diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml
new file mode 100644
index 000000000..40426ba5b
--- /dev/null
+++ b/.github/workflows/docs.yaml
@@ -0,0 +1,49 @@
+name: Docs
+
+on:
+  pull_request:
+    paths:
+      - "docs/**"
+      - "kale/**"
+      - "pyproject.toml"
+      - "uv.lock"
+      - ".readthedocs.yaml"
+      - ".github/workflows/docs.yaml"
+  push:
+    branches:
+      - main
+    paths:
+      - "docs/**"
+      - "kale/**"
+      - "pyproject.toml"
+      - "uv.lock"
+      - ".readthedocs.yaml"
+      - ".github/workflows/docs.yaml"
+
+jobs:
+  build:
+    name: Build documentation
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
+
+      - name: Set up Python
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
+        with:
+          python-version: "3.11"
+
+      - name: Install uv
+        run: pip install uv
+
+      - name: Install docs dependencies
+        run: uv sync --extra docs
+
+      - name: Build Sphinx site
+        run: uv run sphinx-build -b html -W --keep-going docs/source docs/_build/html
+
+      - name: Upload built site
+        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
+        with:
+          name: kale-docs-html
+          path: docs/_build/html
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
new file mode 100644
index 000000000..ff73fbe05
--- /dev/null
+++ b/.readthedocs.yaml
@@ -0,0 +1,20 @@
+# Read the Docs configuration for the Kale documentation site.
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html
+version: 2
+
+build:
+  os: ubuntu-24.04
+  tools:
+    python: "3.11"
+  jobs:
+    post_create_environment:
+      - pip install uv
+    post_install:
+      - UV_PROJECT_ENVIRONMENT=$READTHEDOCS_VIRTUALENV_PATH uv sync --extra docs
+
+sphinx:
+  configuration: docs/source/conf.py
+  fail_on_warning: false
+
+formats:
+  - htmlzip
diff --git a/Makefile b/Makefile
index ca7474d56..1c2eff570 100644
--- a/Makefile
+++ b/Makefile
@@ -6,6 +6,7 @@
         clean clean-venv lock lock-upgrade check-uv \
         jupyter jupyter-kfp watch-labextension \
         docker-build docker-run \
+        docs docs-serve docs-clean \
         release verify check-versions
 
 UV := uv
@@ -186,6 +187,21 @@ jupyter-kfp: ## Start JupyterLab with KFP dev environment (run kfp-serve first!)
 watch-labextension: ## Watch labextension for changes (run in separate terminal)
 	cd labextension && $(JLPM) watch
 
+##@ Documentation
+
+docs: ## Build the documentation site (HTML)
+	@printf "$(BLUE)Building documentation...\n$(NC)"
+	$(UV) sync --extra docs
+	$(UV) run sphinx-build -b html docs/source docs/_build/html
+	@printf "$(GREEN)Docs built: docs/_build/html/index.html\n$(NC)"
+
+docs-serve: docs ## Build and serve the docs locally on port 8000
+	@printf "$(BLUE)Serving docs on http://localhost:8000\n$(NC)"
+	cd docs/_build/html && python3 -m http.server 8000
+
+docs-clean: ## Remove built documentation
+	rm -rf docs/_build
+
 ##@ Release
 
 check-versions: ## Verify backend and labextension versions match
diff --git a/README.md b/README.md
index 590270856..9c2c4fa2c 100644
--- a/README.md
+++ b/README.md
@@ -47,6 +47,8 @@ Kale bridges this gap by providing a simple UI to define Kubeflow Pipelines
 workflows directly from your JupyterLab interface, without the need to change a
 single line of code.
 
+📖 **Documentation:** <https://kale.kubeflow.org>
+
 See the `Kale v2.0 Demo` video at the bottom of the `README` for more details.
 
 Read more about Kale and how it works in this Medium post:
diff --git a/docs/imgs/kale_logo.svg b/docs/imgs/kale_logo.svg
new file mode 100644
index 000000000..ee73ae7b5
--- /dev/null
+++ b/docs/imgs/kale_logo.svg
@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!-- Generator: visioncortex VTracer 0.6.12 -->
+<svg version="1.1" xmlns="http://www.w3.org/2000/svg" width="2800" height="969">
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+<path d="M0,0 L109,0 L109,697 L0,697 Z " fill="#000000" transform="translate(2046,126)"/>
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+<path d="M0,0 L11,1 L33,7 L53,15 L74,26 L86,34 L98,43 L111,54 L131,74 L142,88 L152,102 L163,121 L173,142 L181,164 L185,179 L184,183 L162,193 L160,193 L158,198 L151,210 L142,220 L134,227 L112,234 L89,238 L78,249 L64,268 L55,283 L46,299 L36,322 L27,349 L22,372 L18,404 L0,404 Z " fill="#743ABD" transform="translate(449,502)"/>
+<path d="M0,0 L17,0 L32,3 L45,8 L58,17 L70,29 L79,42 L85,46 L104,53 L123,63 L133,71 L144,85 L156,110 L164,116 L176,123 L178,126 L160,131 L140,137 L118,146 L97,156 L80,166 L64,177 L51,187 L36,200 L18,218 L9,229 L1,239 L0,239 Z " fill="#743ABD" transform="translate(449,40)"/>
+</svg>
diff --git a/docs/imgs/quickstart-01-jupyterlab.png b/docs/imgs/quickstart-01-jupyterlab.png
new file mode 100644
index 000000000..1f074806c
Binary files /dev/null and b/docs/imgs/quickstart-01-jupyterlab.png differ
diff --git a/docs/imgs/quickstart-02-kale-panel.png b/docs/imgs/quickstart-02-kale-panel.png
new file mode 100644
index 000000000..278deedcd
Binary files /dev/null and b/docs/imgs/quickstart-02-kale-panel.png differ
diff --git a/docs/imgs/quickstart-03-cell-tags.png b/docs/imgs/quickstart-03-cell-tags.png
new file mode 100644
index 000000000..5a8feee78
Binary files /dev/null and b/docs/imgs/quickstart-03-cell-tags.png differ
diff --git a/docs/imgs/quickstart-04-panel-config.png b/docs/imgs/quickstart-04-panel-config.png
new file mode 100644
index 000000000..784f00482
Binary files /dev/null and b/docs/imgs/quickstart-04-panel-config.png differ
diff --git a/docs/imgs/quickstart-05-compile-run.png b/docs/imgs/quickstart-05-compile-run.png
new file mode 100644
index 000000000..724733140
Binary files /dev/null and b/docs/imgs/quickstart-05-compile-run.png differ
diff --git a/docs/imgs/quickstart-06-kfp-run.png b/docs/imgs/quickstart-06-kfp-run.png
new file mode 100644
index 000000000..0daa61622
Binary files /dev/null and b/docs/imgs/quickstart-06-kfp-run.png differ
diff --git a/docs/source/_static/custom.css b/docs/source/_static/custom.css
new file mode 100644
index 000000000..3ffebbbf9
--- /dev/null
+++ b/docs/source/_static/custom.css
@@ -0,0 +1,719 @@
+/*
+ * Kale documentation custom styles.
+ *
+ * Mirrors the Kubeflow SDK docs site (sdk.kubeflow.org) so that Kale's
+ * documentation stays visually consistent with the rest of the Kubeflow
+ * project. Layered on top of the Furo Sphinx theme.
+ */
+
+/* ============================================
+   Color Palette
+   ============================================ */
+
+:root {
+    /* Primary colors */
+    --kf-blue: #4299e1;
+    --kf-blue-light: #63b3ed;
+    --kf-blue-dark: #3182ce;
+
+    /* Text colors */
+    --kf-heading: #2d3748;
+    --kf-text: #4a5568;
+    --kf-text-light: #718096;
+
+    /* Backgrounds and borders */
+    --kf-bg-subtle: #f7fafc;
+    --kf-border: #e2e8f0;
+
+    /* Accent */
+    --kf-accent: #81e6d9;
+
+    /* API docs colors */
+    --kf-api-name: #3182ce;
+    --kf-api-param: #4a5568;
+    --kf-api-text: #000;
+}
+
+/* Dark mode overrides */
+@media (prefers-color-scheme: dark) {
+    :root:not([data-theme="light"]) {
+        --kf-blue: #63b3ed;
+        --kf-blue-light: #90cdf4;
+        --kf-blue-dark: #4299e1;
+        --kf-heading: #e2e8f0;
+        --kf-text: #cbd5e0;
+        --kf-text-light: #a0aec0;
+        --kf-bg-subtle: #2d3748;
+        --kf-border: #4a5568;
+        --kf-api-name: #63b3ed;
+        --kf-api-param: #a0aec0;
+        --kf-api-text: #e2e8f0;
+    }
+}
+
+[data-theme="dark"] {
+    --kf-blue: #63b3ed;
+    --kf-blue-light: #90cdf4;
+    --kf-blue-dark: #4299e1;
+    --kf-heading: #e2e8f0;
+    --kf-text: #cbd5e0;
+    --kf-text-light: #a0aec0;
+    --kf-bg-subtle: #2d3748;
+    --kf-border: #4a5568;
+    --kf-api-name: #63b3ed;
+    --kf-api-param: #a0aec0;
+    --kf-api-text: #e2e8f0;
+}
+
+/* ============================================
+   TOP NAVIGATION BAR
+   ============================================ */
+
+/* Override Furo's announcement styling */
+.announcement {
+    background: var(--kf-bg-subtle) !important;
+    border-bottom: 1px solid var(--kf-border) !important;
+    padding: 0 !important;
+    position: fixed !important;
+    top: 0 !important;
+    left: 0 !important;
+    right: 0 !important;
+    z-index: 100 !important;
+}
+
+/* Add padding to page content to account for fixed navbar */
+.page {
+    padding-top: 52px !important;
+}
+
+/* Offset sidebar sticky elements for fixed navbar */
+.sidebar-sticky {
+    top: 52px !important;
+}
+
+.toc-sticky {
+    top: 52px !important;
+}
+
+/* Mobile header offset */
+.mobile-header {
+    top: 52px !important;
+}
+
+.announcement-content {
+    max-width: none !important;
+    padding: 0 !important;
+}
+
+/* Top navigation container */
+.top-nav {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    max-width: 1400px;
+    margin: 0 auto;
+    padding: 0.6rem 1.5rem;
+}
+
+/* Brand section (logo + name) */
+.top-nav-brand {
+    display: flex;
+    align-items: center;
+    gap: 0.5rem;
+    text-decoration: none !important;
+    color: var(--kf-heading) !important;
+    font-weight: 600;
+    font-size: 1.1rem;
+}
+
+.top-nav-brand:hover {
+    color: var(--kf-blue) !important;
+}
+
+.top-nav-logo {
+    height: 28px;
+    width: auto;
+}
+
+/* Navigation links */
+.top-nav-links {
+    display: flex;
+    gap: 0.5rem;
+}
+
+.top-nav-links a {
+    color: var(--kf-text) !important;
+    text-decoration: none !important;
+    padding: 0.4rem 0.75rem;
+    border-radius: 4px;
+    font-size: 0.9rem;
+    transition: all 0.15s ease;
+}
+
+.top-nav-links a:hover {
+    color: var(--kf-blue-dark) !important;
+    background: rgba(66, 153, 225, 0.12);
+}
+
+/* Hide on mobile - let sidebar handle navigation */
+@media (max-width: 768px) {
+    .top-nav-links {
+        display: none;
+    }
+    .top-nav {
+        justify-content: center;
+    }
+}
+
+/* ============================================
+   LOGO
+   ============================================ */
+
+/* Hide logo/name from sidebar - it's in the top navbar now */
+.sidebar-brand {
+    display: none !important;
+}
+
+.sidebar-logo {
+    max-height: 5rem;
+    width: auto;
+}
+
+/* ============================================
+   HEADINGS - Grey with blue accents
+   ============================================ */
+
+h1, h2, h3, h4, h5, h6 {
+    font-weight: 600;
+    color: var(--kf-heading);
+}
+
+h1 {
+    font-size: 2rem;
+    color: var(--kf-heading);
+    border-bottom: 2px solid var(--kf-blue-light);
+    padding-bottom: 0.5rem;
+}
+
+h2 {
+    font-size: 1.4rem;
+    margin-top: 1rem;
+    color: var(--kf-heading);
+    border-bottom: 1px solid var(--kf-border);
+    padding-bottom: 0.3rem;
+}
+
+h3 {
+    font-size: 1.15rem;
+    color: var(--kf-text);
+}
+
+/* ============================================
+   LINKS
+   ============================================ */
+
+a {
+    color: var(--kf-blue);
+}
+
+a:visited {
+    color: var(--kf-blue);
+}
+
+a:hover {
+    color: var(--kf-blue-dark);
+}
+
+/* ============================================
+   SIDEBAR - Calm, muted colors
+   ============================================ */
+
+.sidebar-tree a,
+.sidebar-tree .reference,
+.sidebar-drawer a,
+.toctree-l1 > a,
+.toctree-l2 > a,
+.toctree-l3 > a,
+.caption-text {
+    color: var(--kf-text) !important;
+    transition: color 0.15s;
+}
+
+.sidebar-tree a:hover,
+.sidebar-tree .reference:hover {
+    color: var(--kf-blue) !important;
+}
+
+.sidebar-tree .current > .reference,
+.sidebar-tree .current-page > .reference {
+    color: var(--kf-blue-dark) !important;
+    font-weight: 600;
+}
+
+.sidebar-tree .caption {
+    color: var(--kf-text-light) !important;
+    font-weight: 600;
+    text-transform: uppercase;
+    font-size: 0.75rem;
+    letter-spacing: 0.5px;
+}
+
+/* ============================================
+   API DOCS - Minimal two-color scheme
+   Color 1 (blue): API/method/class names
+   Color 2 (grey): Parameter names
+   Everything else: Uses --kf-api-text (black in light, light in dark)
+   ============================================ */
+
+/* Main class/function container */
+dl.py.class,
+dl.py.function {
+    margin: 2rem 0 !important;
+}
+
+dl.py.method {
+    margin: 1.5rem 0 !important;
+}
+
+/* Remove all hover effects */
+dl.py:hover,
+dl.py.class:hover,
+dl.py.function:hover,
+dl.py.method:hover,
+dl.py dt:hover,
+dl.py dd:hover {
+    background: transparent !important;
+    box-shadow: none !important;
+}
+
+/* Class/function signature header */
+dl.py.class > dt.sig,
+dl.py.function > dt.sig {
+    font-family: ui-monospace, SFMono-Regular, Consolas, monospace;
+    font-size: 1rem;
+    color: var(--kf-api-text) !important;
+    padding: 0.5rem 0;
+    border-bottom: 1px solid var(--kf-border);
+    margin: 0;
+}
+
+/* Class/method/function names - BLUE (the only colored element for names) */
+dl.py dt.sig .sig-name {
+    font-weight: 700;
+    color: var(--kf-api-name) !important;
+}
+
+/* Method signature */
+dl.py.method > dt.sig,
+dl.py.method dt.sig {
+    font-family: ui-monospace, SFMono-Regular, Consolas, monospace;
+    font-size: 0.9rem !important;
+    color: var(--kf-api-text) !important;
+    padding: 0.25rem 0 !important;
+    border-bottom: 1px solid var(--kf-border);
+    margin-top: 1rem;
+}
+
+/* All signature text except names */
+dl.py dt.sig,
+dl.py dt.sig .sig-paren,
+dl.py dt.sig .sig-param,
+dl.py dt.sig .n,
+dl.py dt.sig .o,
+dl.py dt.sig .default_value,
+dl.py dt.sig .p,
+.sig .sig-return,
+.sig .sig-return-typehint {
+    color: var(--kf-api-text) !important;
+    font-style: normal !important;
+}
+
+/* Description body */
+dl.py.class > dd,
+dl.py.function > dd {
+    margin: 0 !important;
+    padding: 0.75rem 0;
+}
+
+dl.py.method > dd {
+    margin: 0 !important;
+    padding: 0.5rem 0 !important;
+}
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+    font-size: 1rem !important;
+    line-height: 1.6;
+    margin: 0.5rem 0;
+}
+
+/* Nested methods within a class */
+dl.py.class dd dl.py.method {
+    margin: 1rem 0 !important;
+}
+
+/* Parameters/Returns/Raises section styling */
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+    margin: 0.75rem 0;
+}
+
+/* Section headers (Parameters, Returns, Raises) - bold */
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+dl.field-list dt.field-even {
+    font-weight: 700 !important;
+    color: var(--kf-api-text) !important;
+    font-size: 1rem !important;
+    text-transform: none !important;
+    margin-bottom: 0.5rem;
+    margin-top: 0.75rem;
+}
+
+dl.field-list > dd,
+dl.field-list dd.field-odd,
+dl.field-list dd.field-even {
+    margin-left: 0 !important;
+    padding-left: 0 !important;
+}
+
+/* Parameter list - use bullet points */
+dl.field-list dd ul {
+    list-style: disc !important;
+    padding-left: 1.5rem !important;
+    margin: 0.25rem 0 !important;
+}
+
+dl.field-list dd ul li {
+    padding: 0.2rem 0;
+    color: var(--kf-api-text) !important;
+}
+
+dl.field-list dd ul li::marker {
+    color: var(--kf-api-text) !important;
+}
+
+/* All text in parameter sections */
+dl.field-list dd,
+dl.field-list dd p,
+dl.field-list dd ul li,
+dl.field-list dd ul li p {
+    color: var(--kf-api-text) !important;
+    font-size: 1rem !important;
+    margin: 0.1rem 0 !important;
+}
+
+/* Parameter names - GREY */
+dl.field-list dd strong,
+dl.field-list dd p strong {
+    color: var(--kf-api-param) !important;
+    font-weight: 600 !important;
+}
+
+/* Type annotations */
+dl.field-list dd .sphinx_autodoc_typehints-type,
+dl.field-list dd .sphinx_autodoc_typehints-type code,
+dl.field-list dd code,
+dl.field-list dd .pre {
+    color: var(--kf-api-text) !important;
+    background: transparent !important;
+    border: none !important;
+    padding: 0 !important;
+}
+
+/* Autosummary - display as simple list, no boxes */
+.table-wrapper.autosummary {
+    border: none !important;
+    overflow: visible;
+    background: transparent !important;
+}
+
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+    width: 100%;
+    border-collapse: collapse;
+    border: none !important;
+    margin: 0.25rem 0;
+    background: transparent !important;
+}
+
+table.autosummary tbody {
+    background: transparent !important;
+}
+
+table.autosummary tr {
+    border: none !important;
+    background: transparent !important;
+}
+
+table.autosummary td {
+    padding: 0.2rem 0;
+    padding-left: 1rem;
+    border: none !important;
+    vertical-align: top;
+    background: transparent !important;
+}
+
+/* Bullet point before each item */
+table.autosummary td:first-child::before {
+    content: "•";
+    color: var(--kf-blue);
+    margin-right: 0.5rem;
+}
+
+table.autosummary td:first-child {
+    font-family: ui-monospace, SFMono-Regular, Consolas, monospace;
+    font-weight: 600;
+    padding-right: 1rem;
+    width: auto;
+}
+
+/* Remove ALL backgrounds and borders from code elements */
+table.autosummary code,
+table.autosummary .pre,
+table.autosummary td code,
+table.autosummary td .pre,
+table.autosummary code.xref,
+table.autosummary .docutils code,
+.autosummary code.xref,
+.autosummary .docutils code,
+.autosummary code.docutils,
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+.autosummary .notranslate {
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+    background-color: transparent !important;
+    border: none !important;
+    padding: 0 !important;
+    box-shadow: none !important;
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+    font-size: 0.9rem;
+}
+
+table.autosummary td:last-child {
+    color: var(--kf-text);
+    font-size: 0.9rem;
+}
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+/* Property/attribute styling */
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+dl.py.attribute > dt.sig {
+    font-family: ui-monospace, SFMono-Regular, Consolas, monospace;
+    font-size: 0.88rem;
+    padding: 0.5rem 0;
+}
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+}
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+}
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+}
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+   ============================================ */
+
+pre {
+    border-radius: 8px;
+    border: none;
+    background: #1e1e1e !important;
+    padding: 1rem !important;
+}
+
+pre code {
+    color: #d4d4d4 !important;
+    background: transparent !important;
+}
+
+/* Syntax highlighting for dark theme */
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+    background: #1e1e1e !important;
+    border-radius: 8px;
+}
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+/* Default text color for code */
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+}
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+}
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+}
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+.highlight .gp { color: #6a9955 !important; } /* prompt */
+.highlight .w { color: #d4d4d4 !important; } /* whitespace */
+
+/* Inline code - keep light */
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+    color: var(--kf-heading);
+    background: var(--kf-bg-subtle);
+    padding: 0.15em 0.4em;
+    border-radius: 3px;
+    font-size: 0.9em;
+}
+
+/* ============================================
+   CARDS
+   ============================================ */
+
+.sd-card {
+    border-radius: 8px;
+    border: 1px solid var(--kf-border);
+    box-shadow: 0 1px 4px rgba(0, 0, 0, 0.04);
+    transition: all 0.2s ease;
+}
+
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+    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.08);
+    border-color: var(--kf-blue-light);
+}
+
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+    color: var(--kf-heading);
+    font-weight: 600;
+}
+
+/* ============================================
+   TABLES
+   ============================================ */
+
+table {
+    border-radius: 6px;
+    overflow: hidden;
+    border: 1px solid var(--kf-border);
+}
+
+th {
+    background: var(--kf-bg-subtle);
+    color: var(--kf-text);
+    font-weight: 600;
+}
+
+
+/* ============================================
+   ADMONITIONS
+   ============================================ */
+
+.admonition {
+    border-radius: 6px;
+    border: none;
+    border-left: 3px solid var(--kf-blue);
+}
+
+.admonition.tip {
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+    border-left-color: var(--kf-accent);
+}
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+    background: rgba(66, 153, 225, 0.1);
+}
+
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+    border-left-color: #f6ad55;
+}
+
+/* ============================================
+   MISC
+   ============================================ */
+
+hr {
+    border: none;
+    height: 1px;
+    background: var(--kf-border);
+    margin: 2rem 0;
+}
+
+p {
+    color: var(--kf-text);
+    line-height: 1.7;
+}
+
+li::marker {
+    color: var(--kf-blue);
+}
+
+/* ============================================
+   LANDING PAGE ENHANCEMENTS
+   ============================================ */
+
+/* Hero tagline styling */
+article > section > p:first-of-type {
+    font-size: 1.2rem;
+    color: var(--kf-text);
+}
+
+/* Section descriptions (italic text after h2) */
+h2 + p > em:only-child,
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+    display: block;
+    font-size: 1rem;
+    color: var(--kf-text-light);
+    font-style: normal;
+    margin-bottom: 1.5rem;
+}
+
+/* Section dividers */
+hr {
+    margin: 1.5rem 0 !important;
+    opacity: 0.4;
+}
+
+/* Borderless cards for feature sections */
+.sd-card.sd-border-0 {
+    border: none !important;
+    box-shadow: none !important;
+    background: transparent !important;
+}
+
+.sd-card.sd-border-0:hover {
+    box-shadow: none !important;
+    border: none !important;
+}
+
+/* Feature card titles (bold text in cards) */
+.sd-card-body strong {
+    color: var(--kf-heading);
+    font-size: 1.1rem;
+}
+
+/* Code blocks in cards - smaller */
+.sd-card .highlight {
+    font-size: 0.8rem;
+}
+
+/* Quick start section code blocks */
+.highlight-bash + .highlight-python {
+    margin-top: -0.5rem;
+}
diff --git a/docs/source/_static/kale-icon.svg b/docs/source/_static/kale-icon.svg
new file mode 100644
index 000000000..ee73ae7b5
--- /dev/null
+++ b/docs/source/_static/kale-icon.svg
@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
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diff --git a/docs/source/_static/kale-symbol.svg b/docs/source/_static/kale-symbol.svg
new file mode 100644
index 000000000..9675c2603
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+++ b/docs/source/_static/kale-symbol.svg
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diff --git a/docs/source/api/cli.md b/docs/source/api/cli.md
new file mode 100644
index 000000000..01dca0ca3
--- /dev/null
+++ b/docs/source/api/cli.md
@@ -0,0 +1,82 @@
+# CLI Reference
+
+Kale ships a single `kale` command-line entry point, declared as
+`[project.scripts]` in `pyproject.toml`. It compiles (and optionally runs) a
+notebook against Kubeflow Pipelines.
+
+## `kale`
+
+The primary CLI. Parses a notebook, builds the pipeline DAG, compiles it to
+KFP v2 DSL, and optionally uploads and runs it.
+
+```bash
+kale --nb path/to/notebook.ipynb [options]
+```
+
+### General options
+
+| Flag                  | Type   | Description                                                          |
+| --------------------- | ------ | -------------------------------------------------------------------- |
+| `--nb`                | str    | Path to the source notebook. **Required.**                           |
+| `--upload_pipeline`   | flag   | Upload the compiled pipeline to KFP.                                 |
+| `--run_pipeline`      | flag   | Upload and then create a KFP run.                                    |
+| `--debug`             | flag   | Enable verbose logging.                                              |
+| `--dev`               | flag   | Bake a local devpi index URL into generated components.              |
+| `--pip-index-urls`    | str    | Comma-separated PEP 503 simple indexes baked into components.        |
+| `--devpi-simple-url`  | str    | Devpi simple URL (used when `--dev` is set).                         |
+
+### Notebook metadata overrides
+
+All the flags in this group override the corresponding fields in the
+notebook's Kale metadata. If both are set, the CLI value wins.
+
+| Flag                      | Description                                                  |
+| ------------------------- | ------------------------------------------------------------ |
+| `--experiment_name`       | KFP experiment name. Default: `Kale-Pipeline-Experiment`.    |
+| `--pipeline_name`         | Name of the deployed pipeline. Default: `kale-pipeline`.     |
+| `--pipeline_description`  | Description shown in the KFP UI.                             |
+| `--docker_image`          | Default base image for every step.                           |
+| `--kfp_host`              | KFP API endpoint, as `<host>:<port>` or a full URL.          |
+| `--storage-class-name`    | Storage class for pipeline-created volumes.                  |
+| `--volume-access-mode`    | Access mode for pipeline-created volumes.                    |
+
+### Examples
+
+Compile only, leave the generated script in `.kale/`:
+
+```bash
+kale --nb examples/base/candies_sharing.ipynb
+```
+
+Compile, upload, and run on a local KFP port-forward:
+
+```bash
+kale --nb examples/base/candies_sharing.ipynb \
+     --kfp_host http://127.0.0.1:8080 \
+     --run_pipeline
+```
+
+Override pipeline naming:
+
+```bash
+kale --nb notebooks/my_pipeline.ipynb \
+     --pipeline_name "weekly-churn" \
+     --experiment_name "production" \
+     --run_pipeline --kfp_host http://127.0.0.1:8080
+```
+
+## Environment variables
+
+A few environment variables affect `kale`:
+
+| Variable                  | Effect                                                                                 |
+| ------------------------- | -------------------------------------------------------------------------------------- |
+| `KF_PIPELINES_ENDPOINT`   | Default KFP API endpoint when `--kfp_host` is not provided.                            |
+| `KF_PIPELINES_UI_ENDPOINT` | KFP UI URL used when rendering run links.                                             |
+| `KALE_PIP_INDEX_URLS`     | Comma-separated list of pip indexes baked into generated components.                   |
+| `KALE_PIP_TRUSTED_HOSTS`  | Trusted hosts for HTTP pip indexes (required when using HTTP URLs).                    |
+| `KALE_DEV_MODE`           | Equivalent to passing `--dev`.                                                         |
+| `KALE_DEVPI_SIMPLE_URL`   | Equivalent to `--devpi-simple-url` when `--dev` is set.                                |
+
+See [Running Pipelines](../user-guide/running-pipelines.md) for concrete scenarios where
+these are useful.
diff --git a/docs/source/api/compiler.rst b/docs/source/api/compiler.rst
new file mode 100644
index 000000000..2720f27b7
--- /dev/null
+++ b/docs/source/api/compiler.rst
@@ -0,0 +1,7 @@
+Compiler
+========
+
+.. automodule:: kale.compiler
+   :members:
+   :exclude-members: Environment, FileSystemLoader, PackageLoader
+   :show-inheritance:
diff --git a/docs/source/api/marshal.rst b/docs/source/api/marshal.rst
new file mode 100644
index 000000000..12f60687e
--- /dev/null
+++ b/docs/source/api/marshal.rst
@@ -0,0 +1,27 @@
+Marshal
+=======
+
+Kale's marshalling system is a small, extensible dispatcher that serializes
+data flowing between pipeline steps. See :doc:`../concepts/data-passing`
+for the conceptual overview; this page is the API reference.
+
+Dispatcher and base class
+-------------------------
+
+.. automodule:: kale.marshal.backend
+   :members:
+   :show-inheritance:
+
+Built-in backends
+-----------------
+
+.. automodule:: kale.marshal.backends
+   :members:
+   :show-inheritance:
+
+Decorator
+---------
+
+.. automodule:: kale.marshal.decorator
+   :members:
+   :show-inheritance:
diff --git a/docs/source/api/pipeline.rst b/docs/source/api/pipeline.rst
new file mode 100644
index 000000000..8b811b364
--- /dev/null
+++ b/docs/source/api/pipeline.rst
@@ -0,0 +1,6 @@
+Pipeline
+========
+
+.. automodule:: kale.pipeline
+   :members:
+   :show-inheritance:
diff --git a/docs/source/api/step.rst b/docs/source/api/step.rst
new file mode 100644
index 000000000..7c8a8750b
--- /dev/null
+++ b/docs/source/api/step.rst
@@ -0,0 +1,6 @@
+Step
+====
+
+.. automodule:: kale.step
+   :members:
+   :show-inheritance:
diff --git a/docs/source/architecture/index.md b/docs/source/architecture/index.md
new file mode 100644
index 000000000..59dc4bd63
--- /dev/null
+++ b/docs/source/architecture/index.md
@@ -0,0 +1,263 @@
+# Architecture
+
+This page is a map of the Kale codebase — what the major components are,
+how they fit together, and what each directory contains. It is aimed at
+contributors who want to navigate the repo with confidence.
+
+## The components
+
+Kale has no server, no operator, and nothing Kale-specific to deploy on
+Kubernetes. Kale has three main components:
+
+1. **The `kale` Python library** — the `kale/` package in this repo. All
+   the "work" Kale does (parsing notebooks, analyzing dependencies,
+   generating KFP v2 DSL, calling the KFP SDK) happens in regular Python
+   function calls inside this library.
+2. **The `kale` CLI** — a thin wrapper in `kale/cli.py` around the library.
+   `kale --nb notebook.ipynb` is basically `import kale; kale.compile(...)`
+   with argument parsing on top.
+3. **The Kale JupyterLab extension** — the `labextension/` package, a
+   JupyterLab 4 extension written in TypeScript and React. It runs in the
+   user's browser and provides the Kale side panel and the per-cell
+   metadata editors.
+
+The interesting question is how the browser-side extension ends up invoking
+functions from a Python library, since one is JavaScript and the other is
+Python. The short answer: the extension starts its own Python kernel inside
+the Jupyter server and calls Kale functions in it over JSON-RPC. The rest
+of this section unpacks what that actually means.
+
+## How the extension talks to the library
+
+### Where the Jupyter server actually runs
+
+When you use JupyterLab there are always _two_ things running, and it's
+worth being explicit about which is where:
+
+- A **web frontend** — HTML/CSS/JavaScript served to your browser. Every
+  JupyterLab extension (including Kale) executes here, in the browser.
+- A **Jupyter server** — a Python process that serves notebooks from disk,
+  spawns and manages kernels, and exposes all of that over HTTP/WebSocket.
+
+Where those two live depends on how you launched JupyterLab. If you run
+`jupyter lab` on your laptop, both are local and your browser talks to
+`localhost`. But if you're using JupyterLab from a Kubeflow Notebook, your
+browser is still on your laptop while the Jupyter server is running in a
+**Pod inside your Kubernetes cluster** — you only reach it through your
+browser the same way you'd reach any web application. The Jupyter server
+always lives next to the files and the kernels it manages, **not** next to
+your browser.
+
+This matters for Kale because everything the `kale` library does — reading
+notebooks off disk, running static analysis, submitting pipelines to KFP —
+has to happen where the Jupyter server is, not where your browser is.
+
+### A Kale-managed kernel
+
+When the Kale side panel is activated in JupyterLab, the extension asks
+the Jupyter server to start a new Python kernel. This kernel is **not**
+the one attached to any notebook you've opened; it's owned by the extension
+itself and invisible in the JupyterLab kernels sidebar. Think of it as a
+hidden Python REPL that Kale uses as its workhorse, with the `kale` library
+already imported.
+
+Because it's a normal Jupyter kernel, it runs exactly where all kernels
+run: inside the Jupyter server. When Jupyter is local, the kernel is local.
+When Jupyter is in a Kubeflow Notebook Pod, the kernel is in that same
+Pod, alongside the user's notebook kernels.
+
+### JSON-RPC over the kernel channel
+
+With that kernel running, the extension needs a way to call specific
+functions in it. It uses JSON-RPC: the extension sends a message naming a
+function (e.g. `nb.compile_notebook`) and its arguments, the kernel runs
+the corresponding Python function, and the result comes back the same way.
+All of the message passing happens over standard Jupyter kernel comms — no
+extra ports, no extra services, no HTTP server that Kale has to host.
+
+Inside the kernel, the call routes through `kale/rpc/` (the dispatcher and
+endpoint modules) into the rest of the Kale library: notebook parsing,
+dependency analysis, DSL compilation, and finally the KFP SDK calls that
+upload and run the pipeline.
+
+A nice consequence of this design is that when the Jupyter server lives
+inside a Kubeflow Notebook Pod, the Kale kernel is _already inside the cluster_.
+It can reach the in-cluster Kubeflow Pipelines API directly,
+using the Pod's service account — no port-forwards, no VPN, no extra
+credentials. Kale "just works" in a Kubeflow installation because the
+Python that calls the KFP SDK is already a cluster citizen.
+
+### Diagram
+
+```
+  Browser (your laptop)
+  ┌──────────────────────────────┐
+  │ JupyterLab UI                │
+  │   Kale labextension (TS/React)│
+  └──────────────┬───────────────┘
+                 │  WebSocket / HTTP
+                 │
+  ─ ─ ─ ─ ─ ─ ─ ─│─ ─ ─ ─ ─ ─ ─ ─ ─  (local ⇄ network boundary)
+                 │
+  Jupyter server (laptop OR Pod in cluster)
+  ┌──────────────▼───────────────┐
+  │ Jupyter server               │
+  │                              │
+  │  ┌────────────────────────┐  │
+  │  │ Kale-managed kernel    │  │
+  │  │   └─ import kale       │  │
+  │  │       └─ kale.rpc/*    │  │
+  │  │           └─ library   │  │──┐
+  │  └────────────────────────┘  │  │ KFP SDK
+  │                              │  │
+  │  (user notebook kernels)     │  │
+  └──────────────────────────────┘  │
+                                    ▼
+                          Kubeflow Pipelines API
+                          (in the same cluster when
+                           Jupyter runs in a KF Notebook)
+```
+
+## Python library layout
+
+The Python package lives at `kale/`. The interesting modules are:
+
+### Core pipeline model
+
+- `pipeline.py` — defines {py:class}`kale.pipeline.Pipeline` (a
+  `networkx.DiGraph` of steps) along with the configuration classes
+  {py:class}`~kale.pipeline.PipelineConfig`, `VolumeConfig`, and
+  `KatibConfig`.
+- `step.py` — defines {py:class}`kale.step.Step` and
+  {py:class}`~kale.step.StepConfig`, plus the `PipelineParam` and
+  `Artifact` named tuples used across the backend.
+
+### Notebook processing
+
+- `processors/nbprocessor.py` — {py:class}`kale.processors.NotebookProcessor`
+  reads an `.ipynb`, parses tags, resolves data dependencies, and returns
+  a ready-to-compile `Pipeline`.
+
+### Compilation
+
+- `compiler.py` — {py:class}`kale.compiler.Compiler` renders the
+  templates in `kale/templates/` to produce KFP v2 DSL.
+- `templates/nb_function_template.jinja2` — per-step component template.
+- `templates/pipeline_template.jinja2` — pipeline wrapper template.
+
+### Marshalling
+
+- `marshal/backend.py` — {py:class}`~kale.marshal.backend.Dispatcher` and
+  {py:class}`~kale.marshal.backend.MarshalBackend` base class.
+- `marshal/backends.py` — nine concrete backends for numpy, pandas,
+  sklearn, XGBoost, PyTorch, Keras, TensorFlow, functions, and a `dill`
+  fallback.
+- `marshal/decorator.py` — the `@marshal` decorator used by the marshal
+  entrypoint.
+
+### Static analysis
+
+- `common/astutils.py` — AST helpers for detecting marshal candidates,
+  parsing metrics print statements, and resolving imports.
+- `common/flakeutils.py` — PyFlakes integration.
+
+### KFP and Kubernetes integration
+
+- `common/kfputils.py` — compile DSL, upload pipelines, create runs via
+  the KFP SDK.
+- `common/k8sutils.py`, `common/podutils.py` — K8s API helpers used by
+  volume management and the in-pod runtime.
+- `common/katibutils.py` — Katib hyperparameter tuning helpers (legacy,
+  under re-evaluation for v2).
+
+### Configuration framework
+
+- `config/config.py` — a small Pydantic-inspired validation framework used
+  by all the `*Config` classes in `pipeline.py` and `step.py`.
+- `config/validators.py` — validators for Kubernetes names, image refs,
+  and other common field types.
+
+### CLI
+
+- `cli.py` — implements the `kale` entry point declared in `pyproject.toml`,
+  which compiles and optionally runs notebooks against KFP.
+
+### RPC layer
+
+These modules are what the Kale-managed kernel executes when the
+labextension calls into it over JSON-RPC (see [](#how-the-extension-talks-to-the-library)).
+
+- `rpc/nb.py` — notebook compilation RPC endpoints.
+- `rpc/kfp.py` — KFP operations (upload, run, list experiments).
+- `rpc/katib.py` — Katib operations.
+- `rpc/run.py` — the JSON-RPC dispatcher.
+- `rpc/errors.py` — error types (`RPCNotFoundError`, etc.).
+
+## JupyterLab extension layout
+
+The JupyterLab extension lives at `labextension/`. It's a standard
+JupyterLab 4 extension built with TypeScript and React, and it runs in
+the user's browser.
+
+Key source files:
+
+- `src/index.ts` — extension activation.
+- `src/widget.tsx` — the main Kale left sidebar widget.
+- `src/widgets/LeftPanel.tsx` — top-level panel with the master toggle,
+  pipeline settings form, and Deploy button.
+- `src/widgets/cell-metadata/CellMetadataEditor.tsx` — per-cell Kale row
+  (cell type dropdown, step name, dependency picker).
+- `src/widgets/deploys-progress/` — progress notifications for running
+  deploys.
+- `src/lib/RPCUtils.tsx` — JSON-RPC client that talks to the
+  Kale-managed Python kernel.
+- `src/lib/CellUtils.ts`, `TagsUtils.ts`, `NotebookUtils.tsx` — helpers for
+  manipulating notebook metadata.
+- `schema/kale-settings.json` — JupyterLab settings schema for any
+  user-facing preferences.
+
+The extension manages the lifecycle of its dedicated Python kernel (the
+"Kale-managed kernel" from the previous section) and dispatches every UI
+action through `RPCUtils` as a JSON-RPC call into that kernel. There is no
+other server or network hop in between — the Jupyter kernel *is* the
+execution environment for the Kale library.
+
+## Data flow end-to-end
+
+Putting all the pieces together, here's what happens when you click
+**Compile and Run** in the Kale side panel on a notebook called
+`my_notebook.ipynb`:
+
+```
+JupyterLab UI (React, in the browser)
+     │ RPCUtils.post("nb.compile_notebook", {path})
+     ▼
+Kale-managed kernel (Python, in the Jupyter server)
+     │ kale/rpc/nb.compile_notebook()
+     ▼
+NotebookProcessor           ── parse tags, build Pipeline DAG
+     │
+     ▼
+Compiler                    ── render Jinja templates, autopep8
+     │
+     ▼
+.kale/my_notebook.kale.py   ── plain KFP v2 DSL
+     │
+     ▼
+kfp.compiler.Compiler       ── compile DSL → YAML IR
+     │
+     ▼
+KFP REST API                ── upload pipeline, create run
+     │
+     ▼
+Kubeflow Pipelines          ── schedule step pods
+     │
+     ▼
+Step pod                    ── load inputs, run user code, save outputs
+```
+
+The generated DSL is a normal KFP v2 pipeline — no runtime dependency on
+Kale beyond the marshalling helper that lives inside each component. This
+means a Kale-produced pipeline keeps running even if Kale is uninstalled,
+and it can be inspected, edited, or re-uploaded by anyone with the KFP
+SDK.
diff --git a/docs/source/concepts/cell-types.md b/docs/source/concepts/cell-types.md
new file mode 100644
index 000000000..6d6671577
--- /dev/null
+++ b/docs/source/concepts/cell-types.md
@@ -0,0 +1,170 @@
+# Cell Types & Annotations
+
+Kale reads Jupyter cell **tags** — strings stored under `metadata.tags` in the
+`.ipynb` — to decide what role each cell plays in the generated pipeline.
+This page documents every tag Kale understands, with examples.
+
+You can set these tags visually through the Kale JupyterLab side panel, or by
+editing the notebook JSON directly.
+
+## The full tag vocabulary
+
+| Tag                         | Example                         | Effect                                                                 |
+| --------------------------- | ------------------------------- | ---------------------------------------------------------------------- |
+| `imports`                   | -                       | Cell is prepended to every pipeline step. **All `import` statements must live in an `imports` cell.** |
+| `functions`                 | -                     | Cell is prepended to every step after `imports`. Put function and class definitions here. |
+| `pipeline-parameters`       | -           | Variables defined here become KFP pipeline parameters.                 |
+| `pipeline-metrics`          | -              | `print()` statements in the cell are converted to KFP pipeline metrics. |
+| `step:<name>`               | `step:train_model`              | Declares (or appends to) a pipeline step named `<name>`.               |
+| `prev:<step_name>`          | `prev:load_data`                | Adds a dependency from the current step to `<step_name>`.              |
+| `skip`                      | -                          | Cell is excluded from the pipeline entirely.                           |
+| `annotation:<key>:<value>`  | `annotation:team:ml`            | Adds a Kubernetes annotation to the step's pod.                        |
+| `label:<key>:<value>`       | `label:env:prod`                | Adds a Kubernetes label to the step's pod.                             |
+| `limit:<resource>:<value>`  | `limit:nvidia.com/gpu:1`        | Adds a Kubernetes resource limit to the step's pod.                    |
+| `image:<image>`             | `image:pytorch/pytorch:2.0`     | Overrides the base image for this step only.                           |
+| `cache:enabled` / `cache:disabled` | `cache:disabled`         | Opts the step into or out of KFP's built-in caching.                   |
+
+
+## Per-cell-type details
+
+### `imports`
+
+The `imports` cell is where **every module import in your notebook must
+live**. Kale prepends this cell's source to every pipeline step's generated
+component, so any step can assume those imports are available.
+
+```python
+# tag: imports
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+```
+
+```{warning}
+If you import a library in a `step` or `functions` cell, Kale will **not**
+add it to the step's `packages_to_install` list, and the step will fail at
+runtime with `ModuleNotFoundError` unless the base image happens to include
+the package.
+```
+
+### `functions`
+
+Put function and class definitions here. Like `imports`, this cell is
+prepended to every step.
+
+```python
+# tag: functions
+def clean(df: pd.DataFrame) -> pd.DataFrame:
+    return df.dropna()
+
+class FeaturePipeline:
+    def __init__(self, model):
+        self.model = model
+```
+
+Keep these definitions **pure**: no top-level executable statements, no
+prints, no imports, no global state mutation.
+
+### `pipeline-parameters`
+
+Variables defined in a `pipeline-parameters` cell become top-level KFP
+pipeline parameters. They become inputs to the `@kfp_dsl.pipeline` function
+and can be overridden at submission time.
+
+```python
+# tag: pipeline-parameters
+learning_rate = 0.01
+batch_size = 128
+num_epochs = 10
+```
+
+Supported parameter types are `int`, `float`, `str`, and `bool`. If you
+declare the same parameter in multiple `pipeline-parameters` cells, the last
+value wins.
+
+### `pipeline-metrics`
+
+Any `print(...)` statements in a `pipeline-metrics` cell are parsed out by
+Kale's AST helper
+({py:func}`kale.common.astutils.parse_metrics_print_statements`) and emitted
+as KFP pipeline metrics, making them visible in the KFP UI's run metrics
+tab.
+
+```python
+# tag: pipeline-metrics
+print("accuracy:", accuracy)
+print("f1:", f1_score)
+```
+
+### `step:<name>`
+
+The workhorse tag. Any cell tagged `step:data_processing` contributes code
+to a pipeline step named `data_processing`. Multiple cells can share the
+same step name — they will be concatenated in notebook order.
+
+```python
+# tag: step:load_data
+df = pd.read_csv("data.csv")
+df = clean(df)
+```
+
+Dependencies are declared with `prev:`:
+
+```python
+# tags: step:train, prev:load_data
+model = RandomForestClassifier()
+model.fit(df.drop("y", axis=1), df["y"])
+```
+
+You can add as many `prev:` tags as you want — one per dependency.
+
+### Per-step configuration
+
+A step cell can carry additional tags to customize its pod spec:
+
+```python
+# tags: step:train_gpu, prev:prepare_data,
+#       image:pytorch/pytorch:2.0-cuda12,
+#       limit:nvidia.com/gpu:1,
+#       annotation:team:ml,
+#       label:env:prod,
+#       cache:disabled
+```
+
+- **`image:<image>`** — use a custom base image for just this step.
+- **`limit:<resource>:<value>`** — request GPU, memory, or any other
+  resource (e.g. `limit:memory:8Gi`).
+- **`annotation:<k>:<v>`** / **`label:<k>:<v>`** — add Kubernetes metadata
+  to the step's pod. Useful for cost allocation, scheduling hints, or
+  integration with observability tooling.
+- **`cache:disabled`** — opt the step out of KFP's caching. Use `cache:enabled`
+  to force caching when it's been disabled globally.
+
+### `skip`
+
+Cells tagged `skip` are dropped from the pipeline. Use them for exploratory
+code or debugging that you want to keep in the notebook but not run on the
+cluster.
+
+```python
+# tag: skip
+df.describe()
+df.plot.hist()
+```
+
+## Best practices
+
+- **Keep `imports` at the top** of the notebook. Don't spread imports across
+  cells — Kale won't pick them up.
+- **Never mutate global state from inside a step**. If you need to configure
+  a library (e.g. `warnings.simplefilter`), do it once in an `imports` or
+  `functions` cell.
+- **Use `pipeline-parameters` for values you want to tweak between runs**.
+  Resist hard-coding hyperparameters inside step cells.
+- **Use `skip` liberally** during development for cells that don't belong in
+  the pipeline, like `df.head()` or plotting code.
+- **Name your steps explicitly** — `step:load_data`, `step:train`,
+  `step:evaluate` — rather than leaving them auto-named.
+
+See [Troubleshooting](../user-guide/troubleshooting.md) for the common failure modes these
+practices prevent.
diff --git a/docs/source/concepts/compilation.md b/docs/source/concepts/compilation.md
new file mode 100644
index 000000000..1f28293b3
--- /dev/null
+++ b/docs/source/concepts/compilation.md
@@ -0,0 +1,124 @@
+# Pipeline Compilation
+
+This page describes what actually happens when you run `kale --nb
+my_notebook.ipynb`, step by step, so you can read the generated code,
+debug problems, and extend Kale.
+
+## Stage-by-stage
+
+### 1. NotebookProcessor
+
+{py:class}`kale.processors.NotebookProcessor` is the entry point. Given a
+notebook path and (optionally) a dictionary of metadata overrides, it:
+
+1. Reads the `.ipynb` via `nbformat`.
+2. Extracts the Kale-specific notebook metadata (pipeline name, image,
+   experiment, volumes, ...).
+3. Walks every cell and classifies it by its Kale tag.
+4. Builds a {py:class}`kale.pipeline.Pipeline` — internally a
+   `networkx.DiGraph` — where each node is a {py:class}`kale.step.Step`
+   carrying its source code, dependencies, inputs and outputs.
+5. Runs
+   {py:func}`kale.common.astutils.get_marshal_candidates`
+   on every step to resolve data dependencies between steps.
+
+The processor returns the `Pipeline` object along with the combined string
+of `imports` + `functions` code, ready to be pasted into every generated
+component.
+
+### 2. Compiler.generate_lightweight_component
+
+For each `Step` in the pipeline, the
+{py:class}`kale.compiler.Compiler` renders
+`kale/templates/nb_function_template.jinja2` to produce a
+`@kfp_dsl.component` function. The template:
+
+- Starts with the shared `imports_and_functions` block so the step can use
+  the same modules and helpers as the notebook.
+- Emits `marshal.load("<var>")` calls for each input the step consumes.
+- Pastes the original cell source code verbatim — Kale does not rewrite
+  your code, it just decorates it.
+- Emits `marshal.save("<var>", <var>)` calls for each output the step
+  produces.
+- Adds a `packages_to_install=[...]` list built from the `imports` cell via
+  {py:func}`kale.compiler.Compiler._get_package_list_from_imports`, which
+  walks the imports AST and resolves module names to pip package names.
+
+### 3. Compiler.generate_pipeline
+
+Next, the compiler renders
+`kale/templates/pipeline_template.jinja2` to generate the top-level
+`@kfp_dsl.pipeline` function. This template:
+
+- Declares the pipeline parameters from the `pipeline-parameters` cell.
+- Instantiates each component in dependency order.
+- Threads parameters into the tasks that need them.
+- Wires task dependencies using KFP's `.after(...)` and input/output
+  references so KFP builds the same DAG Kale has in memory.
+
+### 4. Compiler.generate_dsl
+
+The compiler concatenates:
+
+- A header with imports for the KFP SDK and Kale marshal helpers.
+- All component functions from stage 2.
+- The pipeline function from stage 3.
+- A `__main__` block that invokes `kfp.compiler.Compiler().compile()` so the
+  generated file can be executed directly.
+
+The final text is formatted with `autopep8` and written to
+`.kale/<pipeline_name>.kale.py`. **This file is plain KFP v2 DSL** — no
+Kale-specific runtime, just standard Kubeflow Pipelines code.
+
+### 5. Submission (optional)
+
+If you pass `--run_pipeline` (or the equivalent extension setting),
+{py:func}`kale.common.kfputils.compile_pipeline` invokes the KFP SDK
+compiler to turn the DSL into YAML IR, then uploads the pipeline and
+starts a run via the KFP REST API.
+
+## The `.kale/` directory
+
+After a compile, you'll find the generated files in a `.kale/` directory
+created in the current working directory (not necessarily the same directory
+as the notebook):
+
+```
+.kale/
+├── my_notebook.kale.py    # generated KFP v2 DSL
+└── my_notebook.yaml       # KFP YAML IR (if submission was triggered)
+```
+
+Inspecting this file is the fastest way to debug a misbehaving pipeline.
+You can:
+
+- Read the `packages_to_install` list for each component to catch missing
+  imports.
+- Read the `marshal.load(...)` / `marshal.save(...)` calls to confirm Kale
+  detected the right data dependencies.
+- Run the file directly (`python .kale/my_notebook.kale.py`) to reproduce
+  KFP compilation errors locally.
+
+## Package detection from imports
+
+Kale pulls its `packages_to_install` lists out of your `imports` cell by
+walking the AST. For a line like `import pandas as pd`, it records `pandas`
+as a dependency of every step. For `from sklearn.ensemble import
+RandomForestClassifier`, it records `scikit-learn` (Kale knows about some
+module-name → pip-name mappings).
+
+This is why **all imports must live in `imports` cells**: any module used
+from a `step` cell but imported elsewhere will be missed, and the
+`@kfp_dsl.component` will not declare it in `packages_to_install`, causing
+a `ModuleNotFoundError` at runtime.
+
+## Templates to read
+
+If you want to go deeper, these two files are the source of truth for the
+generated code:
+
+- `kale/templates/nb_function_template.jinja2` — per-step component
+- `kale/templates/pipeline_template.jinja2` — pipeline wrapper
+
+They're ~170 lines of Jinja combined, and they are the clearest way to
+learn exactly what Kale emits.
diff --git a/docs/source/concepts/data-passing.md b/docs/source/concepts/data-passing.md
new file mode 100644
index 000000000..13c53da12
--- /dev/null
+++ b/docs/source/concepts/data-passing.md
@@ -0,0 +1,187 @@
+# Data Passing & Marshalling
+
+One of Kale's main jobs is making multi-step pipelines work as a single
+notebook. You write code across many cells, Kale figures out which variables
+need to move between the resulting pipeline steps, and emits the right
+serialization calls for each object type.
+
+This page explains how that works.
+
+## Detecting data dependencies
+
+When you write this in a notebook:
+
+```python
+# tag: step:load
+df = pd.read_csv("data.csv")
+
+# tags: step:train, prev:load
+model = train(df)
+
+# tags: step:evaluate, prev:train
+score = model.score(df)
+```
+
+Kale needs to know that:
+
+- `df` must be saved at the end of `load` and loaded at the start of `train`
+  and `evaluate`.
+- `model` must be saved at the end of `train` and loaded at the start of
+  `evaluate`.
+
+It discovers this by static AST analysis via
+{py:func}`kale.common.astutils.get_marshal_candidates`. For each cell, Kale
+walks the AST looking for:
+
+- Names assigned at the top level of the cell (these are candidates for
+  **outputs**).
+- Names read but not assigned (these are candidates for **inputs**).
+- Names that come from `imports` / `functions` cells (those are shared
+  across all steps, so they're not treated as data).
+
+The result is a set of variables each step consumes and produces. Kale then
+intersects these sets across the dependency graph: if step `B` depends on
+`A` and reads `df`, and `A` assigns `df`, then `df` becomes an artifact
+flowing from `A` to `B`.
+
+## The marshalling system
+
+Once Kale knows _what_ to pass, it still needs to decide _how_ to serialize
+it. Pickling everything with `dill` works for many Python objects, but
+breaks down for:
+
+- Objects that hold native resources (TensorFlow session graphs, open files,
+  database cursors).
+- Large objects that have a more efficient native format (numpy arrays,
+  parquet-able DataFrames).
+- ML framework models, which have their own `save` / `load` contracts.
+
+Kale solves this with a small, extensible dispatcher system in
+{py:mod}`kale.marshal`.
+
+### `MarshalBackend` and the `Dispatcher`
+
+Every supported type has a dedicated backend class that implements:
+
+- `save(obj, path)` — how to write this object to disk.
+- `load(path)` — how to read it back.
+- `object_type_pattern` — a regex matched against
+  `type(obj).__module__ + "." + type(obj).__qualname__` so the dispatcher
+  knows when to use this backend.
+
+At marshal time, the {py:class}`kale.marshal.backend.Dispatcher` inspects
+the object's type, picks the first backend whose pattern matches, and falls
+back to a generic `dill` backend if none does.
+
+### Built-in backends
+
+| Backend                  | Library       | File extension | Matches                                       |
+| ------------------------ | ------------- | -------------- | --------------------------------------------- |
+| `FunctionBackend`        | (builtin)     | `.pyfn`        | Python `function` objects                     |
+| `SKLearnBackend`         | scikit-learn  | `.joblib`      | `sklearn.*` classes                           |
+| `NumpyBackend`           | numpy         | `.npy`         | `numpy.*`                                     |
+| `PandasBackend`          | pandas        | `.pdpkl`       | `pandas.*(DataFrame|Series)`                  |
+| `XGBoostModelBackend`    | xgboost       | `.json`        | `xgboost.core.Booster`                        |
+| `XGBoostDMatrixBackend`  | xgboost       | `.dmatrix`     | `xgboost.core.DMatrix`                        |
+| `PyTorchBackend`         | pytorch       | `.pt`          | `torch.nn.modules.module.Module` (and subclasses) |
+| `KerasBackend`           | keras         | `.keras`       | `keras.*`                                     |
+| `TensorflowKerasBackend` | tensorflow    | `.tfkeras`     | `tensorflow.python.keras.*`                   |
+| `DillBackend` (fallback) | dill          | `.dillpkl`     | Any object no other backend matches           |
+
+### Extending the dispatcher
+
+You can add a new backend by subclassing
+{py:class}`kale.marshal.backend.MarshalBackend` and registering it with the
+`@register_backend` decorator. Your backend declares an
+`object_type_pattern` and implements `save` / `load`. Kale will pick it up
+automatically at compile time and inject the right calls into the generated
+DSL.
+
+This is the recommended way to support new libraries: open an issue or a PR
+with a new backend rather than forcing your objects through `dill`.
+
+## Common pitfalls
+
+The marshalling model is powerful, but it is still **static** — Kale can
+only see what the AST shows. These are the patterns that trip people up.
+
+### Aliasing
+
+```python
+# step:A
+model1 = model2 = SomeModel()
+
+# step:B (prev: A)
+model2.add_layer(SomeLayer())
+
+# step:C (prev: B)
+print(model1)
+```
+
+Kale saves `model1` and `model2` separately at the end of `A`, so mutating
+`model2` in `B` has no effect on what `C` sees. **Solution**: don't alias
+across steps — give each variable a single name and pass it explicitly.
+
+### Mutating global state
+
+```python
+# imports
+import warnings
+
+# step:A
+warnings.simplefilter("ignore")
+warnings.warn("A", DeprecationWarning)
+
+# step:B (prev: A)
+warnings.warn("B", DeprecationWarning)
+```
+
+Step `B` runs in a fresh container, so `warnings.simplefilter("ignore")`
+never happens there. **Solution**: configure global state in the `imports`
+or `functions` cell, once, so every step gets the same configuration.
+
+### Non-serializable objects
+
+```python
+# step:A
+f = open("log.txt", "a")
+
+# step:B (prev: A)
+f.write("hello")
+```
+
+Kale tries to pickle `f` at the end of `A` and fails — open file handles
+can't be serialized. **Solution**: rebuild the resource at the start of each
+step that needs it, typically via a helper in a `functions` cell:
+
+```python
+# functions
+def get_log_file():
+    return open("log.txt", "a")
+
+# step:A
+with get_log_file() as f:
+    f.write("A")
+
+# step:B (prev: A)
+with get_log_file() as f:
+    f.write("B")
+```
+
+### Star imports
+
+```python
+# imports
+from mymodule import *
+
+# step:A
+result = myfoo()
+```
+
+Kale can't see inside `mymodule`, so it doesn't know that `myfoo` comes from
+there. It may try to marshal `myfoo` as an input to `A` and crash.
+**Solution**: use explicit imports (`from mymodule import myfoo`). Star
+imports should be avoided in Kale notebooks.
+
+See [Troubleshooting](../user-guide/troubleshooting.md) for the wider list of runtime
+issues and how to diagnose them.
diff --git a/docs/source/concepts/index.md b/docs/source/concepts/index.md
new file mode 100644
index 000000000..02da06995
--- /dev/null
+++ b/docs/source/concepts/index.md
@@ -0,0 +1,125 @@
+# How Kale Works
+
+Kale turns an annotated Jupyter notebook into a Kubeflow Pipelines v2 pipeline
+through a small, deterministic compilation pipeline. This page gives you a
+mental model for each stage; the rest of the concepts section drills into
+details.
+
+## The big picture
+
+```
+  .ipynb notebook
+       │
+       ▼
+┌─────────────────────┐
+│ NotebookProcessor   │  parses cell tags, builds a DAG of Steps
+└─────────────────────┘
+       │
+       ▼
+┌─────────────────────┐
+│ Dependency analysis │  static AST analysis finds shared variables
+└─────────────────────┘
+       │
+       ▼
+┌─────────────────────┐
+│ Compiler            │  renders Jinja2 templates → KFP v2 DSL
+└─────────────────────┘
+       │
+       ▼
+  .kale/<name>.kale.py
+       │
+       ▼
+┌─────────────────────┐
+│ KFP SDK             │  compiles DSL → YAML IR → uploads to KFP
+└─────────────────────┘
+       │
+       ▼
+  Running pipeline on Kubeflow
+```
+
+## Stage 1 — Parsing the notebook
+
+When you run `kale --nb <notebook>`, a
+{py:class}`kale.processors.NotebookProcessor` reads the `.ipynb` file with
+`nbformat` and walks every cell. Cell tags (stored in notebook metadata under
+`tags`) tell Kale what to do with the cell:
+
+- Cells tagged `imports` or `functions` are collected and will be **prepended
+  to every pipeline step**, so every step has access to the same set of
+  modules and helpers.
+- Cells tagged `pipeline-parameters` define the KFP parameters of the
+  generated pipeline.
+- Cells tagged `step:<name>` become pipeline steps. Multiple cells can share
+  the same step name and will be concatenated in notebook order.
+- `prev:<other_step>` tags add dependency edges between steps.
+- `skip` cells are excluded entirely — useful for exploration code.
+
+The result is a {py:class}`kale.pipeline.Pipeline` object, which is a
+{py:class}`networkx.DiGraph` where nodes are {py:class}`kale.step.Step`
+instances.
+
+## Stage 2 — Finding data dependencies
+
+Having a DAG of steps is not enough. Kale also needs to know **which variables
+flow between steps** — if step `B` reads a `DataFrame` that step `A`
+produced, Kale has to save it in `A` and load it back in `B`.
+
+To figure this out, Kale uses
+{py:func}`kale.common.astutils.get_marshal_candidates`, a static-analysis
+helper that walks the cell's AST to find:
+
+- Names that are assigned in one step and read in another.
+- Names that escape function bodies via return or assignment.
+- Names introduced by the shared `imports` / `functions` cells (which are
+  **not** treated as data, because they are available everywhere).
+
+The output is a set of "marshal candidates" per step, which Kale later turns
+into save/load calls in the generated DSL.
+
+## Stage 3 — Compiling to KFP v2
+
+Once Kale has a DAG of steps, each annotated with its inputs and outputs, the
+{py:class}`kale.compiler.Compiler` renders two Jinja2 templates:
+
+1. `nb_function_template.jinja2` generates a `@kfp_dsl.component` function
+   per step. Each component:
+   - Starts with the shared imports and function definitions.
+   - Loads inputs via Kale's marshalling dispatcher.
+   - Executes the original cell source code (verbatim).
+   - Saves any outputs that downstream steps will consume.
+
+2. `pipeline_template.jinja2` wraps all components in a single
+   `@kfp_dsl.pipeline` function that wires tasks together according to the
+   dependency graph and plumbs pipeline parameters through.
+
+The result is assembled, formatted with `autopep8`, and written to
+`.kale/<pipeline_name>.kale.py`. You can read it — it's plain KFP v2 DSL —
+and even hand-tweak it if you need to.
+
+## Stage 4 — Submitting to KFP
+
+When you pass `--run_pipeline`, Kale hands the generated script to
+{py:mod}`kale.common.kfputils`, which invokes the KFP SDK compiler to turn
+the DSL into YAML IR, uploads it to KFP, and starts a run via the KFP REST
+API. The pipeline name, experiment name, and KFP host all come from your
+notebook's Kale metadata or from command line overrides.
+
+## Why it's built this way
+
+Kale's compilation pipeline is intentionally a **pure, deterministic
+transformation**: notebook → DSL. That means:
+
+- The output is just Python. No hidden runtime. You can inspect it, debug it,
+  version-control it, and run it without Kale installed (once the pipeline is
+  submitted, it's a KFP pipeline like any other).
+- Cells run **as if they were in a single notebook** — the imports, functions
+  and parameters are available everywhere, and data passing happens behind the
+  scenes.
+- Because the transformation is static, Kale can detect most common mistakes
+  at compile time instead of failing at pipeline runtime.
+
+## Dive deeper
+
+- [Cell Types & Annotations](cell-types.md) — the full tag vocabulary
+- [Data Passing & Marshalling](data-passing.md) — how marshalling works and which types are supported
+- [Pipeline Compilation](compilation.md) — the exact compilation pipeline, file by file
diff --git a/docs/source/conf.py b/docs/source/conf.py
new file mode 100644
index 000000000..c9fa4f1ca
--- /dev/null
+++ b/docs/source/conf.py
@@ -0,0 +1,183 @@
+# Copyright 2026 The Kubeflow Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Sphinx configuration for the Kale documentation site."""
+
+from __future__ import annotations
+
+import os
+import sys
+
+# Make the kale package importable for autodoc.
+sys.path.insert(0, os.path.abspath("../.."))
+
+# -- Project information -----------------------------------------------------
+
+project = "Kale"
+copyright = "2026, The Kubeflow Authors"
+author = "The Kubeflow Authors"
+
+# -- General configuration ---------------------------------------------------
+
+extensions = [
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.viewcode",
+    "sphinx.ext.intersphinx",
+    "sphinx_autodoc_typehints",
+    "sphinx_copybutton",
+    "sphinx_design",
+    "myst_parser",
+]
+
+templates_path = ["_templates"]
+exclude_patterns: list[str] = []
+
+source_suffix = {
+    ".rst": "restructuredtext",
+    ".md": "markdown",
+}
+
+# -- HTML output -------------------------------------------------------------
+
+html_theme = "furo"
+html_title = "Kale"
+html_logo = "_static/kale-symbol.svg"
+html_favicon = "_static/kale-symbol.svg"
+html_static_path = ["_static"]
+html_css_files = ["custom.css"]
+
+# Furo theme options. Mirrors the Kubeflow SDK docs site so that Kale's
+# documentation keeps visual parity with the rest of the Kubeflow project.
+html_theme_options = {
+    "sidebar_hide_name": False,
+    "navigation_with_keys": True,
+    "top_of_page_buttons": ["view", "edit"],
+    "source_repository": "https://github.com/kubeflow/kale",
+    "source_branch": "main",
+    "source_directory": "docs/source/",
+    # Top navigation bar, implemented via Furo's announcement slot and
+    # styled by _static/custom.css (matches sdk.kubeflow.org).
+    "announcement": """
+        <nav class="top-nav">
+            <a href="/index.html" class="top-nav-brand">
+                <svg class="top-nav-logo" width="28" height="28" viewBox="-21 32 884 884" xmlns="http://www.w3.org/2000/svg">
+                    <path d="M0,0 L16,0 L16,864 L0,864 L-3,836 L-8,811 L-14,791 L-24,766 L-34,746 L-47,725 L-59,709 L-68,699 L-75,697 L-90,695 L-106,690 L-118,684 L-128,674 L-135,664 L-141,653 L-161,644 L-171,638 L-183,627 L-189,617 L-193,605 L-196,590 L-207,582 L-216,574 L-225,562 L-229,552 L-231,539 L-231,519 L-241,509 L-249,497 L-253,486 L-254,481 L-254,461 L-250,445 L-252,439 L-260,424 L-264,408 L-264,399 L-262,387 L-255,368 L-254,365 L-259,348 L-261,340 L-262,323 L-260,311 L-255,298 L-249,287 L-240,276 L-239,274 L-239,252 L-237,238 L-232,222 L-224,211 L-215,201 L-202,189 L-199,185 L-195,165 L-190,153 L-184,142 L-175,132 L-167,125 L-153,117 L-143,111 L-139,106 L-135,98 L-130,86 L-121,73 L-112,65 L-96,56 L-83,51 L-68,46 L-60,39 L-51,27 L-40,16 L-26,7 L-11,2 Z" fill="#743ABD" transform="translate(376,44)"/>
+                    <path d="M0,0 L4,0 L6,9 L10,14 L21,23 L31,32 L40,45 L45,57 L47,66 L48,98 L60,116 L67,131 L70,141 L71,157 L68,174 L64,186 L65,195 L71,208 L73,216 L74,229 L70,245 L62,261 L60,265 L61,271 L65,287 L65,305 L61,318 L54,330 L45,340 L42,343 L42,368 L39,380 L32,393 L27,399 L18,406 L15,405 L7,387 L-4,366 L-20,342 L-30,329 L-42,315 L-64,293 L-75,284 L-91,272 L-107,262 L-125,252 L-145,243 L-165,236 L-187,230 L-209,226 L-207,200 L-202,175 L-196,155 L-188,135 L-175,110 L-164,94 L-156,84 L-144,70 L-135,61 L-124,52 L-113,43 L-96,32 L-78,22 L-59,14 L-41,8 L-20,3 Z" fill="#743ABD" transform="translate(656,220)"/>
+                    <path d="M0,0 L11,1 L33,7 L53,15 L74,26 L86,34 L98,43 L111,54 L131,74 L142,88 L152,102 L163,121 L173,142 L181,164 L185,179 L184,183 L162,193 L160,193 L158,198 L151,210 L142,220 L134,227 L112,234 L89,238 L78,249 L64,268 L55,283 L46,299 L36,322 L27,349 L22,372 L18,404 L0,404 Z" fill="#743ABD" transform="translate(449,502)"/>
+                    <path d="M0,0 L17,0 L32,3 L45,8 L58,17 L70,29 L79,42 L85,46 L104,53 L123,63 L133,71 L144,85 L156,110 L164,116 L176,123 L178,126 L160,131 L140,137 L118,146 L97,156 L80,166 L64,177 L51,187 L36,200 L18,218 L9,229 L1,239 L0,239 Z" fill="#743ABD" transform="translate(449,40)"/>
+                </svg>
+                <span>Kale</span>
+            </a>
+            <div class="top-nav-links">
+                <a href="https://github.com/kubeflow/kale/tree/main/examples">Examples</a>
+                <a href="https://github.com/kubeflow/kale">GitHub</a>
+                <a href="https://kubeflow.slack.com">Slack</a>
+                <a href="https://blog.kubeflow.org">Blog</a>
+            </div>
+        </nav>
+    """,
+}
+
+# -- Autodoc -----------------------------------------------------------------
+
+autodoc_default_options = {
+    "members": True,
+    "member-order": "bysource",
+    "undoc-members": False,
+    "exclude-members": "__weakref__,__init__",
+    "show-inheritance": True,
+}
+
+# Generate autosummary pages automatically (matches Kubeflow SDK setup).
+autosummary_generate = True
+autosummary_imported_members = True
+
+# Type hints: show types in the signature so the Kubeflow SDK-style CSS for
+# ``dl.py dt.sig`` elements renders correctly.
+autodoc_typehints = "signature"
+typehints_use_signature = True
+typehints_use_signature_return = True
+typehints_fully_qualified = False
+always_document_param_types = False
+typehints_document_rtype = False
+
+
+import typing as _typing
+
+
+def typehints_formatter(annotation, config=None):  # noqa: ARG001
+    """Render the builtin ``type`` (and ``type[X]``) without a cross-reference.
+
+    Several Kale classes expose a ``type`` attribute (``Artifact.type``,
+    ``VolumeConfig.type``), so a cross-reference to the builtin ``type``
+    collides with them and Sphinx emits an ambiguous-target warning. Rendering
+    the builtin as plain literal text avoids the lookup entirely.
+    """
+    if annotation is type:
+        return "``type``"
+    if _typing.get_origin(annotation) is type:
+        args = _typing.get_args(annotation)
+        inner = ", ".join(
+            getattr(a, "__qualname__", None) or getattr(a, "__name__", None) or repr(a)
+            for a in args
+        )
+        return f"``type[{inner}]``"
+    return None
+
+
+# -- Napoleon (Google-style docstrings) --------------------------------------
+
+napoleon_google_docstring = True
+napoleon_numpy_docstring = False
+napoleon_include_init_with_doc = True
+napoleon_include_private_with_doc = False
+napoleon_include_special_with_doc = True
+napoleon_use_admonition_for_examples = True
+napoleon_use_admonition_for_notes = True
+napoleon_use_admonition_for_references = True
+napoleon_use_ivar = False
+napoleon_use_param = True
+napoleon_use_rtype = True
+napoleon_type_aliases = None
+
+# -- MyST Parser -------------------------------------------------------------
+
+myst_enable_extensions = [
+    "colon_fence",
+    "deflist",
+    "linkify",
+    "substitution",
+    "tasklist",
+]
+myst_heading_anchors = 3
+
+# -- Intersphinx -------------------------------------------------------------
+
+intersphinx_mapping = {
+    "python": ("https://docs.python.org/3", None),
+    "kfp": ("https://kubeflow-pipelines.readthedocs.io/en/stable/", None),
+}
+
+# -- Copy button -------------------------------------------------------------
+
+copybutton_prompt_text = r">>> |\.\.\. |\$ "
+copybutton_prompt_is_regexp = True
+
+# -- Suppress noisy warnings -------------------------------------------------
+
+# Autodoc will frequently fail to import optional ML dependencies when Sphinx
+# runs on Read the Docs; degrade those to warnings that don't fail the build.
+nitpicky = False
+suppress_warnings = ["autodoc.import_object", "config.cache"]
diff --git a/docs/source/contributing.md b/docs/source/contributing.md
new file mode 100644
index 000000000..8ea5a9b3a
--- /dev/null
+++ b/docs/source/contributing.md
@@ -0,0 +1,127 @@
+# Contributing
+
+This page is the shorter, docs-site-friendly version of
+[CONTRIBUTING.md](https://github.com/kubeflow/kale/blob/main/CONTRIBUTING.md)
+in the repository. If you're about to open a PR, start here and then read
+the full document for release-engineering details.
+
+## Before you start
+
+If you haven't already, skim [Architecture](architecture/index.md) — it's a quick tour
+of the codebase and saves a lot of time when you need to find the right
+file to change.
+
+## Prerequisites
+
+- **Python 3.11+** (3.12 is recommended).
+- **uv** — installed automatically by `make dev`, or manually:
+  ```bash
+  curl -LsSf https://astral.sh/uv/install.sh | sh
+  ```
+- **Node.js 22+** and `jlpm` — only required if you're touching the
+  labextension. `jlpm` ships with JupyterLab.
+- **A Kubernetes cluster** (optional) — only needed when testing
+  generated pipelines end-to-end.
+
+## Set up your environment
+
+```bash
+git clone https://github.com/kubeflow/kale.git
+cd kale
+
+make dev          # one-time setup
+make test         # run backend + labextension tests
+make jupyter      # open JupyterLab with the extension loaded
+```
+
+You only need to re-run `make dev` after pulling changes that touch
+`pyproject.toml` / `package.json`, or after `make clean`.
+
+## Make targets you'll use
+
+| Target                 | Description                                      |
+| ---------------------- | ------------------------------------------------ |
+| `make dev`             | One-time environment setup.                      |
+| `make test`            | Run all tests (backend + labextension).          |
+| `make test-backend`    | Backend tests only.                              |
+| `make test-backend-unit` | Fast unit tests only.                          |
+| `make test-labextension` | Labextension tests only.                       |
+| `make lint`            | Run all linters.                                 |
+| `make lint-backend`    | Ruff check on the Python package.                |
+| `make lint-labextension` | ESLint + Prettier on the TypeScript source.    |
+| `make format-backend`  | Auto-fix Ruff findings.                          |
+| `make build`           | Build a wheel.                                   |
+| `make docs`            | Build the docs site (this website).              |
+| `make docs-serve`      | Build and serve docs locally on port 8000.       |
+
+Run `make help` in the repository root for the full list.
+
+## Managing Python dependencies
+
+Edit `pyproject.toml` under the right section:
+
+- Runtime dependencies: `[project.dependencies]`
+- JupyterLab runtime: `[project.optional-dependencies.jupyter]`
+- Dev tools: `[project.optional-dependencies.dev]`
+- Documentation: `[project.optional-dependencies.docs]`
+
+Then update the lockfile with `make lock` and sync the environment with
+`uv sync --all-extras`.
+
+## Pre-commit hooks
+
+Install pre-commit hooks with:
+
+```bash
+uv run pre-commit install
+```
+
+Installed hooks:
+
+- `uv-lock` — ensure `uv.lock` is in sync.
+- `trailing-whitespace`, `end-of-file-fixer` — standard hygiene.
+- `ruff` — Python linting and formatting.
+
+## Contributing to the docs
+
+This docs site lives under `docs/source/` and is built with Sphinx + Furo
++ MyST Parser. To add or edit a page:
+
+1. Drop a new `.md` or `.rst` file into the appropriate directory under
+   `docs/source/`.
+2. Reference it from the toctree in `docs/source/index.md` so it appears
+   in the sidebar.
+3. Run `make docs` to build the site locally.
+4. Run `make docs-serve` to view it at <http://localhost:8000>.
+
+API reference pages use Sphinx autodoc via `.. automodule::`. If you're
+adding or modifying public Python API, make sure you use Google-style
+docstrings (Napoleon is enabled) and add/update the corresponding page in
+`docs/source/api/`.
+
+## Development checklist
+
+Before opening a pull request:
+
+1. `make test` — all tests pass.
+2. `make lint` — no linter findings.
+3. If you changed the generated KFP DSL, update the golden fixtures under
+   `kale/tests/assets/kfp_dsl/`.
+4. If you changed public Python API, update the `docs/source/api/` pages.
+5. If you added or changed a user-visible feature, update the relevant
+   pages under `docs/source/user-guide/` or `docs/source/concepts/`.
+6. Write a clear commit message describing the change and the motivation.
+
+## Releasing
+
+Release procedures are documented in
+[RELEASE.md](https://github.com/kubeflow/kale/blob/main/RELEASE.md). If you
+don't have publish rights, you don't need to worry about this file.
+
+## Getting help
+
+- **GitHub issues** — [github.com/kubeflow/kale/issues](https://github.com/kubeflow/kale/issues)
+- **Slack** — `#kubeflow-ml-experience` on the Kubeflow Slack workspace
+- **WG meetings** — ML Experience WG on the Kubeflow community calendar
+
+Happy hacking!
diff --git a/docs/source/getting-started/installation.md b/docs/source/getting-started/installation.md
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,102 @@
+# Installation
+
+This page walks you through the prerequisites, installation options, and a
+quick sanity check for a working Kale setup.
+
+## Prerequisites
+
+| Requirement        | Version            | Notes                                                                                   |
+| ------------------ | ------------------ | --------------------------------------------------------------------------------------- |
+| Python             | 3.11 or later      | Kale uses modern typing features; older Pythons are not supported.                      |
+| Kubeflow Pipelines | **v2.16.0+**       | Older KFP servers reject the `securityContext` field Kale emits for step pods.          |
+| Kubernetes cluster | any                | `minikube`, `kind`, Docker Desktop, or a managed cluster all work.                      |
+| JupyterLab         | 4.0+ (for the UI)  | Only required if you want to use the Kale side panel inside JupyterLab.                 |
+
+Install KFP by following the
+[official Kubeflow Pipelines installation guide](https://www.kubeflow.org/docs/components/pipelines/operator-guides/installation/).
+Make sure to set `PIPELINE_VERSION=2.16.0` or later. If you are upgrading an
+older environment, ensure your Python deps include `kfp[kubernetes]>=2.16.0`.
+
+## Install from PyPI
+
+```{admonition} Kale v2.0 pre-release
+:class: important
+
+Kale v2.0 is not yet published on PyPI. Until it is, use the "Install from
+source" instructions below. This section will become the recommended path
+once v2.0 ships.
+```
+
+When v2.0 is released, you will be able to install the package directly:
+
+```bash
+pip install "jupyterlab>=4.0.0" "kubeflow-kale[jupyter]"
+jupyter lab
+```
+
+## Install from source
+
+Clone the repository and use the `make` targets provided by the project:
+
+```bash
+git clone https://github.com/kubeflow/kale.git
+cd kale
+
+make dev      # Install Python + Node deps, build and link the labextension
+make jupyter  # Start JupyterLab with the Kale panel
+```
+
+`make dev` takes care of:
+
+- Installing [uv](https://github.com/astral-sh/uv) if it is not already present.
+- Running `uv sync --all-extras` to create the virtual environment and install
+  the Python package in editable mode.
+- Building the JupyterLab 4 extension and linking it so it appears in your
+  JupyterLab UI.
+- Installing pre-commit hooks for linting.
+
+See [Contributing](../contributing.md) for a full breakdown of the available `make` targets
+and the development workflow.
+
+## Try Kale in Docker (no cluster required)
+
+If you just want to play with Kale in an isolated environment, the repo ships
+with a Dockerfile based on the official Kubeflow `jupyter-scipy` image:
+
+```bash
+make docker-build   # Build Kale wheels and bake them into the image
+make docker-run     # Start JupyterLab on http://localhost:8889
+```
+
+To also connect to a KFP cluster, follow the multi-terminal setup in
+[Running Pipelines](../user-guide/running-pipelines.md).
+
+## Verify your installation
+
+1. **Start a cluster and KFP port-forward** (minikube example):
+
+   ```bash
+   minikube start
+   kubectl port-forward -n kubeflow svc/ml-pipeline-ui 8080:80
+   ```
+
+2. **Test the CLI** against an example notebook:
+
+   ```bash
+   kale --nb examples/base/candies_sharing.ipynb \
+        --kfp_host http://127.0.0.1:8080 \
+        --run_pipeline
+   ```
+
+   This compiles the notebook into `.kale/<name>.kale.py`, uploads the
+   pipeline, and starts a run.
+
+3. **Test the JupyterLab extension**:
+   - Open JupyterLab (`make jupyter` or `jupyter lab`).
+   - Open any notebook from `examples/base/`.
+   - Click the Kale icon in the left sidebar.
+   - Toggle the Kale panel on — you should see cell type dropdowns appear on
+     each notebook cell.
+
+If any of these steps fail, head to [Troubleshooting](../user-guide/troubleshooting.md) — the
+most common issues are covered there.
diff --git a/docs/source/getting-started/quickstart.md b/docs/source/getting-started/quickstart.md
new file mode 100644
index 000000000..bd837d46a
--- /dev/null
+++ b/docs/source/getting-started/quickstart.md
@@ -0,0 +1,146 @@
+# Quickstart
+
+This walk-through takes you from a stock Kale install to a running pipeline
+on Kubeflow Pipelines in under ten minutes, using the `candies_sharing`
+example that ships with the repository.
+
+The recommended path is the **JupyterLab UI**: you annotate cells, compile,
+and submit the pipeline without ever leaving the notebook. If you'd rather
+drive Kale from a terminal, the [CLI flow](#advanced-cli-flow) at the bottom
+of this page covers the same journey.
+
+## Prerequisites
+
+Before you start, make sure you have:
+
+- Kale installed, including the JupyterLab extension (see [Installation](installation.md)).
+- A running Kubernetes cluster with Kubeflow Pipelines v2.16.0+ deployed.
+- The KFP API reachable on `http://127.0.0.1:8080` — for a minikube setup you
+  can run:
+  ```bash
+  kubectl port-forward -n kubeflow svc/ml-pipeline-ui 8080:80
+  ```
+
+## 1. Launch JupyterLab with Kale
+
+From the repository root, start the bundled JupyterLab environment:
+
+```bash
+make jupyter
+```
+
+Then open `examples/base/candies_sharing.ipynb` from the file browser. The
+notebook defines a toy pipeline that demonstrates every Kale concept in the
+minimum amount of code, and it ships already annotated with Kale tags.
+
+![JupyterLab right after opening `examples/base/candies_sharing.ipynb` — file browser on the left, notebook in the main area.](../../imgs/quickstart-01-jupyterlab.png)
+
+## 2. Open the Kale side panel
+
+Click the Kale icon in the JupyterLab left sidebar to toggle the Kale
+Deployment Panel. This is the control surface you'll use for the rest of the
+quickstart: it inspects cell tags, compiles the notebook, and submits runs
+to Kubeflow Pipelines.
+
+![The Kale side panel expanded on the left, showing the pipeline metadata fields (name, description, experiment).](../../imgs/quickstart-02-kale-panel.png)
+
+## 3. Review the cell tags
+
+Each cell now has a dropdown showing its Kale cell type. The
+`candies_sharing` notebook uses most of Kale's core tag types:
+
+- **Imports** — all `import` statements go here. Kale prepends this cell to
+  every step in the pipeline.
+- **Pipeline Parameters** — defines values that will become KFP parameters,
+  tweakable at submission time.
+- **Step** — one or more named steps, each with optional dependencies on
+  earlier steps (declared via `prev:<step_name>`).
+
+![Notebook cells with the Kale tag dropdowns visible (imports / pipeline parameters / step).](../../imgs/quickstart-03-cell-tags.png)
+
+See [Cell Types & Annotations](../concepts/cell-types.md) for the full tag vocabulary.
+
+## 4. Configure the pipeline metadata
+
+In the Kale side panel, confirm the basics:
+
+- **Pipeline name** — defaults to the notebook filename.
+- **Experiment** — defaults to `Default` (or the first available KFP
+  experiment).
+
+![The side panel with pipeline name, experiment, and description fields filled out.](../../imgs/quickstart-04-panel-config.png)
+
+## 5. Compile and run from the panel
+
+Click **Compile and Run** at the bottom of the Kale panel. Kale will, in
+order:
+
+1. Parse the notebook and extract the Kale tags from cell metadata.
+2. Build a pipeline DAG (Directed Acyclic Graph) from the `step` and `prev:` annotations.
+3. Detect which variables need to flow between steps.
+4. Generate a KFP v2 DSL Python script under `.kale/`.
+5. Upload the pipeline to KFP and start a new run in the selected experiment.
+
+Each phase updates in place in the panel, with a link to the generated
+`.kale/<notebook>.kale.py` once it exists.
+
+![The Kale panel mid-run, with the phase indicators (parse → compile → upload → run) visible.](../../imgs/quickstart-05-compile-run.png)
+
+## 6. Watch the run in the KFP UI
+
+When the upload finishes, the panel shows a **View run** link pointing at
+the Kubeflow Pipelines UI. Follow it to watch the DAG execute step-by-step;
+click any step to see its logs, artifacts, and the data Kale marshalled in
+and out of it.
+
+![The KFP UI run page with the `candies_sharing` DAG in progress or completed.](../../imgs/quickstart-06-kfp-run.png)
+
+## What's next?
+
+- Learn how Kale detects and moves data between steps in
+  [Data Passing & Marshalling](../concepts/data-passing.md).
+- Explore the rest of the panel (volumes, snapshots, parameters) in
+  [Running Pipelines](../user-guide/running-pipelines.md).
+- Browse the [examples](https://github.com/kubeflow/kale/tree/main/examples) gallery for more realistic pipelines.
+
+---
+
+## Advanced: CLI flow
+
+If you'd rather drive Kale from a terminal — for example in CI, on a remote
+box without a JupyterLab install, or when scripting multi-notebook builds —
+you can do the same thing with the `kale` CLI.
+
+### Compile the notebook
+
+```bash
+kale --nb examples/base/candies_sharing.ipynb
+```
+
+Look inside the `.kale/` directory that was just created:
+
+```bash
+ls .kale/
+# candies_sharing.kale.py     ← generated KFP v2 DSL
+```
+
+Open `candies_sharing.kale.py` — you'll see one `@kfp_dsl.component` function
+per step, a `@kfp_dsl.pipeline` function wiring them together, and a
+`__main__` block that you can run directly to compile the pipeline to YAML.
+
+### Compile and submit in one step
+
+Add `--run_pipeline` to compile **and** submit the pipeline to KFP in one
+shot:
+
+```bash
+kale --nb examples/base/candies_sharing.ipynb \
+     --kfp_host http://127.0.0.1:8080 \
+     --run_pipeline
+```
+
+This uploads the pipeline, creates an experiment (default:
+`Kale-Pipeline-Experiment`), and starts a run. Open the KFP UI at
+<http://127.0.0.1:8080> and navigate to **Runs** to watch it execute.
+
+See [CLI Reference](../api/cli.md) for the complete CLI reference.
diff --git a/docs/source/index.md b/docs/source/index.md
new file mode 100644
index 000000000..71d1dacd9
--- /dev/null
+++ b/docs/source/index.md
@@ -0,0 +1,155 @@
+---
+hide-toc: true
+---
+
+# Kale
+
+```{raw} html
+<div class="kale-hero">
+  <p class="tagline">From Jupyter Notebook to Kubeflow Pipeline — Zero Boilerplate</p>
+</div>
+```
+
+**Kale** (Kubeflow Automated pipeLines Engine) turns annotated Jupyter notebooks
+into production-ready [Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/)
+without requiring you to write a single line of KFP SDK code.
+
+Tag cells in your notebook, let Kale figure out the data dependencies between
+them, and compile the whole thing into a KFP v2 pipeline you can run on any
+Kubeflow cluster.
+
+
+## Why Kale?
+
+::::{grid} 1 2 2 2
+:gutter: 3
+
+:::{grid-item-card} No SDK boilerplate
+Annotate cells, compile, run. Kale generates the KFP v2 DSL for you — no need
+to learn components, artifacts, or Python decorators.
+:::
+
+:::{grid-item-card} Automatic data passing
+Variables flow between steps as if you were still in a single notebook. Kale's
+type-aware marshalling handles numpy, pandas, scikit-learn, PyTorch, Keras,
+TensorFlow, XGBoost and more.
+:::
+
+:::{grid-item-card} JupyterLab integration
+Tag cells visually, define step dependencies, and submit pipelines from the
+Kale side panel inside JupyterLab 4.
+:::
+
+:::{grid-item-card} KFP v2 native
+Compiles to the modern KFP v2 pipeline DSL with full artifact support. Runs on
+any compliant Kubeflow Pipelines backend.
+:::
+::::
+
+## Get started
+
+::::{grid} 1 2 2 2
+:gutter: 3
+
+:::{grid-item-card} {octicon}`rocket` Quickstart
+:link: getting-started/quickstart
+:link-type: doc
+
+Compile and run your first notebook on Kubeflow Pipelines in a few minutes.
+:::
+
+:::{grid-item-card} {octicon}`book` Core Concepts
+:link: concepts/index
+:link-type: doc
+
+Understand cell annotations, data marshalling, and how Kale compiles to KFP.
+:::
+
+:::{grid-item-card} {octicon}`tools` User Guide
+:link: user-guide/annotating-notebooks
+:link-type: doc
+
+Practical walkthroughs for annotating, parameterizing, and running pipelines.
+:::
+
+:::{grid-item-card} {octicon}`code` API Reference
+:link: api/pipeline
+:link-type: doc
+
+Python API for the Pipeline, Step, Compiler and marshalling modules.
+:::
+::::
+
+## Kale in the Kubeflow ecosystem
+
+Kale is part of the Kubeflow **ML Experience Working Group**, alongside the
+[Kubeflow SDK](https://sdk.kubeflow.org/), [Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/)
+and [Kubeflow Notebooks](https://www.kubeflow.org/docs/components/notebooks/).
+It lives at the notebook layer — where data scientists prototype — and bridges
+the gap to the pipeline layer, where production workloads run.
+
+If KFP is the "how" of running ML pipelines on Kubernetes, Kale is the "what
+you meant": take the notebook you already have, and turn it into a pipeline
+without rewriting anything.
+
+## Community
+
+- **GitHub**: [kubeflow/kale](https://github.com/kubeflow/kale)
+- **Slack**: [#kubeflow-ml-experience](https://kubeflow.slack.com/) on the Kubeflow workspace
+- **Working group**: ML Experience WG meetings — see the [Kubeflow community calendar](https://www.kubeflow.org/docs/about/community/)
+- **Issues & feature requests**: [github.com/kubeflow/kale/issues](https://github.com/kubeflow/kale/issues)
+
+```{toctree}
+:hidden:
+:caption: Getting Started
+
+why-kale
+getting-started/installation
+getting-started/quickstart
+```
+
+```{toctree}
+:hidden:
+:caption: Core Concepts
+
+concepts/index
+concepts/cell-types
+concepts/data-passing
+concepts/compilation
+```
+
+```{toctree}
+:hidden:
+:caption: User Guide
+
+user-guide/annotating-notebooks
+user-guide/pipeline-parameters
+user-guide/running-pipelines
+user-guide/troubleshooting
+```
+
+```{toctree}
+:hidden:
+:caption: Architecture
+
+architecture/index
+```
+
+```{toctree}
+:hidden:
+:caption: API Reference
+
+api/pipeline
+api/step
+api/compiler
+api/marshal
+api/cli
+```
+
+```{toctree}
+:hidden:
+:caption: Project
+
+roadmap
+contributing
+```
diff --git a/docs/source/roadmap.md b/docs/source/roadmap.md
new file mode 100644
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--- /dev/null
+++ b/docs/source/roadmap.md
@@ -0,0 +1,93 @@
+# Roadmap
+
+Kale is under active development again after a hiatus, with a re-energized
+maintainer team and a growing roster of contributors. This page captures the
+current direction in broad strokes — it is intentionally not a detailed spec.
+Things will move around as the community gives feedback.
+
+## Where we are
+
+Kale **v2.0** is the headline release for this cycle. It brings the project
+back in sync with the Kubeflow ecosystem:
+
+- Full compatibility with **Kubeflow Pipelines v2** (the KFP v2 DSL and YAML
+  IR, artifact model, and component spec).
+- A **modernized JupyterLab 4.x extension** rewritten for the current
+  labextension API.
+- **Python 3.11+** support across the backend.
+- A cleaner, testable compiler pipeline with golden-file fixtures for the
+  generated KFP DSL.
+
+Tracking issue: [kubeflow/kale#457 — Road to 2.0](https://github.com/kubeflow/kale/issues/457).
+
+## What we're focused on now
+
+### GSoC 2026: composable notebooks
+
+Kale is participating in **Google Summer of Code 2026** under the Kubeflow
+umbrella. The accepted project focuses on **multi-notebook coordination and
+composable pipelines** — letting users build pipelines that span more than
+one notebook, and composing them into larger workflows. This is a concrete,
+community-driven effort landing in Kale over the coming months, and it
+shapes a lot of the near-term priorities below.
+
+If you want to contribute, the GSoC project is a great way to get involved.
+Subscribe to the GitHub milestone for that work, drop a note in the
+ML Experience WG meeting, or ping us on Slack.
+
+## Directional themes
+
+These are high-level directions the maintainers are aligning around. None of
+them are committed features yet; they indicate _where_ we expect Kale to go,
+not a fixed schedule.
+
+### Deeper notebook experience
+
+The notebook is where Kale differentiates, and we want to make that
+experience richer:
+
+- **Incremental execution** — compile and run only the parts of a pipeline
+  that changed since the last run, without recomputing upstream steps.
+- **Dependency visualization** — surface Kale's DAG view directly in the
+  side panel so you can see the pipeline shape while editing.
+- **Run sweeps and experiment comparison** — easy fan-out across parameter
+  grids, with side-by-side metrics comparisons in the KFP UI.
+
+### From development to production
+
+- **Local execution mode** — run a Kale pipeline end-to-end on the local
+  machine for fast feedback, without a Kubernetes cluster in the loop.
+- **Kubeflow SDK integration** — tighter alignment with the emerging
+  [Kubeflow SDK](https://sdk.kubeflow.org/), so that Kale-generated
+  pipelines can interop with hand-written SDK pipelines and components.
+- **Artifact management and model registry** — better default support for
+  typed artifacts, model cards, and registries so that pipelines can hand
+  off to serving and evaluation infrastructure without glue code.
+
+### Community and ecosystem
+
+- **Documentation** — that's what this site is about. Expect the docs to
+  grow in scope as features land.
+- **Contributor onboarding** — simpler setup, better `make` targets,
+  clearer contribution docs, more "good first issue" labeling.
+- **Ecosystem alignment** — closer collaboration with the Kubeflow ML
+  Experience WG, KFP, Notebooks, and Katib maintainers.
+
+## There's much more to come
+
+The maintainers are happy to say: this is the most active Kale has been in
+years, and we're treating the roadmap as a living document. Expect more
+detail to appear on this page as concrete designs emerge from the WG.
+
+## Get involved in shaping the roadmap
+
+- **GitHub issues and discussions** — file issues for bugs, feature
+  requests, and design discussions. Label suggestions welcome.
+- **Milestones and project boards** — see the
+  [milestones](https://github.com/kubeflow/kale/milestones) page for
+  in-flight work.
+- **ML Experience Working Group meetings** — bi-weekly on the Kubeflow
+  community calendar. Kale roadmap updates are a recurring agenda item.
+- **Slack** — `#kubeflow-ml-experience` on the Kubeflow Slack workspace.
+
+The best way to influence the roadmap is to show up with a use case.
diff --git a/docs/source/user-guide/annotating-notebooks.md b/docs/source/user-guide/annotating-notebooks.md
new file mode 100644
index 000000000..ff126520e
--- /dev/null
+++ b/docs/source/user-guide/annotating-notebooks.md
@@ -0,0 +1,113 @@
+# Annotating Notebooks
+
+Kale turns cell tags into pipeline structure. You can set those tags two
+ways:
+
+1. **From the Kale JupyterLab extension** — point-and-click, recommended for
+   interactive development.
+2. **By editing notebook metadata directly** — useful for scripting, code
+   review, and when you don't have JupyterLab running.
+
+Both produce the same tags, so the resulting `.ipynb` looks identical either
+way.
+
+## Using the JupyterLab extension
+
+After you run `make jupyter` (or start JupyterLab any other way with the
+Kale extension installed), open a notebook and click the Kale icon in the
+left sidebar. The Kale panel appears with a master toggle at the top.
+
+When you enable Kale, two things happen:
+
+1. Every notebook cell grows a Kale metadata row at the top showing its cell
+   type (Imports, Functions, Step, ...), step name, and dependencies.
+2. The side panel unlocks pipeline-level settings: pipeline name, experiment
+   name, and description.
+
+### Setting a cell's type
+
+Click the cell type dropdown on the cell's Kale row. You'll see:
+
+- **Imports** — tag the cell as `imports`.
+- **Functions** — tag the cell as `functions`.
+- **Pipeline Parameters** — tag the cell as `pipeline-parameters`.
+- **Pipeline Metrics** — tag the cell as `pipeline-metrics`.
+- **Step** — tag the cell as a pipeline step. When you pick this, you'll be
+  prompted for the step name and given a dropdown of possible dependencies
+  (other steps in the notebook).
+- **Skip Cell** — tag the cell as `skip`.
+
+### Setting step dependencies
+
+For cells tagged as `Step`, the panel lets you pick zero or more previous
+steps. Each choice becomes a `prev:<name>` tag. Kale validates the choices
+so you can't create cycles.
+
+### Pipeline-level settings
+
+The side panel's pipeline settings form is mapped to fields on
+{py:class}`kale.pipeline.PipelineConfig`. Changes are saved into the
+notebook's top-level metadata under the `kubeflow_noteobok` key, so they
+travel with the notebook and show up on the next open.
+
+### Submitting
+
+The **Compile and Run** button invokes the Kale backend from the JupyterLab
+extension, which compiles the pipeline with exactly the same code path as
+`kale --nb ...` on the CLI, then uploads and runs it against the configured
+KFP host.
+
+## Editing metadata by hand
+
+Each cell in a notebook is JSON. Kale tags live in `metadata.tags` as a list
+of strings. A minimal step cell looks like this:
+
+```json
+{
+  "cell_type": "code",
+  "metadata": {
+    "tags": ["step:load_data"]
+  },
+  "source": [
+    "df = pd.read_csv('data.csv')\n"
+  ]
+}
+```
+
+A step with a dependency and a GPU request:
+
+```json
+{
+  "cell_type": "code",
+  "metadata": {
+    "tags": [
+      "step:train",
+      "prev:load_data",
+      "limit:nvidia.com/gpu:1",
+      "image:pytorch/pytorch:2.0-cuda12"
+    ]
+  },
+  "source": ["..."]
+}
+```
+
+Pipeline-level Kale settings live on the notebook (not on a cell) under the
+`metadata.kubeflow_noteobok` key — these are the same fields the side panel
+exposes.
+
+## Organising a notebook for Kale
+
+A notebook that compiles well with Kale usually follows this order:
+
+1. **One `imports` cell** at the top with every `import` statement.
+2. **One or more `functions` cells** below with pure function and class
+   definitions.
+3. **One `pipeline-parameters` cell** declaring tunable inputs.
+4. **A sequence of `step` cells**, each doing one logical thing, with
+   `prev:` tags describing the DAG.
+5. **Optional `pipeline-metrics` cells** at the end of training steps to
+   surface accuracy / loss / etc. in the KFP UI.
+6. **`skip` cells** wherever you want exploratory code to live without
+   affecting the pipeline.
+
+See the [examples](https://github.com/kubeflow/kale/tree/main/examples) gallery for notebooks that follow this pattern.
diff --git a/docs/source/user-guide/pipeline-parameters.md b/docs/source/user-guide/pipeline-parameters.md
new file mode 100644
index 000000000..307b69345
--- /dev/null
+++ b/docs/source/user-guide/pipeline-parameters.md
@@ -0,0 +1,85 @@
+# Pipeline Parameters
+
+Pipeline parameters let you change a pipeline's behavior at submission time
+without recompiling. In Kale, they come from a dedicated cell type:
+
+```python
+# tag: pipeline-parameters
+learning_rate = 0.01
+batch_size = 128
+num_epochs = 10
+model_name = "rf"
+use_gpu = False
+```
+
+Everything you assign in this cell becomes a top-level KFP pipeline
+parameter of the matching type.
+
+## Supported types
+
+Kale infers the parameter type from the Python literal you assign. Supported
+types:
+
+| Python literal     | KFP parameter type |
+| ------------------ | ------------------ |
+| `int` (e.g. `10`)  | `int`              |
+| `float` (e.g. `0.01`) | `float`         |
+| `str` (e.g. `"rf"`) | `str`              |
+| `bool` (e.g. `True`) | `bool`            |
+
+Anything else — lists, dicts, NumPy scalars, objects — will either fail at
+compile time or fall back to string encoding. Stick to the four basic types
+unless you know what you're doing.
+
+## How parameters are used in steps
+
+Once a parameter is declared, you can use it in any step cell as if it were
+a local variable:
+
+```python
+# tags: step:train, prev:load
+model = train(df, lr=learning_rate, epochs=num_epochs)
+```
+
+Kale wires `learning_rate` and `num_epochs` into the `train` component's
+inputs automatically, using KFP's pipeline-parameter plumbing.
+
+## Multiple parameter cells
+
+You can have more than one `pipeline-parameters` cell. Kale concatenates
+them in notebook order, so the **last definition of a parameter wins**.
+This is sometimes convenient for overriding defaults near where they are
+used, but it can also make notebooks hard to read — prefer a single
+parameters cell.
+
+## Overriding parameters at submission time
+
+When you click **Compile and Run** (or pass `--run_pipeline` on the CLI),
+Kale submits a pipeline run with default parameter values. To override
+them:
+
+- **In the KFP UI**: after the pipeline is uploaded, start a new run from
+  the Pipelines tab. The KFP UI will prompt you for each parameter.
+- **Via the KFP SDK**: load the compiled YAML IR and call
+  `client.run_pipeline(..., params={...})`. This is the recommended way to
+  kick off runs programmatically.
+
+## Viewing parameters in the KFP UI
+
+Every run page shows the parameter values it was started with, so you can
+correlate pipeline outputs with the inputs that produced them. Use this in
+combination with `pipeline-metrics` cells to build lightweight experiment
+tracking without needing a separate tracking service.
+
+## Defaults vs. placeholders
+
+The value you assign in the `pipeline-parameters` cell is the **default**.
+It's the value used when no override is provided at submission time. So:
+
+```python
+# tag: pipeline-parameters
+batch_size = 128   # sensible default
+```
+
+reads naturally as "pipeline parameter `batch_size` with default 128", and
+you can still override it on a per-run basis.
diff --git a/docs/source/user-guide/running-pipelines.md b/docs/source/user-guide/running-pipelines.md
new file mode 100644
index 000000000..88397377c
--- /dev/null
+++ b/docs/source/user-guide/running-pipelines.md
@@ -0,0 +1,111 @@
+# Running Pipelines
+
+Once your notebook is annotated, you can compile and run it three ways:
+
+1. **CLI** — `kale --nb ...`, good for scripts and CI.
+2. **JupyterLab extension** — interactive Compile and Run button.
+3. **Compile-only** — inspect the generated KFP DSL before submitting.
+
+All three call into the same underlying Python API, so the behavior is
+identical.
+
+## From the command line
+
+The `kale` CLI is the fastest path to a running pipeline. The core
+invocation is:
+
+```bash
+kale --nb path/to/notebook.ipynb
+```
+
+This compiles the notebook into `.kale/<pipeline_name>.kale.py` and exits.
+To also submit the pipeline to a running KFP instance, add:
+
+```bash
+kale --nb path/to/notebook.ipynb \
+     --kfp_host http://127.0.0.1:8080 \
+     --run_pipeline
+```
+
+### Useful CLI flags
+
+| Flag                   | Effect                                                         |
+| ---------------------- | -------------------------------------------------------------- |
+| `--nb`                 | Path to the notebook (required).                               |
+| `--kfp_host`           | KFP API endpoint for upload and run.                           |
+| `--upload_pipeline`    | Upload the pipeline without starting a run.                    |
+| `--run_pipeline`       | Upload *and* create a run.                                     |
+| `--pipeline_name`      | Override the pipeline name (default comes from notebook metadata). |
+| `--experiment_name`    | Override the KFP experiment (default `Kale-Pipeline-Experiment`). |
+| `--pipeline_description` | Set a pipeline description shown in the KFP UI.              |
+| `--docker_image`       | Override the default base image for all steps.                 |
+| `--debug`              | Keep intermediate files and print verbose logs.                |
+
+See [CLI Reference](../api/cli.md) for the complete list.
+
+## From the JupyterLab extension
+
+Open your notebook in JupyterLab, click the Kale icon in the left sidebar,
+and toggle the Kale panel on. At the bottom of the panel you'll see:
+
+- **Pipeline Name** and **Experiment Name** — override notebook defaults.
+- **Docker Image** — base image used for every step that doesn't declare
+  its own via an `image:` tag.
+- **Compile and Save** — generate the KFP DSL only.
+- **Compile and Run** — generate, upload, and start a run.
+
+The Deploy button streams progress through a notification area at the top
+of the panel, and surfaces the KFP run URL when the run is created so you
+can click straight through to the KFP UI.
+
+## Compile-only mode
+
+When you want to read or debug the generated code before sending it to KFP,
+skip the `--run_pipeline` flag on the CLI (or use **Compile and Save** in
+the extension). You'll end up with:
+
+```
+.kale/
+├── my_notebook.kale.py
+└── my_notebook.yaml       # KFP YAML IR (produced when running the DSL)
+```
+
+The `.kale.py` file is pure KFP v2 DSL. The `.yaml` file is the compiled
+pipeline IR that can be manually uploaded to the KFP UI without using Kale's
+"Compile and Run" button. You can:
+
+- Read it line by line to verify that your step dependencies, inputs, and
+  outputs look right.
+- Run it directly (`python .kale/my_notebook.kale.py`) to reproduce KFP
+  compilation errors locally.
+- Edit it to experiment with changes before committing them to the
+  notebook.
+
+## Monitoring runs
+
+Once a run is submitted, open the KFP UI and navigate to **Runs**. You can:
+
+- Watch step status in real time.
+- Click a step to see its logs, the generated component source, and
+  artifact inputs/outputs.
+- See pipeline parameters and pipeline metrics in the run summary.
+- Compare two runs side by side from the Runs list.
+
+Kale doesn't add any custom tracking on top of KFP — everything runs
+through the standard KFP backend, so anything you can do with a hand-rolled
+KFP pipeline, you can also do with a Kale-generated one.
+
+## Environment variables
+
+A few environment variables are useful when running Kale:
+
+| Variable                 | Purpose                                                                                    |
+| ------------------------ | ------------------------------------------------------------------------------------------ |
+| `KF_PIPELINES_ENDPOINT`  | Default KFP API endpoint if `--kfp_host` is not set.                                       |
+| `KF_PIPELINES_UI_ENDPOINT` | KFP UI URL used when Kale renders run links.                                             |
+| `KALE_PIP_INDEX_URLS`    | Comma-separated pip index URLs baked into the generated components (used for local dev).  |
+| `KALE_PIP_TRUSTED_HOSTS` | Trusted hosts for HTTP pip index URLs.                                                     |
+
+The last two are most useful when testing an unpublished version of Kale
+against a local KFP cluster — see the "Testing with KFP Clusters" section
+of [Contributing](../contributing.md).
diff --git a/docs/source/user-guide/troubleshooting.md b/docs/source/user-guide/troubleshooting.md
new file mode 100644
index 000000000..d858c05e6
--- /dev/null
+++ b/docs/source/user-guide/troubleshooting.md
@@ -0,0 +1,118 @@
+# Troubleshooting
+
+Most Kale problems fall into one of a few categories. This page covers the
+common ones. For anything not listed here, open an issue on
+[GitHub](https://github.com/kubeflow/kale/issues) with the notebook and the
+generated `.kale/<name>.kale.py`.
+
+## `ModuleNotFoundError` in a pipeline step
+
+You'll see this most often on your first pipeline. A step fails with:
+
+```
+ModuleNotFoundError: No module named 'seaborn'
+```
+
+even though you can clearly `import seaborn` in your notebook.
+
+**Cause.** Kale **does not rebuild a Docker image** for your pipeline. It
+uses the base image configured in pipeline metadata (or overridden with
+`--docker_image`), and it installs extra Python packages at step startup
+via `packages_to_install`. That list is built from the `imports` cell only
+— if you import a package in a `step` or `functions` cell, Kale won't add
+it to the install list and the step will fail.
+
+**Fix.**
+
+- Move every `import` statement into an `imports` cell.
+- For packages the AST can't resolve to a pip name, add them to the
+  pipeline's base docker image (or build a custom one).
+
+See [Cell Types & Annotations](../concepts/cell-types.md) for details on the imports rule.
+
+## Pickle / marshal errors
+
+```
+TypeError: cannot pickle '_thread.RLock' object
+```
+
+or
+
+```
+AttributeError: Can't pickle local object 'train.<locals>.inner'
+```
+
+**Cause.** A variable flowing between steps can't be serialized. Kale's
+marshalling system handles many ML-framework types natively (numpy, pandas,
+sklearn, PyTorch, Keras, TF, XGBoost), but falls back to `dill` for
+anything unknown. `dill` can serialize a lot, but not:
+
+- Open file handles, sockets, database connections.
+- Objects holding native thread locks or OS resources.
+- Closures that capture local variables.
+
+**Fix.**
+
+- Don't pass the offending object between steps. Recreate it from scratch
+  at the start of each step that needs it, using a helper in a `functions`
+  cell — see [Data Passing: Non-serializable objects](../concepts/data-passing.md#non-serializable-objects).
+- If the object is a model from an ML library Kale doesn't yet support,
+  consider contributing a new `MarshalBackend` — see
+  [Extending the dispatcher](../concepts/data-passing.md#extending-the-dispatcher).
+
+## Compiler errors from KFP
+
+```
+Internal compiler error: Compiler has produced Argo-incompatible workflow.
+```
+
+Kale's compile step hands the generated DSL to the KFP SDK's compiler,
+which runs `argo lint` if `argo` is on your `PATH`. An outdated `argo`
+binary can produce false positives here.
+
+**Fix.** Remove `argo` from your `PATH` or upgrade it to the version
+recommended by your KFP installation. Kale itself does not require `argo`.
+
+## Aliasing: a variable's value doesn't seem to propagate
+
+```python
+# step:A
+model1 = model2 = SomeModel()
+
+# step:B (prev: A)
+model2.add_layer(SomeLayer())
+
+# step:C (prev: B)
+print(model1)   # still the old model
+```
+
+**Cause.** Kale saves `model1` and `model2` separately in step A, so
+mutations to `model2` in step B don't affect what C loads as `model1`.
+
+**Fix.** Avoid aliasing across steps. Use one name for each object. See
+[Data Passing: Aliasing](../concepts/data-passing.md#aliasing).
+
+## Global state mutated inside a step is not visible to other steps
+
+Each step runs in its own container, so library configuration like
+`warnings.simplefilter`, `logging.basicConfig`, or
+`plt.rcParams[...]` does not persist across steps.
+
+**Fix.** Put all global configuration in the `imports` or `functions`
+cell, so every step applies it at startup.
+
+## `kale` command not found after installing
+
+If you installed Kale inside a `uv` or virtualenv, make sure you're running
+`kale` from an activated environment, or use `uv run kale --nb ...`. The
+`kale` script is registered as a `[project.scripts]` entry in
+`pyproject.toml`, so it only shows up inside the environment where the
+package was installed.
+
+## Still stuck?
+
+- Check the generated `.kale/<name>.kale.py` — it often reveals what Kale
+  thinks the pipeline looks like.
+- Run with `--debug` for verbose output.
+- File an issue on [GitHub](https://github.com/kubeflow/kale/issues) with
+  a minimal reproduction notebook.
diff --git a/docs/source/why-kale.md b/docs/source/why-kale.md
new file mode 100644
index 000000000..ab88c6daa
--- /dev/null
+++ b/docs/source/why-kale.md
@@ -0,0 +1,25 @@
+# Why Kale
+
+```{admonition} Content in progress
+:class: note
+
+This page will go into detail about why Kale exists, who it's for, and how it
+compares to writing KFP components and pipelines by hand. The content is being
+iterated on — expect a fuller write-up shortly.
+```
+
+In the meantime, here's the short version:
+
+- **You already have a notebook.** Kale lets you keep it. Instead of rewriting
+  it as a KFP pipeline, you tag cells with Kale's lightweight annotations and
+  let Kale compile the pipeline for you.
+- **Data passing is automatic.** Kale statically analyses your code to find
+  which variables are shared between steps, and injects the right
+  save/load calls using its type-aware marshalling system.
+- **It's designed for iteration.** You can go back to a pure notebook workflow
+  at any time — nothing Kale does is destructive or locks you in.
+- **It fits into Kubeflow.** Kale compiles to KFP v2 DSL, so your pipelines
+  run on the same infrastructure as any other Kubeflow workload.
+
+For a concrete walk-through, head over to the
+[Quickstart](getting-started/quickstart.md).
diff --git a/kale/compiler.py b/kale/compiler.py
index 8da3e7aa2..4811c7188 100644
--- a/kale/compiler.py
+++ b/kale/compiler.py
@@ -11,6 +11,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+"""Compile a Kale :class:`~kale.pipeline.Pipeline` into a Kubeflow Pipelines v2 DSL script.
+
+This module renders the Jinja2 templates in ``kale/templates/`` to produce a
+ready-to-run KFP v2 pipeline script, formats it, and optionally hands it off to
+the KFP SDK for compilation and submission.
+"""
 
 import argparse
 import logging
diff --git a/kale/marshal/backend.py b/kale/marshal/backend.py
index bde25e8ba..b0d793ff0 100644
--- a/kale/marshal/backend.py
+++ b/kale/marshal/backend.py
@@ -11,6 +11,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+"""Marshal dispatcher and backend base class.
+
+Defines :class:`MarshalBackend`, the abstract base class for type-aware
+serializers that Kale uses to pass data between pipeline steps, and
+:class:`Dispatcher`, which routes objects to the correct backend at
+save/load time.
+"""
 
 import logging
 import os
diff --git a/kale/marshal/backends.py b/kale/marshal/backends.py
index 7cad523d2..781bae986 100644
--- a/kale/marshal/backends.py
+++ b/kale/marshal/backends.py
@@ -11,6 +11,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+"""Built-in marshal backends.
+
+Concrete :class:`~kale.marshal.backend.MarshalBackend` implementations for the
+Python types Kale supports out of the box: numpy arrays, pandas DataFrames,
+scikit-learn estimators, PyTorch / Keras / TensorFlow models, XGBoost
+boosters and DMatrices, and plain Python functions.
+"""
 
 import logging
 
diff --git a/kale/pipeline.py b/kale/pipeline.py
index a327b9650..8a5d70d81 100644
--- a/kale/pipeline.py
+++ b/kale/pipeline.py
@@ -11,6 +11,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+"""Pipeline data model.
+
+Defines :class:`Pipeline` — a :class:`networkx.DiGraph` of :class:`~kale.step.Step`
+nodes — along with the configuration classes that describe pipeline-level
+settings such as the pipeline name, KFP host, volumes, and Katib experiments.
+"""
 
 from collections.abc import Iterable
 import copy
diff --git a/kale/processors/nbprocessor.py b/kale/processors/nbprocessor.py
index e530adba1..132f1a2a0 100644
--- a/kale/processors/nbprocessor.py
+++ b/kale/processors/nbprocessor.py
@@ -11,6 +11,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+"""Notebook → Pipeline conversion.
+
+:class:`NotebookProcessor` reads a Jupyter notebook, parses the Kale-specific
+cell tags and notebook metadata, resolves static data dependencies, and
+produces a ready-to-compile :class:`~kale.pipeline.Pipeline`.
+"""
 
 import logging
 import os
diff --git a/kale/step.py b/kale/step.py
index 472db4adc..4e387d10c 100644
--- a/kale/step.py
+++ b/kale/step.py
@@ -11,6 +11,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+"""Pipeline step data model.
+
+Defines :class:`Step`, the unit of execution inside a Kale :class:`~kale.pipeline.Pipeline`,
+together with :class:`StepConfig` and the :class:`PipelineParam` / :class:`Artifact`
+named tuples used to describe step inputs and outputs.
+"""
 
 from collections.abc import Callable
 import logging
@@ -85,7 +91,7 @@ def add_artifact(self, artifact_name, artifact_type, is_input):
         Artifact_type will be either 'Dataset', 'Model', 'HTML', 'Metrics',
         'ClassificationMetrics' or 'Artifact'.
         This will simplify tracking what should be an Input[Artifact]
-          or Output[Artifact].
+        or Output[Artifact].
 
         Args:
             artifact_name (str): Name of the artifact.
diff --git a/pyproject.toml b/pyproject.toml
index 39b44b3d4..26be76114 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -69,6 +69,15 @@ dev = [
   "pytest-asyncio",
   "pytest-jupyter[server]>=0.6.0",
 ]
+docs = [
+  "sphinx>=9.0",
+  "furo>=2025.0",
+  "sphinx-autodoc-typehints>=2.0",
+  "sphinx-copybutton>=0.5",
+  "myst-parser>=3.0",
+  "sphinx-design>=0.6",
+  "linkify-it-py>=2.0",
+]
 
 [tool.hatch.version]
 path = "kale/__init__.py"
diff --git a/uv.lock b/uv.lock
index 9554a40a1..7c749794c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -4,7 +4,29 @@ requires-python = ">=3.11"
 resolution-markers = [
     "python_full_version >= '3.14'",
     "python_full_version == '3.13.*'",
-    "python_full_version < '3.13'",
+    "python_full_version == '3.12.*'",
+    "python_full_version < '3.12'",
+]
+
+[[package]]
+name = "accessible-pygments"
+version = "0.0.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pygments" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/bc/c1/bbac6a50d02774f91572938964c582fff4270eee73ab822a4aeea4d8b11b/accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872", size = 1377899, upload-time = "2024-05-10T11:23:10.216Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7", size = 1395903, upload-time = "2024-05-10T11:23:08.421Z" },
+]
+
+[[package]]
+name = "alabaster"
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-provides-extras = ["dev", "jupyter"]
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