fix(cli): use UTF-8 for config files

* fix: unify managed-agent tool resolution

* fix: harden managed-agent tool lifecycle

.github/clone-stats/badge.json

@@ -1,7 +1,7 @@
 {
   "schemaVersion": 1,
   "label": "Git Clones",
-  "message": "139,590",
+  "message": "185,599",
   "color": "green",
   "namedLogo": "git"
 }

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

@@ -1,6 +1,6 @@
 {
-  "total_clones": 139590,
-  "last_updated": "2026-07-01T07:33:10Z",
+  "total_clones": 185599,
+  "last_updated": "2026-08-10T07:26:44Z",
   "daily": {
     "2026-03-27": 2189,
     "2026-03-28": 1874,
@@ -97,6 +97,46 @@
     "2026-06-27": 1338,
     "2026-06-28": 1028,
     "2026-06-29": 765,
-    "2026-06-30": 951
+    "2026-06-30": 951,
+    "2026-07-01": 1134,
+    "2026-07-02": 593,
+    "2026-07-03": 537,
+    "2026-07-04": 411,
+    "2026-07-05": 485,
+    "2026-07-06": 555,
+    "2026-07-07": 905,
+    "2026-07-08": 1171,
+    "2026-07-09": 1857,
+    "2026-07-10": 1181,
+    "2026-07-11": 2185,
+    "2026-07-12": 1917,
+    "2026-07-13": 2102,
+    "2026-07-14": 2337,
+    "2026-07-15": 2362,
+    "2026-07-16": 2497,
+    "2026-07-17": 1773,
+    "2026-07-18": 1542,
+    "2026-07-19": 1445,
+    "2026-07-20": 1481,
+    "2026-07-21": 1528,
+    "2026-07-22": 1529,
+    "2026-07-23": 1209,
+    "2026-07-24": 1118,
+    "2026-07-25": 928,
+    "2026-07-26": 740,
+    "2026-07-27": 799,
+    "2026-07-28": 665,
+    "2026-07-29": 745,
+    "2026-07-30": 591,
+    "2026-07-31": 783,
+    "2026-08-01": 567,
+    "2026-08-02": 1248,
+    "2026-08-03": 724,
+    "2026-08-04": 708,
+    "2026-08-05": 647,
+    "2026-08-06": 604,
+    "2026-08-07": 624,
+    "2026-08-08": 706,
+    "2026-08-09": 1076
   }
 }

.github/workflows/ci.yml

@@ -32,6 +32,9 @@ jobs:
       - name: Ruff check
         run: uv run ruff check src/ tests/
 
+      - name: Ruff format check
+        run: uv run ruff format --check src/ tests/
+
   test:
     runs-on: ubuntu-latest
     steps:

Makefile

@@ -0,0 +1,19 @@
+.PHONY: setup build test lint format
+
+# Mirrors .github/workflows/ci.yml so `make test` matches CI locally.
+
+setup:
+	uv sync --extra dev --extra framework-comparison --extra server
+
+build:
+	uv run maturin develop --manifest-path rust/crates/openjarvis-python/Cargo.toml
+
+test: build
+	uv run pytest tests/ -n auto -q --tb=short -m "not live and not cloud and not hub"
+
+lint:
+	uv run ruff check src/ tests/
+	uv run ruff format --check src/ tests/
+
+format:
+	uv run ruff format src/ tests/

README.md

@@ -4,7 +4,8 @@
   <p><i>Personal AI, On Personal Devices.</i></p>
 
   <p>
-    <a href="https://scalingintelligence.stanford.edu/blogs/openjarvis/"><img src="https://img.shields.io/badge/project-OpenJarvis-blue" alt="Project"></a>
+    <a href="https://arxiv.org/abs/2605.17172"><img src="https://img.shields.io/badge/arXiv-2605.17172-b31b1b.svg" alt="arXiv"></a>
+    <a href="https://openjarvis.stanford.edu/"><img src="https://img.shields.io/badge/project-OpenJarvis-blue" alt="Project"></a>
     <a href="https://open-jarvis.github.io/OpenJarvis/"><img src="https://img.shields.io/badge/docs-mkdocs-blue" alt="Docs"></a>
     <img src="https://img.shields.io/badge/python-%3E%3D3.10-blue" alt="Python">
     <img src="https://img.shields.io/badge/license-Apache%202.0-green" alt="License">
@@ -23,7 +24,7 @@
 
 > **[Documentation](https://open-jarvis.github.io/OpenJarvis/)**
 >
-> **[Project Site](https://scalingintelligence.stanford.edu/blogs/openjarvis/)**
+> **[Project Site](https://openjarvis.stanford.edu/)**
 >
 > **[Paper](https://arxiv.org/abs/2605.17172)**
 >

configs/openjarvis/examples/full-system-access.toml

@@ -0,0 +1,39 @@
+# Full system access: unrestricted shell and filesystem
+# Copy to ~/.openjarvis/config.toml
+#
+# WARNING: shell_exec runs arbitrary commands as your user. No command
+# allowlist, no denylist, no working-directory restriction. file_read and
+# file_write aren't restricted to any directory either. Only enable what you
+# actually want the agent to have. tools.enabled is the whole permission grant;
+# there's no second allowlist to configure.
+#
+# On macOS, this config alone does not reach TCC-protected data (Messages,
+# Mail, Photos, Safari). That requires Full Disk Access granted to the process
+# hosting the backend. See docs/user-guide/system-access.md.
+#
+# Usage:
+#   jarvis ask "What's using the most disk space in my home directory?"
+#   jarvis chat        # prompts before each shell_exec call
+
+[engine]
+default = "ollama"
+
+[intelligence]
+default_model = "qwen3.5:9b"
+
+[agent]
+default_agent = "orchestrator"
+max_turns = 10
+
+[tools]
+enabled = [
+    "shell_exec",
+    "file_read",
+    "file_write",
+    "apply_patch",
+    "code_interpreter",
+    "git_status",
+    "git_diff",
+    "think",
+    "calculator",
+]

docs/getting-started/configuration.md

@@ -604,7 +604,7 @@ enforce_tool_confirmation = true
 | `scan_output` | bool | `true` | Whether to scan model output. |
 | `secret_scanner` | bool | `true` | Enable secret detection (API keys, tokens, passwords). |
 | `pii_scanner` | bool | `true` | Enable PII detection (emails, SSNs, credit cards). |
-| `enforce_tool_confirmation` | bool | `true` | Require confirmation before executing tools. |
+| `enforce_tool_confirmation` | bool | `true` | Accepted but **not currently enforced**. Whether you get prompts depends on the entry point. See [System Access](../user-guide/system-access.md#confirmation-behaviour). |
 
 !!! tip "Choosing a security mode"
     Use `"warn"` during development to see what would be flagged without disrupting output.

docs/getting-started/quickstart.md

@@ -135,6 +135,19 @@ cd OpenJarvis
 This launches the backend API server and a React frontend at [http://localhost:5173](http://localhost:5173).
 You get a ChatGPT-like interface with streaming responses, tool use, energy monitoring, and a telemetry dashboard — all running locally on your hardware.
 
+Web search is available through the built-in DuckDuckGo fallback. To use
+Tavily, add `TAVILY_API_KEY` under **Settings → Tools → Web Search** after the
+app starts, or export it before starting quickstart:
+
+```bash
+export TAVILY_API_KEY="tvly-..."
+./scripts/quickstart.sh
+```
+
+The script does not automatically source `.env` files. Run `source .env`
+first if that is where you keep the key. Stop any existing OpenJarvis server
+before restarting so it inherits the updated environment.
+
 To stop all services, press ++ctrl+c++ in the terminal.
 
 !!! tip "Environment variable"

docs/index.md

@@ -215,7 +215,7 @@ OpenJarvis is built around five composable layers. Each has a clean interface an
 
 OpenJarvis is part of [Intelligence Per Watt](https://www.intelligence-per-watt.ai/), a research initiative studying the efficiency of on-device AI systems. Developed at [Hazy Research](https://hazyresearch.stanford.edu/) and the [Scaling Intelligence Lab](https://scalingintelligence.stanford.edu/) at [Stanford SAIL](https://ai.stanford.edu/).
 
-Read the [blog post](https://scalingintelligence.stanford.edu/blogs/openjarvis/) for the full research motivation, architecture details, and experimental results.
+Read the [blog post](https://openjarvis.stanford.edu/) for the full research motivation, architecture details, and experimental results.
 
 ## Citation
 

docs/leaderboard.md

@@ -55,5 +55,5 @@ See how the OpenJarvis community saves money, energy, and compute by running AI
 <div id="leaderboard-pagination" class="lb-pagination"></div>
 
 <p style="font-size:12px;opacity:0.6;margin-top:12px">
-*Dollar savings estimated vs. Claude Opus 4.6 API pricing ($5/1M input, $25/1M output tokens). Assumes local open-source models produce roughly the same number of tokens per request as cloud models.
+*Dollar savings estimated vs. Claude Fable 5 API pricing ($10/1M input, $50/1M output tokens). Assumes local open-source models produce roughly the same number of tokens per request as cloud models.
 </p>

docs/user-guide/security.md

@@ -392,7 +392,7 @@ enforce_tool_confirmation = true
 | `secret_scanner` | `bool` | `true` | Run `SecretScanner` on all text |
 | `pii_scanner` | `bool` | `true` | Run `PIIScanner` on all text |
 | `audit_log_path` | `str` | `~/.openjarvis/audit.db` | Path to the SQLite audit log |
-| `enforce_tool_confirmation` | `bool` | `true` | Require explicit confirmation before tool execution |
+| `enforce_tool_confirmation` | `bool` | `true` | Accepted by the loader but **not currently enforced**. See [System Access](system-access.md#confirmation-behaviour) for when prompts actually happen |
 
 !!! tip "Start with warn, tighten later"
     `mode = "warn"` is a good starting point. It lets you observe what patterns are being triggered without disrupting normal usage. Switch to `"redact"` once you are satisfied that the scanner isn't producing too many false positives for your workload.

docs/user-guide/system-access.md

@@ -0,0 +1,198 @@
+# System Access
+
+How to give an agent access to the machine it runs on, and where the real
+limits are.
+
+!!! warning
+    `shell_exec` runs arbitrary commands as your user. There is no command
+    allowlist, no denylist, and no sandbox unless you turn one on. An agent
+    holding this tool can do anything you can do from a terminal.
+
+---
+
+## Start here: you probably have no tools enabled
+
+If the agent tells you it can't run commands or read files, that's usually not
+a permissions problem. It means no tools were enabled in the first place.
+
+Tools come from `tools.enabled`, falling back to `agent.tools`. Both default to
+empty, and an empty value builds the agent with **zero tools**. Nothing is
+enabled by default.
+
+First check whether you have a config file at all:
+
+```bash
+cat ~/.openjarvis/config.toml
+```
+
+If it isn't there, that's your answer. Create it:
+
+```toml
+[engine]
+default = "ollama"
+
+[intelligence]
+default_model = "qwen3.5:9b"
+
+[agent]
+default_agent = "orchestrator"
+
+[tools]
+enabled = ["shell_exec", "file_read", "file_write", "think"]
+```
+
+There's a fuller version at
+`configs/openjarvis/examples/full-system-access.toml`.
+
+Then confirm the list actually resolved:
+
+```bash
+python -c "from openjarvis.core.config import load_config; print(load_config().tools.enabled)"
+```
+
+---
+
+## What the tools reach
+
+| Tool | Scope |
+|------|-------|
+| `shell_exec` | Any command, as your user. 30s default timeout, 300s max, output capped at 100 KB per stream. |
+| `file_read` | Any readable path. 1 MB cap. |
+| `file_write` | Any writable path. 10 MB cap, can create parent directories. |
+| `apply_patch` | Applies unified diffs to any path. |
+| `code_interpreter` | Python in a subprocess, behind a coarse pattern blocklist. |
+
+`file_read` and `file_write` take an `allowed_dirs` argument that limits them to
+a set of directories, but no config key populates it. When it's empty every path
+is allowed. If you want a filesystem jail today, use the container sandbox
+instead of relying on these tools to enforce one.
+
+### Sensitive filenames
+
+`file_read` and `file_write` refuse names matching a short glob list: `.env`,
+`*.pem`, `id_rsa`, `credentials.*` and a dozen or so others. It matches on the
+filename only, not the path or the contents, and only those two tools consult
+it. `shell_exec`, `apply_patch` and `code_interpreter` skip it entirely, so
+`cat ~/.ssh/id_rsa` through `shell_exec` works fine. Treat it as protection
+against fat fingers, not as a security boundary.
+
+---
+
+## Confirmation behaviour
+
+`shell_exec`, `git_commit` and `agent_kill` are marked `requires_confirmation`.
+What that translates to depends entirely on how you launched the agent:
+
+| Entry point | Behaviour |
+|-------------|-----------|
+| `jarvis chat` | Prompts before each call. |
+| `jarvis ask` | Auto-approves. |
+| `jarvis agent ask` | Auto-approves. Pass `--no-yes` if you want prompts. |
+| HTTP server, desktop app | Auto-approves. Tools you added to an agent's toolkit count as pre-approved. |
+| Embedded via `SystemBuilder` | No callback is wired, so these tools fail closed. |
+
+That last row catches people out. If `shell_exec` returns "requires
+confirmation but no confirmation callback is available", you're constructing the
+agent yourself and need to pass a `confirm_callback`.
+
+!!! note "`enforce_tool_confirmation` doesn't do anything"
+    The config loader accepts `security.enforce_tool_confirmation`, but nothing
+    on the tool execution path reads it. Setting it won't change confirmation
+    behaviour anywhere. Use the table above instead.
+
+---
+
+## macOS: Full Disk Access
+
+On macOS the operating system is the real boundary, not the config. Shell
+access and ordinary file access start working as soon as you enable the tools.
+TCC-protected data does not: Messages, Mail, Photos, Safari history, Contacts
+and Calendar all stay locked, and no config key will change that.
+
+Grant Full Disk Access to whichever process hosts the backend. Child processes
+inherit it:
+
+| How you run OpenJarvis | Grant access to |
+|------------------------|-----------------|
+| CLI (`jarvis ask`, `jarvis chat`) | Your terminal (Terminal, iTerm, Warp) |
+| Desktop app | `OpenJarvis.app`, which spawns `jarvis serve` beneath it |
+| launchd (`deploy/launchd/com.openjarvis.plist`) | The `jarvis` binary, as its own entry |
+
+System Settings, then Privacy & Security, then Full Disk Access, then **+**.
+
+A launchd daemon gets its own TCC context, so granting access to Terminal does
+nothing for it. Add `/usr/local/bin/jarvis` separately.
+
+To check whether the grant took:
+
+```bash
+head -c 16 ~/Library/Messages/chat.db >/dev/null 2>&1 \
+  && echo "granted" || echo "denied"
+```
+
+Restart the host process after you change the setting.
+
+### Driving Mac apps
+
+AppleScript works through `shell_exec`:
+
+```
+osascript -e 'tell application "Music" to play'
+```
+
+macOS asks for Automation permission once per target app, the first time you
+touch it.
+
+---
+
+## What you can't do
+
+There's no computer use. OpenJarvis can't see your screen, move the pointer or
+send keystrokes. No tool for it is registered and no input automation library
+appears anywhere in the codebase, so granting Accessibility or Screen Recording
+buys you nothing on its own.
+
+The `click` and `type` actions you'll find are Playwright, scoped to a browser
+page rather than the desktop.
+
+Some of this is reachable through `shell_exec` if you bring the tooling
+yourself. `screencapture` will take screenshots once you've granted Screen
+Recording, and something like `cliclick` will move the pointer. That gets you
+scripted actions. It doesn't get you an agent that looks at the screen and
+works out where to click.
+
+---
+
+## Narrowing access
+
+Access widens and narrows through `tools.enabled`. Drop entries to take
+capabilities away. That list is the whole grant.
+
+Two stronger isolation options exist. Both are off by default:
+
+```toml
+[sandbox]
+enabled = true          # run tools inside a container
+runtime = "docker"
+
+[security.capabilities]
+enabled = true          # RBAC over declared tool capabilities
+policy_path = "~/.openjarvis/policy.yaml"
+```
+
+!!! note "Capabilities are open by default even once enabled"
+    `CapabilityPolicy` is built with `default_deny=False` and no config key
+    exposes that flag, so an agent with no explicit policy entry gets every
+    capability. Write entries for every agent you mean to restrict.
+
+For anything untrusted, reach for `docker_shell_exec` and
+`code_interpreter_docker` rather than the host-side versions.
+
+---
+
+## See also
+
+- [Security](security.md) for scanners, the audit log and guardrails
+- [Tools](tools.md) for the full registry
+- [Code Assistant](code-assistant.md) for a narrower shell-enabled setup
+- [External MCP Servers](mcp-external-servers.md) for capabilities OpenJarvis doesn't ship

frontend/src/App.tsx

@@ -89,7 +89,7 @@ export default function App() {
           setSavings(data);
           if (optInEnabled && optInDisplayName && data) {
             const claudeEntry = data.per_provider.find(
-              (p) => p.provider === 'claude-opus-4.6',
+              (p) => p.provider === 'claude-fable-5',
             );
             const dollarSavings = claudeEntry ? claudeEntry.total_cost : 0;
             const energySaved = data.per_provider.reduce(

frontend/src/components/Chat/ChatArea.tsx

@@ -22,6 +22,8 @@ export function ChatArea() {
   const navigate = useNavigate();
   const listRef = useRef<HTMLDivElement>(null);
   const shouldAutoScroll = useRef(true);
+  const wasStreaming = useRef(false);
+  const lastScrollTop = useRef(0);
 
   // Check if any data sources are connected
   const [hasConnectedSources, setHasConnectedSources] = useState<boolean | null>(null);
@@ -34,15 +36,34 @@ 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) {
+      shouldAutoScroll.current = true;
+    }
+    wasStreaming.current = streamState.isStreaming;
     if (shouldAutoScroll.current && listRef.current) {
       listRef.current.scrollTop = listRef.current.scrollHeight;
     }
-  }, [messages, streamState.content]);
+  }, [messages, streamState.content, streamState.isStreaming]);
 
   const handleScroll = () => {
     if (!listRef.current) return;
     const { scrollTop, scrollHeight, clientHeight } = listRef.current;
-    shouldAutoScroll.current = scrollHeight - scrollTop - clientHeight < 100;
+    const distance = scrollHeight - scrollTop - clientHeight;
+    const scrolledUp = scrollTop < lastScrollTop.current;
+    lastScrollTop.current = scrollTop;
+    if (scrolledUp && distance >= 1) {
+      // Any upward scroll away from the bottom stops autoscroll immediately,
+      // so streaming content never fights the user (no jitter). Sub-1px
+      // upward movement (elastic bounce settling at the bottom) is ignored.
+      shouldAutoScroll.current = false;
+    } else if (!scrolledUp) {
+      // Re-engage when scrolled back to the bottom. < 2 rather than < 1:
+      // at fractional zoom levels the at-bottom residual can reach 1px,
+      // which would otherwise leave autoscroll permanently disengaged.
+      shouldAutoScroll.current = distance < 2;
+    }
   };
 
   const isEmpty = messages.length === 0 && !streamState.isStreaming;

frontend/src/components/Chat/InputArea.tsx

@@ -466,7 +466,10 @@ 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,

frontend/src/components/Chat/SystemPanel.tsx

@@ -29,8 +29,8 @@ interface TelemetryStats {
 }
 
 const CLOUD_PRICING = [
-  { name: 'GPT-5.3', input: 2.00, output: 10.00, primary: true },
-  { name: 'Claude Opus 4.6', input: 5.00, output: 25.00, primary: false },
+  { name: 'GPT-5.6 Sol', input: 5.00, output: 30.00, primary: true },
+  { name: 'Claude Fable 5', input: 10.00, output: 50.00, primary: false },
   { name: 'Gemini 3.1 Pro', input: 2.00, output: 12.00, primary: false },
 ];
 

frontend/src/components/CommandPalette.tsx

@@ -143,18 +143,17 @@ export function CommandPalette() {
     }
   }, [pullSuccess]);
 
-  const handleSelect = async (modelId: string) => {
+  const handleSelect = async (modelId: string, owner?: string) => {
     const previousModel = selectedModel;
     setSelectedModel(modelId);
     setCommandPaletteOpen(false);
 
     if (modelId !== previousModel) {
-      const { createConversation, setModelLoading, addLogEntry } = useAppStore.getState();
-      createConversation(modelId);
+      const { setModelLoading, addLogEntry } = useAppStore.getState();
       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}` });
@@ -256,7 +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);
     }
   };
 
@@ -366,11 +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}
@@ -382,17 +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/Dashboard/CostComparison.tsx

@@ -2,8 +2,8 @@ import { DollarSign, TrendingDown, Cloud, HardDrive } from 'lucide-react';
 import { useAppStore } from '../../lib/store';
 
 const CLOUD_PRICING = [
-  { name: 'GPT-5.3', input: 2.00, output: 10.00 },
-  { name: 'Claude Opus 4.6', input: 5.00, output: 25.00 },
+  { name: 'GPT-5.6 Sol', input: 5.00, output: 30.00 },
+  { name: 'Claude Fable 5', input: 10.00, output: 50.00 },
   { name: 'Gemini 3.1 Pro', input: 2.00, output: 12.00 },
 ];
 

frontend/src/components/Desktop/SavingsDashboard.tsx

@@ -222,8 +222,8 @@ const styles: Record<string, React.CSSProperties> = {
 };
 
 const PROVIDER_COLORS: Record<string, string> = {
-  'gpt-5.3': colors.green,
-  'claude-opus-4.6': colors.yellow,
+  'gpt-5.6-sol': colors.green,
+  'claude-fable-5': colors.yellow,
   'gemini-3.1-pro': colors.accent,
 };
 

frontend/src/lib/api.auth.test.ts

@@ -6,6 +6,7 @@ import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
 // authHeaders) that source the key and build the header.
 
 const SETTINGS_KEY = 'openjarvis-settings';
+const fetchMock = vi.fn<typeof fetch>();
 
 // Minimal in-memory localStorage stub so the helpers can run under node
 // (no jsdom dependency).
@@ -28,6 +29,8 @@ class MemoryStorage {
 beforeEach(() => {
   vi.resetModules();
   vi.stubEnv('VITE_SUPABASE_ANON_KEY', 'test-anon-key');
+  fetchMock.mockReset();
+  globalThis.fetch = fetchMock;
   (globalThis as unknown as { localStorage: MemoryStorage }).localStorage =
     new MemoryStorage();
 });
@@ -86,3 +89,50 @@ describe('authHeaders', () => {
     });
   });
 });
+
+describe('tool credentials', () => {
+  it('reads credential status from the local server', async () => {
+    fetchMock.mockResolvedValue(
+      new Response(JSON.stringify({ TAVILY_API_KEY: true }), {
+        status: 200,
+        headers: { 'Content-Type': 'application/json' },
+      }),
+    );
+    const { fetchToolCredentialStatus } = await freshApi();
+
+    await expect(fetchToolCredentialStatus('web_search')).resolves.toEqual({
+      TAVILY_API_KEY: true,
+    });
+    expect(fetchMock).toHaveBeenCalledWith(
+      '/v1/tools/web_search/credentials/status',
+      { headers: {} },
+    );
+  });
+
+  it('saves a tool credential through the local server', async () => {
+    fetchMock.mockResolvedValue(new Response('{}', { status: 200 }));
+    const { saveToolCredentials } = await freshApi();
+
+    await saveToolCredentials('web_search', {
+      TAVILY_API_KEY: 'tvly-test',
+    });
+
+    expect(fetchMock).toHaveBeenCalledWith('/v1/tools/web_search/credentials', {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify({ TAVILY_API_KEY: 'tvly-test' }),
+    });
+  });
+
+  it('deletes a tool credential through the local server', async () => {
+    fetchMock.mockResolvedValue(new Response('{}', { status: 200 }));
+    const { deleteToolCredential } = await freshApi();
+
+    await deleteToolCredential('web_search', 'TAVILY_API_KEY');
+
+    expect(fetchMock).toHaveBeenCalledWith(
+      '/v1/tools/web_search/credentials/TAVILY_API_KEY',
+      { method: 'DELETE', headers: {} },
+    );
+  });
+});

frontend/src/lib/api.ts

@@ -218,9 +218,9 @@ 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).
@@ -885,6 +885,25 @@ export async function saveToolCredentials(
   if (!res.ok) throw new Error(`Failed: ${res.status}`);
 }
 
+export async function fetchToolCredentialStatus(
+  toolName: string,
+): Promise<Record<string, boolean>> {
+  const res = await apiFetch(`/v1/tools/${toolName}/credentials/status`);
+  if (!res.ok) throw new Error(`Failed: ${res.status}`);
+  return await res.json();
+}
+
+export async function deleteToolCredential(
+  toolName: string,
+  keyName: string,
+): Promise<void> {
+  const res = await apiFetch(
+    `/v1/tools/${encodeURIComponent(toolName)}/credentials/${encodeURIComponent(keyName)}`,
+    { method: 'DELETE' },
+  );
+  if (!res.ok) throw new Error(`Failed: ${res.status}`);
+}
+
 export interface AgentTraceDetail {
   id: string;
   agent: string;

frontend/src/pages/AgentsPage.tsx

@@ -23,7 +23,6 @@ import {
   fetchAgentTrace,
   fetchManagedAgent,
   fetchAvailableTools,
-  saveToolCredentials,
   fetchModels,
   updateManagedAgent,
   fetchRecommendedModel,
@@ -575,7 +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)',
@@ -609,10 +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}
@@ -3740,8 +3740,8 @@ export function AgentsPage() {
               const paramsB = paramMatch ? parseFloat(paramMatch[1]) : 9;
               const flops = 2 * paramsB * 1e9 * (inTok + outTok);
               const providers = [
-                { label: 'GPT-5.3', inPer1M: 2.0, outPer1M: 10.0 },
-                { label: 'Claude Opus 4.6', inPer1M: 5.0, outPer1M: 25.0 },
+                { label: 'GPT-5.6 Sol', inPer1M: 5.0, outPer1M: 30.0 },
+                { label: 'Claude Fable 5', inPer1M: 10.0, outPer1M: 50.0 },
                 { label: 'Gemini 3.1 Pro', inPer1M: 2.0, outPer1M: 12.0 },
               ];
               const energyWh = (inTok + outTok) / 1000 * 0.4;

frontend/src/pages/SettingsPage.tsx

@@ -27,6 +27,9 @@ import {
   setInferenceSource,
   getCloudKeyStatus,
   saveCloudKey,
+  fetchToolCredentialStatus,
+  saveToolCredentials,
+  deleteToolCredential,
   isTauri,
   type InferenceSource,
 } from '../lib/api';
@@ -56,25 +59,37 @@ function OllamaModelList() {
   );
 }
 
-function ApiKeyInput({ keyName, placeholder }: { keyName: string; placeholder: string }) {
+function ApiKeyInput({
+  keyName,
+  placeholder,
+  toolName,
+}: {
+  keyName: string;
+  placeholder: string;
+  toolName?: string;
+}) {
   const [value, setValue] = useState('');
   const [saved, setSaved] = useState(false);
   const [hasKey, setHasKey] = useState(false);
   const [error, setError] = useState('');
   const desktopKeyStorage = isTauri();
+  const serverToolStorage = !desktopKeyStorage && !!toolName;
+  const canManage = desktopKeyStorage || serverToolStorage;
 
   const refresh = useCallback(async () => {
-    if (!desktopKeyStorage) {
+    if (!canManage) {
       setHasKey(false);
       return;
     }
     try {
-      const status = await getCloudKeyStatus();
+      const status = desktopKeyStorage
+        ? await getCloudKeyStatus()
+        : await fetchToolCredentialStatus(toolName!);
       setHasKey(!!status[keyName]);
     } catch {
       setHasKey(false);
     }
-  }, [desktopKeyStorage, keyName]);
+  }, [canManage, desktopKeyStorage, keyName, toolName]);
 
   useEffect(() => {
     void refresh();
@@ -87,7 +102,13 @@ function ApiKeyInput({ keyName, placeholder }: { keyName: string; placeholder: s
     if (!next) return;
     setError('');
     try {
-      await saveCloudKey(keyName, next);
+      if (desktopKeyStorage) {
+        await saveCloudKey(keyName, next);
+      } else if (toolName) {
+        await saveToolCredentials(toolName, { [keyName]: next });
+      } else {
+        return;
+      }
       setValue('');
       setHasKey(true);
       setSaved(true);
@@ -101,7 +122,13 @@ function ApiKeyInput({ keyName, placeholder }: { keyName: string; placeholder: s
   const remove = async () => {
     setError('');
     try {
-      await saveCloudKey(keyName, '');
+      if (desktopKeyStorage) {
+        await saveCloudKey(keyName, '');
+      } else if (toolName) {
+        await deleteToolCredential(toolName, keyName);
+      } else {
+        return;
+      }
       setValue('');
       setHasKey(false);
       setSaved(true);
@@ -119,8 +146,8 @@ function ApiKeyInput({ keyName, placeholder }: { keyName: string; placeholder: s
         value={value}
         onChange={e => setValue(e.target.value)}
         onBlur={() => { if (value.trim()) void save(value); }}
-        placeholder={hasKey ? 'Saved in secure storage' : placeholder}
-        disabled={!desktopKeyStorage}
+        placeholder={hasKey ? (desktopKeyStorage ? 'Saved in secure storage' : 'Saved by local server') : placeholder}
+        disabled={!canManage}
         className="w-48 px-2 py-1 rounded text-xs"
         style={{ background: 'var(--color-bg)', border: '1px solid var(--color-border)', color: 'var(--color-text)' }} />
       {hasKey && (
@@ -542,7 +569,7 @@ export function SettingsPage() {
           {/* Tools */}
           <Section title="Tools">
             <SettingRow label="Web Search" description="Tavily key for web search tool">
-              <ApiKeyInput keyName="TAVILY_API_KEY" placeholder="tvly-..." />
+              <ApiKeyInput keyName="TAVILY_API_KEY" placeholder="tvly-..." toolName="web_search" />
             </SettingRow>
           </Section>
 
@@ -805,7 +832,7 @@ export function SettingsPage() {
               </p>
               <div className="flex gap-3 mt-3 text-xs">
                 <a
-                  href="https://scalingintelligence.stanford.edu/blogs/openjarvis/"
+                  href="https://openjarvis.stanford.edu/"
                   target="_blank"
                   rel="noopener noreferrer"
                   style={{ color: 'var(--color-accent)' }}

frontend/vite.config.ts

@@ -54,7 +54,15 @@ export default defineConfig({
   server: {
     port: 5173,
     proxy: {
-      '/v1': process.env.VITE_API_URL || 'http://localhost:8000',
+      // ws: true is required for the /v1/agents/events WebSocket. Without it
+      // Vite proxies the HTTP request but not the upgrade, so the socket never
+      // opens — no error, no close event, just silence — and every live agent
+      // view sits empty in dev while working in a production build.
+      '/v1': {
+        target: process.env.VITE_API_URL || 'http://localhost:8000',
+        changeOrigin: true,
+        ws: true,
+      },
       '/health': process.env.VITE_API_URL || 'http://localhost:8000',
       '/api': process.env.VITE_API_URL || 'http://localhost:8000',
     },

mkdocs.yml

@@ -196,6 +196,7 @@ nav:
       - Telemetry: user-guide/telemetry.md
       - Evaluations: user-guide/evaluations.md
       - Benchmarks: user-guide/benchmarks.md
+      - System Access: user-guide/system-access.md
       - Security: user-guide/security.md
       - LLM-guided spec search: user-guide/llm-guided-spec-search.md
   - Leaderboard: leaderboard.md

rust/crates/openjarvis-tools/src/storage/sqlite.rs

@@ -144,16 +144,21 @@ impl MemoryBackend for SQLiteMemory {
     ) -> Result<Vec<RetrievalResult>, OpenJarvisError> {
         let conn = self.conn.lock();
 
+        // Split on any non-alphanumeric character (not just whitespace) so
+        // internal punctuation — apostrophes in particular ("user's") — never
+        // reaches the FTS5 MATCH string. FTS5's query grammar treats an
+        // unescaped `'` as a string delimiter, so passing a raw token like
+        // `user's` through silently fails to parse and yields zero rows with
+        // no visible error. Splitting fully avoids needing to escape anything.
         let words: Vec<String> = query
-            .split_whitespace()
-            .map(|w| w.trim_matches(|c: char| "?.,!;:'\"()[]{}/ ".contains(c)).to_string())
+            .split(|c: char| !c.is_alphanumeric())
+            .map(|w| w.to_string())
             .filter(|w| !w.is_empty())
             .collect();
-        let fts_query = if words.len() == 1 {
-            words[0].clone()
-        } else {
-            words.join(" OR ")
-        };
+        if words.is_empty() {
+            return Ok(Vec::new());
+        }
+        let fts_query = words.join(" OR ");
 
         let mut stmt = conn
             .prepare(
@@ -320,6 +325,27 @@ mod tests {
         assert_eq!(mixed.len(), 2, "mixed-case query should find both documents");
     }
 
+    #[test]
+    fn test_sqlite_apostrophe_in_query() {
+        let mem = SQLiteMemory::in_memory().unwrap();
+        mem.store("The user's name is Trev.", "identity", None).unwrap();
+
+        // A query containing an internal apostrophe must not break FTS5's
+        // MATCH syntax (an unescaped `'` is a string delimiter in FTS5's
+        // query grammar), which previously caused this to silently return
+        // zero results instead of matching or erroring.
+        let multi_word = mem.retrieve("what is the user's name", 5).unwrap();
+        assert!(
+            !multi_word.is_empty(),
+            "query with an internal apostrophe should not silently return zero results"
+        );
+
+        // Bare single-word possessive: exercises the (former) single-word
+        // bypass path that skipped the OR-join entirely.
+        let bare = mem.retrieve("user's", 5).unwrap();
+        assert!(!bare.is_empty(), "single-word possessive query should still match");
+    }
+
     #[test]
     fn test_sqlite_scores_are_positive() {
         let mem = SQLiteMemory::in_memory().unwrap();

scripts/quickstart.sh

@@ -148,7 +148,8 @@ fi
 
 # ── 7. Install Python dependencies ──────────────────────────────────
 info "Installing Python dependencies..."
-uv sync --extra desktop --quiet 2>/dev/null || uv sync --extra desktop
+uv sync --extra desktop --extra tools-search --quiet 2>/dev/null \
+  || uv sync --extra desktop --extra tools-search
 ok "Python dependencies installed"
 
 # ── 7b. Build Rust extension ──────────────────────────────────────
@@ -164,11 +165,17 @@ ok "Frontend dependencies installed"
 
 # ── 9. Start backend ────────────────────────────────────────────────
 info "Starting backend API server on port 8000..."
+if curl -sf http://localhost:8000/health &>/dev/null; then
+  fail "An OpenJarvis server is already running on port 8000. Stop it before re-running quickstart so updated environment variables are applied."
+fi
 uv run jarvis serve --port 8000 &>/dev/null &
-CLEANUP_PIDS+=($!)
+BACKEND_PID=$!
+CLEANUP_PIDS+=("$BACKEND_PID")
 sleep 3
 
-if curl -sf http://localhost:8000/health &>/dev/null; then
+if ! kill -0 "$BACKEND_PID" 2>/dev/null; then
+  fail "Backend exited during startup. Run 'uv run jarvis serve --port 8000' to see the error."
+elif curl -sf http://localhost:8000/health &>/dev/null; then
   ok "Backend running at http://localhost:8000"
 else
   warn "Backend may still be starting..."

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,

src/openjarvis/agents/errors.py

@@ -2,6 +2,8 @@
 
 from __future__ import annotations
 
+from openjarvis.engine._base import looks_like_context_length_error
+
 
 class AgentTickError(Exception):
     """Base class for agent tick errors."""
@@ -64,6 +66,14 @@ def classify_error(exc: Exception) -> AgentTickError:
 
     msg = str(exc).lower()
 
+    # A context-window overflow is deterministic: retrying the identical
+    # over-length request can never succeed, so fail fast instead of burning
+    # the retry budget on it.
+    if getattr(exc, "is_context_length_error", False) or (
+        looks_like_context_length_error(msg)
+    ):
+        return FatalError(str(exc))
+
     # Check fatal patterns first (more specific)
     if isinstance(exc, PermissionError):
         return FatalError(str(exc))
@@ -90,6 +100,11 @@ def retry_delay(attempt: int) -> int:
 def suggest_action(error: AgentTickError) -> str:
     """Return a human-readable suggested action for the given error."""
     msg = str(error).lower()
+    if looks_like_context_length_error(msg):
+        return (
+            "Conversation too long for the model's context window \u2014 "
+            "start a new chat or shorten the conversation"
+        )
     if any(p in msg for p in ("rate limit", "rate_limit", "429", "too many requests")):
         return "Rate limited \u2014 agent will auto-retry on next tick"
     if any(p in msg for p in ("timeout", "timed out", "connection", "unavailable")):

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,
@@ -102,9 +111,7 @@ class AgentExecutor:
         )
         return agent.run(input_text)
 
-    def execute_tick(
-        self, agent_id: str, *, lock_already_held: bool = False
-    ) -> None:
+    def execute_tick(self, agent_id: str, *, lock_already_held: bool = False) -> None:
         """Run one tick for the given agent.
 
         1. Acquire concurrency guard (start_tick)
@@ -126,9 +133,7 @@ class AgentExecutor:
                 self._manager.start_tick(agent_id)
                 self._set_activity(agent_id, "Preparing tick...")
             except ValueError:
-                logger.warning(
-                    "Agent %s already running, skipping tick", agent_id
-                )
+                logger.warning("Agent %s already running, skipping tick", agent_id)
                 return
 
         agent = self._manager.get_agent(agent_id)
@@ -252,7 +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")
@@ -261,6 +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
@@ -304,64 +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``
@@ -383,7 +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()
@@ -392,6 +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"]
@@ -408,27 +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
@@ -444,7 +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.
@@ -474,9 +552,7 @@ class AgentExecutor:
                 tick_note = f"Previous tick: {first_sentence}"
 
         if instruction:
-            input_text = (
-                f"Current date: {today}\n\nStanding instruction: {instruction}"
-            )
+            input_text = f"Current date: {today}\n\nStanding instruction: {instruction}"
             if tick_note:
                 input_text += f"\n\n{tick_note}"
         else:
@@ -561,21 +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, "
@@ -665,7 +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/hybrid/_base.py

@@ -303,7 +303,9 @@ def _openrouter_limiter() -> _OpenRouterLimiter:
     if _OPENROUTER_LIMITER is None:
         with _OPENROUTER_LIMITER_LOCK:
             if _OPENROUTER_LIMITER is None:
-                max_concurrent = int(os.environ.get("OJ_OPENROUTER_MAX_CONCURRENT", "20") or 20)
+                max_concurrent = int(
+                    os.environ.get("OJ_OPENROUTER_MAX_CONCURRENT", "20") or 20
+                )
                 rpm = int(os.environ.get("OJ_OPENROUTER_RPM", "60") or 60)
                 _OPENROUTER_LIMITER = _OpenRouterLimiter(max_concurrent, rpm)
     return _OPENROUTER_LIMITER
@@ -319,8 +321,14 @@ def _serialize_block(block: Any) -> Dict[str, Any]:
     """
     out: Dict[str, Any] = {"type": getattr(block, "type", type(block).__name__)}
     for attr in (
-        "id", "name", "input", "text", "thinking", "signature",
-        "tool_use_id", "content",
+        "id",
+        "name",
+        "input",
+        "text",
+        "thinking",
+        "signature",
+        "tool_use_id",
+        "content",
     ):
         if hasattr(block, attr):
             val = getattr(block, attr)
@@ -341,14 +349,16 @@ def _serialize_openai_tool_calls(tool_calls: Any) -> List[Dict[str, Any]]:
         return out
     for tc in tool_calls:
         fn = getattr(tc, "function", None)
-        out.append({
-            "id": getattr(tc, "id", None),
-            "type": getattr(tc, "type", "function"),
-            "function": {
-                "name": getattr(fn, "name", None) if fn else None,
-                "arguments": getattr(fn, "arguments", None) if fn else None,
-            },
-        })
+        out.append(
+            {
+                "id": getattr(tc, "id", None),
+                "type": getattr(tc, "type", "function"),
+                "function": {
+                    "name": getattr(fn, "name", None) if fn else None,
+                    "arguments": getattr(fn, "arguments", None) if fn else None,
+                },
+            }
+        )
     return out
 
 
@@ -473,32 +483,46 @@ class LocalCloudAgent(BaseAgent):
         srv = getattr(msg.usage, "server_tool_use", None)
         n_searches = getattr(srv, "web_search_requests", 0) if srv else 0
         content_blocks = [_serialize_block(b) for b in msg.content]
-        tool_use_blocks = [b for b in content_blocks if b.get("type") in (
-            "tool_use", "server_tool_use",
-        )]
-        tool_result_blocks = [b for b in content_blocks if b.get("type") in (
-            "web_search_tool_result", "tool_result",
-        )]
-        _record_event({
-            "kind": "anthropic",
-            "role": trace_role,
-            "model": model,
-            "system": system,
-            "user": user,
-            "response": text,
-            "content_blocks": content_blocks,
-            "tool_calls": tool_use_blocks,
-            "tool_results": tool_result_blocks,
-            "tokens_in": msg.usage.input_tokens,
-            "tokens_out": msg.usage.output_tokens,
-            "n_web_searches": n_searches,
-            "tools_declared": tools,
-            "tool_choice": tool_choice,
-            "output_config": output_config,
-            "stop_reason": getattr(msg, "stop_reason", None),
-            "latency_s": latency,
-            "ts": time.time(),
-        })
+        tool_use_blocks = [
+            b
+            for b in content_blocks
+            if b.get("type")
+            in (
+                "tool_use",
+                "server_tool_use",
+            )
+        ]
+        tool_result_blocks = [
+            b
+            for b in content_blocks
+            if b.get("type")
+            in (
+                "web_search_tool_result",
+                "tool_result",
+            )
+        ]
+        _record_event(
+            {
+                "kind": "anthropic",
+                "role": trace_role,
+                "model": model,
+                "system": system,
+                "user": user,
+                "response": text,
+                "content_blocks": content_blocks,
+                "tool_calls": tool_use_blocks,
+                "tool_results": tool_result_blocks,
+                "tokens_in": msg.usage.input_tokens,
+                "tokens_out": msg.usage.output_tokens,
+                "n_web_searches": n_searches,
+                "tools_declared": tools,
+                "tool_choice": tool_choice,
+                "output_config": output_config,
+                "stop_reason": getattr(msg, "stop_reason", None),
+                "latency_s": latency,
+                "ts": time.time(),
+            }
+        )
         return text, msg.usage.input_tokens, msg.usage.output_tokens, n_searches
 
     @staticmethod
@@ -550,24 +574,26 @@ class LocalCloudAgent(BaseAgent):
         u = resp.usage
         p = getattr(u, "prompt_tokens", 0) if u else 0
         c = getattr(u, "completion_tokens", 0) if u else 0
-        _record_event({
-            "kind": "openai",
-            "role": trace_role,
-            "model": model,
-            "system": system,
-            "user": user,
-            "response": text,
-            "tool_calls": tool_calls,
-            "reasoning_content": reasoning,
-            "tokens_in": p,
-            "tokens_out": c,
-            "response_format": response_format,
-            "tools_declared": tools,
-            "tool_choice": tool_choice,
-            "finish_reason": getattr(choice, "finish_reason", None),
-            "latency_s": latency,
-            "ts": time.time(),
-        })
+        _record_event(
+            {
+                "kind": "openai",
+                "role": trace_role,
+                "model": model,
+                "system": system,
+                "user": user,
+                "response": text,
+                "tool_calls": tool_calls,
+                "reasoning_content": reasoning,
+                "tokens_in": p,
+                "tokens_out": c,
+                "response_format": response_format,
+                "tools_declared": tools,
+                "tool_choice": tool_choice,
+                "finish_reason": getattr(choice, "finish_reason", None),
+                "latency_s": latency,
+                "ts": time.time(),
+            }
+        )
         return text, p, c
 
     @staticmethod
@@ -614,12 +640,10 @@ class LocalCloudAgent(BaseAgent):
         from openai import OpenAI
 
         if model.startswith("openrouter/"):
-            model = model[len("openrouter/"):]
+            model = model[len("openrouter/") :]
         api_key = os.environ.get("OPENROUTER_API_KEY")
         if not api_key:
-            raise RuntimeError(
-                "OPENROUTER_API_KEY is not set; cannot call OpenRouter."
-            )
+            raise RuntimeError("OPENROUTER_API_KEY is not set; cannot call OpenRouter.")
         client = OpenAI(
             base_url="https://openrouter.ai/api/v1",
             api_key=api_key,
@@ -658,21 +682,23 @@ class LocalCloudAgent(BaseAgent):
         u = resp.usage
         p = getattr(u, "prompt_tokens", 0) if u else 0
         c = getattr(u, "completion_tokens", 0) if u else 0
-        _record_event({
-            "kind": "openrouter",
-            "role": trace_role,
-            "model": model,
-            "system": system,
-            "user": user,
-            "response": text,
-            "tool_calls": tool_calls,
-            "reasoning_content": reasoning,
-            "tokens_in": p,
-            "tokens_out": c,
-            "finish_reason": getattr(choice, "finish_reason", None),
-            "latency_s": latency,
-            "ts": time.time(),
-        })
+        _record_event(
+            {
+                "kind": "openrouter",
+                "role": trace_role,
+                "model": model,
+                "system": system,
+                "user": user,
+                "response": text,
+                "tool_calls": tool_calls,
+                "reasoning_content": reasoning,
+                "tokens_in": p,
+                "tokens_out": c,
+                "finish_reason": getattr(choice, "finish_reason", None),
+                "latency_s": latency,
+                "ts": time.time(),
+            }
+        )
         return text, p, c
 
     @staticmethod
@@ -701,7 +727,9 @@ class LocalCloudAgent(BaseAgent):
         from google import genai
         from google.genai import types
 
-        client = genai.Client(http_options=types.HttpOptions(timeout=int(timeout * 1000)))
+        client = genai.Client(
+            http_options=types.HttpOptions(timeout=int(timeout * 1000))
+        )
         cfg = types.GenerateContentConfig(
             temperature=temperature,
             max_output_tokens=max_tokens,
@@ -728,21 +756,23 @@ class LocalCloudAgent(BaseAgent):
             finish_reason = str(resp.candidates[0].finish_reason)
         except Exception:
             pass
-        _record_event({
-            "kind": "gemini",
-            "role": trace_role,
-            "model": model,
-            "system": system,
-            "user": user,
-            "response": text,
-            "tokens_in": p,
-            "tokens_out": c,
-            "temperature": temperature,
-            "max_tokens": max_tokens,
-            "finish_reason": finish_reason,
-            "latency_s": latency,
-            "ts": time.time(),
-        })
+        _record_event(
+            {
+                "kind": "gemini",
+                "role": trace_role,
+                "model": model,
+                "system": system,
+                "user": user,
+                "response": text,
+                "tokens_in": p,
+                "tokens_out": c,
+                "temperature": temperature,
+                "max_tokens": max_tokens,
+                "finish_reason": finish_reason,
+                "latency_s": latency,
+                "ts": time.time(),
+            }
+        )
         return text, p, c
 
     @staticmethod
@@ -797,27 +827,29 @@ class LocalCloudAgent(BaseAgent):
         u = resp.usage
         p = getattr(u, "prompt_tokens", 0) if u else 0
         c = getattr(u, "completion_tokens", 0) if u else 0
-        _record_event({
-            "kind": "vllm",
-            "role": trace_role,
-            "model": model,
-            "endpoint": endpoint,
-            "system": system,
-            "user": user,
-            "response": text,
-            "tool_calls": tool_calls,
-            "reasoning_content": reasoning,
-            "tokens_in": p,
-            "tokens_out": c,
-            "temperature": temperature,
-            "max_tokens": max_tokens,
-            "enable_thinking": enable_thinking,
-            "tools_declared": tools,
-            "tool_choice": tool_choice,
-            "finish_reason": getattr(choice, "finish_reason", None),
-            "latency_s": latency,
-            "ts": time.time(),
-        })
+        _record_event(
+            {
+                "kind": "vllm",
+                "role": trace_role,
+                "model": model,
+                "endpoint": endpoint,
+                "system": system,
+                "user": user,
+                "response": text,
+                "tool_calls": tool_calls,
+                "reasoning_content": reasoning,
+                "tokens_in": p,
+                "tokens_out": c,
+                "temperature": temperature,
+                "max_tokens": max_tokens,
+                "enable_thinking": enable_thinking,
+                "tools_declared": tools,
+                "tool_choice": tool_choice,
+                "finish_reason": getattr(choice, "finish_reason", None),
+                "latency_s": latency,
+                "ts": time.time(),
+            }
+        )
         return text, p, c
 
     @staticmethod
@@ -884,33 +916,37 @@ class LocalCloudAgent(BaseAgent):
             n_searches = getattr(srv, "web_search_requests", 0) if srv else 0
             content_blocks = [_serialize_block(b) for b in msg.content]
             tool_use_blocks = [
-                b for b in content_blocks
+                b
+                for b in content_blocks
                 if b.get("type") in ("tool_use", "server_tool_use")
             ]
             tool_result_blocks = [
-                b for b in content_blocks
+                b
+                for b in content_blocks
                 if b.get("type") in ("web_search_tool_result", "tool_result")
             ]
             stop_reason = getattr(msg, "stop_reason", None)
-            _record_event({
-                "kind": "anthropic",
-                "role": trace_role,
-                "model": model,
-                "system": system if turn == 0 else None,
-                "user": user if turn == 0 else None,
-                "turn": turn,
-                "response": text,
-                "content_blocks": content_blocks,
-                "tool_calls": tool_use_blocks,
-                "tool_results": tool_result_blocks,
-                "tokens_in": msg.usage.input_tokens,
-                "tokens_out": msg.usage.output_tokens,
-                "n_web_searches": n_searches,
-                "tools_declared": tools,
-                "stop_reason": stop_reason,
-                "latency_s": latency,
-                "ts": time.time(),
-            })
+            _record_event(
+                {
+                    "kind": "anthropic",
+                    "role": trace_role,
+                    "model": model,
+                    "system": system if turn == 0 else None,
+                    "user": user if turn == 0 else None,
+                    "turn": turn,
+                    "response": text,
+                    "content_blocks": content_blocks,
+                    "tool_calls": tool_use_blocks,
+                    "tool_results": tool_result_blocks,
+                    "tokens_in": msg.usage.input_tokens,
+                    "tokens_out": msg.usage.output_tokens,
+                    "n_web_searches": n_searches,
+                    "tools_declared": tools,
+                    "stop_reason": stop_reason,
+                    "latency_s": latency,
+                    "ts": time.time(),
+                }
+            )
             p_total += msg.usage.input_tokens
             c_total += msg.usage.output_tokens
             n_searches_total += n_searches
@@ -920,9 +956,7 @@ class LocalCloudAgent(BaseAgent):
             # here — break and let the caller (or future loop variant)
             # handle it. Only ``server_tool_use`` blocks (web_search)
             # are auto-continued by Anthropic itself.
-            client_tool_use = any(
-                b.get("type") == "tool_use" for b in content_blocks
-            )
+            client_tool_use = any(b.get("type") == "tool_use" for b in content_blocks)
             if client_tool_use:
                 break
             if stop_reason == "end_turn" or stop_reason is None:
@@ -931,10 +965,12 @@ class LocalCloudAgent(BaseAgent):
             # (server side) — Anthropic returned mid-thought. Append the
             # assistant turn and ask it to continue.
             messages.append({"role": "assistant", "content": msg.content})
-            messages.append({
-                "role": "user",
-                "content": "Continue.",
-            })
+            messages.append(
+                {
+                    "role": "user",
+                    "content": "Continue.",
+                }
+            )
         return last_text, p_total, c_total, n_searches_total, turns
 
     @staticmethod
@@ -1008,8 +1044,12 @@ class LocalCloudAgent(BaseAgent):
                     continue
                 raise
         if resp is None:
-            raise last_exc if last_exc is not None else RuntimeError(
-                "openai responses.create failed for all web_search tool names"
+            raise (
+                last_exc
+                if last_exc is not None
+                else RuntimeError(
+                    "openai responses.create failed for all web_search tool names"
+                )
             )
         _bump_cloud_calls()
         latency = time.time() - t0
@@ -1033,30 +1073,35 @@ class LocalCloudAgent(BaseAgent):
             text = "".join(chunks)
 
         n_searches = sum(
-            1 for item in output_items
-            if getattr(item, "type", None) in (
-                "web_search_call", "web_search_tool_call",
+            1
+            for item in output_items
+            if getattr(item, "type", None)
+            in (
+                "web_search_call",
+                "web_search_tool_call",
             )
         )
         u = getattr(resp, "usage", None)
         p = int(getattr(u, "input_tokens", 0) or 0) if u else 0
         c = int(getattr(u, "output_tokens", 0) or 0) if u else 0
-        _record_event({
-            "kind": "openai_agent",
-            "role": trace_role,
-            "model": model,
-            "system": system,
-            "user": user,
-            "response": text,
-            "output_items": _jsonable(output_items),
-            "tokens_in": p,
-            "tokens_out": c,
-            "n_web_searches": n_searches,
-            "tools_declared": [{"type": used_tool_name}],
-            "stop_reason": getattr(resp, "status", None),
-            "latency_s": latency,
-            "ts": time.time(),
-        })
+        _record_event(
+            {
+                "kind": "openai_agent",
+                "role": trace_role,
+                "model": model,
+                "system": system,
+                "user": user,
+                "response": text,
+                "output_items": _jsonable(output_items),
+                "tokens_in": p,
+                "tokens_out": c,
+                "n_web_searches": n_searches,
+                "tools_declared": [{"type": used_tool_name}],
+                "stop_reason": getattr(resp, "status", None),
+                "latency_s": latency,
+                "ts": time.time(),
+            }
+        )
         return text, p, c, n_searches, 1
 
     @staticmethod
@@ -1129,23 +1174,25 @@ class LocalCloudAgent(BaseAgent):
                 n_searches = len(web_search_queries)
         except Exception:  # noqa: BLE001
             pass
-        _record_event({
-            "kind": "gemini_agent",
-            "role": trace_role,
-            "model": model,
-            "system": system,
-            "user": user,
-            "response": text,
-            "tokens_in": p,
-            "tokens_out": c,
-            "n_web_searches": n_searches,
-            "web_search_queries": web_search_queries,
-            "temperature": temperature,
-            "max_tokens": max_tokens,
-            "finish_reason": finish_reason,
-            "latency_s": latency,
-            "ts": time.time(),
-        })
+        _record_event(
+            {
+                "kind": "gemini_agent",
+                "role": trace_role,
+                "model": model,
+                "system": system,
+                "user": user,
+                "response": text,
+                "tokens_in": p,
+                "tokens_out": c,
+                "n_web_searches": n_searches,
+                "web_search_queries": web_search_queries,
+                "temperature": temperature,
+                "max_tokens": max_tokens,
+                "finish_reason": finish_reason,
+                "latency_s": latency,
+                "ts": time.time(),
+            }
+        )
         return text, p, c, n_searches, 1
 
     def _call_cloud(
@@ -1269,8 +1316,13 @@ class LocalCloudAgent(BaseAgent):
             # Persist the trace before the trace state is closed (and even on
             # hard failure, so we get a record of what we did before it broke).
             self._write_trace_log(
-                context, input, answer, meta if "meta" in locals() else {},
-                events, soft_reason, exc_obj,
+                context,
+                input,
+                answer,
+                meta if "meta" in locals() else {},
+                events,
+                soft_reason,
+                exc_obj,
             )
             _close_trace()
             _close_call_counts()
@@ -1326,9 +1378,7 @@ class LocalCloudAgent(BaseAgent):
                 "metadata": meta,
                 "events": events,
                 "soft_error": soft_reason,
-                "error": (
-                    f"{type(exc).__name__}: {exc}" if exc is not None else None
-                ),
+                "error": (f"{type(exc).__name__}: {exc}" if exc is not None else None),
             }
             (out_dir / f"{task_id}.json").write_text(
                 json.dumps(blob, indent=2, default=str)

src/openjarvis/agents/hybrid/_energy.py

@@ -96,6 +96,7 @@ class EnergyCollector:
             return self
         try:
             import pynvml  # type: ignore[import-not-found]
+
             pynvml.nvmlInit()
             total = pynvml.nvmlDeviceGetCount()
             self.gpu_indices = _resolve_gpu_indices(total)

src/openjarvis/agents/hybrid/_openai_retry.py

@@ -174,12 +174,15 @@ def _is_retryable(exc: BaseException) -> bool:
         import openai
     except ImportError:
         return False
-    if isinstance(exc, (
-        openai.RateLimitError,
-        openai.APITimeoutError,
-        openai.APIConnectionError,
-        openai.InternalServerError,
-    )):
+    if isinstance(
+        exc,
+        (
+            openai.RateLimitError,
+            openai.APITimeoutError,
+            openai.APIConnectionError,
+            openai.InternalServerError,
+        ),
+    ):
         return True
     if isinstance(exc, openai.APIStatusError):
         status = getattr(exc, "status_code", None)
@@ -196,7 +199,7 @@ def _sleep_for(attempt: int, exc: BaseException) -> float:
         # Respect a server-provided hint, but clamp to our cap so a
         # pathological header can't stall the run for hours.
         return min(_RETRY_CAP, hinted) + random.uniform(0, 0.5)
-    base = min(_RETRY_CAP, _RETRY_BASE * (2 ** attempt))
+    base = min(_RETRY_CAP, _RETRY_BASE * (2**attempt))
     # Full jitter — better tail behavior than equal jitter when many
     # workers wake at the same moment.
     return random.uniform(0.0, base)
@@ -237,16 +240,19 @@ def _wrap_create(orig: Callable[..., Any]) -> Callable[..., Any]:
                         import openai
                     except ImportError:
                         raise
-                    if not isinstance(exc, (
-                        openai.APIConnectionError,
-                        openai.APITimeoutError,
-                        openai.InternalServerError,
-                    )):
+                    if not isinstance(
+                        exc,
+                        (
+                            openai.APIConnectionError,
+                            openai.APITimeoutError,
+                            openai.InternalServerError,
+                        ),
+                    ):
                         raise
                     local_last_exc = exc
                     if attempt >= 2:
                         break
-                    time.sleep(2 ** attempt)
+                    time.sleep(2**attempt)
             assert local_last_exc is not None
             raise local_last_exc
 
@@ -266,6 +272,7 @@ def _wrap_create(orig: Callable[..., Any]) -> Callable[..., Any]:
                 # stay parseable in the runner log.
                 try:
                     import sys
+
                     print(
                         f"[openai-retry] attempt {attempt + 1}/{_MAX_RETRIES} "
                         f"{type(exc).__name__}: {str(exc)[:120]} — "
@@ -333,9 +340,7 @@ def patch_openai_globally() -> None:
             from openai.resources.chat import completions as _comp_mod_async
 
             cls = getattr(_comp_mod_async, "AsyncCompletions", None)
-            if cls is not None and not getattr(
-                cls.create, "_hybrid_patched", False
-            ):
+            if cls is not None and not getattr(cls.create, "_hybrid_patched", False):
                 # Async wrapper is structurally different — only patch
                 # the bumped defaults via __init__; full retry loop on
                 # async would need an async wrapper. Leave that for the

src/openjarvis/agents/hybrid/_prices.py

@@ -11,27 +11,27 @@ from __future__ import annotations
 
 # USD per million tokens, (input, output). Local models = 0.
 PRICES: dict[str, tuple[float, float]] = {
-    "claude-opus-4-7":             (5.00, 25.0),
-    "claude-sonnet-4-6":           (3.00, 15.0),
-    "claude-haiku-4-5":            (1.00, 5.00),
-    "claude-haiku-4-5-20251001":   (1.00, 5.00),
-    "gpt-5.5":                     (5.00, 30.0),
-    "gpt-5":                       (1.25, 10.0),
-    "gpt-5-mini":                  (0.25, 2.00),
-    "gpt-5-mini-2025-08-07":       (0.25, 2.00),
-    "gpt-4o":                      (0.15, 0.60),
+    "claude-opus-4-7": (5.00, 25.0),
+    "claude-sonnet-4-6": (3.00, 15.0),
+    "claude-haiku-4-5": (1.00, 5.00),
+    "claude-haiku-4-5-20251001": (1.00, 5.00),
+    "gpt-5.5": (5.00, 30.0),
+    "gpt-5": (1.25, 10.0),
+    "gpt-5-mini": (0.25, 2.00),
+    "gpt-5-mini-2025-08-07": (0.25, 2.00),
+    "gpt-4o": (0.15, 0.60),
     # Gemini Developer API prices (USD per 1M tokens). Pro models use tiered
     # pricing above 200K prompt tokens; GAIA prompts stay under that tier, so
     # charge the low-context standard rate.
-    "gemini-3.1-pro-preview":       (2.00, 12.0),
+    "gemini-3.1-pro-preview": (2.00, 12.0),
     "gemini-3.1-pro-preview-customtools": (2.00, 12.0),
-    "gemini-2.5-pro":              (1.25, 10.0),
-    "gemini-2.5-flash":            (0.30, 2.50),
-    "gemini-2.5-flash-lite":       (0.10, 0.40),
+    "gemini-2.5-pro": (1.25, 10.0),
+    "gemini-2.5-flash": (0.30, 2.50),
+    "gemini-2.5-flash-lite": (0.10, 0.40),
     # OpenRouter slugs (used by toolorchestra paper-match pool).
     # Prices are OpenRouter list (USD/1M tokens), 2026-05 snapshot.
-    "qwen/qwen-2.5-coder-32b-instruct":  (0.08, 0.18),
-    "qwen/qwen3-32b":                    (0.10, 0.30),
+    "qwen/qwen-2.5-coder-32b-instruct": (0.08, 0.18),
+    "qwen/qwen3-32b": (0.10, 0.30),
     "meta-llama/llama-3.3-70b-instruct": (0.13, 0.39),
 }
 
@@ -64,11 +64,7 @@ def is_reasoning_model(model: str) -> bool:
     before emitting visible answer text. At max_tokens=4096 these silently
     truncate with empty answers on GAIA (26/100 GPT-5, 18/100 Gemini Pro)."""
     m = (model or "").lower()
-    return (
-        is_gpt5_family(model)
-        or "gemini-2.5-pro" in m
-        or "gemini-3.1-pro" in m
-    )
+    return is_gpt5_family(model) or "gemini-2.5-pro" in m or "gemini-3.1-pro" in m
 
 
 def default_max_output_tokens(model: str) -> int:

src/openjarvis/agents/hybrid/advisors.py

@@ -80,9 +80,7 @@ def _resolve_local_model(endpoint: str, registry_model: str) -> str:
     a model id (e.g. ``Qwen3.5-9B``) that's different from what's loaded.
     """
     try:
-        with urllib.request.urlopen(
-            endpoint.rstrip("/") + "/models", timeout=5
-        ) as r:
+        with urllib.request.urlopen(endpoint.rstrip("/") + "/models", timeout=5) as r:
             data = json.loads(r.read())
         served = [m["id"] for m in data.get("data", [])]
     except Exception:
@@ -158,14 +156,15 @@ class AdvisorsAgent(LocalCloudAgent):
         # only the cloud executor passes do. With web_search on, dispatch
         # to the search-capable agent loop for the configured provider.
         if use_ws:
-            (initial_resp, e1_in, e1_out, n_s1, e1_turns,
-             e1_search_cost) = self._executor_search(
-                user=f"Question:\n{question}",
-                system=EXECUTOR_INITIAL_SYS,
-                max_tokens=executor_max_tokens,
-                ws_max_uses=ws_max_uses,
-                max_turns=gaia_max_turns,
-                query=question,
+            (initial_resp, e1_in, e1_out, n_s1, e1_turns, e1_search_cost) = (
+                self._executor_search(
+                    user=f"Question:\n{question}",
+                    system=EXECUTOR_INITIAL_SYS,
+                    max_tokens=executor_max_tokens,
+                    ws_max_uses=ws_max_uses,
+                    max_turns=gaia_max_turns,
+                    query=question,
+                )
             )
             n_searches_total += n_s1
             search_cost_total += e1_search_cost
@@ -186,7 +185,8 @@ class AdvisorsAgent(LocalCloudAgent):
             )
         local_model = _resolve_local_model(self._local_endpoint, self._local_model)
         advisor_prompt = ADVISOR_TEMPLATE.format(
-            question=question, initial_response=initial_resp,
+            question=question,
+            initial_response=initial_resp,
         )
         advisor_text, adv_in, adv_out = self._call_vllm(
             local_model,
@@ -206,14 +206,15 @@ class AdvisorsAgent(LocalCloudAgent):
             f"answer-format rules."
         )
         if use_ws:
-            (final_answer, e2_in, e2_out, n_s2, e2_turns,
-             e2_search_cost) = self._executor_search(
-                user=final_user,
-                system=EXECUTOR_FINAL_SYS,
-                max_tokens=executor_max_tokens,
-                ws_max_uses=ws_max_uses,
-                max_turns=gaia_max_turns,
-                query=question,
+            (final_answer, e2_in, e2_out, n_s2, e2_turns, e2_search_cost) = (
+                self._executor_search(
+                    user=final_user,
+                    system=EXECUTOR_FINAL_SYS,
+                    max_tokens=executor_max_tokens,
+                    ws_max_uses=ws_max_uses,
+                    max_turns=gaia_max_turns,
+                    query=question,
+                )
             )
             n_searches_total += n_s2
             search_cost_total += e2_search_cost
@@ -424,8 +425,10 @@ class AdvisorsAgent(LocalCloudAgent):
 
         tokens_local = adv_in + adv_out
         tokens_cloud = (
-            initial_out["tokens_in"] + initial_out["tokens_out"]
-            + final_out["tokens_in"] + final_out["tokens_out"]
+            initial_out["tokens_in"]
+            + initial_out["tokens_out"]
+            + final_out["tokens_in"]
+            + final_out["tokens_out"]
         )
         cost = initial_out["cost_usd"] + final_out["cost_usd"]
         meta: Dict[str, Any] = {

src/openjarvis/agents/hybrid/archon.py

@@ -73,6 +73,7 @@ ARCHON_SWE_RANKER_SYS = (
 
 # ---------- Stubs for Archon's eager-imported heavy deps we don't need ----------
 
+
 def _stub_archon_imports() -> None:
     """``utils.py`` imports groq/google/litellm/dotenv at module load. Stub
     the ones we don't use so the import chain doesn't fail when those
@@ -97,6 +98,7 @@ def _add_archon_to_path() -> None:
 
 # ---------- Anthropic patch for Opus 4.7 ----------
 
+
 def _patch_anthropic_for_opus() -> None:
     from anthropic.resources.messages import messages as _msgs_mod
 
@@ -129,8 +131,10 @@ def _tally() -> Dict[str, int]:
     counts = getattr(_TALLY_LOCAL, "counts", None)
     if counts is None:
         counts = {
-            "cloud_prompt": 0, "cloud_completion": 0,
-            "local_prompt": 0, "local_completion": 0,
+            "cloud_prompt": 0,
+            "cloud_completion": 0,
+            "local_prompt": 0,
+            "local_completion": 0,
             "n_web_searches": 0,
         }
         _TALLY_LOCAL.counts = counts
@@ -141,8 +145,10 @@ def _tally() -> Dict[str, int]:
 
 def _reset_tally() -> None:
     _TALLY_LOCAL.counts = {
-        "cloud_prompt": 0, "cloud_completion": 0,
-        "local_prompt": 0, "local_completion": 0,
+        "cloud_prompt": 0,
+        "cloud_completion": 0,
+        "local_prompt": 0,
+        "local_completion": 0,
         "n_web_searches": 0,
     }
 
@@ -169,6 +175,7 @@ def _make_local_generator(local_endpoint: str, local_model: str):
 
     def local_gen(model, messages, max_tokens=2048, temperature=0.7, **_kw):  # type: ignore[no-untyped-def]
         import time as _time
+
         t0 = _time.time()
         try:
             resp = client.chat.completions.create(
@@ -179,31 +186,35 @@ def _make_local_generator(local_endpoint: str, local_model: str):
             )
             _bump_local_calls()
         except Exception as e:
-            _record_event({
-                "kind": "archon_local_gen_error",
-                "model": local_model,
-                "messages": messages,
-                "error": f"{type(e).__name__}: {e}",
-                "ts": _time.time(),
-            })
+            _record_event(
+                {
+                    "kind": "archon_local_gen_error",
+                    "model": local_model,
+                    "messages": messages,
+                    "error": f"{type(e).__name__}: {e}",
+                    "ts": _time.time(),
+                }
+            )
             return f"[local-vllm error: {e!r}]"
         u = resp.usage
         if u:
             _tally()["local_prompt"] += getattr(u, "prompt_tokens", 0) or 0
             _tally()["local_completion"] += getattr(u, "completion_tokens", 0) or 0
         text = (resp.choices[0].message.content or "").strip()
-        _record_event({
-            "kind": "archon_local_gen",
-            "model": local_model,
-            "messages": messages,
-            "response": text,
-            "tokens_in": getattr(u, "prompt_tokens", 0) if u else 0,
-            "tokens_out": getattr(u, "completion_tokens", 0) if u else 0,
-            "temperature": temperature,
-            "max_tokens": max_tokens,
-            "latency_s": _time.time() - t0,
-            "ts": _time.time(),
-        })
+        _record_event(
+            {
+                "kind": "archon_local_gen",
+                "model": local_model,
+                "messages": messages,
+                "response": text,
+                "tokens_in": getattr(u, "prompt_tokens", 0) if u else 0,
+                "tokens_out": getattr(u, "completion_tokens", 0) if u else 0,
+                "temperature": temperature,
+                "max_tokens": max_tokens,
+                "latency_s": _time.time() - t0,
+                "ts": _time.time(),
+            }
+        )
         return text
 
     return local_gen
@@ -218,10 +229,13 @@ def _wrap_archon_cloud_generators() -> None:
 
     def gen_openai(model, messages, max_tokens=2048, temperature=0.7, **_kw):  # type: ignore[no-untyped-def]
         import time as _time
+
         client = _OAI()
         kwargs: Dict[str, Any] = dict(
-            model=model, messages=messages,
-            max_tokens=max_tokens, temperature=temperature,
+            model=model,
+            messages=messages,
+            max_tokens=max_tokens,
+            temperature=temperature,
         )
         # GPT-5/o1/o3 reject non-default temperature and use max_completion_tokens.
         if model.startswith(("gpt-5", "o1", "o3")):
@@ -236,20 +250,23 @@ def _wrap_archon_cloud_generators() -> None:
             _tally()["cloud_prompt"] += getattr(u, "prompt_tokens", 0) or 0
             _tally()["cloud_completion"] += getattr(u, "completion_tokens", 0) or 0
         text = (resp.choices[0].message.content or "").strip()
-        _record_event({
-            "kind": "archon_cloud_openai",
-            "model": model,
-            "messages": messages,
-            "response": text,
-            "tokens_in": getattr(u, "prompt_tokens", 0) if u else 0,
-            "tokens_out": getattr(u, "completion_tokens", 0) if u else 0,
-            "latency_s": _time.time() - t0,
-            "ts": _time.time(),
-        })
+        _record_event(
+            {
+                "kind": "archon_cloud_openai",
+                "model": model,
+                "messages": messages,
+                "response": text,
+                "tokens_in": getattr(u, "prompt_tokens", 0) if u else 0,
+                "tokens_out": getattr(u, "completion_tokens", 0) if u else 0,
+                "latency_s": _time.time() - t0,
+                "ts": _time.time(),
+            }
+        )
         return text
 
     def gen_anthropic(model, messages, max_tokens=2048, temperature=0.7, **_kw):  # type: ignore[no-untyped-def]
         import time as _time
+
         client = _anth.Anthropic(timeout=600.0)
         system = ""
         msgs = []
@@ -259,7 +276,10 @@ def _wrap_archon_cloud_generators() -> None:
             else:
                 msgs.append(m)
         kwargs: Dict[str, Any] = dict(
-            model=model, system=system, messages=msgs, max_tokens=max_tokens,
+            model=model,
+            system=system,
+            messages=msgs,
+            max_tokens=max_tokens,
         )
         if not model.startswith(NO_TEMP_PREFIXES):
             kwargs["temperature"] = temperature
@@ -277,24 +297,27 @@ def _wrap_archon_cloud_generators() -> None:
         srv = getattr(u, "server_tool_use", None) if u else None
         n_searches = getattr(srv, "web_search_requests", 0) if srv else 0
         _tally()["n_web_searches"] += int(n_searches)
-        _record_event({
-            "kind": "archon_cloud_anthropic",
-            "model": model,
-            "system": system,
-            "messages": msgs,
-            "response": text.strip(),
-            "tokens_in": getattr(u, "input_tokens", 0) if u else 0,
-            "tokens_out": getattr(u, "output_tokens", 0) if u else 0,
-            "n_web_searches": int(n_searches),
-            "tools_declared": kwargs.get("tools"),
-            "latency_s": _time.time() - t0,
-            "ts": _time.time(),
-        })
+        _record_event(
+            {
+                "kind": "archon_cloud_anthropic",
+                "model": model,
+                "system": system,
+                "messages": msgs,
+                "response": text.strip(),
+                "tokens_in": getattr(u, "input_tokens", 0) if u else 0,
+                "tokens_out": getattr(u, "output_tokens", 0) if u else 0,
+                "n_web_searches": int(n_searches),
+                "tools_declared": kwargs.get("tools"),
+                "latency_s": _time.time() - t0,
+                "ts": _time.time(),
+            }
+        )
         return text.strip()
 
     from archon.completions.components.Generator import (
         GENERATE_MAP as _GMAP,  # type: ignore[import-not-found]
     )
+
     _GMAP["OpenAI_API"] = gen_openai
     _GMAP["Anthropic_API"] = gen_anthropic
 
@@ -330,7 +353,9 @@ def _patch_archon_prompts() -> None:
     orig = _p.make_fuser_prompt
 
     def patched(conv, references, critiques=None, length_control=False):  # type: ignore[no-untyped-def]
-        base = orig(conv, references, critiques=critiques, length_control=length_control)
+        base = orig(
+            conv, references, critiques=critiques, length_control=length_control
+        )
         return base + _FUSER_FORMAT_REMINDER
 
     patched._hybrid_format_patched = True  # type: ignore[attr-defined]
@@ -339,6 +364,7 @@ def _patch_archon_prompts() -> None:
     from archon.completions.components import (
         Fuser as _F,  # type: ignore[import-not-found]
     )
+
     _F.make_fuser_prompt = patched
 
 
@@ -354,6 +380,7 @@ def _apply_patches_once() -> None:
     _patch_anthropic_for_opus()
     # Trigger Archon imports so GENERATE_MAP exists.
     import archon.completions.components.Generator  # type: ignore[import-not-found]  # noqa: F401
+
     _wrap_archon_cloud_generators()
     _patch_archon_prompts()
     _PATCHES_APPLIED = True
@@ -361,49 +388,62 @@ def _apply_patches_once() -> None:
 
 # ---------- Architecture presets ----------
 
+
 def _presets():
     return {
         "ensemble_rank_fuse": lambda K, local_model, ranker_model, fuser_model, max_tokens, temperature: [
-            [{
-                "type": "generator",
-                "model": local_model,
-                "model_type": "vllm_local",
-                "top_k": 1,
-                "temperature": temperature,
-                "max_tokens": max_tokens,
-                "samples": K,
-            }],
-            [{
-                "type": "ranker",
-                "model": ranker_model,
-                "model_type": "Anthropic_API" if ranker_model.startswith("claude") else "OpenAI_API",
-                "top_k": min(K, 5),
-                "temperature": 0.0,
-                "max_tokens": max_tokens,
-            }],
-            [{
-                "type": "fuser",
-                "model": fuser_model,
-                "model_type": "Anthropic_API" if fuser_model.startswith("claude") else "OpenAI_API",
-                "temperature": 0.0,
-                "max_tokens": max_tokens,
-                "samples": 1,
-            }],
+            [
+                {
+                    "type": "generator",
+                    "model": local_model,
+                    "model_type": "vllm_local",
+                    "top_k": 1,
+                    "temperature": temperature,
+                    "max_tokens": max_tokens,
+                    "samples": K,
+                }
+            ],
+            [
+                {
+                    "type": "ranker",
+                    "model": ranker_model,
+                    "model_type": "Anthropic_API"
+                    if ranker_model.startswith("claude")
+                    else "OpenAI_API",
+                    "top_k": min(K, 5),
+                    "temperature": 0.0,
+                    "max_tokens": max_tokens,
+                }
+            ],
+            [
+                {
+                    "type": "fuser",
+                    "model": fuser_model,
+                    "model_type": "Anthropic_API"
+                    if fuser_model.startswith("claude")
+                    else "OpenAI_API",
+                    "temperature": 0.0,
+                    "max_tokens": max_tokens,
+                    "samples": 1,
+                }
+            ],
         ],
         # ``single_local`` honors the cfg ``max_tokens`` (passed positionally
         # like ``ensemble_rank_fuse``). Previously it hard-coded 2048, which
         # cut Qwen off mid-reasoning before it could emit the GAIA
         # ``FINAL ANSWER:`` line — the scorer then had nothing to extract.
         "single_local": lambda K, local_model, ranker_model, fuser_model, max_tokens, temperature: [
-            [{
-                "type": "generator",
-                "model": local_model,
-                "model_type": "vllm_local",
-                "top_k": 1,
-                "temperature": 0.0,
-                "max_tokens": max_tokens,
-                "samples": 1,
-            }],
+            [
+                {
+                    "type": "generator",
+                    "model": local_model,
+                    "model_type": "vllm_local",
+                    "top_k": 1,
+                    "temperature": 0.0,
+                    "max_tokens": max_tokens,
+                    "samples": 1,
+                }
+            ],
         ],
     }
 
@@ -464,7 +504,12 @@ class ArchonAgent(LocalCloudAgent):
         )
 
         layers = presets[arch](
-            K, self._local_model, ranker_model, fuser_model, max_tokens, temperature,
+            K,
+            self._local_model,
+            ranker_model,
+            fuser_model,
+            max_tokens,
+            temperature,
         )
         archon_cfg = {"name": f"hybrid-archon-{arch}", "layers": layers}
 
@@ -480,10 +525,12 @@ class ArchonAgent(LocalCloudAgent):
         archon = Archon(archon_cfg)
 
         try:
-            answer = archon.generate([
-                {"role": "system", "content": "You are a helpful assistant."},
-                {"role": "user", "content": input},
-            ])
+            answer = archon.generate(
+                [
+                    {"role": "system", "content": "You are a helpful assistant."},
+                    {"role": "user", "content": input},
+                ]
+            )
         except Exception:
             # Re-raise so the base ``run()`` / runner's ``_run_one_inner``
             # records this in the row's ``error`` field instead of stashing
@@ -518,10 +565,10 @@ class ArchonAgent(LocalCloudAgent):
             "tool_calls": int(n_searches),
             "traces": {
                 "architecture": arch,
-                "n_samples":    K,
+                "n_samples": K,
                 "ranker_model": ranker_model,
-                "fuser_model":  fuser_model,
-                "local_model":  self._local_model,
+                "fuser_model": fuser_model,
+                "local_model": self._local_model,
                 "tokens_breakdown": dict(_tally()),
                 "web_search_enabled": ws_enabled,
                 "n_web_searches": n_searches,
@@ -568,26 +615,30 @@ class ArchonAgent(LocalCloudAgent):
                 turn_max_tokens=turn_max_tokens,
                 trace_prefix=f"archon_gen{k}",
             )
-            candidates.append({
-                "idx": k,
-                "summary": out["final_summary"],
-                "patch": out["patch"],
-                "framed": out["answer"],
-                "tokens_in": out["tokens_in"],
-                "tokens_out": out["tokens_out"],
-                "turns": out["turns"],
-            })
+            candidates.append(
+                {
+                    "idx": k,
+                    "summary": out["final_summary"],
+                    "patch": out["patch"],
+                    "framed": out["answer"],
+                    "tokens_in": out["tokens_in"],
+                    "tokens_out": out["tokens_out"],
+                    "turns": out["turns"],
+                }
+            )
             total_tokens_local += out["tokens_in"] + out["tokens_out"]
-            self.record_trace_event({
-                "kind": "archon_swe_candidate",
-                "idx": k,
-                "patch_chars": len(out["patch"]),
-                "summary": out["final_summary"],
-            })
+            self.record_trace_event(
+                {
+                    "kind": "archon_swe_candidate",
+                    "idx": k,
+                    "patch_chars": len(out["patch"]),
+                    "summary": out["final_summary"],
+                }
+            )
 
         # Ranker: cloud picks the best candidate.
         ranker_user = (
-            f"Issue:\n{task.get('problem_statement','')}\n\n"
+            f"Issue:\n{task.get('problem_statement', '')}\n\n"
             f"K = {K} candidate patches:\n\n"
             + "\n\n".join(
                 f"=== Candidate {c['idx']} ===\nSummary: {c['summary']}\n"
@@ -615,13 +666,15 @@ class ArchonAgent(LocalCloudAgent):
             chosen_idx = 0
         chosen = candidates[chosen_idx]
 
-        self.record_trace_event({
-            "kind": "archon_swe_rank",
-            "chosen_idx": chosen_idx,
-            "ranker_raw": ranker_text,
-            "tokens_in": r_in,
-            "tokens_out": r_out,
-        })
+        self.record_trace_event(
+            {
+                "kind": "archon_swe_rank",
+                "chosen_idx": chosen_idx,
+                "ranker_raw": ranker_text,
+                "tokens_in": r_in,
+                "tokens_out": r_out,
+            }
+        )
 
         meta = {
             "tokens_local": total_tokens_local,
@@ -635,8 +688,12 @@ class ArchonAgent(LocalCloudAgent):
                 "swe_mode": True,
                 "K": K,
                 "candidates": [
-                    {"idx": c["idx"], "summary": c["summary"],
-                     "patch_chars": len(c["patch"]), "turns": c["turns"]}
+                    {
+                        "idx": c["idx"],
+                        "summary": c["summary"],
+                        "patch_chars": len(c["patch"]),
+                        "turns": c["turns"],
+                    }
                     for c in candidates
                 ],
                 "chosen_idx": chosen_idx,

src/openjarvis/agents/hybrid/baseline_cloud.py

@@ -82,7 +82,11 @@ class BaselineCloudAgent(LocalCloudAgent):
                 max_turns=int(cfg.get("swe_max_turns", 30)),
                 bash_timeout=int(cfg.get("swe_bash_timeout_s", 120)),
                 output_cap=int(cfg.get("swe_output_cap", 10_000)),
-                turn_max_tokens=int(cfg.get("cloud_max_tokens", default_max_output_tokens(self._cloud_model))),
+                turn_max_tokens=int(
+                    cfg.get(
+                        "cloud_max_tokens", default_max_output_tokens(self._cloud_model)
+                    )
+                ),
                 trace_prefix="baseline_cloud",
             )
             meta = {
@@ -112,7 +116,11 @@ class BaselineCloudAgent(LocalCloudAgent):
             text, p_tok, c_tok, n_searches, turns = self._call_anthropic_agent(
                 self._cloud_model,
                 user=input,
-                max_tokens=int(cfg.get("cloud_max_tokens", default_max_output_tokens(self._cloud_model))),
+                max_tokens=int(
+                    cfg.get(
+                        "cloud_max_tokens", default_max_output_tokens(self._cloud_model)
+                    )
+                ),
                 temperature=0.0,
                 tools=[build_web_search_tool(ws_max_uses)],
                 max_turns=gaia_max_turns,
@@ -145,17 +153,23 @@ class BaselineCloudAgent(LocalCloudAgent):
             # wired here. Skip cleanly rather than fake one. Cells that
             # want web_search must run on Anthropic until those backends
             # are wired.
-            self.record_trace_event({
-                "kind": "web_search_skipped",
-                "reason": "non_anthropic_endpoint",
-                "endpoint": self._cloud_endpoint,
-            })
+            self.record_trace_event(
+                {
+                    "kind": "web_search_skipped",
+                    "reason": "non_anthropic_endpoint",
+                    "endpoint": self._cloud_endpoint,
+                }
+            )
 
         # One-shot direct cloud call. GAIA only — SWE goes through the
         # mini-SWE-agent loop above (now supports anthropic/openai/gemini).
         text, p_tok, c_tok = self._call_cloud(
             user=input,
-            max_tokens=int(cfg.get("cloud_max_tokens", default_max_output_tokens(self._cloud_model))),
+            max_tokens=int(
+                cfg.get(
+                    "cloud_max_tokens", default_max_output_tokens(self._cloud_model)
+                )
+            ),
             temperature=0.0,
         )
         meta = {

src/openjarvis/agents/hybrid/conductor.py

@@ -89,19 +89,20 @@ CONDUCTOR_STRICTER = (
     "Your previous response was not valid JSON or was missing required fields. "
     "Reply with ONLY a single JSON object — no prose, no code fences, no commentary "
     "— containing exactly the three keys model_id (list[int]), subtasks (list[str]), "
-    "and access_list (list[list[int] or \"all\"]) of equal length, at most 5 entries, "
+    'and access_list (list[list[int] or "all"]) of equal length, at most 5 entries, '
     "and access_list[0] must be [] (an empty list)."
 )
 
 
 # ---------- Plan parsing ----------
 
+
 def _strip_fences(s: str) -> str:
     s = s.strip()
     if s.startswith("```"):
         first_nl = s.find("\n")
         if first_nl != -1:
-            s = s[first_nl + 1:]
+            s = s[first_nl + 1 :]
         if s.endswith("```"):
             s = s[:-3]
         s = s.strip()
@@ -119,9 +120,7 @@ def _try_literal(s: str):
     """Fallback for the paper's literal Python-list output style."""
     out = {}
     for key in ("model_id", "subtasks", "access_list"):
-        m = re.search(
-            rf"{key}\s*=\s*(\[[^\]]*\](?:\s*\+\s*\[[^\]]*\])*)", s, re.DOTALL
-        )
+        m = re.search(rf"{key}\s*=\s*(\[[^\]]*\](?:\s*\+\s*\[[^\]]*\])*)", s, re.DOTALL)
         if not m:
             return None
         try:
@@ -154,7 +153,7 @@ def _validate_plan(plan: Any, n_workers: int) -> Optional[str]:
         if a == "all":
             continue
         if not isinstance(a, list):
-            return f"access_list[{i}] must be list or \"all\""
+            return f'access_list[{i}] must be list or "all"'
         for j in a:
             if not isinstance(j, int) or not (0 <= j < i):
                 return f"access_list[{i}] has bad ref {j!r}"
@@ -174,17 +173,18 @@ def _parse_plan(text: str, n_workers: int):
 
 # ---------- Worker pool ----------
 
+
 def _vllm_alive(base_url: str) -> bool:
     try:
-        with urllib.request.urlopen(
-            base_url.rstrip("/") + "/models", timeout=3
-        ) as r:
+        with urllib.request.urlopen(base_url.rstrip("/") + "/models", timeout=3) as r:
             return r.status == 200
     except Exception:
         return False
 
 
-def _default_pool(local_model: Optional[str], local_endpoint: Optional[str]) -> List[Dict[str, Any]]:
+def _default_pool(
+    local_model: Optional[str], local_endpoint: Optional[str]
+) -> List[Dict[str, Any]]:
     """Default worker pool — faithful to the Sakana Conductor paper (arXiv 2512.04388).
 
     The paper composes a heterogeneous 7-worker pool spanning three frontier
@@ -206,98 +206,112 @@ def _default_pool(local_model: Optional[str], local_endpoint: Optional[str]) ->
     del local_model, local_endpoint  # paper default carries no local worker
     pool: List[Dict[str, Any]] = []
     if not os.environ.get("OJ_CONDUCTOR_DISABLE_GEMINI"):
-        pool.append({
-            "id": len(pool),
-            "name": "gemini-pro",
-            "endpoint": "gemini",
-            "model": "gemini-2.5-pro",
-            "description": (
-                "Google Gemini 2.5 Pro. Frontier multimodal reasoner with a "
-                "very large context window. Strong at long-document synthesis, "
-                "multi-hop factual reasoning, and tasks that benefit from "
-                "wide retrieval. Slower and pricier than mid-tier workers."
-            ),
-        })
+        pool.append(
+            {
+                "id": len(pool),
+                "name": "gemini-pro",
+                "endpoint": "gemini",
+                "model": "gemini-2.5-pro",
+                "description": (
+                    "Google Gemini 2.5 Pro. Frontier multimodal reasoner with a "
+                    "very large context window. Strong at long-document synthesis, "
+                    "multi-hop factual reasoning, and tasks that benefit from "
+                    "wide retrieval. Slower and pricier than mid-tier workers."
+                ),
+            }
+        )
     if not os.environ.get("OJ_CONDUCTOR_DISABLE_ANTHROPIC"):
-        pool.append({
-            "id": len(pool),
-            "name": "claude-sonnet-4",
-            "endpoint": "anthropic",
-            "model": "claude-sonnet-4-6",
-            "description": (
-                "Anthropic Claude Sonnet 4. Strong general-purpose reasoner "
-                "with careful instruction following and reliable formatting. "
-                "Good default for code, structured writing, and decisive "
-                "steps where accuracy matters more than raw throughput."
-            ),
-        })
+        pool.append(
+            {
+                "id": len(pool),
+                "name": "claude-sonnet-4",
+                "endpoint": "anthropic",
+                "model": "claude-sonnet-4-6",
+                "description": (
+                    "Anthropic Claude Sonnet 4. Strong general-purpose reasoner "
+                    "with careful instruction following and reliable formatting. "
+                    "Good default for code, structured writing, and decisive "
+                    "steps where accuracy matters more than raw throughput."
+                ),
+            }
+        )
     if not os.environ.get("OJ_CONDUCTOR_DISABLE_OPENAI"):
-        pool.append({
-            "id": len(pool),
-            "name": "gpt-5",
-            "endpoint": "openai",
-            "model": "gpt-5",
-            "description": (
-                "OpenAI GPT-5. Frontier-tier broad-knowledge model. Best for "
-                "open-domain factual recall, creative generation, and "
-                "ambiguous questions where coverage matters. Expensive; use "
-                "for steps where breadth of world knowledge is the bottleneck."
-            ),
-        })
+        pool.append(
+            {
+                "id": len(pool),
+                "name": "gpt-5",
+                "endpoint": "openai",
+                "model": "gpt-5",
+                "description": (
+                    "OpenAI GPT-5. Frontier-tier broad-knowledge model. Best for "
+                    "open-domain factual recall, creative generation, and "
+                    "ambiguous questions where coverage matters. Expensive; use "
+                    "for steps where breadth of world knowledge is the bottleneck."
+                ),
+            }
+        )
     if not os.environ.get("OJ_CONDUCTOR_DISABLE_OPENROUTER"):
-        pool.append({
-            "id": len(pool),
-            "name": "deepseek-r1-distill-qwen-32b",
-            "endpoint": "openrouter",
-            "model": "deepseek/deepseek-r1-distill-qwen-32b",
-            "description": (
-                "DeepSeek R1 distilled into Qwen-32B (open weights via "
-                "OpenRouter). Specialized for chain-of-thought math, logic, "
-                "and competitive-programming-style problems. Verbose; "
-                "produces extensive reasoning traces before the final answer."
-            ),
-        })
-        pool.append({
-            "id": len(pool),
-            "name": "gemma3-27b-it",
-            "endpoint": "openrouter",
-            "model": "google/gemma-3-27b-it",
-            "description": (
-                "Google Gemma 3 27B Instruct (open weights via OpenRouter). "
-                "Mid-size instruction-tuned model. Cheap and fast; solid at "
-                "concise summarization, extraction, and short-form Q&A on "
-                "given context. Weaker than the frontier workers on multi-step "
-                "reasoning."
-            ),
-        })
-        pool.append({
-            "id": len(pool),
-            "name": "qwen3-32b",
-            "endpoint": "openrouter",
-            "model": "qwen/qwen3-32b",
-            "description": (
-                "Qwen3-32B in non-thinking mode (open weights via OpenRouter). "
-                "Fast general-purpose dialogue and instruction following. "
-                "Use when the step is straightforward generation, "
-                "summarization, or formatting — does NOT spend tokens on "
-                "internal reasoning."
-            ),
-        })
-        pool.append({
-            "id": len(pool),
-            "name": "qwen3-32b-thinking",
-            "endpoint": "openrouter",
-            "model": "qwen/qwen3-32b",
-            "extra_body": {"reasoning": {"effort": "medium"}},
-            "description": (
-                "Qwen3-32B with reasoning enabled (open weights via "
-                "OpenRouter). Same backbone as 'qwen3-32b' but spends tokens "
-                "on an internal chain of thought before answering. Stronger "
-                "on math, code, and multi-step logic; slower and consumes "
-                "more completion tokens. Prefer this for hard reasoning "
-                "steps; prefer the non-thinking variant for plain dialogue."
-            ),
-        })
+        pool.append(
+            {
+                "id": len(pool),
+                "name": "deepseek-r1-distill-qwen-32b",
+                "endpoint": "openrouter",
+                "model": "deepseek/deepseek-r1-distill-qwen-32b",
+                "description": (
+                    "DeepSeek R1 distilled into Qwen-32B (open weights via "
+                    "OpenRouter). Specialized for chain-of-thought math, logic, "
+                    "and competitive-programming-style problems. Verbose; "
+                    "produces extensive reasoning traces before the final answer."
+                ),
+            }
+        )
+        pool.append(
+            {
+                "id": len(pool),
+                "name": "gemma3-27b-it",
+                "endpoint": "openrouter",
+                "model": "google/gemma-3-27b-it",
+                "description": (
+                    "Google Gemma 3 27B Instruct (open weights via OpenRouter). "
+                    "Mid-size instruction-tuned model. Cheap and fast; solid at "
+                    "concise summarization, extraction, and short-form Q&A on "
+                    "given context. Weaker than the frontier workers on multi-step "
+                    "reasoning."
+                ),
+            }
+        )
+        pool.append(
+            {
+                "id": len(pool),
+                "name": "qwen3-32b",
+                "endpoint": "openrouter",
+                "model": "qwen/qwen3-32b",
+                "description": (
+                    "Qwen3-32B in non-thinking mode (open weights via OpenRouter). "
+                    "Fast general-purpose dialogue and instruction following. "
+                    "Use when the step is straightforward generation, "
+                    "summarization, or formatting — does NOT spend tokens on "
+                    "internal reasoning."
+                ),
+            }
+        )
+        pool.append(
+            {
+                "id": len(pool),
+                "name": "qwen3-32b-thinking",
+                "endpoint": "openrouter",
+                "model": "qwen/qwen3-32b",
+                "extra_body": {"reasoning": {"effort": "medium"}},
+                "description": (
+                    "Qwen3-32B with reasoning enabled (open weights via "
+                    "OpenRouter). Same backbone as 'qwen3-32b' but spends tokens "
+                    "on an internal chain of thought before answering. Stronger "
+                    "on math, code, and multi-step logic; slower and consumes "
+                    "more completion tokens. Prefer this for hard reasoning "
+                    "steps; prefer the non-thinking variant for plain dialogue."
+                ),
+            }
+        )
     # Reassign ids contiguously in case env-gates skipped some entries.
     for new_id, entry in enumerate(pool):
         entry["id"] = new_id
@@ -363,16 +377,17 @@ def _resolve_worker_pool(
                 f"Invalid worker_pool entry [{wid_repr}]: 'id' must be an int"
             )
         if wid in seen_ids:
-            raise ValueError(
-                f"Invalid worker_pool entry [{wid}]: duplicate id"
-            )
+            raise ValueError(f"Invalid worker_pool entry [{wid}]: duplicate id")
         seen_ids.add(wid)
         if not entry.get("name") or not isinstance(entry["name"], str):
             raise ValueError(
                 f"Invalid worker_pool entry [{wid}]: 'name' must be a non-empty string"
             )
         endpoint = entry.get("endpoint") or entry.get("type")
-        if not isinstance(endpoint, str) or endpoint.lower() not in _CONDUCTOR_VALID_ENDPOINTS:
+        if (
+            not isinstance(endpoint, str)
+            or endpoint.lower() not in _CONDUCTOR_VALID_ENDPOINTS
+        ):
             raise ValueError(
                 f"Invalid worker_pool entry [{wid}]: 'endpoint' must be one of "
                 f"{_CONDUCTOR_VALID_ENDPOINTS} (got {endpoint!r})"
@@ -466,9 +481,9 @@ def _search_capable_indices(
     if search_backend == "tavily":
         return [w["id"] for w in workers]
     return [
-        w["id"] for w in workers
-        if (w.get("endpoint") or "openai").lower()
-        in _SEARCH_CAPABLE_WORKER_ENDPOINTS
+        w["id"]
+        for w in workers
+        if (w.get("endpoint") or "openai").lower() in _SEARCH_CAPABLE_WORKER_ENDPOINTS
     ]
 
 
@@ -497,7 +512,9 @@ def _build_conductor_prompt(
     if capable:
         cap_str = ", ".join(str(i) for i in capable)
         if search_backend == "tavily":
-            capability = "External Tavily search results will be prepended to worker prompts"
+            capability = (
+                "External Tavily search results will be prepended to worker prompts"
+            )
         else:
             capability = "Only these model indices can perform live web search"
         constraint = (
@@ -687,12 +704,18 @@ def _swe_worker_step(
     ep = (worker.get("endpoint") or "openai").lower()
     if ep == "vllm":
         backbone, model, endpoint, is_local = (
-            "local", worker["model"], worker.get("base_url"), True,
+            "local",
+            worker["model"],
+            worker.get("base_url"),
+            True,
         )
         cloud_endpoint = "anthropic"  # unused on the local path
     elif ep == "anthropic":
         backbone, model, endpoint, is_local = (
-            "cloud", worker["model"], None, False,
+            "cloud",
+            worker["model"],
+            None,
+            False,
         )
         cloud_endpoint = "anthropic"
     else:
@@ -718,7 +741,11 @@ def _swe_worker_step(
     )
     return (
         out["final_summary"] or out["answer"],
-        out["tokens_in"], out["tokens_out"], is_local, 0, int(out["turns"]),
+        out["tokens_in"],
+        out["tokens_out"],
+        is_local,
+        0,
+        int(out["turns"]),
     )
 
 
@@ -819,21 +846,22 @@ class ConductorAgent(LocalCloudAgent):
         if plan is None:
             fallback_used = True
             plan = {
-                "model_id":    [len(workers) - 1],
-                "subtasks":    [question],
+                "model_id": [len(workers) - 1],
+                "subtasks": [question],
                 "access_list": [[]],
             }
 
-        self.record_trace_event({
-            "kind": "conductor_plan",
-            "plan": plan,
-            "fallback_used": fallback_used,
-            "parse_attempts": parse_attempts,
-            "workers": [
-                {k: v for k, v in w.items() if k != "api_key"}
-                for w in workers
-            ],
-        })
+        self.record_trace_event(
+            {
+                "kind": "conductor_plan",
+                "plan": plan,
+                "fallback_used": fallback_used,
+                "parse_attempts": parse_attempts,
+                "workers": [
+                    {k: v for k, v in w.items() if k != "api_key"} for w in workers
+                ],
+            }
+        )
 
         # 2. Execute
         # If we're on a SWE-bench task AND cfg["swe_use_agent_loop"] is on,
@@ -867,14 +895,15 @@ class ConductorAgent(LocalCloudAgent):
         # constraint — reuse them here.
         if ws_enabled and search_backend != "tavily" and not swe_mode:
             search_workers = [
-                w for w in workers
+                w
+                for w in workers
                 if (w.get("endpoint") or "openai").lower()
                 in _SEARCH_CAPABLE_WORKER_ENDPOINTS
             ]
             if not search_workers:
-                endpoints = sorted({
-                    (w.get("endpoint") or "openai").lower() for w in workers
-                })
+                endpoints = sorted(
+                    {(w.get("endpoint") or "openai").lower() for w in workers}
+                )
                 raise ValueError(
                     f"web_search.enabled=true but the worker pool has no "
                     f"search-capable worker (endpoints present: {endpoints}); "
@@ -885,39 +914,43 @@ class ConductorAgent(LocalCloudAgent):
                 )
         # ``ws_tool`` doubles as the enable marker passed to `_call_worker`
         # (truthy => route search-capable workers through their agent loop).
-        ws_tool = (
-            build_web_search_tool(ws_max_uses) if ws_enabled else None
-        )
+        ws_tool = build_web_search_tool(ws_max_uses) if ws_enabled else None
 
         try:
             if swe_mode:
-                shared_workdir = Path(tempfile.mkdtemp(
-                    prefix=f"conductor-swe-{task_meta.get('task_id','x')}-"
-                ))
+                shared_workdir = Path(
+                    tempfile.mkdtemp(
+                        prefix=f"conductor-swe-{task_meta.get('task_id', 'x')}-"
+                    )
+                )
                 _clone_repo(task_meta["repo"], task_meta["base_commit"], shared_workdir)
-                self.record_trace_event({
-                    "kind": "conductor_swe_workdir",
-                    "workdir": str(shared_workdir),
-                    "repo": task_meta["repo"],
-                    "base_commit": task_meta["base_commit"],
-                })
+                self.record_trace_event(
+                    {
+                        "kind": "conductor_swe_workdir",
+                        "workdir": str(shared_workdir),
+                        "repo": task_meta["repo"],
+                        "base_commit": task_meta["base_commit"],
+                    }
+                )
 
             for i, (mid, subtask, access) in enumerate(
                 zip(plan["model_id"], plan["subtasks"], plan["access_list"])
             ):
                 worker = workers[mid]
                 prompt = _build_step_prompt(question, subtask, steps, access)
-                self.record_trace_event({
-                    "kind": "conductor_step_dispatch",
-                    "step_idx": i,
-                    "worker_id": mid,
-                    "worker_name": worker["name"],
-                    "worker_model": worker["model"],
-                    "subtask": subtask,
-                    "access": access,
-                    "prompt": prompt,
-                    "swe_mode": swe_mode,
-                })
+                self.record_trace_event(
+                    {
+                        "kind": "conductor_step_dispatch",
+                        "step_idx": i,
+                        "worker_id": mid,
+                        "worker_name": worker["name"],
+                        "worker_model": worker["model"],
+                        "subtask": subtask,
+                        "access": access,
+                        "prompt": prompt,
+                        "swe_mode": swe_mode,
+                    }
+                )
 
                 worker_ep = (worker.get("endpoint") or "openai").lower()
                 # Post-hoc routing check: if web_search is on but the
@@ -926,38 +959,49 @@ class ConductorAgent(LocalCloudAgent):
                 # may legitimately not need search; see Task-3 planner
                 # constraint that tries to prevent this upfront).
                 if (
-                    ws_enabled and search_backend != "tavily" and not swe_mode
+                    ws_enabled
+                    and search_backend != "tavily"
+                    and not swe_mode
                     and worker_ep not in _SEARCH_CAPABLE_WORKER_ENDPOINTS
                 ):
-                    self.record_trace_event({
-                        "kind": "conductor_search_routing_warning",
-                        "step_idx": i,
-                        "worker_id": mid,
-                        "worker_name": worker["name"],
-                        "worker_endpoint": worker_ep,
-                        "warning": (
-                            f"web_search enabled but step {i} routed to "
-                            f"search-incapable worker {worker['name']!r} "
-                            f"(endpoint {worker_ep!r}); this step cannot "
-                            "ground and may answer blind."
-                        ),
-                    })
+                    self.record_trace_event(
+                        {
+                            "kind": "conductor_search_routing_warning",
+                            "step_idx": i,
+                            "worker_id": mid,
+                            "worker_name": worker["name"],
+                            "worker_endpoint": worker_ep,
+                            "warning": (
+                                f"web_search enabled but step {i} routed to "
+                                f"search-incapable worker {worker['name']!r} "
+                                f"(endpoint {worker_ep!r}); this step cannot "
+                                "ground and may answer blind."
+                            ),
+                        }
+                    )
 
                 extra_cost = 0.0
                 if swe_mode:
                     text, w_in, w_out, is_local, n_searches, bash_turns = (
                         _swe_worker_step(
-                            worker, task_meta, prompt, cfg, shared_workdir, i,
+                            worker,
+                            task_meta,
+                            prompt,
+                            cfg,
+                            shared_workdir,
+                            i,
                         )
                     )
                     tool_calls += bash_turns
                 else:
-                    (
-                        text, w_in, w_out, is_local, n_searches, extra_cost
-                    ) = _call_worker(
-                        worker, prompt, cfg,
-                        web_search_tool=ws_tool,
-                        web_search_max_uses=ws_max_uses,
+                    (text, w_in, w_out, is_local, n_searches, extra_cost) = (
+                        _call_worker(
+                            worker,
+                            prompt,
+                            cfg,
+                            web_search_tool=ws_tool,
+                            web_search_max_uses=ws_max_uses,
+                        )
                     )
 
                 if is_local:
@@ -971,17 +1015,19 @@ class ConductorAgent(LocalCloudAgent):
                     cost += extra_cost
                 n_web_searches_total += n_searches
                 tool_calls += n_searches
-                steps.append({
-                    "step_idx": i,
-                    "model_id": mid,
-                    "worker_name": worker["name"],
-                    "worker_model": worker["model"],
-                    "subtask": subtask,
-                    "access": access,
-                    "output": text,
-                    "tokens_in": w_in,
-                    "tokens_out": w_out,
-                })
+                steps.append(
+                    {
+                        "step_idx": i,
+                        "model_id": mid,
+                        "worker_name": worker["name"],
+                        "worker_model": worker["model"],
+                        "subtask": subtask,
+                        "access": access,
+                        "output": text,
+                        "tokens_in": w_in,
+                        "tokens_out": w_out,
+                    }
+                )
                 final_answer = text
 
             # For SWE mode, the authoritative patch is whatever lives in
@@ -992,7 +1038,8 @@ class ConductorAgent(LocalCloudAgent):
                 if patch.strip():
                     final_answer = (
                         f"{final_answer}\n\n```diff\n{patch}```"
-                        if final_answer else f"```diff\n{patch}```"
+                        if final_answer
+                        else f"```diff\n{patch}```"
                     )
         finally:
             if shared_workdir is not None:
@@ -1003,8 +1050,7 @@ class ConductorAgent(LocalCloudAgent):
         tokens_cloud += conductor_p_in + conductor_p_out
 
         traces = [
-            (s["step_idx"], s["model_id"], s["subtask"], s["output"])
-            for s in steps
+            (s["step_idx"], s["model_id"], s["subtask"], s["output"]) for s in steps
         ]
 
         meta = {
@@ -1023,8 +1069,7 @@ class ConductorAgent(LocalCloudAgent):
                 "n_web_searches": n_web_searches_total,
                 "parse_attempts": parse_attempts,
                 "workers": [
-                    {k: v for k, v in w.items() if k != "api_key"}
-                    for w in workers
+                    {k: v for k, v in w.items() if k != "api_key"} for w in workers
                 ],
             },
         }

src/openjarvis/agents/hybrid/mini_swe_agent.py

@@ -172,11 +172,16 @@ def _clone_repo(repo: str, base_commit: str, dest: Path) -> None:
     url = f"https://github.com/{repo}.git"
     subprocess.run(
         ["git", "clone", "--quiet", url, str(dest)],
-        check=True, timeout=300, capture_output=True,
+        check=True,
+        timeout=300,
+        capture_output=True,
     )
     subprocess.run(
         ["git", "checkout", "--quiet", base_commit],
-        cwd=str(dest), check=True, timeout=120, capture_output=True,
+        cwd=str(dest),
+        check=True,
+        timeout=120,
+        capture_output=True,
     )
 
 
@@ -265,10 +270,14 @@ def _run_bash(
     stderr = _decode_bash_output(stderr_b, exit_code)
     truncated = False
     if len(stdout) > output_cap:
-        stdout = stdout[:output_cap] + f"\n…[+{len(stdout) - output_cap} chars truncated]"
+        stdout = (
+            stdout[:output_cap] + f"\n…[+{len(stdout) - output_cap} chars truncated]"
+        )
         truncated = True
     if len(stderr) > output_cap:
-        stderr = stderr[:output_cap] + f"\n…[+{len(stderr) - output_cap} chars truncated]"
+        stderr = (
+            stderr[:output_cap] + f"\n…[+{len(stderr) - output_cap} chars truncated]"
+        )
         truncated = True
     return {
         "stdout": stdout,
@@ -297,7 +306,10 @@ def _extract_diff(workdir: Path) -> str:
     """``git diff`` against the base commit — the final SWE-bench patch."""
     proc = subprocess.run(
         ["git", "diff", "--no-color"],
-        cwd=str(workdir), capture_output=True, text=True, timeout=60,
+        cwd=str(workdir),
+        capture_output=True,
+        text=True,
+        timeout=60,
     )
     return proc.stdout
 
@@ -320,10 +332,11 @@ def _anthropic_assistant_block(block: Any) -> Dict[str, Any]:
 
 # ---------- Reusable agent-loop entry point ----------
 
+
 def run_swe_agent_loop(
     task: Dict[str, Any],
     *,
-    backbone: str,                          # "cloud" or "local"
+    backbone: str,  # "cloud" or "local"
     backbone_model: str,
     cloud_endpoint: str = "anthropic",
     local_endpoint: Optional[str] = None,
@@ -387,32 +400,33 @@ def run_swe_agent_loop(
 
     own_workdir = workdir is None
     if own_workdir:
-        workdir = Path(tempfile.mkdtemp(
-            prefix=f"mini-swe-{task.get('task_id','x')}-"
-        ))
+        workdir = Path(tempfile.mkdtemp(prefix=f"mini-swe-{task.get('task_id', 'x')}-"))
         try:
             _clone_repo(repo, base_commit, workdir)
         except Exception:
             shutil.rmtree(workdir, ignore_errors=True)
             raise
 
-    _record_event({
-        "kind": f"{trace_prefix}_setup",
-        "repo": repo,
-        "base_commit": base_commit,
-        "workdir": str(workdir),
-        "owns_workdir": own_workdir,
-        "backbone": backbone,
-        "backbone_model": backbone_model,
-        "ts": time.time(),
-    })
+    _record_event(
+        {
+            "kind": f"{trace_prefix}_setup",
+            "repo": repo,
+            "base_commit": base_commit,
+            "workdir": str(workdir),
+            "owns_workdir": own_workdir,
+            "backbone": backbone,
+            "backbone_model": backbone_model,
+            "ts": time.time(),
+        }
+    )
 
     user_prompt = initial_prompt or task.get("problem_statement") or ""
 
     try:
         if backbone == "cloud":
             result = _loop_cloud(
-                user_prompt, workdir,
+                user_prompt,
+                workdir,
                 model=backbone_model,
                 cloud_endpoint=cloud_endpoint,
                 max_turns=max_turns,
@@ -423,9 +437,12 @@ def run_swe_agent_loop(
             )
         elif backbone == "local":
             if not local_endpoint:
-                raise ValueError("run_swe_agent_loop(backbone='local') needs local_endpoint")
+                raise ValueError(
+                    "run_swe_agent_loop(backbone='local') needs local_endpoint"
+                )
             result = _loop_local(
-                user_prompt, workdir,
+                user_prompt,
+                workdir,
                 model=backbone_model,
                 endpoint=local_endpoint,
                 max_turns=max_turns,
@@ -440,7 +457,7 @@ def run_swe_agent_loop(
             raise ValueError(f"unsupported backbone: {backbone!r}")
 
         patch = _extract_diff(workdir)
-        framed = (result["final_summary"] or "[mini-swe-agent produced no summary text]")
+        framed = result["final_summary"] or "[mini-swe-agent produced no summary text]"
         if patch.strip():
             framed = f"{framed}\n\n```diff\n{patch}```"
 
@@ -450,11 +467,16 @@ def run_swe_agent_loop(
             "final_summary": result["final_summary"],
             "tokens_in": result["tokens_in"],
             "tokens_out": result["tokens_out"],
-            "tokens_local": result["tokens_in"] + result["tokens_out"] if backbone == "local" else 0,
-            "tokens_cloud": result["tokens_in"] + result["tokens_out"] if backbone == "cloud" else 0,
+            "tokens_local": result["tokens_in"] + result["tokens_out"]
+            if backbone == "local"
+            else 0,
+            "tokens_cloud": result["tokens_in"] + result["tokens_out"]
+            if backbone == "cloud"
+            else 0,
             "cost_usd": (
                 estimate_cost(backbone_model, result["tokens_in"], result["tokens_out"])
-                if backbone == "cloud" else 0.0
+                if backbone == "cloud"
+                else 0.0
             ),
             "turns": result["turns"],
             "max_turns_hit": result["max_turns_hit"],
@@ -467,6 +489,7 @@ def run_swe_agent_loop(
 
 # ---------- Cloud loop (dispatcher → per-endpoint multi-turn tool loops) ----------
 
+
 def _loop_cloud(
     problem: str,
     workdir: Path,
@@ -485,24 +508,36 @@ def _loop_cloud(
     to unblock the 8 SWE cells that were stuck on Anthropic-only support."""
     if cloud_endpoint == "anthropic":
         return _loop_cloud_anthropic(
-            problem, workdir,
-            model=model, max_turns=max_turns,
-            bash_timeout=bash_timeout, output_cap=output_cap,
-            turn_max_tokens=turn_max_tokens, trace_prefix=trace_prefix,
+            problem,
+            workdir,
+            model=model,
+            max_turns=max_turns,
+            bash_timeout=bash_timeout,
+            output_cap=output_cap,
+            turn_max_tokens=turn_max_tokens,
+            trace_prefix=trace_prefix,
         )
     if cloud_endpoint == "openai":
         return _loop_cloud_openai(
-            problem, workdir,
-            model=model, max_turns=max_turns,
-            bash_timeout=bash_timeout, output_cap=output_cap,
-            turn_max_tokens=turn_max_tokens, trace_prefix=trace_prefix,
+            problem,
+            workdir,
+            model=model,
+            max_turns=max_turns,
+            bash_timeout=bash_timeout,
+            output_cap=output_cap,
+            turn_max_tokens=turn_max_tokens,
+            trace_prefix=trace_prefix,
         )
     if cloud_endpoint == "gemini":
         return _loop_cloud_gemini(
-            problem, workdir,
-            model=model, max_turns=max_turns,
-            bash_timeout=bash_timeout, output_cap=output_cap,
-            turn_max_tokens=turn_max_tokens, trace_prefix=trace_prefix,
+            problem,
+            workdir,
+            model=model,
+            max_turns=max_turns,
+            bash_timeout=bash_timeout,
+            output_cap=output_cap,
+            turn_max_tokens=turn_max_tokens,
+            trace_prefix=trace_prefix,
         )
     raise ValueError(
         f"mini-SWE-agent cloud backbone unsupported endpoint: {cloud_endpoint!r}"
@@ -521,6 +556,7 @@ def _loop_cloud_anthropic(
     trace_prefix: str,
 ) -> Dict[str, Any]:
     import anthropic
+
     client = anthropic.Anthropic(timeout=600.0, max_retries=5)
     messages: List[Dict[str, Any]] = [{"role": "user", "content": problem}]
 
@@ -553,30 +589,39 @@ def _loop_cloud_anthropic(
             btype = getattr(block, "type", None)
             if btype == "tool_use":
                 tool_uses.append((block.id, block.name, dict(block.input or {})))
-                content_blocks.append({
-                    "type": "tool_use", "id": block.id, "name": block.name,
-                    "input": dict(block.input or {}),
-                })
+                content_blocks.append(
+                    {
+                        "type": "tool_use",
+                        "id": block.id,
+                        "name": block.name,
+                        "input": dict(block.input or {}),
+                    }
+                )
             elif hasattr(block, "text"):
                 text_parts.append(block.text)
                 content_blocks.append({"type": "text", "text": block.text})
             else:
                 content_blocks.append({"type": btype or "unknown"})
 
-        _record_event({
-            "kind": f"{trace_prefix}_turn",
-            "turn": turn,
-            "stop_reason": msg.stop_reason,
-            "tokens_in": msg.usage.input_tokens,
-            "tokens_out": msg.usage.output_tokens,
-            "latency_s": latency,
-            "content_blocks": content_blocks,
-            "ts": time.time(),
-        })
+        _record_event(
+            {
+                "kind": f"{trace_prefix}_turn",
+                "turn": turn,
+                "stop_reason": msg.stop_reason,
+                "tokens_in": msg.usage.input_tokens,
+                "tokens_out": msg.usage.output_tokens,
+                "latency_s": latency,
+                "content_blocks": content_blocks,
+                "ts": time.time(),
+            }
+        )
 
-        messages.append({"role": "assistant", "content": [
-            _anthropic_assistant_block(b) for b in msg.content
-        ]})
+        messages.append(
+            {
+                "role": "assistant",
+                "content": [_anthropic_assistant_block(b) for b in msg.content],
+            }
+        )
 
         if not tool_uses:
             final_text = "\n".join(text_parts).strip()
@@ -586,28 +631,40 @@ def _loop_cloud_anthropic(
         for tu_id, tu_name, tu_input in tool_uses:
             if tu_name != "bash":
                 obs = f"unknown tool: {tu_name!r}"
-                _record_event({
-                    "kind": f"{trace_prefix}_unknown_tool",
-                    "turn": turn, "name": tu_name, "input": tu_input,
-                    "ts": time.time(),
-                })
+                _record_event(
+                    {
+                        "kind": f"{trace_prefix}_unknown_tool",
+                        "turn": turn,
+                        "name": tu_name,
+                        "input": tu_input,
+                        "ts": time.time(),
+                    }
+                )
             else:
                 command = str(tu_input.get("command", ""))
                 result = _run_bash(
-                    command, workdir,
-                    timeout=bash_timeout, output_cap=output_cap,
+                    command,
+                    workdir,
+                    timeout=bash_timeout,
+                    output_cap=output_cap,
+                )
+                _record_event(
+                    {
+                        "kind": f"{trace_prefix}_bash",
+                        "turn": turn,
+                        "command": command,
+                        **result,
+                        "ts": time.time(),
+                    }
                 )
-                _record_event({
-                    "kind": f"{trace_prefix}_bash",
-                    "turn": turn, "command": command,
-                    **result, "ts": time.time(),
-                })
                 obs = _format_observation(result)
-            tool_result_blocks.append({
-                "type": "tool_result",
-                "tool_use_id": tu_id,
-                "content": obs,
-            })
+            tool_result_blocks.append(
+                {
+                    "type": "tool_result",
+                    "tool_use_id": tu_id,
+                    "content": obs,
+                }
+            )
         messages.append({"role": "user", "content": tool_result_blocks})
 
     return {
@@ -621,6 +678,7 @@ def _loop_cloud_anthropic(
 
 # ---------- Cloud loop (OpenAI multi-turn with function tools) ----------
 
+
 def _loop_cloud_openai(
     problem: str,
     workdir: Path,
@@ -646,6 +704,7 @@ def _loop_cloud_openai(
       ``_loop_local`` behavior).
     """
     from openai import OpenAI
+
     client = OpenAI(timeout=600.0)
 
     messages: List[Dict[str, Any]] = [
@@ -681,21 +740,27 @@ def _loop_cloud_openai(
         tool_calls = list(getattr(message, "tool_calls", None) or [])
         text = message.content or ""
 
-        _record_event({
-            "kind": f"{trace_prefix}_turn",
-            "turn": turn,
-            "endpoint": "openai",
-            "finish_reason": choice.finish_reason,
-            "tokens_in": getattr(u, "prompt_tokens", 0) if u else 0,
-            "tokens_out": getattr(u, "completion_tokens", 0) if u else 0,
-            "latency_s": latency,
-            "text": text,
-            "tool_calls": [
-                {"id": tc.id, "name": tc.function.name, "arguments": tc.function.arguments}
-                for tc in tool_calls
-            ],
-            "ts": time.time(),
-        })
+        _record_event(
+            {
+                "kind": f"{trace_prefix}_turn",
+                "turn": turn,
+                "endpoint": "openai",
+                "finish_reason": choice.finish_reason,
+                "tokens_in": getattr(u, "prompt_tokens", 0) if u else 0,
+                "tokens_out": getattr(u, "completion_tokens", 0) if u else 0,
+                "latency_s": latency,
+                "text": text,
+                "tool_calls": [
+                    {
+                        "id": tc.id,
+                        "name": tc.function.name,
+                        "arguments": tc.function.arguments,
+                    }
+                    for tc in tool_calls
+                ],
+                "ts": time.time(),
+            }
+        )
 
         # Append the assistant turn (including any tool_calls) so the
         # follow-up tool messages have the right call ids to reference.
@@ -711,7 +776,8 @@ def _loop_cloud_openai(
         if tool_calls:
             assistant_msg["tool_calls"] = [
                 {
-                    "id": tc.id, "type": "function",
+                    "id": tc.id,
+                    "type": "function",
                     "function": {
                         "name": tc.function.name,
                         "arguments": tc.function.arguments,
@@ -734,21 +800,26 @@ def _loop_cloud_openai(
                 and not text.strip()
                 and turn < max_turns
             ):
-                messages.append({
-                    "role": "user",
-                    "content": (
-                        "Your previous response was truncated by the token limit "
-                        "before producing a tool call or final summary. Retry: "
-                        "either issue ONE bash tool call (short command, no large "
-                        "output) or send a brief one-line final summary with no "
-                        "tool calls to end the loop."
-                    ),
-                })
-                _record_event({
-                    "kind": f"{trace_prefix}_recover",
-                    "turn": turn, "reason": "length_truncation_no_tool_call",
-                    "ts": time.time(),
-                })
+                messages.append(
+                    {
+                        "role": "user",
+                        "content": (
+                            "Your previous response was truncated by the token limit "
+                            "before producing a tool call or final summary. Retry: "
+                            "either issue ONE bash tool call (short command, no large "
+                            "output) or send a brief one-line final summary with no "
+                            "tool calls to end the loop."
+                        ),
+                    }
+                )
+                _record_event(
+                    {
+                        "kind": f"{trace_prefix}_recover",
+                        "turn": turn,
+                        "reason": "length_truncation_no_tool_call",
+                        "ts": time.time(),
+                    }
+                )
                 continue
             # No tool call → the model is done. Same termination rule as
             # the Anthropic branch.
@@ -762,28 +833,40 @@ def _loop_cloud_openai(
                 args = {}
             if tc.function.name != "bash":
                 obs = f"unknown tool: {tc.function.name!r}"
-                _record_event({
-                    "kind": f"{trace_prefix}_unknown_tool",
-                    "turn": turn, "name": tc.function.name, "input": args,
-                    "ts": time.time(),
-                })
+                _record_event(
+                    {
+                        "kind": f"{trace_prefix}_unknown_tool",
+                        "turn": turn,
+                        "name": tc.function.name,
+                        "input": args,
+                        "ts": time.time(),
+                    }
+                )
             else:
                 command = str(args.get("command", ""))
                 result = _run_bash(
-                    command, workdir,
-                    timeout=bash_timeout, output_cap=output_cap,
+                    command,
+                    workdir,
+                    timeout=bash_timeout,
+                    output_cap=output_cap,
+                )
+                _record_event(
+                    {
+                        "kind": f"{trace_prefix}_bash",
+                        "turn": turn,
+                        "command": command,
+                        **result,
+                        "ts": time.time(),
+                    }
                 )
-                _record_event({
-                    "kind": f"{trace_prefix}_bash",
-                    "turn": turn, "command": command,
-                    **result, "ts": time.time(),
-                })
                 obs = _format_observation(result)
-            messages.append({
-                "role": "tool",
-                "tool_call_id": tc.id,
-                "content": obs,
-            })
+            messages.append(
+                {
+                    "role": "tool",
+                    "tool_call_id": tc.id,
+                    "content": obs,
+                }
+            )
 
     return {
         "tokens_in": tokens_in,
@@ -796,6 +879,7 @@ def _loop_cloud_openai(
 
 # ---------- Cloud loop (Gemini multi-turn with function tools) ----------
 
+
 def _loop_cloud_gemini(
     problem: str,
     workdir: Path,
@@ -831,13 +915,15 @@ def _loop_cloud_gemini(
     from google.genai import types
 
     client = genai.Client(http_options=types.HttpOptions(timeout=600_000))
-    bash_tool = types.Tool(function_declarations=[
-        types.FunctionDeclaration(
-            name="bash",
-            description=BASH_TOOL_ANTHROPIC["description"],
-            parameters=BASH_TOOL_GEMINI_PARAMETERS,
-        ),
-    ])
+    bash_tool = types.Tool(
+        function_declarations=[
+            types.FunctionDeclaration(
+                name="bash",
+                description=BASH_TOOL_ANTHROPIC["description"],
+                parameters=BASH_TOOL_GEMINI_PARAMETERS,
+            ),
+        ]
+    )
 
     contents: List[types.Content] = [
         types.Content(role="user", parts=[types.Part(text=problem)]),
@@ -859,7 +945,9 @@ def _loop_cloud_gemini(
         )
         t0 = time.time()
         resp = client.models.generate_content(
-            model=model, contents=contents, config=cfg,
+            model=model,
+            contents=contents,
+            config=cfg,
         )
         _bump_cloud_calls()
         latency = time.time() - t0
@@ -896,21 +984,22 @@ def _loop_cloud_gemini(
         except Exception:
             pass
 
-        _record_event({
-            "kind": f"{trace_prefix}_turn",
-            "turn": turn,
-            "endpoint": "gemini",
-            "finish_reason": finish_reason,
-            "tokens_in": p,
-            "tokens_out": c,
-            "latency_s": latency,
-            "text": "\n".join(text_parts),
-            "tool_calls": [
-                {"name": name, "arguments": args}
-                for name, args in function_calls
-            ],
-            "ts": time.time(),
-        })
+        _record_event(
+            {
+                "kind": f"{trace_prefix}_turn",
+                "turn": turn,
+                "endpoint": "gemini",
+                "finish_reason": finish_reason,
+                "tokens_in": p,
+                "tokens_out": c,
+                "latency_s": latency,
+                "text": "\n".join(text_parts),
+                "tool_calls": [
+                    {"name": name, "arguments": args} for name, args in function_calls
+                ],
+                "ts": time.time(),
+            }
+        )
 
         # Append the model's content as-is so the next turn sees its own
         # prior function_call parts (Gemini requires this for the
@@ -934,28 +1023,37 @@ def _loop_cloud_gemini(
             # treat genuine ``STOP`` with text as a final answer.
             fr_str = str(finish_reason or "")
             empty_text = not any(t.strip() for t in text_parts)
-            recoverable = empty_text and turn < max_turns and (
-                "MALFORMED_FUNCTION_CALL" in fr_str
-                or "MAX_TOKENS" in fr_str
+            recoverable = (
+                empty_text
+                and turn < max_turns
+                and ("MALFORMED_FUNCTION_CALL" in fr_str or "MAX_TOKENS" in fr_str)
             )
             if recoverable:
-                contents.append(types.Content(
-                    role="user",
-                    parts=[types.Part(text=(
-                        "Your previous response had no parsable function call "
-                        "and no final text (finish_reason="
-                        f"{fr_str}). Retry: either issue ONE well-formed "
-                        "`bash` function call (short command, valid JSON-ish "
-                        "args) or send a brief final text message with no "
-                        "function call to end the loop."
-                    ))],
-                ))
-                _record_event({
-                    "kind": f"{trace_prefix}_recover",
-                    "turn": turn,
-                    "reason": f"empty_response_{fr_str}",
-                    "ts": time.time(),
-                })
+                contents.append(
+                    types.Content(
+                        role="user",
+                        parts=[
+                            types.Part(
+                                text=(
+                                    "Your previous response had no parsable function call "
+                                    "and no final text (finish_reason="
+                                    f"{fr_str}). Retry: either issue ONE well-formed "
+                                    "`bash` function call (short command, valid JSON-ish "
+                                    "args) or send a brief final text message with no "
+                                    "function call to end the loop."
+                                )
+                            )
+                        ],
+                    )
+                )
+                _record_event(
+                    {
+                        "kind": f"{trace_prefix}_recover",
+                        "turn": turn,
+                        "reason": f"empty_response_{fr_str}",
+                        "ts": time.time(),
+                    }
+                )
                 continue
             final_text = "\n".join(text_parts).strip()
             break
@@ -964,26 +1062,39 @@ def _loop_cloud_gemini(
         for name, args in function_calls:
             if name != "bash":
                 obs = f"unknown tool: {name!r}"
-                _record_event({
-                    "kind": f"{trace_prefix}_unknown_tool",
-                    "turn": turn, "name": name, "input": args,
-                    "ts": time.time(),
-                })
+                _record_event(
+                    {
+                        "kind": f"{trace_prefix}_unknown_tool",
+                        "turn": turn,
+                        "name": name,
+                        "input": args,
+                        "ts": time.time(),
+                    }
+                )
             else:
                 command = str(args.get("command", ""))
                 result = _run_bash(
-                    command, workdir,
-                    timeout=bash_timeout, output_cap=output_cap,
+                    command,
+                    workdir,
+                    timeout=bash_timeout,
+                    output_cap=output_cap,
+                )
+                _record_event(
+                    {
+                        "kind": f"{trace_prefix}_bash",
+                        "turn": turn,
+                        "command": command,
+                        **result,
+                        "ts": time.time(),
+                    }
                 )
-                _record_event({
-                    "kind": f"{trace_prefix}_bash",
-                    "turn": turn, "command": command,
-                    **result, "ts": time.time(),
-                })
                 obs = _format_observation(result)
-            response_parts.append(types.Part.from_function_response(
-                name=name, response={"output": obs},
-            ))
+            response_parts.append(
+                types.Part.from_function_response(
+                    name=name,
+                    response={"output": obs},
+                )
+            )
         contents.append(types.Content(role="user", parts=response_parts))
 
     return {
@@ -1013,10 +1124,14 @@ def _get_tiktoken_enc() -> Any:
         return _TIKTOKEN_ENC
     try:
         import tiktoken
+
         _TIKTOKEN_ENC = tiktoken.get_encoding("cl100k_base")
     except Exception as exc:
         if not _TIKTOKEN_WARNED:
-            print(f"[mini_swe_agent] tiktoken unavailable ({exc!r}); falling back to len(s)//4", flush=True)
+            print(
+                f"[mini_swe_agent] tiktoken unavailable ({exc!r}); falling back to len(s)//4",
+                flush=True,
+            )
             _TIKTOKEN_WARNED = True
         _TIKTOKEN_ENC = False
     return _TIKTOKEN_ENC
@@ -1038,7 +1153,7 @@ def _estimate_prompt_tokens(messages: List[Dict[str, Any]]) -> int:
             s = "\n".join(parts)
         else:
             s = ""
-        for tc in (m.get("tool_calls") or []):
+        for tc in m.get("tool_calls") or []:
             try:
                 s += "\n" + (tc["function"]["arguments"] or "")
                 s += "\n" + (tc["function"].get("name") or "")
@@ -1119,7 +1234,7 @@ def _compact_local_messages(
     before_tokens = _estimate_prompt_tokens(messages)
     new_messages: List[Dict[str, Any]] = list(messages)
     n_tool_elided = 0
-    for (s, e) in old_turns:
+    for s, e in old_turns:
         for k in range(s, e):
             m = new_messages[k]
             if m.get("role") != "tool":
@@ -1140,17 +1255,19 @@ def _compact_local_messages(
             n_tool_elided += 1
 
     after_stage1_tokens = _estimate_prompt_tokens(new_messages)
-    _record_event({
-        "kind": f"{trace_prefix}_compact",
-        "stage": "1",
-        "msgs_before": len(messages),
-        "msgs_after": len(new_messages),
-        "before_tokens": before_tokens,
-        "after_tokens": after_stage1_tokens,
-        "n_tool_elided": n_tool_elided,
-        "n_turns_folded": 0,
-        "ts": time.time(),
-    })
+    _record_event(
+        {
+            "kind": f"{trace_prefix}_compact",
+            "stage": "1",
+            "msgs_before": len(messages),
+            "msgs_after": len(new_messages),
+            "before_tokens": before_tokens,
+            "after_tokens": after_stage1_tokens,
+            "n_tool_elided": n_tool_elided,
+            "n_turns_folded": 0,
+            "ts": time.time(),
+        }
+    )
 
     if after_stage1_tokens <= compact_at_tokens:
         return new_messages
@@ -1163,12 +1280,21 @@ def _compact_local_messages(
 
     summary_input = [
         {"role": "system", "content": _COMPACT_PROMPT},
-        {"role": "user", "content": json.dumps(
-            [{"role": m.get("role"),
-              "content": m.get("content") if isinstance(m.get("content"), str) else str(m.get("content"))[:4000]}
-             for m in middle],
-            default=str,
-        )[:60_000]},
+        {
+            "role": "user",
+            "content": json.dumps(
+                [
+                    {
+                        "role": m.get("role"),
+                        "content": m.get("content")
+                        if isinstance(m.get("content"), str)
+                        else str(m.get("content"))[:4000],
+                    }
+                    for m in middle
+                ],
+                default=str,
+            )[:60_000],
+        },
     ]
     summary = ""
     try:
@@ -1194,18 +1320,20 @@ def _compact_local_messages(
     }
     folded = [system_msg, initial_user, synthetic, *tail]
     after_stage2_tokens = _estimate_prompt_tokens(folded)
-    _record_event({
-        "kind": f"{trace_prefix}_compact",
-        "stage": "2",
-        "msgs_before": len(new_messages),
-        "msgs_after": len(folded),
-        "before_tokens": after_stage1_tokens,
-        "after_tokens": after_stage2_tokens,
-        "n_tool_elided": n_tool_elided,
-        "n_turns_folded": n_turns_folded,
-        "summary_chars": len(summary),
-        "ts": time.time(),
-    })
+    _record_event(
+        {
+            "kind": f"{trace_prefix}_compact",
+            "stage": "2",
+            "msgs_before": len(new_messages),
+            "msgs_after": len(folded),
+            "before_tokens": after_stage1_tokens,
+            "after_tokens": after_stage2_tokens,
+            "n_tool_elided": n_tool_elided,
+            "n_turns_folded": n_turns_folded,
+            "summary_chars": len(summary),
+            "ts": time.time(),
+        }
+    )
     return folded
 
 
@@ -1231,6 +1359,7 @@ def _loop_local(
     # but still saw 28k-input 400s on the n=100 SWE sweep (the keep window
     # alone routinely exceeded the budget once bash outputs piled up).
     from openai import OpenAI
+
     client = OpenAI(base_url=endpoint, api_key="EMPTY", timeout=600.0)
 
     messages: List[Dict[str, Any]] = [
@@ -1243,10 +1372,16 @@ def _loop_local(
     turns = 0
     for turn in range(1, max_turns + 1):
         turns = turn
-        if compact_at_tokens > 0 and _estimate_prompt_tokens(messages) > compact_at_tokens:
+        if (
+            compact_at_tokens > 0
+            and _estimate_prompt_tokens(messages) > compact_at_tokens
+        ):
             messages = _compact_local_messages(
-                messages, client=client, model=model,
-                keep_last=compact_keep_last, trace_prefix=trace_prefix,
+                messages,
+                client=client,
+                model=model,
+                keep_last=compact_keep_last,
+                trace_prefix=trace_prefix,
                 compact_at_tokens=compact_at_tokens,
             )
         t0 = time.time()
@@ -1268,23 +1403,27 @@ def _loop_local(
             # server walled the call. Compact aggressively (keep_last=1)
             # and retry once. Re-raise on anything else or on a second
             # failure — the runner records the row as errored.
+            from openjarvis.engine._base import looks_like_context_length_error
+
             msg = str(exc)
-            is_ctx = (
-                "maximum context length" in msg
-                or "context length" in msg.lower() and "exceed" in msg.lower()
-            )
+            is_ctx = looks_like_context_length_error(msg)
             if not is_ctx:
                 raise
-            _record_event({
-                "kind": f"{trace_prefix}_emergency_compact",
-                "turn": turn,
-                "error": msg[:300],
-                "tokens_before": _estimate_prompt_tokens(messages),
-                "ts": time.time(),
-            })
+            _record_event(
+                {
+                    "kind": f"{trace_prefix}_emergency_compact",
+                    "turn": turn,
+                    "error": msg[:300],
+                    "tokens_before": _estimate_prompt_tokens(messages),
+                    "ts": time.time(),
+                }
+            )
             messages = _compact_local_messages(
-                messages, client=client, model=model,
-                keep_last=1, trace_prefix=trace_prefix,
+                messages,
+                client=client,
+                model=model,
+                keep_last=1,
+                trace_prefix=trace_prefix,
                 compact_at_tokens=max(8_000, compact_at_tokens // 2),
             )
             resp = client.chat.completions.create(
@@ -1306,20 +1445,26 @@ def _loop_local(
         tool_calls = list(getattr(message, "tool_calls", None) or [])
         text = message.content or ""
 
-        _record_event({
-            "kind": f"{trace_prefix}_turn",
-            "turn": turn,
-            "finish_reason": choice.finish_reason,
-            "tokens_in": getattr(u, "prompt_tokens", 0) if u else 0,
-            "tokens_out": getattr(u, "completion_tokens", 0) if u else 0,
-            "latency_s": latency,
-            "text": text,
-            "tool_calls": [
-                {"id": tc.id, "name": tc.function.name, "arguments": tc.function.arguments}
-                for tc in tool_calls
-            ],
-            "ts": time.time(),
-        })
+        _record_event(
+            {
+                "kind": f"{trace_prefix}_turn",
+                "turn": turn,
+                "finish_reason": choice.finish_reason,
+                "tokens_in": getattr(u, "prompt_tokens", 0) if u else 0,
+                "tokens_out": getattr(u, "completion_tokens", 0) if u else 0,
+                "latency_s": latency,
+                "text": text,
+                "tool_calls": [
+                    {
+                        "id": tc.id,
+                        "name": tc.function.name,
+                        "arguments": tc.function.arguments,
+                    }
+                    for tc in tool_calls
+                ],
+                "ts": time.time(),
+            }
+        )
 
         # Match the OpenAI cloud branch: content="" (not None) when only
         # tool_calls are present; omit ``tool_calls`` entirely when there
@@ -1333,7 +1478,8 @@ def _loop_local(
         if tool_calls:
             assistant_local_msg["tool_calls"] = [
                 {
-                    "id": tc.id, "type": "function",
+                    "id": tc.id,
+                    "type": "function",
                     "function": {
                         "name": tc.function.name,
                         "arguments": tc.function.arguments,
@@ -1357,20 +1503,28 @@ def _loop_local(
             else:
                 command = str(args.get("command", ""))
                 result = _run_bash(
-                    command, workdir,
-                    timeout=bash_timeout, output_cap=output_cap,
+                    command,
+                    workdir,
+                    timeout=bash_timeout,
+                    output_cap=output_cap,
+                )
+                _record_event(
+                    {
+                        "kind": f"{trace_prefix}_bash",
+                        "turn": turn,
+                        "command": command,
+                        **result,
+                        "ts": time.time(),
+                    }
                 )
-                _record_event({
-                    "kind": f"{trace_prefix}_bash",
-                    "turn": turn, "command": command,
-                    **result, "ts": time.time(),
-                })
                 obs = _format_observation(result)
-            messages.append({
-                "role": "tool",
-                "tool_call_id": tc.id,
-                "content": obs,
-            })
+            messages.append(
+                {
+                    "role": "tool",
+                    "tool_call_id": tc.id,
+                    "content": obs,
+                }
+            )
 
     return {
         "tokens_in": tokens_in,
@@ -1383,6 +1537,7 @@ def _loop_local(
 
 # ---------- Standalone agent ----------
 
+
 @AgentRegistry.register("mini_swe_agent")
 class MiniSWEAgent(LocalCloudAgent):
     """Single-model bash-loop agent for SWE-bench-shaped tasks.
@@ -1410,10 +1565,7 @@ class MiniSWEAgent(LocalCloudAgent):
             task = context.metadata.get("task") or {}
 
         backbone = cfg.get("backbone", "cloud")
-        model = (
-            self._cloud_model if backbone == "cloud"
-            else (self._local_model or "")
-        )
+        model = self._cloud_model if backbone == "cloud" else (self._local_model or "")
 
         out = run_swe_agent_loop(
             task,

src/openjarvis/agents/hybrid/minions.py

@@ -87,7 +87,7 @@ MINIONS_FIRST_TURN_SCHEMA = {
             "type": "object",
             "properties": {
                 "reasoning": {"type": "string"},
-                "message":   {"type": "string"},
+                "message": {"type": "string"},
             },
             "required": ["reasoning", "message"],
             "additionalProperties": False,
@@ -104,7 +104,7 @@ MINIONS_CONVERSATION_SCHEMA = {
                     "type": "object",
                     "properties": {
                         "decision": {"const": "request_additional_info"},
-                        "message":  {"type": "string"},
+                        "message": {"type": "string"},
                     },
                     "required": ["decision", "message"],
                     "additionalProperties": False,
@@ -113,7 +113,7 @@ MINIONS_CONVERSATION_SCHEMA = {
                     "type": "object",
                     "properties": {
                         "decision": {"const": "provide_final_answer"},
-                        "answer":   {"type": "string"},
+                        "answer": {"type": "string"},
                     },
                     "required": ["decision", "answer"],
                     "additionalProperties": False,
@@ -126,8 +126,8 @@ MINIONS_CONVERSATION_SCHEMA = {
 # Markers from Minions's supervisor prompts (prompts/minion.py). Any one
 # being present in the call's messages/system is a strong Minions signal.
 MINIONS_PROMPT_MARKERS = (
-    "small language model that has read",   # SUPERVISOR_INITIAL_PROMPT
-    "provide_final_answer",                  # SUPERVISOR_CONVERSATION_PROMPT
+    "small language model that has read",  # SUPERVISOR_INITIAL_PROMPT
+    "provide_final_answer",  # SUPERVISOR_CONVERSATION_PROMPT
     "request_additional_info",
 )
 
@@ -171,6 +171,7 @@ def _stub_missing_imports() -> None:
     """
     try:
         import mistralai
+
         if not hasattr(mistralai, "Mistral"):
             mistralai.Mistral = type("Mistral", (), {})  # type: ignore[attr-defined]
     except ImportError:
@@ -215,12 +216,12 @@ def _patch_anthropic_globally() -> None:
                 model = kwargs.get("model", "")
                 if model.startswith(NO_TEMP_PREFIXES):
                     kwargs.pop("temperature", None)
-                    if (
-                        "output_config" not in kwargs
-                        and _looks_like_minions_call(kwargs)
+                    if "output_config" not in kwargs and _looks_like_minions_call(
+                        kwargs
                     ):
                         kwargs["output_config"] = _minions_turn_schema(kwargs)
                 return orig(self, **kwargs)
+
             patched._hybrid_patched = True  # type: ignore[attr-defined]
             return patched
 
@@ -358,6 +359,7 @@ def _apply_patches_once() -> None:
 
 # ---------- Pre-fetch helper (GAIA only) ----------
 
+
 def _prefetch_context(
     question: str,
     cloud_endpoint: str,
@@ -376,7 +378,10 @@ def _prefetch_context(
     and zeros — the protocol still runs.
     """
     out: Dict[str, Any] = {
-        "text": "", "tokens": 0, "cost_usd": 0.0, "n_searches": 0,
+        "text": "",
+        "tokens": 0,
+        "cost_usd": 0.0,
+        "n_searches": 0,
     }
     if search_backend == "tavily":
         try:
@@ -412,10 +417,12 @@ def _prefetch_context(
             tool_choice={"type": "any"},
         )
         from openjarvis.agents.hybrid._prices import cost as _cost_usd
+
         out.update(
             text=text,
             tokens=p + c,
-            cost_usd=_cost_usd(cloud_model, p, c) + n_searches * WEB_SEARCH_COST_PER_CALL,
+            cost_usd=_cost_usd(cloud_model, p, c)
+            + n_searches * WEB_SEARCH_COST_PER_CALL,
             n_searches=n_searches,
         )
     except Exception as e:
@@ -423,9 +430,7 @@ def _prefetch_context(
     return out
 
 
-def _context_for(
-    task: Optional[Dict[str, Any]], prefetched: str = ""
-) -> List[str]:
+def _context_for(task: Optional[Dict[str, Any]], prefetched: str = "") -> List[str]:
     """Minions wants a context list."""
     bits: List[str] = []
     task = task or {}
@@ -440,6 +445,7 @@ def _context_for(
 
 # ---------- Main agent ----------
 
+
 @AgentRegistry.register("minions")
 class MinionsAgent(LocalCloudAgent):
     """HazyResearch Minions supervisor/worker protocol. See module docstring."""
@@ -451,6 +457,7 @@ class MinionsAgent(LocalCloudAgent):
         # 400/529, KeyError on missing schema fields.
         try:
             import anthropic
+
             if isinstance(exc, anthropic.BadRequestError):
                 return f"{type(exc).__name__}: {str(exc)[:120]}"
         except Exception:
@@ -518,8 +525,7 @@ class MinionsAgent(LocalCloudAgent):
             local=True,
         )
         cloud_max_tokens = int(
-            cfg.get("cloud_max_tokens")
-            or default_max_output_tokens(self._cloud_model)
+            cfg.get("cloud_max_tokens") or default_max_output_tokens(self._cloud_model)
         )
         if self._cloud_endpoint == "openai":
             cloud_client = OpenAIClient(
@@ -566,9 +572,14 @@ class MinionsAgent(LocalCloudAgent):
         #   - enabled = false  → prefetch OFF
         #   - enabled = true   → prefetch ON (honors max_uses)
         prefetch: Dict[str, Any] = {
-            "text": "", "tokens": 0, "cost_usd": 0.0, "n_searches": 0,
+            "text": "",
+            "tokens": 0,
+            "cost_usd": 0.0,
+            "n_searches": 0,
         }
-        ws_block = cfg.get("web_search") if isinstance(cfg.get("web_search"), dict) else None
+        ws_block = (
+            cfg.get("web_search") if isinstance(cfg.get("web_search"), dict) else None
+        )
         ws_enabled, ws_max_uses = web_search_cfg(cfg)
         # If the cell explicitly set web_search.enabled = false, honor that.
         # If it set web_search.enabled = true, honor max_uses. If it didn't
@@ -588,14 +599,16 @@ class MinionsAgent(LocalCloudAgent):
             )
 
         if prefetch.get("text"):
-            self.record_trace_event({
-                "kind": "minions_prefetch",
-                "n_searches": prefetch["n_searches"],
-                "tokens": prefetch["tokens"],
-                "cost_usd": prefetch["cost_usd"],
-                "text": prefetch["text"],
-                "error": prefetch.get("error"),
-            })
+            self.record_trace_event(
+                {
+                    "kind": "minions_prefetch",
+                    "n_searches": prefetch["n_searches"],
+                    "tokens": prefetch["tokens"],
+                    "cost_usd": prefetch["cost_usd"],
+                    "text": prefetch["text"],
+                    "error": prefetch.get("error"),
+                }
+            )
 
         out = protocol(
             task=input,  # full formatted prompt (with bench instruction)
@@ -607,15 +620,17 @@ class MinionsAgent(LocalCloudAgent):
         # The Minions library doesn't go through our SDK helpers, so the
         # auto-trace missed every turn. Record the protocol output directly —
         # supervisor_messages + worker_messages contain the full conversation.
-        self.record_trace_event({
-            "kind": "minions_protocol",
-            "mode": mode,
-            "supervisor_messages": out.get("supervisor_messages"),
-            "worker_messages": out.get("worker_messages"),
-            "timing": out.get("timing"),
-            "log_file": out.get("log_file"),
-            "final_answer": out.get("final_answer", ""),
-        })
+        self.record_trace_event(
+            {
+                "kind": "minions_protocol",
+                "mode": mode,
+                "supervisor_messages": out.get("supervisor_messages"),
+                "worker_messages": out.get("worker_messages"),
+                "timing": out.get("timing"),
+                "log_file": out.get("log_file"),
+                "final_answer": out.get("final_answer", ""),
+            }
+        )
 
         local_usage = out.get("local_usage")
         remote_usage = out.get("remote_usage")
@@ -650,7 +665,6 @@ class MinionsAgent(LocalCloudAgent):
         }
         return out.get("final_answer", ""), meta
 
-
     # ------------------------------------------------------------------
     # SWE-bench variant
     # ------------------------------------------------------------------
@@ -668,21 +682,23 @@ class MinionsAgent(LocalCloudAgent):
         # 1. Cloud supervisor writes a high-level plan (no tools).
         plan_text, p_in, p_out = self._call_cloud(
             user=(
-                f"Issue:\n{task.get('problem_statement','')}\n\n"
-                f"Repo: {task.get('repo','')}\n"
-                f"Base commit: {task.get('base_commit','')}\n\n"
-                f"{task.get('hints_text','')}"
+                f"Issue:\n{task.get('problem_statement', '')}\n\n"
+                f"Repo: {task.get('repo', '')}\n"
+                f"Base commit: {task.get('base_commit', '')}\n\n"
+                f"{task.get('hints_text', '')}"
             ),
             system=MINIONS_SWE_PLANNER_SYS,
             max_tokens=int(cfg.get("supervisor_max_tokens", 1024)),
             temperature=0.0,
         )
-        self.record_trace_event({
-            "kind": "minions_swe_plan",
-            "plan": plan_text,
-            "tokens_in": p_in,
-            "tokens_out": p_out,
-        })
+        self.record_trace_event(
+            {
+                "kind": "minions_swe_plan",
+                "plan": plan_text,
+                "tokens_in": p_in,
+                "tokens_out": p_out,
+            }
+        )
         supervisor_cost = self.cost_usd(self._cloud_model, p_in, p_out)
 
         # 2. Local worker runs mini-SWE-agent with the plan as context.

src/openjarvis/agents/hybrid/skillorchestra.py

@@ -47,33 +47,33 @@ from openjarvis.core.registry import AgentRegistry
 # would seed before any oracle update.
 
 SKILL_CATALOG: Dict[str, str] = {
-    "factual_recall":     "Recall named entities, dates, places, well-known facts from training data without external lookup.",
+    "factual_recall": "Recall named entities, dates, places, well-known facts from training data without external lookup.",
     "multi_step_reasoning": "Chain several inference steps together (e.g. compose dates, traverse relationships, decompose then aggregate).",
-    "arithmetic":         "Exact numeric computation on values already given in the question.",
-    "web_grounding":      "Question needs information likely NOT in a small model's parametric memory (rare facts, recent events, niche sources).",
+    "arithmetic": "Exact numeric computation on values already given in the question.",
+    "web_grounding": "Question needs information likely NOT in a small model's parametric memory (rare facts, recent events, niche sources).",
     "long_text_extraction": "Read a long supplied document/context and extract a specific piece.",
-    "format_compliance":  "Strict output formatting (e.g. GAIA's `FINAL ANSWER: <answer>` rule, comma-separated lists with no units).",
-    "code_or_logic":      "Write or trace code, or apply logical/symbolic constraints precisely.",
+    "format_compliance": "Strict output formatting (e.g. GAIA's `FINAL ANSWER: <answer>` rule, comma-separated lists with no units).",
+    "code_or_logic": "Write or trace code, or apply logical/symbolic constraints precisely.",
 }
 
 DEFAULT_AGENT_COMPETENCE: Dict[str, Dict[str, float]] = {
     "local-qwen-27b": {
-        "factual_recall":       0.25,
+        "factual_recall": 0.25,
         "multi_step_reasoning": 0.30,
-        "arithmetic":           0.55,
-        "web_grounding":        0.10,
+        "arithmetic": 0.55,
+        "web_grounding": 0.10,
         "long_text_extraction": 0.55,
-        "format_compliance":    0.65,
-        "code_or_logic":        0.45,
+        "format_compliance": 0.65,
+        "code_or_logic": 0.45,
     },
     "cloud-opus-4-7": {
-        "factual_recall":       0.85,
+        "factual_recall": 0.85,
         "multi_step_reasoning": 0.88,
-        "arithmetic":           0.85,
-        "web_grounding":        0.70,
+        "arithmetic": 0.85,
+        "web_grounding": 0.70,
         "long_text_extraction": 0.90,
-        "format_compliance":    0.92,
-        "code_or_logic":        0.90,
+        "format_compliance": 0.92,
+        "code_or_logic": 0.90,
     },
 }
 
@@ -189,9 +189,7 @@ def _score_agents(
     lam = 0.5
     scores: Dict[str, Dict[str, float]] = {}
     for aid, comps in competence.items():
-        comp = sum(
-            skill_weights.get(sid, 0.0) * comps[sid] for sid in SKILL_CATALOG
-        )
+        comp = sum(skill_weights.get(sid, 0.0) * comps[sid] for sid in SKILL_CATALOG)
         cost_pen = lam * cost.get(aid, 0.0)
         scores[aid] = {
             "competence": comp,
@@ -313,14 +311,16 @@ class SkillOrchestraAgent(LocalCloudAgent):
         if chosen not in competence:
             chosen = max(scored, key=lambda a: scored[a]["final_score"])
 
-        self.record_trace_event({
-            "kind": "skillorchestra_route",
-            "chosen_agent": chosen,
-            "skill_weights": skill_weights,
-            "agent_scores": scored,
-            "reasoning": decision.get("reasoning", ""),
-            "router_raw": router_text,
-        })
+        self.record_trace_event(
+            {
+                "kind": "skillorchestra_route",
+                "chosen_agent": chosen,
+                "skill_weights": skill_weights,
+                "agent_scores": scored,
+                "reasoning": decision.get("reasoning", ""),
+                "router_raw": router_text,
+            }
+        )
 
         tokens_local = 0
         tokens_cloud = r_in + r_out

src/openjarvis/agents/hybrid/skillorchestra/agent.py

@@ -41,8 +41,12 @@ from .orchestrator import run_orchestrator
 from .stage_router import StageSkillHandbook
 
 _VALID_STRATEGIES = {
-    "none", "router_decides", "analyze_model_decide",
-    "weighted_avg", "weakest_skill", "strongest_skill",
+    "none",
+    "router_decides",
+    "analyze_model_decide",
+    "weighted_avg",
+    "weakest_skill",
+    "strongest_skill",
 }
 
 
@@ -146,7 +150,11 @@ class SkillOrchestraAgent(LocalCloudAgent):
             strategy = "none"
 
         return run_orchestrator(
-            self, input, cfg=cfg, handbook=handbook, strategy=strategy,
+            self,
+            input,
+            cfg=cfg,
+            handbook=handbook,
+            strategy=strategy,
         )
 
 

src/openjarvis/agents/hybrid/skillorchestra/orchestrator.py

@@ -58,6 +58,7 @@ _STAGE_DEFAULT_ALIAS = {
 # Orchestrator decision step (raw SDK — needs tool_use blocks back)
 # ---------------------------------------------------------------------------
 
+
 def _orchestrate_step(
     agent: Any,
     *,
@@ -126,9 +127,7 @@ def _orchestrate_step(
         from google import genai
         from google.genai import types
 
-        client = genai.Client(
-            http_options=types.HttpOptions(timeout=600_000)
-        )
+        client = genai.Client(http_options=types.HttpOptions(timeout=600_000))
         cfg = types.GenerateContentConfig(
             temperature=1.0,
             max_output_tokens=max_tokens,
@@ -169,16 +168,18 @@ def _orchestrate_step(
         )
 
     cost = agent.cost_usd(model, p, c)
-    agent.record_trace_event({
-        "kind": "skillorchestra_orchestrate",
-        "model": model,
-        "endpoint": endpoint,
-        "prompt": user,
-        "response": text,
-        "tool_calls": tool_calls,
-        "tokens_in": p,
-        "tokens_out": c,
-    })
+    agent.record_trace_event(
+        {
+            "kind": "skillorchestra_orchestrate",
+            "model": model,
+            "endpoint": endpoint,
+            "prompt": user,
+            "response": text,
+            "tool_calls": tool_calls,
+            "tokens_in": p,
+            "tokens_out": c,
+        }
+    )
     return text, tool_calls, p, c, cost
 
 
@@ -186,6 +187,7 @@ def _orchestrate_step(
 # Context assembly  —  eval_frames.py:1305-1351
 # ---------------------------------------------------------------------------
 
+
 def _build_context(
     doc_list: List[Tuple[str, str]],
     code_list: List[Tuple[str, str]],
@@ -223,6 +225,7 @@ def _build_context(
 # Main loop  —  eval_frames.py:run_single
 # ---------------------------------------------------------------------------
 
+
 def run_orchestrator(
     agent: Any,
     problem: str,
@@ -245,12 +248,14 @@ def run_orchestrator(
     # MODEL_NAME). Defaults to the cell's cloud model when that endpoint
     # supports tool calls, else Opus. ``router_model`` / ``router_endpoint``
     # are accepted as back-compat aliases (pre-restructure cfg key names).
-    orch_endpoint = (cfg.get("orchestrator_endpoint")
-                     or cfg.get("router_endpoint")
-                     or agent._cloud_endpoint).lower()
-    orch_model = (cfg.get("orchestrator_model")
-                  or cfg.get("router_model")
-                  or agent._cloud_model)
+    orch_endpoint = (
+        cfg.get("orchestrator_endpoint")
+        or cfg.get("router_endpoint")
+        or agent._cloud_endpoint
+    ).lower()
+    orch_model = (
+        cfg.get("orchestrator_model") or cfg.get("router_model") or agent._cloud_model
+    )
     if orch_endpoint not in ("anthropic", "openai", "gemini"):
         orch_endpoint, orch_model = "anthropic", "claude-opus-4-7"
     orch_max_tokens = int(cfg.get("orchestrator_max_tokens", 4096))
@@ -281,7 +286,9 @@ def run_orchestrator(
         if handbook is not None and strategy != "none":
             sa = parse_skill_analysis(orch_text)
             rr = get_routing_strategy(strategy, handbook).select_model(
-                stage, sa, tool_call_model=tool_alias,
+                stage,
+                sa,
+                tool_call_model=tool_alias,
             )
             return rr.model_alias
         return tool_alias or _STAGE_DEFAULT_ALIAS[stage]
@@ -290,7 +297,10 @@ def run_orchestrator(
         used_rounds = step + 1
         is_last = step == max_rounds - 1
         context_str = _build_context(
-            doc_list, code_list, attempt_list, char_cap=char_cap,
+            doc_list,
+            code_list,
+            attempt_list,
+            char_cap=char_cap,
         )
 
         if handbook is not None and strategy != "none":
@@ -301,14 +311,14 @@ def run_orchestrator(
                 handbook=handbook,
             )
         else:
-            user = (
-                f"Problem: {problem}\n\n{context_str}\n\n"
-                "Choose an appropriate tool."
-            )
+            user = f"Problem: {problem}\n\n{context_str}\n\nChoose an appropriate tool."
 
         text, tcalls, p, c, ocost = _orchestrate_step(
-            agent, user=user, model=orch_model,
-            endpoint=orch_endpoint, max_tokens=orch_max_tokens,
+            agent,
+            user=user,
+            model=orch_model,
+            endpoint=orch_endpoint,
+            max_tokens=orch_max_tokens,
         )
         tokens_cloud += p + c
         cost_usd += ocost
@@ -337,23 +347,29 @@ def run_orchestrator(
             tool_alias = (tc.get("input") or {}).get("model")
             stage = _TOOL_STAGE.get(tool, "answer")
             chosen_alias = _route(stage, tool_alias, text)
-            spec: ModelSpec = pool.get(chosen_alias) or pool[
-                _STAGE_DEFAULT_ALIAS[stage]
-            ]
-            route_log.append({
-                "step": step,
-                "tool": tool,
-                "orchestrator_alias": tool_alias,
-                "routed_alias": chosen_alias,
-                "routed_model": spec.model,
-                "is_local": spec.is_local,
-            })
+            spec: ModelSpec = (
+                pool.get(chosen_alias) or pool[_STAGE_DEFAULT_ALIAS[stage]]
+            )
+            route_log.append(
+                {
+                    "step": step,
+                    "tool": tool,
+                    "orchestrator_alias": tool_alias,
+                    "routed_alias": chosen_alias,
+                    "routed_model": spec.model,
+                    "is_local": spec.is_local,
+                }
+            )
             tool_calls_n += 1
 
             if tool == "search":
                 res = run_search(
-                    agent, spec, context_str=context_str, problem=problem,
-                    retriever_url=retriever_url, web_search_max_uses=ws_max_uses,
+                    agent,
+                    spec,
+                    context_str=context_str,
+                    problem=problem,
+                    retriever_url=retriever_url,
+                    web_search_max_uses=ws_max_uses,
                     search_backend=search_backend,
                     tavily_max_results=tavily_max_results,
                 )
@@ -363,13 +379,19 @@ def run_orchestrator(
                 web_uses += res.get("web_search_uses", 0)
             elif tool in ("enhance_reasoning", "code"):
                 res = run_code(
-                    agent, spec, context_str=context_str, problem=problem,
+                    agent,
+                    spec,
+                    context_str=context_str,
+                    problem=problem,
                     bash_timeout_s=code_timeout,
                 )
                 code_list.append((res["generated_code"], res["exec_result"]))
             else:  # answer
                 res = run_answer(
-                    agent, spec, context_str=context_str, problem=problem,
+                    agent,
+                    spec,
+                    context_str=context_str,
+                    problem=problem,
                     max_tokens=answer_max_tokens,
                 )
                 final_pred = res["pred"]
@@ -385,12 +407,14 @@ def run_orchestrator(
         if finish:
             break
 
-    agent.record_trace_event({
-        "kind": "skillorchestra_route_log",
-        "strategy": strategy,
-        "rounds_used": used_rounds,
-        "routes": route_log,
-    })
+    agent.record_trace_event(
+        {
+            "kind": "skillorchestra_route_log",
+            "strategy": strategy,
+            "rounds_used": used_rounds,
+            "routes": route_log,
+        }
+    )
 
     meta = {
         "tokens_local": tokens_local,

src/openjarvis/agents/hybrid/skillorchestra/pool.py

@@ -32,8 +32,14 @@ from typing import Any, Dict, List, Optional, Tuple
 STAGE_ALIASES: Dict[str, List[str]] = {
     "search": ["search-1", "search-2", "search-3"],
     "reasoning": ["reasoner-1", "reasoner-2", "reasoner-3"],
-    "answer": ["answer-1", "answer-2", "answer-3", "answer-4",
-               "answer-math-1", "answer-math-2"],
+    "answer": [
+        "answer-1",
+        "answer-2",
+        "answer-3",
+        "answer-4",
+        "answer-math-1",
+        "answer-math-2",
+    ],
 }
 
 # Every alias the orchestrator can emit, flat.
@@ -42,9 +48,13 @@ ALL_ALIASES: List[str] = [a for aliases in STAGE_ALIASES.values() for a in alias
 # Default tier: which aliases collapse onto the cloud model vs the local
 # model. Dearer ``-1``/``-2`` (+ answer-math-1) -> cloud; cheaper -> local.
 _CLOUD_ALIASES = {
-    "search-1", "search-2",
-    "reasoner-1", "reasoner-2",
-    "answer-1", "answer-2", "answer-math-1",
+    "search-1",
+    "search-2",
+    "reasoner-1",
+    "reasoner-2",
+    "answer-1",
+    "answer-2",
+    "answer-math-1",
 }
 
 
@@ -54,8 +64,8 @@ class ModelSpec:
 
     alias: str
     model: str
-    endpoint: str          # "anthropic" | "openai" | "gemini" | "http://..."
-    kind: str              # "cloud" | "local"
+    endpoint: str  # "anthropic" | "openai" | "gemini" | "http://..."
+    kind: str  # "cloud" | "local"
 
     @property
     def is_local(self) -> bool:
@@ -87,7 +97,10 @@ def build_pool(
             pool[alias] = ModelSpec(alias, cloud_model, cloud_endpoint, "cloud")
         else:
             pool[alias] = ModelSpec(
-                alias, local_model, local_endpoint, "local"  # type: ignore[arg-type]
+                alias,
+                local_model,
+                local_endpoint,
+                "local",  # type: ignore[arg-type]
             )
 
     for alias, spec in (overrides or {}).items():
@@ -133,18 +146,30 @@ def call_alias(
     ep = spec.endpoint.lower()
     if ep == "anthropic":
         text, p, c, _ = agent._call_anthropic(
-            spec.model, user=user, system=system,
-            max_tokens=max_tokens, temperature=temperature, trace_role="cloud",
+            spec.model,
+            user=user,
+            system=system,
+            max_tokens=max_tokens,
+            temperature=temperature,
+            trace_role="cloud",
         )
     elif ep == "openai":
         text, p, c = agent._call_openai(
-            spec.model, user=user, system=system,
-            max_tokens=max_tokens, temperature=temperature, trace_role="cloud",
+            spec.model,
+            user=user,
+            system=system,
+            max_tokens=max_tokens,
+            temperature=temperature,
+            trace_role="cloud",
         )
     elif ep == "gemini":
         text, p, c = agent._call_gemini(
-            spec.model, user=user, system=system,
-            max_tokens=max_tokens, temperature=temperature, trace_role="cloud",
+            spec.model,
+            user=user,
+            system=system,
+            max_tokens=max_tokens,
+            temperature=temperature,
+            trace_role="cloud",
         )
     else:
         raise ValueError(f"unsupported pool endpoint: {spec.endpoint!r}")

src/openjarvis/agents/hybrid/skillorchestra/stage_router.py

@@ -94,7 +94,12 @@ class StageSkillHandbook:
             "answer": {},
         }
         self.model_profiles: Dict[str, ModelProfile] = {}
-        self.usage_patterns: Dict[str, Any] = {"stages": {}, "guidelines": {}, "models": {}, "raw": {}}
+        self.usage_patterns: Dict[str, Any] = {
+            "stages": {},
+            "guidelines": {},
+            "models": {},
+            "raw": {},
+        }
         self.routing_insights: List[str] = []
         self.learning_history: List[Dict[str, Any]] = []
         self.version = "1.0.0"
@@ -102,7 +107,10 @@ class StageSkillHandbook:
         self.updated_at = ""
 
     def get_model_skill_scores(self) -> Dict[str, Dict[str, float]]:
-        return {alias: profile.skill_scores for alias, profile in self.model_profiles.items()}
+        return {
+            alias: profile.skill_scores
+            for alias, profile in self.model_profiles.items()
+        }
 
     def get_models_for_stage(self, stage: str) -> List[ModelProfile]:
         return [p for p in self.model_profiles.values() if p.stage == stage]
@@ -135,19 +143,29 @@ class StageSkillHandbook:
 
     def format_model_performance(self, stage: str) -> str:
         profiles = self.get_models_for_stage(stage)
-        valid_prefixes = {"search": ["search-"], "code": ["reasoner-", "code-"], "answer": ["answer-"]}
+        valid_prefixes = {
+            "search": ["search-"],
+            "code": ["reasoner-", "code-"],
+            "answer": ["answer-"],
+        }
         prefixes = valid_prefixes.get(stage, [])
 
         lines = []
         for p in profiles:
             if not any(p.model_alias.startswith(prefix) for prefix in prefixes):
                 continue
-            has_data = (p.skill_scores and len(p.skill_scores) > 0) or p.strengths or p.weaknesses
+            has_data = (
+                (p.skill_scores and len(p.skill_scores) > 0)
+                or p.strengths
+                or p.weaknesses
+            )
             if p.total_attempts > 0 or has_data:
                 lines.append(f"\n### {p.model_alias} ({p.actual_model})")
                 if p.total_attempts > 0:
                     rate = p.total_successes / p.total_attempts
-                    lines.append(f"Overall: {rate:.0%} success ({p.total_successes}/{p.total_attempts})")
+                    lines.append(
+                        f"Overall: {rate:.0%} success ({p.total_successes}/{p.total_attempts})"
+                    )
                 else:
                     lines.append("Overall: 0% overall")
                 if p.skill_scores:
@@ -157,7 +175,9 @@ class StageSkillHandbook:
                         for sid, s in p.skill_scores.items()
                         if (sid.split(".")[0] if "." in sid else sid) in ("code", stage)
                     }
-                    for skill_id, score in sorted(stage_skill_scores.items(), key=lambda x: x[1], reverse=True):
+                    for skill_id, score in sorted(
+                        stage_skill_scores.items(), key=lambda x: x[1], reverse=True
+                    ):
                         lines.append(f"  - {skill_id}: {score:.0%}")
                 if p.strengths:
                     lines.append(f"Strengths: {', '.join(p.strengths[:3])}")
@@ -220,7 +240,9 @@ def parse_skill_analysis(output: str) -> Optional[SkillAnalysis]:
     try:
         data = json.loads(match.group(1).strip())
         required_skills = [
-            SkillWeight(skill_id=s.get("skill_id", ""), percentage=float(s.get("percentage", 0)))
+            SkillWeight(
+                skill_id=s.get("skill_id", ""), percentage=float(s.get("percentage", 0))
+            )
             for s in data.get("required_skills", [])
         ]
         return SkillAnalysis(
@@ -272,7 +294,14 @@ class RoutingStrategy:
         if stage == "reasoning":
             return ["reasoner-1", "reasoner-2", "reasoner-3"]
         if stage == "answer":
-            return ["answer-1", "answer-2", "answer-3", "answer-4", "answer-math-1", "answer-math-2"]
+            return [
+                "answer-1",
+                "answer-2",
+                "answer-3",
+                "answer-4",
+                "answer-math-1",
+                "answer-math-2",
+            ]
         return []
 
     def select_model(
@@ -292,9 +321,17 @@ class RouterDecidesStrategy(RoutingStrategy):
         tool_call_model: Optional[str] = None,
     ) -> ModelRoutingResult:
         if tool_call_model:
-            return ModelRoutingResult(tool_call_model, "router_decides_from_tool_call", 1.0)
-        defaults = {"search": "search-1", "reasoning": "reasoner-1", "answer": "answer-1"}
-        return ModelRoutingResult(defaults.get(stage, "answer-1"), "router_decides_fallback", 0.5)
+            return ModelRoutingResult(
+                tool_call_model, "router_decides_from_tool_call", 1.0
+            )
+        defaults = {
+            "search": "search-1",
+            "reasoning": "reasoner-1",
+            "answer": "answer-1",
+        }
+        return ModelRoutingResult(
+            defaults.get(stage, "answer-1"), "router_decides_fallback", 0.5
+        )
 
 
 class AnalyzeModelDecideStrategy(RoutingStrategy):
@@ -305,17 +342,33 @@ class AnalyzeModelDecideStrategy(RoutingStrategy):
         tool_call_model: Optional[str] = None,
     ) -> ModelRoutingResult:
         if tool_call_model:
-            return ModelRoutingResult(tool_call_model, "analyze_model_decide_with_skill_analysis", 1.0)
-        defaults = {"search": "search-1", "reasoning": "reasoner-1", "answer": "answer-1"}
-        return ModelRoutingResult(defaults.get(stage, "answer-1"), "analyze_model_decide_fallback", 0.5)
+            return ModelRoutingResult(
+                tool_call_model, "analyze_model_decide_with_skill_analysis", 1.0
+            )
+        defaults = {
+            "search": "search-1",
+            "reasoning": "reasoner-1",
+            "answer": "answer-1",
+        }
+        return ModelRoutingResult(
+            defaults.get(stage, "answer-1"), "analyze_model_decide_fallback", 0.5
+        )
 
 
 class WeightedAverageStrategy(RoutingStrategy):
     COST_TIERS = {
-        "search-3": 1, "search-2": 2, "search-1": 3,
-        "reasoner-3": 1, "reasoner-2": 2, "reasoner-1": 3,
-        "answer-math-2": 1, "answer-4": 1, "answer-3": 2,
-        "answer-math-1": 2, "answer-2": 3, "answer-1": 4,
+        "search-3": 1,
+        "search-2": 2,
+        "search-1": 3,
+        "reasoner-3": 1,
+        "reasoner-2": 2,
+        "reasoner-1": 3,
+        "answer-math-2": 1,
+        "answer-4": 1,
+        "answer-3": 2,
+        "answer-math-1": 2,
+        "answer-2": 3,
+        "answer-1": 4,
     }
 
     def select_model(
@@ -326,9 +379,17 @@ class WeightedAverageStrategy(RoutingStrategy):
     ) -> ModelRoutingResult:
         if not skill_analysis or not skill_analysis.required_skills:
             if tool_call_model:
-                return ModelRoutingResult(tool_call_model, "weighted_avg_no_skills_use_tool_call", 0.7)
-            defaults = {"search": "search-1", "reasoning": "reasoner-1", "answer": "answer-1"}
-            return ModelRoutingResult(defaults.get(stage, "answer-1"), "weighted_avg_no_skills_fallback", 0.5)
+                return ModelRoutingResult(
+                    tool_call_model, "weighted_avg_no_skills_use_tool_call", 0.7
+                )
+            defaults = {
+                "search": "search-1",
+                "reasoning": "reasoner-1",
+                "answer": "answer-1",
+            }
+            return ModelRoutingResult(
+                defaults.get(stage, "answer-1"), "weighted_avg_no_skills_fallback", 0.5
+            )
 
         models = self._get_models_for_stage(stage)
         model_scores = {}
@@ -341,15 +402,25 @@ class WeightedAverageStrategy(RoutingStrategy):
                 score = scores.get(sid, 0.0)
                 weighted_sum += weight * score
                 total_weight += weight
-            model_scores[model] = weighted_sum / total_weight if total_weight > 0 else 0.5
+            model_scores[model] = (
+                weighted_sum / total_weight if total_weight > 0 else 0.5
+            )
 
         if not model_scores:
-            defaults = {"search": "search-1", "reasoning": "reasoner-1", "answer": "answer-1"}
-            return ModelRoutingResult(defaults.get(stage, "answer-1"), "weighted_avg_no_model_scores", 0.5)
+            defaults = {
+                "search": "search-1",
+                "reasoning": "reasoner-1",
+                "answer": "answer-1",
+            }
+            return ModelRoutingResult(
+                defaults.get(stage, "answer-1"), "weighted_avg_no_model_scores", 0.5
+            )
         max_score = max(model_scores.values())
         best = [m for m, s in model_scores.items() if abs(s - max_score) < 0.001]
         best.sort(key=lambda m: self.COST_TIERS.get(m, 999))
-        return ModelRoutingResult(best[0], "weighted_avg_from_skill_analysis", max_score, model_scores)
+        return ModelRoutingResult(
+            best[0], "weighted_avg_from_skill_analysis", max_score, model_scores
+        )
 
 
 class WeakestSkillStrategy(RoutingStrategy):
@@ -361,18 +432,36 @@ class WeakestSkillStrategy(RoutingStrategy):
     ) -> ModelRoutingResult:
         if not skill_analysis or not skill_analysis.required_skills:
             if tool_call_model:
-                return ModelRoutingResult(tool_call_model, "weakest_skill_no_skills_use_tool_call", 0.7)
-            defaults = {"search": "search-1", "reasoning": "reasoner-1", "answer": "answer-1"}
-            return ModelRoutingResult(defaults.get(stage, "answer-1"), "weakest_skill_no_skills_fallback", 0.5)
+                return ModelRoutingResult(
+                    tool_call_model, "weakest_skill_no_skills_use_tool_call", 0.7
+                )
+            defaults = {
+                "search": "search-1",
+                "reasoning": "reasoner-1",
+                "answer": "answer-1",
+            }
+            return ModelRoutingResult(
+                defaults.get(stage, "answer-1"), "weakest_skill_no_skills_fallback", 0.5
+            )
         weakest = min(skill_analysis.required_skills, key=lambda s: s.percentage)
         sid = self._find_skill_id(stage, weakest.skill_id) or weakest.skill_id
         models = self._get_models_for_stage(stage)
-        model_scores = {m: self._model_skill_scores.get(m, {}).get(sid, 0.5) for m in models}
+        model_scores = {
+            m: self._model_skill_scores.get(m, {}).get(sid, 0.5) for m in models
+        }
         if not model_scores:
-            defaults = {"search": "search-1", "reasoning": "reasoner-1", "answer": "answer-1"}
-            return ModelRoutingResult(defaults.get(stage, "answer-1"), "weakest_skill_no_model_scores", 0.5)
+            defaults = {
+                "search": "search-1",
+                "reasoning": "reasoner-1",
+                "answer": "answer-1",
+            }
+            return ModelRoutingResult(
+                defaults.get(stage, "answer-1"), "weakest_skill_no_model_scores", 0.5
+            )
         best = max(model_scores, key=model_scores.get)
-        return ModelRoutingResult(best, f"weakest_skill_{weakest.skill_id}", model_scores[best], model_scores)
+        return ModelRoutingResult(
+            best, f"weakest_skill_{weakest.skill_id}", model_scores[best], model_scores
+        )
 
 
 class StrongestSkillStrategy(RoutingStrategy):
@@ -384,18 +473,41 @@ class StrongestSkillStrategy(RoutingStrategy):
     ) -> ModelRoutingResult:
         if not skill_analysis or not skill_analysis.required_skills:
             if tool_call_model:
-                return ModelRoutingResult(tool_call_model, "strongest_skill_no_skills_use_tool_call", 0.7)
-            defaults = {"search": "search-1", "reasoning": "reasoner-1", "answer": "answer-1"}
-            return ModelRoutingResult(defaults.get(stage, "answer-1"), "strongest_skill_no_skills_fallback", 0.5)
+                return ModelRoutingResult(
+                    tool_call_model, "strongest_skill_no_skills_use_tool_call", 0.7
+                )
+            defaults = {
+                "search": "search-1",
+                "reasoning": "reasoner-1",
+                "answer": "answer-1",
+            }
+            return ModelRoutingResult(
+                defaults.get(stage, "answer-1"),
+                "strongest_skill_no_skills_fallback",
+                0.5,
+            )
         strongest = max(skill_analysis.required_skills, key=lambda s: s.percentage)
         sid = self._find_skill_id(stage, strongest.skill_id) or strongest.skill_id
         models = self._get_models_for_stage(stage)
-        model_scores = {m: self._model_skill_scores.get(m, {}).get(sid, 0.5) for m in models}
+        model_scores = {
+            m: self._model_skill_scores.get(m, {}).get(sid, 0.5) for m in models
+        }
         if not model_scores:
-            defaults = {"search": "search-1", "reasoning": "reasoner-1", "answer": "answer-1"}
-            return ModelRoutingResult(defaults.get(stage, "answer-1"), "strongest_skill_no_model_scores", 0.5)
+            defaults = {
+                "search": "search-1",
+                "reasoning": "reasoner-1",
+                "answer": "answer-1",
+            }
+            return ModelRoutingResult(
+                defaults.get(stage, "answer-1"), "strongest_skill_no_model_scores", 0.5
+            )
         best = max(model_scores, key=model_scores.get)
-        return ModelRoutingResult(best, f"strongest_skill_{strongest.skill_id}", model_scores[best], model_scores)
+        return ModelRoutingResult(
+            best,
+            f"strongest_skill_{strongest.skill_id}",
+            model_scores[best],
+            model_scores,
+        )
 
 
 ROUTING_STRATEGIES = {

src/openjarvis/agents/hybrid/skillorchestra/tools.py

@@ -41,8 +41,7 @@ _SEARCH_CAPABLE_ENDPOINTS = ("anthropic", "openai", "gemini")
 
 _SEARCH_DESC = "Search for missing information."
 _CODE_DESC = (
-    "Write and execute Python code to compute intermediate results for "
-    "the problem."
+    "Write and execute Python code to compute intermediate results for the problem."
 )
 _ANSWER_DESC = (
     "Extract the final answer when you have gathered enough information "
@@ -52,8 +51,14 @@ _ANSWER_DESC = (
 _ENUMS = {
     "search": ["search-1", "search-2", "search-3"],
     "enhance_reasoning": ["reasoner-1", "reasoner-2", "reasoner-3"],
-    "answer": ["answer-1", "answer-2", "answer-3", "answer-4",
-               "answer-math-1", "answer-math-2"],
+    "answer": [
+        "answer-1",
+        "answer-2",
+        "answer-3",
+        "answer-4",
+        "answer-math-1",
+        "answer-math-2",
+    ],
 }
 
 
@@ -76,15 +81,17 @@ def anthropic_tools() -> List[Dict[str, Any]]:
         ("enhance_reasoning", _CODE_DESC),
         ("answer", _ANSWER_DESC),
     ):
-        out.append({
-            "name": name,
-            "description": desc,
-            "input_schema": {
-                "type": "object",
-                "properties": {"model": _model_prop(name)},
-                "required": ["model"],
-            },
-        })
+        out.append(
+            {
+                "name": name,
+                "description": desc,
+                "input_schema": {
+                    "type": "object",
+                    "properties": {"model": _model_prop(name)},
+                    "required": ["model"],
+                },
+            }
+        )
     return out
 
 
@@ -96,18 +103,20 @@ def openai_tools() -> List[Dict[str, Any]]:
         ("enhance_reasoning", _CODE_DESC),
         ("answer", _ANSWER_DESC),
     ):
-        out.append({
-            "type": "function",
-            "function": {
-                "name": name,
-                "description": desc,
-                "parameters": {
-                    "type": "object",
-                    "properties": {"model": _model_prop(name)},
-                    "required": ["model"],
+        out.append(
+            {
+                "type": "function",
+                "function": {
+                    "name": name,
+                    "description": desc,
+                    "parameters": {
+                        "type": "object",
+                        "properties": {"model": _model_prop(name)},
+                        "required": ["model"],
+                    },
                 },
-            },
-        })
+            }
+        )
     return out
 
 
@@ -119,15 +128,17 @@ def gemini_tools() -> List[Dict[str, Any]]:
         ("enhance_reasoning", _CODE_DESC),
         ("answer", _ANSWER_DESC),
     ):
-        out.append({
-            "name": name,
-            "description": desc,
-            "parameters": {
-                "type": "object",
-                "properties": {"model": _model_prop(name)},
-                "required": ["model"],
-            },
-        })
+        out.append(
+            {
+                "name": name,
+                "description": desc,
+                "parameters": {
+                    "type": "object",
+                    "properties": {"model": _model_prop(name)},
+                    "required": ["model"],
+                },
+            }
+        )
     return out
 
 
@@ -135,6 +146,7 @@ def gemini_tools() -> List[Dict[str, Any]]:
 # enhance_reasoning / code  —  eval_frames.py:659-812
 # ---------------------------------------------------------------------------
 
+
 def run_code(
     agent: Any,
     spec: ModelSpec,
@@ -150,7 +162,8 @@ def run_code(
     rather than raising — the orchestrator learns the model can't code.
     """
     prompt = (
-        context_str.strip() + "\n\n"
+        context_str.strip()
+        + "\n\n"
         + f"Question: {problem}\nInstead of directly answering the question, "
         "please write additional python code that will give intermidiate "
         "results after execution. Wrap the code within ```python and ```. "
@@ -158,7 +171,11 @@ def run_code(
         "initialization."
     )
     text, p, c, cost = call_alias(
-        agent, spec, user=prompt, max_tokens=8000, temperature=1.0,
+        agent,
+        spec,
+        user=prompt,
+        max_tokens=8000,
+        temperature=1.0,
     )
     generated_code = ""
     if "```python" in text:
@@ -197,6 +214,7 @@ def run_code(
 # answer  —  eval_frames.py:814-997
 # ---------------------------------------------------------------------------
 
+
 def run_answer(
     agent: Any,
     spec: ModelSpec,
@@ -219,11 +237,15 @@ def run_answer(
     boxed = False
 
     if "qwen3" in model_l and "235" not in model_l:
-        system = "Please reason step by step, and put your final answer within \\boxed{}."
+        system = (
+            "Please reason step by step, and put your final answer within \\boxed{}."
+        )
         user = base
         boxed = True
     elif "qwen2.5-math" in model_l or "qwen-2.5-math" in model_l:
-        system = "Please reason step by step, and put your final answer within \\boxed{}."
+        system = (
+            "Please reason step by step, and put your final answer within \\boxed{}."
+        )
         user = base
         boxed = True
     else:
@@ -236,8 +258,12 @@ def run_answer(
         )
 
     text, p, c, cost = call_alias(
-        agent, spec, user=user, system=system,
-        max_tokens=max_tokens, temperature=1.0,
+        agent,
+        spec,
+        user=user,
+        system=system,
+        max_tokens=max_tokens,
+        temperature=1.0,
     )
 
     pred = ""
@@ -268,6 +294,7 @@ def run_answer(
 # search  —  eval_frames.py:999-1096
 # ---------------------------------------------------------------------------
 
+
 def run_search(
     agent: Any,
     spec: ModelSpec,
@@ -288,13 +315,18 @@ def run_search(
     OpenJarvis substitution for the missing FAISS wiki index).
     """
     prompt = (
-        context_str.strip() + "\n\n"
+        context_str.strip()
+        + "\n\n"
         + f"Question: {problem}\nInstead of directly answering the question, "
         "please think hard and write a concise query to search Wikipedia. "
         "Wrap the query within <query> and </query>."
     )
     text, p, c, cost = call_alias(
-        agent, spec, user=prompt, max_tokens=8000, temperature=1.0,
+        agent,
+        spec,
+        user=prompt,
+        max_tokens=8000,
+        temperature=1.0,
     )
     if "<query>" in text:
         query = text.split("<query>")[-1].split("</query>")[0].strip()
@@ -322,7 +354,9 @@ def run_search(
         }
         try:
             results = requests.post(
-                f"{retriever_url.rstrip('/')}/retrieve", json=payload, timeout=120,
+                f"{retriever_url.rstrip('/')}/retrieve",
+                json=payload,
+                timeout=120,
             ).json()
             for r in results[0]:
                 doc = r.get("document", {})

src/openjarvis/agents/hybrid/skillorchestra/types.py

@@ -20,6 +20,7 @@ from typing import Any, Dict, List, Optional
 # BetaCompetence
 # ---------------------------------------------------------------------------
 
+
 @dataclass
 class BetaCompetence:
     """Bayesian competence estimate for an agent on a specific skill.
@@ -80,6 +81,7 @@ class BetaCompetence:
 # CostStats
 # ---------------------------------------------------------------------------
 
+
 @dataclass
 class CostStats:
     """Execution cost statistics for an agent under a specific mode.
@@ -110,11 +112,17 @@ class CostStats:
         """Incremental running-average update."""
         n = self.total_executions
         self.avg_prompt_tokens = (self.avg_prompt_tokens * n + prompt_tokens) / (n + 1)
-        self.avg_completion_tokens = (self.avg_completion_tokens * n + completion_tokens) / (n + 1)
+        self.avg_completion_tokens = (
+            self.avg_completion_tokens * n + completion_tokens
+        ) / (n + 1)
         self.avg_latency_s = (self.avg_latency_s * n + latency_s) / (n + 1)
         self.avg_cost_usd = (self.avg_cost_usd * n + cost_usd) / (n + 1)
-        self.avg_completion_cost_usd = (self.avg_completion_cost_usd * n + completion_cost_usd) / (n + 1)
-        self.avg_prompt_cost_usd = (self.avg_prompt_cost_usd * n + prompt_cost_usd) / (n + 1)
+        self.avg_completion_cost_usd = (
+            self.avg_completion_cost_usd * n + completion_cost_usd
+        ) / (n + 1)
+        self.avg_prompt_cost_usd = (self.avg_prompt_cost_usd * n + prompt_cost_usd) / (
+            n + 1
+        )
         self.total_executions = n + 1
 
     def to_dict(self) -> Dict[str, Any]:
@@ -137,6 +145,7 @@ class CostStats:
 # RoutingInsight
 # ---------------------------------------------------------------------------
 
+
 @dataclass
 class RoutingInsight:
     """A single routing insight learned from execution traces"""
@@ -165,6 +174,7 @@ class RoutingInsight:
 # ModeMetadata
 # ---------------------------------------------------------------------------
 
+
 @dataclass
 class ModeMetadata:
     """Mode-level routing metadata."""
@@ -197,6 +207,7 @@ class ModeMetadata:
 # Skill
 # ---------------------------------------------------------------------------
 
+
 @dataclass
 class SkillProvenance:
     """Tracks how and why a skill was discovered."""
@@ -231,7 +242,7 @@ class Skill:
     indicators: List[str] = field(default_factory=list)
     examples: List[str] = field(default_factory=list)
     mode: str = ""
-    parent_skill_id: Optional[str] = None # for hierarchical skills
+    parent_skill_id: Optional[str] = None  # for hierarchical skills
     provenance: SkillProvenance = field(default_factory=SkillProvenance)
 
     def to_dict(self) -> Dict[str, Any]:
@@ -273,6 +284,7 @@ class Skill:
 # AgentProfile
 # ---------------------------------------------------------------------------
 
+
 @dataclass
 class AgentProfile:
     """Agent profile for skill-aware orchestration."""
@@ -309,9 +321,7 @@ class AgentProfile:
         """Update competence estimate for a skill."""
         self.get_competence_dist(skill_id).update(success)
 
-    def weighted_competence(
-        self, skill_weights: Dict[str, float]
-    ) -> float:
+    def weighted_competence(self, skill_weights: Dict[str, float]) -> float:
         """Compute weighted competence: sum w_{t,sigma} * alpha/(alpha+beta)."""
         if not skill_weights:
             return 0.5
@@ -334,9 +344,7 @@ class AgentProfile:
         ]
         return sum(scores) / len(scores) if scores else 0.0
 
-    def category_competence_for_skills(
-        self, active_skill_ids: List[str]
-    ) -> float:
+    def category_competence_for_skills(self, active_skill_ids: List[str]) -> float:
         """Category-level competence for hierarchical tie-breaking.
 
         Extracts parent categories from active_skill_ids (e.g. 'entertainment_knowledge'
@@ -349,7 +357,9 @@ class AgentProfile:
             categories.add(cat)
         if not categories:
             return 0.0
-        return sum(self.category_competence(cat) for cat in categories) / len(categories)
+        return sum(self.category_competence(cat) for cat in categories) / len(
+            categories
+        )
 
     @property
     def overall_success_rate(self) -> float:
@@ -390,8 +400,12 @@ class AgentProfile:
             "skill_scores": skill_scores,
             "skill_attempts": skill_attempts,
             "skill_successes": skill_successes,
-            "total_attempts": self.total_attempts if self.total_attempts > 0 else skill_total_attempts,
-            "total_successes": self.total_successes if self.total_attempts > 0 else skill_total_successes,
+            "total_attempts": self.total_attempts
+            if self.total_attempts > 0
+            else skill_total_attempts,
+            "total_successes": self.total_successes
+            if self.total_attempts > 0
+            else skill_total_successes,
             "cost_stats": self.cost_stats.to_dict(),
             "routing_signals": self.routing_signals,
             "strengths": self.strengths,

src/openjarvis/agents/hybrid/toolorchestra.py

@@ -172,8 +172,12 @@ RL_ALL_TOOLS: Dict[str, Dict[str, List[str]]] = {
     "enhance_reasoning": {"model": ["reasoner-1", "reasoner-2", "reasoner-3"]},
     "answer": {
         "model": [
-            "answer-1", "answer-2", "answer-3", "answer-4",
-            "answer-math-1", "answer-math-2",
+            "answer-1",
+            "answer-2",
+            "answer-3",
+            "answer-4",
+            "answer-math-1",
+            "answer-math-2",
         ],
     },
     "search": {"model": ["search-1", "search-2", "search-3"]},
@@ -188,10 +192,14 @@ RL_ALL_TOOLS: Dict[str, Dict[str, List[str]]] = {
 # so the substitution is deferred until we know the cell's resolved local/cloud
 # pair. Worker dicts share the schema validated by `_resolve_worker_pool`.
 
-def _expert_for(slot: str, local_model: Optional[str],
-                local_endpoint: Optional[str],
-                cloud_model: str,
-                cloud_endpoint: str = "anthropic") -> Dict[str, Any]:
+
+def _expert_for(
+    slot: str,
+    local_model: Optional[str],
+    local_endpoint: Optional[str],
+    cloud_model: str,
+    cloud_endpoint: str = "anthropic",
+) -> Dict[str, Any]:
     """Map an upstream model slot (`answer-1`, `search-3`, …) to a worker spec.
 
     Routing policy:
@@ -357,6 +365,7 @@ def _paper_expert_for(
 
 # ---- Tavily + Modal helpers -------------------------------------------------
 
+
 def _call_tavily_search(
     query: str,
     max_results: int = 5,
@@ -390,7 +399,9 @@ def _call_modal_python(code: str, timeout_s: int = 60) -> Tuple[str, int]:
         # Python image too. We rely on stdlib only — no extra pip installs.
         image = modal.Image.debian_slim(python_version="3.12")
         sb = modal.Sandbox.create(
-            "python", "-c", code,
+            "python",
+            "-c",
+            code,
             app=app,
             image=image,
             timeout=int(timeout_s),
@@ -485,53 +496,77 @@ def _paper_pool(
     """
     pool: List[Dict[str, Any]] = []
     if local_model and local_endpoint:
-        pool.append({
+        pool.append(
+            {
+                "id": len(pool),
+                "name": "local-qwen",
+                "type": "vllm",
+                "model": local_model,
+                "base_url": local_endpoint,
+                "description": "Local Qwen vLLM (paper uses Qwen3-32B).",
+            }
+        )
+    pool.append(
+        {
             "id": len(pool),
-            "name": "local-qwen",
-            "type": "vllm",
-            "model": local_model,
-            "base_url": local_endpoint,
-            "description": "Local Qwen vLLM (paper uses Qwen3-32B).",
-        })
-    pool.append({
-        "id": len(pool), "name": "tavily-search",
-        "type": "tavily-search", "model": "tavily",
-        "description": "Tavily web search.",
-    })
-    pool.append({
-        "id": len(pool), "name": "modal-python",
-        "type": "modal-python", "model": "modal-python",
-        "description": "Modal Sandbox for one-shot Python exec.",
-    })
-    pool.append({
-        "id": len(pool), "name": "code-specialist",
-        "type": "openrouter", "model": _PAPER_CODER_OPENROUTER,
-        "description": "Qwen-2.5-Coder-32B via OpenRouter (paper).",
-    })
-    pool.append({
-        "id": len(pool), "name": "generalist-llama",
-        "type": "openrouter", "model": _PAPER_GENERALIST_TIER3_OPENROUTER,
-        "description": "Llama-3.3-70B-Instruct via OpenRouter (paper tier-3).",
-    })
-    pool.append({
-        "id": len(pool), "name": "generalist-gpt5",
-        "type": "openai", "model": "gpt-5",
-        "description": "GPT-5 frontier generalist.",
-    })
-    pool.append({
-        "id": len(pool), "name": "generalist-gpt5-mini",
-        "type": "openai", "model": "gpt-5-mini",
-        "description": "GPT-5-mini mid generalist.",
-    })
+            "name": "tavily-search",
+            "type": "tavily-search",
+            "model": "tavily",
+            "description": "Tavily web search.",
+        }
+    )
+    pool.append(
+        {
+            "id": len(pool),
+            "name": "modal-python",
+            "type": "modal-python",
+            "model": "modal-python",
+            "description": "Modal Sandbox for one-shot Python exec.",
+        }
+    )
+    pool.append(
+        {
+            "id": len(pool),
+            "name": "code-specialist",
+            "type": "openrouter",
+            "model": _PAPER_CODER_OPENROUTER,
+            "description": "Qwen-2.5-Coder-32B via OpenRouter (paper).",
+        }
+    )
+    pool.append(
+        {
+            "id": len(pool),
+            "name": "generalist-llama",
+            "type": "openrouter",
+            "model": _PAPER_GENERALIST_TIER3_OPENROUTER,
+            "description": "Llama-3.3-70B-Instruct via OpenRouter (paper tier-3).",
+        }
+    )
+    pool.append(
+        {
+            "id": len(pool),
+            "name": "generalist-gpt5",
+            "type": "openai",
+            "model": "gpt-5",
+            "description": "GPT-5 frontier generalist.",
+        }
+    )
+    pool.append(
+        {
+            "id": len(pool),
+            "name": "generalist-gpt5-mini",
+            "type": "openai",
+            "model": "gpt-5-mini",
+            "description": "GPT-5-mini mid generalist.",
+        }
+    )
     return pool
 
 
 # Regex for ``<tool_call>{...}</tool_call>`` blocks emitted by Orchestrator-8B
 # when the vLLM tool parser doesn't catch them (e.g. `qwen3_xml` parser on a
 # hermes-style template). Captures the JSON payload.
-_TOOL_CALL_TAG_RE = re.compile(
-    r"<tool_call>\s*(\{.*?\})\s*</tool_call>", re.DOTALL
-)
+_TOOL_CALL_TAG_RE = re.compile(r"<tool_call>\s*(\{.*?\})\s*</tool_call>", re.DOTALL)
 
 
 def _parse_rl_tool_call(content: str, sdk_tool_calls: Any) -> Optional[Dict[str, Any]]:
@@ -608,7 +643,7 @@ def _strip_fences(s: str) -> str:
     if s.startswith("```"):
         first_nl = s.find("\n")
         if first_nl != -1:
-            s = s[first_nl + 1:]
+            s = s[first_nl + 1 :]
         if s.endswith("```"):
             s = s[:-3]
         s = s.strip()
@@ -660,6 +695,7 @@ def _extract_final_answer_text(text: str) -> str:
 
 # ---------- Worker pool ----------
 
+
 def _default_pool(
     local_model: Optional[str],
     local_endpoint: Optional[str],
@@ -677,17 +713,19 @@ def _default_pool(
         ep = "anthropic"
     pool: List[Dict[str, Any]] = []
     if local_model and local_endpoint:
-        pool.append({
-            "id": len(pool),
-            "name": "local-qwen",
-            "type": "vllm",
-            "model": local_model,
-            "base_url": local_endpoint,
-            "description": (
-                "Open-weights Qwen3.5 served locally. Cheap and fast. Good at "
-                "concise extraction, formatting, arithmetic on given data."
-            ),
-        })
+        pool.append(
+            {
+                "id": len(pool),
+                "name": "local-qwen",
+                "type": "vllm",
+                "model": local_model,
+                "base_url": local_endpoint,
+                "description": (
+                    "Open-weights Qwen3.5 served locally. Cheap and fast. Good at "
+                    "concise extraction, formatting, arithmetic on given data."
+                ),
+            }
+        )
     if ep == "openai":
         search_type = "openai-web-search"
         search_model = cloud_model
@@ -700,36 +738,42 @@ def _default_pool(
         search_type = "anthropic-web-search"
         search_model = _DEFAULT_WEB_SEARCH_MODEL
         search_desc = "Anthropic server-side web_search."
-    pool.append({
-        "id": len(pool),
-        "name": "web-search",
-        "type": search_type,
-        "model": search_model,
-        "description": (
-            f"{search_desc} Use for facts that need a lookup "
-            "(recent events, rare names/dates, niche sources). Returns a digest."
-        ),
-    })
-    pool.append({
-        "id": len(pool),
-        "name": f"frontier-{ep}",
-        "type": ep,
-        "model": cloud_model,
-        "description": (
-            "Frontier reasoning model. Use for hard multi-step reasoning, "
-            "code review, or a final synthesis pass. Expensive — use sparingly."
-        ),
-    })
-    pool.append({
-        "id": len(pool),
-        "name": "frontier-openai-mini",
-        "type": "openai",
-        "model": "gpt-5-mini",
-        "description": (
-            "Mid-tier OpenAI model. Solid general knowledge and reasoning at a "
-            "fraction of frontier cost."
-        ),
-    })
+    pool.append(
+        {
+            "id": len(pool),
+            "name": "web-search",
+            "type": search_type,
+            "model": search_model,
+            "description": (
+                f"{search_desc} Use for facts that need a lookup "
+                "(recent events, rare names/dates, niche sources). Returns a digest."
+            ),
+        }
+    )
+    pool.append(
+        {
+            "id": len(pool),
+            "name": f"frontier-{ep}",
+            "type": ep,
+            "model": cloud_model,
+            "description": (
+                "Frontier reasoning model. Use for hard multi-step reasoning, "
+                "code review, or a final synthesis pass. Expensive — use sparingly."
+            ),
+        }
+    )
+    pool.append(
+        {
+            "id": len(pool),
+            "name": "frontier-openai-mini",
+            "type": "openai",
+            "model": "gpt-5-mini",
+            "description": (
+                "Mid-tier OpenAI model. Solid general knowledge and reasoning at a "
+                "fraction of frontier cost."
+            ),
+        }
+    )
     return pool
 
 
@@ -743,12 +787,21 @@ def _default_pool(
 #   `modal-python`   — One-shot Python exec in a fresh Modal Sandbox (the
 #                      paper's "Python sandbox" inside `enhance_reasoning`).
 _TOOLORCH_VALID_TYPES = (
-    "vllm", "openai", "anthropic", "anthropic-web-search",
-    "openai-web-search", "gemini", "gemini-web-search", "tavily-search",
-    "openrouter", "modal-python",
+    "vllm",
+    "openai",
+    "anthropic",
+    "anthropic-web-search",
+    "openai-web-search",
+    "gemini",
+    "gemini-web-search",
+    "tavily-search",
+    "openrouter",
+    "modal-python",
 )
 _TOOLORCH_SEARCH_TYPES = (
-    "anthropic-web-search", "openai-web-search", "gemini-web-search",
+    "anthropic-web-search",
+    "openai-web-search",
+    "gemini-web-search",
     "tavily-search",
 )
 
@@ -808,9 +861,7 @@ def _resolve_worker_pool(
                 f"Invalid worker_pool entry [{wid_repr}]: 'id' must be an int"
             )
         if wid in seen_ids:
-            raise ValueError(
-                f"Invalid worker_pool entry [{wid}]: duplicate id"
-            )
+            raise ValueError(f"Invalid worker_pool entry [{wid}]: duplicate id")
         seen_ids.add(wid)
         if not entry.get("name") or not isinstance(entry["name"], str):
             raise ValueError(
@@ -850,7 +901,10 @@ def _resolve_worker_pool(
                 raise ValueError(
                     f"Invalid worker_pool entry [{wid}]: 'model' must be a string when set"
                 )
-            if wtype in ("openai-web-search", "gemini-web-search") and model not in PRICES:
+            if (
+                wtype in ("openai-web-search", "gemini-web-search")
+                and model not in PRICES
+            ):
                 raise ValueError(
                     f"Invalid worker_pool entry [{wid}]: model {model!r} "
                     f"is not in PRICES (known: {sorted(PRICES)})"
@@ -958,7 +1012,9 @@ def _call_worker(
         text, p, c, n_searches, _ = LocalCloudAgent._call_openai_agent(
             worker["model"],
             user=prompt,
-            max_tokens=max(max_tok, 16384) if is_gpt5_family(worker["model"]) else max_tok,
+            max_tokens=max(max_tok, 16384)
+            if is_gpt5_family(worker["model"])
+            else max_tok,
             temperature=eff_temp,
         )
         extra = n_searches * OPENAI_WEB_SEARCH_COST_PER_CALL
@@ -975,7 +1031,8 @@ def _call_worker(
     if wtype == "tavily-search":
         max_results = int(cfg.get("tavily_max_results", 5))
         text, p, c, extra, n_searches = _call_tavily_search(
-            str(prompt), max_results=max_results,
+            str(prompt),
+            max_results=max_results,
         )
         return text, p, c, False, extra, n_searches
     if wtype == "openrouter":
@@ -1045,8 +1102,12 @@ def _swe_call_worker(
     is_local = backbone == "local"
     return (
         out["final_summary"] or out["answer"],
-        out["tokens_in"], out["tokens_out"],
-        is_local, 0.0, 0, int(out["turns"]),
+        out["tokens_in"],
+        out["tokens_out"],
+        is_local,
+        0.0,
+        0,
+        int(out["turns"]),
     )
 
 
@@ -1134,20 +1195,24 @@ class ToolOrchestraAgent(LocalCloudAgent):
         )
         shared_workdir: Optional[Path] = None
         if swe_mode:
-            shared_workdir = Path(tempfile.mkdtemp(
-                prefix=f"toolorch-swe-{task_meta.get('task_id','x')}-"
-            ))
+            shared_workdir = Path(
+                tempfile.mkdtemp(
+                    prefix=f"toolorch-swe-{task_meta.get('task_id', 'x')}-"
+                )
+            )
             try:
                 _clone_repo(task_meta["repo"], task_meta["base_commit"], shared_workdir)
             except Exception:
                 shutil.rmtree(shared_workdir, ignore_errors=True)
                 raise
-            self.record_trace_event({
-                "kind": "toolorchestra_swe_workdir",
-                "workdir": str(shared_workdir),
-                "repo": task_meta["repo"],
-                "base_commit": task_meta["base_commit"],
-            })
+            self.record_trace_event(
+                {
+                    "kind": "toolorchestra_swe_workdir",
+                    "workdir": str(shared_workdir),
+                    "repo": task_meta["repo"],
+                    "base_commit": task_meta["base_commit"],
+                }
+            )
 
         # try/finally guards ``shared_workdir`` against exceptions raised
         # anywhere in the turn loop, the worker calls, the fallback, or
@@ -1185,15 +1250,22 @@ class ToolOrchestraAgent(LocalCloudAgent):
                 cost += self.cost_usd(self._cloud_model, o_in, o_out)
 
                 action = _parse_action(text)
-                history.append({
-                    "role": "orchestrator", "turn": turn, "raw": text, "action": action,
-                })
-                self.record_trace_event({
-                    "kind": "toolorchestra_action",
-                    "turn": turn,
-                    "action": action,
-                    "raw": text,
-                })
+                history.append(
+                    {
+                        "role": "orchestrator",
+                        "turn": turn,
+                        "raw": text,
+                        "action": action,
+                    }
+                )
+                self.record_trace_event(
+                    {
+                        "kind": "toolorchestra_action",
+                        "turn": turn,
+                        "action": action,
+                        "raw": text,
+                    }
+                )
 
                 if action is None:
                     parse_failures += 1
@@ -1217,12 +1289,21 @@ class ToolOrchestraAgent(LocalCloudAgent):
                         continue
                     worker = workers[wid]
                     if swe_mode and shared_workdir is not None:
-                        (w_text, w_in, w_out, is_local, extra_cost,
-                         n_searches, bash_turns) = (
-                            _swe_call_worker(
-                                worker, str(w_input), cfg, task_meta,
-                                shared_workdir, turn,
-                            )
+                        (
+                            w_text,
+                            w_in,
+                            w_out,
+                            is_local,
+                            extra_cost,
+                            n_searches,
+                            bash_turns,
+                        ) = _swe_call_worker(
+                            worker,
+                            str(w_input),
+                            cfg,
+                            task_meta,
+                            shared_workdir,
+                            turn,
                         )
                         tool_calls += bash_turns
                     else:
@@ -1236,17 +1317,19 @@ class ToolOrchestraAgent(LocalCloudAgent):
                         cost += self.cost_usd(worker["model"], w_in, w_out) + extra_cost
                     n_web_searches_total += n_searches
                     tool_calls += n_searches
-                    history.append({
-                        "role": "worker",
-                        "turn": turn,
-                        "worker_id": wid,
-                        "worker_name": worker["name"],
-                        "worker_model": worker["model"],
-                        "output": w_text,
-                        "tokens_in": w_in,
-                        "tokens_out": w_out,
-                        "n_web_searches": n_searches,
-                    })
+                    history.append(
+                        {
+                            "role": "worker",
+                            "turn": turn,
+                            "worker_id": wid,
+                            "worker_name": worker["name"],
+                            "worker_model": worker["model"],
+                            "output": w_text,
+                            "tokens_in": w_in,
+                            "tokens_out": w_out,
+                            "n_web_searches": n_searches,
+                        }
+                    )
                     continue
                 # Unknown action kind — treat as parse failure.
                 parse_failures += 1
@@ -1258,18 +1341,22 @@ class ToolOrchestraAgent(LocalCloudAgent):
                 # Search workers are excluded — they answer fact-lookup
                 # questions, not synthesis.
                 non_search = [
-                    w for w in workers
-                    if w.get("type") not in _TOOLORCH_SEARCH_TYPES
+                    w for w in workers if w.get("type") not in _TOOLORCH_SEARCH_TYPES
                 ] or workers
                 worker = max(
                     non_search,
                     key=lambda w: PRICES.get(w.get("model", ""), (0.0, 0.0))[1],
                 )
                 if swe_mode and shared_workdir is not None:
-                    (ans, w_in, w_out, is_local, extra_cost, _,
-                     bash_turns) = _swe_call_worker(
-                        worker, question, cfg, task_meta,
-                        shared_workdir, max_turns + 1,
+                    (ans, w_in, w_out, is_local, extra_cost, _, bash_turns) = (
+                        _swe_call_worker(
+                            worker,
+                            question,
+                            cfg,
+                            task_meta,
+                            shared_workdir,
+                            max_turns + 1,
+                        )
                     )
                     tool_calls += bash_turns
                 else:
@@ -1281,17 +1368,19 @@ class ToolOrchestraAgent(LocalCloudAgent):
                 else:
                     tokens_cloud += w_in + w_out
                     cost += self.cost_usd(worker["model"], w_in, w_out) + extra_cost
-                history.append({
-                    "role": "worker",
-                    "turn": max_turns + 1,
-                    "worker_id": worker["id"],
-                    "worker_name": worker["name"],
-                    "worker_model": worker["model"],
-                    "output": ans,
-                    "tokens_in": w_in,
-                    "tokens_out": w_out,
-                    "fallback": True,
-                })
+                history.append(
+                    {
+                        "role": "worker",
+                        "turn": max_turns + 1,
+                        "worker_id": worker["id"],
+                        "worker_name": worker["name"],
+                        "worker_model": worker["model"],
+                        "output": ans,
+                        "tokens_in": w_in,
+                        "tokens_out": w_out,
+                        "fallback": True,
+                    }
+                )
                 final_answer = ans
 
             # In SWE mode, the authoritative output is the working-tree diff —
@@ -1301,7 +1390,8 @@ class ToolOrchestraAgent(LocalCloudAgent):
                 if patch.strip():
                     final_answer = (
                         f"{final_answer}\n\n```diff\n{patch}```"
-                        if final_answer else f"```diff\n{patch}```"
+                        if final_answer
+                        else f"```diff\n{patch}```"
                     )
 
             meta = {
@@ -1371,20 +1461,24 @@ class ToolOrchestraAgent(LocalCloudAgent):
         )
         shared_workdir: Optional[Path] = None
         if swe_mode:
-            shared_workdir = Path(tempfile.mkdtemp(
-                prefix=f"toolorch-rl-swe-{task_meta.get('task_id','x')}-"
-            ))
+            shared_workdir = Path(
+                tempfile.mkdtemp(
+                    prefix=f"toolorch-rl-swe-{task_meta.get('task_id', 'x')}-"
+                )
+            )
             try:
                 _clone_repo(task_meta["repo"], task_meta["base_commit"], shared_workdir)
             except Exception:
                 shutil.rmtree(shared_workdir, ignore_errors=True)
                 raise
-            self.record_trace_event({
-                "kind": "toolorchestra_rl_swe_workdir",
-                "workdir": str(shared_workdir),
-                "repo": task_meta["repo"],
-                "base_commit": task_meta["base_commit"],
-            })
+            self.record_trace_event(
+                {
+                    "kind": "toolorchestra_rl_swe_workdir",
+                    "workdir": str(shared_workdir),
+                    "repo": task_meta["repo"],
+                    "base_commit": task_meta["base_commit"],
+                }
+            )
 
         # ``context_str`` mirrors the upstream's running context — accumulates
         # search documents and code/exec snippets across turns. We keep this
@@ -1433,40 +1527,53 @@ class ToolOrchestraAgent(LocalCloudAgent):
                     temperature=orch_temp,
                     tools=RL_TOOLS_SPEC,
                 )
-                self.record_trace_event({
-                    "kind": "vllm",
-                    "role": "orchestrator",
-                    "model": orch_model,
-                    "endpoint": orch_endpoint,
-                    "system": RL_ORCHESTRATOR_SYS,
-                    "user": user,
-                    "response": text,
-                    "tool_calls": [
-                        {
-                            "id": getattr(tc, "id", None),
-                            "type": getattr(tc, "type", None),
-                            "function": {
-                                "name": getattr(getattr(tc, "function", None), "name", None),
-                                "arguments": getattr(getattr(tc, "function", None), "arguments", None),
-                            },
-                        }
-                        for tc in (sdk_tool_calls or [])
-                    ],
-                    "tokens_in": o_in,
-                    "tokens_out": o_out,
-                })
+                self.record_trace_event(
+                    {
+                        "kind": "vllm",
+                        "role": "orchestrator",
+                        "model": orch_model,
+                        "endpoint": orch_endpoint,
+                        "system": RL_ORCHESTRATOR_SYS,
+                        "user": user,
+                        "response": text,
+                        "tool_calls": [
+                            {
+                                "id": getattr(tc, "id", None),
+                                "type": getattr(tc, "type", None),
+                                "function": {
+                                    "name": getattr(
+                                        getattr(tc, "function", None), "name", None
+                                    ),
+                                    "arguments": getattr(
+                                        getattr(tc, "function", None), "arguments", None
+                                    ),
+                                },
+                            }
+                            for tc in (sdk_tool_calls or [])
+                        ],
+                        "tokens_in": o_in,
+                        "tokens_out": o_out,
+                    }
+                )
                 tokens_local += o_in + o_out
 
                 action = _parse_rl_tool_call(text, sdk_tool_calls)
-                history.append({
-                    "role": "orchestrator", "turn": turn, "raw": text, "action": action,
-                })
-                self.record_trace_event({
-                    "kind": "toolorchestra_rl_action",
-                    "turn": turn,
-                    "action": action,
-                    "raw": text,
-                })
+                history.append(
+                    {
+                        "role": "orchestrator",
+                        "turn": turn,
+                        "raw": text,
+                        "action": action,
+                    }
+                )
+                self.record_trace_event(
+                    {
+                        "kind": "toolorchestra_rl_action",
+                        "turn": turn,
+                        "action": action,
+                        "raw": text,
+                    }
+                )
 
                 if action is None:
                     parse_failures += 1
@@ -1479,8 +1586,10 @@ class ToolOrchestraAgent(LocalCloudAgent):
                 slot = args.get("model", "")
 
                 # Validate against the upstream tool/arg schema.
-                valid = name in RL_ALL_TOOLS and isinstance(slot, str) and (
-                    slot in RL_ALL_TOOLS[name]["model"]
+                valid = (
+                    name in RL_ALL_TOOLS
+                    and isinstance(slot, str)
+                    and (slot in RL_ALL_TOOLS[name]["model"])
                 )
                 if not valid:
                     parse_failures += 1
@@ -1501,8 +1610,11 @@ class ToolOrchestraAgent(LocalCloudAgent):
                 # framing).
                 if paper_mode:
                     worker = _paper_expert_for(
-                        slot, self._local_model, self._local_endpoint,
-                        self._cloud_model, self._cloud_endpoint,
+                        slot,
+                        self._local_model,
+                        self._local_endpoint,
+                        self._cloud_model,
+                        self._cloud_endpoint,
                     )
                     # In paper mode, `enhance_reasoning` is always the coder
                     # specialist regardless of the orchestrator's chosen tier.
@@ -1516,7 +1628,10 @@ class ToolOrchestraAgent(LocalCloudAgent):
                         }
                 else:
                     worker = _expert_for(
-                        slot, self._local_model, self._local_endpoint, self._cloud_model,
+                        slot,
+                        self._local_model,
+                        self._local_endpoint,
+                        self._cloud_model,
                         self._cloud_endpoint,
                     )
 
@@ -1572,13 +1687,25 @@ class ToolOrchestraAgent(LocalCloudAgent):
                 # bash_turns=0; vllm/anthropic-typed workers run the loop.
                 bash_turns = 0
                 if swe_mode and shared_workdir is not None and name != "search":
-                    (w_text, w_in, w_out, is_local, extra_cost,
-                     n_searches, bash_turns) = _swe_call_worker(
-                        worker, w_input, cfg, task_meta, shared_workdir, turn,
+                    (
+                        w_text,
+                        w_in,
+                        w_out,
+                        is_local,
+                        extra_cost,
+                        n_searches,
+                        bash_turns,
+                    ) = _swe_call_worker(
+                        worker,
+                        w_input,
+                        cfg,
+                        task_meta,
+                        shared_workdir,
+                        turn,
                     )
                 else:
-                    w_text, w_in, w_out, is_local, extra_cost, n_searches = _call_worker(
-                        worker, w_input, cfg
+                    w_text, w_in, w_out, is_local, extra_cost, n_searches = (
+                        _call_worker(worker, w_input, cfg)
                     )
                 if is_local:
                     tokens_local += w_in + w_out
@@ -1597,13 +1724,13 @@ class ToolOrchestraAgent(LocalCloudAgent):
                 # when no python block is found.
                 modal_exec_output: Optional[str] = None
                 modal_exec_rc: Optional[int] = None
-                if (paper_mode and name == "enhance_reasoning"
-                        and not swe_mode):
+                if paper_mode and name == "enhance_reasoning" and not swe_mode:
                     code = _extract_first_python_block(w_text)
                     if code:
                         timeout_s = int(cfg.get("modal_python_timeout_s", 60))
                         modal_exec_output, modal_exec_rc = _call_modal_python(
-                            code, timeout_s=timeout_s,
+                            code,
+                            timeout_s=timeout_s,
                         )
                         tool_calls += 1
                         w_text = (
@@ -1611,27 +1738,29 @@ class ToolOrchestraAgent(LocalCloudAgent):
                             f"(rc={modal_exec_rc})]\n{modal_exec_output}"
                         )
 
-                history.append({
-                    "role": "worker",
-                    "turn": turn,
-                    "tool": name,
-                    "slot": slot,
-                    "worker_model": worker["model"],
-                    "worker_type": worker["type"],
-                    "output": w_text,
-                    "tokens_in": w_in,
-                    "tokens_out": w_out,
-                    "n_web_searches": n_searches,
-                    "bash_turns": bash_turns,
-                    "modal_exec_rc": modal_exec_rc,
-                })
+                history.append(
+                    {
+                        "role": "worker",
+                        "turn": turn,
+                        "tool": name,
+                        "slot": slot,
+                        "worker_model": worker["model"],
+                        "worker_type": worker["type"],
+                        "output": w_text,
+                        "tokens_in": w_in,
+                        "tokens_out": w_out,
+                        "n_web_searches": n_searches,
+                        "bash_turns": bash_turns,
+                        "modal_exec_rc": modal_exec_rc,
+                    }
+                )
 
                 # Update accumulated context for the next turn.
                 if name == "search":
                     # Treat the search worker's response as a document.
                     doc_list.append(w_text)
                     ctx_docs = "\n\n".join(
-                        f"Doc {i+1}: {d}" for i, d in enumerate(doc_list)
+                        f"Doc {i + 1}: {d}" for i, d in enumerate(doc_list)
                     )
                     # Crude char-level cap mirrors the upstream's ~24k token cap.
                     context_str = ("Documents:\n" + ctx_docs)[-24000:]
@@ -1648,15 +1777,23 @@ class ToolOrchestraAgent(LocalCloudAgent):
                 # it can still touch the workdir and emit a diff.
                 expert_fn = _paper_expert_for if paper_mode else _expert_for
                 worker = expert_fn(
-                    "answer-1", self._local_model, self._local_endpoint,
-                    self._cloud_model, self._cloud_endpoint,
+                    "answer-1",
+                    self._local_model,
+                    self._local_endpoint,
+                    self._cloud_model,
+                    self._cloud_endpoint,
                 )
                 fb_bash_turns = 0
                 if swe_mode and shared_workdir is not None:
-                    (ans, w_in, w_out, is_local, extra_cost,
-                     _, fb_bash_turns) = _swe_call_worker(
-                        worker, question, cfg, task_meta,
-                        shared_workdir, max_turns + 1,
+                    (ans, w_in, w_out, is_local, extra_cost, _, fb_bash_turns) = (
+                        _swe_call_worker(
+                            worker,
+                            question,
+                            cfg,
+                            task_meta,
+                            shared_workdir,
+                            max_turns + 1,
+                        )
                     )
                     tool_calls += fb_bash_turns
                 else:
@@ -1668,19 +1805,21 @@ class ToolOrchestraAgent(LocalCloudAgent):
                 else:
                     tokens_cloud += w_in + w_out
                     cost += self.cost_usd(worker["model"], w_in, w_out) + extra_cost
-                history.append({
-                    "role": "worker",
-                    "turn": max_turns + 1,
-                    "tool": "answer",
-                    "slot": "answer-1",
-                    "worker_model": worker["model"],
-                    "worker_type": worker["type"],
-                    "output": ans,
-                    "tokens_in": w_in,
-                    "tokens_out": w_out,
-                    "bash_turns": fb_bash_turns,
-                    "fallback": True,
-                })
+                history.append(
+                    {
+                        "role": "worker",
+                        "turn": max_turns + 1,
+                        "tool": "answer",
+                        "slot": "answer-1",
+                        "worker_model": worker["model"],
+                        "worker_type": worker["type"],
+                        "output": ans,
+                        "tokens_in": w_in,
+                        "tokens_out": w_out,
+                        "bash_turns": fb_bash_turns,
+                        "fallback": True,
+                    }
+                )
                 final_answer = ans
 
             # In SWE mode, the authoritative output is the working-tree diff —
@@ -1690,7 +1829,8 @@ class ToolOrchestraAgent(LocalCloudAgent):
                 if patch.strip():
                     final_answer = (
                         f"{final_answer}\n\n```diff\n{patch}```"
-                        if final_answer else f"```diff\n{patch}```"
+                        if final_answer
+                        else f"```diff\n{patch}```"
                     )
 
             meta = {

src/openjarvis/agents/opencode.py

@@ -327,9 +327,7 @@ class OpenCodeAgent(BaseAgent):
             self._ensure_server()
         except RuntimeError as exc:
             self._emit_turn_end(turns=1, error=True)
-            return AgentResult(
-                content=str(exc), turns=1, metadata={"error": True}
-            )
+            return AgentResult(content=str(exc), turns=1, metadata={"error": True})
 
         data: dict = {}
         turn_parts: List[dict] = []

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/research_loop.py

@@ -106,8 +106,8 @@ SEARCH_TOOL_SPEC: Dict[str, Any] = {
                     "type": "array",
                     "description": (
                         "Restrict the search to one or more connectors. Use this "
-                        "whenever the user names a data source (e.g. \"in my "
-                        "Granola notes\" → ['granola']; \"check Slack and Gmail\" "
+                        'whenever the user names a data source (e.g. "in my '
+                        'Granola notes" → [\'granola\']; "check Slack and Gmail" '
                         "→ ['slack', 'gmail']). Valid IDs include: gmail, slack, "
                         "granola, notion, obsidian, gcalendar, gdrive, gmail_imap, "
                         "outlook, imessage, whatsapp, apple_notes, apple_contacts, "
@@ -206,7 +206,9 @@ def shape_results_for_model(
         if i < detailed_top:
             base["snippet"] = h.content_snippet
             if h.thread_context:
-                base["thread"] = _trim_thread_context(h.thread_context, thread_ctx_per_hit)
+                base["thread"] = _trim_thread_context(
+                    h.thread_context, thread_ctx_per_hit
+                )
         out_hits.append(base)
     return {
         "num_results": len(hits),
@@ -221,7 +223,9 @@ def _hit_date(timestamp: str) -> str:
     if not timestamp:
         return ""
     try:
-        return datetime.fromisoformat(timestamp.replace("Z", "+00:00")).date().isoformat()
+        return (
+            datetime.fromisoformat(timestamp.replace("Z", "+00:00")).date().isoformat()
+        )
     except (ValueError, AttributeError):
         return str(timestamp)[:10]
 
@@ -239,7 +243,7 @@ def _bare_doc_id(source: str, document_id: str) -> str:
         return ""
     prefix = f"{source}:"
     if source and document_id.startswith(prefix):
-        return document_id[len(prefix):]
+        return document_id[len(prefix) :]
     return document_id
 
 
@@ -520,6 +524,7 @@ class ResearchAgent:
     def _parse_time_range(raw: Any):
         if not raw or not isinstance(raw, dict):
             return None
+
         def _maybe(v):
             if not v:
                 return None
@@ -527,6 +532,7 @@ class ResearchAgent:
                 return datetime.fromisoformat(str(v).replace("Z", "+00:00"))
             except ValueError:
                 return None
+
         start = _maybe(raw.get("start"))
         end = _maybe(raw.get("end"))
         if start is None and end is None:
@@ -557,9 +563,16 @@ class ResearchAgent:
                 "query": query,
                 "person": person,
                 "time_range": (
-                    {"start": time_range[0].isoformat() if time_range and time_range[0] else None,
-                     "end": time_range[1].isoformat() if time_range and time_range[1] else None}
-                    if time_range else None
+                    {
+                        "start": time_range[0].isoformat()
+                        if time_range and time_range[0]
+                        else None,
+                        "end": time_range[1].isoformat()
+                        if time_range and time_range[1]
+                        else None,
+                    }
+                    if time_range
+                    else None
                 ),
                 "sources": sources,
                 "limit": limit,
@@ -690,9 +703,7 @@ class ResearchAgent:
                     )
                     continue
                 fallback = "(model returned no content and no tool calls)"
-                self._emit(
-                    {"type": "final_answer", "text": fallback, "sources": []}
-                )
+                self._emit({"type": "final_answer", "text": fallback, "sources": []})
                 return ResearchResult(
                     answer=fallback,
                     iterations=iterations,
@@ -718,7 +729,11 @@ class ResearchAgent:
                 name = tc.get("name", "")
                 raw_args = tc.get("arguments", "{}") or "{}"
                 try:
-                    args = json.loads(raw_args) if isinstance(raw_args, str) else dict(raw_args)
+                    args = (
+                        json.loads(raw_args)
+                        if isinstance(raw_args, str)
+                        else dict(raw_args)
+                    )
                 except json.JSONDecodeError:
                     args = {}
 
@@ -766,7 +781,10 @@ class ResearchAgent:
                         )
                     else:
                         self._emit(
-                            {"type": "clarify_call", "question": str(args.get("question", ""))}
+                            {
+                                "type": "clarify_call",
+                                "question": str(args.get("question", "")),
+                            }
                         )
                         inv = self._execute_clarify(args)
                         invocations.append(inv)
@@ -845,9 +863,7 @@ class ResearchAgent:
                 "and the model returned no text response)"
             )
         answer, final_sources = _finalize(answer)
-        self._emit(
-            {"type": "final_answer", "text": answer, "sources": final_sources}
-        )
+        self._emit({"type": "final_answer", "text": answer, "sources": final_sources})
         return ResearchResult(
             answer=answer,
             iterations=iterations,

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/channels/twitter_channel.py

@@ -72,15 +72,16 @@ class _OAuth1Auth:
             all_params[key] = value
 
         param_str = "&".join(
-            f"{_pct(k)}={_pct(v)}"
-            for k, v in sorted(all_params.items())
+            f"{_pct(k)}={_pct(v)}" for k, v in sorted(all_params.items())
         )
         base_string = f"{method}&{_pct(base_url)}&{_pct(param_str)}"
 
         signing_key = f"{_pct(self._consumer_secret)}&{_pct(self._access_secret)}"
         signature = base64.b64encode(
             hmac.new(
-                signing_key.encode(), base_string.encode(), hashlib.sha1,
+                signing_key.encode(),
+                base_string.encode(),
+                hashlib.sha1,
             ).digest(),
         ).decode()
 
@@ -146,7 +147,8 @@ class TwitterChannel(BaseChannel):
         self._api_secret = api_secret or os.environ.get("TWITTER_API_SECRET", "")
         self._access_token = access_token or os.environ.get("TWITTER_ACCESS_TOKEN", "")
         self._access_secret = access_secret or os.environ.get(
-            "TWITTER_ACCESS_SECRET", "",
+            "TWITTER_ACCESS_SECRET",
+            "",
         )
         self._bot_user_id = bot_user_id or os.environ.get("TWITTER_BOT_USER_ID", "")
         self._poll_interval = poll_interval
@@ -162,8 +164,10 @@ class TwitterChannel(BaseChannel):
 
     def _oauth(self) -> _OAuth1Auth:
         return _OAuth1Auth(
-            self._api_key, self._api_secret,
-            self._access_token, self._access_secret,
+            self._api_key,
+            self._api_secret,
+            self._access_token,
+            self._access_secret,
         )
 
     # -- connection lifecycle -----------------------------------------------
@@ -179,7 +183,8 @@ class TwitterChannel(BaseChannel):
         self._status = ChannelStatus.CONNECTING
 
         self._listener_thread = threading.Thread(
-            target=self._poll_mentions, daemon=True,
+            target=self._poll_mentions,
+            daemon=True,
         )
         self._listener_thread.start()
         self._status = ChannelStatus.CONNECTED
@@ -276,7 +281,10 @@ class TwitterChannel(BaseChannel):
                     params["since_id"] = self._since_id
 
                 resp = httpx.get(
-                    url, headers=headers, params=params, timeout=10.0,
+                    url,
+                    headers=headers,
+                    params=params,
+                    timeout=10.0,
                 )
                 if resp.status_code < 300:
                     data = resp.json()

src/openjarvis/cli/_banner.py

@@ -9,12 +9,12 @@ from __future__ import annotations
 # readable capital J (the bottom-left \___/ hook), unlike the cramped prior
 # art where the J read as an I.
 _WORDMARK = (
-    '  ___                       _                  _     ',
-    ' / _ \\ _ __   ___ _ __     | | __ _ _ ____   _(_)___ ',
+    "  ___                       _                  _     ",
+    " / _ \\ _ __   ___ _ __     | | __ _ _ ____   _(_)___ ",
     "| | | | '_ \\ / _ \\ '_ \\ _  | |/ _` | '__\\ \\ / / / __|",
-    '| |_| | |_) |  __/ | | | |_| | (_| | |   \\ V /| \\__ \\',
-    ' \\___/| .__/ \\___|_| |_|\\___/ \\__,_|_|    \\_/ |_|___/',
-    '      |_|                                            ',
+    "| |_| | |_) |  __/ | | | |_| | (_| | |   \\ V /| \\__ \\",
+    " \\___/| .__/ \\___|_| |_|\\___/ \\__,_|_|    \\_/ |_|___/",
+    "      |_|                                            ",
 )
 
 _TAGLINE = "Personal AI, On Personal Devices"

src/openjarvis/cli/ask.py

@@ -20,6 +20,7 @@ from openjarvis.core.events import EventBus, EventType
 from openjarvis.core.types import Message, Role
 from openjarvis.engine import (
     EngineConnectionError,
+    EngineContextLengthError,
     discover_engines,
     discover_models,
     get_engine,
@@ -881,6 +882,11 @@ def ask(
                 capability_policy=sec.capability_policy,
                 memory_files_config=effective_mf,
             )
+        except EngineContextLengthError as exc:
+            # Not a reachability problem — pointing the user at server/host
+            # config (hint_no_engine) would be misleading here.
+            console.print(f"[red]{exc}[/red]")
+            sys.exit(1)
         except EngineConnectionError as exc:
             console.print(f"[red]Engine error:[/red] {exc}")
             console.print(hint_no_engine())
@@ -990,6 +996,11 @@ def ask(
                 temperature=temperature,
                 max_tokens=max_tokens,
             )
+    except EngineContextLengthError as exc:
+        # Not a reachability problem — pointing the user at server/host
+        # config (hint_no_engine) would be misleading here.
+        console.print(f"[red]{exc}[/red]")
+        sys.exit(1)
     except EngineConnectionError as exc:
         console.print(f"[red]Engine error:[/red] {exc}")
         console.print(hint_no_engine())

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

@@ -81,14 +81,28 @@ def start(
     if agent_name:
         cmd.extend(["--agent", agent_name])
 
-    # Start as background process
+    # Start as background process, fully detached from the launching terminal.
+    #
+    # ``start_new_session`` is POSIX-only: CPython's Windows ``_execute_child``
+    # names the parameter ``unused_start_new_session`` and ignores it. Relying
+    # on it there leaves the server sharing its parent's console, so closing
+    # that console — or logging off — delivers CTRL_CLOSE_EVENT and kills the
+    # daemon. DETACHED_PROCESS gives it no console at all; the new process
+    # group additionally stops a Ctrl-C in the parent reaching it.
     DEFAULT_CONFIG_DIR.mkdir(parents=True, exist_ok=True)
     log_fh = open(_LOG_FILE, "a")  # noqa: SIM115
+    spawn_kwargs: dict = {}
+    if sys.platform == "win32":
+        spawn_kwargs["creationflags"] = (
+            subprocess.DETACHED_PROCESS | subprocess.CREATE_NEW_PROCESS_GROUP
+        )
+    else:
+        spawn_kwargs["start_new_session"] = True
     proc = subprocess.Popen(
         cmd,
         stdout=log_fh,
         stderr=log_fh,
-        start_new_session=True,
+        **spawn_kwargs,
     )
     _write_pid(proc.pid)
 

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/serve.py

@@ -10,6 +10,7 @@ from rich.console import Console
 
 from openjarvis.cli._banner import print_banner
 from openjarvis.core.config import load_config
+from openjarvis.core.credentials import inject_credentials
 from openjarvis.core.events import EventBus
 from openjarvis.core.paths import get_config_dir
 from openjarvis.engine import (
@@ -122,6 +123,11 @@ def serve(
         )
         sys.exit(1)
 
+    # Tool credentials saved through the browser UI live in the OpenJarvis
+    # credential store. Restore them before engines and tools are constructed
+    # so availability checks and tool instances see the same environment.
+    inject_credentials()
+
     config = load_config()
 
     # Resolve host/port from CLI args or config
@@ -273,6 +279,15 @@ 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
@@ -284,11 +299,6 @@ 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
@@ -325,12 +335,13 @@ 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 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:
@@ -383,10 +394,6 @@ 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
@@ -426,29 +433,23 @@ 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 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,
@@ -458,6 +459,8 @@ 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)
 
@@ -475,23 +478,24 @@ 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
@@ -586,6 +590,7 @@ 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,
@@ -594,6 +599,7 @@ def serve(
                 capability_policy=sec.capability_policy,
                 agent_manager=agent_manager,
                 agent_executor=executor,
+                _mcp_clients=mcp_clients,
             )
             executor.set_system(system)
 
@@ -685,10 +691,13 @@ 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,
@@ -717,6 +726,4 @@ def serve(
             "authenticated requests to your instance."
         )
 
-    import uvicorn
-
     uvicorn.run(app, host=bind_host, port=bind_port, log_level="info")

src/openjarvis/cli/skill_cmd.py

@@ -199,7 +199,16 @@ def _get_resolver(source: str, url: str = ""):
     default="",
     help="Repo URL (required when source is 'github').",
 )
-def install(query: str, with_scripts: bool, force: bool, url: str):
+@click.option(
+    "--yes-dangerous",
+    is_flag=True,
+    default=False,
+    help=(
+        "Confirm installing an unreviewed skill that requests dangerous "
+        "capabilities (shell/network-listen/filesystem-write)."
+    ),
+)
+def install(query: str, with_scripts: bool, force: bool, url: str, yes_dangerous: bool):
     """Install a skill from a source.
 
     Example: ``jarvis skill install hermes:apple-notes``
@@ -233,7 +242,12 @@ def install(query: str, with_scripts: bool, force: bool, url: str):
     from openjarvis.skills.tool_translator import ToolTranslator
 
     importer = SkillImporter(parser=SkillParser(), tool_translator=ToolTranslator())
-    result = importer.import_skill(matches[0], with_scripts=with_scripts, force=force)
+    result = importer.import_skill(
+        matches[0],
+        with_scripts=with_scripts,
+        force=force,
+        confirm_dangerous=yes_dangerous,
+    )
 
     if result.success:
         if result.skipped:
@@ -270,6 +284,15 @@ def install(query: str, with_scripts: bool, force: bool, url: str):
     help="Import scripts/ directories.",
 )
 @click.option("--force", is_flag=True, default=False, help="Re-import existing skills.")
+@click.option(
+    "--yes-dangerous",
+    is_flag=True,
+    default=False,
+    help=(
+        "Confirm installing unreviewed skills that request dangerous "
+        "capabilities (shell/network-listen/filesystem-write)."
+    ),
+)
 def sync(
     source: str,
     category: str,
@@ -277,6 +300,7 @@ def sync(
     search: str,
     with_scripts: bool,
     force: bool,
+    yes_dangerous: bool,
 ):
     """Bulk install + update from a source (or all configured sources)."""
     console = Console()
@@ -343,9 +367,20 @@ def sync(
 
         installed_count = 0
         for resolved in skills_to_import:
-            r = importer.import_skill(resolved, with_scripts=with_scripts, force=force)
+            r = importer.import_skill(
+                resolved,
+                with_scripts=with_scripts,
+                force=force,
+                confirm_dangerous=yes_dangerous,
+            )
             if r.success and not r.skipped:
                 installed_count += 1
+            elif not r.success and r.requires_confirmation:
+                console.print(
+                    f"  [yellow]Skipped {resolved.name}: requests dangerous "
+                    f"capabilities {r.dangerous_capabilities} "
+                    "(re-run with --yes-dangerous to install)[/yellow]"
+                )
         console.print(f"  Imported {installed_count}/{len(skills_to_import)} skills")
         total_installed += installed_count
 

src/openjarvis/connectors/embeddings.py

@@ -124,7 +124,8 @@ class OllamaEmbedder:
         elif arr.shape[0] != self._dim:
             logger.warning(
                 "OllamaEmbedder.embed: dim drift (expected %d, got %d)",
-                self._dim, arr.shape[0],
+                self._dim,
+                arr.shape[0],
             )
             return None
         return arr.tobytes()
@@ -143,9 +144,7 @@ class OllamaEmbedder:
 # ---------------------------------------------------------------------------
 
 
-def decode_embedding(
-    blob: Optional[bytes], *, dtype=None
-) -> Optional[np.ndarray]:
+def decode_embedding(blob: Optional[bytes], *, dtype=None) -> Optional[np.ndarray]:
     """Reconstruct a 1-D vector from a BLOB written by ``OllamaEmbedder.embed``.
 
     Returns ``None`` when the input is missing or zero-length so callers can

src/openjarvis/connectors/granola.py

@@ -345,9 +345,7 @@ class GranolaConnector(BaseConnector):
 
                 attendees: List[Dict[str, Any]] = note.get("attendees") or []
                 participants: List[str] = [
-                    (a.get("email") or "").lower()
-                    for a in attendees
-                    if a.get("email")
+                    (a.get("email") or "").lower() for a in attendees if a.get("email")
                 ]
                 participants_raw: List[str] = [
                     a.get("name") or a.get("email") or ""

src/openjarvis/connectors/hybrid_search.py

@@ -54,13 +54,17 @@ _CALENDAR_REQUEST_TERMS = _CALENDAR_TERMS | {
     "meetings",
     "schedule",
 }
-_GCALENDAR_GENERIC_TERMS = _UPCOMING_TERMS | _CALENDAR_TERMS | {
-    "appointment",
-    "appointments",
-    "meeting",
-    "meetings",
-    "schedule",
-}
+_GCALENDAR_GENERIC_TERMS = (
+    _UPCOMING_TERMS
+    | _CALENDAR_TERMS
+    | {
+        "appointment",
+        "appointments",
+        "meeting",
+        "meetings",
+        "schedule",
+    }
+)
 _QUERY_STOPWORDS = {
     "a",
     "all",
@@ -636,8 +640,7 @@ class HybridSearch:
             ids,
         ).fetchall()
         timestamps = {
-            row["id"]: _parse_timestamp_for_timeline(row["timestamp"])
-            for row in rows
+            row["id"]: _parse_timestamp_for_timeline(row["timestamp"]) for row in rows
         }
 
         def _keeps_item(item: Tuple[str, float, float, float]) -> bool:

src/openjarvis/connectors/pipeline.py

@@ -48,7 +48,7 @@ def _derive_source_id(doc: Document) -> str:
         return doc.source_id
     prefix = f"{doc.source}:"
     if doc.doc_id.startswith(prefix):
-        return doc.doc_id[len(prefix):]
+        return doc.doc_id[len(prefix) :]
     return doc.doc_id
 
 
@@ -56,6 +56,7 @@ def _content_hash(text: str) -> str:
     """SHA-256 hex digest of UTF-8-encoded chunk content."""
     return hashlib.sha256(text.encode("utf-8")).hexdigest()
 
+
 if TYPE_CHECKING:
     from openjarvis.connectors.attachment_store import AttachmentStore
 

src/openjarvis/connectors/slack_connector.py

@@ -205,8 +205,7 @@ def _validate_user_token(token: str) -> None:
         raise SlackTokenError("Slack token is empty.")
     if token.startswith(_BOT_TOKEN_PREFIX):
         raise SlackTokenError(
-            "Bot tokens (xoxb-) can't read DMs. "
-            "Use a User OAuth Token (xoxp-) instead."
+            "Bot tokens (xoxb-) can't read DMs. Use a User OAuth Token (xoxp-) instead."
         )
     if not token.startswith(_USER_TOKEN_PREFIX):
         raise SlackTokenError(
@@ -436,9 +435,7 @@ class SlackConnector(BaseConnector):
         all_channels: List[Dict[str, Any]] = []
         channels_cursor = ""
         while True:
-            channels_resp = _slack_api_conversations_list(
-                token, cursor=channels_cursor
-            )
+            channels_resp = _slack_api_conversations_list(token, cursor=channels_cursor)
             if not channels_resp.get("ok", True):
                 err = str(channels_resp.get("error", "list_failed"))
                 self._last_error = f"Slack conversations.list failed: {err}"
@@ -446,8 +443,7 @@ class SlackConnector(BaseConnector):
                 return
             all_channels.extend(channels_resp.get("channels", []))
             channels_cursor = (
-                channels_resp.get("response_metadata", {}).get("next_cursor", "")
-                or ""
+                channels_resp.get("response_metadata", {}).get("next_cursor", "") or ""
             )
             if not channels_cursor:
                 break

src/openjarvis/core/config.py

@@ -12,9 +12,18 @@ import os
 import platform
 import shutil
 import subprocess
-from dataclasses import dataclass, field
+from dataclasses import dataclass, field, is_dataclass
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Dict, List, Optional
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Dict,
+    List,
+    Optional,
+    get_args,
+    get_origin,
+    get_type_hints,
+)
 
 from openjarvis.core.paths import (
     ConfigurationError,
@@ -1710,10 +1719,16 @@ def _apply_toml_section(target: Any, section: Dict[str, Any]) -> None:
     """Overlay TOML key/value pairs onto a dataclass instance.
 
     Recursively handles nested dicts when the target attribute is itself
-    a dataclass.  Normalises TOML arrays to comma-separated strings — both
-    for dataclass fields annotated as ``str`` and for backward-compat
-    property setters that expect string input.
+    a dataclass, including dict entries in lists of dataclasses.  Normalises
+    TOML arrays to comma-separated strings — both for dataclass fields annotated
+    as ``str`` and for backward-compat property setters that expect string input.
     """
+    try:
+        type_hints = get_type_hints(type(target))
+    except (NameError, TypeError):
+        # Some config types contain optional runtime-only forward references.
+        type_hints = {}
+
     for key, value in section.items():
         if hasattr(target, key):
             if isinstance(value, dict):
@@ -1728,14 +1743,35 @@ def _apply_toml_section(target: Any, section: Dict[str, Any]) -> None:
                 # property setters (e.g. reward_weights, default_tools).
                 if isinstance(value, list):
                     is_str_field = False
+                    item_dataclass = None
                     if hasattr(target, "__dataclass_fields__"):
                         field_obj = target.__dataclass_fields__.get(key)
-                        if field_obj is not None and field_obj.type in ("str", str):
-                            is_str_field = True
-                        elif field_obj is None:
+                        if field_obj is not None:
+                            field_type = type_hints.get(key, field_obj.type)
+                            type_args = get_args(field_type)
+                            if (
+                                get_origin(field_type) is list
+                                and len(type_args) == 1
+                                and is_dataclass(type_args[0])
+                            ):
+                                item_dataclass = type_args[0]
+                            elif field_obj.type in ("str", str):
+                                is_str_field = True
+                        else:
                             # Property, not a real field — normalise to string
                             is_str_field = True
-                    if is_str_field:
+
+                    if item_dataclass is not None:
+                        converted = []
+                        for item in value:
+                            if isinstance(item, dict):
+                                nested = item_dataclass()
+                                _apply_toml_section(nested, item)
+                                converted.append(nested)
+                            else:
+                                converted.append(item)
+                        value = converted
+                    elif is_str_field:
                         value = ",".join(str(v) for v in value)
                 setattr(target, key, value)
 

src/openjarvis/core/credentials.py

@@ -67,6 +67,24 @@ def load_credentials(path: Path | None = None) -> dict[str, dict[str, str]]:
         return tomllib.load(f)
 
 
+def _validate_credential_key(tool_name: str, key: str) -> None:
+    allowed = TOOL_CREDENTIALS.get(tool_name, [])
+    if key not in allowed:
+        raise ValueError(f"Unknown credential key '{key}' for tool '{tool_name}'")
+
+
+def _write_credentials(creds: dict[str, dict[str, str]], path: Path) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    lines: list[str] = []
+    for section, kvs in creds.items():
+        lines.append(f"[{section}]")
+        for k, v in kvs.items():
+            lines.append(f'{k} = "{v}"')
+        lines.append("")
+    path.write_text("\n".join(lines))
+    os.chmod(path, 0o600)
+
+
 def save_credential(
     tool_name: str,
     key: str,
@@ -75,9 +93,7 @@ def save_credential(
     path: Path | None = None,
 ) -> None:
     """Save a single credential key, validate, write file, and set os.environ."""
-    allowed = TOOL_CREDENTIALS.get(tool_name, [])
-    if key not in allowed:
-        raise ValueError(f"Unknown credential key '{key}' for tool '{tool_name}'")
+    _validate_credential_key(tool_name, key)
     stripped = value.strip()
     if not stripped:
         raise ValueError("Credential value must not be empty")
@@ -88,20 +104,32 @@ def save_credential(
         if tool_name not in creds:
             creds[tool_name] = {}
         creds[tool_name][key] = stripped
-
-        p.parent.mkdir(parents=True, exist_ok=True)
-        lines: list[str] = []
-        for section, kvs in creds.items():
-            lines.append(f"[{section}]")
-            for k, v in kvs.items():
-                lines.append(f'{k} = "{v}"')
-            lines.append("")
-        p.write_text("\n".join(lines))
-        os.chmod(p, 0o600)
+        _write_credentials(creds, p)
 
     os.environ[key] = stripped
 
 
+def delete_credential(
+    tool_name: str,
+    key: str,
+    *,
+    path: Path | None = None,
+) -> None:
+    """Delete a persisted credential and remove it from the running process."""
+    _validate_credential_key(tool_name, key)
+    p = Path(path) if path else _default_path()
+    with _LOCK:
+        creds = load_credentials(path=p)
+        tool_creds = creds.get(tool_name)
+        if tool_creds is not None:
+            tool_creds.pop(key, None)
+            if not tool_creds:
+                creds.pop(tool_name, None)
+            _write_credentials(creds, p)
+
+    os.environ.pop(key, None)
+
+
 def get_credential_status(tool_name: str) -> dict[str, bool]:
     """Return {KEY: bool} for each required key indicating if set in env."""
     keys = TOOL_CREDENTIALS.get(tool_name, [])

src/openjarvis/engine/__init__.py

@@ -9,7 +9,9 @@ import openjarvis.engine.ollama  # noqa: F401
 import openjarvis.engine.openai_compat_engines  # noqa: F401
 from openjarvis.engine._base import (
     EngineConnectionError,
+    EngineContextLengthError,
     InferenceEngine,
+    looks_like_context_length_error,
     messages_to_dicts,
 )
 from openjarvis.engine._discovery import discover_engines, discover_models, get_engine
@@ -23,9 +25,11 @@ for _optional in ("cloud", "litellm", "gemma_cpp"):
 
 __all__ = [
     "EngineConnectionError",
+    "EngineContextLengthError",
     "InferenceEngine",
     "discover_engines",
     "discover_models",
     "get_engine",
+    "looks_like_context_length_error",
     "messages_to_dicts",
 ]

src/openjarvis/engine/_base.py

@@ -13,6 +13,46 @@ class EngineConnectionError(Exception):
     """Raised when an engine is unreachable."""
 
 
+class EngineContextLengthError(EngineConnectionError):
+    """The prompt exceeds the served model's maximum context window.
+
+    Subclasses ``EngineConnectionError`` so existing ``except
+    EngineConnectionError`` handlers keep catching it, while callers that want a
+    distinct, user-facing "conversation too long" message can branch on this type
+    (or the ``is_context_length_error`` marker) instead of surfacing a generic
+    engine failure.
+    """
+
+    is_context_length_error: bool = True
+
+
+# Substrings that identify an error body as a context-window overflow (vLLM,
+# SGLang, and OpenAI-compatible servers phrase this a few different ways).
+# Every marker is anchored on "context" on purpose: generic phrases like
+# "please reduce" or "too many tokens" also appear in unrelated 400 bodies
+# (max_tokens validation, rate limiting, oversized images) and would
+# misclassify those as "conversation too long".
+CONTEXT_LENGTH_MARKERS = (
+    "context length",
+    "maximum context",
+    "context window",
+    "maximum_context",
+    "context_length_exceeded",
+)
+
+
+def looks_like_context_length_error(text: str) -> bool:
+    """True when *text* reads like a context-window overflow error.
+
+    The single shared heuristic for recognizing vendor context-overflow
+    phrasings — used by the engine layer (typing upstream 400s), agent error
+    classification, and the server stream bridge, so a new vendor phrasing
+    only ever needs to be added here.
+    """
+    low = (text or "").lower()
+    return any(marker in low for marker in CONTEXT_LENGTH_MARKERS)
+
+
 _REASONING_METADATA_KEYS = ("reasoning_content", "thinking")
 
 
@@ -80,8 +120,11 @@ def estimate_prompt_tokens(messages: Sequence[Message]) -> int:
 
 
 __all__ = [
+    "CONTEXT_LENGTH_MARKERS",
     "EngineConnectionError",
+    "EngineContextLengthError",
     "InferenceEngine",
     "estimate_prompt_tokens",
+    "looks_like_context_length_error",
     "messages_to_dicts",
 ]

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/_http_async.py

@@ -0,0 +1,128 @@
+"""Shared async-HTTP plumbing for engines that stream over httpx.
+
+Home of the pieces the OpenAI-compat and Ollama engines were each hand-rolling:
+the async-client factory (with the configured timeout applied), a cached
+long-lived client so consecutive streams reuse pooled connections instead of
+paying a fresh TCP/TLS handshake per turn, the transport-error set that maps to
+``EngineConnectionError``, and the non-2xx → engine-error translation.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from typing import NoReturn
+
+import httpx
+
+from openjarvis.engine._base import (
+    EngineConnectionError,
+    EngineContextLengthError,
+    looks_like_context_length_error,
+)
+
+logger = logging.getLogger(__name__)
+
+# Transport failures that map to EngineConnectionError on the streaming paths.
+# ``RemoteProtocolError``/``ReadError`` cover a server dying MID-STREAM (peer
+# closed between tokens); a wedged read trips the configured timeout
+# (TimeoutException). Kept exactly this narrow on purpose:
+# ``asyncio.CancelledError``/``GeneratorExit`` are NOT ``httpx.TransportError``
+# subclasses and must keep propagating for correct cancellation.
+STREAM_TRANSPORT_ERRORS = (
+    httpx.ConnectError,
+    httpx.TimeoutException,
+    httpx.RemoteProtocolError,
+    httpx.ReadError,
+)
+
+_CONTEXT_LENGTH_USER_MESSAGE = (
+    "The conversation is too long for the model's context window. "
+    "Start a new chat or shorten the conversation, then try again."
+)
+
+
+class AsyncHTTPEngineMixin:
+    """Async streaming plumbing shared by httpx-backed engines.
+
+    Expects the engine to provide ``engine_id``, ``_host``, ``_timeout``, an
+    ``_async_transport`` test seam (``httpx.MockTransport`` in tests, ``None``
+    in production), and optionally ``_headers``.
+    """
+
+    engine_id: str
+    _host: str
+    _timeout: float
+    _async_transport: httpx.AsyncBaseTransport | None
+
+    # Set True by engines whose upstream reports context-window overflows in
+    # 400 bodies (OpenAI-compat servers). Ollama has no such signal.
+    _stream_400_signals_context_length: bool = False
+
+    # Lazily-created shared client (and the loop it belongs to). Class-level
+    # ``None`` defaults keep engine ``__init__``s free of mixin bookkeeping.
+    _async_client: httpx.AsyncClient | None = None
+    _async_client_loop: asyncio.AbstractEventLoop | None = None
+
+    def _make_async_client(self) -> httpx.AsyncClient:
+        """Build an async client that honours the configured timeout."""
+        return httpx.AsyncClient(
+            base_url=self._host,
+            timeout=self._timeout,
+            headers=getattr(self, "_headers", None),
+            transport=self._async_transport,
+        )
+
+    def _get_async_client(self) -> httpx.AsyncClient:
+        """Return the shared async client for the running event loop.
+
+        Reusing one client across calls preserves connection pooling — without
+        it every conversation turn pays a fresh TCP (and TLS) handshake. The
+        client is cached per event loop: pooled connections die with their
+        loop, so CLI flows that run ``asyncio.run()`` per turn transparently
+        get a fresh client while a long-lived server loop keeps one pool.
+        """
+        loop = asyncio.get_running_loop()
+        client = self._async_client
+        if client is None or client.is_closed or self._async_client_loop is not loop:
+            # Any previous client belonged to a finished loop; its pooled
+            # connections are already dead, so just drop the reference.
+            client = self._make_async_client()
+            self._async_client = client
+            self._async_client_loop = loop
+        return client
+
+    def _close_async_client(self) -> None:
+        """Best-effort close of the shared async client (for ``close()``)."""
+        client = self._async_client
+        loop = self._async_client_loop
+        self._async_client = None
+        self._async_client_loop = None
+        if client is None or client.is_closed:
+            return
+        try:
+            if loop is not None and not loop.is_closed():
+                if loop.is_running():
+                    loop.create_task(client.aclose())
+                else:
+                    loop.run_until_complete(client.aclose())
+        except Exception:  # noqa: BLE001 — cleanup must never mask the close
+            logger.debug("Async client did not close cleanly", exc_info=True)
+
+    def _raise_stream_http_error(self, status: int, detail: str) -> NoReturn:
+        """Map a non-success streaming HTTP response to a clean engine error."""
+        detail = (detail or "").strip()
+        if (
+            status == 400
+            and self._stream_400_signals_context_length
+            and looks_like_context_length_error(detail)
+        ):
+            raise EngineContextLengthError(_CONTEXT_LENGTH_USER_MESSAGE)
+        detail_suffix = f": {detail}" if detail else ""
+        raise EngineConnectionError(
+            f"{self.engine_id} engine at {self._host} returned HTTP "
+            f"{status}{detail_suffix}"
+        )
+
+
+__all__ = ["AsyncHTTPEngineMixin", "STREAM_TRANSPORT_ERRORS"]

src/openjarvis/engine/_openai_compat.py

@@ -12,18 +12,27 @@ import httpx
 from openjarvis.core.types import Message
 from openjarvis.engine._base import (
     EngineConnectionError,
+    EngineContextLengthError,
     InferenceEngine,
     estimate_prompt_tokens,
     messages_to_dicts,
 )
+from openjarvis.engine._http_async import (
+    STREAM_TRANSPORT_ERRORS,
+    AsyncHTTPEngineMixin,
+)
 from openjarvis.engine._stubs import StreamChunk
 
 logger = logging.getLogger(__name__)
 
 
-class _OpenAICompatibleEngine(InferenceEngine):
+class _OpenAICompatibleEngine(AsyncHTTPEngineMixin, InferenceEngine):
     """Base for engines that serve the OpenAI ``/v1/chat/completions`` API."""
 
+    # vLLM/SGLang report context-window overflows in 400 bodies; the shared
+    # ``_raise_stream_http_error`` types those as ``EngineContextLengthError``.
+    _stream_400_signals_context_length = True
+
     engine_id: str = ""
     _default_host: str = "http://localhost:8000"
     _api_prefix: str = "/v1"
@@ -50,6 +59,16 @@ class _OpenAICompatibleEngine(InferenceEngine):
         headers = (
             {"Authorization": f"Bearer {self._api_key}"} if self._api_key else None
         )
+        # Used by the shared async streaming plumbing (AsyncHTTPEngineMixin) so
+        # the bounded request timeout is applied to streaming reads, not just
+        # the synchronous methods (a wedged token read fails at ``timeout``
+        # rather than hanging the caller for the httpx default).
+        self._timeout = timeout
+        self._headers = headers
+        # Injection seam for tests: an ``httpx.MockTransport`` swapped in here lets
+        # the async stream path be exercised with a mocked transport and no real
+        # server. ``None`` in production so httpx uses its default networking.
+        self._async_transport: httpx.AsyncBaseTransport | None = None
         self._client = httpx.Client(
             base_url=self._host, timeout=timeout, headers=headers
         )
@@ -168,11 +187,26 @@ class _OpenAICompatibleEngine(InferenceEngine):
         # Default to tool_choice=auto when tools are provided
         if "tools" in payload and "tool_choice" not in payload:
             payload["tool_choice"] = "auto"
+        url = f"{self._api_prefix}/chat/completions"
         try:
-            url = f"{self._api_prefix}/chat/completions"
-            with self._client.stream("POST", url, json=payload) as resp:
-                resp.raise_for_status()
-                for line in resp.iter_lines():
+            # ASYNC streaming: ``httpx.AsyncClient`` + ``aiter_lines`` never
+            # blocks the event loop between tokens (the previous SYNC
+            # ``httpx.Client`` + ``iter_lines`` inside this ``async def`` blocked
+            # the single uvicorn worker on every inter-token wait, serializing all
+            # concurrent chats and letting one wedged read freeze the whole API).
+            # The shared client keeps pooled connections across turns.
+            client = self._get_async_client()
+            async with client.stream("POST", url, json=payload) as resp:
+                # ``not is_success`` covers 3xx as well as 4xx/5xx. With
+                # ``follow_redirects`` off (the default) an unexpected redirect
+                # would otherwise fall through to ``aiter_lines`` and surface as
+                # a silent EMPTY stream instead of a clean engine error.
+                if not resp.is_success:
+                    # Load the (short) error body before touching ``.text``:
+                    # a streaming response is otherwise unread.
+                    await resp.aread()
+                    self._raise_stream_http_error(resp.status_code, resp.text)
+                async for line in resp.aiter_lines():
                     if not line.startswith("data:"):
                         continue
                     data_str = line[len("data:") :].strip()
@@ -186,7 +220,11 @@ class _OpenAICompatibleEngine(InferenceEngine):
                     content = delta.get("content")
                     if content:
                         yield content
-        except (httpx.ConnectError, httpx.TimeoutException) as exc:
+        except STREAM_TRANSPORT_ERRORS as exc:
+            # A wedged upstream read trips ``timeout`` (ReadTimeout) and is mapped
+            # here, so the request fails cleanly at the configured bound instead
+            # of hanging indefinitely (see STREAM_TRANSPORT_ERRORS for why the
+            # set is exactly this narrow).
             raise EngineConnectionError(
                 f"{self.engine_id} engine not reachable at {self._host}"
             ) from exc
@@ -212,11 +250,20 @@ class _OpenAICompatibleEngine(InferenceEngine):
         }
         if "tools" in payload and "tool_choice" not in payload:
             payload["tool_choice"] = "auto"
+        url = f"{self._api_prefix}/chat/completions"
         try:
-            url = f"{self._api_prefix}/chat/completions"
-            with self._client.stream("POST", url, json=payload) as resp:
-                resp.raise_for_status()
-                for line in resp.iter_lines():
+            # ASYNC streaming (see ``stream``): non-blocking shared client so
+            # rich streaming never stalls the event loop and honours ``timeout``.
+            client = self._get_async_client()
+            async with client.stream("POST", url, json=payload) as resp:
+                # ``not is_success`` covers 3xx as well as 4xx/5xx. With
+                # ``follow_redirects`` off (the default) an unexpected redirect
+                # would otherwise fall through to ``aiter_lines`` and surface as
+                # a silent EMPTY stream instead of a clean engine error.
+                if not resp.is_success:
+                    await resp.aread()
+                    self._raise_stream_http_error(resp.status_code, resp.text)
+                async for line in resp.aiter_lines():
                     if not line.startswith("data:"):
                         continue
                     data_str = line[len("data:") :].strip()
@@ -240,7 +287,10 @@ class _OpenAICompatibleEngine(InferenceEngine):
                             finish_reason=finish,
                             usage=usage,
                         )
-        except (httpx.ConnectError, httpx.TimeoutException) as exc:
+        except STREAM_TRANSPORT_ERRORS as exc:
+            # See ``stream``: transport failures (incl. a mid-stream server
+            # disconnect) map to a clean error; the set is kept narrow so
+            # cancellation still propagates.
             raise EngineConnectionError(
                 f"{self.engine_id} engine not reachable at {self._host}"
             ) from exc
@@ -279,6 +329,9 @@ class _OpenAICompatibleEngine(InferenceEngine):
 
     def close(self) -> None:
         self._client.close()
+        self._close_async_client()
 
 
-__all__ = ["_OpenAICompatibleEngine"]
+# ``EngineContextLengthError`` moved to ``openjarvis.engine._base``; re-exported
+# here for callers/tests that import it from this module.
+__all__ = ["_OpenAICompatibleEngine", "EngineContextLengthError"]

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/litellm.py

@@ -123,8 +123,12 @@ class LiteLLMEngine(InferenceEngine):
             call_kwargs["api_base"] = self._api_base
         call_kwargs.update(kwargs)
 
-        resp = litellm.completion(**call_kwargs)
-        for chunk in resp:
+        # ``acompletion`` + ``async for``: the sync ``litellm.completion`` used
+        # before made a blocking network call (and blocking per-chunk reads)
+        # inside this ``async def``, stalling the whole event loop between
+        # tokens — the same bug the httpx engines' streaming paths had.
+        resp = await litellm.acompletion(**call_kwargs)
+        async for chunk in resp:
             delta = chunk.choices[0].delta if chunk.choices else None
             if delta and delta.content:
                 yield delta.content

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/engine/ollama.py

@@ -18,6 +18,10 @@ from openjarvis.engine._base import (
     estimate_prompt_tokens,
     messages_to_dicts,
 )
+from openjarvis.engine._http_async import (
+    STREAM_TRANSPORT_ERRORS,
+    AsyncHTTPEngineMixin,
+)
 from openjarvis.engine._stubs import StreamChunk
 
 logger = logging.getLogger(__name__)
@@ -80,11 +84,15 @@ def _default_num_ctx() -> int:
 
 
 @EngineRegistry.register("ollama")
-class OllamaEngine(InferenceEngine):
+class OllamaEngine(AsyncHTTPEngineMixin, InferenceEngine):
     """Ollama backend via its native HTTP API."""
 
     engine_id = "ollama"
 
+    # Ollama has no context-length overflow signal in its 400 bodies, so the
+    # shared ``_raise_stream_http_error`` keeps its default (no
+    # ``EngineContextLengthError`` branch, unlike the OpenAI-compat engines).
+
     _DEFAULT_HOST = "http://localhost:11434"
 
     def __init__(
@@ -98,6 +106,14 @@ class OllamaEngine(InferenceEngine):
             env_host = os.environ.get("OLLAMA_HOST")
             host = env_host or self._DEFAULT_HOST
         self._host = host.rstrip("/")
+        # Used by the shared async streaming plumbing (AsyncHTTPEngineMixin) so a
+        # wedged token read is bounded by ``timeout`` instead of hanging the
+        # single event loop for the httpx default.
+        self._timeout = timeout
+        # Injection seam for tests: an ``httpx.MockTransport`` swapped in here drives
+        # the async stream path with no real Ollama server. ``None`` in production so
+        # httpx uses its default networking.
+        self._async_transport: httpx.AsyncBaseTransport | None = None
         self._client = httpx.Client(base_url=self._host, timeout=timeout)
         # Last stream usage — captured from Ollama's final chunk
         self._last_stream_usage: Dict[str, int] = {}
@@ -263,9 +279,26 @@ class OllamaEngine(InferenceEngine):
         elif kwargs["think"] is not None:
             payload["think"] = kwargs["think"]
         try:
-            with self._client.stream("POST", "/api/chat", json=payload) as resp:
-                resp.raise_for_status()
-                for line in resp.iter_lines():
+            # ASYNC streaming: ``httpx.AsyncClient`` + ``aiter_lines`` never
+            # blocks the event loop between tokens (the previous SYNC
+            # ``self._client`` + ``iter_lines`` inside this ``async def`` blocked
+            # the single uvicorn worker on every inter-token wait, serializing all
+            # concurrent chats and letting one wedged read freeze the whole API).
+            # The shared client keeps pooled connections across turns.
+            client = self._get_async_client()
+            async with client.stream("POST", "/api/chat", json=payload) as resp:
+                # ``not is_success`` covers 3xx as well as 4xx/5xx and maps
+                # to ``EngineConnectionError`` (matching the OpenAI-compat
+                # path) instead of leaking a raw ``httpx.HTTPStatusError``.
+                # With redirects off (the default) an unexpected 3xx would
+                # otherwise fall through to ``aiter_lines`` and surface as a
+                # silent EMPTY stream rather than a clean engine error.
+                if not resp.is_success:
+                    # Read the (short) error body before touching ``.text``:
+                    # a streaming response is otherwise unread.
+                    await resp.aread()
+                    self._raise_stream_http_error(resp.status_code, resp.text)
+                async for line in resp.aiter_lines():
                     if not line.strip():
                         continue
                     try:
@@ -290,7 +323,10 @@ class OllamaEngine(InferenceEngine):
                             "total_tokens": full_prompt + comp,
                         }
                         break
-        except (httpx.ConnectError, httpx.TimeoutException) as exc:
+        except STREAM_TRANSPORT_ERRORS as exc:
+            # Transport failures (incl. a mid-stream server disconnect) map to a
+            # clean error; the set is kept narrow (see STREAM_TRANSPORT_ERRORS)
+            # so cancellation still propagates.
             raise EngineConnectionError(
                 f"Ollama not reachable at {self._host}"
             ) from exc
@@ -356,19 +392,34 @@ class OllamaEngine(InferenceEngine):
     ) -> AsyncIterator[StreamChunk]:
         """Execute the streaming request and yield parsed StreamChunks."""
         try:
-            with self._client.stream("POST", "/api/chat", json=payload) as resp:
+            # ASYNC streaming (see ``stream``): shared ``AsyncClient`` +
+            # ``aiter_lines`` so rich streaming never stalls the event loop and
+            # honours ``timeout``.
+            client = self._get_async_client()
+            async with client.stream("POST", "/api/chat", json=payload) as resp:
                 if resp.status_code == 400 and retry_without_tools:
                     # Model doesn't support tools — retry without them.
+                    # PRESERVED: this specific 400 path must still trigger the
+                    # tools-less retry; only OTHER non-2xx responses map to
+                    # EngineConnectionError below.
                     payload.pop("tools", None)
                     async for c in self._run_stream(
                         payload, messages, retry_without_tools=False
                     ):
                         yield c
                     return
-                resp.raise_for_status()
+                # ``not is_success`` covers 3xx as well as 4xx/5xx and maps
+                # to ``EngineConnectionError`` (matching the OpenAI-compat
+                # path) instead of leaking a raw ``httpx.HTTPStatusError``.
+                # With redirects off (the default) an unexpected 3xx would
+                # otherwise fall through to ``aiter_lines`` and surface as a
+                # silent EMPTY stream rather than a clean engine error.
+                if not resp.is_success:
+                    await resp.aread()
+                    self._raise_stream_http_error(resp.status_code, resp.text)
 
                 finish_reason: str | None = None
-                for line in resp.iter_lines():
+                async for line in resp.aiter_lines():
                     if not line.strip():
                         continue
                     try:
@@ -441,7 +492,10 @@ class OllamaEngine(InferenceEngine):
                             usage=dict(self._last_stream_usage),
                         )
                         break
-        except (httpx.ConnectError, httpx.TimeoutException) as exc:
+        except STREAM_TRANSPORT_ERRORS as exc:
+            # See ``stream``: transport failures (incl. a mid-stream server
+            # disconnect) map to a clean error; the set is kept narrow so
+            # cancellation still propagates.
             raise EngineConnectionError(
                 f"Ollama not reachable at {self._host}"
             ) from exc
@@ -474,6 +528,7 @@ class OllamaEngine(InferenceEngine):
 
     def close(self) -> None:
         self._client.close()
+        self._close_async_client()
 
 
 __all__ = ["OllamaEngine"]

src/openjarvis/evals/backends/external/_subprocess_runner.py

@@ -203,6 +203,7 @@ def _try_start_nvml() -> Optional[_Sampler]:
     try:
         # Suppress legacy pynvml deprecation FutureWarning (#389).
         import warnings as _warnings
+
         with _warnings.catch_warnings():
             _warnings.filterwarnings(
                 "ignore",

src/openjarvis/evals/core/runner.py

@@ -175,8 +175,7 @@ class EvalRunner:
             before = len(records)
             records = [r for r in records if r.record_id in wanted]
             LOGGER.info(
-                "Filtering %s to %d/%d records via record_ids "
-                "(first 3: %s)",
+                "Filtering %s to %d/%d records via record_ids (first 3: %s)",
                 cfg.benchmark,
                 len(records),
                 before,
@@ -324,8 +323,7 @@ class EvalRunner:
                 energy_joules=energy_j,
                 power_watts=power_w,
                 gpu_utilization_pct=full.get("gpu_utilization_pct", 0.0) or 0.0,
-                throughput_tok_per_sec=full.get("throughput_tok_per_sec", 0.0)
-                or 0.0,
+                throughput_tok_per_sec=full.get("throughput_tok_per_sec", 0.0) or 0.0,
                 trace_data=full.get("trace_data"),
                 framework=full.get(
                     "framework",
@@ -960,9 +958,7 @@ class EvalRunner:
 
         # Continuous-score reporting: skip None (errored) entries; clamp values
         # outside [0,1] are already handled in _extract_continuous_score.
-        cont_scores = [
-            float(r.score) for r in results if r.score is not None
-        ]
+        cont_scores = [float(r.score) for r in results if r.score is not None]
         if cont_scores:
             mean_cont = sum(cont_scores) / len(cont_scores)
             median_cont = statistics.median(cont_scores)

src/openjarvis/evals/datasets/terminalbench_v2_1.py

@@ -85,7 +85,12 @@ class TerminalBenchV21Dataset(DatasetProvider):
                 "git binary not found. Install git to clone TerminalBench V2.1 tasks."
             )
         self._repo_dir.parent.mkdir(parents=True, exist_ok=True)
-        LOGGER.info("Cloning %s (branch %s) into %s", self._repo_url, self._branch, self._repo_dir)
+        LOGGER.info(
+            "Cloning %s (branch %s) into %s",
+            self._repo_url,
+            self._branch,
+            self._repo_dir,
+        )
         subprocess.run(
             [
                 "git",
@@ -110,9 +115,7 @@ class TerminalBenchV21Dataset(DatasetProvider):
     ) -> None:
         repo = self._ensure_repo()
         task_dirs = sorted(
-            d
-            for d in repo.iterdir()
-            if d.is_dir() and (d / "task.toml").exists()
+            d for d in repo.iterdir() if d.is_dir() and (d / "task.toml").exists()
         )
 
         if self._task_ids:

src/openjarvis/evals/execution/terminalbench_v2_1_env.py

@@ -49,8 +49,7 @@ class TerminalBenchV21TaskEnv:
         task_dir = self._metadata.get("task_dir")
         if not docker_image or not task_dir:
             raise ValueError(
-                "TerminalBenchV21TaskEnv missing 'docker_image' or 'task_dir' "
-                "metadata"
+                "TerminalBenchV21TaskEnv missing 'docker_image' or 'task_dir' metadata"
             )
 
         tests_dir = Path(task_dir) / "tests"
@@ -90,9 +89,7 @@ class TerminalBenchV21TaskEnv:
         )
         if start.returncode != 0:
             self._metadata["tbv21_env_error"] = start.stderr[:500]
-            raise RuntimeError(
-                f"docker run failed for {task_id}: {start.stderr[:300]}"
-            )
+            raise RuntimeError(f"docker run failed for {task_id}: {start.stderr[:300]}")
 
         self._started = True
         self._metadata["tbv21_container"] = name

src/openjarvis/evals/scorers/liveresearch.py

@@ -120,7 +120,6 @@ Be a rigorous evaluator. Reserve scores of 9-10 for genuinely excellent work.
 A score of 5 represents adequate but unremarkable quality."""
 
 
-
 # Optional permissive JSON parser (json5 if available; fallback otherwise).
 try:
     import json5 as _json5  # type: ignore[import-not-found]
@@ -147,7 +146,7 @@ def _escape_newlines_inside_strings(text: str) -> str:
                 out.append(ch)
                 escape_next = True
                 continue
-            if ch == "\"":
+            if ch == '"':
                 out.append(ch)
                 in_string = False
                 continue
@@ -162,7 +161,7 @@ def _escape_newlines_inside_strings(text: str) -> str:
                 continue
             out.append(ch)
         else:
-            if ch == "\"":
+            if ch == '"':
                 in_string = True
             out.append(ch)
     return "".join(out)
@@ -217,7 +216,7 @@ def _parse_judge_response(raw: str) -> Dict[str, Any]:
                 esc = False
             elif char == "\\":
                 esc = True
-            elif char == "\"":
+            elif char == '"':
                 in_str = False
             continue
         if char == "{":
@@ -226,7 +225,7 @@ def _parse_judge_response(raw: str) -> Dict[str, Any]:
             depth += 1
         if depth > 0:
             current.append(char)
-        if char == "\"" and depth > 0:
+        if char == '"' and depth > 0:
             in_str = True
         elif char == "}":
             depth -= 1

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/mcp/loader.py

@@ -82,9 +82,7 @@ def load_mcp_tools_from_config(
 
     for server_cfg in server_list:
         try:
-            cfg = (
-                json.loads(server_cfg) if isinstance(server_cfg, str) else server_cfg
-            )
+            cfg = json.loads(server_cfg) if isinstance(server_cfg, str) else server_cfg
             name = cfg.get("name", "<unnamed>")
             url = cfg.get("url")
             token = cfg.get("token")

src/openjarvis/operators/manager.py

@@ -1,4 +1,5 @@
 """Operator manager — lifecycle management for autonomous operators."""
+
 from __future__ import annotations
 
 import logging
@@ -271,20 +272,14 @@ class OperatorManager:
             "total_cost": summary.total_cost,
             "total_latency": summary.total_latency,
             "total_energy_joules": summary.total_energy_joules,
-            "avg_throughput_tok_per_sec": (
-                summary.avg_throughput_tok_per_sec
-            ),
+            "avg_throughput_tok_per_sec": (summary.avg_throughput_tok_per_sec),
             "avg_gpu_utilization_pct": summary.avg_gpu_utilization_pct,
             "avg_energy_per_output_token_joules": (
                 summary.avg_energy_per_output_token_joules
             ),
             "avg_throughput_per_watt": summary.avg_throughput_per_watt,
-            "total_prefill_energy_joules": (
-                summary.total_prefill_energy_joules
-            ),
-            "total_decode_energy_joules": (
-                summary.total_decode_energy_joules
-            ),
+            "total_prefill_energy_joules": (summary.total_prefill_energy_joules),
+            "total_decode_energy_joules": (summary.total_decode_energy_joules),
             "avg_mean_itl_ms": summary.avg_mean_itl_ms,
             "avg_median_itl_ms": summary.avg_median_itl_ms,
             "avg_p95_itl_ms": summary.avg_p95_itl_ms,

src/openjarvis/recipes/data/operators/openjarvis_twitter_bot_prompt.md

@@ -15,7 +15,7 @@ HARD RULE: Every reply MUST be ≤280 characters. Count before sending.
 - GitHub: https://github.com/open-jarvis/OpenJarvis
 - Docs: https://open-jarvis.github.io/OpenJarvis/
 - Discord: https://discord.gg/wfXEkpPX
-- Blog: https://scalingintelligence.stanford.edu/blogs/openjarvis/
+- Blog: https://openjarvis.stanford.edu/
 - Install: `git clone https://github.com/open-jarvis/OpenJarvis.git && cd OpenJarvis && uv sync`
 - CLI commands (ONLY these exist):
   - `jarvis init` — auto-detects hardware, configures engine

src/openjarvis/server/api_routes.py

@@ -306,9 +306,7 @@ async def memory_index(req: MemoryIndexRequest, request: Request):
                 for d in workspace.split(os.pathsep)
                 if d.strip()
             ]
-            if not any(
-                target == root or root in target.parents for root in roots
-            ):
+            if not any(target == root or root in target.parents for root in roots):
                 raise HTTPException(
                     status_code=403,
                     detail="Path is outside the allowed workspace directories.",
@@ -743,8 +741,11 @@ async def websocket_chat_stream(websocket: WebSocket):
                                 )
                     except TypeError:
                         # stream() didn't return an iterable; fall back to
-                        # generate()
-                        result = engine.generate(messages, model=model)
+                        # generate(). It makes a blocking upstream call, so run
+                        # it in a worker thread to keep the event loop free.
+                        result = await asyncio.to_thread(
+                            engine.generate, messages, model=model
+                        )
                         content = (
                             result.get("content", "")
                             if isinstance(
@@ -769,8 +770,11 @@ async def websocket_chat_stream(websocket: WebSocket):
                         ended_at=_time.time(),
                     )
                 else:
-                    # No stream method — single-shot generate
-                    result = engine.generate(messages, model=model)
+                    # No stream method — single-shot generate. Blocking upstream
+                    # call, so run in a worker thread to keep the event loop free.
+                    result = await asyncio.to_thread(
+                        engine.generate, messages, model=model
+                    )
                     content = (
                         result.get("content", "")
                         if isinstance(

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/auth_middleware.py

@@ -59,7 +59,6 @@ class AuthMiddleware(BaseHTTPMiddleware):
         )
 
 
-
 def generate_api_key() -> str:
     """Generate a new API key with ``oj_sk_`` prefix."""
     return f"oj_sk_{secrets.token_urlsafe(32)}"

src/openjarvis/server/comparison.py

@@ -215,8 +215,8 @@ COMPARISON_HTML = """\
         <tr>
           <th></th>
           <th>OpenJarvis (Local)</th>
-          <th>GPT-5.3</th>
-          <th>Claude Opus 4.6</th>
+          <th>GPT-5.6 Sol</th>
+          <th>Claude Fable 5</th>
           <th>Gemini 3.1 Pro</th>
         </tr>
       </thead>
@@ -261,11 +261,11 @@ COMPARISON_HTML = """\
         <div class="cc-value">$0.00/mo</div>
       </div>
       <div class="calc-card cloud">
-        <div class="cc-label">GPT-5.3</div>
+        <div class="cc-label">GPT-5.6 Sol</div>
         <div class="cc-value" id="calc-gpt">--</div>
       </div>
       <div class="calc-card cloud">
-        <div class="cc-label">Claude Opus 4.6</div>
+        <div class="cc-label">Claude Fable 5</div>
         <div class="cc-value" id="calc-claude">--</div>
       </div>
       <div class="calc-card cloud">
@@ -292,13 +292,13 @@ COMPARISON_HTML = """\
 <script>
 // Embedded data -- avoids API calls, keeps the page static and fast.
 const CLOUD_PRICING = {
-  "gpt-5.3": {
-    input_per_1m: 2.00, output_per_1m: 10.00,
-    label: "GPT-5.3"
+  "gpt-5.6-sol": {
+    input_per_1m: 5.00, output_per_1m: 30.00,
+    label: "GPT-5.6 Sol"
   },
-  "claude-opus-4.6": {
-    input_per_1m: 5.00, output_per_1m: 25.00,
-    label: "Claude Opus 4.6"
+  "claude-fable-5": {
+    input_per_1m: 10.00, output_per_1m: 50.00,
+    label: "Claude Fable 5"
   },
   "gemini-3.1-pro": {
     input_per_1m: 2.00, output_per_1m: 12.00,
@@ -376,8 +376,8 @@ function updateTable() {
   const sc = SCENARIOS[activeScenario];
   const i = sc.avg_input_tokens, o = sc.avg_output_tokens;
   const c = sc.calls_per_month;
-  const gpt = calcMonthlyCost(c, i, o, 'gpt-5.3');
-  const claude = calcMonthlyCost(c, i, o, 'claude-opus-4.6');
+  const gpt = calcMonthlyCost(c, i, o, 'gpt-5.6-sol');
+  const claude = calcMonthlyCost(c, i, o, 'claude-fable-5');
   const gemini = calcMonthlyCost(c, i, o, 'gemini-3.1-pro');
 
   document.getElementById('t-gpt-m').textContent = fmtDollar(gpt);
@@ -410,8 +410,8 @@ function updateCalc() {
   const avgOut = tpc - avgIn;
   const callsPerMonth = cpd * 30;
 
-  const gpt = calcMonthlyCost(callsPerMonth, avgIn, avgOut, 'gpt-5.3');
-  const claude = calcMonthlyCost(callsPerMonth, avgIn, avgOut, 'claude-opus-4.6');
+  const gpt = calcMonthlyCost(callsPerMonth, avgIn, avgOut, 'gpt-5.6-sol');
+  const claude = calcMonthlyCost(callsPerMonth, avgIn, avgOut, 'claude-fable-5');
   const gemini = calcMonthlyCost(callsPerMonth, avgIn, avgOut, 'gemini-3.1-pro');
 
   document.getElementById('calc-gpt').textContent = fmtDollar(gpt) + '/mo';

src/openjarvis/server/dashboard.py

@@ -184,7 +184,7 @@ DASHBOARD_HTML = """\
   <div class="providers">
     <div class="provider-card openai">
       <div class="pname">OpenAI</div>
-      <div class="pmodel">GPT-5.3 &mdash; $2.00 / $10.00 per 1M tokens</div>
+      <div class="pmodel">GPT-5.6 Sol &mdash; $5.00 / $30.00 per 1M tokens</div>
       <div class="savings-amount" id="save-openai">$0.00</div>
       <div class="breakdown">
         <div class="item">
@@ -199,7 +199,7 @@ DASHBOARD_HTML = """\
     </div>
     <div class="provider-card anthropic">
       <div class="pname">Anthropic</div>
-      <div class="pmodel">Claude Opus 4.6 &mdash; $5.00 / $25.00 per 1M tokens</div>
+      <div class="pmodel">Claude Fable 5 &mdash; $10.00 / $50.00 per 1M tokens</div>
       <div class="savings-amount" id="save-anthropic">$0.00</div>
       <div class="breakdown">
         <div class="item">
@@ -281,12 +281,12 @@ DASHBOARD_HTML = """\
   <div class="providers-heading">Energy &amp; Compute Avoided</div>
   <div class="metrics-row">
     <div class="metric-card">
-      <div class="mheading">Energy Saved (vs GPT-5.3)</div>
+      <div class="mheading">Energy Saved (vs GPT-5.6 Sol)</div>
       <div class="mvalue green" id="energy-joules">0 <span class="munit">J</span></div>
       <div class="msub" id="energy-kwh">0 kWh of cloud datacenter energy avoided</div>
     </div>
     <div class="metric-card">
-      <div class="mheading">FLOPs Avoided (vs GPT-5.3)</div>
+      <div class="mheading">FLOPs Avoided (vs GPT-5.6 Sol)</div>
       <div class="mvalue purple" id="flops-val">0 <span class="munit">FLOP</span></div>
       <div class="msub" id="flops-sub">cloud compute operations not needed</div>
     </div>
@@ -354,8 +354,8 @@ async function refresh() {
       providerMap[p.provider] = p;
     });
 
-    // OpenAI / GPT-5.3
-    const oa = providerMap['gpt-5.3'] || {};
+    // OpenAI / GPT-5.6 Sol
+    const oa = providerMap['gpt-5.6-sol'] || {};
     document.getElementById('save-openai')
       .textContent = fmtDollar(oa.total_cost || 0);
     document.getElementById('save-openai-in')
@@ -363,8 +363,8 @@ async function refresh() {
     document.getElementById('save-openai-out')
       .textContent = fmtDollar(oa.output_cost || 0);
 
-    // Anthropic / Claude Opus 4.6
-    const an = providerMap['claude-opus-4.6'] || {};
+    // Anthropic / Claude Fable 5
+    const an = providerMap['claude-fable-5'] || {};
     document.getElementById('save-anthropic')
       .textContent = fmtDollar(an.total_cost || 0);
     document.getElementById('save-anthropic-in')
@@ -384,13 +384,13 @@ async function refresh() {
     // Monthly projections
     const proj = d.monthly_projection || {};
     document.getElementById('proj-openai')
-      .textContent = fmtDollar(proj['gpt-5.3'] || 0);
+      .textContent = fmtDollar(proj['gpt-5.6-sol'] || 0);
     document.getElementById('proj-anthropic')
-      .textContent = fmtDollar(proj['claude-opus-4.6'] || 0);
+      .textContent = fmtDollar(proj['claude-fable-5'] || 0);
     document.getElementById('proj-google')
       .textContent = fmtDollar(proj['gemini-3.1-pro'] || 0);
 
-    // Energy / FLOPs (use GPT-5.3 as reference)
+    // Energy / FLOPs (use GPT-5.6 Sol as reference)
     const ej = oa.energy_joules || 0;
     const eWh = oa.energy_wh || 0;
     const fl = oa.flops || 0;

src/openjarvis/server/research_router.py

@@ -238,6 +238,7 @@ class _LiveGPUSampler:
         try:
             # Suppress legacy pynvml deprecation FutureWarning (#389).
             import warnings as _warnings
+
             with _warnings.catch_warnings():
                 _warnings.filterwarnings(
                     "ignore",
@@ -248,9 +249,7 @@ class _LiveGPUSampler:
 
             pynvml.nvmlInit()
             count = pynvml.nvmlDeviceGetCount()
-            self._handles = [
-                pynvml.nvmlDeviceGetHandleByIndex(i) for i in range(count)
-            ]
+            self._handles = [pynvml.nvmlDeviceGetHandleByIndex(i) for i in range(count)]
             self._pynvml = pynvml
             self._available = bool(self._handles)
             if not self._available:
@@ -528,9 +527,7 @@ async def _stream_research(
                 for piece in _chunk_synthesis(final_answer or ""):
                     yield _sse({"type": "synthesis", "text": piece})
                 if final_sources:
-                    yield _sse(
-                        {"type": "final_sources", "sources": final_sources}
-                    )
+                    yield _sse({"type": "final_sources", "sources": final_sources})
                 continue
 
             yield _sse(event)
@@ -539,9 +536,7 @@ async def _stream_research(
         # client still gets the error frame (emitted above) followed by done.
         # The done frame also carries the deduped sources so a client that
         # only listens for ``done`` still gets the canonical citation list.
-        yield _sse(
-            {"type": "done", "usage": final_usage, "sources": final_sources}
-        )
+        yield _sse({"type": "done", "usage": final_usage, "sources": final_sources})
     except Exception as exc:  # noqa: BLE001
         # Consumer loop crashed unexpectedly (e.g. JSON serialization fault,
         # logic bug). Surface a clean error frame rather than letting the
@@ -553,9 +548,7 @@ async def _stream_research(
                 "message": f"Research failed: {type(exc).__name__}: {exc}",
             }
         )
-        yield _sse(
-            {"type": "done", "usage": final_usage, "sources": final_sources}
-        )
+        yield _sse({"type": "done", "usage": final_usage, "sources": final_sources})
     finally:
         # The worker may still be cleaning up (rarely) — make sure we don't
         # leak a dangling task. Swallow any straggler exception so a worker

src/openjarvis/server/routes.py

@@ -59,23 +59,34 @@ def _ensure_identity_prompt(messages: list[Message], app_config) -> list[Message
     If any message already carries a system role, the caller has supplied
     their own grounding and we leave the list untouched (no double-prompting).
 
-    Resolution of the identity text: ``app_config.agent.default_system_prompt``
-    when a config is wired onto ``app.state``; otherwise fall back to
-    ``load_config()``. Config resolution is wrapped so a broken/missing
-    config degrades to "no injection" rather than crashing the endpoint, but
-    the failure is logged (per REVIEW.md — never silently swallow).
+    Resolution of the identity text: the config comes from ``app.state`` when
+    wired, otherwise ``load_config()``; the prompt itself is assembled by
+    ``SystemPromptBuilder`` from ``agent.default_system_prompt`` plus the
+    persona files (SOUL.md/MEMORY.md/USER.md), matching
+    ``_build_managed_system_prompt`` in ``agent_manager_routes.py``. Config
+    resolution is wrapped so a broken/missing config degrades to "no
+    injection" rather than crashing the endpoint, but the failure is logged
+    (per REVIEW.md — never silently swallow).
     """
     if any(m.role == Role.SYSTEM for m in messages):
         return messages
 
     prompt = ""
     try:
-        if app_config is not None:
-            prompt = app_config.agent.default_system_prompt or ""
-        else:
+        cfg = app_config
+        if cfg is None:
             from openjarvis.core.config import load_config
 
-            prompt = load_config().agent.default_system_prompt or ""
+            cfg = load_config()
+
+        from openjarvis.prompt.builder import SystemPromptBuilder
+
+        builder = SystemPromptBuilder(
+            agent_template=cfg.agent.default_system_prompt or "",
+            memory_files_config=getattr(cfg, "memory_files", None),
+            system_prompt_config=getattr(cfg, "system_prompt", None),
+        )
+        prompt = builder.build()
     except Exception:
         logging.getLogger("openjarvis.server").debug(
             "Identity system prompt resolution failed; "
@@ -231,8 +242,13 @@ async def chat_completions(request_body: ChatCompletionRequest, request: Request
     # tools (e.g. injecting MCP tools through this endpoint and wanting
     # the agent to execute them), add an explicit opt-in header rather
     # than removing this guard — silent re-routing is what produced #414.
+    # ``_handle_agent`` (sync ``agent.run()``) and ``_handle_direct`` (sync
+    # ``engine.generate()``) both make blocking upstream calls; run them in a
+    # worker thread so a slow/wedged non-streaming request can't stall the
+    # event loop and every other concurrent request with it.
     if agent is not None and not request_body.tools:
-        response = _handle_agent(
+        response = await asyncio.to_thread(
+            _handle_agent,
             agent,
             model,
             request_body,
@@ -242,7 +258,8 @@ async def chat_completions(request_body: ChatCompletionRequest, request: Request
         )
     else:
         bus = getattr(request.app.state, "bus", None)
-        response = _handle_direct(
+        response = await asyncio.to_thread(
+            _handle_direct,
             engine,
             model,
             request_body,
@@ -319,6 +336,34 @@ 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,
@@ -524,12 +569,13 @@ 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:
@@ -609,7 +655,7 @@ 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"
@@ -651,11 +697,7 @@ async def _handle_stream(
     """
     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)
@@ -670,7 +712,10 @@ 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()
@@ -775,7 +820,7 @@ 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(),
             )
@@ -808,7 +853,7 @@ 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()
@@ -825,24 +870,40 @@ 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()
 
     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/savings.py

@@ -23,19 +23,19 @@ from openjarvis.core.types import TOKEN_COUNTING_VERSION  # noqa: E402,F401
 # ---------------------------------------------------------------------------
 
 CLOUD_PRICING: Dict[str, Dict[str, float]] = {
-    "gpt-5.3": {
-        "input_per_1m": 2.00,
-        "output_per_1m": 10.00,
-        "label": "GPT-5.3",
+    "gpt-5.6-sol": {
+        "input_per_1m": 5.00,
+        "output_per_1m": 30.00,
+        "label": "GPT-5.6 Sol",
         "provider": "OpenAI",
         "params_b": 200.0,
         "energy_wh_per_1k_tokens": 0.4,
         "flops_per_token": 3.0e12,
     },
-    "claude-opus-4.6": {
-        "input_per_1m": 5.00,
-        "output_per_1m": 25.00,
-        "label": "Claude Opus 4.6",
+    "claude-fable-5": {
+        "input_per_1m": 10.00,
+        "output_per_1m": 50.00,
+        "label": "Claude Fable 5",
         "provider": "Anthropic",
         "params_b": 137.0,
         "energy_wh_per_1k_tokens": 0.5,

src/openjarvis/server/session_store.py

@@ -25,9 +25,11 @@ class SessionStore:
     def __init__(self, db_path: str = "") -> None:
         if not db_path:
             db_path = str(get_config_dir() / "sessions.db")
-        from openjarvis.security.file_utils import secure_create
+        # Ensure the parent directory exists (skip for :memory:)
+        if db_path != ":memory:":
+            from openjarvis.security.file_utils import secure_create
 
-        secure_create(Path(db_path))
+            secure_create(Path(db_path))
         self._db = sqlite3.connect(db_path, check_same_thread=False)
         self._db.row_factory = sqlite3.Row
         self._create_tables()

src/openjarvis/server/stream_bridge.py

@@ -16,6 +16,7 @@ from fastapi.responses import StreamingResponse
 
 from openjarvis.agents._stubs import AgentContext, BaseAgent
 from openjarvis.core.events import Event, EventBus, EventType
+from openjarvis.engine._base import looks_like_context_length_error
 from openjarvis.server.models import (
     ChatCompletionChunk,
     ChatCompletionRequest,
@@ -194,8 +195,10 @@ class AgentStreamBridge:
                 logger.error("Agent stream error: %s", exc, exc_info=True)
 
                 error_str = str(exc)
-                if "context length" in error_str.lower() or (
-                    "400" in error_str and "too long" in error_str.lower()
+                if (
+                    getattr(exc, "is_context_length_error", False)
+                    or looks_like_context_length_error(error_str)
+                    or ("400" in error_str and "too long" in error_str.lower())
                 ):
                     error_content = (
                         "The input is too long for the model's context window. "

src/openjarvis/server/webhook_routes.py

@@ -116,13 +116,9 @@ def create_webhook_router(
         # Fail closed: an unconfigured token means we cannot verify the sender,
         # so reject rather than trust unsigned input.
         if not twilio_auth_token:
-            logger.error(
-                "Twilio webhook rejected: TWILIO_AUTH_TOKEN not configured."
-            )
+            logger.error("Twilio webhook rejected: TWILIO_AUTH_TOKEN not configured.")
             return Response("Webhook signature verification not configured", 403)
-        if not _validate_twilio_signature(
-            twilio_auth_token, url, params, signature
-        ):
+        if not _validate_twilio_signature(twilio_auth_token, url, params, signature):
             return Response("Invalid signature", status_code=403)
 
         from_number = params.get("From", "")
@@ -266,9 +262,7 @@ def create_webhook_router(
         auth = request.headers.get("Authorization", "")
         # Fail closed when no password is configured.
         if not bluebubbles_password:
-            logger.error(
-                "BlueBubbles webhook rejected: password not configured."
-            )
+            logger.error("BlueBubbles webhook rejected: password not configured.")
             return Response("Webhook authentication not configured", 403)
         if not hmac.compare_digest(auth, bluebubbles_password):
             return Response("Invalid password", status_code=403)
@@ -321,9 +315,7 @@ def create_webhook_router(
 
         # Fail closed: reject when no app secret is configured to verify HMAC.
         if not whatsapp_app_secret:
-            logger.error(
-                "WhatsApp webhook rejected: app secret not configured."
-            )
+            logger.error("WhatsApp webhook rejected: app secret not configured.")
             return Response("Webhook signature verification not configured", 403)
         signature = request.headers.get("X-Hub-Signature-256", "")
         expected = (
@@ -373,9 +365,7 @@ def create_webhook_router(
         # Fail closed: require a configured channel + webhook secret to verify
         # the sender before processing any inbound message.
         if sb is None or not getattr(sb, "webhook_secret", ""):
-            logger.error(
-                "SendBlue webhook rejected: webhook_secret not configured."
-            )
+            logger.error("SendBlue webhook rejected: webhook_secret not configured.")
             return Response("Webhook secret not configured", status_code=403)
         header_secret = request.headers.get("x-sendblue-secret", "")
         if not hmac.compare_digest(header_secret, sb.webhook_secret):