chore: update clone traffic data [skip ci]

* fix(server): preserve grounded agent stream content

* test(server): cover active grounded stream path

* fix(server): retain agent stream bridge

.github/clone-stats/badge.json

@@ -1,7 +1,7 @@
 {
   "schemaVersion": 1,
   "label": "Git Clones",
-  "message": "117,047",
+  "message": "190,252",
   "color": "green",
   "namedLogo": "git"
 }

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

@@ -1,6 +1,6 @@
 {
-  "total_clones": 117047,
-  "last_updated": "2026-06-14T07:38:47Z",
+  "total_clones": 190252,
+  "last_updated": "2026-08-14T07:19:51Z",
   "daily": {
     "2026-03-27": 2189,
     "2026-03-28": 1874,
@@ -80,6 +80,67 @@
     "2026-06-10": 1310,
     "2026-06-11": 2564,
     "2026-06-12": 1313,
-    "2026-06-13": 2804
+    "2026-06-13": 2804,
+    "2026-06-14": 1543,
+    "2026-06-15": 1379,
+    "2026-06-16": 1317,
+    "2026-06-17": 1170,
+    "2026-06-18": 1408,
+    "2026-06-19": 1350,
+    "2026-06-20": 1437,
+    "2026-06-21": 1426,
+    "2026-06-22": 1350,
+    "2026-06-23": 1468,
+    "2026-06-24": 1635,
+    "2026-06-25": 1640,
+    "2026-06-26": 1338,
+    "2026-06-27": 1338,
+    "2026-06-28": 1028,
+    "2026-06-29": 765,
+    "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,
+    "2026-08-10": 1060,
+    "2026-08-11": 2182,
+    "2026-08-12": 641,
+    "2026-08-13": 770
   }
 }

.github/workflows/ci.yml

@@ -23,6 +23,8 @@ jobs:
 
       - name: Install uv
         uses: astral-sh/setup-uv@v8.0.0
+        with:
+          enable-cache: true
 
       - name: Install dependencies
         run: uv sync --extra dev --extra framework-comparison --extra server
@@ -30,6 +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:
@@ -55,6 +60,8 @@ jobs:
 
       - name: Install uv
         uses: astral-sh/setup-uv@v8.0.0
+        with:
+          enable-cache: true
 
       - name: Install dependencies
         run: uv sync --extra dev --extra framework-comparison --extra server
@@ -63,8 +70,13 @@ jobs:
         run: uv run maturin develop --manifest-path rust/crates/openjarvis-python/Cargo.toml
 
       - name: Run tests
+        # COVERAGE_CORE=sysmon uses CPython 3.12's sys.monitoring backend,
+        # which is dramatically cheaper than the default C trace function.
+        # -n auto fans the suite out across all runner cores via pytest-xdist.
+        env:
+          COVERAGE_CORE: sysmon
         run: |
-          uv run pytest tests/ -v --tb=short -m "not live and not cloud and not hub" \
+          uv run pytest tests/ -n auto -q --tb=short -m "not live and not cloud and not hub" \
             --cov=openjarvis \
             --cov-report=term-missing \
             --cov-report=xml \
@@ -107,6 +119,8 @@ jobs:
 
       - name: Install uv
         uses: astral-sh/setup-uv@v8.0.0
+        with:
+          enable-cache: true
 
       - name: Install dependencies
         run: uv sync --extra dev --extra server

.github/workflows/desktop.yml

@@ -40,7 +40,8 @@ jobs:
             libappindicator3-dev \
             librsvg2-dev \
             patchelf \
-            libxdo-dev
+            libxdo-dev \
+            libdbus-1-dev
 
       - name: Setup Node.js
         uses: actions/setup-node@v6
@@ -120,6 +121,104 @@ jobs:
           # latest release tag (#526).
           fetch-depth: 0
 
+      # Validate Apple credentials BEFORE the expensive work. Notarization is
+      # the very last thing `tauri-action` does, so a bad credential or a
+      # lapsed account agreement previously surfaced ~10 minutes in — after the
+      # Rust toolchain, npm install, two Ollama sidecar downloads and a
+      # universal cargo build — as a single opaque line:
+      #
+      #   failed to bundle project: failed codesign application: failed to
+      #   notarize app: Error: HTTP status code: 403. ...
+      #
+      # `notarytool history` is a read-only call (it submits nothing) that
+      # exercises the identical auth path, so every credential/account failure
+      # mode reaches us here first, in seconds, with the specific cause named.
+      # `xcrun` is preinstalled on macOS runners, hence placement before the
+      # toolchain steps rather than next to "Configure Apple signing".
+      - name: Preflight Apple notarization credentials
+        if: matrix.platform == 'macos-14'
+        env:
+          CERT: ${{ secrets.APPLE_CERTIFICATE }}
+          A_ID: ${{ secrets.APPLE_ID }}
+          A_PASS: ${{ secrets.APPLE_PASSWORD }}
+          A_TEAM: ${{ secrets.APPLE_TEAM_ID }}
+        shell: bash
+        run: |
+          set -uo pipefail
+
+          # Mirror the skip logic in "Configure Apple signing": without a
+          # certificate the build is unsigned and never notarizes, so there is
+          # nothing to preflight. Tag builds still hard-fail there.
+          if [ -z "$CERT" ]; then
+            echo "No Apple certificate configured; skipping notarization preflight."
+            exit 0
+          fi
+
+          missing=""
+          [ -z "$A_ID" ]   && missing="$missing APPLE_ID"
+          [ -z "$A_PASS" ] && missing="$missing APPLE_PASSWORD"
+          [ -z "$A_TEAM" ] && missing="$missing APPLE_TEAM_ID"
+          if [ -n "$missing" ]; then
+            echo "::error::APPLE_CERTIFICATE is set but notarization secrets are missing:$missing"
+            echo "::error::Signing would succeed and notarization would then fail. Set them or clear APPLE_CERTIFICATE."
+            exit 1
+          fi
+
+          # Retry only to absorb transient network faults. Credential and
+          # account errors are deterministic, so we classify and exit on the
+          # first definitive answer rather than retrying into the same wall.
+          attempt=1
+          while [ "$attempt" -le 3 ]; do
+            out=$(xcrun notarytool history \
+              --apple-id "$A_ID" \
+              --team-id "$A_TEAM" \
+              --password "$A_PASS" \
+              --output-format json 2>&1)
+            rc=$?
+
+            if [ $rc -eq 0 ]; then
+              echo "Apple notarization preflight OK — credentials valid, team reachable, agreements in effect."
+              exit 0
+            fi
+
+            case "$out" in
+              *"Invalid credentials"*|*"401"*)
+                echo "::error::Apple notarization preflight failed: invalid credentials (HTTP 401)."
+                echo "::error::APPLE_PASSWORD must be an app-specific password from appleid.apple.com,"
+                echo "::error::generated while signed in as the SAME Apple ID as APPLE_ID. A regular"
+                echo "::error::Apple ID password will not work, and a password minted under a different"
+                echo "::error::Apple ID authenticates as that other account."
+                exit 1
+                ;;
+              *"Invalid or inaccessible developer team ID"*)
+                echo "::error::Apple notarization preflight failed: APPLE_ID is not a member of team APPLE_TEAM_ID (HTTP 403)."
+                echo "::error::The Team ID must match the signing certificate. Read it from the cert's"
+                echo "::error::subject, where it appears as: Developer ID Application: NAME (TEAMID)."
+                echo "::error::If you belong to several teams, confirm APPLE_ID is a member of this one."
+                exit 1
+                ;;
+              *"required agreement"*|*"agreement"*)
+                echo "::error::Apple notarization preflight failed: the team has no in-effect agreement (HTTP 403)."
+                echo "::error::Apple reissues the Developer Program License Agreement periodically and"
+                echo "::error::notarization is refused until it is accepted. ONLY THE ACCOUNT HOLDER can"
+                echo "::error::accept it — team Admins cannot. Sign in to the account that owns this team:"
+                echo "::error::  1. https://developer.apple.com/account -> review any pending agreement"
+                echo "::error::  2. App Store Connect -> Business -> accept anything pending there too"
+                echo "::error::Certificates stay valid while this is outstanding, so signing still works."
+                exit 1
+                ;;
+            esac
+
+            echo "Preflight attempt ${attempt}/3 failed with a non-credential error."
+            echo "$out" | tail -5
+            attempt=$((attempt + 1))
+            [ "$attempt" -le 3 ] && sleep 10
+          done
+
+          echo "::error::Apple notarization preflight failed after 3 attempts. Last output:"
+          echo "$out" | tail -20
+          exit 1
+
       - name: Install system dependencies (Linux)
         if: matrix.platform == 'ubuntu-22.04'
         run: |
@@ -130,7 +229,8 @@ jobs:
             libappindicator3-dev \
             librsvg2-dev \
             patchelf \
-            libxdo-dev
+            libxdo-dev \
+            libdbus-1-dev
 
       - name: Install Rust stable
         uses: dtolnay/rust-toolchain@stable
@@ -252,6 +352,10 @@ jobs:
           TAURI_SIGNING_PRIVATE_KEY: ${{ secrets.TAURI_SIGNING_PRIVATE_KEY }}
           TAURI_SIGNING_PRIVATE_KEY_PASSWORD: ${{ secrets.TAURI_SIGNING_PRIVATE_KEY_PASSWORD }}
           TAURI_CONFIG: '{"version":"${{ steps.release-info.outputs.tauri_version }}","bundle":{"externalBin":["binaries/ollama"]}}'
+          # tauri-action runs beforeBuildCommand (npm run build:tauri -> vite
+          # build), which requires this at build time (#587). Strict for
+          # releases: a missing/empty secret fails the build by design.
+          VITE_SUPABASE_ANON_KEY: ${{ secrets.VITE_SUPABASE_ANON_KEY }}
         with:
           projectPath: frontend
           tauriScript: npx tauri

.github/workflows/docs.yml

@@ -41,6 +41,26 @@ jobs:
       - name: Install dependencies
         run: uv sync --extra docs
 
+      # Inject the public Supabase anon key so the savings leaderboard works on
+      # the published docs site. Missing/empty (e.g. fork PRs) leaves the
+      # leaderboard gracefully disabled. The key is read from env (not inlined)
+      # and JSON-encoded into a JS string literal to avoid any injection.
+      - name: Inject leaderboard Supabase anon key
+        env:
+          OPENJARVIS_LEADERBOARD_ANON: ${{ secrets.VITE_SUPABASE_ANON_KEY }}
+        run: |
+          python3 - <<'PY'
+          import json, os, pathlib
+
+          key = os.environ.get("OPENJARVIS_LEADERBOARD_ANON", "")
+          pathlib.Path("docs/javascripts/leaderboard-config.js").write_text(
+              "// Generated at docs-build time from the VITE_SUPABASE_ANON_KEY secret.\n"
+              "window.OPENJARVIS_SUPABASE_ANON_KEY = " + json.dumps(key) + ";\n",
+              encoding="utf-8",
+          )
+          print("leaderboard anon key:", "set" if key else "empty (leaderboard disabled)")
+          PY
+
       - name: Build documentation
         run: uv run mkdocs build
 

.github/workflows/frontend.yml

@@ -35,3 +35,8 @@ jobs:
       - run: npm ci
       - run: npx tsc --noEmit
       - run: npm run build
+        env:
+          # Optional: when the secret is unset the build still succeeds and the
+          # leaderboard is disabled (see src/lib/supabase.ts). No placeholder,
+          # so a keyless CI build doesn't bake in a bogus anon key.
+          VITE_SUPABASE_ANON_KEY: ${{ secrets.VITE_SUPABASE_ANON_KEY }}

.github/workflows/pypi-publish.yml

@@ -55,6 +55,8 @@ jobs:
           cache-dependency-path: frontend/package-lock.json
 
       - name: Build frontend and bundle into package
+        env:
+          VITE_SUPABASE_ANON_KEY: ${{ secrets.VITE_SUPABASE_ANON_KEY }}
         run: |
           set -euo pipefail
           cd frontend

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",
+]

deploy/docker/Dockerfile

@@ -1,34 +1,83 @@
+# Base images are pinned to an immutable digest (in addition to a human-readable
+# tag) so every build resolves the exact same layers — reproducible builds and
+# safe rollbacks (#563).
+
 # Stage 1: Build frontend SPA
-FROM node:22-slim AS frontend
+FROM node:22.23.0-slim@sha256:d9f850096136edbc402debdd8729579a288aac64574ada0ff4db26b6ae58b0b2 AS frontend
+# Public Supabase anon key for the savings leaderboard; empty by default so
+# the image's leaderboard stays disabled (#589). Pass --build-arg to enable.
+ARG OPENJARVIS_LEADERBOARD_PUBLIC_ANON=
 
 WORKDIR /frontend
 COPY frontend/package.json frontend/package-lock.json* ./
 RUN npm ci --ignore-scripts 2>/dev/null || npm install
 COPY frontend/ .
-RUN npm run build
+RUN VITE_SUPABASE_ANON_KEY="${OPENJARVIS_LEADERBOARD_PUBLIC_ANON}" npm run build
 
 # Stage 2: Build Python package
-FROM python:3.12-slim-bookworm AS builder
+FROM python:3.12.13-slim-bookworm@sha256:76d4b7b6305788c6b4c6a19d6a22a3921bf802e9af4d5e1e5bd771208dba74bf AS builder
+
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends build-essential ca-certificates curl && \
+    rm -rf /var/lib/apt/lists/*
+
+ENV PATH="/root/.cargo/bin:${PATH}"
+
+RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | \
+        sh -s -- -y --profile minimal --default-toolchain none && \
+    rustup toolchain install 1.88 --profile minimal && \
+    rustup default 1.88
 
 WORKDIR /app
-COPY pyproject.toml README.md ./
+
+# Install dependencies from the committed lockfile (#567). `uv export --frozen`
+# reads uv.lock as-is (no re-resolution) and emits a fully pinned, hash-verified
+# requirements set; `--no-deps` then installs exactly that set. This is a
+# separate layer from the source copy so dependency installs stay cached when
+# only application code changes.
+COPY pyproject.toml uv.lock README.md ./
+RUN pip install --no-cache-dir uv && \
+    uv export --frozen --no-dev --extra server --no-emit-project > requirements.txt && \
+    uv pip install --system --no-deps -r requirements.txt && \
+    uv pip install --system --no-deps "maturin>=1.12.6,<2"
+
+# Copy the source and the non-src force-include paths (see pyproject
+# [tool.hatch.build.targets.wheel.force-include]) before building the project.
 COPY src/ src/
+COPY rust/ rust/
 COPY scripts/install scripts/install
 COPY deploy/windows deploy/windows
 
 # Copy built frontend into the server static directory
 COPY --from=frontend /src/openjarvis/server/static src/openjarvis/server/static/
 
-RUN pip install --no-cache-dir uv && \
-    uv pip install --system ".[server]"
+# Install the project itself without re-resolving dependencies.
+RUN uv pip install --system --no-deps . && \
+    maturin build --release \
+        --manifest-path rust/crates/openjarvis-python/Cargo.toml \
+        --interpreter python3 \
+        --out /tmp/openjarvis-rust-wheel && \
+    uv pip install --system --no-deps /tmp/openjarvis-rust-wheel/*.whl && \
+    python3 -c "import openjarvis_rust; print('openjarvis_rust ok')" && \
+    python3 -m pip uninstall -y maturin && \
+    rm -rf /tmp/openjarvis-rust-wheel rust
 
 # Stage 3: Runtime
-FROM python:3.12-slim-bookworm
+FROM python:3.12.13-slim-bookworm@sha256:76d4b7b6305788c6b4c6a19d6a22a3921bf802e9af4d5e1e5bd771208dba74bf
 
 COPY --from=builder /usr/local /usr/local
 COPY --from=builder /app /app
 WORKDIR /app
 
+# Run as an unprivileged user — the server needs no root privileges, so dropping
+# them limits the blast radius of a compromise (#565). The app writes only to
+# $HOME (config/cache/state), which is owned by this user.
+RUN groupadd --system --gid 10001 openjarvis && \
+    useradd --system --uid 10001 --gid openjarvis \
+        --create-home --home-dir /home/openjarvis openjarvis
+ENV HOME=/home/openjarvis
+USER openjarvis
+
 EXPOSE 8000
 
 ENTRYPOINT ["jarvis"]

deploy/docker/Dockerfile.gpu

@@ -1,32 +1,69 @@
+# Base images are pinned to an immutable digest (in addition to a human-readable
+# tag) so every build resolves the exact same layers — reproducible builds and
+# safe rollbacks (#563).
+
 # Stage 1: Build frontend SPA
-FROM node:22-slim AS frontend
+FROM node:22.23.0-slim@sha256:d9f850096136edbc402debdd8729579a288aac64574ada0ff4db26b6ae58b0b2 AS frontend
+# Public Supabase anon key for the savings leaderboard; empty by default so
+# the image's leaderboard stays disabled (#589). Pass --build-arg to enable.
+ARG OPENJARVIS_LEADERBOARD_PUBLIC_ANON=
 
 WORKDIR /frontend
 COPY frontend/package.json frontend/package-lock.json* ./
 RUN npm ci --ignore-scripts 2>/dev/null || npm install
 COPY frontend/ .
-RUN npm run build
+RUN VITE_SUPABASE_ANON_KEY="${OPENJARVIS_LEADERBOARD_PUBLIC_ANON}" npm run build
 
 # Stage 2: Build Python package (NVIDIA CUDA 12.4)
-FROM nvidia/cuda:12.4.0-runtime-ubuntu22.04 AS builder
+FROM nvidia/cuda:12.4.0-runtime-ubuntu22.04@sha256:af8bd179ed3bf69d4b63b19a763662a6141f0f62ef099283f68d0b14b4bab0e3 AS builder
 
 RUN apt-get update && \
-    apt-get install -y --no-install-recommends python3 python3-pip python3-venv && \
+    apt-get install -y --no-install-recommends \
+        build-essential \
+        ca-certificates \
+        curl \
+        python3 \
+        python3-dev \
+        python3-pip \
+        python3-venv && \
     rm -rf /var/lib/apt/lists/*
 
+ENV PATH="/root/.cargo/bin:${PATH}"
+
+RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | \
+        sh -s -- -y --profile minimal --default-toolchain none && \
+    rustup toolchain install 1.88 --profile minimal && \
+    rustup default 1.88
+
 WORKDIR /app
-COPY pyproject.toml README.md ./
+
+# Install dependencies from the committed lockfile (#567). See deploy/docker/Dockerfile
+# for the rationale behind the frozen export + --no-deps install.
+COPY pyproject.toml uv.lock README.md ./
+RUN pip install --no-cache-dir uv && \
+    uv export --frozen --no-dev --extra server --no-emit-project > requirements.txt && \
+    uv pip install --system --no-deps -r requirements.txt && \
+    uv pip install --system --no-deps "maturin>=1.12.6,<2"
+
 COPY src/ src/
+COPY rust/ rust/
 COPY scripts/install scripts/install
 COPY deploy/windows deploy/windows
 
 COPY --from=frontend /src/openjarvis/server/static src/openjarvis/server/static/
 
-RUN pip install --no-cache-dir uv && \
-    uv pip install --system ".[server]"
+RUN uv pip install --system --no-deps . && \
+    maturin build --release \
+        --manifest-path rust/crates/openjarvis-python/Cargo.toml \
+        --interpreter python3 \
+        --out /tmp/openjarvis-rust-wheel && \
+    uv pip install --system --no-deps /tmp/openjarvis-rust-wheel/*.whl && \
+    python3 -c "import openjarvis_rust; print('openjarvis_rust ok')" && \
+    python3 -m pip uninstall -y maturin && \
+    rm -rf /tmp/openjarvis-rust-wheel rust
 
 # Stage 3: Runtime
-FROM nvidia/cuda:12.4.0-runtime-ubuntu22.04
+FROM nvidia/cuda:12.4.0-runtime-ubuntu22.04@sha256:af8bd179ed3bf69d4b63b19a763662a6141f0f62ef099283f68d0b14b4bab0e3
 
 RUN apt-get update && \
     apt-get install -y --no-install-recommends python3 python3-pip && \
@@ -36,6 +73,14 @@ COPY --from=builder /usr/local /usr/local
 COPY --from=builder /app /app
 WORKDIR /app
 
+# Run as an unprivileged user (#565). NVIDIA device nodes (/dev/nvidia*) are
+# world-accessible, so GPU workloads do not require root.
+RUN groupadd --system --gid 10001 openjarvis && \
+    useradd --system --uid 10001 --gid openjarvis \
+        --create-home --home-dir /home/openjarvis openjarvis
+ENV HOME=/home/openjarvis
+USER openjarvis
+
 EXPOSE 8000
 
 ENTRYPOINT ["jarvis"]

deploy/docker/Dockerfile.gpu.rocm

@@ -1,32 +1,69 @@
+# Base images are pinned to an immutable digest (in addition to a human-readable
+# tag) so every build resolves the exact same layers — reproducible builds and
+# safe rollbacks (#563).
+
 # Stage 1: Build frontend SPA
-FROM node:22-slim AS frontend
+FROM node:22.23.0-slim@sha256:d9f850096136edbc402debdd8729579a288aac64574ada0ff4db26b6ae58b0b2 AS frontend
+# Public Supabase anon key for the savings leaderboard; empty by default so
+# the image's leaderboard stays disabled (#589). Pass --build-arg to enable.
+ARG OPENJARVIS_LEADERBOARD_PUBLIC_ANON=
 
 WORKDIR /frontend
 COPY frontend/package.json frontend/package-lock.json* ./
 RUN npm ci --ignore-scripts 2>/dev/null || npm install
 COPY frontend/ .
-RUN npm run build
+RUN VITE_SUPABASE_ANON_KEY="${OPENJARVIS_LEADERBOARD_PUBLIC_ANON}" npm run build
 
 # Stage 2: Build Python package (AMD ROCm 7.2)
-FROM rocm/dev-ubuntu-22.04:7.2 AS builder
+FROM rocm/dev-ubuntu-22.04:7.2@sha256:05af5f04a06b04676d4c7438997d0deadaeb7478961ad621376e199bf3aeb644 AS builder
 
 RUN apt-get update && \
-    apt-get install -y --no-install-recommends python3 python3-pip python3-venv && \
+    apt-get install -y --no-install-recommends \
+        build-essential \
+        ca-certificates \
+        curl \
+        python3 \
+        python3-dev \
+        python3-pip \
+        python3-venv && \
     rm -rf /var/lib/apt/lists/*
 
+ENV PATH="/root/.cargo/bin:${PATH}"
+
+RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | \
+        sh -s -- -y --profile minimal --default-toolchain none && \
+    rustup toolchain install 1.88 --profile minimal && \
+    rustup default 1.88
+
 WORKDIR /app
-COPY pyproject.toml README.md ./
+
+# Install dependencies from the committed lockfile (#567). See deploy/docker/Dockerfile
+# for the rationale behind the frozen export + --no-deps install.
+COPY pyproject.toml uv.lock README.md ./
+RUN pip install --no-cache-dir uv && \
+    uv export --frozen --no-dev --extra server --no-emit-project > requirements.txt && \
+    uv pip install --system --no-deps -r requirements.txt && \
+    uv pip install --system --no-deps "maturin>=1.12.6,<2"
+
 COPY src/ src/
+COPY rust/ rust/
 COPY scripts/install scripts/install
 COPY deploy/windows deploy/windows
 
 COPY --from=frontend /src/openjarvis/server/static src/openjarvis/server/static/
 
-RUN pip install --no-cache-dir uv && \
-    uv pip install --system ".[server]"
+RUN uv pip install --system --no-deps . && \
+    maturin build --release \
+        --manifest-path rust/crates/openjarvis-python/Cargo.toml \
+        --interpreter python3 \
+        --out /tmp/openjarvis-rust-wheel && \
+    uv pip install --system --no-deps /tmp/openjarvis-rust-wheel/*.whl && \
+    python3 -c "import openjarvis_rust; print('openjarvis_rust ok')" && \
+    python3 -m pip uninstall -y maturin && \
+    rm -rf /tmp/openjarvis-rust-wheel rust
 
 # Stage 3: Runtime
-FROM rocm/dev-ubuntu-22.04:7.2
+FROM rocm/dev-ubuntu-22.04:7.2@sha256:05af5f04a06b04676d4c7438997d0deadaeb7478961ad621376e199bf3aeb644
 
 RUN apt-get update && \
     apt-get install -y --no-install-recommends python3 python3-pip && \
@@ -36,6 +73,18 @@ COPY --from=builder /usr/local /usr/local
 COPY --from=builder /app /app
 WORKDIR /app
 
+# Run as an unprivileged user (#565). ROCm GPU access is gated by the `video` and
+# `render` groups (see group_add in docker-compose.gpu.rocm.yml), so the user is
+# added to both; root is not required.
+RUN groupadd --system --gid 10001 openjarvis && \
+    useradd --system --uid 10001 --gid openjarvis \
+        --create-home --home-dir /home/openjarvis openjarvis && \
+    (getent group video  >/dev/null || groupadd --system video) && \
+    (getent group render >/dev/null || groupadd --system render) && \
+    usermod -aG video,render openjarvis
+ENV HOME=/home/openjarvis
+USER openjarvis
+
 EXPOSE 8000
 
 ENTRYPOINT ["jarvis"]

deploy/docker/Dockerfile.sandbox

@@ -1,15 +1,69 @@
-FROM python:3.12-slim
+# Base images are pinned to an immutable digest (in addition to a human-readable
+# tag) so every build resolves the exact same layers (#563).
 
-# Install Node.js 22
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    curl ca-certificates && \
-    curl -fsSL https://deb.nodesource.com/setup_22.x | bash - && \
-    apt-get install -y nodejs && \
+# Node.js is sourced from the official, digest-pinned image rather than piping a
+# remote setup script into bash (`curl ... | bash -`), which performed no
+# checksum or signature verification of the downloaded installer (#566). The
+# image digest is the integrity check, and the copy is architecture-agnostic.
+FROM node:22.23.0-slim@sha256:d9f850096136edbc402debdd8729579a288aac64574ada0ff4db26b6ae58b0b2 AS node
+
+FROM python:3.12.13-slim-bookworm@sha256:76d4b7b6305788c6b4c6a19d6a22a3921bf802e9af4d5e1e5bd771208dba74bf AS builder
+
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends build-essential ca-certificates curl && \
     rm -rf /var/lib/apt/lists/*
 
+ENV PATH="/root/.cargo/bin:${PATH}"
+
+RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | \
+        sh -s -- -y --profile minimal --default-toolchain none && \
+    rustup toolchain install 1.88 --profile minimal && \
+    rustup default 1.88
+
 WORKDIR /app
+
+# Install dependencies from the committed lockfile (#567): `uv export --frozen`
+# reads uv.lock as-is and emits a pinned, hash-verified set installed with
+# --no-deps (no re-resolution). Copied first so this layer caches independently
+# of application source.
+COPY pyproject.toml uv.lock README.md ./
+RUN pip install --no-cache-dir uv && \
+    uv export --frozen --no-dev --extra server --no-emit-project > requirements.txt && \
+    uv pip install --system --no-deps -r requirements.txt && \
+    uv pip install --system --no-deps "maturin>=1.12.6,<2"
+
 COPY . .
-RUN pip install --no-cache-dir ".[server]"
+
+# Install the project itself without re-resolving dependencies.
+RUN uv pip install --system --no-deps . && \
+    maturin build --release \
+        --manifest-path rust/crates/openjarvis-python/Cargo.toml \
+        --interpreter python3 \
+        --out /tmp/openjarvis-rust-wheel && \
+    uv pip install --system --no-deps /tmp/openjarvis-rust-wheel/*.whl && \
+    python3 -c "import openjarvis_rust; print('openjarvis_rust ok')" && \
+    python3 -m pip uninstall -y maturin && \
+    rm -rf /tmp/openjarvis-rust-wheel rust/target
+
+FROM python:3.12.13-slim-bookworm@sha256:76d4b7b6305788c6b4c6a19d6a22a3921bf802e9af4d5e1e5bd771208dba74bf
+
+# libstdc++6 + ca-certificates are the only runtime requirements of the Node
+# binary copied below (the python slim image already provides libc/libgcc).
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends ca-certificates libstdc++6 && \
+    rm -rf /var/lib/apt/lists/*
+
+COPY --from=builder /usr/local /usr/local
+COPY --from=builder /app /app
+
+# Transplant the Node.js runtime from the official image. Both images are Debian
+# bookworm, so the glibc/libstdc++ ABI matches.
+COPY --from=node /usr/local/bin/node /usr/local/bin/node
+COPY --from=node /usr/local/lib/node_modules /usr/local/lib/node_modules
+RUN ln -sf /usr/local/lib/node_modules/npm/bin/npm-cli.js /usr/local/bin/npm && \
+    ln -sf /usr/local/lib/node_modules/npm/bin/npx-cli.js /usr/local/bin/npx
+
+WORKDIR /app
 
 LABEL openjarvis-sandbox=true
 

deploy/docker/docker-compose.gpu.nvidia.yml

@@ -18,7 +18,9 @@ services:
               capabilities: [gpu]
 
   ollama:
-    image: ollama/ollama:latest
+    # Pinned to a fixed version + digest for reproducible deployments (#563);
+    # must match the tag in docker-compose.yml.
+    image: ollama/ollama:0.30.10@sha256:bfc9c6d53cc6989aa5131a6fde6b162b2802d4d337657f3253b5f69579bddeee
     environment:
       - NVIDIA_VISIBLE_DEVICES=all
       - NVIDIA_DRIVER_CAPABILITIES=compute,utility

deploy/docker/docker-compose.yml

@@ -18,7 +18,9 @@ services:
     restart: unless-stopped
 
   ollama:
-    image: ollama/ollama:latest
+    # Pinned to a fixed version + digest for reproducible deployments and
+    # predictable rollbacks (#563). Bump deliberately, not implicitly via :latest.
+    image: ollama/ollama:0.30.10@sha256:bfc9c6d53cc6989aa5131a6fde6b162b2802d4d337657f3253b5f69579bddeee
     ports:
       - "11434:11434"
     volumes:

deploy/systemd/openjarvis.service

@@ -14,7 +14,27 @@ Environment=HOME=/opt/openjarvis
 #   OPENJARVIS_API_KEY=<key>      (generate one: `jarvis auth generate-key`)
 # It is not prefixed with "-", so the unit fails to start if the file is
 # missing — preventing an accidentally unauthenticated public server.
+# Keep secrets here (mode 0600, owned by root) rather than inline Environment=
+# lines, which leak into `systemctl show` and the journal.
 EnvironmentFile=/etc/openjarvis/env
 
+# --- Sandboxing / hardening (#564) ---
+# Conservative set: tightens the unit without blocking the server's normal I/O
+# or local GPU inference. ProtectSystem=strict makes the whole filesystem
+# read-only except ReadWritePaths, so $HOME (config/cache/state under
+# /opt/openjarvis) stays writable.
+NoNewPrivileges=true
+ProtectSystem=strict
+ReadWritePaths=/opt/openjarvis
+ProtectHome=true
+PrivateTmp=true
+ProtectControlGroups=true
+ProtectKernelLogs=true
+ProtectKernelModules=true
+ProtectKernelTunables=true
+RestrictRealtime=true
+RestrictSUIDSGID=true
+LockPersonality=true
+
 [Install]
 WantedBy=multi-user.target

deploy/windows/README.md

@@ -20,8 +20,8 @@ What it does:
 4. Installs `uv` (https://astral.sh/uv) if absent.
 5. Clones the OpenJarvis repository to `%LOCALAPPDATA%\OpenJarvis`
    (override with `$env:OPENJARVIS_HOME`).
-6. Runs `uv sync --extra server` so the FastAPI server entry point is
-   importable.
+6. Runs `uv sync --extra desktop --group desktop-native` so the FastAPI server,
+   speech backend, and native extension are importable.
 7. Optionally prompts to register a scheduled task that auto-starts the
    server at logon.
 
@@ -105,7 +105,7 @@ To pull the latest:
 ```powershell
 cd "$env:LOCALAPPDATA\OpenJarvis\src"
 git pull --ff-only
-uv sync --extra server
+uv sync --extra desktop --group desktop-native
 ```
 
 Or re-run the installer with `-Force`:

deploy/windows/install.ps1

@@ -16,8 +16,8 @@
       4. Install uv (https://astral.sh/uv) if absent.
       5. Clone the OpenJarvis repository to $env:LOCALAPPDATA\OpenJarvis
          (override with $env:OPENJARVIS_HOME).
-      6. Run `uv sync --extra server` so the FastAPI server entry point
-         is importable.
+      6. Run `uv sync --extra desktop --group desktop-native` so the FastAPI
+         server, speech backend, and native extension are importable.
       7. Optionally register the scheduled-task service (see
          deploy/windows/jarvis-service.ps1).
 
@@ -279,13 +279,13 @@ if (Test-Path (Join-Path $srcDir '.git')) {
 }
 
 # ---------------------------------------------------------------------------
-# 6. uv sync --extra server
+# 6. uv sync --extra desktop --group desktop-native
 # ---------------------------------------------------------------------------
 
-Write-Info "Running 'uv sync --extra server' in $srcDir (this can take a few minutes)..."
+Write-Info "Running 'uv sync --extra desktop --group desktop-native' in $srcDir (this can take a few minutes)..."
 Push-Location $srcDir
 try {
-    & $uvExe sync --extra server
+    & $uvExe sync --extra desktop --group desktop-native
     if ($LASTEXITCODE -ne 0) {
         Write-Fail "uv sync failed with exit code $LASTEXITCODE. Check the output above."
     }

docs/downloads.md

@@ -109,7 +109,7 @@ If you prefer to run each step yourself:
     ```bash
     git clone https://github.com/open-jarvis/OpenJarvis.git
     cd OpenJarvis
-    uv sync --extra server
+    uv sync --extra desktop
     cd frontend && npm install && cd ..
     ```
 

docs/getting-started/configuration.md

@@ -17,6 +17,46 @@ The configuration file lives at:
 
 OpenJarvis creates the `~/.openjarvis/` directory and populates it with a default config when you run `jarvis init`.
 
+## Relocating the OpenJarvis directory
+
+OpenJarvis keeps **all** of its state — config, databases, caches, logs,
+credentials, skills, recipes, connectors — under a **single root** so it never
+clutters your home directory beyond one folder. By default that root is
+`~/.openjarvis`, but you can move it.
+
+The root is resolved in priority order:
+
+1. **`$OPENJARVIS_HOME`** — explicit override. Honored by both the installer
+   and the Python runtime.
+2. **`$XDG_DATA_HOME/openjarvis`** — used when `$XDG_DATA_HOME` is set (a single
+   `openjarvis` directory nested under it, per the XDG Base Directory spec).
+3. **`~/.openjarvis`** — the default. With no environment variables set, the
+   resolved path is exactly this, so existing installs are untouched.
+
+```bash
+# Relocate the whole install + runtime tree at install time:
+OPENJARVIS_HOME=~/apps/openjarvis curl -fsSL https://open-jarvis.github.io/OpenJarvis/install.sh | bash
+
+# Or for a single run / your shell profile:
+export OPENJARVIS_HOME=~/apps/openjarvis
+```
+
+Confirm where your data lives with:
+
+```bash
+jarvis config path
+```
+
+!!! note "Migration"
+    Because the default is unchanged, **no data migration is required** for
+    existing installs. If you set `OPENJARVIS_HOME` (or `XDG_DATA_HOME`) on a
+    machine that already has data in `~/.openjarvis`, OpenJarvis will look in
+    the new location and not see your old data — move it yourself if you want
+    to keep it: `mv ~/.openjarvis "$OPENJARVIS_HOME"`.
+
+`$OPENJARVIS_CONFIG` still points at an explicit `config.toml` file
+independently of the root, if you need to override just the config file path.
+
 ## Generating Configuration
 
 ### First-Time Setup
@@ -564,7 +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/installation.md

@@ -41,7 +41,7 @@ If you prefer to run each step yourself:
     ```bash
     git clone https://github.com/open-jarvis/OpenJarvis.git
     cd OpenJarvis
-    uv sync --extra server
+    uv sync --extra desktop
     uv run maturin develop -m rust/crates/openjarvis-python/Cargo.toml
     cd frontend && npm install && cd ..
     ```
@@ -278,6 +278,7 @@ OpenJarvis uses optional extras to keep the base installation lightweight.
 
 | Extra | Install Command | Description |
 |-------|----------------|-------------|
+| `desktop` | `uv sync --extra desktop` | Desktop/API server plus local speech input |
 | `server` | `uv sync --extra server` | OpenAI-compatible API server (`jarvis serve`) |
 | `dev` | `uv sync --extra dev` | Development and testing tools |
 | `docs` | `uv sync --extra docs` | Documentation build tools |
@@ -285,7 +286,7 @@ OpenJarvis uses optional extras to keep the base installation lightweight.
 Combine extras:
 
 ```bash
-uv sync --extra server --extra memory-faiss --extra inference-cloud
+uv sync --extra desktop --extra memory-faiss --extra inference-cloud
 ```
 
 ## Setting Up an Inference Backend

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/getting-started/windows-native.md

@@ -8,7 +8,7 @@ avoid a Linux VM; WSL2 remains the smoother experience for most users.
 ## What you get
 
 - A PowerShell installer that probes prerequisites, installs `uv`,
-  clones the repo, and runs `uv sync --extra server`.
+  clones the repo, and runs `uv sync --extra desktop --group desktop-native`.
 - An optional Windows scheduled-task service equivalent to the systemd
   unit and launchd plist.
 - Loopback default — the service binds `127.0.0.1` so no API key is
@@ -38,7 +38,7 @@ The installer will:
 4. Install `uv` if absent (via the official `astral.sh/uv` PowerShell
    installer).
 5. Clone the repo to `%LOCALAPPDATA%\OpenJarvis\src`.
-6. Run `uv sync --extra server`.
+6. Run `uv sync --extra desktop --group desktop-native`.
 7. Prompt to register the scheduled-task service (skip with
    `-SkipService`).
 

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/javascripts/leaderboard-config.js

@@ -0,0 +1,12 @@
+// Public Supabase config for the savings leaderboard.
+//
+// This file is loaded *before* leaderboard.js and supplies the anon key it
+// reads from `window.OPENJARVIS_SUPABASE_ANON_KEY`. The key is injected at
+// docs-build time from the VITE_SUPABASE_ANON_KEY repo secret (see
+// .github/workflows/docs.yml). It is intentionally empty here so that local
+// `mkdocs build` and fork pull requests — which have no secret — render the
+// graceful "Leaderboard not configured yet" message instead of failing.
+//
+// The anon key is public by design: Supabase Row-Level Security protects the
+// data, so shipping it in the public docs bundle is expected.
+window.OPENJARVIS_SUPABASE_ANON_KEY = "";

docs/javascripts/leaderboard.js

@@ -1,9 +1,9 @@
 (function () {
   "use strict";
 
-  var SUPABASE_URL = "https://mtbtgpwzrbostweaanpr.supabase.co";
-  var SUPABASE_ANON_KEY =
-    "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6Im10YnRncHd6cmJvc3R3ZWFhbnByIiwicm9sZSI6ImFub24iLCJpYXQiOjE3NzMxODk0OTQsImV4cCI6MjA4ODc2NTQ5NH0._xMlqCfljtXpwPj54H-ghxfLFO-jiq4W2WhpU8vVL1c";
+  var SUPABASE_URL =
+    window.OPENJARVIS_SUPABASE_URL || "https://mtbtgpwzrbostweaanpr.supabase.co";
+  var SUPABASE_ANON_KEY = window.OPENJARVIS_SUPABASE_ANON_KEY || "";
 
   var PAGE_SIZE = 50;
   var allRows = [];

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/agents.md

@@ -19,6 +19,71 @@ Agents are the agentic logic layer of OpenJarvis. They determine how a query is
 
 ---
 
+## Persistent Persona: SOUL.md, MEMORY.md, USER.md
+
+Every agent's system prompt is assembled at conversation start by the `SystemPromptBuilder`, which injects up to three optional Markdown files -- the **persistent persona**. They are plain text you own and edit, loaded at the start of each conversation. There is no vector database or embedding cache behind them.
+
+| File | What it holds | Example line |
+|------|---------------|--------------|
+| `SOUL.md` | How the agent should behave -- tone, length, what to push back on | `Be concise. Challenge weak assumptions.` |
+| `MEMORY.md` | Facts about you, your projects, your preferences | `I deploy to Postgres, never MySQL.` |
+| `USER.md` | Who you are -- role, team, context | `Backend engineer at Acme, on the payments team.` |
+
+This persona is distinct from the retrieval [memory backend](memory.md): the persona is always-on Markdown context loaded into the prompt, while the memory backend is searchable long-term storage the agent queries on demand.
+
+### Where they live
+
+By default the files are read from the config directory:
+
+```
+~/.openjarvis/SOUL.md
+~/.openjarvis/MEMORY.md
+~/.openjarvis/USER.md
+```
+
+(The config directory honors `$OPENJARVIS_HOME` / `$XDG_DATA_HOME` when set.) The paths are configurable under `[memory_files]`:
+
+```toml
+[memory_files]
+soul_path    = "~/.openjarvis/SOUL.md"
+memory_path  = "~/.openjarvis/MEMORY.md"
+user_path    = "~/.openjarvis/USER.md"
+persona_name = ""    # optional named persona -- see below
+```
+
+### How they're loaded
+
+At the start of each conversation, `SystemPromptBuilder` reads each file as UTF-8 and adds its contents as a section of the system prompt, after the agent template and before the skill catalog:
+
+- **All three are optional.** A missing or empty file is skipped, so any subset works and an install with no persona files behaves exactly as before.
+- **Edits apply to the next conversation.** The files are read once when a conversation's prompt is built, so there is no restart or re-indexing -- edit or delete a line and it takes effect the next time you start a conversation.
+- **Each section is length-capped.** Files are truncated to a per-section character budget so a large `MEMORY.md` cannot crowd out the rest of the prompt.
+
+### Named personas
+
+A single install can answer as different personas without changing global config. A named persona lives in its own directory:
+
+```
+~/.openjarvis/personas/<name>/SOUL.md
+~/.openjarvis/personas/<name>/MEMORY.md
+~/.openjarvis/personas/<name>/USER.md
+```
+
+Select one per invocation, or opt out entirely:
+
+```bash
+jarvis ask --persona work  "summarize my open PRs"
+jarvis ask --persona none  "what is 2 + 2?"     # inject no persona
+```
+
+Set `persona_name` under `[memory_files]` to make a named persona the default. `persona_name = "none"` (equivalently `--persona none`) disables persona injection for that run.
+
+### Editing them
+
+`SOUL.md`, `MEMORY.md`, and `USER.md` are plain Markdown -- open them in any editor. `MEMORY.md` and `USER.md` can also be updated by the agent itself through the `memory_manage` and `user_profile_manage` tools when those are enabled, so the agent can record a new fact mid-conversation. These tools always target the default `MEMORY.md` and `USER.md` (under `~/.openjarvis/`), never a named persona's copies -- edit those by hand.
+
+---
+
 ## BaseAgent ABC
 
 All agents extend the abstract `BaseAgent` class.

docs/user-guide/evaluations.md

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

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/package-lock.json

@@ -11,7 +11,7 @@
         "@base-ui/react": "^1.3.0",
         "@fontsource-variable/geist": "^5.2.8",
         "@tailwindcss/vite": "^4.2.1",
-        "@tauri-apps/api": "^2",
+        "@tauri-apps/api": "^2.11.1",
         "@tauri-apps/plugin-autostart": "^2",
         "@tauri-apps/plugin-dialog": "^2.7.0",
         "@tauri-apps/plugin-global-shortcut": "^2",
@@ -42,7 +42,7 @@
         "zustand": "^5.0.11"
       },
       "devDependencies": {
-        "@tauri-apps/cli": "^2",
+        "@tauri-apps/cli": "^2.11.4",
         "@types/react": "^19.0.0",
         "@types/react-dom": "^19.0.0",
         "@vitejs/plugin-react": "^4.3.4",
@@ -3720,9 +3720,9 @@
       }
     },
     "node_modules/@tauri-apps/api": {
-      "version": "2.10.1",
-      "resolved": "https://registry.npmjs.org/@tauri-apps/api/-/api-2.10.1.tgz",
-      "integrity": "sha512-hKL/jWf293UDSUN09rR69hrToyIXBb8CjGaWC7gfinvnQrBVvnLr08FeFi38gxtugAVyVcTa5/FD/Xnkb1siBw==",
+      "version": "2.11.1",
+      "resolved": "https://registry.npmjs.org/@tauri-apps/api/-/api-2.11.1.tgz",
+      "integrity": "sha512-M2FPuYND2m+wh5hfW9ZpSdxMPdEJovPBWwoHJmwUpysTYNHaOkVFN419m/K0LIgjb/7KU2vBgsUepJWugQCvAA==",
       "license": "Apache-2.0 OR MIT",
       "funding": {
         "type": "opencollective",
@@ -3730,9 +3730,9 @@
       }
     },
     "node_modules/@tauri-apps/cli": {
-      "version": "2.10.1",
-      "resolved": "https://registry.npmjs.org/@tauri-apps/cli/-/cli-2.10.1.tgz",
-      "integrity": "sha512-jQNGF/5quwORdZSSLtTluyKQ+o6SMa/AUICfhf4egCGFdMHqWssApVgYSbg+jmrZoc8e1DscNvjTnXtlHLS11g==",
+      "version": "2.11.4",
+      "resolved": "https://registry.npmjs.org/@tauri-apps/cli/-/cli-2.11.4.tgz",
+      "integrity": "sha512-R8xGtMpwyetawSqm9kYOuMmEqkhUbvcUy8n0aNXIxollKBLESUu5f4Fx+64hgASYm1H+jSWq6jCW6zqTnH6hqQ==",
       "dev": true,
       "license": "Apache-2.0 OR MIT",
       "bin": {
@@ -3746,23 +3746,23 @@
         "url": "https://opencollective.com/tauri"
       },
       "optionalDependencies": {
-        "@tauri-apps/cli-darwin-arm64": "2.10.1",
-        "@tauri-apps/cli-darwin-x64": "2.10.1",
-        "@tauri-apps/cli-linux-arm-gnueabihf": "2.10.1",
-        "@tauri-apps/cli-linux-arm64-gnu": "2.10.1",
-        "@tauri-apps/cli-linux-arm64-musl": "2.10.1",
-        "@tauri-apps/cli-linux-riscv64-gnu": "2.10.1",
-        "@tauri-apps/cli-linux-x64-gnu": "2.10.1",
-        "@tauri-apps/cli-linux-x64-musl": "2.10.1",
-        "@tauri-apps/cli-win32-arm64-msvc": "2.10.1",
-        "@tauri-apps/cli-win32-ia32-msvc": "2.10.1",
-        "@tauri-apps/cli-win32-x64-msvc": "2.10.1"
+        "@tauri-apps/cli-darwin-arm64": "2.11.4",
+        "@tauri-apps/cli-darwin-x64": "2.11.4",
+        "@tauri-apps/cli-linux-arm-gnueabihf": "2.11.4",
+        "@tauri-apps/cli-linux-arm64-gnu": "2.11.4",
+        "@tauri-apps/cli-linux-arm64-musl": "2.11.4",
+        "@tauri-apps/cli-linux-riscv64-gnu": "2.11.4",
+        "@tauri-apps/cli-linux-x64-gnu": "2.11.4",
+        "@tauri-apps/cli-linux-x64-musl": "2.11.4",
+        "@tauri-apps/cli-win32-arm64-msvc": "2.11.4",
+        "@tauri-apps/cli-win32-ia32-msvc": "2.11.4",
+        "@tauri-apps/cli-win32-x64-msvc": "2.11.4"
       }
     },
     "node_modules/@tauri-apps/cli-darwin-arm64": {
-      "version": "2.10.1",
-      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-darwin-arm64/-/cli-darwin-arm64-2.10.1.tgz",
-      "integrity": "sha512-Z2OjCXiZ+fbYZy7PmP3WRnOpM9+Fy+oonKDEmUE6MwN4IGaYqgceTjwHucc/kEEYZos5GICve35f7ZiizgqEnQ==",
+      "version": "2.11.4",
+      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-darwin-arm64/-/cli-darwin-arm64-2.11.4.tgz",
+      "integrity": "sha512-1ryOF3ZhpZ/nemHV5zVwBQBz9jDGKmKPvWPADOhc83ig0P4bMc2iER4NbC6r9sjeIZ6RVQ4g3RZIYvezhcl4TQ==",
       "cpu": [
         "arm64"
       ],
@@ -3777,9 +3777,9 @@
       }
     },
     "node_modules/@tauri-apps/cli-darwin-x64": {
-      "version": "2.10.1",
-      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-darwin-x64/-/cli-darwin-x64-2.10.1.tgz",
-      "integrity": "sha512-V/irQVvjPMGOTQqNj55PnQPVuH4VJP8vZCN7ajnj+ZS8Kom1tEM2hR3qbbIRoS3dBKs5mbG8yg1WC+97dq17Pw==",
+      "version": "2.11.4",
+      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-darwin-x64/-/cli-darwin-x64-2.11.4.tgz",
+      "integrity": "sha512-uFsGQAAfuyz1k/yGLmkWfkBlgKAqZfxqlHmLWx81QU27RJWfmbNHCIq8T8w1e+VClleIuZUjpHWfoE4E3DLo3A==",
       "cpu": [
         "x64"
       ],
@@ -3794,9 +3794,9 @@
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     },
     "node_modules/@tauri-apps/cli-linux-arm-gnueabihf": {
-      "version": "2.10.1",
-      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-linux-arm-gnueabihf/-/cli-linux-arm-gnueabihf-2.10.1.tgz",
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+      "version": "2.11.4",
+      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-linux-arm-gnueabihf/-/cli-linux-arm-gnueabihf-2.11.4.tgz",
+      "integrity": "sha512-IaHZn5CdBL21oUmjiVOS1ctw6Ip1O0pjp70FwOWmYz1myWe0SY96ZIj2FYf7pT0m8bI2h/hrs5ZbEXXh44/MkQ==",
       "cpu": [
         "arm"
       ],
@@ -3811,13 +3811,16 @@
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     },
     "node_modules/@tauri-apps/cli-linux-arm64-gnu": {
-      "version": "2.10.1",
-      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-linux-arm64-gnu/-/cli-linux-arm64-gnu-2.10.1.tgz",
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+      "version": "2.11.4",
+      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-linux-arm64-gnu/-/cli-linux-arm64-gnu-2.11.4.tgz",
+      "integrity": "sha512-N41/ukTRVe6XSuUTESuFdGeOW2i7k62tK+6gHK5Kd5/q5RPvvi19GaWAVPPb9u95HSGmTChSolBfzynUsssFaA==",
       "cpu": [
         "arm64"
       ],
       "dev": true,
+      "libc": [
+        "glibc"
+      ],
       "license": "Apache-2.0 OR MIT",
       "optional": true,
       "os": [
@@ -3828,13 +3831,16 @@
       }
     },
     "node_modules/@tauri-apps/cli-linux-arm64-musl": {
-      "version": "2.10.1",
-      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-linux-arm64-musl/-/cli-linux-arm64-musl-2.10.1.tgz",
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+      "version": "2.11.4",
+      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-linux-arm64-musl/-/cli-linux-arm64-musl-2.11.4.tgz",
+      "integrity": "sha512-v277UnT/fB64xAfSroL5N3Km3tLmvATWqJJw/wRI+g6o+HkeD0slyE7gOhNs1MbjE41R7bQOTxMVoL3aomUJmw==",
       "cpu": [
         "arm64"
       ],
       "dev": true,
+      "libc": [
+        "musl"
+      ],
       "license": "Apache-2.0 OR MIT",
       "optional": true,
       "os": [
@@ -3845,13 +3851,16 @@
       }
     },
     "node_modules/@tauri-apps/cli-linux-riscv64-gnu": {
-      "version": "2.10.1",
-      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-linux-riscv64-gnu/-/cli-linux-riscv64-gnu-2.10.1.tgz",
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+      "version": "2.11.4",
+      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-linux-riscv64-gnu/-/cli-linux-riscv64-gnu-2.11.4.tgz",
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       "cpu": [
         "riscv64"
       ],
       "dev": true,
+      "libc": [
+        "glibc"
+      ],
       "license": "Apache-2.0 OR MIT",
       "optional": true,
       "os": [
@@ -3862,13 +3871,16 @@
       }
     },
     "node_modules/@tauri-apps/cli-linux-x64-gnu": {
-      "version": "2.10.1",
-      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-linux-x64-gnu/-/cli-linux-x64-gnu-2.10.1.tgz",
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+      "version": "2.11.4",
+      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-linux-x64-gnu/-/cli-linux-x64-gnu-2.11.4.tgz",
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       "cpu": [
         "x64"
       ],
       "dev": true,
+      "libc": [
+        "glibc"
+      ],
       "license": "Apache-2.0 OR MIT",
       "optional": true,
       "os": [
@@ -3879,13 +3891,16 @@
       }
     },
     "node_modules/@tauri-apps/cli-linux-x64-musl": {
-      "version": "2.10.1",
-      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-linux-x64-musl/-/cli-linux-x64-musl-2.10.1.tgz",
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+      "version": "2.11.4",
+      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-linux-x64-musl/-/cli-linux-x64-musl-2.11.4.tgz",
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       "cpu": [
         "x64"
       ],
       "dev": true,
+      "libc": [
+        "musl"
+      ],
       "license": "Apache-2.0 OR MIT",
       "optional": true,
       "os": [
@@ -3896,9 +3911,9 @@
       }
     },
     "node_modules/@tauri-apps/cli-win32-arm64-msvc": {
-      "version": "2.10.1",
-      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-win32-arm64-msvc/-/cli-win32-arm64-msvc-2.10.1.tgz",
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+      "version": "2.11.4",
+      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-win32-arm64-msvc/-/cli-win32-arm64-msvc-2.11.4.tgz",
+      "integrity": "sha512-ld5Ehb598m0VkYyylRPNeCFsBe/km0jxis6KgMpl3IGY6I/i1RwQXO05I1AsXUXO2WC6AvB/Lw4qTf/asiuEiQ==",
       "cpu": [
         "arm64"
       ],
@@ -3913,9 +3928,9 @@
       }
     },
     "node_modules/@tauri-apps/cli-win32-ia32-msvc": {
-      "version": "2.10.1",
-      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-win32-ia32-msvc/-/cli-win32-ia32-msvc-2.10.1.tgz",
-      "integrity": "sha512-gXyxgEzsFegmnWywYU5pEBURkcFN/Oo45EAwvZrHMh+zUSEAvO5E8TXsgPADYm31d1u7OQU3O3HsYfVBf2moHw==",
+      "version": "2.11.4",
+      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-win32-ia32-msvc/-/cli-win32-ia32-msvc-2.11.4.tgz",
+      "integrity": "sha512-12Hxi0XX/H5VFxO/bGgHkFWhml9VMgEOu9CidjeCeTNQ1l6fpUlbiGgSP7CLI3PFtW9/FfbeHieZ+kyWK5H7CA==",
       "cpu": [
         "ia32"
       ],
@@ -3930,9 +3945,9 @@
       }
     },
     "node_modules/@tauri-apps/cli-win32-x64-msvc": {
-      "version": "2.10.1",
-      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-win32-x64-msvc/-/cli-win32-x64-msvc-2.10.1.tgz",
-      "integrity": "sha512-6Cn7YpPFwzChy0ERz6djKEmUehWrYlM+xTaNzGPgZocw3BD7OfwfWHKVWxXzdjEW2KfKkHddfdxK1XXTYqBRLg==",
+      "version": "2.11.4",
+      "resolved": "https://registry.npmjs.org/@tauri-apps/cli-win32-x64-msvc/-/cli-win32-x64-msvc-2.11.4.tgz",
+      "integrity": "sha512-+vDiqBIU5dMISg/wNvX3sF+ZHfgJGJ5T0AcO+EHNXV9GGAG+P5fzodlDXD3QdKCRgZxMoCm5PPvj3BqLNjBthw==",
       "cpu": [
         "x64"
       ],

frontend/package.json

@@ -18,7 +18,7 @@
     "@base-ui/react": "^1.3.0",
     "@fontsource-variable/geist": "^5.2.8",
     "@tailwindcss/vite": "^4.2.1",
-    "@tauri-apps/api": "^2",
+    "@tauri-apps/api": "^2.11.1",
     "@tauri-apps/plugin-autostart": "^2",
     "@tauri-apps/plugin-dialog": "^2.7.0",
     "@tauri-apps/plugin-global-shortcut": "^2",
@@ -49,7 +49,7 @@
     "zustand": "^5.0.11"
   },
   "devDependencies": {
-    "@tauri-apps/cli": "^2",
+    "@tauri-apps/cli": "^2.11.4",
     "@types/react": "^19.0.0",
     "@types/react-dom": "^19.0.0",
     "@vitejs/plugin-react": "^4.3.4",

frontend/src-tauri/Cargo.toml

@@ -24,9 +24,22 @@ serde_json = "1"
 reqwest = { version = "0.12", features = ["json", "multipart"] }
 tokio = { version = "1", features = ["full"] }
 
+# Cloud API keys are stored in the OS credential store via `keyring`. keyring v3
+# enables NO backend by default — without an explicit per-platform feature it
+# silently falls back to a non-persistent in-memory mock, so keys would not
+# survive an app restart. Each desktop target opts into its native store.
 [target.'cfg(target_os = "macos")'.dependencies]
 objc = "0.2"
 dispatch = "0.2"
+keyring = { version = "3", features = ["apple-native"] }
+
+[target.'cfg(target_os = "windows")'.dependencies]
+keyring = { version = "3", features = ["windows-native"] }
+
+[target.'cfg(target_os = "linux")'.dependencies]
+# Blocking Secret Service backend (no internal async runtime, so it is safe to
+# call from the tokio-driven Tauri commands). Needs libdbus-1-dev at build time.
+keyring = { version = "3", features = ["sync-secret-service", "crypto-rust"] }
 
 [features]
 default = ["custom-protocol"]

frontend/src-tauri/src/lib.rs

@@ -8,8 +8,10 @@ use tokio::sync::Mutex;
 
 const OLLAMA_PORT: u16 = 11434;
 const JARVIS_PORT: u16 = 8000;
+const DESKTOP_UV_SYNC_COMMAND: &str =
+    "uv sync --extra desktop --extra inference-cloud --extra inference-google --group desktop-native";
 
-/// Small, fast model pulled at startup so the app opens quickly.
+/// Small, fast model used when startup needs a default Ollama tag.
 const STARTUP_MODEL: &str = "qwen3.5:4b";
 
 /// Tiny fallback model if even the startup model can't be pulled.
@@ -104,7 +106,7 @@ fn default_local_model(ram_gb: f64) -> &'static str {
 struct BootPlan {
     /// Whether to start and wait for the bundled Ollama.
     launch_ollama: bool,
-    /// The single Ollama model to pull (None for custom endpoints).
+    /// The preferred Ollama model (None for custom endpoints).
     model_to_pull: Option<String>,
     /// Optional `(engine_key, bare_host)` override for a custom endpoint,
     /// e.g. `("lmstudio", "http://localhost:1234")`. Written into
@@ -608,6 +610,69 @@ async fn wait_for_jarvis_health(
 }
 
 async fn ollama_has_model(model: &str) -> bool {
+    let models = ollama_model_names().await;
+    matching_installed_model(&models, model).is_some()
+}
+
+fn parse_ollama_model_names(body: &serde_json::Value) -> Vec<String> {
+    body.get("models")
+        .and_then(|m| m.as_array())
+        .map(|models| {
+            models
+                .iter()
+                .filter_map(|m| {
+                    m.get("name")
+                        .or_else(|| m.get("model"))
+                        .and_then(|n| n.as_str())
+                })
+                .filter(|name| !name.trim().is_empty())
+                .map(|name| name.to_string())
+                .collect()
+        })
+        .unwrap_or_default()
+}
+
+fn model_names_match(installed: &str, requested: &str) -> bool {
+    installed == requested
+        || installed.strip_suffix(":latest") == Some(requested)
+        || requested.strip_suffix(":latest") == Some(installed)
+}
+
+fn matching_installed_model(models: &[String], requested: &str) -> Option<String> {
+    models
+        .iter()
+        .find(|model| model_names_match(model, requested))
+        .cloned()
+}
+
+fn model_name_looks_embedding_only(model: &str) -> bool {
+    let name = model.to_ascii_lowercase();
+    ["embed", "embedding", "rerank", "minilm", "bge-", "bge_", "e5-", "e5_"]
+        .iter()
+        .any(|marker| name.contains(marker))
+}
+
+fn preferred_installed_model(models: &[String]) -> Option<String> {
+    models
+        .iter()
+        .find(|model| !model.trim().is_empty() && !model_name_looks_embedding_only(model))
+        .or_else(|| models.iter().find(|model| !model.trim().is_empty()))
+        .cloned()
+}
+
+fn startup_installed_model(requested_model: &str, installed_models: &[String]) -> Option<String> {
+    matching_installed_model(installed_models, requested_model)
+        .or_else(|| preferred_installed_model(installed_models))
+}
+
+fn should_persist_resolved_model(cfg: &InferenceConfig) -> bool {
+    cfg.model
+        .as_deref()
+        .map(|model| model.trim().is_empty())
+        .unwrap_or(true)
+}
+
+async fn ollama_model_names() -> Vec<String> {
     let url = format!("http://127.0.0.1:{}/api/tags", OLLAMA_PORT);
     let client = reqwest::Client::builder()
         .timeout(Duration::from_secs(5))
@@ -615,21 +680,10 @@ async fn ollama_has_model(model: &str) -> bool {
         .unwrap();
     if let Ok(resp) = client.get(&url).send().await {
         if let Ok(body) = resp.json::<serde_json::Value>().await {
-            if let Some(models) = body.get("models").and_then(|m| m.as_array()) {
-                return models.iter().any(|m| {
-                    m.get("name")
-                        .and_then(|n| n.as_str())
-                        .map(|n| {
-                            n == model
-                                || n.strip_suffix(":latest") == Some(model)
-                                || model.strip_suffix(":latest") == Some(n)
-                        })
-                        .unwrap_or(false)
-                });
-            }
+            return parse_ollama_model_names(&body);
         }
     }
-    false
+    Vec::new()
 }
 
 async fn pull_model(model: &str) -> Result<(), String> {
@@ -679,13 +733,21 @@ fn format_uv_sync_failure(
     let code = exit_code
         .map(|c| c.to_string())
         .unwrap_or_else(|| "unknown".to_string());
+    let tail = uv_sync_stderr_tail(stderr, 800);
+    let rust_hint = if looks_like_rust_extension_build_error(stderr) {
+        format!("\n\n{}", rust_toolchain_install_hint())
+    } else {
+        String::new()
+    };
     format!(
         "`uv sync` failed in {} (exit {}). Last output:\n\n{}\n\n\
          Try opening a terminal in that directory and running \
-         `uv sync --extra server` manually for the full output.",
+         `{}` manually for the full output.{}",
         root.display(),
         code,
-        uv_sync_stderr_tail(stderr, 800),
+        tail,
+        DESKTOP_UV_SYNC_COMMAND,
+        rust_hint,
     )
 }
 
@@ -734,6 +796,122 @@ fn format_uv_sync_spawn_error(root: &std::path::Path, uv_bin: &str, err: &str) -
     )
 }
 
+fn rust_toolchain_install_hint() -> &'static str {
+    "The desktop app needs the Rust toolchain to build `openjarvis_rust`. \
+     Install Rust from https://rustup.rs. On Windows, also install Visual Studio \
+     Build Tools with the C++ workload, then relaunch."
+}
+
+fn looks_like_rust_extension_build_error(stderr: &str) -> bool {
+    let lower = stderr.to_ascii_lowercase();
+    [
+        "openjarvis-rust",
+        "openjarvis_rust",
+        "maturin",
+        "cargo",
+        "rustc",
+        "link.exe",
+        "visual studio",
+    ]
+    .iter()
+    .any(|marker| lower.contains(marker))
+}
+
+fn format_missing_rust_toolchain() -> String {
+    format!(
+        "Could not find Rust's `cargo` command. {}\n\n\
+         If Rust is already installed, close and relaunch the desktop app so \
+         PATH includes `~/.cargo/bin`.",
+        rust_toolchain_install_hint(),
+    )
+}
+
+fn format_extension_import_failure(root: &std::path::Path, stderr: &str) -> String {
+    let tail = uv_sync_stderr_tail(stderr, 4000);
+    format!(
+        "`openjarvis_rust` is still not importable after building. Last output:\n\n{}\n\n\
+         Run these manually for the full build log:\n\n\
+           cd {}\n\
+           {}\n\
+           uv run python -c \"import openjarvis_rust\"",
+        if tail.is_empty() {
+            "(no stderr output)"
+        } else {
+            &tail
+        },
+        root.display(),
+        DESKTOP_UV_SYNC_COMMAND,
+    )
+}
+
+fn add_cargo_bin_to_path(cmd: &mut tokio::process::Command) {
+    let mut paths: Vec<std::path::PathBuf> = std::env::var_os("PATH")
+        .map(|path| std::env::split_paths(&path).collect())
+        .unwrap_or_default();
+    paths.insert(
+        0,
+        std::path::PathBuf::from(home_dir())
+            .join(".cargo")
+            .join("bin"),
+    );
+    if let Ok(joined) = std::env::join_paths(paths) {
+        cmd.env("PATH", joined);
+    }
+}
+
+async fn verify_openjarvis_rust_extension(
+    root: &std::path::Path,
+    uv_bin: &str,
+) -> Result<(), String> {
+    let mut cmd = tokio::process::Command::new(uv_bin);
+    cmd.args(["run", "python", "-c", "import openjarvis_rust"])
+        .stdout(std::process::Stdio::null())
+        .stderr(std::process::Stdio::piped())
+        .current_dir(root);
+    prepare_subprocess_for_appimage(&mut cmd);
+    add_cargo_bin_to_path(&mut cmd);
+
+    match cmd.output().await {
+        Ok(out) if out.status.success() => Ok(()),
+        Ok(out) => {
+            let stderr = String::from_utf8_lossy(&out.stderr);
+            Err(format_extension_import_failure(root, &stderr))
+        }
+        Err(e) => Err(format!(
+            "Could not verify `openjarvis_rust`: {}. Verify uv is installed at `{}`.",
+            e, uv_bin
+        )),
+    }
+}
+
+fn port_owner_hint() -> String {
+    if cfg!(target_os = "windows") {
+        format!("netstat -ano | findstr :{}", JARVIS_PORT)
+    } else {
+        format!("lsof -i :{}", JARVIS_PORT)
+    }
+}
+
+fn format_port_unavailable(port: u16, reason: &str) -> String {
+    format!(
+        "Port {} is not available: {}. Stop the process using that port or \
+         change the OpenJarvis port, then relaunch.\n\nTo identify it:\n  {}",
+        port,
+        reason,
+        port_owner_hint(),
+    )
+}
+
+fn check_jarvis_port_available() -> Result<(), String> {
+    match std::net::TcpListener::bind(("127.0.0.1", JARVIS_PORT)) {
+        Ok(listener) => {
+            drop(listener);
+            Ok(())
+        }
+        Err(err) => Err(format_port_unavailable(JARVIS_PORT, &err.to_string())),
+    }
+}
+
 // ---------------------------------------------------------------------------
 // Backend boot sequence (runs in background after app launch)
 // ---------------------------------------------------------------------------
@@ -751,7 +929,7 @@ async fn boot_backend(backend: SharedBackend, status: SharedStatus) {
         .into();
     }
 
-    // For the Ollama path, the model pull may fall back to FALLBACK_MODEL; we
+    // For the Ollama path, model resolution may fall back to FALLBACK_MODEL; we
     // record what is actually available here so the serve command below uses
     // it instead of the originally-planned tag. None on the custom path.
     let mut serve_model_override: Option<String> = None;
@@ -798,8 +976,8 @@ async fn boot_backend(backend: SharedBackend, status: SharedStatus) {
             s.detail = "Inference engine ready.".into();
         }
 
-        // Phase 2: Pull the single default model (see default_local_model /
-        // boot_plan). We deliberately do NOT pull any others.
+        // Phase 2: Resolve one model to serve. Prefer an installed model on
+        // first run so startup does not depend on a download succeeding.
         let model = plan
             .model_to_pull
             .clone()
@@ -810,41 +988,63 @@ async fn boot_backend(backend: SharedBackend, status: SharedStatus) {
             s.detail = format!("Checking for {}...", model);
         }
 
-        if !ollama_has_model(&model).await {
+        let installed_models = ollama_model_names().await;
+        let resolved_model = if let Some(installed) = startup_installed_model(&model, &installed_models) {
+            installed
+        } else {
             {
                 let mut s = status.lock().await;
                 s.detail = format!("Downloading {}... (this may take a minute)", model);
             }
-            if let Err(e) = pull_model(&model).await {
-                // If the chosen model fails, try the tiny fallback
-                eprintln!("Warning: failed to pull {}: {}", model, e);
-                if !ollama_has_model(FALLBACK_MODEL).await {
-                    {
-                        let mut s = status.lock().await;
-                        s.detail = format!("Downloading {}...", FALLBACK_MODEL);
-                    }
-                    if let Err(e2) = pull_model(FALLBACK_MODEL).await {
-                        let mut s = status.lock().await;
-                        s.error = Some(format!("Failed to download model: {}", e2));
-                        return;
+            match pull_model(&model).await {
+                Ok(()) => model.clone(),
+                Err(e) => {
+                    eprintln!("Warning: failed to pull {}: {}", model, e);
+
+                    // If a local model appeared while pulling, use it instead of
+                    // making startup depend on another network pull.
+                    if let Some(installed) = preferred_installed_model(&ollama_model_names().await) {
+                        installed
+                    } else if ollama_has_model(FALLBACK_MODEL).await {
+                        FALLBACK_MODEL.to_string()
+                    } else {
+                        {
+                            let mut s = status.lock().await;
+                            s.detail = format!("Downloading {}...", FALLBACK_MODEL);
+                        }
+                        if let Err(e2) = pull_model(FALLBACK_MODEL).await {
+                            if let Some(installed) =
+                                preferred_installed_model(&ollama_model_names().await)
+                            {
+                                installed
+                            } else {
+                                let mut s = status.lock().await;
+                                s.error = Some(format!("Failed to download model: {}", e2));
+                                return;
+                            }
+                        } else {
+                            FALLBACK_MODEL.to_string()
+                        }
                     }
                 }
             }
+        };
+
+        if resolved_model != model {
+            let mut s = status.lock().await;
+            s.detail = format!("Using installed model {}.", resolved_model);
         }
 
-        // The pull may have fallen back to FALLBACK_MODEL; serve and persist
-        // whatever is actually available now, not the originally-planned tag.
-        let resolved_model = if ollama_has_model(&model).await {
-            model
-        } else {
-            FALLBACK_MODEL.to_string()
-        };
         serve_model_override = Some(resolved_model.clone());
 
-        // Persist the resolved model so Settings shows it and future boots reuse it.
-        let mut persisted = cfg.clone();
-        persisted.model = Some(resolved_model);
-        let _ = write_inference_config(&persisted);
+        // Persist only first-run/default resolution. If the user explicitly
+        // configured a model, do not overwrite that choice with a temporary
+        // fallback selected just to keep startup nonfatal.
+        if should_persist_resolved_model(&cfg) {
+            let mut persisted = cfg.clone();
+            persisted.model = Some(resolved_model);
+            let _ = write_inference_config(&persisted);
+        }
 
         {
             let mut s = status.lock().await;
@@ -1097,11 +1297,6 @@ async fn boot_backend(backend: SharedBackend, status: SharedStatus) {
                 // Something else (a different web server, a stale process,
                 // a 4xx-returning instance) is on our port. Don't kill it —
                 // give the user actionable info instead.
-                let lsof_hint = if cfg!(target_os = "windows") {
-                    format!("netstat -ano | findstr :{}", JARVIS_PORT)
-                } else {
-                    format!("lsof -i :{}", JARVIS_PORT)
-                };
                 let mut s = status.lock().await;
                 s.error = Some(format!(
                     "Port {} is already in use by another service (it answered \
@@ -1109,7 +1304,7 @@ async fn boot_backend(backend: SharedBackend, status: SharedStatus) {
                      OpenJarvis port, then relaunch.\n\nTo identify it:\n  {}",
                     JARVIS_PORT,
                     resp.status(),
-                    lsof_hint,
+                    port_owner_hint(),
                 ));
                 return;
             }
@@ -1119,8 +1314,21 @@ async fn boot_backend(backend: SharedBackend, status: SharedStatus) {
         }
     }
 
+    if let Err(err) = check_jarvis_port_available() {
+        let mut s = status.lock().await;
+        s.error = Some(err);
+        return;
+    }
+
     let root = project_root.as_ref().unwrap();
 
+    let cargo_bin = resolve_bin("cargo");
+    if !std::path::Path::new(&cargo_bin).exists() && cargo_bin == "cargo" {
+        let mut s = status.lock().await;
+        s.error = Some(format_missing_rust_toolchain());
+        return;
+    }
+
     // Install dependencies automatically (handles fresh clones).
     //
     // Previously we ran `uv sync` with both stdout AND stderr piped to
@@ -1143,15 +1351,19 @@ async fn boot_backend(backend: SharedBackend, status: SharedStatus) {
     sync_cmd
         .args([
             "sync",
-            "--extra", "server",
+            "--extra", "desktop",
             "--extra", "inference-cloud",
             "--extra", "inference-google",
+            // openjarvis_rust lives in a uv dependency group (not the published
+            // `desktop` extra) so pip installs from PyPI don't require it (#584).
+            "--group", "desktop-native",
         ])
         .stdout(std::process::Stdio::null())
         .stderr(std::process::Stdio::piped())
         .current_dir(root);
     // Avoid LD_LIBRARY_PATH leak when running inside an AppImage (#455).
     prepare_subprocess_for_appimage(&mut sync_cmd);
+    add_cargo_bin_to_path(&mut sync_cmd);
     let sync_output = sync_cmd.output().await;
     match sync_output {
         Ok(out) if !out.status.success() => {
@@ -1168,6 +1380,16 @@ async fn boot_backend(backend: SharedBackend, status: SharedStatus) {
         Ok(_) => {} // success — fall through
     }
 
+    {
+        let mut s = status.lock().await;
+        s.detail = "Verifying Rust extension (openjarvis_rust)...".into();
+    }
+    if let Err(err) = verify_openjarvis_rust_extension(root, &uv_bin).await {
+        let mut s = status.lock().await;
+        s.error = Some(err);
+        return;
+    }
+
     {
         let mut s = status.lock().await;
         s.detail = format!("Starting API server from {}...", root.display());
@@ -1204,7 +1426,7 @@ async fn boot_backend(backend: SharedBackend, status: SharedStatus) {
     // additions aren't accidentally stripped.
     prepare_subprocess_for_appimage(&mut cmd);
 
-    // Inject cloud API keys from ~/.openjarvis/cloud-keys.env
+    // Inject cloud API keys from secure desktop storage.
     for (key, value) in read_cloud_keys() {
         cmd.env(&key, &value);
     }
@@ -1664,11 +1886,29 @@ async fn transcribe_audio(
         .send()
         .await
         .map_err(|e| format!("Connection failed: {}", e))?;
-    let body: serde_json::Value = resp
-        .json()
+    let status = resp.status();
+    let body = resp
+        .text()
         .await
         .map_err(|e| format!("Invalid response: {}", e))?;
-    Ok(body)
+    if !status.is_success() {
+        let detail = serde_json::from_str::<serde_json::Value>(&body)
+            .ok()
+            .and_then(|value| {
+                value
+                    .get("detail")
+                    .and_then(|detail| detail.as_str())
+                    .map(str::to_string)
+            })
+            .filter(|detail| !detail.is_empty())
+            .unwrap_or(body);
+        return Err(format!(
+            "Transcription failed ({}): {}",
+            status.as_u16(),
+            detail
+        ));
+    }
+    serde_json::from_str(&body).map_err(|e| format!("Invalid response: {}", e))
 }
 
 /// Submit savings to Supabase leaderboard.
@@ -1702,17 +1942,111 @@ async fn submit_savings(
 // Cloud API key management
 // ---------------------------------------------------------------------------
 
-/// Path to the cloud keys file (~/.openjarvis/cloud-keys.env).
-fn cloud_keys_path() -> std::path::PathBuf {
+const SECURE_KEY_SERVICE: &str = "OpenJarvis Cloud Keys";
+const MANAGED_CLOUD_KEY_NAMES: &[&str] = &[
+    "OPENAI_API_KEY",
+    "ANTHROPIC_API_KEY",
+    "GEMINI_API_KEY",
+    "GOOGLE_API_KEY",
+    "OPENROUTER_API_KEY",
+    "MINIMAX_API_KEY",
+    "TAVILY_API_KEY",
+];
+
+/// Legacy path used by older desktop builds. New saves never write here.
+fn legacy_cloud_keys_path() -> std::path::PathBuf {
     let home = home_dir();
     std::path::PathBuf::from(home)
         .join(".openjarvis")
         .join("cloud-keys.env")
 }
 
-/// Read cloud keys from disk and return as key=value pairs.
-fn read_cloud_keys() -> Vec<(String, String)> {
-    let path = cloud_keys_path();
+fn validate_cloud_key_name(key_name: &str) -> Result<(), String> {
+    let valid = !key_name.is_empty()
+        && key_name.len() <= 128
+        && key_name.ends_with("_API_KEY")
+        && key_name
+            .chars()
+            .all(|ch| ch.is_ascii_uppercase() || ch.is_ascii_digit() || ch == '_');
+    if valid {
+        Ok(())
+    } else {
+        Err(format!("Invalid API key name: {}", key_name))
+    }
+}
+
+fn engine_api_key_name(engine: &str) -> String {
+    let normalized: String = engine
+        .chars()
+        .map(|ch| {
+            if ch.is_ascii_alphanumeric() {
+                ch.to_ascii_uppercase()
+            } else {
+                '_'
+            }
+        })
+        .collect();
+    let trimmed = normalized.trim_matches('_');
+    let engine_name = if trimmed.is_empty() {
+        CUSTOM_FALLBACK_ENGINE.to_ascii_uppercase()
+    } else {
+        trimmed.to_string()
+    };
+    format!("{}_API_KEY", engine_name)
+}
+
+fn managed_cloud_key_names() -> Vec<String> {
+    let mut names: Vec<String> = MANAGED_CLOUD_KEY_NAMES
+        .iter()
+        .map(|name| (*name).to_string())
+        .collect();
+
+    let cfg = read_inference_config();
+    if matches!(&cfg.kind, SourceKind::Custom) {
+        let engine = cfg.engine.unwrap_or_else(|| CUSTOM_FALLBACK_ENGINE.to_string());
+        let key_name = engine_api_key_name(&engine);
+        if validate_cloud_key_name(&key_name).is_ok() {
+            names.push(key_name);
+        }
+    }
+
+    names.sort();
+    names.dedup();
+    names
+}
+
+fn secure_store_get(key_name: &str) -> Result<Option<String>, String> {
+    validate_cloud_key_name(key_name)?;
+    let entry = keyring::Entry::new(SECURE_KEY_SERVICE, key_name)
+        .map_err(|err| format!("Failed to open secure key storage for {}: {}", key_name, err))?;
+    match entry.get_password() {
+        Ok(value) => Ok(Some(value)),
+        Err(keyring::Error::NoEntry) => Ok(None),
+        Err(err) => Err(format!("Failed to read {} from secure key storage: {}", key_name, err)),
+    }
+}
+
+fn secure_store_set(key_name: &str, key_value: &str) -> Result<(), String> {
+    validate_cloud_key_name(key_name)?;
+    let entry = keyring::Entry::new(SECURE_KEY_SERVICE, key_name)
+        .map_err(|err| format!("Failed to open secure key storage for {}: {}", key_name, err))?;
+    if key_value.is_empty() {
+        return match entry.delete_credential() {
+            Ok(()) => Ok(()),
+            Err(keyring::Error::NoEntry) => Ok(()),
+            Err(err) => Err(format!(
+                "Failed to remove {} from secure key storage: {}",
+                key_name, err
+            )),
+        };
+    }
+    entry
+        .set_password(key_value)
+        .map_err(|err| format!("Failed to save {} in secure key storage: {}", key_name, err))
+}
+
+fn read_legacy_cloud_keys() -> Vec<(String, String)> {
+    let path = legacy_cloud_keys_path();
     let mut keys = Vec::new();
     if let Ok(contents) = std::fs::read_to_string(&path) {
         for line in contents.lines() {
@@ -1728,47 +2062,68 @@ fn read_cloud_keys() -> Vec<(String, String)> {
     keys
 }
 
-/// Save a single cloud API key to the keys file.
-#[tauri::command]
-async fn save_cloud_key(key_name: String, key_value: String) -> Result<(), String> {
-    let path = cloud_keys_path();
-    // Ensure directory exists
-    if let Some(parent) = path.parent() {
-        let _ = std::fs::create_dir_all(parent);
+fn migrate_legacy_cloud_keys() {
+    let path = legacy_cloud_keys_path();
+    if !path.exists() {
+        return;
     }
 
-    // Read existing keys, update/add the one being saved
-    let mut keys: Vec<(String, String)> = read_cloud_keys()
+    let legacy_keys = read_legacy_cloud_keys();
+    if legacy_keys.is_empty() {
+        let _ = std::fs::remove_file(&path);
+        return;
+    }
+
+    let mut migrated_all = true;
+    for (key, value) in legacy_keys {
+        if value.is_empty() {
+            continue;
+        }
+        if secure_store_set(&key, &value).is_err() {
+            migrated_all = false;
+        }
+    }
+
+    if migrated_all {
+        let _ = std::fs::remove_file(path);
+    }
+}
+
+/// Read cloud keys from secure desktop storage and return key=value pairs.
+fn read_cloud_keys() -> Vec<(String, String)> {
+    migrate_legacy_cloud_keys();
+    managed_cloud_key_names()
         .into_iter()
-        .filter(|(k, _)| k != &key_name)
-        .collect();
-    if !key_value.is_empty() {
-        keys.push((key_name, key_value));
-    }
+        .filter_map(|key| match secure_store_get(&key) {
+            Ok(Some(value)) if !value.is_empty() => Some((key, value)),
+            _ => None,
+        })
+        .collect()
+}
 
-    // Write back
-    let content: String = keys
-        .iter()
-        .map(|(k, v)| format!("{}={}", k, v))
-        .collect::<Vec<_>>()
-        .join("\n");
-    std::fs::write(&path, content + "\n").map_err(|e| format!("Failed to save key: {}", e))?;
-
-    // Set permissions to owner-only (chmod 600)
-    #[cfg(unix)]
-    {
-        use std::os::unix::fs::PermissionsExt;
-        let _ = std::fs::set_permissions(&path, std::fs::Permissions::from_mode(0o600));
-    }
-
-    // Tell the running server to hot-reload its cloud engine so the user
-    // doesn't need to restart the app after entering an API key.
+async fn reload_cloud_keys(keys: Vec<(String, String)>) {
     let reload_url = format!("http://127.0.0.1:{}/v1/cloud/reload", JARVIS_PORT);
+    let key_map: serde_json::Map<String, serde_json::Value> = keys
+        .into_iter()
+        .map(|(key, value)| (key, serde_json::Value::String(value)))
+        .collect();
     let _ = reqwest::Client::new()
         .post(&reload_url)
+        .json(&serde_json::json!({ "keys": key_map }))
         .timeout(std::time::Duration::from_secs(10))
         .send()
         .await;
+}
+
+/// Save a single cloud API key to secure desktop storage.
+#[tauri::command]
+async fn save_cloud_key(key_name: String, key_value: String) -> Result<(), String> {
+    let key_value = key_value.trim().to_string();
+    secure_store_set(&key_name, &key_value)?;
+
+    // Tell the running server to hot-reload its cloud engine so the user
+    // doesn't need to restart the app after entering an API key.
+    reload_cloud_keys(vec![(key_name, key_value)]).await;
 
     Ok(())
 }
@@ -1776,10 +2131,13 @@ async fn save_cloud_key(key_name: String, key_value: String) -> Result<(), Strin
 /// Get which cloud providers have keys configured (without exposing values).
 #[tauri::command]
 async fn get_cloud_key_status() -> Result<serde_json::Value, String> {
-    let keys = read_cloud_keys();
-    let status: Vec<serde_json::Value> = keys
-        .iter()
-        .map(|(k, v)| serde_json::json!({ "key": k, "set": !v.is_empty() }))
+    migrate_legacy_cloud_keys();
+    let status: Vec<serde_json::Value> = managed_cloud_key_names()
+        .into_iter()
+        .map(|key| {
+            let set = matches!(secure_store_get(&key), Ok(Some(value)) if !value.is_empty());
+            serde_json::json!({ "key": key, "set": set })
+        })
         .collect();
     Ok(serde_json::json!(status))
 }
@@ -1791,8 +2149,8 @@ async fn get_inference_source() -> Result<InferenceConfig, String> {
 }
 
 /// Persist the chosen inference source. `host` is normalized to a bare base
-/// URL. For custom endpoints, an optional API key is stored in cloud-keys.env
-/// under `<ENGINE>_API_KEY`. Applies on next app launch.
+/// URL. For custom endpoints, an optional API key is stored in secure desktop
+/// storage under `<ENGINE>_API_KEY`. Applies on next app launch.
 #[tauri::command]
 async fn set_inference_source(
     kind: String,
@@ -1824,7 +2182,7 @@ async fn set_inference_source(
                 .engine
                 .clone()
                 .unwrap_or_else(|| CUSTOM_FALLBACK_ENGINE.to_string());
-            let key_name = format!("{}_API_KEY", engine.to_ascii_uppercase());
+            let key_name = engine_api_key_name(&engine);
             // Save the key before persisting the config: if the key can't be
             // written, surface it and DON'T record a custom source whose
             // credential is missing (which would fail confusingly at runtime).
@@ -2510,9 +2868,12 @@ pub fn run() {
 #[cfg(test)]
 mod tests {
     use super::{
-        boot_plan, default_local_model, format_uv_sync_failure, format_uv_sync_spawn_error,
-        normalize_host, parse_inference_config, upsert_engine_host, uv_sync_stderr_tail,
-        InferenceConfig, SourceKind,
+        boot_plan, default_local_model, format_extension_import_failure,
+        format_missing_rust_toolchain, format_port_unavailable, format_uv_sync_failure,
+        format_uv_sync_spawn_error, matching_installed_model, model_names_match, normalize_host,
+        parse_inference_config, parse_ollama_model_names, preferred_installed_model,
+        should_persist_resolved_model, startup_installed_model, upsert_engine_host,
+        uv_sync_stderr_tail, InferenceConfig, SourceKind, DESKTOP_UV_SYNC_COMMAND,
     };
     use std::path::Path;
 
@@ -2556,7 +2917,7 @@ mod tests {
         assert!(msg.contains("exit 2"));
         assert!(msg.contains("/home/u/.openjarvis/src"));
         assert!(msg.contains("failed to resolve numpy==2.1.3"));
-        assert!(msg.contains("uv sync --extra server")); // actionable next step
+        assert!(msg.contains(DESKTOP_UV_SYNC_COMMAND)); // actionable next step
     }
 
     #[test]
@@ -2579,6 +2940,49 @@ mod tests {
         assert!(msg.contains("No such file or directory"));
     }
 
+    #[test]
+    fn missing_rust_toolchain_message_names_cargo_and_installer() {
+        let msg = format_missing_rust_toolchain();
+        assert!(msg.contains("cargo"));
+        assert!(msg.contains("https://rustup.rs"));
+        assert!(msg.contains("openjarvis_rust"));
+        assert!(msg.contains("Visual Studio Build Tools"));
+    }
+
+    #[test]
+    fn uv_sync_rust_failure_mentions_toolchain() {
+        let msg = format_uv_sync_failure(
+            Path::new("C:\\Users\\me\\OpenJarvis"),
+            Some(1),
+            "maturin failed: linker `link.exe` not found while building openjarvis-rust",
+        );
+        assert!(msg.contains("exit 1"));
+        assert!(msg.contains("link.exe"));
+        assert!(msg.contains("https://rustup.rs"));
+        assert!(msg.contains("Visual Studio Build Tools"));
+    }
+
+    #[test]
+    fn extension_import_failure_names_verification_command() {
+        let msg = format_extension_import_failure(
+            Path::new("C:\\Users\\me\\OpenJarvis"),
+            "ModuleNotFoundError: No module named 'openjarvis_rust'",
+        );
+        assert!(msg.contains("openjarvis_rust"));
+        assert!(msg.contains(DESKTOP_UV_SYNC_COMMAND));
+        assert!(msg.contains("uv run python -c \"import openjarvis_rust\""));
+        assert!(msg.contains("ModuleNotFoundError"));
+    }
+
+    #[test]
+    fn port_unavailable_message_names_port_and_owner_hint() {
+        let msg = format_port_unavailable(8000, "address already in use");
+        assert!(msg.contains("Port 8000 is not available"));
+        assert!(msg.contains("address already in use"));
+        assert!(msg.contains("To identify it"));
+        assert!(msg.contains("8000"));
+    }
+
     #[test]
     fn default_local_model_picks_second_largest_that_fits() {
         // QWEN35_MODELS min_ram ladder: 4,6,8,12,24,32,96 GB
@@ -2594,6 +2998,97 @@ mod tests {
         assert_eq!(default_local_model(1.0), super::FALLBACK_MODEL);
     }
 
+    #[test]
+    fn parse_ollama_model_names_reads_nonempty_names() {
+        let body = serde_json::json!({
+            "models": [
+                {"name": "llama3.2:latest"},
+                {"name": ""},
+                {"name": "qwen3.5:4b"},
+                {"model": "mistral:latest"}
+            ]
+        });
+        assert_eq!(
+            parse_ollama_model_names(&body),
+            vec![
+                "llama3.2:latest".to_string(),
+                "qwen3.5:4b".to_string(),
+                "mistral:latest".to_string()
+            ]
+        );
+    }
+
+    #[test]
+    fn model_names_match_treats_latest_as_optional() {
+        assert!(model_names_match("llama3.2:latest", "llama3.2"));
+        assert!(model_names_match("llama3.2", "llama3.2:latest"));
+        assert!(model_names_match("qwen3.5:4b", "qwen3.5:4b"));
+        assert!(!model_names_match("llama3.2:latest", "qwen3.5:4b"));
+    }
+
+    #[test]
+    fn installed_model_helpers_pick_matching_or_first_model() {
+        let models = vec!["llama3.2:latest".to_string(), "qwen3.5:4b".to_string()];
+        assert_eq!(
+            matching_installed_model(&models, "llama3.2"),
+            Some("llama3.2:latest".to_string())
+        );
+        assert_eq!(
+            preferred_installed_model(&models),
+            Some("llama3.2:latest".to_string())
+        );
+    }
+
+    #[test]
+    fn preferred_installed_model_skips_embedding_names_when_chat_model_exists() {
+        let models = vec![
+            "nomic-embed-text:latest".to_string(),
+            "llama3.2:latest".to_string(),
+        ];
+        assert_eq!(
+            preferred_installed_model(&models),
+            Some("llama3.2:latest".to_string())
+        );
+    }
+
+    #[test]
+    fn startup_installed_model_uses_existing_model_for_defaults() {
+        let models = vec!["llama3.2:latest".to_string()];
+        assert_eq!(
+            startup_installed_model("qwen3.5:4b", &models),
+            Some("llama3.2:latest".to_string())
+        );
+    }
+
+    #[test]
+    fn startup_installed_model_uses_existing_model_when_configured_model_missing() {
+        let models = vec!["llama3.2:latest".to_string()];
+        assert_eq!(
+            startup_installed_model("qwen3.5:4b", &models),
+            Some("llama3.2:latest".to_string())
+        );
+    }
+
+    #[test]
+    fn resolved_model_is_only_persisted_when_no_model_was_configured() {
+        let default_cfg = InferenceConfig { kind: SourceKind::Ollama, ..Default::default() };
+        assert!(should_persist_resolved_model(&default_cfg));
+
+        let empty_cfg = InferenceConfig {
+            kind: SourceKind::Ollama,
+            model: Some(" ".into()),
+            ..Default::default()
+        };
+        assert!(should_persist_resolved_model(&empty_cfg));
+
+        let user_cfg = InferenceConfig {
+            kind: SourceKind::Ollama,
+            model: Some("qwen3.5:9b".into()),
+            ..Default::default()
+        };
+        assert!(!should_persist_resolved_model(&user_cfg));
+    }
+
     #[test]
     fn parse_defaults_to_ollama_when_file_missing_or_garbage() {
         assert!(matches!(parse_inference_config("").kind, SourceKind::Ollama));

frontend/src/App.tsx

@@ -31,7 +31,6 @@ export default function App() {
   const prevModelRef = useRef<string>('');
   const setModels = useAppStore((s) => s.setModels);
   const setModelsLoading = useAppStore((s) => s.setModelsLoading);
-  const setSelectedModel = useAppStore((s) => s.setSelectedModel);
   const selectedModel = useAppStore((s) => s.selectedModel);
   const setServerInfo = useAppStore((s) => s.setServerInfo);
   const setSavings = useAppStore((s) => s.setSavings);
@@ -70,7 +69,6 @@ export default function App() {
     fetchModels()
       .then((m) => {
         setModels(m);
-        if (!selectedModel && m.length > 0) setSelectedModel(m[0].id);
       })
       .catch(() => setModels([]))
       .finally(() => setModelsLoading(false));
@@ -89,7 +87,7 @@ 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

@@ -15,6 +15,7 @@ function getGreeting(): string {
 }
 
 export function ChatArea() {
+  const activeId = useAppStore((s) => s.activeId);
   const messages = useAppStore((s) => s.messages);
   const streamState = useAppStore((s) => s.streamState);
   const systemPanelOpen = useAppStore((s) => s.systemPanelOpen);
@@ -22,6 +23,10 @@ export function ChatArea() {
   const navigate = useNavigate();
   const listRef = useRef<HTMLDivElement>(null);
   const shouldAutoScroll = useRef(true);
+  const wasStreaming = useRef(false);
+  const lastScrollTop = useRef(0);
+  const isCurrentChatStreaming = streamState.isStreaming && streamState.conversationId === activeId;
+  const currentStreamContent = isCurrentChatStreaming ? streamState.content : '';
 
   // Check if any data sources are connected
   const [hasConnectedSources, setHasConnectedSources] = useState<boolean | null>(null);
@@ -34,18 +39,37 @@ 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 (isCurrentChatStreaming && !wasStreaming.current) {
+      shouldAutoScroll.current = true;
+    }
+    wasStreaming.current = isCurrentChatStreaming;
     if (shouldAutoScroll.current && listRef.current) {
       listRef.current.scrollTop = listRef.current.scrollHeight;
     }
-  }, [messages, streamState.content]);
+  }, [messages, currentStreamContent, isCurrentChatStreaming]);
 
   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;
+  const isEmpty = messages.length === 0 && !isCurrentChatStreaming;
 
   const PanelIcon = systemPanelOpen ? PanelRightClose : PanelRightOpen;
 
@@ -153,12 +177,12 @@ export function ChatArea() {
                 <MessageBubble
                   key={msg.id}
                   message={msg}
-                  isLive={isLastAssistant && streamState.isStreaming}
+                  isLive={isLastAssistant && isCurrentChatStreaming}
                 />
               );
             })}
             {(() => {
-              if (!streamState.isStreaming || streamState.content !== '') return null;
+              if (!isCurrentChatStreaming || streamState.content !== '') return null;
               // For research messages the ResearchTimeline handles its own
               // pre-content loading state — suppress the generic dots.
               const last = messages[messages.length - 1];

frontend/src/components/Chat/InputArea.tsx

@@ -5,6 +5,7 @@ import { useAppStore, generateId } from '../../lib/store';
 import { streamChat, streamResearch } from '../../lib/sse';
 import { fetchSavings, getBase } from '../../lib/api';
 import { listConnectors, getSyncStatus } from '../../lib/connectors-api';
+import { serializeToolCallArguments } from '../../lib/tool-call';
 import { MicButton } from './MicButton';
 import { useSpeech } from '../../hooks/useSpeech';
 import type {
@@ -96,8 +97,15 @@ export function InputArea() {
   const deepResearch = useAppStore((s) => s.deepResearch);
   const setDeepResearch = useAppStore((s) => s.setDeepResearch);
   const corpusSync = useResearchCorpusSync(deepResearch);
+  const isCurrentChatStreaming = streamState.isStreaming && streamState.conversationId === activeId;
 
-  const { state: speechState, available: speechAvailable, startRecording, stopRecording } = useSpeech();
+  const {
+    state: speechState,
+    error: speechError,
+    available: speechAvailable,
+    startRecording,
+    stopRecording,
+  } = useSpeech();
 
   // Abort in-flight stream when the user switches models mid-generation.
   // This prevents errors from trying to continue a stream with a stale model.
@@ -122,6 +130,12 @@ export function InputArea() {
     : streamState.isStreaming ? 'streaming'
     : undefined;
 
+  useEffect(() => {
+    if (speechError) {
+      toast.error(speechError, { duration: 8000 });
+    }
+  }, [speechError]);
+
   const handleMicClick = useCallback(async () => {
     if (speechState === 'recording') {
       try {
@@ -214,6 +228,7 @@ export function InputArea() {
     let ttftMs: number | undefined;
 
     setStreamState({
+      conversationId: convId,
       isStreaming: true,
       phase: deepResearch ? 'Researching...' : 'Generating...',
       elapsedMs: 0,
@@ -231,7 +246,11 @@ export function InputArea() {
 
     try {
       if (deepResearch) {
-        for await (const ev of streamResearch(content, controller.signal)) {
+        for await (const ev of streamResearch(
+          content,
+          selectedModel,
+          controller.signal,
+        )) {
           if (ev.type === 'search_call') {
             const trace: ResearchSearchTrace = {
               id: generateId(),
@@ -371,7 +390,7 @@ export function InputArea() {
             const tc: ToolCallInfo = {
               id: generateId(),
               tool: data.tool,
-              arguments: data.arguments || '',
+              arguments: serializeToolCallArguments(data.arguments),
               status: 'running',
             };
             toolCalls.push(tc);
@@ -382,7 +401,7 @@ export function InputArea() {
             updateLastAssistant(convId, accumulatedContent, [...toolCalls]);
             useAppStore.getState().addLogEntry({
               timestamp: Date.now(), level: 'info', category: 'tool',
-              message: `Calling ${data.tool}(${data.arguments || ''})`,
+              message: `Calling ${data.tool}(${serializeToolCallArguments(data.arguments)})`,
             });
           } catch {}
         } else if (eventName === 'tool_call_end') {
@@ -450,7 +469,10 @@ export function InputArea() {
       }
       const totalMs = Date.now() - startTime;
       const _CLOUD_PREFIXES = ['gpt-', 'o1-', 'o3-', 'o4-', 'claude-', 'gemini-', 'openrouter/', 'MiniMax-', 'chatgpt-'];
-      const engineLabel = _CLOUD_PREFIXES.some(p => selectedModel.startsWith(p)) ? 'cloud' : 'ollama';
+      const selectedOwner = useAppStore.getState().models.find((m) => m.id === selectedModel)?.owned_by;
+      const engineLabel = selectedOwner === 'litellm'
+        ? 'litellm'
+        : _CLOUD_PREFIXES.some(p => selectedModel.startsWith(p)) ? 'cloud' : 'ollama';
       const telemetry: MessageTelemetry = {
         engine: engineLabel,
         model_id: selectedModel,
@@ -583,7 +605,7 @@ export function InputArea() {
           style={{ color: 'var(--color-text)', maxHeight: '200px' }}
           disabled={streamState.isStreaming || modelLoading}
         />
-        {streamState.isStreaming ? (
+        {isCurrentChatStreaming ? (
           <button
             onClick={stopStreaming}
             className="p-2 rounded-xl transition-colors shrink-0 cursor-pointer"
@@ -602,7 +624,7 @@ export function InputArea() {
             />
             <button
               onClick={sendMessage}
-              disabled={!input.trim() || modelLoading || !selectedModel}
+              disabled={streamState.isStreaming || !input.trim() || modelLoading || !selectedModel}
               title={selectedModel ? 'Send message' : 'Pick a model first (⌘K)'}
               className="p-2 rounded-xl transition-colors shrink-0 cursor-pointer disabled:opacity-30 disabled:cursor-default"
               style={{

frontend/src/components/Chat/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/Chat/ToolCallCard.tsx

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

frontend/src/components/CommandPalette.tsx

@@ -1,7 +1,15 @@
-import { useState, useRef, useEffect } from 'react';
+import { useState, useRef, useEffect, useCallback } from 'react';
 import { Search, Cpu, X, Download, Loader2, Trash2, Check, Cloud, Key, Eye, EyeOff } from 'lucide-react';
 import { useAppStore } from '../lib/store';
-import { pullModel, deleteModel, fetchModels, preloadModel, isTauri } from '../lib/api';
+import {
+  pullModel,
+  deleteModel,
+  fetchModels,
+  preloadModel,
+  isTauri,
+  getCloudKeyStatus,
+  saveCloudKey,
+} from '../lib/api';
 
 /** Popular models that users can download from the catalogue. */
 const CATALOGUE_MODELS = [
@@ -23,7 +31,6 @@ const CATALOGUE_MODELS = [
 interface CloudProvider {
   name: string;
   envKey: string;
-  storageKey: string;
   models: Array<{ id: string; desc: string }>;
 }
 
@@ -31,7 +38,6 @@ const CLOUD_PROVIDERS: CloudProvider[] = [
   {
     name: 'OpenAI',
     envKey: 'OPENAI_API_KEY',
-    storageKey: 'openjarvis-openai-key',
     models: [
       { id: 'gpt-4o', desc: 'GPT-4o — fast, multimodal' },
       { id: 'gpt-4o-mini', desc: 'GPT-4o Mini — cheap, fast' },
@@ -41,7 +47,6 @@ const CLOUD_PROVIDERS: CloudProvider[] = [
   {
     name: 'Anthropic',
     envKey: 'ANTHROPIC_API_KEY',
-    storageKey: 'openjarvis-anthropic-key',
     models: [
       { id: 'claude-sonnet-4-6', desc: 'Claude Sonnet 4.6 — balanced' },
       { id: 'claude-opus-4-6', desc: 'Claude Opus 4.6 — most capable' },
@@ -51,7 +56,6 @@ const CLOUD_PROVIDERS: CloudProvider[] = [
   {
     name: 'Google',
     envKey: 'GEMINI_API_KEY',
-    storageKey: 'openjarvis-gemini-key',
     models: [
       { id: 'gemini-2.5-pro', desc: 'Gemini 2.5 Pro — flagship' },
       { id: 'gemini-2.5-flash', desc: 'Gemini 2.5 Flash — fast' },
@@ -61,7 +65,6 @@ const CLOUD_PROVIDERS: CloudProvider[] = [
   {
     name: 'OpenRouter',
     envKey: 'OPENROUTER_API_KEY',
-    storageKey: 'openjarvis-openrouter-key',
     models: [
       { id: 'openrouter/auto', desc: 'Auto — best model for the task' },
       { id: 'openrouter/anthropic/claude-sonnet-4', desc: 'Claude Sonnet 4 via OpenRouter' },
@@ -70,16 +73,6 @@ const CLOUD_PROVIDERS: CloudProvider[] = [
   },
 ];
 
-function getStoredKey(storageKey: string): string {
-  try { return localStorage.getItem(storageKey) || ''; } catch { return ''; }
-}
-function setStoredKey(storageKey: string, value: string): void {
-  try {
-    if (value) localStorage.setItem(storageKey, value);
-    else localStorage.removeItem(storageKey);
-  } catch {}
-}
-
 type Tab = 'installed' | 'catalogue' | 'cloud';
 
 export function CommandPalette() {
@@ -92,11 +85,10 @@ export function CommandPalette() {
   const [deleting, setDeleting] = useState<string | null>(null);
   const [customModel, setCustomModel] = useState('');
   const [showKeys, setShowKeys] = useState<Record<string, boolean>>({});
-  const [apiKeys, setApiKeys] = useState<Record<string, string>>(() => {
-    const keys: Record<string, string> = {};
-    for (const p of CLOUD_PROVIDERS) keys[p.storageKey] = getStoredKey(p.storageKey);
-    return keys;
-  });
+  const [apiKeys, setApiKeys] = useState<Record<string, string>>({});
+  const [cloudKeyStatus, setCloudKeyStatus] = useState<Record<string, boolean>>({});
+  const [cloudKeyError, setCloudKeyError] = useState<string | null>(null);
+  const [savingKey, setSavingKey] = useState<string | null>(null);
   const inputRef = useRef<HTMLInputElement>(null);
 
   const models = useAppStore((s) => s.models);
@@ -106,6 +98,20 @@ export function CommandPalette() {
   const setCommandPaletteOpen = useAppStore((s) => s.setCommandPaletteOpen);
 
   const installedIds = new Set(models.map((m) => m.id));
+  const desktopKeyStorage = isTauri();
+
+  const refreshCloudKeyStatus = useCallback(async () => {
+    if (!desktopKeyStorage) {
+      setCloudKeyStatus({});
+      return;
+    }
+    try {
+      setCloudKeyStatus(await getCloudKeyStatus());
+      setCloudKeyError(null);
+    } catch (e: any) {
+      setCloudKeyError(e?.message || 'Failed to read cloud key status');
+    }
+  }, [desktopKeyStorage]);
 
   const filtered = tab === 'installed'
     ? (query
@@ -122,6 +128,10 @@ export function CommandPalette() {
     inputRef.current?.focus();
   }, []);
 
+  useEffect(() => {
+    void refreshCloudKeyStatus();
+  }, [refreshCloudKeyStatus]);
+
   useEffect(() => {
     setSelectedIdx(0);
   }, [query, tab]);
@@ -133,18 +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}` });
@@ -210,24 +219,29 @@ export function CommandPalette() {
   };
 
   const handleSaveKey = async (provider: CloudProvider, value: string) => {
-    setStoredKey(provider.storageKey, value);
-    setApiKeys((prev) => ({ ...prev, [provider.storageKey]: value }));
+    const keyValue = value.trim();
+    setSavingKey(provider.envKey);
+    setCloudKeyError(null);
 
-    // Also save to Tauri backend so the server process picks up the key
-    if (isTauri()) {
-      try {
-        const { invoke } = await import('@tauri-apps/api/core');
-        await invoke('save_cloud_key', { keyName: provider.envKey, keyValue: value });
-      } catch {}
+    try {
+      await saveCloudKey(provider.envKey, keyValue);
+      setApiKeys((prev) => ({ ...prev, [provider.envKey]: '' }));
+      await refreshCloudKeyStatus();
+      useAppStore.getState().addLogEntry({
+        timestamp: Date.now(), level: 'info', category: 'model',
+        message: `${provider.name} API key ${keyValue ? 'saved' : 'removed'}. Refreshing model list...`,
+      });
+      await refreshModels();
+    } catch (e: any) {
+      setCloudKeyError(e?.message || `Failed to save ${provider.name} API key`);
+    } finally {
+      setSavingKey(null);
     }
+  };
 
-    useAppStore.getState().addLogEntry({
-      timestamp: Date.now(), level: 'info', category: 'model',
-      message: `${provider.name} API key ${value ? 'saved' : 'removed'}. Refreshing model list…`,
-    });
-
-    // Refresh the model list so cloud models appear immediately.
-    await refreshModels();
+  const handleKeyBlur = (provider: CloudProvider) => {
+    const draft = apiKeys[provider.envKey] || '';
+    if (draft.trim()) void handleSaveKey(provider, draft);
   };
 
   const handleKeyDown = (e: React.KeyboardEvent) => {
@@ -241,7 +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);
     }
   };
 
@@ -323,6 +338,11 @@ export function CommandPalette() {
             <Check size={12} /> Downloaded {pullSuccess} successfully
           </div>
         )}
+        {tab === 'cloud' && cloudKeyError && (
+          <div className="px-4 py-2 text-xs" style={{ color: 'var(--color-error)', background: 'rgba(220,38,38,0.05)' }}>
+            {cloudKeyError}
+          </div>
+        )}
 
         {/* Results */}
         <div className="max-h-[400px] overflow-y-auto py-2">
@@ -346,11 +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}
@@ -362,17 +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>
                 );
               })
@@ -431,13 +457,17 @@ export function CommandPalette() {
             /* ── Cloud Models tab ── */
             <div className="px-4 py-2">
               <div className="text-[11px] mb-3" style={{ color: 'var(--color-text-tertiary)' }}>
-                Add your API keys to use cloud models. Keys are stored locally on your device only.
+                {desktopKeyStorage
+                  ? 'Add your API keys to use cloud models. Keys are stored in secure desktop storage.'
+                  : 'Configure cloud provider keys in the server environment to use cloud models.'}
               </div>
 
               {CLOUD_PROVIDERS.map((provider) => {
-                const key = apiKeys[provider.storageKey] || '';
-                const hasKey = !!key;
-                const isVisible = showKeys[provider.storageKey];
+                const key = apiKeys[provider.envKey] || '';
+                const hasSavedKey = !!cloudKeyStatus[provider.envKey];
+                const hasKey = hasSavedKey || !!key.trim();
+                const isVisible = showKeys[provider.envKey];
+                const isSaving = savingKey === provider.envKey;
 
                 return (
                   <div key={provider.name} className="mb-4">
@@ -458,26 +488,28 @@ export function CommandPalette() {
                         <input
                           type={isVisible ? 'text' : 'password'}
                           value={key}
-                          onChange={(e) => setApiKeys((prev) => ({ ...prev, [provider.storageKey]: e.target.value }))}
-                          onBlur={() => handleSaveKey(provider, apiKeys[provider.storageKey] || '')}
-                          placeholder={`${provider.envKey}`}
+                          onChange={(e) => setApiKeys((prev) => ({ ...prev, [provider.envKey]: e.target.value }))}
+                          onBlur={() => handleKeyBlur(provider)}
+                          placeholder={hasSavedKey ? 'Saved in secure storage' : provider.envKey}
+                          disabled={!desktopKeyStorage || isSaving}
                           className="flex-1 text-xs px-2 py-1.5 bg-transparent outline-none font-mono"
                           style={{ color: 'var(--color-text)' }}
                         />
                         <button
-                          onClick={() => setShowKeys((prev) => ({ ...prev, [provider.storageKey]: !prev[provider.storageKey] }))}
+                          onClick={() => setShowKeys((prev) => ({ ...prev, [provider.envKey]: !prev[provider.envKey] }))}
                           className="px-2 cursor-pointer" style={{ color: 'var(--color-text-tertiary)' }}
                         >
                           {isVisible ? <EyeOff size={12} /> : <Eye size={12} />}
                         </button>
                       </div>
-                      {hasKey && (
+                      {hasSavedKey && (
                         <button
                           onClick={() => handleSaveKey(provider, '')}
+                          disabled={isSaving}
                           className="px-2 py-1 rounded-lg text-[10px] cursor-pointer"
-                          style={{ color: 'var(--color-error)', border: '1px solid var(--color-error)' }}
+                          style={{ color: 'var(--color-error)', border: '1px solid var(--color-error)', opacity: isSaving ? 0.5 : 1 }}
                         >
-                          Remove
+                          {isSaving ? 'Saving' : 'Remove'}
                         </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

@@ -1,6 +1,7 @@
 import { useState, useEffect, useCallback } from 'react';
 import type React from 'react';
 import { invoke } from '@tauri-apps/api/core';
+import { LEADERBOARD_ENABLED, SUPABASE_ANON_KEY, SUPABASE_URL } from '../../lib/supabase';
 
 // ---------------------------------------------------------------------------
 // Types
@@ -221,8 +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,
 };
 
@@ -279,9 +280,6 @@ function getOrCreateAnonId(): string {
   return id;
 }
 
-const SUPABASE_URL = 'https://mtbtgpwzrbostweaanpr.supabase.co';
-const SUPABASE_KEY = 'eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6Im10YnRncHd6cmJvc3R3ZWFhbnByIiwicm9sZSI6ImFub24iLCJpYXQiOjE3NzMxODk0OTQsImV4cCI6MjA4ODc2NTQ5NH0._xMlqCfljtXpwPj54H-ghxfLFO-jiq4W2WhpU8vVL1c';
-
 const REFRESH_INTERVAL_MS = 5000;
 
 export function SavingsDashboard({ apiUrl }: { apiUrl: string }) {
@@ -318,15 +316,16 @@ export function SavingsDashboard({ apiUrl }: { apiUrl: string }) {
     return () => clearInterval(timer);
   }, [fetchData]);
 
-  // Share savings to Supabase when opted in and data changes
+  // Share savings to Supabase when opted in and data changes. Skipped entirely
+  // when no anon key was built in (leaderboard disabled).
   useEffect(() => {
-    if (!optInEnabled || !displayName || !data) return;
+    if (!LEADERBOARD_ENABLED || !optInEnabled || !displayName || !data) return;
     const dollarSavings = data.per_provider.reduce((s, p) => s + p.total_cost, 0);
     const energySaved = data.per_provider.reduce((s, p) => s + (p.energy_wh || 0), 0);
     const flopsSaved = data.per_provider.reduce((s, p) => s + (p.flops || 0), 0);
     invoke('submit_savings', {
       supabaseUrl: SUPABASE_URL,
-      supabaseKey: SUPABASE_KEY,
+      supabaseKey: SUPABASE_ANON_KEY,
       payload: {
         anon_id: anonId,
         display_name: displayName,

frontend/src/components/SetupScreen.tsx

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

frontend/src/components/Sidebar/ConversationList.tsx

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

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

@@ -1,4 +1,4 @@
-import { afterEach, beforeEach, describe, expect, it } from 'vitest';
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
 
 // Regression for #266: the frontend must send the local API key as a Bearer
 // token on /v1 + /api requests, or `jarvis serve` with a key configured 401s
@@ -6,6 +6,7 @@ import { afterEach, beforeEach, describe, expect, it } 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).
@@ -26,11 +27,16 @@ 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();
 });
 
 afterEach(() => {
+  vi.unstubAllEnvs();
   (globalThis as unknown as { localStorage?: MemoryStorage }).localStorage =
     undefined;
 });
@@ -83,3 +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

@@ -1,12 +1,11 @@
 import type { ModelInfo, SavingsData, ServerInfo } from '../types';
+import { SUPABASE_ANON_KEY, SUPABASE_URL } from './supabase';
+import { serializeToolCallArguments } from './tool-call';
 
 // ---------------------------------------------------------------------------
-// Supabase config — safe to embed (RLS protects writes)
+// Supabase config
 // ---------------------------------------------------------------------------
 
-const SUPABASE_URL = import.meta.env.VITE_SUPABASE_URL || 'https://mtbtgpwzrbostweaanpr.supabase.co';
-const SUPABASE_ANON_KEY = import.meta.env.VITE_SUPABASE_ANON_KEY || 'eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6Im10YnRncHd6cmJvc3R3ZWFhbnByIiwicm9sZSI6ImFub24iLCJpYXQiOjE3NzMxODk0OTQsImV4cCI6MjA4ODc2NTQ5NH0._xMlqCfljtXpwPj54H-ghxfLFO-jiq4W2WhpU8vVL1c';
-
 declare global {
   interface Window {
     __TAURI_INTERNALS__?: unknown;
@@ -15,6 +14,31 @@ declare global {
 
 export const isTauri = () => typeof window !== 'undefined' && !!window.__TAURI_INTERNALS__;
 
+export type CloudKeyStatus = Record<string, boolean>;
+
+export async function getCloudKeyStatus(): Promise<CloudKeyStatus> {
+  if (!isTauri()) return {};
+  try {
+    const { invoke } = await import('@tauri-apps/api/core');
+    const rows = await invoke<Array<{ key: string; set: boolean }>>('get_cloud_key_status');
+    return Object.fromEntries(rows.map((row) => [row.key, row.set]));
+  } catch (e: any) {
+    throw new Error(e?.message ?? e ?? 'Failed to read cloud key status');
+  }
+}
+
+export async function saveCloudKey(keyName: string, keyValue: string): Promise<void> {
+  if (!isTauri()) {
+    throw new Error('Cloud API keys can be saved in the desktop app only.');
+  }
+  try {
+    const { invoke } = await import('@tauri-apps/api/core');
+    await invoke('save_cloud_key', { keyName, keyValue });
+  } catch (e: any) {
+    throw new Error(e?.message ?? e ?? 'Failed to save cloud key');
+  }
+}
+
 // Cached API base URL fetched from the Tauri backend at startup.
 // This avoids hardcoding the port — the Rust backend is the single
 // source of truth for JARVIS_PORT.
@@ -195,9 +219,9 @@ export async function deleteModel(modelName: string): Promise<void> {
 
 const _CLOUD_PREFIXES = ['gpt-', 'o1-', 'o3-', 'o4-', 'claude-', 'gemini-', 'openrouter/'];
 
-export async function preloadModel(modelName: string): Promise<void> {
+export async function preloadModel(modelName: string, owner?: string): Promise<void> {
   // Cloud models don't need Ollama preloading
-  if (_CLOUD_PREFIXES.some(p => modelName.startsWith(p))) {
+  if (owner === 'litellm' || _CLOUD_PREFIXES.some(p => modelName.startsWith(p))) {
     return;
   }
   // Trigger Ollama to load the model into memory (empty prompt, no generation).
@@ -317,8 +341,9 @@ export async function transcribeAudio(audioBlob: Blob, filename = 'recording.web
         audioData: Array.from(new Uint8Array(buffer)),
         filename,
       });
-    } catch {
-      // Fall through to fetch
+    } catch (err) {
+      const msg = err instanceof Error ? err.message : String(err);
+      throw new Error(msg || 'Transcription failed');
     }
   }
   const formData = new FormData();
@@ -327,7 +352,16 @@ export async function transcribeAudio(audioBlob: Blob, filename = 'recording.web
     method: 'POST',
     body: formData,
   });
-  if (!res.ok) throw new Error(`Transcription failed: ${res.status}`);
+  if (!res.ok) {
+    let detail = "";
+    try {
+      const body = await res.json();
+      detail = typeof body.detail === 'string' ? body.detail : "";
+    } catch {
+      // Keep the status-only message below when the body is not JSON.
+    }
+    throw new Error(detail || `Transcription failed: ${res.status}`);
+  }
   return res.json();
 }
 
@@ -708,7 +742,7 @@ export async function sendAgentMessage(
               const parsed = JSON.parse(data);
               callbacks?.onToolCallStart?.({
                 tool: parsed.tool,
-                arguments: parsed.arguments ?? '',
+                arguments: serializeToolCallArguments(parsed.arguments),
               });
             } catch {
               /* skip */
@@ -852,6 +886,25 @@ 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/lib/model-capabilities.test.ts

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

frontend/src/lib/model-capabilities.ts

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

frontend/src/lib/sse.ts

@@ -60,6 +60,7 @@ export async function* streamChat(
 
 export async function* streamResearch(
   query: string,
+  model?: string,
   signal?: AbortSignal,
 ): AsyncGenerator<ResearchEvent> {
   // /api/research is mounted at the server root — strip any trailing /v1
@@ -68,7 +69,7 @@ export async function* streamResearch(
   const response = await fetch(`${base}/api/research`, {
     method: 'POST',
     headers: authHeaders({ 'Content-Type': 'application/json' }),
-    body: JSON.stringify({ query }),
+    body: JSON.stringify({ query, ...(model ? { model } : {}) }),
     signal,
   });
 
@@ -106,4 +107,3 @@ export async function* streamResearch(
     reader.releaseLock();
   }
 }
-

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

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

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

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

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

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

frontend/src/lib/store.ts

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

frontend/src/lib/supabase.ts

@@ -0,0 +1,11 @@
+export const SUPABASE_URL =
+  import.meta.env.VITE_SUPABASE_URL || 'https://mtbtgpwzrbostweaanpr.supabase.co';
+
+// The Supabase anon key is optional at build time. When it is unset the public
+// savings leaderboard is disabled rather than failing the build — this keeps
+// the `openjarvis` package and desktop app buildable without coupling
+// publishability to a leaderboard credential. Set VITE_SUPABASE_ANON_KEY at
+// build time (from a repo secret) to enable the leaderboard.
+export const SUPABASE_ANON_KEY = import.meta.env.VITE_SUPABASE_ANON_KEY ?? '';
+
+export const LEADERBOARD_ENABLED = SUPABASE_ANON_KEY.length > 0;

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

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

frontend/src/lib/tool-call.ts

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

frontend/src/pages/AgentsPage.tsx

@@ -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/GetStartedPage.tsx

@@ -417,7 +417,7 @@ function SelfHostedView() {
           <p className="text-xs" style={{ color: 'var(--color-text-secondary)' }}>
             Launch the API server to get the full UI in your browser:
           </p>
-          <CodeBlock code={"git clone https://github.com/open-jarvis/OpenJarvis.git\ncd OpenJarvis\nuv sync --extra server\njarvis serve --port 8000"} />
+          <CodeBlock code={"git clone https://github.com/open-jarvis/OpenJarvis.git\ncd OpenJarvis\nuv sync --extra desktop\njarvis serve --port 8000"} />
           <p className="text-xs" style={{ color: 'var(--color-text-tertiary)' }}>
             The chat, dashboard, energy profiling, and cost comparison all run
             locally on your machine.

frontend/src/pages/SettingsPage.tsx

@@ -19,9 +19,24 @@ import {
   RefreshCw,
 } from 'lucide-react';
 import { useAppStore, type ThemeMode } from '../lib/store';
-import { checkHealth, fetchSpeechHealth, getMemoryStats, getInferenceSource, setInferenceSource, type InferenceSource } from '../lib/api';
+import {
+  checkHealth,
+  fetchSpeechHealth,
+  getMemoryStats,
+  getInferenceSource,
+  setInferenceSource,
+  getCloudKeyStatus,
+  saveCloudKey,
+  fetchToolCredentialStatus,
+  saveToolCredentials,
+  deleteToolCredential,
+  isTauri,
+  type InferenceSource,
+} from '../lib/api';
 import { isAutoUpdateDisabled, setAutoUpdateDisabled } from '../components/Desktop/UpdateChecker';
 
+const CLOUD_KEY_STATUS_CHANGED = 'openjarvis-cloud-key-status-changed';
+
 function OllamaModelList() {
   const [models, setModels] = useState<Array<{ name: string; size: number }>>([]);
   useEffect(() => {
@@ -44,32 +59,135 @@ function OllamaModelList() {
   );
 }
 
-function ApiKeyInput({ storageKey, placeholder }: { storageKey: string; placeholder: string }) {
-  const [value, setValue] = useState(() => {
-    try { return localStorage.getItem(storageKey) || ''; } catch { return ''; }
-  });
+function ApiKeyInput({
+  keyName,
+  placeholder,
+  toolName,
+}: {
+  keyName: string;
+  placeholder: string;
+  toolName?: string;
+}) {
+  const [value, setValue] = useState('');
   const [saved, setSaved] = useState(false);
-  const save = (v: string) => {
-    setValue(v);
-    try { if (v) localStorage.setItem(storageKey, v); else localStorage.removeItem(storageKey); } catch {}
-    setSaved(true);
-    setTimeout(() => setSaved(false), 2000);
+  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 (!canManage) {
+      setHasKey(false);
+      return;
+    }
+    try {
+      const status = desktopKeyStorage
+        ? await getCloudKeyStatus()
+        : await fetchToolCredentialStatus(toolName!);
+      setHasKey(!!status[keyName]);
+    } catch {
+      setHasKey(false);
+    }
+  }, [canManage, desktopKeyStorage, keyName, toolName]);
+
+  useEffect(() => {
+    void refresh();
+    window.addEventListener(CLOUD_KEY_STATUS_CHANGED, refresh);
+    return () => window.removeEventListener(CLOUD_KEY_STATUS_CHANGED, refresh);
+  }, [refresh]);
+
+  const save = async (v: string) => {
+    const next = v.trim();
+    if (!next) return;
+    setError('');
+    try {
+      if (desktopKeyStorage) {
+        await saveCloudKey(keyName, next);
+      } else if (toolName) {
+        await saveToolCredentials(toolName, { [keyName]: next });
+      } else {
+        return;
+      }
+      setValue('');
+      setHasKey(true);
+      setSaved(true);
+      window.dispatchEvent(new Event(CLOUD_KEY_STATUS_CHANGED));
+      setTimeout(() => setSaved(false), 2000);
+    } catch (e: any) {
+      setError(e?.message || 'Failed to save API key');
+    }
   };
+
+  const remove = async () => {
+    setError('');
+    try {
+      if (desktopKeyStorage) {
+        await saveCloudKey(keyName, '');
+      } else if (toolName) {
+        await deleteToolCredential(toolName, keyName);
+      } else {
+        return;
+      }
+      setValue('');
+      setHasKey(false);
+      setSaved(true);
+      window.dispatchEvent(new Event(CLOUD_KEY_STATUS_CHANGED));
+      setTimeout(() => setSaved(false), 2000);
+    } catch (e: any) {
+      setError(e?.message || 'Failed to remove API key');
+    }
+  };
+
   return (
     <div className="flex items-center gap-2">
-      <input type="password" value={value} onChange={e => save(e.target.value)} placeholder={placeholder}
+      <input
+        type="password"
+        value={value}
+        onChange={e => setValue(e.target.value)}
+        onBlur={() => { if (value.trim()) void save(value); }}
+        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 && (
+        <button
+          onClick={() => void remove()}
+          className="px-2 py-1 rounded text-[10px] cursor-pointer"
+          style={{ color: 'var(--color-error)', border: '1px solid var(--color-error)' }}
+        >
+          Remove
+        </button>
+      )}
       {saved && <span className="text-[10px]" style={{ color: 'var(--color-success)' }}>Saved</span>}
+      {error && <span className="text-[10px]" style={{ color: 'var(--color-error)' }}>{error}</span>}
     </div>
   );
 }
 
-function CloudProviderStatus({ label, storageKey }: { label: string; storageKey: string }) {
+function CloudProviderStatus({ label, keyName }: { label: string; keyName: string }) {
   const [hasKey, setHasKey] = useState(false);
+  const desktopKeyStorage = isTauri();
+
+  const refresh = useCallback(async () => {
+    if (!desktopKeyStorage) {
+      setHasKey(false);
+      return;
+    }
+    try {
+      const status = await getCloudKeyStatus();
+      setHasKey(!!status[keyName]);
+    } catch {
+      setHasKey(false);
+    }
+  }, [desktopKeyStorage, keyName]);
+
   useEffect(() => {
-    try { setHasKey(!!localStorage.getItem(storageKey)); } catch { setHasKey(false); }
-  }, [storageKey]);
+    void refresh();
+    window.addEventListener(CLOUD_KEY_STATUS_CHANGED, refresh);
+    return () => window.removeEventListener(CLOUD_KEY_STATUS_CHANGED, refresh);
+  }, [refresh]);
+
   return (
     <span className="flex items-center gap-1 text-xs" style={{ color: 'var(--color-text-secondary)' }}>
       <span style={{
@@ -424,10 +542,10 @@ export function SettingsPage() {
             </div>
             <SettingRow label="Cloud providers" description="Green dot means API key is configured">
               <div className="flex flex-wrap gap-3">
-                <CloudProviderStatus label="OpenAI" storageKey="openjarvis-openai-key" />
-                <CloudProviderStatus label="Anthropic" storageKey="openjarvis-anthropic-key" />
-                <CloudProviderStatus label="Google" storageKey="openjarvis-gemini-key" />
-                <CloudProviderStatus label="OpenRouter" storageKey="openjarvis-openrouter-key" />
+                <CloudProviderStatus label="OpenAI" keyName="OPENAI_API_KEY" />
+                <CloudProviderStatus label="Anthropic" keyName="ANTHROPIC_API_KEY" />
+                <CloudProviderStatus label="Google" keyName="GEMINI_API_KEY" />
+                <CloudProviderStatus label="OpenRouter" keyName="OPENROUTER_API_KEY" />
               </div>
             </SettingRow>
           </Section>
@@ -435,23 +553,23 @@ export function SettingsPage() {
           {/* API Keys */}
           <Section title="API Keys">
             <SettingRow label="OpenAI" description="GPT-4, GPT-3.5, etc.">
-              <ApiKeyInput storageKey="openjarvis-openai-key" placeholder="sk-..." />
+              <ApiKeyInput keyName="OPENAI_API_KEY" placeholder="sk-..." />
             </SettingRow>
             <SettingRow label="Anthropic" description="Claude models">
-              <ApiKeyInput storageKey="openjarvis-anthropic-key" placeholder="sk-ant-..." />
+              <ApiKeyInput keyName="ANTHROPIC_API_KEY" placeholder="sk-ant-..." />
             </SettingRow>
             <SettingRow label="Google" description="Gemini models">
-              <ApiKeyInput storageKey="openjarvis-gemini-key" placeholder="AI..." />
+              <ApiKeyInput keyName="GEMINI_API_KEY" placeholder="AI..." />
             </SettingRow>
             <SettingRow label="OpenRouter" description="Multi-provider routing">
-              <ApiKeyInput storageKey="openjarvis-openrouter-key" placeholder="sk-or-..." />
+              <ApiKeyInput keyName="OPENROUTER_API_KEY" placeholder="sk-or-..." />
             </SettingRow>
           </Section>
 
           {/* Tools */}
           <Section title="Tools">
-            <SettingRow label="Web Search" description="SerpAPI or Tavily key for web search tool">
-              <ApiKeyInput storageKey="openjarvis-search-key" placeholder="API key..." />
+            <SettingRow label="Web Search" description="Tavily key for web search tool">
+              <ApiKeyInput keyName="TAVILY_API_KEY" placeholder="tvly-..." toolName="web_search" />
             </SettingRow>
           </Section>
 
@@ -714,7 +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/src/types/index.ts

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

frontend/src/vite-env.d.ts

@@ -1,7 +1,9 @@
 /// <reference types="vite/client" />
 
 interface ImportMetaEnv {
-  readonly VITE_API_URL: string;
+  readonly VITE_API_URL?: string;
+  readonly VITE_SUPABASE_URL?: string;
+  readonly VITE_SUPABASE_ANON_KEY?: string;
 }
 
 interface ImportMeta {

frontend/vite.config.ts

@@ -4,6 +4,9 @@ import react from '@vitejs/plugin-react';
 import tailwindcss from '@tailwindcss/vite';
 import { VitePWA } from 'vite-plugin-pwa';
 
+// VITE_SUPABASE_ANON_KEY is intentionally NOT required here: a missing key
+// disables the savings leaderboard at runtime (see src/lib/supabase.ts) rather
+// than failing the build, so the package/app stays publishable without it.
 export default defineConfig({
   resolve: {
     alias: {
@@ -51,7 +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

@@ -127,6 +127,7 @@ markdown_extensions:
   - pymdownx.tilde
 
 extra_javascript:
+  - javascripts/leaderboard-config.js
   - javascripts/leaderboard.js
   - https://cdn.jsdelivr.net/npm/@docsearch/js@3
   - javascripts/docsearch-init.js
@@ -195,6 +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

pyproject.toml

@@ -48,6 +48,7 @@ dev = [
     "pytest>=8",
     "pytest-asyncio>=0.24",
     "pytest-cov>=5",
+    "pytest-xdist>=3",
     "respx>=0.22",
     "ruff>=0.4",
     "pre-commit>=3.0",
@@ -84,6 +85,13 @@ server = [
     "pydantic>=2.0",
     "python-multipart>=0.0.9",
 ]
+desktop = [
+    "fastapi>=0.110",
+    "uvicorn>=0.30",
+    "pydantic>=2.0",
+    "python-multipart>=0.0.9",
+    "faster-whisper>=1.0",
+]
 openhands = ["openhands-sdk>=1.0; python_version >= '3.12'"]
 gpu-metrics = ["pynvml>=12.0"]
 energy-amd = ["amdsmi>=6.1"]
@@ -178,6 +186,9 @@ git_describe_command = [
 # Such builds can inject the real version via SETUPTOOLS_SCM_PRETEND_VERSION.
 fallback_version = "0.0.0+unknown"
 
+[tool.uv.sources]
+openjarvis-rust = { path = "rust/crates/openjarvis-python" }
+
 [tool.hatch.build.targets.wheel]
 packages = ["src/openjarvis"]
 
@@ -227,3 +238,12 @@ select = ["E", "F", "I", "W"]
 dev = [
     "maturin>=1.12.6",
 ]
+# openjarvis_rust is the native PyO3 extension, built from the local Rust
+# workspace. It lives in a uv dependency group (PEP 735) — not the published
+# `desktop` extra — so `uv sync --group desktop-native` builds it from source
+# for the desktop app, while `pip install openjarvis[desktop]` from PyPI does
+# NOT try to resolve openjarvis-rust from PyPI, where it isn't published
+# (dependency groups are excluded from wheel metadata). See #584 / #615.
+desktop-native = [
+    "openjarvis-rust",
+]

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/install/install.sh

@@ -210,6 +210,14 @@ if ! command -v python3 >/dev/null 2>&1; then
 fi
 
 # ---- env ----
+# OpenJarvis keeps ALL of its state (install tree + runtime data, configs,
+# databases, caches, logs) under a single root so it never clutters $HOME
+# beyond one directory. Relocate it by exporting OPENJARVIS_HOME before
+# running the installer, e.g.:
+#     OPENJARVIS_HOME=~/apps/openjarvis curl ... | bash
+# The Python runtime honors the same override (and, when OPENJARVIS_HOME is
+# unset, $XDG_DATA_HOME/openjarvis if XDG_DATA_HOME is set). With nothing set
+# the root is ~/.openjarvis, so existing installs are untouched.
 OPENJARVIS_HOME="${OPENJARVIS_HOME:-$HOME/.openjarvis}"
 OPENJARVIS_REPO_URL="${OPENJARVIS_REPO_URL:-https://github.com/open-jarvis/OpenJarvis.git}"
 SRC_DIR="$OPENJARVIS_HOME/src"

scripts/quickstart.sh

@@ -148,7 +148,8 @@ fi
 
 # ── 7. Install Python dependencies ──────────────────────────────────
 info "Installing Python dependencies..."
-uv sync --extra server --quiet 2>/dev/null || uv sync --extra server
+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,
@@ -151,6 +155,9 @@ class BaseAgent(ABC):
         conversation messages, and finally the user input.
         """
         messages: list[Message] = []
+        context_messages = (
+            list(context.conversation.messages) if context is not None else []
+        )
         # Check if the context already supplies a system message
         _context_has_system = (
             context
@@ -172,9 +179,28 @@ class BaseAgent(ABC):
             except Exception:
                 effective_system_prompt = None
         if effective_system_prompt:
+            context_system_text = "\n\n".join(
+                message.text
+                for message in context_messages
+                if message.role == Role.SYSTEM
+                and message.metadata.get("memory_context")
+                and message.text
+            )
+            if context_system_text:
+                effective_system_prompt = (
+                    f"{effective_system_prompt}\n\n{context_system_text}"
+                )
+                context_messages = [
+                    message
+                    for message in context_messages
+                    if not (
+                        message.role == Role.SYSTEM
+                        and message.metadata.get("memory_context")
+                    )
+                ]
             messages.append(Message(role=Role.SYSTEM, content=effective_system_prompt))
-        if context and context.conversation.messages:
-            messages.extend(context.conversation.messages)
+        if context_messages:
+            messages.extend(context_messages)
         messages.append(Message(role=Role.USER, content=input))
         return messages
 

src/openjarvis/agents/claude_code.py

@@ -19,6 +19,7 @@ from typing import Any, List, Optional
 
 from openjarvis.agents._stubs import AgentContext, AgentResult, BaseAgent
 from openjarvis.core.events import EventBus
+from openjarvis.core.paths import get_config_dir
 from openjarvis.core.registry import AgentRegistry
 from openjarvis.core.types import ToolResult
 from openjarvis.engine._stubs import InferenceEngine
@@ -103,7 +104,7 @@ class ClaudeCodeAgent(BaseAgent):
                 "Install it from https://nodejs.org/ or via your package manager."
             )
 
-        dest = Path.home() / ".openjarvis" / "claude_code_runner"
+        dest = get_config_dir() / "claude_code_runner"
         dest.mkdir(parents=True, exist_ok=True)
 
         # Copy runner files if missing or outdated

src/openjarvis/agents/digest_store.py

@@ -9,6 +9,8 @@ from datetime import datetime
 from pathlib import Path
 from typing import Dict, List, Optional
 
+from openjarvis.core.paths import get_config_dir
+
 
 @dataclass
 class DigestArtifact:
@@ -30,7 +32,7 @@ class DigestStore:
 
     def __init__(self, db_path: str = "") -> None:
         if not db_path:
-            db_path = str(Path.home() / ".openjarvis" / "digest.db")
+            db_path = str(get_config_dir() / "digest.db")
         self._db_path = db_path
         self._conn = sqlite3.connect(db_path, check_same_thread=False)
         self._conn.execute("PRAGMA journal_mode=WAL")

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

@@ -73,12 +73,19 @@ WEB_SEARCH_COST_PER_CALL = 0.01
 # $0.01/call number — kept as a separate constant so it can drift.
 OPENAI_WEB_SEARCH_COST_PER_CALL = 0.01
 
-# Gemini Google-Search grounding: billed at $35 per 1000 grounded
-# *requests* (2025-12 public list price for the Grounding-with-Google-Search
-# tool, charged once per request that uses the tool regardless of how many
-# internal queries it issues). We charge per grounded request, not per
-# `web_search_queries` entry.
-GEMINI_SEARCH_COST_PER_CALL = 0.035
+# Gemini 3 Google-Search grounding: billed at $14 per 1000 search queries.
+# `_call_gemini_agent` reports the model's `web_search_queries`, so this is
+# charged per query, not per outer generate_content request.
+GEMINI_SEARCH_COST_PER_CALL = 0.014
+
+# Tavily Search, advanced depth: 2 API credits per search request at $0.008
+# per credit on the public pay-as-you-go plan. WebSearchTool captures actual
+# credits when Tavily returns usage metadata; this is the fallback estimate.
+TAVILY_SEARCH_COST_PER_CREDIT = 0.008
+TAVILY_ADVANCED_SEARCH_CREDITS = 2
+TAVILY_SEARCH_COST_PER_CALL = (
+    TAVILY_SEARCH_COST_PER_CREDIT * TAVILY_ADVANCED_SEARCH_CREDITS
+)
 
 ANTHROPIC_WEB_SEARCH_TOOL = {
     "type": "web_search_20250305",
@@ -101,6 +108,40 @@ def build_web_search_tool(max_uses: int = 8) -> Dict[str, Any]:
     }
 
 
+def tavily_search_context(
+    query: str,
+    *,
+    max_results: int = 5,
+) -> Dict[str, Any]:
+    """Run OpenJarvis WebSearchTool and return accounting-friendly metadata."""
+    from openjarvis.tools.web_search import WebSearchTool
+
+    tool = WebSearchTool(max_results=max_results)
+    res = tool.execute(query=query, max_results=max_results)
+    meta = dict(res.metadata or {})
+    engine = str(meta.get("engine") or "unknown")
+    credits = 0
+    cost_usd = 0.0
+    if engine == "tavily":
+        try:
+            credits = int(meta.get("credits") or TAVILY_ADVANCED_SEARCH_CREDITS)
+        except (TypeError, ValueError):
+            credits = TAVILY_ADVANCED_SEARCH_CREDITS
+        cost_usd = credits * TAVILY_SEARCH_COST_PER_CREDIT
+    text = res.content or ""
+    if not res.success and not text:
+        text = "(no search results)"
+    return {
+        "text": text,
+        "success": bool(res.success),
+        "engine": engine,
+        "credits": credits,
+        "cost_usd": cost_usd,
+        "n_searches": 1 if (query or "").strip() else 0,
+        "error": None if res.success else text,
+    }
+
+
 def web_search_cfg(method_cfg: Optional[Dict[str, Any]]) -> Tuple[bool, int]:
     """Parse ``method_cfg.web_search = { enabled, max_uses }``.
 
@@ -262,7 +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
@@ -278,8 +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)
@@ -300,14 +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
 
 
@@ -432,32 +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
@@ -509,24 +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
@@ -573,12 +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,
@@ -617,21 +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
@@ -660,7 +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,
@@ -687,21 +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
@@ -756,27 +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
@@ -843,33 +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
@@ -879,9 +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:
@@ -890,10 +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
@@ -967,8 +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
@@ -992,30 +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
@@ -1088,23 +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(
@@ -1228,8 +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()
@@ -1285,9 +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)
@@ -1322,6 +1413,9 @@ __all__ = [
     "LocalCloudAgent",
     "NO_TEMP_PREFIXES",
     "OPENAI_WEB_SEARCH_COST_PER_CALL",
+    "TAVILY_ADVANCED_SEARCH_CREDITS",
+    "TAVILY_SEARCH_COST_PER_CALL",
+    "TAVILY_SEARCH_COST_PER_CREDIT",
     "WEB_SEARCH_COST_PER_CALL",
     "_bump_cloud_calls",
     "_bump_local_calls",
@@ -1329,5 +1423,6 @@ __all__ = [
     "estimate_cost",
     "is_gpt5_family",
     "supports_temperature",
+    "tavily_search_context",
     "web_search_cfg",
 ]

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

@@ -11,24 +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":                       (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), 2025-12 list price.
-    # 2.5 Pro uses tiered pricing (>200K context = $2.50/$15); we charge the
-    # low-context tier since GAIA / SWE-bench prompts stay well under 200K.
-    "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),
+    "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-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),
     # 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),
 }
 
@@ -61,7 +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
+    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

@@ -34,6 +34,7 @@ from openjarvis.agents.hybrid._base import (
     WEB_SEARCH_COST_PER_CALL,
     LocalCloudAgent,
     build_web_search_tool,
+    tavily_search_context,
     web_search_cfg,
 )
 from openjarvis.agents.hybrid.mini_swe_agent import (
@@ -79,9 +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:
@@ -135,7 +134,12 @@ class AdvisorsAgent(LocalCloudAgent):
         advisor_temperature = float(cfg.get("advisor_temperature", 0.2))
 
         ws_enabled, ws_max_uses = web_search_cfg(cfg)
-        if ws_enabled and self._cloud_endpoint not in _SEARCH_CAPABLE_ENDPOINTS:
+        search_backend = str(cfg.get("search_backend", "provider")).lower()
+        if (
+            ws_enabled
+            and search_backend != "tavily"
+            and self._cloud_endpoint not in _SEARCH_CAPABLE_ENDPOINTS
+        ):
             raise ValueError(
                 f"web_search.enabled=true but cloud_endpoint={self._cloud_endpoint!r}; "
                 "server-side web_search is wired for anthropic / openai / gemini "
@@ -146,19 +150,24 @@ class AdvisorsAgent(LocalCloudAgent):
         use_ws = ws_enabled
         gaia_max_turns = int(cfg.get("gaia_max_turns", 8))
         n_searches_total = 0
+        search_cost_total = 0.0
 
         # 1. Initial executor pass — advisor (Qwen) doesn't get tools;
         # 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 = 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,
+            (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
         else:
             initial_resp, e1_in, e1_out = self._call_cloud(
                 user=f"Question:\n{question}",
@@ -176,7 +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,
@@ -196,14 +206,18 @@ class AdvisorsAgent(LocalCloudAgent):
             f"answer-format rules."
         )
         if use_ws:
-            final_answer, e2_in, e2_out, n_s2, e2_turns = 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,
+            (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
         else:
             final_answer, e2_in, e2_out = self._call_cloud(
                 user=final_user,
@@ -216,7 +230,10 @@ class AdvisorsAgent(LocalCloudAgent):
         tokens_local = adv_in + adv_out
         tokens_cloud = e1_in + e1_out + e2_in + e2_out
         cost = self.cost_usd(self._cloud_model, e1_in + e2_in, e1_out + e2_out)
-        cost += n_searches_total * _search_cost_per_call(self._cloud_endpoint)
+        if search_backend == "tavily":
+            cost += search_cost_total
+        else:
+            cost += n_searches_total * _search_cost_per_call(self._cloud_endpoint)
 
         meta: Dict[str, Any] = {
             "tokens_local": tokens_local,
@@ -233,7 +250,9 @@ class AdvisorsAgent(LocalCloudAgent):
                 "initial_response": initial_resp,
                 "advisor_feedback": advisor_text,
                 "web_search_enabled": use_ws,
+                "search_backend": search_backend,
                 "n_web_searches": n_searches_total,
+                "search_cost_usd": search_cost_total,
                 "note": "inference-only advisor (untrained); lower bound on the technique.",
             },
         }
@@ -251,16 +270,40 @@ class AdvisorsAgent(LocalCloudAgent):
         max_tokens: int,
         ws_max_uses: int,
         max_turns: int,
-    ) -> Tuple[str, int, int, int, int]:
+        query: Optional[str] = None,
+    ) -> Tuple[str, int, int, int, int, float]:
         """Run a search-capable executor pass for the configured cloud.
 
         Dispatches by ``self._cloud_endpoint`` to the matching ``_base``
-        agent loop. Returns the shared 5-tuple ``(text, p_tok, c_tok,
-        n_searches, turns)``. The endpoint is assumed already validated
-        against ``_SEARCH_CAPABLE_ENDPOINTS`` by the caller.
+        agent loop, or through Tavily when ``method_cfg.search_backend`` is
+        ``"tavily"``. Returns ``(text, p_tok, c_tok, n_searches, turns,
+        search_cost_usd)``.
         """
+        if str(self._cfg.get("search_backend", "provider")).lower() == "tavily":
+            res = tavily_search_context(
+                query or user,
+                max_results=int(self._cfg.get("tavily_max_results", 5)),
+            )
+            grounded_user = (
+                f"Web search results:\n{res['text']}\n\n"
+                f"Using the search results above, answer this request:\n{user}"
+            )
+            text, p, c = self._call_cloud(
+                user=grounded_user,
+                system=system,
+                max_tokens=max_tokens,
+                temperature=0.0,
+            )
+            return (
+                text,
+                p,
+                c,
+                int(res["n_searches"]),
+                1,
+                float(res["cost_usd"]),
+            )
         if self._cloud_endpoint == "anthropic":
-            return self._call_anthropic_agent(
+            text, p, c, n_searches, turns = self._call_anthropic_agent(
                 self._cloud_model,
                 user=user,
                 system=system,
@@ -269,8 +312,9 @@ class AdvisorsAgent(LocalCloudAgent):
                 tools=[build_web_search_tool(ws_max_uses)],
                 max_turns=max_turns,
             )
+            return text, p, c, n_searches, turns, 0.0
         if self._cloud_endpoint == "openai":
-            return self._call_openai_agent(
+            text, p, c, n_searches, turns = self._call_openai_agent(
                 self._cloud_model,
                 user=user,
                 system=system,
@@ -278,8 +322,9 @@ class AdvisorsAgent(LocalCloudAgent):
                 temperature=0.0,
                 max_turns=max_turns,
             )
+            return text, p, c, n_searches, turns, 0.0
         if self._cloud_endpoint == "gemini":
-            return self._call_gemini_agent(
+            text, p, c, n_searches, turns = self._call_gemini_agent(
                 self._cloud_model,
                 user=user,
                 system=system,
@@ -287,6 +332,7 @@ class AdvisorsAgent(LocalCloudAgent):
                 temperature=0.0,
                 max_turns=max_turns,
             )
+            return text, p, c, n_searches, turns, 0.0
         # Genuinely unsupported (openrouter / vllm / unknown). The caller
         # guard should have caught this; raise defensively.
         raise ValueError(
@@ -379,8 +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

@@ -46,6 +46,7 @@ from openjarvis.agents.hybrid._base import (
     WEB_SEARCH_COST_PER_CALL,
     LocalCloudAgent,
     build_web_search_tool,
+    tavily_search_context,
     web_search_cfg,
 )
 from openjarvis.agents.hybrid._prices import (
@@ -88,19 +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()
@@ -118,9 +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:
@@ -153,7 +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}"
@@ -173,17 +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
@@ -205,98 +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
@@ -362,16 +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})"
@@ -456,12 +472,18 @@ def _format_worker_pool(workers: List[Dict[str, Any]]) -> str:
     )
 
 
-def _search_capable_indices(workers: List[Dict[str, Any]]) -> List[int]:
+def _search_capable_indices(
+    workers: List[Dict[str, Any]],
+    *,
+    search_backend: str = "provider",
+) -> List[int]:
     """Indices of workers whose endpoint can run server-side web search."""
+    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
     ]
 
 
@@ -470,6 +492,7 @@ def _build_conductor_prompt(
     workers: List[Dict[str, Any]],
     *,
     web_search_enabled: bool = False,
+    search_backend: str = "provider",
 ) -> str:
     """Build the planner prompt.
 
@@ -485,12 +508,18 @@ def _build_conductor_prompt(
     )
     if not web_search_enabled:
         return base
-    capable = _search_capable_indices(workers)
+    capable = _search_capable_indices(workers, search_backend=search_backend)
     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"
+            )
+        else:
+            capability = "Only these model indices can perform live web search"
         constraint = (
             "\n\nWEB SEARCH CONSTRAINT:\n"
-            f"Only these model indices can perform live web search: [{cap_str}]. "
+            f"{capability}: [{cap_str}]. "
             "Any step that needs to look up facts, current events, or other "
             "information not reliably known from memory MUST be routed to one "
             "of those indices. Steps routed to any other model can only use "
@@ -550,8 +579,8 @@ def _call_worker(
     *,
     web_search_tool: Optional[Dict[str, Any]] = None,
     web_search_max_uses: int = 8,
-) -> Tuple[str, int, int, bool, int]:
-    """Returns (text, p_tok, c_tok, is_local, n_web_searches).
+) -> Tuple[str, int, int, bool, int, float]:
+    """Returns (text, p_tok, c_tok, is_local, n_web_searches, extra_cost).
 
     ``web_search_tool``: a truthy marker that web_search is enabled for
     this run. When set AND the worker endpoint is search-capable
@@ -565,6 +594,22 @@ def _call_worker(
     max_tok = int(cfg.get("worker_max_tokens", 4096))
     temp = float(cfg.get("worker_temperature", 0.2))
     use_ws = web_search_tool is not None
+    search_backend = str(cfg.get("search_backend", "provider")).lower()
+    extra_cost = 0.0
+    if use_ws and search_backend == "tavily":
+        res = tavily_search_context(
+            prompt,
+            max_results=int(cfg.get("tavily_max_results", 5)),
+        )
+        prompt = (
+            f"Web search results:\n{res['text']}\n\n"
+            f"Using the search results above, answer this request:\n{prompt}"
+        )
+        extra_cost = float(res["cost_usd"])
+        use_ws = False
+        tavily_searches = int(res["n_searches"])
+    else:
+        tavily_searches = 0
 
     if ep == "vllm":
         text, p, c = LocalCloudAgent._call_vllm(
@@ -575,7 +620,7 @@ def _call_worker(
             temperature=temp,
             enable_thinking=False,
         )
-        return text, p, c, True, 0
+        return text, p, c, True, tavily_searches, extra_cost
     if ep == "openai":
         if use_ws:
             text, p, c, n_searches, _ = LocalCloudAgent._call_openai_agent(
@@ -584,14 +629,14 @@ def _call_worker(
                 max_tokens=max_tok,
                 temperature=(1.0 if is_gpt5_family(worker["model"]) else temp),
             )
-            return text, p, c, False, n_searches
+            return text, p, c, False, n_searches, 0.0
         text, p, c = LocalCloudAgent._call_openai(
             worker["model"],
             user=prompt,
             max_tokens=max_tok,
             temperature=(1.0 if is_gpt5_family(worker["model"]) else temp),
         )
-        return text, p, c, False, 0
+        return text, p, c, False, tavily_searches, extra_cost
     if ep == "openrouter":
         # OpenRouter is OpenAI-compatible; the helper handles the
         # base_url + OPENROUTER_API_KEY plumbing. No server-side web
@@ -607,7 +652,7 @@ def _call_worker(
             temperature=temp,
             extra_body=extra_body if isinstance(extra_body, dict) else None,
         )
-        return text, p, c, False, 0
+        return text, p, c, False, tavily_searches, extra_cost
     if ep == "anthropic":
         eff_temp = temp if supports_temperature(worker["model"]) else 0.0
         anthropic_kwargs: Dict[str, Any] = dict(
@@ -620,7 +665,7 @@ def _call_worker(
         text, p, c, n_searches = LocalCloudAgent._call_anthropic(
             worker["model"], **anthropic_kwargs
         )
-        return text, p, c, False, n_searches
+        return text, p, c, False, n_searches or tavily_searches, extra_cost
     if ep == "gemini":
         # Gemini Developer API via google-genai. With web_search on, route
         # through the Google-Search-grounded agent loop; otherwise plain
@@ -632,14 +677,14 @@ def _call_worker(
                 max_tokens=max_tok,
                 temperature=temp,
             )
-            return text, p, c, False, n_searches
+            return text, p, c, False, n_searches, 0.0
         text, p, c = LocalCloudAgent._call_gemini(
             worker["model"],
             user=prompt,
             max_tokens=max_tok,
             temperature=temp,
         )
-        return text, p, c, False, 0
+        return text, p, c, False, tavily_searches, extra_cost
     raise ValueError(f"unsupported worker endpoint: {ep!r}")
 
 
@@ -659,12 +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:
@@ -672,7 +723,7 @@ def _swe_worker_step(
         # backbones today (the loop's tool-call format is Anthropic- or
         # OpenAI-via-vllm-shaped only). Fall back to one-shot for those —
         # SWE-bench-wise they were already weak; this preserves behavior.
-        text, p, c, is_local, n_searches = _call_worker(worker, prompt, cfg)
+        text, p, c, is_local, n_searches, _extra = _call_worker(worker, prompt, cfg)
         return text, p, c, is_local, n_searches, 0
     out = run_swe_agent_loop(
         task,
@@ -690,7 +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"]),
     )
 
 
@@ -753,13 +808,17 @@ class ConductorAgent(LocalCloudAgent):
             and bool(task_meta_early.get("base_commit"))
         )
         ws_enabled, ws_max_uses = web_search_cfg(cfg)
+        search_backend = str(cfg.get("search_backend", "provider")).lower()
         planner_ws = ws_enabled and not swe_mode_early
 
         # 1. Plan — when web_search is on (GAIA), the prompt names which
         # worker indices can actually search, so the planner routes
         # research steps to a search-capable worker.
         user = _build_conductor_prompt(
-            question, workers, web_search_enabled=planner_ws,
+            question,
+            workers,
+            web_search_enabled=planner_ws,
+            search_backend=search_backend,
         )
         plan_text, p_in, p_out = self._call_cloud(
             user=user,
@@ -787,21 +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,
@@ -833,16 +893,17 @@ class ConductorAgent(LocalCloudAgent):
         # memory. Fail loud instead of degrading silently.
         # ``ws_enabled`` / ``ws_max_uses`` computed up front for the planner
         # constraint — reuse them here.
-        if ws_enabled and not swe_mode:
+        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}); "
@@ -853,39 +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
@@ -894,35 +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 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 = _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:
@@ -930,20 +1009,25 @@ class ConductorAgent(LocalCloudAgent):
                 else:
                     tokens_cloud += w_in + w_out
                     cost += self.cost_usd(worker["model"], w_in, w_out)
-                    cost += n_searches * _worker_search_cost_per_call(worker_ep)
+                    if search_backend != "tavily":
+                        cost += n_searches * _worker_search_cost_per_call(worker_ep)
+                if search_backend == "tavily":
+                    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
@@ -954,7 +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:
@@ -965,8 +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 = {
@@ -981,11 +1065,11 @@ class ConductorAgent(LocalCloudAgent):
                 "plan": plan,
                 "fallback_used": fallback_used,
                 "web_search_enabled": ws_enabled,
+                "search_backend": search_backend,
                 "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

@@ -48,12 +48,13 @@ from openjarvis.agents.hybrid._base import (
     WEB_SEARCH_COST_PER_CALL,
     LocalCloudAgent,
     build_web_search_tool,
+    tavily_search_context,
     web_search_cfg,
 )
 from openjarvis.agents.hybrid._openai_retry import (
     patch_openai_globally as _patch_openai_globally,
 )
-from openjarvis.agents.hybrid._prices import NO_TEMP_PREFIXES
+from openjarvis.agents.hybrid._prices import NO_TEMP_PREFIXES, default_max_output_tokens
 from openjarvis.agents.hybrid.mini_swe_agent import run_swe_agent_loop
 from openjarvis.core.registry import AgentRegistry
 
@@ -86,7 +87,7 @@ MINIONS_FIRST_TURN_SCHEMA = {
             "type": "object",
             "properties": {
                 "reasoning": {"type": "string"},
-                "message":   {"type": "string"},
+                "message": {"type": "string"},
             },
             "required": ["reasoning", "message"],
             "additionalProperties": False,
@@ -103,7 +104,7 @@ MINIONS_CONVERSATION_SCHEMA = {
                     "type": "object",
                     "properties": {
                         "decision": {"const": "request_additional_info"},
-                        "message":  {"type": "string"},
+                        "message": {"type": "string"},
                     },
                     "required": ["decision", "message"],
                     "additionalProperties": False,
@@ -112,7 +113,7 @@ MINIONS_CONVERSATION_SCHEMA = {
                     "type": "object",
                     "properties": {
                         "decision": {"const": "provide_final_answer"},
-                        "answer":   {"type": "string"},
+                        "answer": {"type": "string"},
                     },
                     "required": ["decision", "answer"],
                     "additionalProperties": False,
@@ -125,8 +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",
 )
 
@@ -170,6 +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:
@@ -214,12 +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
 
@@ -357,11 +359,14 @@ def _apply_patches_once() -> None:
 
 # ---------- Pre-fetch helper (GAIA only) ----------
 
+
 def _prefetch_context(
     question: str,
     cloud_endpoint: str,
     cloud_model: str,
     max_uses: int = 8,
+    search_backend: str = "provider",
+    tavily_max_results: int = 5,
 ) -> Dict[str, Any]:
     """Use Anthropic web_search to fetch real source material the worker can read.
 
@@ -373,8 +378,27 @@ 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:
+            res = tavily_search_context(question, max_results=tavily_max_results)
+            out.update(
+                text=res["text"],
+                cost_usd=float(res["cost_usd"]),
+                n_searches=int(res["n_searches"]),
+                tokens=0,
+                engine=res.get("engine"),
+                credits=res.get("credits"),
+            )
+            if res.get("error"):
+                out["error"] = res["error"]
+        except Exception as e:
+            out["error"] = f"{type(e).__name__}: {e}"
+        return out
     if cloud_endpoint != "anthropic" or not (question or "").strip():
         return out
     try:
@@ -393,10 +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:
@@ -404,9 +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 {}
@@ -421,6 +445,7 @@ def _context_for(
 
 # ---------- Main agent ----------
 
+
 @AgentRegistry.register("minions")
 class MinionsAgent(LocalCloudAgent):
     """HazyResearch Minions supervisor/worker protocol. See module docstring."""
@@ -432,6 +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:
@@ -498,18 +524,21 @@ class MinionsAgent(LocalCloudAgent):
             max_tokens=cfg.get("worker_max_tokens", 4096),
             local=True,
         )
+        cloud_max_tokens = int(
+            cfg.get("cloud_max_tokens") or default_max_output_tokens(self._cloud_model)
+        )
         if self._cloud_endpoint == "openai":
             cloud_client = OpenAIClient(
                 model_name=self._cloud_model,
                 temperature=0.0,
-                max_tokens=4096,
+                max_tokens=cloud_max_tokens,
             )
         elif self._cloud_endpoint == "anthropic":
             # Temperature stripping is handled by the global patch above for Opus 4.7+.
             cloud_client = AnthropicClient(
                 model_name=self._cloud_model,
                 temperature=0.0,
-                max_tokens=4096,
+                max_tokens=cloud_max_tokens,
             )
         elif self._cloud_endpoint == "gemini":
             # The vendored Minion library already special-cases GeminiClient
@@ -520,7 +549,7 @@ class MinionsAgent(LocalCloudAgent):
             cloud_client = GeminiClient(
                 model_name=self._cloud_model,
                 temperature=0.0,
-                max_tokens=4096,
+                max_tokens=cloud_max_tokens,
             )
         else:
             raise ValueError(f"unsupported cloud endpoint: {self._cloud_endpoint!r}")
@@ -543,9 +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
@@ -560,17 +594,21 @@ class MinionsAgent(LocalCloudAgent):
                 self._cloud_endpoint,
                 self._cloud_model,
                 max_uses=ws_max_uses,
+                search_backend=str(cfg.get("search_backend", "provider")).lower(),
+                tavily_max_results=int(cfg.get("tavily_max_results", 5)),
             )
 
         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)
@@ -582,15 +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")
@@ -625,7 +665,6 @@ class MinionsAgent(LocalCloudAgent):
         }
         return out.get("final_answer", ""), meta
 
-
     # ------------------------------------------------------------------
     # SWE-bench variant
     # ------------------------------------------------------------------
@@ -643,21 +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/runner.py

@@ -38,13 +38,14 @@ except ModuleNotFoundError:
 from openjarvis.agents._stubs import AgentContext, AgentResult
 from openjarvis.agents.hybrid._energy import EnergyCollector
 from openjarvis.agents.hybrid._prompts import format_prompt as _format_prompt
+from openjarvis.core.paths import get_config_dir
 
 PACKAGE_DIR = Path(__file__).parent
 DEFAULT_REGISTRY_DIR = PACKAGE_DIR / "registry"
 DEFAULT_EXPERIMENTS_DIR = Path(
     os.environ.get(
         "OPENJARVIS_HYBRID_EXPERIMENTS_DIR",
-        Path.home() / ".openjarvis" / "experiments" / "hybrid",
+        get_config_dir() / "experiments" / "hybrid",
     )
 )
 DEFAULT_SUBSETS_DIR = DEFAULT_EXPERIMENTS_DIR / "subsets"
@@ -122,6 +123,7 @@ def load_registry(registry_dir: Optional[Path] = None) -> Dict[str, Dict[str, An
 
 # ---------- Bench dispatch ----------
 
+
 def _load_gaia_tasks(n: Optional[int]) -> List[Dict[str, Any]]:
     """GAIA validation. Each task is a dict with `task_id` + `question`."""
     from openjarvis.evals.datasets.gaia import GAIADataset
@@ -137,12 +139,14 @@ def _load_gaia_tasks(n: Optional[int]) -> List[Dict[str, Any]]:
         # id round-trip.
         md = rec.metadata or {}
         task_id = md.get("task_id") or rec.record_id
-        out.append({
-            "task_id": task_id,
-            "question": md.get("question", rec.problem),
-            "reference": rec.reference,
-            "metadata": dict(md),
-        })
+        out.append(
+            {
+                "task_id": task_id,
+                "question": md.get("question", rec.problem),
+                "reference": rec.reference,
+                "metadata": dict(md),
+            }
+        )
     return out
 
 
@@ -155,19 +159,21 @@ def _load_swebench_tasks(n: Optional[int]) -> List[Dict[str, Any]]:
     out: List[Dict[str, Any]] = []
     for rec in ds.iter_records():
         md = rec.metadata or {}
-        out.append({
-            "task_id": md.get("instance_id", rec.record_id),
-            "repo": md.get("repo", ""),
-            "base_commit": md.get("base_commit", ""),
-            "problem_statement": md.get("problem_statement", rec.problem),
-            "hints_text": md.get("hints_text", ""),
-            "test_patch": md.get("test_patch", ""),
-            "FAIL_TO_PASS": md.get("FAIL_TO_PASS", []),
-            "PASS_TO_PASS": md.get("PASS_TO_PASS", []),
-            "version": md.get("version"),
-            "reference": rec.reference,
-            "metadata": dict(md),
-        })
+        out.append(
+            {
+                "task_id": md.get("instance_id", rec.record_id),
+                "repo": md.get("repo", ""),
+                "base_commit": md.get("base_commit", ""),
+                "problem_statement": md.get("problem_statement", rec.problem),
+                "hints_text": md.get("hints_text", ""),
+                "test_patch": md.get("test_patch", ""),
+                "FAIL_TO_PASS": md.get("FAIL_TO_PASS", []),
+                "PASS_TO_PASS": md.get("PASS_TO_PASS", []),
+                "version": md.get("version"),
+                "reference": rec.reference,
+                "metadata": dict(md),
+            }
+        )
     return out
 
 
@@ -206,7 +212,9 @@ def _load_subset_file(subset_path: str) -> Dict[str, Any]:
                 f"subset {p.name} has no 'task_ids' field; got keys {list(data.keys())}"
             )
         return data
-    raise ValueError(f"subset {p.name} must be a list or dict; got {type(data).__name__}")
+    raise ValueError(
+        f"subset {p.name} must be a list or dict; got {type(data).__name__}"
+    )
 
 
 def _apply_subset(
@@ -332,7 +340,11 @@ def _score_swebench(
 
     patch = extract_patch(answer)
     if patch is None:
-        return {"success": False, "score": 0.0, "details": {"reason": "no_patch_extracted"}}
+        return {
+            "success": False,
+            "score": 0.0,
+            "details": {"reason": "no_patch_extracted"},
+        }
 
     record = EvalRecord(
         record_id=task["task_id"],
@@ -368,6 +380,7 @@ def score(
 
 # ---------- Cell run ----------
 
+
 def _cell_dir(cell_name: str, root: Path) -> Path:
     d = root / cell_name
     d.mkdir(parents=True, exist_ok=True)
@@ -440,8 +453,10 @@ def _error_row(task: Dict[str, Any], t0: float, error: str) -> Dict[str, Any]:
     return {
         "task_id": task["task_id"],
         "answer": "",
-        "tokens_local": 0, "tokens_cloud": 0,
-        "cost_usd": 0.0, "latency_s": time.time() - t0,
+        "tokens_local": 0,
+        "tokens_cloud": 0,
+        "cost_usd": 0.0,
+        "latency_s": time.time() - t0,
         "web_search_uses": 0,
         "tool_calls": 0,
         "n_cloud_calls": 0,
@@ -456,11 +471,13 @@ def _run_one_inner(
 ) -> Dict[str, Any]:
     """Run the agent on one task. Returns a hybrid-shape row."""
     prompt = _format_prompt(task)
-    ctx = AgentContext(metadata={
-        "task": task,
-        "task_id": task["task_id"],
-        "log_dir": log_dir,
-    })
+    ctx = AgentContext(
+        metadata={
+            "task": task,
+            "task_id": task["task_id"],
+            "log_dir": log_dir,
+        }
+    )
     t0 = time.time()
     try:
         result: AgentResult = agent.run(prompt, ctx)
@@ -534,11 +551,12 @@ def _run_one(
     if worker.is_alive():
         print(
             f"[timeout] task={task['task_id']} exceeded "
-            f"{task_timeout_s/60:.1f}m — abandoning worker, recording error row",
+            f"{task_timeout_s / 60:.1f}m — abandoning worker, recording error row",
             flush=True,
         )
         return _error_row(
-            task, t0,
+            task,
+            t0,
             f"TaskTimeout: task exceeded the {task_timeout_s:.0f}s hybrid "
             "per-task wall-clock cap (likely a hung network or Modal-harness "
             "call); worker thread abandoned, task left for resume.",
@@ -558,7 +576,7 @@ def _heartbeat(done: int, total: int, row: Dict[str, Any], t_start: float) -> No
     print(
         f"[{done}/{total}] {ok} task={row['task_id']} score={sc_str} "
         f"local={row['tokens_local']} cloud={row['tokens_cloud']} "
-        f"${row['cost_usd']:.3f} {row['latency_s']:.1f}s eta={eta/60:.1f}m",
+        f"${row['cost_usd']:.3f} {row['latency_s']:.1f}s eta={eta / 60:.1f}m",
         flush=True,
     )
 
@@ -644,9 +662,9 @@ def _write_summary(
     summary_path.write_text(json.dumps(summary, indent=2))
     print(
         f"[summary] {cell_name}: n={n_done}/{cell['n']} err={n_err} "
-        f"acc={acc:.3f} cost=${total_cost:.2f} time={wall/60:.1f}m "
-        f"energy={energy_j/1000:.1f}kJ "
-        f"(session +{elapsed/60:.1f}m +{energy_j_session/1000:.1f}kJ, "
+        f"acc={acc:.3f} cost=${total_cost:.2f} time={wall / 60:.1f}m "
+        f"energy={energy_j / 1000:.1f}kJ "
+        f"(session +{elapsed / 60:.1f}m +{energy_j_session / 1000:.1f}kJ, "
         f"processed={n_processed})",
         flush=True,
     )
@@ -664,8 +682,11 @@ def run_cell(
     out_dir = _cell_dir(cell_name, out_root)
     with _cell_lock(out_dir, cell_name):
         _run_cell_locked(
-            cell_name, cell, out_dir,
-            do_score=do_score, resume=resume,
+            cell_name,
+            cell,
+            out_dir,
+            do_score=do_score,
+            resume=resume,
         )
 
 
@@ -727,7 +748,7 @@ def _run_cell_locked(
     mcfg = cell.get("method_cfg") or {}
     task_timeout_s = float(mcfg.get("task_timeout_s", DEFAULT_TASK_TIMEOUT_S))
     if task_timeout_s > 0:
-        print(f"[task-timeout] {task_timeout_s/60:.1f}m per task", flush=True)
+        print(f"[task-timeout] {task_timeout_s / 60:.1f}m per task", flush=True)
 
     agent = _build_agent(cell)
 
@@ -739,18 +760,25 @@ def _run_cell_locked(
 
     def _process(task: Dict[str, Any]) -> None:
         row = _run_one(
-            agent, cell["bench"], task, log_dir,
+            agent,
+            cell["bench"],
+            task,
+            log_dir,
             task_timeout_s=task_timeout_s,
         )
         scored: Optional[Dict[str, Any]] = None
         if do_score and row.get("error") is None:
             try:
                 scored = score(
-                    cell["bench"], task, row["answer"], cell_name=cell_name,
+                    cell["bench"],
+                    task,
+                    row["answer"],
+                    cell_name=cell_name,
                 )
             except Exception as e:
                 scored = {
-                    "success": False, "score": 0.0,
+                    "success": False,
+                    "score": 0.0,
                     "details": {"score_error": str(e)},
                 }
         full_row = {**row, "score": scored}
@@ -789,9 +817,7 @@ def _run_cell_locked(
         while not watchdog_stop.wait(60.0):
             try:
                 cur = (
-                    results_path.stat().st_mtime
-                    if results_path.exists()
-                    else last_seen
+                    results_path.stat().st_mtime if results_path.exists() else last_seen
                 )
             except Exception:
                 cur = last_seen
@@ -830,7 +856,11 @@ def _run_cell_locked(
     watchdog_stop.set()
 
     _write_summary(
-        out_dir, cell_name, cell, tasks, t_start,
+        out_dir,
+        cell_name,
+        cell,
+        tasks,
+        t_start,
         n_processed=len(pending),
         energy_j_session=energy.energy_j_total,
     )
@@ -838,6 +868,7 @@ def _run_cell_locked(
 
 # ---------- CLI ----------
 
+
 def main(argv: Optional[List[str]] = None) -> int:
     p = argparse.ArgumentParser(
         prog="python -m openjarvis.agents.hybrid.runner",
@@ -855,13 +886,18 @@ def main(argv: Optional[List[str]] = None) -> int:
         help="Override experiments output root.",
     )
     p.add_argument("--no-score", action="store_true", help="Skip scoring.")
-    p.add_argument("--no-resume", action="store_true", help="Don't resume from results.jsonl.")
+    p.add_argument(
+        "--no-resume", action="store_true", help="Don't resume from results.jsonl."
+    )
     args = p.parse_args(argv)
 
     reg_dir = Path(args.registry_dir) if args.registry_dir else None
     cells = load_registry(reg_dir)
     if not cells:
-        print(f"[error] no cells found in {reg_dir or DEFAULT_REGISTRY_DIR}", file=sys.stderr)
+        print(
+            f"[error] no cells found in {reg_dir or DEFAULT_REGISTRY_DIR}",
+            file=sys.stderr,
+        )
         return 2
     if args.cell not in cells:
         print(
@@ -871,7 +907,8 @@ def main(argv: Optional[List[str]] = None) -> int:
         return 2
     root = Path(args.root) if args.root else None
     run_cell(
-        args.cell, cells[args.cell],
+        args.cell,
+        cells[args.cell],
         do_score=not args.no_score,
         resume=not args.no_resume,
         root=root,

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,
     },
 }
 
@@ -149,6 +149,17 @@ def _build_router_schema(agent_ids: List[str]) -> Dict[str, Any]:
     }
 
 
+def _openai_response_format(schema: Dict[str, Any]) -> Dict[str, Any]:
+    return {
+        "type": "json_schema",
+        "json_schema": {
+            "name": "skillorchestra_route",
+            "schema": schema["format"]["schema"],
+            "strict": True,
+        },
+    }
+
+
 def _parse_router_json(text: str) -> Dict[str, Any]:
     s = (text or "").strip()
     try:
@@ -178,9 +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,
@@ -196,6 +205,70 @@ class SkillOrchestraAgent(LocalCloudAgent):
 
     agent_id = "skillorchestra"
 
+    def _route_call(
+        self,
+        *,
+        question: str,
+        router_sys: str,
+        router_schema: Dict[str, Any],
+        router_max: int,
+    ) -> Tuple[str, int, int]:
+        user = f"Question:\n{question}"
+        if self._cloud_endpoint == "anthropic":
+            kwargs: Dict[str, Any] = {
+                "user": user,
+                "system": router_sys,
+                "max_tokens": router_max,
+                "output_config": router_schema,
+            }
+            if supports_temperature(self._cloud_model):
+                kwargs["temperature"] = 0.0
+            text, r_in, r_out, _ = self._call_anthropic(
+                self._cloud_model,
+                **kwargs,
+            )
+            return text, r_in, r_out
+        if self._cloud_endpoint == "openai":
+            return self._call_openai(
+                self._cloud_model,
+                user=user,
+                system=router_sys,
+                max_tokens=router_max,
+                temperature=0.0,
+                response_format=_openai_response_format(router_schema),
+            )
+        if self._cloud_endpoint == "gemini":
+            return self._call_gemini(
+                self._cloud_model,
+                user=user,
+                system=router_sys,
+                max_tokens=router_max,
+                temperature=0.0,
+            )
+        raise ValueError(
+            f"SkillOrchestra router unsupported cloud_endpoint={self._cloud_endpoint!r}"
+        )
+
+    def _executor_call(
+        self,
+        *,
+        question: str,
+        max_tokens: int,
+    ) -> Tuple[str, int, int]:
+        if self._cloud_endpoint == "anthropic":
+            text, w_in, w_out, _ = self._call_anthropic(
+                self._cloud_model,
+                user=question,
+                max_tokens=max_tokens,
+                temperature=0.0,
+            )
+            return text, w_in, w_out
+        return self._call_cloud(
+            user=question,
+            max_tokens=max_tokens,
+            temperature=0.0,
+        )
+
     def _is_soft_failure(self, exc: BaseException) -> Optional[str]:
         # Empty/unbalanced router JSON — treat as soft failure to match the
         # hybrid adapter's behavior (matches `err=1` rows in the n=30 cell).
@@ -220,33 +293,13 @@ class SkillOrchestraAgent(LocalCloudAgent):
         router_sys = _build_router_sys(competence, cost)
         router_schema = _build_router_schema(agent_ids)
 
-        # 1. Route — Anthropic only (output_config schema is Anthropic-specific
-        # in the hybrid adapter). If you need OpenAI routing, swap the prompt
-        # to JSON-mode and bypass output_config.
-        if self._cloud_endpoint != "anthropic":
-            raise ValueError(
-                "SkillOrchestra router requires cloud_endpoint='anthropic'; "
-                f"got {self._cloud_endpoint!r}"
-            )
         router_max = int(cfg.get("router_max_tokens", 1024))
-        # Strip temperature for Opus 4.7+; Anthropic's output_config does the schema.
-        if supports_temperature(self._cloud_model):
-            router_text, r_in, r_out, _ = self._call_anthropic(
-                self._cloud_model,
-                user=f"Question:\n{question}",
-                system=router_sys,
-                max_tokens=router_max,
-                temperature=0.0,
-                output_config=router_schema,
-            )
-        else:
-            router_text, r_in, r_out, _ = self._call_anthropic(
-                self._cloud_model,
-                user=f"Question:\n{question}",
-                system=router_sys,
-                max_tokens=router_max,
-                output_config=router_schema,
-            )
+        router_text, r_in, r_out = self._route_call(
+            question=question,
+            router_sys=router_sys,
+            router_schema=router_schema,
+            router_max=router_max,
+        )
 
         decision = _parse_router_json(router_text)
         skill_weights: Dict[str, float] = decision.get("skill_weights") or {}
@@ -258,14 +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
@@ -329,11 +384,9 @@ class SkillOrchestraAgent(LocalCloudAgent):
                 tokens_cloud += out["tokens_in"] + out["tokens_out"]
                 run_cost += out["cost_usd"]
             else:
-                ans, w_in, w_out, _ = self._call_anthropic(
-                    self._cloud_model,
-                    user=question,
+                ans, w_in, w_out = self._executor_call(
+                    question=question,
                     max_tokens=int(cfg.get("cloud_max_tokens", 4096)),
-                    temperature=0.0,
                 )
                 tokens_cloud += w_in + w_out
                 run_cost += self.cost_usd(self._cloud_model, w_in, w_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

@@ -31,7 +31,14 @@ from .stage_router import (
     get_routing_strategy,
     parse_skill_analysis,
 )
-from .tools import anthropic_tools, openai_tools, run_answer, run_code, run_search
+from .tools import (
+    anthropic_tools,
+    gemini_tools,
+    openai_tools,
+    run_answer,
+    run_code,
+    run_search,
+)
 
 # tool name -> routing stage (stage_router uses "reasoning" for code).
 _TOOL_STAGE = {
@@ -51,6 +58,7 @@ _STAGE_DEFAULT_ALIAS = {
 # Orchestrator decision step (raw SDK — needs tool_use blocks back)
 # ---------------------------------------------------------------------------
 
+
 def _orchestrate_step(
     agent: Any,
     *,
@@ -115,24 +123,63 @@ def _orchestrate_step(
         u = resp.usage
         p = getattr(u, "prompt_tokens", 0) if u else 0
         c = getattr(u, "completion_tokens", 0) if u else 0
+    elif endpoint == "gemini":
+        from google import genai
+        from google.genai import types
+
+        client = genai.Client(http_options=types.HttpOptions(timeout=600_000))
+        cfg = types.GenerateContentConfig(
+            temperature=1.0,
+            max_output_tokens=max_tokens,
+            tools=[types.Tool(function_declarations=gemini_tools())],
+        )
+        resp = client.models.generate_content(
+            model=model,
+            contents=user,
+            config=cfg,
+        )
+        text = (resp.text or "") if hasattr(resp, "text") else ""
+        tool_calls = []
+        try:
+            parts = resp.candidates[0].content.parts or []
+        except Exception:  # noqa: BLE001
+            parts = []
+        for part in parts:
+            fc = getattr(part, "function_call", None)
+            if fc is None:
+                continue
+            name = getattr(fc, "name", None)
+            if not isinstance(name, str) or not name:
+                continue
+            args = getattr(fc, "args", None) or {}
+            try:
+                args = dict(args)
+            except Exception:  # noqa: BLE001
+                args = {}
+            tool_calls.append({"name": name, "input": args})
+        um = getattr(resp, "usage_metadata", None)
+        p = int(getattr(um, "prompt_token_count", 0) or 0) if um else 0
+        c = int(getattr(um, "candidates_token_count", 0) or 0) if um else 0
     else:
         raise ValueError(
             f"orchestrator endpoint {endpoint!r} unsupported — route the "
-            "orchestrator through anthropic/openai (set method_cfg."
+            "orchestrator through anthropic/openai/gemini (set method_cfg."
             "orchestrator_endpoint)."
         )
 
     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
 
 
@@ -140,6 +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]],
@@ -177,6 +225,7 @@ def _build_context(
 # Main loop  —  eval_frames.py:run_single
 # ---------------------------------------------------------------------------
 
+
 def run_orchestrator(
     agent: Any,
     problem: str,
@@ -192,18 +241,22 @@ def run_orchestrator(
     code_timeout = int(cfg.get("code_timeout_s", 60))
     answer_max_tokens = int(cfg.get("answer_max_tokens", 40000))
     ws_max_uses = int(cfg.get("web_search_max_uses", 5))
+    search_backend = str(cfg.get("search_backend", "provider")).lower()
+    tavily_max_results = int(cfg.get("tavily_max_results", 5))
 
     # The orchestrator model: a fixed model per run (the original's
     # 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)
-    if orch_endpoint not in ("anthropic", "openai"):
+    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))
 
@@ -233,7 +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]
@@ -242,7 +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":
@@ -253,14 +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
@@ -289,23 +347,31 @@ 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,
                 )
                 docs = res["search_results_data"]
                 joined = "\n---\n".join(d for d in docs if d)[:char_cap]
@@ -313,13 +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"]
@@ -335,12 +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

@@ -27,6 +27,7 @@ from .._base import (
     OPENAI_WEB_SEARCH_COST_PER_CALL,
     WEB_SEARCH_COST_PER_CALL,
     build_web_search_tool,
+    tavily_search_context,
 )
 from .pool import ModelSpec, call_alias
 
@@ -40,8 +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 "
@@ -51,8 +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",
+    ],
 }
 
 
@@ -75,15 +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
 
 
@@ -95,9 +103,33 @@ def openai_tools() -> List[Dict[str, Any]]:
         ("enhance_reasoning", _CODE_DESC),
         ("answer", _ANSWER_DESC),
     ):
-        out.append({
-            "type": "function",
-            "function": {
+        out.append(
+            {
+                "type": "function",
+                "function": {
+                    "name": name,
+                    "description": desc,
+                    "parameters": {
+                        "type": "object",
+                        "properties": {"model": _model_prop(name)},
+                        "required": ["model"],
+                    },
+                },
+            }
+        )
+    return out
+
+
+def gemini_tools() -> List[Dict[str, Any]]:
+    """The 3 orchestrator tools in Gemini function-declaration shape."""
+    out = []
+    for name, desc in (
+        ("search", _SEARCH_DESC),
+        ("enhance_reasoning", _CODE_DESC),
+        ("answer", _ANSWER_DESC),
+    ):
+        out.append(
+            {
                 "name": name,
                 "description": desc,
                 "parameters": {
@@ -105,8 +137,8 @@ def openai_tools() -> List[Dict[str, Any]]:
                     "properties": {"model": _model_prop(name)},
                     "required": ["model"],
                 },
-            },
-        })
+            }
+        )
     return out
 
 
@@ -114,6 +146,7 @@ def openai_tools() -> List[Dict[str, Any]]:
 # enhance_reasoning / code  —  eval_frames.py:659-812
 # ---------------------------------------------------------------------------
 
+
 def run_code(
     agent: Any,
     spec: ModelSpec,
@@ -129,7 +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 ```. "
@@ -137,7 +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:
@@ -176,6 +214,7 @@ def run_code(
 # answer  —  eval_frames.py:814-997
 # ---------------------------------------------------------------------------
 
+
 def run_answer(
     agent: Any,
     spec: ModelSpec,
@@ -198,11 +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:
@@ -215,8 +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 = ""
@@ -247,6 +294,7 @@ def run_answer(
 # search  —  eval_frames.py:999-1096
 # ---------------------------------------------------------------------------
 
+
 def run_search(
     agent: Any,
     spec: ModelSpec,
@@ -256,6 +304,8 @@ def run_search(
     retriever_url: Optional[str] = None,
     topk: int = 150,
     web_search_max_uses: int = 5,
+    search_backend: str = "provider",
+    tavily_max_results: int = 5,
 ) -> Dict[str, Any]:
     """Write a search query with ``spec``, then retrieve documents.
 
@@ -265,13 +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()
@@ -283,7 +338,12 @@ def run_search(
     contents: List[str] = []
     search_uses = 0
 
-    if retriever_url:
+    if search_backend == "tavily":
+        res = tavily_search_context(query, max_results=tavily_max_results)
+        contents.append(res["text"])
+        search_uses = int(res["n_searches"])
+        cost += float(res["cost_usd"])
+    elif retriever_url:
         # Faithful path — the original FAISS retriever service.
         import requests
 
@@ -294,7 +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/manager.py

@@ -11,10 +11,11 @@ import logging
 import sqlite3
 import time
 import uuid
-from pathlib import Path
 from typing import Any, Dict, List, Optional
 from uuid import uuid4
 
+from openjarvis.core.paths import get_config_dir
+
 logger = logging.getLogger(__name__)
 
 _CREATE_AGENTS = """\
@@ -537,7 +538,7 @@ class AgentManager:
             pass
 
         # User templates
-        user_dir = Path("~/.openjarvis/templates").expanduser()
+        user_dir = get_config_dir() / "templates"
         if user_dir.is_dir():
             for f in user_dir.glob("*.toml"):
                 try:

src/openjarvis/agents/morning_digest.py

@@ -13,6 +13,7 @@ from typing import Any, List, Optional
 
 from openjarvis.agents._stubs import AgentContext, AgentResult, ToolUsingAgent
 from openjarvis.agents.digest_store import DigestArtifact, DigestStore
+from openjarvis.core.paths import get_config_dir
 from openjarvis.core.registry import AgentRegistry
 from openjarvis.core.types import Message, Role, ToolCall
 
@@ -21,7 +22,7 @@ def _load_persona(persona_name: str) -> str:
     """Load a persona prompt file by name."""
     search_paths = [
         Path("configs/openjarvis/prompts/personas") / f"{persona_name}.md",
-        Path.home() / ".openjarvis" / "prompts" / "personas" / f"{persona_name}.md",
+        get_config_dir() / "prompts" / "personas" / f"{persona_name}.md",
     ]
     for p in search_paths:
         if p.exists():
@@ -202,7 +203,7 @@ class MorningDigestAgent(ToolUsingAgent):
         tts_text = re.sub(r"\*{1,2}([^*]+)\*{1,2}", r"\1", tts_text)
         tts_text = tts_text.strip()
 
-        output_dir = str(Path.home() / ".openjarvis" / "digests")
+        output_dir = str(get_config_dir() / "digests")
         tts_call = ToolCall(
             id="digest-tts-1",
             name="text_to_speech",

src/openjarvis/agents/native_openhands.py

@@ -19,6 +19,7 @@ from openjarvis.agents.prompt_loader import (
 from openjarvis.core.events import EventBus
 from openjarvis.core.registry import AgentRegistry
 from openjarvis.core.types import Message, Role, ToolCall, ToolResult
+from openjarvis.engine._base import estimate_prompt_tokens
 from openjarvis.engine._stubs import InferenceEngine
 from openjarvis.tools._stubs import BaseTool, build_tool_descriptions
 
@@ -116,8 +117,7 @@ class NativeOpenHandsAgent(ToolUsingAgent):
         max_prompt_tokens: int = 3000,
     ) -> list[Message]:
         """Truncate messages if estimated token count exceeds limit."""
-        total_chars = sum(len(m.content) for m in messages)
-        estimated_tokens = total_chars // 4
+        estimated_tokens = estimate_prompt_tokens(messages)
         if estimated_tokens <= max_prompt_tokens:
             return messages
         # Find the last user message and truncate its content
@@ -125,7 +125,7 @@ class NativeOpenHandsAgent(ToolUsingAgent):
             if messages[i].role == Role.USER:
                 excess_tokens = estimated_tokens - max_prompt_tokens
                 excess_chars = excess_tokens * 4
-                original = messages[i].content
+                original = messages[i].content or ""
                 if len(original) > excess_chars + 200:
                     truncated = original[: len(original) - excess_chars]
                     messages[i] = Message(
@@ -258,7 +258,7 @@ class NativeOpenHandsAgent(ToolUsingAgent):
                 # still emitted before re-raising.
                 self._emit_turn_end(turns=1, error=True)
                 raise
-            content = self._strip_think_tags(result.get("content", ""))
+            content = self._strip_think_tags(result.get("content") or "")
             usage = result.get("usage", {})
             self._emit_turn_end(turns=1)
             return AgentResult(
@@ -315,7 +315,7 @@ class NativeOpenHandsAgent(ToolUsingAgent):
             for k in total_usage:
                 total_usage[k] += usage.get(k, 0)
 
-            content = result.get("content", "")
+            content = result.get("content") or ""
             # Strip think tags so they don't interfere with parsing
             content = self._strip_think_tags(content)
             last_content = content

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

@@ -37,12 +37,14 @@ 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
 
 from openjarvis.agents._stubs import AgentContext, AgentResult, ToolUsingAgent
 from openjarvis.core.config import load_config
+from openjarvis.core.paths import get_config_dir
 from openjarvis.core.registry import AgentRegistry
 from openjarvis.core.types import Message, Role, ToolCall
 from openjarvis.tools.approval_store import (
@@ -55,6 +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:
 
@@ -251,14 +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
 
@@ -298,6 +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
@@ -342,8 +371,8 @@ class ProactiveAgent(ToolUsingAgent):
         return self._approval_store
 
     def _build_system_prompt(self) -> str:
-        user_md = _load_md_file(Path.home() / ".openjarvis" / "USER.md")
-        memory_md = _load_md_file(Path.home() / ".openjarvis" / "MEMORY.md")
+        user_md = _load_md_file(get_config_dir() / "USER.md")
+        memory_md = _load_md_file(get_config_dir() / "MEMORY.md")
         now = datetime.now()
         context_block = ""
         if user_md or memory_md:
@@ -483,13 +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,
                     }
                 ),
@@ -591,15 +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,
+            )