycle(self):
+        class FakeChannel:
+            def __init__(self, **kwargs):
+                self.connect = MagicMock()
+
+        with (
+            patch.object(ChannelRegistry, "contains", return_value=True),
+            patch.object(ChannelRegistry, "get", return_value=FakeChannel),
+        ):
+            channel = _build_notification_channel("twilio:15551234567")
+
+        channel.connect.assert_called_once_with()

tests/agents/test_scheduler.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import tempfile
+import threading
 import time
 from pathlib import Path
 from unittest.mock import MagicMock
@@ -117,6 +118,48 @@ class TestSchedulerBasic:
         assert executor.execute_tick.call_count >= 1
         executor.execute_tick.assert_called_with(agent["id"])
 
+    def test_two_phase_stop_retains_and_drains_active_worker(self, manager):
+        """Shutdown quiesces later ticks and can wait again after cancellation."""
+
+        from openjarvis.agents.scheduler import AgentScheduler
+
+        started = threading.Event()
+        release = threading.Event()
+        calls: list[str] = []
+
+        class _BlockingExecutor:
+            def execute_tick(self, agent_id):
+                calls.append(agent_id)
+                started.set()
+                release.wait(timeout=2)
+
+        scheduler = AgentScheduler(
+            manager=manager,
+            executor=_BlockingExecutor(),
+            tick_interval=0.01,
+        )
+        agents = [
+            manager.create_agent(
+                name=f"test-{index}",
+                agent_type="monitor_operative",
+                config={"schedule_type": "interval", "schedule_value": 0},
+            )
+            for index in range(2)
+        ]
+        for agent in agents:
+            scheduler.register_agent(agent["id"])
+
+        scheduler.start()
+        assert started.wait(timeout=1)
+        scheduler.request_stop()
+        assert scheduler.wait_stopped(timeout=0.01) is False
+        assert scheduler._thread is not None
+
+        release.set()
+        assert scheduler.wait_stopped(timeout=1) is True
+        assert scheduler._thread is None
+        assert calls == [agents[0]["id"]]
+
     def test_skips_paused_agents(self, manager):
         from openjarvis.agents.scheduler import AgentScheduler
 

tests/agents/test_tool_resolver.py

@@ -0,0 +1,284 @@
+"""Focused tests for canonical managed-agent tool resolution (#688)."""
+
+from __future__ import annotations
+
+from collections import Counter
+
+import pytest
+
+from openjarvis.agents import tool_resolver
+from openjarvis.connectors.store import KnowledgeStore
+from openjarvis.core.registry import ToolRegistry
+from openjarvis.core.types import ToolResult
+from openjarvis.tools import description_loader
+from openjarvis.tools._stubs import BaseTool, ToolSpec
+
+
+class _AlphaTool(BaseTool):
+    tool_id = "alpha"
+
+    @property
+    def spec(self) -> ToolSpec:
+        return ToolSpec(name="alpha", description="Alpha test tool")
+
+    def execute(self, **params) -> ToolResult:
+        return ToolResult(tool_name="alpha", content="alpha", success=True)
+
+
+class _BetaTool(BaseTool):
+    tool_id = "beta"
+
+    @property
+    def spec(self) -> ToolSpec:
+        return ToolSpec(name="beta", description="Beta test tool")
+
+    def execute(self, **params) -> ToolResult:
+        return ToolResult(tool_name="beta", content="beta", success=True)
+
+
+class _NativeSharedTool(BaseTool):
+    tool_id = "shared"
+
+    @property
+    def spec(self) -> ToolSpec:
+        return ToolSpec(name="shared", description="Native shared tool")
+
+    def execute(self, **params) -> ToolResult:
+        return ToolResult(tool_name="shared", content="native", success=True)
+
+
+class _MCPSharedTool(BaseTool):
+    tool_id = "shared"
+
+    @property
+    def spec(self) -> ToolSpec:
+        return ToolSpec(name="shared", description="MCP name collision")
+
+    def execute(self, **params) -> ToolResult:
+        return ToolResult(tool_name="shared", content="mcp", success=True)
+
+
+class _MCPOnlyTool(BaseTool):
+    tool_id = "mcp_only"
+
+    @property
+    def spec(self) -> ToolSpec:
+        return ToolSpec(name="mcp_only", description="MCP-only test tool")
+
+    def execute(self, **params) -> ToolResult:
+        return ToolResult(tool_name="mcp_only", content="mcp-only", success=True)
+
+
+@pytest.fixture(autouse=True)
+def _use_explicit_test_registrations(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Keep these unit tests independent of import-time registry population."""
+
+    monkeypatch.setattr(tool_resolver, "ensure_registries_populated", lambda: None)
+
+
+def test_deep_research_grants_are_live_deduplicated_and_use_selected_model(
+    tmp_path,
+) -> None:
+    """Agent-type grants must beat duplicate bare configured tools."""
+
+    db_path = tmp_path / "knowledge.db"
+    with KnowledgeStore(db_path=db_path) as store:
+        store.store(
+            "The RESOLVER_SENTINEL decision was approved.",
+            source="test",
+            doc_type="note",
+        )
+
+    engine = object()
+    resolved = tool_resolver.resolve_agent_tools(
+        {
+            "agent_type": "deep_research",
+            "config": {
+                # Both names are already supplied by the agent-type grant.
+                "tools": ["knowledge_search", "think", "think"],
+            },
+        },
+        engine=engine,
+        model="agent-selected-model",
+        knowledge_db_path=db_path,
+    )
+
+    try:
+        names = [tool.spec.name for tool in resolved.instances]
+        assert set(names) == {
+            "knowledge_search",
+            "knowledge_sql",
+            "scan_chunks",
+            "think",
+        }
+        assert all(count == 1 for count in Counter(names).values())
+
+        search = resolved.by_name["knowledge_search"]
+        result = search.execute(query="RESOLVER_SENTINEL")
+        assert result.success is True
+        assert "RESOLVER_SENTINEL" in result.content
+
+        scan = resolved.by_name["scan_chunks"]
+        assert scan._engine is engine
+        assert scan._model == "agent-selected-model"
+    finally:
+        # All three knowledge tools share this store connection.
+        resolved.by_name["knowledge_sql"]._store.close()
+
+
+@pytest.mark.parametrize(
+    "tool_config",
+    [
+        ["alpha", "beta", "alpha"],
+        " alpha, beta, alpha ",
+    ],
+)
+def test_configured_tools_normalize_lists_and_comma_separated_strings(
+    tool_config,
+) -> None:
+    ToolRegistry.register_value("alpha", _AlphaTool)
+    ToolRegistry.register_value("beta", _BetaTool)
+
+    resolved = tool_resolver.resolve_agent_tools(
+        {"agent_type": "simple", "config": {"tools": tool_config}},
+        engine=object(),
+        model="test-model",
+    )
+
+    assert [tool.spec.name for tool in resolved.instances] == ["alpha", "beta"]
+    assert [spec["function"]["name"] for spec in resolved.openai_specs] == [
+        "alpha",
+        "beta",
+    ]
+
+
+def test_registered_tool_advertisement_matches_to_openai_function(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Runtime description overrides must reach canonical advertisements."""
+
+    ToolRegistry.register_value("alpha", _AlphaTool)
+    monkeypatch.setattr(
+        description_loader,
+        "get_tool_description_override",
+        lambda name: "Runtime alpha description" if name == "alpha" else None,
+    )
+
+    resolved = tool_resolver.resolve_agent_tools(
+        {"agent_type": "simple", "config": {"tools": ["alpha"]}},
+        engine=object(),
+        model="test-model",
+    )
+
+    tool = resolved.by_name["alpha"]
+    assert resolved.openai_specs == [tool.to_openai_function()]
+    assert (
+        resolved.openai_specs[0]["function"]["description"]
+        == "Runtime alpha description"
+    )
+
+
+def test_explicit_config_schema_takes_priority_over_tool_advertisement(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    ToolRegistry.register_value("alpha", _AlphaTool)
+    monkeypatch.setattr(
+        description_loader,
+        "get_tool_description_override",
+        lambda name: "Runtime alpha description" if name == "alpha" else None,
+    )
+    custom_spec = {
+        "type": "function",
+        "function": {
+            "name": "alpha",
+            "description": "Agent-specific alpha description",
+            "parameters": {
+                "type": "object",
+                "properties": {"query": {"type": "string"}},
+                "required": ["query"],
+            },
+        },
+    }
+
+    resolved = tool_resolver.resolve_agent_tools(
+        {"agent_type": "simple", "config": {"tools": [custom_spec]}},
+        engine=object(),
+        model="test-model",
+    )
+
+    assert resolved.openai_specs == [custom_spec]
+    assert resolved.by_name["alpha"].to_openai_function() == custom_spec
+
+
+def test_invalid_tool_advertisement_falls_back_to_tool_spec() -> None:
+    class _InvalidAdvertisementTool(_AlphaTool):
+        def to_openai_function(self) -> dict[str, object]:
+            raise RuntimeError("broken advertisement")
+
+    ToolRegistry.register_value("alpha", _InvalidAdvertisementTool)
+
+    resolved = tool_resolver.resolve_agent_tools(
+        {"agent_type": "simple", "config": {"tools": ["alpha"]}},
+        engine=object(),
+        model="test-model",
+    )
+
+    assert resolved.openai_specs == [
+        {
+            "type": "function",
+            "function": {
+                "name": "alpha",
+                "description": "Alpha test tool",
+                "parameters": {},
+            },
+        }
+    ]
+
+
+def test_mcp_tools_merge_after_native_tools_without_name_collisions() -> None:
+    ToolRegistry.register_value("shared", _NativeSharedTool)
+    mcp_shared = _MCPSharedTool()
+    mcp_only = _MCPOnlyTool()
+    client = object()
+
+    resolved = tool_resolver.resolve_agent_tools(
+        {
+            "agent_type": "simple",
+            "config": {"tools": ["shared", "shared"]},
+        },
+        engine=object(),
+        model="test-model",
+        mcp_tools=[mcp_shared, mcp_only, mcp_only],
+        mcp_clients=[client],
+    )
+
+    assert [tool.spec.name for tool in resolved.instances] == ["shared", "mcp_only"]
+    assert isinstance(resolved.by_name["shared"], _NativeSharedTool)
+    assert resolved.by_name["mcp_only"] is mcp_only
+    assert resolved.mcp_clients == [client]
+    assert [spec["function"]["name"] for spec in resolved.openai_specs] == [
+        "shared",
+        "mcp_only",
+    ]
+
+
+def test_mcp_tools_can_be_disabled_per_agent() -> None:
+    ToolRegistry.register_value("shared", _NativeSharedTool)
+
+    class _MustNotIterate:
+        def __iter__(self):
+            raise AssertionError("MCP tools must not be inspected after opt-out")
+
+    resolved = tool_resolver.resolve_agent_tools(
+        {
+            "agent_type": "simple",
+            "config": {"tools": ["shared"], "mcp_tools": False},
+        },
+        engine=object(),
+        model="test-model",
+        mcp_tools=_MustNotIterate(),
+        mcp_clients=_MustNotIterate(),
+    )
+
+    assert [tool.spec.name for tool in resolved.instances] == ["shared"]
+    assert resolved.mcp_clients == []

tests/cli/test_chat_cmd.py

@@ -18,6 +18,7 @@ from openjarvis.core.config import JarvisConfig
 from openjarvis.core.events import Event, EventBus, EventType
 from openjarvis.core.registry import AgentRegistry, ToolRegistry
 from openjarvis.core.types import ToolCall, ToolResult
+from openjarvis.memory.store import LocalFactStore
 from openjarvis.tools._stubs import BaseTool, ToolSpec
 
 
@@ -97,6 +98,79 @@ class TestReadInput:
 
 
 class TestChatAgents:
+    def test_direct_chat_injects_auto_memory_facts(self, tmp_path) -> None:
+        facts_path = tmp_path / "facts.jsonl"
+        LocalFactStore(facts_path).add(
+            "The user's favorite color is blue",
+            source="auto",
+        )
+
+        engine = MagicMock()
+        engine.engine_id = "mock"
+        engine.generate.return_value = {"content": "Blue."}
+        config = JarvisConfig()
+        config.intelligence.default_model = "test-model"
+        config.memory.enabled = True
+        config.memory.facts_path = str(facts_path)
+        config.agent.context_from_memory = True
+
+        with (
+            patch("openjarvis.cli.chat_cmd.load_config", return_value=config),
+            patch("openjarvis.engine.get_engine", return_value=("mock", engine)),
+            patch("openjarvis.intelligence.register_builtin_models"),
+            patch("openjarvis.memory.build_memory_service", return_value=None),
+            patch("openjarvis.cli.ask._get_memory_backend", return_value=None),
+        ):
+            result = CliRunner().invoke(
+                chat,
+                ["--model", "test-model"],
+                input="What is my favorite color?\n/quit\n",
+            )
+
+        assert result.exit_code == 0
+        messages = engine.generate.call_args.args[0]
+        assert messages[0].role.value == "system"
+        assert "favorite color is blue" in messages[0].content
+
+    def test_chat_generation_survives_fact_store_failure(self) -> None:
+        class _FailingMemoryService:
+            def start(self) -> None:
+                pass
+
+            def stop(self, timeout: float = 2.0) -> None:
+                pass
+
+            def list_facts(self):
+                raise OSError("fact store unavailable")
+
+        engine = MagicMock()
+        engine.engine_id = "mock"
+        engine.generate.return_value = {"content": "Still working."}
+        config = JarvisConfig()
+        config.intelligence.default_model = "test-model"
+        config.memory.enabled = True
+        config.agent.context_from_memory = True
+
+        with (
+            patch("openjarvis.cli.chat_cmd.load_config", return_value=config),
+            patch("openjarvis.engine.get_engine", return_value=("mock", engine)),
+            patch("openjarvis.intelligence.register_builtin_models"),
+            patch(
+                "openjarvis.memory.build_memory_service",
+                return_value=_FailingMemoryService(),
+            ),
+            patch("openjarvis.cli.ask._get_memory_backend", return_value=None),
+        ):
+            result = CliRunner().invoke(
+                chat,
+                ["--model", "test-model"],
+                input="hello\n/quit\n",
+            )
+
+        assert result.exit_code == 0
+        assert "Still working." in result.output
+        engine.generate.assert_called_once()
+
     def test_simple_agent_does_not_receive_tool_only_kwargs(self) -> None:
         engine = MagicMock()
         engine.engine_id = "mock"

tests/cli/test_cli.py

@@ -138,6 +138,38 @@ class TestCLI:
         content = config_path.read_text()
         assert "[engine]" in content
 
+    def test_init_preset_uses_utf8_for_config_copy(self, tmp_path: Path) -> None:
+        """Preset installation reads and writes shipped TOML as UTF-8."""
+        config_dir = tmp_path / ".openjarvis"
+        config_path = config_dir / "config.toml"
+        original_read_text = Path.read_text
+        original_write_text = Path.write_text
+
+        def read_text(path: Path, *args: object, **kwargs: object) -> str:
+            if path.name == "chat-simple.toml":
+                assert kwargs.get("encoding") == "utf-8"
+            return original_read_text(path, *args, **kwargs)
+
+        def write_text(path: Path, data: str, *args: object, **kwargs: object) -> int:
+            if path == config_path:
+                assert kwargs.get("encoding") == "utf-8"
+            return original_write_text(path, data, *args, **kwargs)
+
+        with (
+            mock.patch("openjarvis.cli.init_cmd.DEFAULT_CONFIG_DIR", config_dir),
+            mock.patch("openjarvis.cli.init_cmd.DEFAULT_CONFIG_PATH", config_path),
+            mock.patch.object(Path, "read_text", autospec=True, side_effect=read_text),
+            mock.patch.object(
+                Path, "write_text", autospec=True, side_effect=write_text
+            ),
+        ):
+            result = CliRunner().invoke(cli, ["init", "--preset", "chat-simple"])
+
+        assert result.exit_code == 0
+        assert "lightweight conversational AI" in config_path.read_text(
+            encoding="utf-8"
+        )
+
 
 class TestStartupResilience:
     """Importing the CLI must not force heavy/native deps (#404, #309).

tests/cli/test_config_cmd.py

@@ -4,6 +4,7 @@ from __future__ import annotations
 
 import json
 from pathlib import Path
+from unittest import mock
 
 import pytest
 from click.testing import CliRunner
@@ -109,18 +110,34 @@ temperature = 0.7
         except json.JSONDecodeError:
             pytest.fail(f"Output is not valid JSON: {result.output}")
 
-    def test_config_show_toml_displays_raw_content(self, tmp_path: Path) -> None:
-        """Test that config show toml displays the raw TOML content."""
+    @pytest.mark.parametrize("output_format", ["toml", "json"])
+    def test_config_show_uses_utf8_for_config_file(
+        self, tmp_path: Path, output_format: str
+    ) -> None:
+        """Test that config show reads UTF-8 config files explicitly."""
         # Create a temporary config file
         config_file = tmp_path / "test_config.toml"
-        config_file.write_text('[engine]\ndefault = "ollama"\n')
-
-        result = CliRunner().invoke(
-            cli, ["config", "show", "toml", "--path", str(config_file)]
+        config_file.write_text(
+            '# Preset comment — stored as UTF-8\n[engine]\ndefault = "ollama"\n',
+            encoding="utf-8",
         )
+        original_read_text = Path.read_text
+
+        def read_text(path: Path, *args: object, **kwargs: object) -> str:
+            if path == config_file:
+                assert kwargs.get("encoding") == "utf-8"
+            return original_read_text(path, *args, **kwargs)
+
+        with mock.patch.object(Path, "read_text", autospec=True, side_effect=read_text):
+            result = CliRunner().invoke(
+                cli, ["config", "show", output_format, "--path", str(config_file)]
+            )
 
         assert result.exit_code == 0
-        assert "[engine]" in result.output
+        if output_format == "toml":
+            assert "[engine]" in result.output
+        else:
+            assert '"engine"' in result.output
         assert "ollama" in result.output
 
     def test_config_show_json_displays_parsed_content(self, tmp_path: Path) -> None:

tests/cli/test_config_set.py

@@ -60,6 +60,42 @@ class TestConfigSet:
         assert "vllm" in content
         assert "qwen2.5:3b" in content
 
+    def test_set_uses_utf8_for_existing_config(self, tmp_path: Path) -> None:
+        """config set preserves a UTF-8 config regardless of the system locale."""
+        config_file = tmp_path / "config.toml"
+        config_file.write_text(
+            '# Preset comment — stored as UTF-8\n[engine]\ndefault = "ollama"\n',
+            encoding="utf-8",
+        )
+        original_read_text = Path.read_text
+        original_write_text = Path.write_text
+
+        def read_text(path: Path, *args: object, **kwargs: object) -> str:
+            if path == config_file:
+                assert kwargs.get("encoding") == "utf-8"
+            return original_read_text(path, *args, **kwargs)
+
+        def write_text(path: Path, data: str, *args: object, **kwargs: object) -> int:
+            if path == config_file:
+                assert kwargs.get("encoding") == "utf-8"
+            return original_write_text(path, data, *args, **kwargs)
+
+        with (
+            mock.patch.dict(os.environ, {"OPENJARVIS_CONFIG": str(config_file)}),
+            mock.patch.object(Path, "read_text", autospec=True, side_effect=read_text),
+            mock.patch.object(
+                Path, "write_text", autospec=True, side_effect=write_text
+            ),
+        ):
+            result = CliRunner().invoke(
+                cli, ["config", "set", "engine.default", "vllm"]
+            )
+
+        assert result.exit_code == 0
+        content = config_file.read_text(encoding="utf-8")
+        assert "Preset comment — stored as UTF-8" in content
+        assert "vllm" in content
+
     def test_set_invalid_key_rejected(self, tmp_path: Path) -> None:
         """config set rejects unknown keys."""
         config_file = tmp_path / "config.toml"

tests/cli/test_daemon_cmd.py

@@ -2,14 +2,17 @@
 
 from __future__ import annotations
 
+import os
 import subprocess
+import sys
+import time
 from pathlib import Path
 from unittest.mock import MagicMock, patch
 
 from click.testing import CliRunner
 
 from openjarvis.cli import cli
-from openjarvis.cli.daemon_cmd import _read_pid, _write_pid
+from openjarvis.cli.daemon_cmd import _pid_alive, _read_pid, _write_pid
 
 
 class TestDaemonCommands:
@@ -45,12 +48,12 @@ class TestDaemonCommands:
             assert _read_pid() is None
 
     def test_write_and_read_pid(self, tmp_path: Path) -> None:
-        """Write a PID, then read it back (mock os.kill to succeed)."""
+        """Write a PID, then read it back with a successful liveness probe."""
         pid_file = tmp_path / "server.pid"
         with (
             patch("openjarvis.cli.daemon_cmd._PID_FILE", pid_file),
             patch("openjarvis.cli.daemon_cmd.DEFAULT_CONFIG_DIR", tmp_path),
-            patch("os.kill", return_value=None),
+            patch("openjarvis.cli.daemon_cmd._pid_alive", return_value=True),
         ):
             _write_pid(12345)
             assert pid_file.exists()
@@ -82,6 +85,53 @@ class TestDaemonCommands:
         assert "already running" in result.output
 
 
+class TestPidLiveness:
+    """Regression coverage for Windows-safe PID liveness checks."""
+
+    def test_pid_alive_current_process(self) -> None:
+        assert _pid_alive(os.getpid()) is True
+
+    def test_pid_alive_nonpositive(self) -> None:
+        assert _pid_alive(0) is False
+        assert _pid_alive(-1) is False
+
+    def test_pid_alive_dead_pid(self) -> None:
+        proc = subprocess.Popen([sys.executable, "-c", "pass"])
+        proc.wait()
+
+        for _ in range(20):
+            if not _pid_alive(proc.pid):
+                break
+            time.sleep(0.1)
+
+        assert _pid_alive(proc.pid) is False
+
+    def test_read_pid_stale_pid_returns_none(self, tmp_path: Path) -> None:
+        proc = subprocess.Popen([sys.executable, "-c", "pass"])
+        proc.wait()
+        pid_file = tmp_path / "server.pid"
+        pid_file.write_text(str(proc.pid))
+
+        with patch("openjarvis.cli.daemon_cmd._PID_FILE", pid_file):
+            assert _read_pid() is None
+
+        assert not pid_file.exists()
+
+    def test_read_pid_live_pid_returns_it(self, tmp_path: Path) -> None:
+        proc = subprocess.Popen([sys.executable, "-c", "import time; time.sleep(10)"])
+        try:
+            pid_file = tmp_path / "server.pid"
+            pid_file.write_text(str(proc.pid))
+
+            with patch("openjarvis.cli.daemon_cmd._PID_FILE", pid_file):
+                assert _read_pid() == proc.pid
+
+            assert pid_file.exists()
+        finally:
+            proc.terminate()
+            proc.wait()
+
+
 class TestDaemonDetachment:
     """The spawned server must outlive the console that started it.
 

tests/cli/test_install_detect.py

@@ -35,7 +35,7 @@ def test_editable_git_install_detected(tmp_path, monkeypatch):
     info = detect_install()
     assert info.kind == "editable-git"
     assert "git pull" in info.upgrade_command
-    assert "uv sync" in info.upgrade_command
+    assert info.upgrade_command.endswith("uv sync --inexact")
     assert info.repo_root == repo
 
 

tests/cli/test_self_update.py

@@ -21,7 +21,7 @@ def _mock_info(kind: str = "pypi") -> InstallInfo:
         upgrade_command={
             "pypi": "pip install --upgrade openjarvis",
             "uv-tool": "uv tool upgrade openjarvis",
-            "editable-git": "cd /tmp/repo && git pull && uv sync",
+            "editable-git": "cd /tmp/repo && git pull && uv sync --inexact",
             "unknown": "pip install --upgrade openjarvis",
         }[kind],
     )
@@ -90,6 +90,26 @@ def test_editable_git_uses_shell_true():
     assert kwargs.get("shell") is True
 
 
+def test_editable_git_preserves_extra_dependencies():
+    """The update sync must not remove packages from prior extras/groups."""
+    mock_proc = MagicMock(returncode=0)
+    with (
+        patch(
+            "openjarvis.cli.self_update_cmd.detect_install",
+            return_value=_mock_info("editable-git"),
+        ),
+        patch(
+            "openjarvis.cli.self_update_cmd.subprocess.run",
+            return_value=mock_proc,
+        ) as mock_run,
+    ):
+        result = CliRunner().invoke(self_update, ["-y"])
+
+    assert result.exit_code == 0
+    assert "uv sync --inexact" in result.output
+    assert "uv sync --inexact" in mock_run.call_args.args[0]
+
+
 def test_failed_upgrade_propagates_exit_code():
     mock_proc = MagicMock(returncode=3)
     with (

tests/cli/test_serve_single_build.py

@@ -101,6 +101,7 @@ def _run_serve(tmp_path, monkeypatch, *, build_spy, set_system_spy):
     (``uvicorn.run`` is a no-op) and no real engine is contacted.
     """
     from openjarvis.core.config import JarvisConfig
+    from openjarvis.core.registry import MemoryRegistry
 
     _repopulate_registries()
 
@@ -112,6 +113,9 @@ def _run_serve(tmp_path, monkeypatch, *, build_spy, set_system_spy):
     config.sessions.enabled = True
     config.sessions.db_path = str(tmp_path / "sessions.db")
     config.memory.db_path = str(tmp_path / "memory.db")
+    # Disabling prompt-context injection must not disable the backend needed
+    # by explicitly configured memory tools in managed-agent ticks.
+    config.agent.context_from_memory = False
     config.telemetry.enabled = False
     config.traces.enabled = False
     config.channel.enabled = False
@@ -122,6 +126,16 @@ def _run_serve(tmp_path, monkeypatch, *, build_spy, set_system_spy):
     config.intelligence.default_model = "test-model"
 
     engine = _fake_engine()
+    # Keep this wiring test independent of the optional native memory runtime.
+    # The assertion is that serve resolves and passes a backend even when
+    # prompt-context injection is disabled, not that SQLite itself works.
+    memory_backend = MagicMock(name="memory_backend")
+    monkeypatch.setattr(MemoryRegistry, "contains", MagicMock(return_value=True))
+    monkeypatch.setattr(
+        MemoryRegistry,
+        "create",
+        MagicMock(return_value=memory_backend),
+    )
 
     monkeypatch.setattr(serve_mod, "load_config", lambda *a, **k: config)
     monkeypatch.setattr(serve_mod, "get_engine", lambda *a, **k: ("mock", engine))

tests/cli/test_serve_tools.py

@@ -0,0 +1,53 @@
+"""Regression tests for tool selection during ``jarvis serve`` startup."""
+
+from __future__ import annotations
+
+import pytest
+
+from openjarvis.cli.serve import _resolve_allowed_tools
+from openjarvis.core.config import JarvisConfig
+
+
+@pytest.mark.parametrize(
+    "configured",
+    [
+        "code_interpreter,file_read",
+        ["code_interpreter", "file_read"],
+    ],
+)
+def test_tools_enabled_is_used_by_serve(configured):
+    config = JarvisConfig()
+    config.tools.enabled = configured
+
+    allowed, explicit = _resolve_allowed_tools(config)
+
+    assert allowed == {"code_interpreter", "file_read"}
+    assert explicit is True
+
+
+def test_tools_enabled_takes_precedence_over_legacy_agent_tools():
+    config = JarvisConfig()
+    config.tools.enabled = "file_read"
+    config.agent.tools = "calculator"
+
+    allowed, explicit = _resolve_allowed_tools(config)
+
+    assert allowed == {"file_read"}
+    assert explicit is True
+
+
+def test_agent_tools_remains_a_backward_compatible_fallback():
+    config = JarvisConfig()
+    config.agent.tools = "file_read"
+
+    allowed, explicit = _resolve_allowed_tools(config)
+
+    assert allowed == {"file_read"}
+    assert explicit is True
+
+
+def test_serve_defaults_tools_when_no_selection_is_configured():
+    allowed, explicit = _resolve_allowed_tools(JarvisConfig())
+
+    assert allowed == {"think", "calculator", "web_search"}
+    assert explicit is False

tests/engine/test_cloud_stream_full.py

@@ -3,6 +3,8 @@ and _prepare_anthropic_messages."""
 
 from __future__ import annotations
 
+import sys
+from types import ModuleType, SimpleNamespace
 from typing import Any, List
 from unittest.mock import MagicMock
 
@@ -63,6 +65,36 @@ def _openai_tool_call_delta(
     return tc
 
 
+class _GoogleConfig:
+    def __init__(self, **kwargs: Any) -> None:
+        self.__dict__.update(kwargs)
+
+
+def _google_stream_chunk(
+    *parts: Any,
+    text: str | None = None,
+    usage_metadata: Any = None,
+) -> Any:
+    candidates = []
+    if parts:
+        candidates = [SimpleNamespace(content=SimpleNamespace(parts=list(parts)))]
+    return SimpleNamespace(
+        text=text,
+        candidates=candidates,
+        usage_metadata=usage_metadata,
+    )
+
+
+def _google_types_modules() -> dict[str, ModuleType]:
+    types = ModuleType("google.genai.types")
+    types.GenerateContentConfig = _GoogleConfig
+    genai = ModuleType("google.genai")
+    genai.types = types
+    google = ModuleType("google")
+    google.genai = genai
+    return {"google": google, "google.genai": genai, "google.genai.types": types}
+
+
 # ---------------------------------------------------------------------------
 # _stream_full_openai tests
 # ---------------------------------------------------------------------------
@@ -413,6 +445,316 @@ def test_prepare_anthropic_messages_tool_calls():
     assert blocks[1]["input"] == {"city": "Berlin"}
 
 
+# ---------------------------------------------------------------------------
+# _stream_full_google tests
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_stream_full_google_text_only(monkeypatch: pytest.MonkeyPatch):
+    """Google text chunks retain their content and finish normally."""
+    client = MagicMock()
+    client.models.generate_content_stream.return_value = iter(
+        [_google_stream_chunk(text="Hello"), _google_stream_chunk(text=" world")]
+    )
+    engine = _make_cloud_engine(google_client=client)
+    engine._thought_sigs = {}
+    messages = [Message(role=Role.USER, content="hi")]
+    modules = _google_types_modules()
+
+    with monkeypatch.context() as patch:
+        for name, module in modules.items():
+            patch.setitem(sys.modules, name, module)
+        result = [
+            chunk
+            async for chunk in engine.stream_full(messages, model="gemini-2.5-flash")
+        ]
+
+    assert [chunk.content for chunk in result[:-1]] == ["Hello", " world"]
+    assert result[-1].finish_reason == "stop"
+
+
+@pytest.mark.asyncio
+async def test_stream_full_google_preserves_tool_calls(monkeypatch: pytest.MonkeyPatch):
+    """Google function_call parts become OpenAI-compatible tool call chunks."""
+    function_call = SimpleNamespace(name="get_weather", args={"city": "Berlin"})
+    part = SimpleNamespace(
+        function_call=function_call, text=None, thought_signature=b"sig"
+    )
+    client = MagicMock()
+    client.models.generate_content_stream.return_value = iter(
+        [_google_stream_chunk(part)]
+    )
+    engine = _make_cloud_engine(google_client=client)
+    engine._thought_sigs = {}
+    messages = [Message(role=Role.USER, content="weather")]
+    modules = _google_types_modules()
+
+    with monkeypatch.context() as patch:
+        for name, module in modules.items():
+            patch.setitem(sys.modules, name, module)
+        result = [
+            chunk
+            async for chunk in engine.stream_full(
+                messages,
+                model="gemini-2.5-flash",
+                tools=[
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "description": "Get weather",
+                            "parameters": {"type": "object", "properties": {}},
+                        },
+                    }
+                ],
+            )
+        ]
+
+    tool_call = result[0].tool_calls[0]
+    assert tool_call["index"] == 0
+    assert tool_call["id"].startswith("google_")
+    assert tool_call["type"] == "function"
+    assert tool_call["function"] == {
+        "name": "get_weather",
+        "arguments": '{"city": "Berlin"}',
+    }
+    assert tool_call["thought_signature"] == b"sig"
+    assert engine._thought_sigs[tool_call["id"]] == b"sig"
+    assert result[-1].finish_reason == "tool_calls"
+    config = client.models.generate_content_stream.call_args.kwargs["config"]
+    assert config.tools == [
+        {
+            "function_declarations": [
+                {
+                    "name": "get_weather",
+                    "description": "Get weather",
+                    "parameters": {"type": "object", "properties": {}},
+                }
+            ]
+        }
+    ]
+
+
+@pytest.mark.asyncio
+async def test_stream_full_google_preserves_mixed_and_multiple_calls(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    """Google streams retain mixed text and multiple tool calls."""
+    weather = SimpleNamespace(name="get_weather", args={"city": "Berlin"})
+    calendar = SimpleNamespace(name="get_calendar", args={"day": "Monday"})
+    text_part = SimpleNamespace(text="I'll check.", function_call=None)
+    weather_part = SimpleNamespace(
+        function_call=weather, text=None, thought_signature=None
+    )
+    calendar_part = SimpleNamespace(
+        function_call=calendar, text=None, thought_signature=None
+    )
+    client = MagicMock()
+    client.models.generate_content_stream.return_value = iter(
+        [
+            _google_stream_chunk(text_part, weather_part),
+            _google_stream_chunk(calendar_part),
+        ]
+    )
+    engine = _make_cloud_engine(google_client=client)
+    engine._thought_sigs = {}
+    modules = _google_types_modules()
+
+    with monkeypatch.context() as patch:
+        for name, module in modules.items():
+            patch.setitem(sys.modules, name, module)
+        result = [
+            chunk
+            async for chunk in engine.stream_full(
+                [Message(role=Role.USER, content="plan")], model="gemini-2.5-flash"
+            )
+        ]
+
+    assert result[0].content == "I'll check."
+    weather_call = result[1].tool_calls[0]
+    calendar_call = result[2].tool_calls[0]
+    assert weather_call["index"] == 0
+    assert weather_call["function"] == {
+        "name": "get_weather",
+        "arguments": '{"city": "Berlin"}',
+    }
+    assert calendar_call["index"] == 1
+    assert calendar_call["function"] == {
+        "name": "get_calendar",
+        "arguments": '{"day": "Monday"}',
+    }
+    assert weather_call["id"] != calendar_call["id"]
+    assert result[-1].finish_reason == "tool_calls"
+
+
+@pytest.mark.asyncio
+async def test_stream_full_google_keeps_parallel_same_name_calls_distinct(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    """Parallel invocations of one function receive unique indexes and IDs."""
+    paris = SimpleNamespace(name="get_weather", args={"city": "Paris"})
+    london = SimpleNamespace(name="get_weather", args={"city": "London"})
+    parts = [
+        SimpleNamespace(function_call=paris, text=None, thought_signature=b"sig"),
+        SimpleNamespace(function_call=london, text=None, thought_signature=None),
+    ]
+    client = MagicMock()
+    client.models.generate_content_stream.return_value = iter(
+        [_google_stream_chunk(*parts)]
+    )
+    engine = _make_cloud_engine(google_client=client)
+    engine._thought_sigs = {}
+
+    with monkeypatch.context() as patch:
+        for name, module in _google_types_modules().items():
+            patch.setitem(sys.modules, name, module)
+        result = [
+            chunk
+            async for chunk in engine.stream_full(
+                [Message(role=Role.USER, content="Weather in Paris and London")],
+                model="gemini-3-flash-preview",
+            )
+        ]
+
+    calls = result[0].tool_calls
+    assert [call["index"] for call in calls] == [0, 1]
+    assert calls[0]["id"] != calls[1]["id"]
+    assert [call["function"]["arguments"] for call in calls] == [
+        '{"city": "Paris"}',
+        '{"city": "London"}',
+    ]
+
+
+@pytest.mark.asyncio
+async def test_stream_full_google_ids_are_unique_across_requests(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    """Shared engines keep signatures isolated between conversations."""
+    first_part = SimpleNamespace(
+        function_call=SimpleNamespace(name="get_weather", args={"city": "Paris"}),
+        text=None,
+        thought_signature=b"paris-sig",
+    )
+    second_part = SimpleNamespace(
+        function_call=SimpleNamespace(name="get_weather", args={"city": "London"}),
+        text=None,
+        thought_signature=b"london-sig",
+    )
+    client = MagicMock()
+    client.models.generate_content_stream.side_effect = [
+        iter([_google_stream_chunk(first_part)]),
+        iter([_google_stream_chunk(second_part)]),
+    ]
+    engine = _make_cloud_engine(google_client=client)
+    engine._thought_sigs = {}
+
+    with monkeypatch.context() as patch:
+        for name, module in _google_types_modules().items():
+            patch.setitem(sys.modules, name, module)
+        first = [
+            chunk
+            async for chunk in engine.stream_full(
+                [Message(role=Role.USER, content="Weather in Paris")],
+                model="gemini-3-flash-preview",
+            )
+        ]
+        second = [
+            chunk
+            async for chunk in engine.stream_full(
+                [Message(role=Role.USER, content="Weather in London")],
+                model="gemini-3-flash-preview",
+            )
+        ]
+
+    first_id = first[0].tool_calls[0]["id"]
+    second_id = second[0].tool_calls[0]["id"]
+    assert first_id != second_id
+    assert engine._thought_sigs[first_id] == b"paris-sig"
+    assert engine._thought_sigs[second_id] == b"london-sig"
+
+
+@pytest.mark.asyncio
+async def test_stream_full_google_emits_final_usage(monkeypatch: pytest.MonkeyPatch):
+    """Google's final usage metadata is normalized onto the terminal chunk."""
+    usage = SimpleNamespace(prompt_token_count=12, candidates_token_count=5)
+    client = MagicMock()
+    client.models.generate_content_stream.return_value = iter(
+        [
+            _google_stream_chunk(text="Hello"),
+            _google_stream_chunk(usage_metadata=usage),
+        ]
+    )
+    engine = _make_cloud_engine(google_client=client)
+    engine._thought_sigs = {}
+
+    with monkeypatch.context() as patch:
+        for name, module in _google_types_modules().items():
+            patch.setitem(sys.modules, name, module)
+        result = [
+            chunk
+            async for chunk in engine.stream_full(
+                [Message(role=Role.USER, content="hi")],
+                model="gemini-2.5-flash",
+            )
+        ]
+
+    assert result[-1].finish_reason == "stop"
+    assert result[-1].usage == {
+        "prompt_tokens": 12,
+        "completion_tokens": 5,
+        "total_tokens": 17,
+    }
+
+
+@pytest.mark.asyncio
+async def test_stream_full_google_replays_signature_on_part(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    """A saved Gemini signature is replayed beside, not inside, function_call."""
+    client = MagicMock()
+    client.models.generate_content_stream.return_value = iter([])
+    engine = _make_cloud_engine(google_client=client)
+    engine._thought_sigs = {"google_get_weather_0": b"sig"}
+    messages = [
+        Message(role=Role.USER, content="weather"),
+        Message(
+            role=Role.ASSISTANT,
+            content=None,
+            tool_calls=[
+                ToolCall(
+                    id="google_get_weather_0",
+                    name="get_weather",
+                    arguments='{"city": "Berlin"}',
+                )
+            ],
+        ),
+        Message(role=Role.TOOL, name="get_weather", content='{"temp": 20}'),
+    ]
+
+    with monkeypatch.context() as patch:
+        for name, module in _google_types_modules().items():
+            patch.setitem(sys.modules, name, module)
+        result = [
+            chunk
+            async for chunk in engine.stream_full(
+                messages, model="gemini-3-flash-preview"
+            )
+        ]
+
+    contents = client.models.generate_content_stream.call_args.kwargs["contents"]
+    assert contents[1]["parts"] == [
+        {
+            "function_call": {
+                "name": "get_weather",
+                "args": {"city": "Berlin"},
+            },
+            "thought_signature": b"sig",
+        }
+    ]
+    assert result[-1].finish_reason == "stop"
+
+
 # ---------------------------------------------------------------------------
 # stream_full routing tests
 # ---------------------------------------------------------------------------

tests/engine/test_discovery.py

@@ -8,10 +8,12 @@ from openjarvis.core.config import JarvisConfig
 from openjarvis.core.registry import EngineRegistry
 from openjarvis.engine._base import InferenceEngine
 from openjarvis.engine._discovery import (
+    _make_engine,
     discover_engines,
     discover_models,
     get_engine,
 )
+from openjarvis.engine.litellm import LiteLLMEngine
 
 
 class _FakeEngine(InferenceEngine):
@@ -131,6 +133,24 @@ class TestDiscoverModels:
         assert result == {"ollama": ["m1", "m2"], "vllm": ["m3"]}
 
 
+class TestLiteLLMDiscovery:
+    def test_configured_default_model_is_advertised(self) -> None:
+        """Regression for #713: discovery must configure LiteLLM's model.
+
+        LiteLLM cannot enumerate every model supported by every provider, so
+        ``LiteLLMEngine.list_models()`` advertises the configured default
+        model.  Dropping that value while constructing the engine leaves the
+        API and Web UI with an empty model list.
+        """
+        cfg = JarvisConfig()
+        cfg.intelligence.default_model = "groq/llama-3.3-70b-versatile"
+        EngineRegistry.register_value("litellm", LiteLLMEngine)
+
+        engine = _make_engine("litellm", cfg)
+
+        assert engine.list_models() == ["groq/llama-3.3-70b-versatile"]
+
+
 class TestGetEngine:
     def test_fallback_when_default_unhealthy(self) -> None:
         _reg("bad", "bad")

tests/engine/test_engine_wrappers.py

@@ -120,6 +120,9 @@ async def test_multi_routes_stream_full_by_model():
     engine_b.list_models = lambda: ["model-b"]
 
     multi = MultiEngine([("a", engine_a), ("b", engine_b)])
+    assert multi.engine_key_for("model-a") == "a"
+    assert multi.engine_key_for("model-b") == "b"
+    assert multi.engine_key_for("missing") is None
 
     # Route to engine A
     result_a = []

tests/evals/datasets/test_taubench.py

@@ -0,0 +1,98 @@
+"""Tests for the TauBench optional dependency boundary."""
+
+from __future__ import annotations
+
+import builtins
+import sys
+from types import ModuleType
+from unittest.mock import Mock
+
+import pytest
+
+from openjarvis.evals.datasets import taubench
+
+
+def _mock_direct_url(monkeypatch, direct_url):
+    distribution = Mock()
+    distribution.read_text.return_value = direct_url
+    monkeypatch.setattr(
+        taubench.metadata, "distribution", Mock(return_value=distribution)
+    )
+
+
+def test_ensure_tau2_accepts_the_pinned_source_revision(monkeypatch):
+    monkeypatch.setitem(sys.modules, "tau2", ModuleType("tau2"))
+    _mock_direct_url(
+        monkeypatch,
+        (
+            '{"url": "https://github.com/sierra-research/tau2-bench.git", '
+            '"vcs_info": {"vcs": "git", '
+            f'"commit_id": "{taubench.TAU2_REVISION}"}}}}'
+        ),
+    )
+
+    taubench._ensure_tau2()
+
+
+def test_ensure_tau2_requires_explicit_pinned_install(monkeypatch):
+    monkeypatch.setitem(sys.modules, "tau2", None)
+    monkeypatch.setattr(
+        taubench.metadata,
+        "distribution",
+        Mock(side_effect=taubench.metadata.PackageNotFoundError),
+    )
+
+    with pytest.raises(ImportError) as exc_info:
+        taubench._ensure_tau2()
+
+    message = str(exc_info.value)
+    assert "does not install at runtime" in message
+    assert taubench.TAU2_REVISION in message
+    assert "uv pip install" in message
+
+
+@pytest.mark.parametrize(
+    "direct_url",
+    [
+        # Editable install left behind by the previous runtime installer.
+        '{"url": "file:///home/user/.openjarvis/cache/tau2-bench", '
+        '"dir_info": {"editable": true}}',
+        # A git install from an arbitrary upstream revision.
+        '{"url": "https://github.com/sierra-research/tau2-bench.git", '
+        '"vcs_info": {"vcs": "git", "commit_id": "deadbeef"}}',
+        # Registry installs do not carry PEP 610 direct-origin metadata.
+        None,
+    ],
+)
+def test_ensure_tau2_rejects_unpinned_install(monkeypatch, direct_url):
+    _mock_direct_url(monkeypatch, direct_url)
+    original_import = builtins.__import__
+
+    def guarded_import(name, *args, **kwargs):
+        if name == "tau2":
+            raise AssertionError("unverified tau2 package was imported")
+        return original_import(name, *args, **kwargs)
+
+    monkeypatch.setattr(builtins, "__import__", guarded_import)
+
+    with pytest.raises(ImportError) as exc_info:
+        taubench._ensure_tau2()
+
+    message = str(exc_info.value)
+    assert "does not match" in message
+    assert taubench.TAU2_REVISION in message
+    assert "--force-reinstall" in message
+
+
+def test_verify_requirements_reports_install_instruction(monkeypatch):
+    monkeypatch.setitem(sys.modules, "tau2", None)
+    monkeypatch.setattr(
+        taubench.metadata,
+        "distribution",
+        Mock(side_effect=taubench.metadata.PackageNotFoundError),
+    )
+
+    issues = taubench.TauBenchDataset().verify_requirements()
+
+    assert len(issues) == 1
+    assert taubench.TAU2_REVISION in issues[0]

tests/mcp/test_client.py

@@ -2,10 +2,15 @@
 
 from __future__ import annotations
 
+import threading
+import time
+from concurrent.futures import ThreadPoolExecutor
+from unittest.mock import MagicMock
+
 import pytest
 
 from openjarvis.mcp.client import MCPClient
-from openjarvis.mcp.protocol import MCPError
+from openjarvis.mcp.protocol import MCPError, MCPResponse
 from openjarvis.mcp.server import MCPServer
 from openjarvis.mcp.transport import InProcessTransport
 from openjarvis.tools._stubs import ToolSpec
@@ -101,3 +106,101 @@ class TestMCPClient:
         result = client.call_tool("think")
         # Think tool echoes empty thought
         assert result["isError"] is False
+
+    def test_shared_client_serializes_transport_round_trips(self):
+        """Concurrent agents cannot consume one another's MCP responses."""
+
+        class _ConcurrencyProbeTransport:
+            def __init__(self):
+                self.active = 0
+                self.max_active = 0
+                self.lock = threading.Lock()
+
+            def send(self, request):
+                with self.lock:
+                    self.active += 1
+                    self.max_active = max(self.max_active, self.active)
+                time.sleep(0.01)
+                with self.lock:
+                    self.active -= 1
+                return MCPResponse(result={"tools": []}, id=request.id)
+
+            def send_notification(self, request):
+                return None
+
+            def close(self):
+                return None
+
+        transport = _ConcurrencyProbeTransport()
+        shared_client = MCPClient(transport)
+
+        with ThreadPoolExecutor(max_workers=8) as pool:
+            list(pool.map(lambda _: shared_client.list_tools(), range(24)))
+
+        assert transport.max_active == 1
+
+    def test_close_interrupts_blocked_request_and_rejects_queued_request(self):
+        """Shutdown reaches the transport without waiting on an in-flight call."""
+
+        class _BlockingTransport:
+            def __init__(self):
+                self.send_started = threading.Event()
+                self.send_released = threading.Event()
+                self.close_called = threading.Event()
+                self.send_count = 0
+
+            def send(self, request):
+                self.send_count += 1
+                self.send_started.set()
+                self.send_released.wait()
+                raise RuntimeError("transport closed")
+
+            def send_notification(self, request):
+                return None
+
+            def close(self):
+                self.close_called.set()
+                self.send_released.set()
+
+        transport = _BlockingTransport()
+        shared_client = MCPClient(transport)
+
+        with ThreadPoolExecutor(max_workers=3) as pool:
+            blocked_request = pool.submit(shared_client.list_tools)
+            assert transport.send_started.wait(timeout=1)
+
+            queued_request = pool.submit(shared_client.list_tools)
+            close_call = pool.submit(shared_client.close)
+
+            try:
+                close_reached_transport = transport.close_called.wait(timeout=1)
+            finally:
+                # Keep the test failure-safe against a regression that makes
+                # close wait behind the blocked request.
+                transport.send_released.set()
+
+            close_call.result(timeout=1)
+            assert close_reached_transport
+            with pytest.raises(RuntimeError, match="transport closed"):
+                blocked_request.result(timeout=1)
+            with pytest.raises(RuntimeError, match="MCP client is closed"):
+                queued_request.result(timeout=1)
+
+        assert transport.send_count == 1
+
+    def test_close_retries_transport_cleanup_after_failure(self):
+        """A failed close keeps requests blocked but permits cleanup retry."""
+
+        transport = MagicMock()
+        transport.close.side_effect = [RuntimeError("terminate timed out"), None]
+        client = MCPClient(transport)
+
+        with pytest.raises(RuntimeError, match="terminate timed out"):
+            client.close()
+        with pytest.raises(RuntimeError, match="MCP client is closed"):
+            client.list_tools()
+
+        client.close()
+        client.close()
+
+        assert transport.close.call_count == 2

tests/mcp/test_discovery.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import json
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -181,6 +182,132 @@ class TestClientPersistence:
         assert len(builder._mcp_clients) == 3
 
 
+def test_builder_retains_full_mcp_pool_for_managed_agents() -> None:
+    """Global primary-agent filters must not trim managed-agent MCP tools."""
+
+    from openjarvis.core.config import JarvisConfig
+    from openjarvis.system import SystemBuilder
+
+    config = JarvisConfig()
+    config.tools.mcp.servers = json.dumps(
+        [{"name": "test", "url": "http://localhost:8080/mcp"}]
+    )
+    external = _make_mock_tool("mcp_only")
+    builder = SystemBuilder(config).tools(["native_only"])
+
+    with (
+        patch("openjarvis.mcp.server.MCPServer") as mcp_server_cls,
+        patch.object(
+            builder,
+            "_discover_external_mcp",
+            return_value=[external],
+        ),
+    ):
+        mcp_server_cls.return_value.get_tools.return_value = []
+        primary_tools = builder._resolve_tools(
+            config,
+            engine=MagicMock(),
+            model="test-model",
+            memory_backend=None,
+        )
+
+    assert primary_tools == []
+    assert builder._mcp_tools == [external]
+
+
+def test_builder_global_mcp_disable_prevents_discovery() -> None:
+    """A global MCP disable is honored by every managed-agent entry path."""
+
+    from openjarvis.core.config import JarvisConfig
+    from openjarvis.system import SystemBuilder
+
+    config = JarvisConfig()
+    config.tools.mcp.enabled = False
+    config.tools.mcp.servers = json.dumps(
+        [{"name": "disabled", "url": "http://localhost:8080/mcp"}]
+    )
+    builder = SystemBuilder(config)
+
+    with (
+        patch("openjarvis.mcp.server.MCPServer") as mcp_server_cls,
+        patch.object(builder, "_discover_external_mcp") as discover,
+    ):
+        mcp_server_cls.return_value.get_tools.return_value = []
+        builder._resolve_tools(
+            config,
+            engine=MagicMock(),
+            model="test-model",
+            memory_backend=None,
+        )
+
+    discover.assert_not_called()
+    assert builder._mcp_tools == []
+
+
+def test_reused_builder_transfers_only_current_build_mcp_state() -> None:
+    """Each built system exclusively owns its own MCP clients and tools."""
+
+    from openjarvis.core.config import JarvisConfig
+    from openjarvis.system import SystemBuilder
+
+    config = JarvisConfig()
+    config.telemetry.enabled = False
+    config.traces.enabled = False
+    config.skills.enabled = False
+    config.agent_manager.enabled = False
+    config.tools.mcp.servers = json.dumps(
+        [{"name": "test", "url": "http://localhost:8080/mcp"}]
+    )
+
+    engine = MagicMock(spec=["health", "can_serve", "generate", "list_models", "close"])
+    engine.health.return_value = True
+    first_tool = _make_mock_tool("first_mcp_tool")
+    second_tool = _make_mock_tool("second_mcp_tool")
+    first_client = MagicMock()
+    second_client = MagicMock()
+    discoveries = iter([(first_tool, first_client), (second_tool, second_client)])
+
+    builder = (
+        SystemBuilder(config)
+        .engine_instance(engine)
+        .model("test-model")
+        .tools([])
+        .telemetry(False)
+        .traces(False)
+        .speech(False)
+    )
+
+    def _discover(_server_cfg):
+        tool, client = next(discoveries)
+        builder._mcp_clients.append(client)
+        return [tool]
+
+    with (
+        patch.object(builder, "_discover_external_mcp", side_effect=_discover),
+        patch.object(builder, "_resolve_memory", return_value=None),
+    ):
+        first_system = builder.build()
+        assert first_system.mcp_tools == [first_tool]
+        assert first_system._mcp_clients == [first_client]
+        assert builder._mcp_tools == []
+        assert builder._mcp_clients == []
+        first_system.close()
+
+        second_system = builder.build()
+
+    try:
+        assert second_system.mcp_tools == [second_tool]
+        assert second_system._mcp_clients == [second_client]
+        assert first_client not in second_system._mcp_clients
+        assert builder._mcp_tools == []
+        assert builder._mcp_clients == []
+    finally:
+        second_system.close()
+
+    first_client.close.assert_called_once()
+    second_client.close.assert_called_once()
+
+
 class TestStringConfig:
     @patch(_PATCH_PROVIDER)
     @patch(_PATCH_CLIENT)

tests/memory/test_context.py

@@ -7,6 +7,7 @@ from typing import Any, Dict, List, Optional
 
 from openjarvis.core.events import EventBus, EventType
 from openjarvis.core.types import Message, Role
+from openjarvis.memory.store import Fact
 from openjarvis.tools.storage._stubs import MemoryBackend, RetrievalResult
 from openjarvis.tools.storage.context import (
     ContextConfig,
@@ -167,6 +168,113 @@ def test_inject_context_no_results_returns_original():
     assert augmented is messages
 
 
+def test_inject_context_adds_auto_memory_facts_without_backend():
+    messages = [Message(role=Role.USER, content="What is my favorite color?")]
+    facts = [Fact(text="The user's favorite color is blue", source="auto")]
+
+    augmented = inject_context("favorite color", messages, None, facts=facts)
+
+    assert len(augmented) == 2
+    assert augmented[0].role == Role.SYSTEM
+    assert "remembered from prior conversations" in augmented[0].content
+    assert "favorite color is blue" in augmented[0].content
+
+
+def test_inject_context_prioritizes_newest_facts_within_token_budget():
+    messages = [Message(role=Role.USER, content="What do you remember?")]
+    facts = [
+        Fact(text="old fact uses four tokens"),
+        Fact(text="new fact uses four tokens"),
+    ]
+
+    augmented = inject_context(
+        "remember",
+        messages,
+        None,
+        config=ContextConfig(max_context_tokens=5),
+        facts=facts,
+    )
+
+    assert "new fact uses four tokens" in augmented[0].content
+    assert "old fact uses four tokens" not in augmented[0].content
+
+
+def test_inject_context_merges_with_existing_system_message():
+    messages = [
+        Message(role=Role.SYSTEM, content="You are OpenJarvis."),
+        Message(role=Role.USER, content="What is my favorite color?"),
+    ]
+    facts = [Fact(text="The user's favorite color is blue")]
+
+    augmented = inject_context("favorite color", messages, None, facts=facts)
+
+    system_messages = [m for m in augmented if m.role == Role.SYSTEM]
+    assert len(system_messages) == 1
+    assert "You are OpenJarvis." in system_messages[0].content
+    assert "favorite color is blue" in system_messages[0].content
+    assert messages[0].content == "You are OpenJarvis."
+
+
+def test_inject_context_collapses_multiple_system_messages():
+    messages = [
+        Message(role=Role.SYSTEM, content="Identity."),
+        Message(role=Role.SYSTEM, content="Persona."),
+        Message(role=Role.USER, content="What do you remember?"),
+    ]
+
+    augmented = inject_context(
+        "remember",
+        messages,
+        None,
+        facts=[Fact(text="User likes jazz")],
+    )
+
+    system_messages = [m for m in augmented if m.role == Role.SYSTEM]
+    assert len(system_messages) == 1
+    assert "Identity." in system_messages[0].content
+    assert "Persona." in system_messages[0].content
+    assert "User likes jazz" in system_messages[0].content
+
+
+def test_inject_context_reserves_budget_for_retrieved_documents():
+    backend = _FakeMemory(
+        [RetrievalResult(content="d1 d2 d3 d4 d5", score=1.0, source="doc")]
+    )
+    facts = [
+        Fact(text="old1 old2 old3 old4 old5"),
+        Fact(text="new1 new2 new3 new4 new5"),
+    ]
+
+    augmented = inject_context(
+        "query",
+        [Message(role=Role.USER, content="query")],
+        backend,
+        config=ContextConfig(max_context_tokens=10),
+        facts=facts,
+    )
+
+    assert "new1 new2 new3 new4 new5" in augmented[0].content
+    assert "d1 d2 d3 d4 d5" in augmented[0].content
+    assert "old1 old2 old3 old4 old5" not in augmented[0].content
+
+
+def test_inject_context_prefers_large_document_that_fits_total_budget():
+    backend = _FakeMemory(
+        [RetrievalResult(content="d1 d2 d3 d4 d5 d6 d7 d8", score=1.0)]
+    )
+
+    augmented = inject_context(
+        "query",
+        [Message(role=Role.USER, content="query")],
+        backend,
+        config=ContextConfig(max_context_tokens=10),
+        facts=[Fact(text="f1 f2 f3 f4 f5")],
+    )
+
+    assert "d1 d2 d3 d4 d5 d6 d7 d8" in augmented[0].content
+    assert "f1 f2 f3 f4 f5" not in augmented[0].content
+
+
 def test_inject_context_publishes_event():
     bus = EventBus(record_history=True)
     results = [

tests/memory/test_fact_store.py

@@ -7,7 +7,11 @@ import json
 import pytest
 
 from openjarvis.core.registry import FactStoreRegistry
-from openjarvis.memory.store import LocalFactStore, create_fact_store
+from openjarvis.memory.store import (
+    LocalFactStore,
+    create_fact_store,
+    load_configured_facts,
+)
 
 
 def test_add_and_list(tmp_path):
@@ -145,3 +149,28 @@ def test_create_fact_store_default_path_uses_openjarvis_home(tmp_path, monkeypat
 def test_create_fact_store_unknown_backend(tmp_path):
     with pytest.raises(ValueError):
         create_fact_store("cloud", path=tmp_path / "f.jsonl")
+
+
+def test_load_configured_facts_reads_enabled_store(tmp_path):
+    from types import SimpleNamespace
+
+    path = tmp_path / "facts.jsonl"
+    LocalFactStore(path).add("User likes jazz", source="auto")
+    config = SimpleNamespace(
+        memory=SimpleNamespace(
+            enabled=True,
+            backend="local",
+            facts_path=str(path),
+            max_facts=1000,
+        )
+    )
+
+    assert [fact.text for fact in load_configured_facts(config)] == ["User likes jazz"]
+
+
+def test_load_configured_facts_skips_disabled_memory():
+    from types import SimpleNamespace
+
+    config = SimpleNamespace(memory=SimpleNamespace(enabled=False))
+
+    assert load_configured_facts(config) == []

tests/sdk/test_system.py

@@ -12,6 +12,34 @@ from openjarvis.system import JarvisSystem, SystemBuilder
 
 
 class TestJarvisSystem:
+    def test_new_fields_do_not_shift_existing_positional_arguments(self):
+        """Adding mcp_tools must not reinterpret legacy positional calls."""
+        config = JarvisConfig()
+        bus = EventBus()
+        engine = MagicMock()
+        agent = MagicMock()
+        tools = [MagicMock()]
+        tool_executor = MagicMock()
+        memory_backend = MagicMock()
+
+        system = JarvisSystem(
+            config,
+            bus,
+            engine,
+            "mock",
+            "test-model",
+            agent,
+            "simple",
+            tools,
+            tool_executor,
+            memory_backend,
+        )
+
+        assert system.tools is tools
+        assert system.tool_executor is tool_executor
+        assert system.memory_backend is memory_backend
+        assert system.mcp_tools == []
+
     def test_ask_direct_mode(self):
         engine = MagicMock()
         engine.generate.return_value = {

tests/server/test_agent_manager_routes.py

@@ -4,6 +4,8 @@ from __future__ import annotations
 
 import json
 import tempfile
+import threading
+from concurrent.futures import ThreadPoolExecutor
 from pathlib import Path
 from types import SimpleNamespace
 from unittest.mock import AsyncMock, MagicMock, patch
@@ -602,3 +604,81 @@ class TestLightweightSystemEngineResolution:
             engine=MagicMock(), model="m", config=self._cfg(None, "llamacpp")
         )
         assert captured["key"] == "llamacpp"
+
+    def test_caches_tool_memory_backend_when_prompt_context_is_disabled(
+        self,
+        monkeypatch,
+    ):
+        pytest.importorskip("fastapi")
+        from openjarvis.server import agent_manager_routes as amr
+
+        backend = object()
+        resolver = MagicMock(return_value=backend)
+        monkeypatch.setattr(amr, "_resolve_memory_backend", resolver)
+        config = SimpleNamespace(
+            agent=SimpleNamespace(context_from_memory=False),
+            memory=SimpleNamespace(default_backend="sqlite", db_path="memory.db"),
+        )
+        runtime = SimpleNamespace(
+            memory_backend=None,
+            _owns_memory_backend=False,
+            channel_backend=None,
+            channel_bridge=None,
+            knowledge_db_path=None,
+        )
+
+        system = amr._LightweightSystem(
+            engine=MagicMock(),
+            model="m",
+            config=config,
+            runtime=runtime,
+        )
+
+        resolver.assert_called_once_with(config)
+        assert system.memory_backend is backend
+        assert runtime.memory_backend is backend
+        assert runtime._owns_memory_backend is True
+
+    def test_memory_backend_lazy_init_is_synchronized(self, monkeypatch):
+        pytest.importorskip("fastapi")
+        from openjarvis.server import agent_manager_routes as amr
+
+        backend = object()
+        resolver_calls = 0
+        calls_lock = threading.Lock()
+        duplicate_entered = threading.Event()
+        start = threading.Barrier(8)
+
+        def _resolve(config):
+            nonlocal resolver_calls
+            with calls_lock:
+                resolver_calls += 1
+                call_number = resolver_calls
+                if call_number > 1:
+                    duplicate_entered.set()
+            # A check-then-create race lets another worker enter while the
+            # first resolver is blocked here. The locked implementation times
+            # out once, publishes the backend, and all other workers reuse it.
+            if call_number == 1:
+                duplicate_entered.wait(timeout=0.2)
+            return backend
+
+        monkeypatch.setattr(amr, "_resolve_memory_backend", _resolve)
+        config = SimpleNamespace()
+        runtime = SimpleNamespace(
+            memory_backend=None,
+            _owns_memory_backend=False,
+            _managed_runtime_stopping=False,
+        )
+
+        def _get_backend():
+            start.wait(timeout=2)
+            return amr._get_or_create_memory_backend(runtime, config)
+
+        with ThreadPoolExecutor(max_workers=8) as pool:
+            results = list(pool.map(lambda _: _get_backend(), range(8)))
+
+        assert resolver_calls == 1
+        assert results == [backend] * 8
+        assert runtime.memory_backend is backend
+        assert runtime._owns_memory_backend is True

tests/server/test_deep_research_tools_wiring.py

@@ -2,7 +2,9 @@
 
 from __future__ import annotations
 
+import json
 from pathlib import Path
+from types import SimpleNamespace
 from unittest.mock import MagicMock
 
 import pytest
@@ -15,6 +17,82 @@ except ImportError:
     HAS_FASTAPI = False
 
 from openjarvis.connectors.store import KnowledgeStore
+from openjarvis.core.registry import ToolRegistry
+from openjarvis.core.types import Role, ToolResult
+from openjarvis.tools._stubs import BaseTool, ToolSpec
+
+
+class _ConfiguredResearchProbe(BaseTool):
+    """Configured native tool used to exercise the Deep Research SSE path."""
+
+    tool_id = "configured_research_probe_682"
+    calls = 0
+
+    @property
+    def spec(self) -> ToolSpec:
+        return ToolSpec(
+            name=self.tool_id,
+            description="Configured Deep Research probe",
+            parameters={
+                "type": "object",
+                "properties": {"value": {"type": "string"}},
+                "required": ["value"],
+            },
+        )
+
+    def execute(self, **params) -> ToolResult:
+        type(self).calls += 1
+        return ToolResult(
+            tool_name=self.tool_id,
+            content=f"configured:{params['value']}",
+        )
+
+
+class _MCPResearchProbe(BaseTool):
+    """MCP-shaped adapter that must be merged into the same toolkit."""
+
+    tool_id = "mcp_research_probe_682"
+
+    @property
+    def spec(self) -> ToolSpec:
+        return ToolSpec(name=self.tool_id, description="MCP Deep Research probe")
+
+    def execute(self, **params) -> ToolResult:
+        return ToolResult(tool_name=self.tool_id, content="mcp")
+
+
+class _ScriptedDeepResearchEngine:
+    """Call the configured probe once, then return a final answer."""
+
+    def __init__(self) -> None:
+        self.turns = 0
+        self.advertised_names: list[str] = []
+        self.observed_tool_result = ""
+
+    def generate(self, messages, *, model, **kwargs):
+        self.turns += 1
+        self.advertised_names = [
+            spec["function"]["name"] for spec in kwargs.get("tools", [])
+        ]
+        if self.turns == 1:
+            return {
+                "content": "",
+                "tool_calls": [
+                    {
+                        "id": "call-configured-research-probe",
+                        "type": "function",
+                        "function": {
+                            "name": _ConfiguredResearchProbe.tool_id,
+                            "arguments": json.dumps({"value": "sentinel"}),
+                        },
+                    }
+                ],
+                "usage": {},
+            }
+
+        tool_messages = [message for message in messages if message.role is Role.TOOL]
+        self.observed_tool_result = tool_messages[-1].content
+        return {"content": "complete", "tool_calls": [], "usage": {}}
 
 
 @pytest.mark.skipif(not HAS_FASTAPI, reason="fastapi not installed")
@@ -53,3 +131,111 @@ def test_deep_research_tools_returns_empty_when_no_db() -> None:
     )
 
     assert tools == []
+
+
+@pytest.mark.skipif(not HAS_FASTAPI, reason="fastapi not installed")
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "with_knowledge_db",
+    [False, True],
+    ids=["without-knowledge-db", "with-knowledge-db"],
+)
+async def test_server_deep_research_merges_and_executes_all_tool_sources(
+    tmp_path: Path,
+    with_knowledge_db: bool,
+    monkeypatch,
+) -> None:
+    """Configured and MCP tools reach Deep Research with or without its DB."""
+
+    from openjarvis.server import agent_manager_routes as routes
+
+    start_worker = MagicMock(wraps=routes._start_managed_worker)
+    monkeypatch.setattr(routes, "_start_managed_worker", start_worker)
+
+    db_path = tmp_path / "knowledge.db"
+    if with_knowledge_db:
+        store = KnowledgeStore(str(db_path))
+        store.store("test content", source="test", doc_type="note")
+        store.close()
+
+    if not ToolRegistry.contains(_ConfiguredResearchProbe.tool_id):
+        ToolRegistry.register_value(
+            _ConfiguredResearchProbe.tool_id,
+            _ConfiguredResearchProbe,
+        )
+    _ConfiguredResearchProbe.calls = 0
+
+    mcp_tool = _MCPResearchProbe()
+    app_state = SimpleNamespace(
+        config=SimpleNamespace(memory_files=None, system_prompt=None),
+        memory_backend=None,
+        channel_backend=None,
+        channel_bridge=None,
+        knowledge_db_path=str(db_path),
+        _mcp_clients=[object()],
+        _mcp_tools_cache=(
+            [mcp_tool.to_openai_function()],
+            {mcp_tool.spec.name: mcp_tool},
+        ),
+    )
+    manager = MagicMock()
+    manager.list_messages.return_value = []
+    engine = _ScriptedDeepResearchEngine()
+
+    response = await routes._stream_managed_agent(
+        manager=manager,
+        agent_record={
+            "id": "agent-deep-research-682",
+            "name": "Deep Research Agent",
+            "agent_type": "deep_research",
+            "config": {
+                "model": "test-model",
+                "max_turns": 3,
+                "tools": [_ConfiguredResearchProbe.tool_id],
+            },
+        },
+        user_content="Use the configured research probe",
+        message_id="message-deep-research-682",
+        engine=engine,
+        bus=None,
+        app_state=app_state,
+    )
+
+    body_parts: list[str] = []
+    async for part in response.body_iterator:
+        body_parts.append(part.decode() if isinstance(part, bytes) else part)
+
+    expected_names = {
+        _ConfiguredResearchProbe.tool_id,
+        _MCPResearchProbe.tool_id,
+    }
+    knowledge_names = {
+        "knowledge_search",
+        "knowledge_sql",
+        "scan_chunks",
+        "think",
+    }
+    if with_knowledge_db:
+        expected_names.update(knowledge_names)
+
+    assert set(engine.advertised_names) == expected_names
+    assert len(engine.advertised_names) == len(expected_names)
+    assert not with_knowledge_db or knowledge_names.issubset(engine.advertised_names)
+    assert with_knowledge_db or knowledge_names.isdisjoint(engine.advertised_names)
+    assert engine.turns == 2
+    assert _ConfiguredResearchProbe.calls == 1
+    assert engine.observed_tool_result == "configured:sentinel"
+    assert "data: [DONE]" in "".join(body_parts)
+    start_worker.assert_called_once()
+    assert start_worker.call_args.kwargs["name"].startswith(
+        "managed-agent-deep-research-"
+    )
+    assert app_state._managed_workers == set()
+
+    manager.store_agent_response.assert_called_once()
+    stored = manager.store_agent_response.call_args
+    assert stored.args[:2] == ("agent-deep-research-682", "complete")
+    persisted_calls = stored.kwargs["tool_calls"]
+    assert persisted_calls[0]["tool"] == _ConfiguredResearchProbe.tool_id
+    assert persisted_calls[0]["result"] == "configured:sentinel"
+    assert persisted_calls[0]["success"] is True

tests/server/test_managed_agent_resolved_tools.py

@@ -0,0 +1,302 @@
+"""SSE regression coverage for canonical managed-agent tool resolution."""
+
+from __future__ import annotations
+
+import json
+from types import SimpleNamespace
+from unittest.mock import MagicMock
+
+import pytest
+
+pytest.importorskip("fastapi")
+
+from openjarvis.core.registry import ToolRegistry  # noqa: E402
+from openjarvis.core.types import Role, ToolResult  # noqa: E402
+from openjarvis.engine._stubs import StreamChunk  # noqa: E402
+from openjarvis.tools._stubs import BaseTool, ToolSpec  # noqa: E402
+
+
+class _StatefulConfiguredTool(BaseTool):
+    """A tool whose result identifies the exact instance that executed."""
+
+    tool_id = "stateful_probe"
+    instances: list["_StatefulConfiguredTool"] = []
+
+    def __init__(self) -> None:
+        self.instance_id = len(self.instances) + 1
+        self.calls = 0
+        self.instances.append(self)
+
+    @property
+    def spec(self) -> ToolSpec:
+        return ToolSpec(
+            name="stateful_probe",
+            description=f"configured-instance-{self.instance_id}",
+            parameters={
+                "type": "object",
+                "properties": {"value": {"type": "string"}},
+                "required": ["value"],
+            },
+        )
+
+    def execute(self, **params) -> ToolResult:
+        self.calls += 1
+        return ToolResult(
+            tool_name=self.spec.name,
+            content=(
+                f"instance={self.instance_id};calls={self.calls};"
+                f"value={params['value']}"
+            ),
+        )
+
+
+class _CollidingMCPTool(BaseTool):
+    """An MCP-shaped collision that must lose to the configured native tool."""
+
+    tool_id = "mcp_stateful_probe"
+
+    def __init__(self) -> None:
+        self.calls = 0
+
+    @property
+    def spec(self) -> ToolSpec:
+        return ToolSpec(
+            name="stateful_probe",
+            description="mcp-collision",
+            parameters={"type": "object", "properties": {}},
+        )
+
+    def execute(self, **params) -> ToolResult:
+        self.calls += 1
+        return ToolResult(tool_name=self.spec.name, content="wrong MCP instance")
+
+
+class _ToolCallingEngine:
+    """Advertise the toolkit, request one call, then observe its result."""
+
+    def __init__(
+        self,
+        tool_name: str = "stateful_probe",
+        arguments: dict | None = None,
+    ) -> None:
+        self.tool_name = tool_name
+        self.arguments = arguments or {"value": "sentinel"}
+        self.turns = 0
+        self.advertised_specs: list[dict] = []
+        self.observed_tool_result = ""
+
+    async def stream_full(self, messages, *, model, **kwargs):
+        self.turns += 1
+        self.advertised_specs = list(kwargs.get("tools", []))
+
+        if self.turns == 1:
+            yield StreamChunk(
+                tool_calls=[
+                    {
+                        "index": 0,
+                        "id": f"call-{self.tool_name}",
+                        "type": "function",
+                        "function": {
+                            "name": self.tool_name,
+                            "arguments": json.dumps(self.arguments),
+                        },
+                    }
+                ],
+                finish_reason="tool_calls",
+            )
+            return
+
+        tool_messages = [message for message in messages if message.role is Role.TOOL]
+        self.observed_tool_result = tool_messages[-1].content
+        yield StreamChunk(content="complete")
+        yield StreamChunk(finish_reason="stop")
+
+
+class _FinalOnlyEngine:
+    async def stream_full(self, messages, *, model, **kwargs):
+        yield StreamChunk(content="complete")
+        yield StreamChunk(finish_reason="stop")
+
+
+@pytest.mark.asyncio
+async def test_sse_advertises_and_executes_the_same_resolved_tool_instance() -> None:
+    """The schema and dispatch map must come from one first-wins toolkit."""
+
+    from openjarvis.server.agent_manager_routes import _stream_managed_agent
+
+    _StatefulConfiguredTool.instances.clear()
+    ToolRegistry.register_value("stateful_probe", _StatefulConfiguredTool)
+
+    colliding_mcp = _CollidingMCPTool()
+    mcp_spec = colliding_mcp.to_openai_function()
+    app_state = SimpleNamespace(
+        config=SimpleNamespace(memory_files=None, system_prompt=None),
+        memory_backend=None,
+        channel_backend=None,
+        channel_bridge=None,
+        _mcp_clients=[object()],
+        _mcp_tools_cache=(
+            [mcp_spec],
+            {"stateful_probe": colliding_mcp},
+        ),
+    )
+    manager = MagicMock()
+    manager.list_messages.return_value = []
+    engine = _ToolCallingEngine()
+    custom_spec = {
+        "type": "function",
+        "function": {
+            "name": "stateful_probe",
+            "description": "custom configured schema",
+            "parameters": {
+                "type": "object",
+                "properties": {"value": {"type": "string"}},
+                "required": ["value"],
+            },
+        },
+    }
+
+    response = await _stream_managed_agent(
+        manager=manager,
+        agent_record={
+            "id": "agent-stateful",
+            "name": "Stateful Agent",
+            "agent_type": "simple",
+            "config": {
+                "model": "test-model",
+                "max_turns": 3,
+                "tools": [custom_spec],
+            },
+        },
+        user_content="Use the stateful probe",
+        message_id="message-stateful",
+        engine=engine,
+        bus=None,
+        app_state=app_state,
+    )
+
+    body_parts: list[str] = []
+    async for part in response.body_iterator:
+        body_parts.append(part.decode() if isinstance(part, bytes) else part)
+
+    assert engine.turns == 2
+    assert len(_StatefulConfiguredTool.instances) == 1
+    configured_instance = _StatefulConfiguredTool.instances[0]
+    assert configured_instance.calls == 1
+    assert colliding_mcp.calls == 0
+
+    advertised = [
+        spec
+        for spec in engine.advertised_specs
+        if spec.get("function", {}).get("name") == "stateful_probe"
+    ]
+    assert len(advertised) == 1
+    assert advertised[0] is custom_spec
+    assert advertised[0]["function"]["description"] == "custom configured schema"
+    assert engine.observed_tool_result == "instance=1;calls=1;value=sentinel"
+    assert "data: [DONE]" in "".join(body_parts)
+
+
+@pytest.mark.asyncio
+async def test_sse_mcp_opt_out_skips_discovery(monkeypatch) -> None:
+    """Opting out skips request-local discovery and hides MCP specs."""
+
+    from openjarvis.server import agent_manager_routes as routes
+
+    discovery = MagicMock(side_effect=AssertionError("MCP discovery must not run"))
+    monkeypatch.setattr(routes, "_get_mcp_tools", discovery)
+    manager = MagicMock()
+    manager.list_messages.return_value = []
+    app_state = SimpleNamespace(
+        config=SimpleNamespace(memory_files=None, system_prompt=None),
+        memory_backend=None,
+        channel_backend=None,
+        channel_bridge=None,
+    )
+
+    response = await routes._stream_managed_agent(
+        manager=manager,
+        agent_record={
+            "id": "agent-no-mcp",
+            "name": "No MCP",
+            "agent_type": "simple",
+            "config": {"model": "test-model", "mcp_tools": False},
+        },
+        user_content="Answer directly",
+        message_id="message-no-mcp",
+        engine=_FinalOnlyEngine(),
+        bus=None,
+        app_state=app_state,
+    )
+
+    async for _ in response.body_iterator:
+        pass
+
+    discovery.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_sse_memory_tools_resolve_backend_when_context_injection_is_off(
+    monkeypatch,
+) -> None:
+    """Prompt context opt-out must not disable explicit memory tools."""
+
+    from openjarvis.server import agent_manager_routes as routes
+    from openjarvis.tools.storage_tools import MemoryStoreTool
+
+    if not ToolRegistry.contains("memory_store"):
+        ToolRegistry.register_value("memory_store", MemoryStoreTool)
+
+    backend = MagicMock()
+    backend.store.return_value = "doc-1"
+    resolver = MagicMock(return_value=backend)
+    monkeypatch.setattr(routes, "_resolve_memory_backend", resolver)
+
+    manager = MagicMock()
+    manager.list_messages.return_value = []
+    app_config = SimpleNamespace(
+        memory_files=None,
+        system_prompt=None,
+        agent=SimpleNamespace(context_from_memory=False),
+        memory=SimpleNamespace(default_backend="sqlite", db_path="memory.db"),
+    )
+    app_state = SimpleNamespace(
+        config=app_config,
+        memory_backend=None,
+        channel_backend=None,
+        channel_bridge=None,
+        _mcp_clients=[],
+        _mcp_tools_cache=([], {}),
+    )
+    engine = _ToolCallingEngine(
+        tool_name="memory_store",
+        arguments={"content": "remember me"},
+    )
+
+    response = await routes._stream_managed_agent(
+        manager=manager,
+        agent_record={
+            "id": "agent-memory-tool",
+            "name": "Memory Tool Agent",
+            "agent_type": "simple",
+            "config": {
+                "model": "test-model",
+                "max_turns": 3,
+                "tools": ["memory_store"],
+            },
+        },
+        user_content="Remember this",
+        message_id="message-memory-tool",
+        engine=engine,
+        bus=None,
+        app_state=app_state,
+    )
+
+    async for _ in response.body_iterator:
+        pass
+
+    resolver.assert_called_once_with(app_config)
+    assert app_state.memory_backend is backend
+    assert app_state._owns_memory_backend is True
+    backend.store.assert_called_once_with("remember me", source="")
+    assert engine.observed_tool_result == "Stored as doc-1"

tests/server/test_mcp_tools_cache.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import json
+import threading
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -118,7 +119,7 @@ def test_does_not_cache_empty_results(mock_load_config: MagicMock):
 
     with (
         patch("openjarvis.mcp.transport.StreamableHTTPTransport"),
-        patch("openjarvis.mcp.client.MCPClient"),
+        patch("openjarvis.mcp.client.MCPClient") as MockClient,
         patch("openjarvis.tools.mcp_adapter.MCPToolProvider") as MockProvider,
     ):
         # First call: discovery returns empty
@@ -127,6 +128,8 @@ def test_does_not_cache_empty_results(mock_load_config: MagicMock):
 
         tools1, _ = _get_mcp_tools(app_state)
         assert len(tools1) == 0
+        MockClient.return_value.close.assert_called_once_with()
+        assert getattr(app_state, "_mcp_clients", []) == []
 
         # Verify no cache was set (empty result)
         assert getattr(app_state, "_mcp_tools_cache", None) is None
@@ -152,3 +155,298 @@ def test_handles_config_load_failure(mock_load_config: MagicMock):
 
     assert tools == []
     assert adapters == {}
+
+
+@patch("openjarvis.core.config.load_config")
+def test_uses_preloaded_full_system_pool(mock_load_config: MagicMock):
+    """Server and scheduled paths reuse one unfiltered MCP discovery."""
+
+    from openjarvis.server.agent_manager_routes import _get_mcp_tools
+
+    adapter = _make_adapter("preloaded_tool")
+    app_state = _FakeAppState()
+    app_state.mcp_tools = [adapter]
+
+    tools, adapters = _get_mcp_tools(app_state)
+
+    mock_load_config.assert_not_called()
+    assert tools[0]["function"]["name"] == "preloaded_tool"
+    assert adapters == {"preloaded_tool": adapter}
+
+
+@patch("openjarvis.core.config.load_config")
+def test_preloaded_duplicate_names_are_first_wins(mock_load_config: MagicMock):
+    """SSE and executor paths choose the same adapter on name collisions."""
+
+    from openjarvis.server.agent_manager_routes import _get_mcp_tools
+
+    first = _make_adapter("duplicate")
+    second = _make_adapter("duplicate")
+    app_state = _FakeAppState()
+    app_state.mcp_tools = [first, second]
+
+    tools, adapters = _get_mcp_tools(app_state)
+
+    mock_load_config.assert_not_called()
+    assert len(tools) == 1
+    assert adapters == {"duplicate": first}
+
+
+def test_app_shutdown_stops_scheduler_before_closing_shared_mcp_clients() -> None:
+    """Shutdown quiesces and drains every user of the shared MCP pool."""
+
+    from fastapi.testclient import TestClient
+
+    from openjarvis.core.config import JarvisConfig
+    from openjarvis.server.agent_manager_routes import _start_managed_worker
+    from openjarvis.server.app import create_app
+
+    events: list[str] = []
+    release_worker = threading.Event()
+    worker_finished = threading.Event()
+
+    class _Scheduler:
+        def request_stop(self):
+            events.append("scheduler-stop")
+
+        def wait_stopped(self, timeout=10):
+            events.append("scheduler-wait")
+            return True
+
+    scheduler = _Scheduler()
+    mcp_client = MagicMock()
+    memory_backend = MagicMock()
+    channel_bridge = MagicMock()
+
+    def _close_mcp():
+        events.append("mcp")
+        release_worker.set()
+
+    mcp_client.close.side_effect = _close_mcp
+    memory_backend.close.side_effect = lambda: events.append("memory")
+    channel_bridge.disconnect.side_effect = lambda: events.append("channel")
+    config = JarvisConfig()
+    config.analytics.enabled = False
+    config.traces.enabled = False
+
+    app = create_app(
+        MagicMock(),
+        "test-model",
+        config=config,
+        channel_bridge=channel_bridge,
+        agent_scheduler=scheduler,
+        mcp_clients=[mcp_client],
+        memory_backend=memory_backend,
+        own_memory_backend=True,
+    )
+
+    def _worker():
+        release_worker.wait(timeout=2)
+        events.append("worker-finished")
+        worker_finished.set()
+
+    _start_managed_worker(app.state, _worker, name="test-managed-worker")
+
+    with TestClient(app):
+        pass
+
+    mcp_client.close.assert_called_once_with()
+    memory_backend.close.assert_called_once_with()
+    channel_bridge.disconnect.assert_called_once_with()
+    assert worker_finished.is_set()
+    assert app.state.memory_backend is None
+    assert app.state._owns_memory_backend is False
+    assert app.state._managed_runtime_stopping is True
+    assert app.state._managed_workers == set()
+    assert events.index("channel") < events.index("mcp")
+    assert events.index("scheduler-stop") < events.index("mcp")
+    assert events.index("mcp") < events.index("worker-finished")
+    assert events.index("worker-finished") < events.index("memory")
+    with pytest.raises(RuntimeError, match="shutting down"):
+        _start_managed_worker(app.state, lambda: None, name="too-late-worker")
+
+
+def test_shutdown_interrupts_mcp_client_during_lazy_initialization() -> None:
+    """A client is registered before initialize() can block on transport I/O."""
+
+    from fastapi.testclient import TestClient
+
+    from openjarvis.core.config import JarvisConfig
+    from openjarvis.server.agent_manager_routes import (
+        _get_mcp_tools,
+        _start_managed_worker,
+    )
+    from openjarvis.server.app import create_app
+
+    initialize_started = threading.Event()
+    initialize_released = threading.Event()
+    discovery_finished = threading.Event()
+
+    class _BlockingClient:
+        def __init__(self):
+            self.closed = False
+            self.close_calls = 0
+
+        def initialize(self):
+            initialize_started.set()
+            initialize_released.wait(timeout=2)
+            if self.closed:
+                raise RuntimeError("transport closed during initialize")
+
+        def close(self):
+            self.close_calls += 1
+            self.closed = True
+            initialize_released.set()
+
+    client = _BlockingClient()
+    config = JarvisConfig()
+    config.analytics.enabled = False
+    config.traces.enabled = False
+    app = create_app(MagicMock(), "test-model", config=config)
+    mcp_config = _make_config(
+        servers_json=json.dumps([{"name": "blocking", "url": "http://localhost:9999"}])
+    )
+
+    def _discover():
+        try:
+            _get_mcp_tools(app.state)
+        finally:
+            discovery_finished.set()
+
+    with (
+        patch("openjarvis.core.config.load_config", return_value=mcp_config),
+        patch("openjarvis.mcp.transport.StreamableHTTPTransport"),
+        patch("openjarvis.mcp.client.MCPClient", return_value=client),
+    ):
+        _start_managed_worker(
+            app.state,
+            _discover,
+            name="blocking-mcp-discovery",
+        )
+        assert initialize_started.wait(timeout=2)
+        with app.state._mcp_clients_lock:
+            assert client in app.state._mcp_clients
+
+        with TestClient(app):
+            pass
+
+    assert client.close_calls >= 1
+    assert discovery_finished.is_set()
+    assert app.state._managed_workers == set()
+    assert getattr(app.state, "_mcp_tools_cache", None) is None
+
+
+def test_app_shutdown_closes_lazily_created_memory_backend(monkeypatch) -> None:
+    """A backend opened by a managed route is owned and closed by the app."""
+
+    from fastapi.testclient import TestClient
+
+    from openjarvis.core.config import JarvisConfig
+    from openjarvis.server import agent_manager_routes as routes
+    from openjarvis.server.app import create_app
+
+    backend = MagicMock()
+    monkeypatch.setattr(routes, "_resolve_memory_backend", lambda config: backend)
+    config = JarvisConfig()
+    config.analytics.enabled = False
+    config.traces.enabled = False
+    app = create_app(MagicMock(), "test-model", config=config)
+
+    assert routes._get_or_create_memory_backend(app.state, config) is backend
+    assert app.state._owns_memory_backend is True
+
+    with TestClient(app):
+        pass
+
+    backend.close.assert_called_once_with()
+    assert app.state.memory_backend is None
+
+
+def test_app_shutdown_keeps_owned_memory_open_for_live_worker(monkeypatch) -> None:
+    """A timed-out worker must never resume against a closed backend."""
+
+    from fastapi.testclient import TestClient
+
+    from openjarvis.core.config import JarvisConfig
+    from openjarvis.server import app as app_module
+    from openjarvis.server.agent_manager_routes import _start_managed_worker
+
+    monkeypatch.setattr(app_module, "_MANAGED_SHUTDOWN_GRACE_SECONDS", 0.01)
+    monkeypatch.setattr(app_module, "_MANAGED_SHUTDOWN_DRAIN_SECONDS", 0.01)
+
+    release_worker = threading.Event()
+    worker_holds_memory_lock = threading.Event()
+    shutdown_finished = threading.Event()
+    shutdown_errors: list[BaseException] = []
+    backend = MagicMock()
+    config = JarvisConfig()
+    config.analytics.enabled = False
+    config.traces.enabled = False
+    app = app_module.create_app(
+        MagicMock(),
+        "test-model",
+        config=config,
+        memory_backend=backend,
+        own_memory_backend=True,
+    )
+
+    def _hold_memory_lock():
+        with app.state._memory_backend_lock:
+            worker_holds_memory_lock.set()
+            release_worker.wait(timeout=2)
+
+    worker = _start_managed_worker(
+        app.state,
+        _hold_memory_lock,
+        name="memory-using-straggler",
+    )
+    assert worker_holds_memory_lock.wait(timeout=2)
+
+    def _shutdown_app():
+        try:
+            with TestClient(app):
+                pass
+        except BaseException as exc:
+            shutdown_errors.append(exc)
+        finally:
+            shutdown_finished.set()
+
+    shutdown_thread = threading.Thread(target=_shutdown_app, daemon=True)
+    shutdown_thread.start()
+
+    try:
+        assert shutdown_finished.wait(timeout=1)
+        assert shutdown_errors == []
+        backend.close.assert_not_called()
+        assert app.state.memory_backend is backend
+        assert app.state._owns_memory_backend is True
+    finally:
+        release_worker.set()
+        worker.join(timeout=2)
+        shutdown_thread.join(timeout=2)
+
+
+def test_app_shutdown_leaves_borrowed_memory_backend_open() -> None:
+    """An injected backend remains owned by its caller unless opted in."""
+
+    from fastapi.testclient import TestClient
+
+    from openjarvis.core.config import JarvisConfig
+    from openjarvis.server.app import create_app
+
+    backend = MagicMock()
+    config = JarvisConfig()
+    config.analytics.enabled = False
+    config.traces.enabled = False
+    app = create_app(
+        MagicMock(),
+        "test-model",
+        config=config,
+        memory_backend=backend,
+    )
+
+    with TestClient(app):
+        pass
+
+    backend.close.assert_not_called()
+    assert app.state.memory_backend is backend

tests/server/test_model_management.py

@@ -213,6 +213,7 @@ class TestStreamingResilience:
         engine = _make_engine()
         agent = MagicMock()
         agent.agent_id = "simple"
+        agent._tools = []
         agent.run.return_value = AgentResult(
             content="agent response",
             turns=1,
@@ -265,6 +266,28 @@ class TestModelsEndpointExtended:
         assert "qwen3.5:9b" in ids
         assert "qwen3:0.6b" in ids
 
+    def test_models_list_filters_embedding_only_models(self):
+        engine = _make_engine(
+            models=["nomic-embed-text", "all-minilm:latest", "qwen3.5:4b"],
+        )
+        client = TestClient(create_app(engine, "qwen3.5:4b"))
+
+        resp = client.get("/v1/models")
+
+        assert resp.status_code == 200
+        assert [m["id"] for m in resp.json()["data"]] == ["qwen3.5:4b"]
+
+    def test_models_list_returns_empty_when_only_embedders_are_installed(self):
+        engine = _make_engine(
+            models=["nomic-embed-text", "hf.co/BAAI/bge-m3:latest"],
+        )
+        client = TestClient(create_app(engine, "nomic-embed-text"))
+
+        resp = client.get("/v1/models")
+
+        assert resp.status_code == 200
+        assert resp.json()["data"] == []
+
     def test_models_empty_engine(self):
         """When engine.list_models() returns empty, endpoint still succeeds."""
         engine = _make_engine(models=[])

tests/server/test_recommended_model.py

@@ -50,3 +50,42 @@ def test_parse_param_count():
     assert _parse_param_count("qwen3.5:0.8b") == 0.8
     assert _parse_param_count("qwen3.5:35b") == 35.0
     assert _parse_param_count("gpt-4o") == 0.0
+
+
+@pytest.mark.skipif(not HAS_FASTAPI, reason="fastapi not installed")
+def test_recommended_model_skips_embed_only():
+    """Embed-only models must never be recommended for chat."""
+    from openjarvis.server.agent_manager_routes import _pick_recommended_model
+
+    models = [
+        "nomic-embed-text",
+        "qwen3.5:4b",
+        "mxbai-embed-large",
+        "qwen3.5:9b",
+    ]
+    result = _pick_recommended_model(models)
+    assert result["model"] == "qwen3.5:4b"
+    assert "embed" not in result["model"]
+
+
+@pytest.mark.skipif(not HAS_FASTAPI, reason="fastapi not installed")
+def test_recommended_model_embed_only_returns_empty():
+    """If only embedders are installed, recommend nothing (not nomic-embed)."""
+    from openjarvis.server.agent_manager_routes import _pick_recommended_model
+
+    result = _pick_recommended_model(["nomic-embed-text", "mxbai-embed-large"])
+    assert result["model"] == ""
+    assert "No local chat model" in result["reason"]
+
+
+@pytest.mark.skipif(not HAS_FASTAPI, reason="fastapi not installed")
+def test_is_embed_only_model():
+    from openjarvis.server.model_capabilities import is_embed_only_model
+
+    assert is_embed_only_model("nomic-embed-text")
+    assert is_embed_only_model("mxbai-embed-large")
+    assert is_embed_only_model("text-embedding-3-small")
+    assert is_embed_only_model("all-minilm:latest")
+    assert is_embed_only_model("hf.co/BAAI/bge-m3:latest")
+    assert not is_embed_only_model("qwen3.5:4b")
+    assert not is_embed_only_model("codegemma:7b")

tests/server/test_routes.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import json
-from unittest.mock import MagicMock
+from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
@@ -11,6 +11,7 @@ fastapi = pytest.importorskip("fastapi")
 from fastapi.testclient import TestClient  # noqa: E402
 
 from openjarvis.core.events import EventBus, EventType  # noqa: E402
+from openjarvis.core.types import Role  # noqa: E402
 from openjarvis.server.app import create_app  # noqa: E402
 
 # ---------------------------------------------------------------------------
@@ -534,6 +535,94 @@ class TestChatCompletions:
                     content += delta_content
         assert content == "Hello world"
 
+    def test_streaming_without_client_tools_uses_configured_agent(self):
+        """Server-side tools remain available to streaming web clients (#735)."""
+        from openjarvis.agents.orchestrator import OrchestratorAgent
+        from openjarvis.core.types import ToolResult
+        from openjarvis.tools._stubs import BaseTool, ToolSpec
+
+        executions: list[str] = []
+
+        class _FileReadTool(BaseTool):
+            @property
+            def spec(self):
+                return ToolSpec(
+                    name="file_read",
+                    description="Read a file",
+                    parameters={
+                        "type": "object",
+                        "properties": {"path": {"type": "string"}},
+                    },
+                )
+
+            def execute(self, **params):
+                executions.append(params["path"])
+                return ToolResult(
+                    tool_name="file_read",
+                    content="README fixture contents",
+                    success=True,
+                )
+
+        engine = _make_engine(content="ENGINE BYPASS")
+        engine.generate.side_effect = [
+            {
+                "content": "",
+                "tool_calls": [
+                    {
+                        "id": "call_1",
+                        "name": "file_read",
+                        "arguments": '{"path": "README.md"}',
+                    }
+                ],
+                "usage": {},
+            },
+            {
+                "content": "README fixture contents",
+                "finish_reason": "stop",
+                "usage": {},
+            },
+        ]
+        agent = OrchestratorAgent(
+            engine,
+            "test-model",
+            tools=[_FileReadTool()],
+            bus=EventBus(),
+            max_turns=3,
+            temperature=0.7,
+            max_tokens=128,
+            system_prompt="Use the configured tools.",
+        )
+        app = create_app(
+            engine,
+            "test-model",
+            agent=agent,
+            bus=EventBus(),
+            config=_test_config(),
+        )
+        client = TestClient(app)
+
+        resp = client.post(
+            "/v1/chat/completions",
+            json={
+                "model": "test-model",
+                "messages": [{"role": "user", "content": "Read README.md"}],
+                "stream": True,
+            },
+        )
+
+        assert resp.status_code == 200
+        content = ""
+        for line in resp.text.strip().split("\n"):
+            if not line.startswith("data:") or "[DONE]" in line:
+                continue
+            data = json.loads(line[5:].strip())
+            delta = data.get("choices", [{}])[0].get("delta", {})
+            content += delta.get("content") or ""
+
+        assert content == "README fixture contents"
+        assert executions == ["README.md"]
+        assert engine.generate.call_count == 2
+
     def test_streaming_with_tools_emits_tool_calls_and_bypasses_agent(self):
         """Regression for the streaming analog of #414.
 
@@ -758,6 +847,47 @@ class TestIdentityPromptInjection:
         assert len(system_msgs) == 1
         assert system_msgs[0].content == "Be terse."
 
+    def test_stream_uses_grounded_agent_result_without_replay(self):
+        """Regression for #734: web streaming emits the agent's final answer."""
+        from openjarvis.core.events import EventBus
+
+        captured: list = []
+        engine = _make_capturing_engine(captured)
+        agent = _make_agent(content="My name is Jarvis Prime.")
+        agent._tools = [object()]
+        agent._engine = engine
+        client = TestClient(
+            create_app(
+                engine,
+                "test-model",
+                agent=agent,
+                bus=EventBus(),
+                config=_identity_config(),
+            )
+        )
+
+        resp = client.post(
+            "/v1/chat/completions",
+            json={
+                "model": "test-model",
+                "messages": [{"role": "user", "content": "who are you?"}],
+                "stream": True,
+            },
+        )
+
+        assert resp.status_code == 200
+        streamed_content = ""
+        for line in resp.text.splitlines():
+            if not line.startswith("data: {"):
+                continue
+            payload = json.loads(line.removeprefix("data: "))
+            choices = payload.get("choices", [])
+            if choices and choices[0]["delta"].get("content"):
+                streamed_content += choices[0]["delta"]["content"]
+        assert streamed_content == "My name is Jarvis Prime."
+        assert captured == []
+        agent.run.assert_called_once()
+
     def test_direct_injects_identity_when_absent(self):
         captured: list = []
         engine = _make_capturing_engine(captured)
@@ -798,6 +928,101 @@ class TestIdentityPromptInjection:
         assert len(system_msgs) == 1
         assert system_msgs[0].content == "Be terse."
 
+    def test_direct_merges_identity_and_auto_memory_into_one_system_message(self):
+        from openjarvis.memory.store import Fact
+
+        class _MemoryService:
+            def list_facts(self):
+                return [Fact(text="The user's favorite color is blue")]
+
+        captured: list = []
+        engine = _make_capturing_engine(captured)
+        cfg = _identity_config()
+        cfg.agent.context_from_memory = True
+        client = TestClient(
+            create_app(
+                engine,
+                "test-model",
+                config=cfg,
+                memory_service=_MemoryService(),
+            )
+        )
+
+        resp = client.post(
+            "/v1/chat/completions",
+            json={
+                "model": "test-model",
+                "messages": [{"role": "user", "content": "What is my favorite color?"}],
+            },
+        )
+
+        assert resp.status_code == 200
+        messages = engine.generate.call_args.args[0]
+        system_messages = [m for m in messages if m.role == Role.SYSTEM]
+        assert len(system_messages) == 1
+        assert "OpenJarvis" in system_messages[0].content
+        assert "favorite color is blue" in system_messages[0].content
+
+    def test_memory_context_preserves_assistant_tool_calls(self):
+        from openjarvis.memory.store import Fact
+
+        class _MemoryService:
+            def list_facts(self):
+                return [Fact(text="User likes jazz")]
+
+        captured: list = []
+        engine = _make_capturing_engine(captured)
+        cfg = _identity_config()
+        cfg.agent.context_from_memory = True
+        client = TestClient(
+            create_app(
+                engine,
+                "test-model",
+                config=cfg,
+                memory_service=_MemoryService(),
+            )
+        )
+
+        resp = client.post(
+            "/v1/chat/completions",
+            json={
+                "model": "test-model",
+                "messages": [
+                    {"role": "user", "content": "Run the lookup"},
+                    {
+                        "role": "assistant",
+                        "content": "",
+                        "tool_calls": [
+                            {
+                                "id": "call_1",
+                                "type": "function",
+                                "function": {
+                                    "name": "lookup",
+                                    "arguments": '{"query":"jazz"}',
+                                },
+                            }
+                        ],
+                    },
+                    {
+                        "role": "tool",
+                        "content": "result",
+                        "tool_call_id": "call_1",
+                    },
+                    {"role": "user", "content": "What did it find?"},
+                ],
+            },
+        )
+
+        assert resp.status_code == 200
+        messages = engine.generate.call_args.args[0]
+        assistant = next(
+            message for message in messages if message.role == Role.ASSISTANT
+        )
+        assert assistant.tool_calls is not None
+        assert assistant.tool_calls[0].id == "call_1"
+        assert assistant.tool_calls[0].name == "lookup"
+        assert assistant.tool_calls[0].arguments == '{"query":"jazz"}'
+
     def test_direct_injects_soul_persona_when_present(self, tmp_path):
         """Regression: /v1/chat/completions previously injected only the bare
         ``default_system_prompt`` blurb via a hand-rolled lookup, bypassing
@@ -883,6 +1108,64 @@ class TestModelsEndpoint:
         data = resp.json()
         assert len(data["data"]) == 3
 
+    def test_configured_litellm_model_is_listed(self):
+        """Regression for #713: LiteLLM models must reach the Web UI."""
+        model = "groq/llama-3.3-70b-versatile"
+        engine = _make_engine(models=[model])
+        engine.engine_id = "litellm"
+        app = create_app(
+            engine,
+            model,
+            engine_name="litellm",
+            config=_test_config(),
+        )
+
+        with patch(
+            "openjarvis.server.cloud_router.list_local_models",
+            new_callable=AsyncMock,
+        ) as list_local_models:
+            list_local_models.return_value = []
+            client = TestClient(app)
+            resp = client.get("/v1/models")
+
+        assert resp.status_code == 200
+        assert [item["id"] for item in resp.json()["data"]] == [model]
+        assert resp.json()["data"][0]["owned_by"] == "litellm"
+
+    def test_litellm_provider_model_streams_through_active_engine(self):
+        """A LiteLLM ``provider/model`` ID must not bypass its engine."""
+        model = "groq/llama-3.3-70b-versatile"
+        engine = _make_engine(models=[model])
+        engine.engine_id = "litellm"
+        app = create_app(
+            engine,
+            model,
+            engine_name="litellm",
+            config=_test_config(),
+        )
+
+        async def direct_cloud_tokens():
+            yield "wrong backend"
+
+        with patch(
+            "openjarvis.server.cloud_router.stream_cloud",
+            return_value=direct_cloud_tokens(),
+        ) as stream_cloud:
+            client = TestClient(app)
+            resp = client.post(
+                "/v1/chat/completions",
+                json={
+                    "model": model,
+                    "messages": [{"role": "user", "content": "hello"}],
+                    "stream": True,
+                },
+            )
+
+        assert resp.status_code == 200
+        stream_cloud.assert_not_called()
+        assert "Hello" in resp.text
+        assert '"engine": "litellm"' in resp.text
+
 
 # ---------------------------------------------------------------------------
 # Health endpoint tests

tests/server/test_stream_bridge.py

@@ -0,0 +1,92 @@
+"""Regression tests for streaming completed agent responses."""
+
+from __future__ import annotations
+
+import asyncio
+import json
+from unittest.mock import MagicMock
+
+import pytest
+
+pytest.importorskip("fastapi")
+
+from openjarvis.agents._stubs import AgentResult  # noqa: E402
+from openjarvis.core.events import EventBus  # noqa: E402
+from openjarvis.core.types import ToolResult  # noqa: E402
+from openjarvis.server.models import ChatCompletionRequest  # noqa: E402
+from openjarvis.server.stream_bridge import AgentStreamBridge  # noqa: E402
+
+
+def _streamed_content(events: list[str]) -> str:
+    """Join assistant content from OpenAI-compatible data chunks."""
+    content = []
+    for event in events:
+        if not event.startswith("data: {"):
+            continue
+        payload = json.loads(event.removeprefix("data: ").strip())
+        choices = payload.get("choices")
+        if choices and choices[0]["delta"].get("content"):
+            content.append(choices[0]["delta"]["content"])
+    return "".join(content)
+
+
+def test_stream_replays_grounded_agent_result_without_second_inference():
+    grounded_content = "My name is Jarvis. The tool reports 72 degrees."
+    agent = MagicMock()
+    agent._model = "configured-model"
+    agent.run.return_value = AgentResult(
+        content=grounded_content,
+        tool_results=[
+            ToolResult(tool_name="weather", content="72 degrees", success=True)
+        ],
+        metadata={"prompt_tokens": 10, "completion_tokens": 12, "total_tokens": 22},
+    )
+
+    async def ungrounded_replay(*args, **kwargs):
+        raise AssertionError("stream_full must not run after agent.run")
+        yield  # pragma: no cover
+
+    agent._engine.stream_full = ungrounded_replay
+    request = ChatCompletionRequest(
+        model="requested-model",
+        messages=[{"role": "user", "content": "Who are you, and what's outside?"}],
+        stream=True,
+    )
+    bridge = AgentStreamBridge(agent, EventBus(), request.model, request)
+
+    async def collect_events() -> list[str]:
+        return [event async for event in bridge.stream()]
+
+    events = asyncio.run(collect_events())
+
+    assert _streamed_content(events) == grounded_content
+    assert any(event.startswith("event: tool_results\n") for event in events)
+    agent.run.assert_called_once()
+    assert agent._model == "configured-model"
+
+
+def test_tool_call_start_serializes_arguments_for_sse_without_mutating_event():
+    bridge = object.__new__(AgentStreamBridge)
+    event_data = {
+        "tool": "web_search",
+        "arguments": {"query": "python"},
+        "agent": "agent-1",
+    }
+
+    event = bridge._format_named_event("tool_call_start", event_data)
+    payload = json.loads(event.split("data: ", 1)[1])
+
+    assert payload["arguments"] == '{"query": "python"}'
+    assert event_data["arguments"] == {"query": "python"}
+
+
+def test_tool_call_start_preserves_already_serialized_arguments():
+    bridge = object.__new__(AgentStreamBridge)
+
+    event = bridge._format_named_event(
+        "tool_call_start",
+        {"tool": "web_search", "arguments": '{"query":"python"}'},
+    )
+    payload = json.loads(event.split("data: ", 1)[1])
+
+    assert payload["arguments"] == '{"query":"python"}'

tests/server/test_ws_bridge.py

@@ -2,7 +2,9 @@
 
 from __future__ import annotations
 
+import asyncio
 import time
+from types import SimpleNamespace
 
 import pytest
 
@@ -60,3 +62,102 @@ class TestWSBridge:
             time.sleep(0.05)  # Let call_soon_threadsafe deliver to queue
             data = ws.receive_json()
             assert data["data"]["agent_id"] == "agent-A"
+
+    def test_client_disconnect_stops_handler(self, event_bus):
+        async def exercise():
+            from openjarvis.server.ws_bridge import create_ws_router
+
+            class FakeWebSocket:
+                app = SimpleNamespace(state=SimpleNamespace(api_key=""))
+                query_params = {}
+                headers = {}
+
+                async def accept(self):
+                    pass
+
+                async def receive(self):
+                    return {"type": "websocket.disconnect"}
+
+            endpoint = create_ws_router(event_bus).routes[0].endpoint
+            await asyncio.wait_for(endpoint(FakeWebSocket()), timeout=1)
+
+        asyncio.run(exercise())
+
+    def test_simultaneous_client_message_does_not_drop_event(self, event_bus):
+        async def exercise():
+            from openjarvis.server.ws_bridge import create_ws_router
+
+            class FakeWebSocket:
+                def __init__(self):
+                    self.app = SimpleNamespace(state=SimpleNamespace(api_key=""))
+                    self.query_params = {}
+                    self.headers = {}
+                    self.sent = []
+                    self.receive_count = 0
+                    self.disconnect = asyncio.Event()
+
+                async def accept(self):
+                    pass
+
+                async def receive(self):
+                    self.receive_count += 1
+                    if self.receive_count == 1:
+                        event_bus.publish(
+                            EventType.AGENT_TICK_START, {"agent_id": "not-dropped"}
+                        )
+                        return {"type": "websocket.receive", "text": "client message"}
+                    await self.disconnect.wait()
+                    return {"type": "websocket.disconnect"}
+
+                async def send_json(self, payload):
+                    self.sent.append(payload)
+                    self.disconnect.set()
+
+            websocket = FakeWebSocket()
+            endpoint = create_ws_router(event_bus).routes[0].endpoint
+
+            await asyncio.wait_for(endpoint(websocket), timeout=1)
+
+            assert websocket.sent[0]["data"]["agent_id"] == "not-dropped"
+
+        asyncio.run(exercise())
+
+    def test_cancelling_handler_cleans_up_child_tasks(self, event_bus):
+        async def exercise():
+            from openjarvis.server.ws_bridge import create_ws_router
+
+            class FakeWebSocket:
+                def __init__(self):
+                    self.app = SimpleNamespace(state=SimpleNamespace(api_key=""))
+                    self.query_params = {}
+                    self.headers = {}
+                    self.receiving = asyncio.Event()
+                    self.receive_cancelled = asyncio.Event()
+
+                async def accept(self):
+                    pass
+
+                async def receive(self):
+                    self.receiving.set()
+                    try:
+                        await asyncio.Event().wait()
+                    finally:
+                        self.receive_cancelled.set()
+
+            websocket = FakeWebSocket()
+            endpoint = create_ws_router(event_bus).routes[0].endpoint
+            handler = asyncio.create_task(endpoint(websocket))
+            await websocket.receiving.wait()
+
+            handler.cancel()
+            with pytest.raises(asyncio.CancelledError):
+                await handler
+
+            assert websocket.receive_cancelled.is_set()
+            assert not [
+                task
+                for task in asyncio.all_tasks()
+                if task is not asyncio.current_task() and not task.done()
+            ]
+
+        asyncio.run(exercise())

tests/telemetry/test_instrumented_engine.py

@@ -71,6 +71,17 @@ class TestInstrumentedEngine:
         assert record.prompt_tokens == 10
         assert record.completion_tokens == 5
 
+    def test_generate_records_cost(self, mock_engine, bus):
+        mock_engine.generate.return_value["cost_usd"] = 0.0015
+        ie = InstrumentedEngine(mock_engine, bus)
+        messages = [Message(role=Role.USER, content="Hi")]
+        ie.generate(messages, model="test")
+
+        event = next(
+            e for e in bus.history if e.event_type == EventType.TELEMETRY_RECORD
+        )
+        assert event.data["record"].cost_usd == pytest.approx(0.0015)
+
     def test_list_models_delegates(self, mock_engine, bus):
         ie = InstrumentedEngine(mock_engine, bus)
         assert ie.list_models() == ["test-model"]

tests/tools/test_tool_registration.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import importlib
+import subprocess
 import sys
 
 from openjarvis.core.registry import ToolRegistry
@@ -68,6 +69,10 @@ EXPECTED_TOOLS = {
     "kg_add_relation",
     "kg_query",
     "kg_neighbors",
+    # knowledge_sql.py
+    "knowledge_sql",
+    # scan_chunks.py
+    "scan_chunks",
 }
 
 
@@ -100,3 +105,25 @@ def test_all_builtin_tools_registered():
     assert not missing, (
         f"Tools not registered (missing import in __init__.py?): {sorted(missing)}"
     )
+
+
+def test_package_import_registers_deep_research_tools():
+    """Registration must not depend on another module being imported first."""
+    result = subprocess.run(
+        [
+            sys.executable,
+            "-c",
+            (
+                "import openjarvis.tools; "
+                "from openjarvis.core.registry import ToolRegistry; "
+                "expected = {'knowledge_sql', 'scan_chunks'}; "
+                "missing = expected - set(ToolRegistry.keys()); "
+                "assert not missing, f'Missing tools: {sorted(missing)}'"
+            ),
+        ],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+
+    assert result.returncode == 0, result.stderr

tools/pearl-reference-oracle/README.md

@@ -1,111 +0,0 @@
-# Pearl reference oracle (OpenJarvis Phase 0 deliverable)
-
-Phase 0-B of [Spec B](../../docs/design/2026-05-05-apple-silicon-pearl-mining-design.md)
-called for "build a Python reference oracle for NoisyGEMM, validate against the
-Pearl CUDA reference."
-
-**Phase 0 found the oracle already exists upstream**, in two complementary forms:
-
-| Layer | Upstream location | What it covers |
-|---|---|---|
-| Pure-Rust mining algorithm exposed to Python | `pearl/py-pearl-mining` | The complete `mine()` + `verify_plain_proof()` cycle. CPU-only. Hardware-portable. |
-| PyTorch reference of production NoisyGEMM | `pearl/miner/miner-base/src/miner_base/noisy_gemm.py` | The same NoisyGEMM that vllm-miner accelerates with H100 CUDA. Bit-exact denoising verified by upstream test (`tests/test_noisy_gemm.py:92`). |
-
-So this directory contains:
-
-1. `smoke_test.py` — a runnable script that **actually mines a block on this machine** using the upstream Rust path, demonstrating the v1 architecture works on Apple Silicon (or any platform where `py-pearl-mining` builds).
-2. This README documenting where the reference math lives.
-
-## What this is *not*
-
-This is **not a reimplementation** of NoisyGEMM. The original Spec B planned for that;
-Phase 0 made it unnecessary. If you're tempted to write `noisy_gemm.py` here, stop —
-read `pearl/miner/miner-base/src/miner_base/noisy_gemm.py` instead.
-
-## Setup
-
-You need:
-
-- macOS arm64 (M1/M2/M3/M4) **or** Linux x86_64 / aarch64
-- Python 3.12 (`uv venv --python 3.12 .venv` is the easiest)
-- Rust 1.78+ (any recent toolchain — verified with 1.94 on macOS arm64)
-- The Pearl source tree somewhere on disk
-
-Build the wheel and install it (one-time, ~60 s on a fast Mac, ~5 min on first build):
-
-```bash
-# from the Pearl repo root
-cd py-pearl-mining
-uv pip install maturin
-maturin build --release --interpreter "$(which python)"
-
-# install the resulting wheel
-uv pip install target/wheels/py_pearl_mining-*.whl
-```
-
-Or if Pearl publishes to PyPI in the future:
-
-```bash
-uv pip install py-pearl-mining
-```
-
-## Run the smoke test
-
-```bash
-python smoke_test.py
-```
-
-Actual output on Apple Silicon M2 Max (numbers will vary by hardware and run):
-
-```
-host: macOS-26.4.1-arm64-arm-64bit (arm64)
-python: 3.12.1
-[ok] pearl_mining loaded from <site-packages>/pearl_mining/__init__.py
-[ok] PUBLICDATA_SIZE=164  MERKLE_LEAF_SIZE=1024
-[ok] mine(m=256, n=128, k=1024, rank=32) returned a proof in 0.119 s
-       proof.m=256 proof.n=128 proof.k=1024  noise_rank=32
-       a.row_indices=[177, 185, 241, 249]  bt.row_indices=[80, 81, 88, 89, 112, 113, 120, 121]
-[ok] verify_plain_proof: ok=True ('Mining solution verified successfully', 0.2 ms)
-
-[ok] all checks passed — Pearl mining works on this host
-```
-
-The `a.row_indices` and `bt.row_indices` values above are not constants — they're
-`(offset + ROWS_PATTERN)` and `(offset + COLS_PATTERN)` for whichever offset the
-miner happened to find a jackpot at. The smoke test verifies the *deltas* match
-the configured `PeriodicPattern`, not the absolute values.
-
-If it succeeds, this host can mine Pearl using the OpenJarvis `cpu-pearl` provider
-(see Spec B §13). If it fails, the `[fail]` line tells you which step broke.
-
-## What this proves (and what it doesn't)
-
-**Proves:**
-
-- The Pearl mining algorithm executes correctly on this host's CPU.
-- Generated proofs verify under `verify_plain_proof`. (This is the same check
-  validators run on the inputs to the ZK proof.)
-- The whole stack — `pearl-blake3`, `zk-pow`, `py-pearl-mining` — builds and
-  loads as a native CPython extension.
-
-**Does NOT prove:**
-
-- Network-difficulty hashrate. The smoke test uses
-  `nbits=0x1D2FFFFF` (test difficulty), much easier than mainnet. Real mining
-  expected hashrate on Apple Silicon CPU is several orders of magnitude lower
-  per share — see Spec B §1.5.6.
-- ZK proof generation throughput. The smoke test calls `verify_plain_proof`,
-  not `generate_proof`. Plonky2 STARK proving takes seconds-to-minutes of CPU
-  per block (Spec B Open Q10).
-- That this host can keep up with the network's block production rate.
-
-## When to update this
-
-- When Pearl bumps `py-pearl-mining` API: re-run the smoke test against the
-  new ref pinned in `OpenJarvis/src/openjarvis/mining/_constants.py`.
-- When Pearl publishes a Mac wheel to PyPI: simplify the install instructions
-  above, drop the local `maturin build` step.
-- When Spec B v2 adds the PyTorch-MPS reference path: extend `smoke_test.py`
-  with an MPS path comparison. The `miner-base` reference is already in
-  PyTorch, so the v2 smoke test would be a different test invoking
-  `miner_base.NoisyGemm` and comparing CPU vs MPS outputs for parity.

tools/pearl-reference-oracle/smoke_test.py

@@ -1,139 +0,0 @@
-"""Pearl mining smoke test — runs an end-to-end mine + verify cycle.
-
-Verifies that this host can run Pearl's pure-Rust mining algorithm via the
-`pearl_mining` Python package. Used as Phase 0-B of the OpenJarvis Apple Silicon
-mining spec ([Spec B]).
-
-Exit codes:
-    0   all checks passed
-    1   pearl_mining import failed
-    2   mine() failed
-    3   verify_plain_proof rejected the proof
-    4   timing or sanity check failed
-
-[Spec B]: ../../docs/design/2026-05-05-apple-silicon-pearl-mining-design.md
-"""
-
-from __future__ import annotations
-
-import platform
-import sys
-import time
-
-# Test fixture values — match upstream Pearl's tests/test_python_api.py so we are
-# testing the same code path that Pearl's own CI exercises. Do not change
-# without re-syncing with upstream.
-DEFAULT_NBITS = 0x1D2FFFFF
-DEFAULT_M = 256
-DEFAULT_N = 128
-DEFAULT_K = 1024
-DEFAULT_RANK = 32
-ROWS_PATTERN = [0, 8, 64, 72]
-COLS_PATTERN = [0, 1, 8, 9, 32, 33, 40, 41]
-
-
-def _ok(msg: str) -> None:
-    print(f"[ok] {msg}")
-
-
-def _fail(msg: str, code: int) -> None:
-    print(f"[fail] {msg}")
-    sys.exit(code)
-
-
-def main() -> None:
-    print(f"host: {platform.platform()} ({platform.machine()})")
-    print(f"python: {sys.version.split()[0]}")
-
-    try:
-        import pearl_mining
-    except ImportError as e:
-        _fail(f"could not import pearl_mining — install with `uv pip install py-pearl-mining` or build from source: {e}", 1)
-
-    _ok(f"pearl_mining loaded from {pearl_mining.__file__}")
-    _ok(
-        f"PUBLICDATA_SIZE={pearl_mining.PUBLICDATA_SIZE}  "
-        f"MERKLE_LEAF_SIZE={pearl_mining.MERKLE_LEAF_SIZE}"
-    )
-
-    block_header = pearl_mining.IncompleteBlockHeader(
-        version=0,
-        prev_block=b"\x00" * 32,
-        merkle_root=b"0123456789abcdef" * 2,
-        timestamp=0x66666666,
-        nbits=DEFAULT_NBITS,
-    )
-    mining_config = pearl_mining.MiningConfiguration(
-        common_dim=DEFAULT_K,
-        rank=DEFAULT_RANK,
-        mma_type=pearl_mining.MMAType.Int7xInt7ToInt32,
-        rows_pattern=pearl_mining.PeriodicPattern.from_list(ROWS_PATTERN),
-        cols_pattern=pearl_mining.PeriodicPattern.from_list(COLS_PATTERN),
-        reserved=pearl_mining.MiningConfiguration.RESERVED,
-    )
-
-    t0 = time.perf_counter()
-    try:
-        plain_proof = pearl_mining.mine(
-            DEFAULT_M,
-            DEFAULT_N,
-            DEFAULT_K,
-            block_header,
-            mining_config,
-            signal_range=None,
-            wrong_jackpot_hash=False,
-        )
-    except Exception as e:
-        _fail(f"mine() raised: {e!r}", 2)
-    t_mine = time.perf_counter() - t0
-
-    _ok(
-        f"mine(m={DEFAULT_M}, n={DEFAULT_N}, k={DEFAULT_K}, rank={DEFAULT_RANK}) "
-        f"returned a proof in {t_mine:.3f} s"
-    )
-    print(
-        f"       proof.m={plain_proof.m} proof.n={plain_proof.n} proof.k={plain_proof.k}  "
-        f"noise_rank={plain_proof.noise_rank}"
-    )
-    print(
-        f"       a.row_indices={plain_proof.a.row_indices}  "
-        f"bt.row_indices={plain_proof.bt.row_indices}"
-    )
-
-    t0 = time.perf_counter()
-    ok, msg = pearl_mining.verify_plain_proof(block_header, plain_proof)
-    t_verify_ms = (time.perf_counter() - t0) * 1000
-
-    if not ok:
-        _fail(f"verify_plain_proof rejected our proof: {msg}", 3)
-
-    _ok(f"verify_plain_proof: ok=True ({msg!r}, {t_verify_ms:.1f} ms)")
-
-    if plain_proof.m != DEFAULT_M or plain_proof.n != DEFAULT_N or plain_proof.k != DEFAULT_K:
-        _fail("plain_proof dimensions do not match request", 4)
-    if plain_proof.noise_rank != DEFAULT_RANK:
-        _fail("plain_proof noise_rank does not match request", 4)
-
-    # Row indices are (offset + base_index) for some valid offset within the
-    # matrix dimension — see threads_partition() in zk-pow/src/ffi/mine.rs.
-    # We can't assert an absolute value (different offsets are valid every run),
-    # but we can assert the deltas match the pattern shape.
-    a_idxs = list(plain_proof.a.row_indices)
-    bt_idxs = list(plain_proof.bt.row_indices)
-    a_deltas = [v - a_idxs[0] for v in a_idxs]
-    bt_deltas = [v - bt_idxs[0] for v in bt_idxs]
-    if a_deltas != ROWS_PATTERN:
-        _fail(f"a.row_indices deltas ({a_deltas}) != ROWS_PATTERN ({ROWS_PATTERN})", 4)
-    if bt_deltas != COLS_PATTERN:
-        _fail(f"bt.row_indices deltas ({bt_deltas}) != COLS_PATTERN ({COLS_PATTERN})", 4)
-
-    print()
-    print("[ok] all checks passed — Pearl mining works on this host")
-    print()
-    print("Note: this used test difficulty (nbits=0x1D2FFFFF), not mainnet.")
-    print("Real-network shares per second will be many orders of magnitude lower.")
-    print("See docs/design/2026-05-05-apple-silicon-pearl-mining-design.md §1.5.6")
-
-
-if __name__ == "__main__":
-    main()