diff --git a/demos/demos_databases_apis/graphviz/graphviz.ipynb b/demos/demos_databases_apis/graphviz/graphviz.ipynb
index 6dd0269b1a..72f6ac01cb 100644
--- a/demos/demos_databases_apis/graphviz/graphviz.ipynb
+++ b/demos/demos_databases_apis/graphviz/graphviz.ipynb
@@ -6,9 +6,13 @@
     "id": "fEjoJ5eBnuKZ"
    },
    "source": [
-    "# Graphistry <> graphviz integration quickstart\n",
+    "# Graphviz Layouts for Graphistry Visualization\n",
     "\n",
-    "The [graphviz engine](https://graphviz.org/) is popular for layout of small graphs and rendering to static images. The Graphistry Python bindings to graphviz enable using pygraphistry as usual for quickly loading and manipulating your data, and then benefiting from graphviz for layout, and optionally, rendering.\n",
+    "The [graphviz engine](https://graphviz.org/) is popular for high-quality layout of small graphs, especially trees and directed acyclic graphs (DAGs).\n",
+    "\n",
+    "This notebook shows how to use graphviz for **layout**, then visualize interactively with Graphistry's GPU-accelerated renderer.\n",
+    "\n",
+    "**For static image export** (SVG, PNG for docs/reports), see the [static_rendering.ipynb](static_rendering.ipynb) notebook.\n",
     "\n",
     "The example below shows laying out and rendering company ownership data that is in a tree and benefits from graphviz's high-quality layout engine."
    ]
@@ -33,24 +37,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 1,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:22.280959Z",
+     "iopub.status.busy": "2025-12-10T07:05:22.280803Z",
+     "iopub.status.idle": "2025-12-10T07:05:22.283760Z",
+     "shell.execute_reply": "2025-12-10T07:05:22.283115Z"
+    },
     "id": "3XMNgAvIM9Ep",
     "outputId": "b391eb13-0650-433b-bd2b-c905cdef9e18"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-      "  Building wheel for graphistry (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#!apt-get install graphviz graphviz-dev\n",
     "\n",
@@ -68,46 +69,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 2,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 35
     },
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:22.285848Z",
+     "iopub.status.busy": "2025-12-10T07:05:22.285652Z",
+     "iopub.status.idle": "2025-12-10T07:05:24.152836Z",
+     "shell.execute_reply": "2025-12-10T07:05:24.152154Z"
+    },
     "id": "Cnhc-A4_M2Ad",
     "outputId": "0f2fb73f-72a2-4fae-9b28-cea26b85d0ad"
    },
    "outputs": [
     {
      "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "type": "string"
-      },
       "text/plain": [
-       "'0.34.5+12.g4dba3e6'"
+       "'0+unknown'"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "from typing import Any, Dict, Literal, Optional\n",
-    "import logging\n",
-    "try:\n",
-    "  import pygraphviz as pgv\n",
-    "except (ImportError, ModuleNotFoundError):\n",
-    "  logging.error(\"ImportError: Did you install pygraphviz and the supporting native packages?\")\n",
-    "  raise\n",
-    "\n",
-    "import pandas as pd\n",
-    "import graphistry\n",
-    "from graphistry import Plottable\n",
-    "graphistry.register(api=3, username=FILL_ME_IN, password=FILL_ME_IN)\n",
-    "\n",
-    "graphistry.__version__"
-   ]
+   "source": "from typing import Any, Dict, Literal, Optional\nimport logging\ntry:\n  import pygraphviz as pgv\nexcept (ImportError, ModuleNotFoundError):\n  logging.error(\"ImportError: Did you install pygraphviz and the supporting native packages?\")\n  raise\n\nimport pandas as pd\nimport graphistry\nfrom graphistry import Plottable\ngraphistry.register(api=3, username=FILL_ME_IN, password=FILL_ME_IN)\n\ngraphistry.__version__"
   },
   {
    "cell_type": "markdown",
@@ -122,8 +111,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:24.196306Z",
+     "iopub.status.busy": "2025-12-10T07:05:24.195989Z",
+     "iopub.status.idle": "2025-12-10T07:05:24.203292Z",
+     "shell.execute_reply": "2025-12-10T07:05:24.202546Z"
+    },
     "id": "8-7OAzDml0RV"
    },
    "outputs": [],
@@ -161,8 +156,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 4,
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:24.205170Z",
+     "iopub.status.busy": "2025-12-10T07:05:24.204950Z",
+     "iopub.status.idle": "2025-12-10T07:05:24.208318Z",
+     "shell.execute_reply": "2025-12-10T07:05:24.207591Z"
+    },
     "id": "7TmvBE5iI8Tu"
    },
    "outputs": [],
@@ -172,8 +173,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 5,
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:24.210087Z",
+     "iopub.status.busy": "2025-12-10T07:05:24.209895Z",
+     "iopub.status.idle": "2025-12-10T07:05:24.213687Z",
+     "shell.execute_reply": "2025-12-10T07:05:24.212914Z"
+    },
     "id": "y7eC5hOCwfE1"
    },
    "outputs": [],
@@ -196,12 +203,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 6,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 543
     },
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:24.215732Z",
+     "iopub.status.busy": "2025-12-10T07:05:24.215530Z",
+     "iopub.status.idle": "2025-12-10T07:05:26.814150Z",
+     "shell.execute_reply": "2025-12-10T07:05:26.813498Z"
+    },
     "id": "KiOxkJR_YKrh",
     "outputId": "2b9af5e5-b199-452a-839b-d8cc7cc0ea50"
    },
@@ -210,7 +223,7 @@
      "data": {
       "text/html": [
        "\n",
-       "            <iframe id=\"f2b2456c-1d5c-45c6-b3f8-ca01642c3563\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=278a461208e44b72883cb95a68f5e126&type=arrow&viztoken=66da7fa4-b7c3-44ed-9e5d-8bdea9e2136e&usertag=3c690b8f-pygraphistry-0.34.5+12.g4dba3e6&splashAfter=1727948559&info=true&play=0&edgeCurvature=0\"\n",
+       "            <iframe id=\"8eba87eb-46c1-4f8f-9f4b-7270073c5df0\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=8f40adcaeabe4977a7a1d4bfad054dfc&type=arrow&viztoken=c05b12f2-bd42-4d05-9489-251e7ba54cf8&usertag=faa52067-pygraphistry-0+unknown&splashAfter=1765350341&info=true&play=0&edgeCurvature=0\"\n",
        "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
        "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
        "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
@@ -220,7 +233,7 @@
        "        \n",
        "            <script>\n",
        "                try {\n",
-       "                  $(\"#f2b2456c-1d5c-45c6-b3f8-ca01642c3563\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                  $(\"#8eba87eb-46c1-4f8f-9f4b-7270073c5df0\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
        "                } catch (e) { console.error('exn catching scroll', e); }\n",
        "            </script>\n",
        "        "
@@ -229,7 +242,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -241,10 +254,194 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": "# Static render via Graphviz (auto-displays in notebook, returns bytes)\n# Falls back to layout if no x/y\ng.plot_static(format='svg')\n"
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:26.815906Z",
+     "iopub.status.busy": "2025-12-10T07:05:26.815736Z",
+     "iopub.status.idle": "2025-12-10T07:05:26.864235Z",
+     "shell.execute_reply": "2025-12-10T07:05:26.863433Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"1796pt\" height=\"404pt\" viewBox=\"0.00 0.00 1796.10 404.00\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 400)\">\n",
+       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-400 1792.1,-400 1792.1,4 -4,4\"/>\n",
+       "<!-- 1862294673469042014 -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>1862294673469042014</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"441.14\" cy=\"-234\" rx=\"141.07\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"441.14\" y=\"-230.3\" font-family=\"Times,serif\" font-size=\"14.00\">Hsbc Finance (Netherlands)</text>\n",
+       "</g>\n",
+       "<!-- 16634236373777089526 -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>16634236373777089526</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"163.14\" cy=\"-90\" rx=\"163.27\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"163.14\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">HSBC PROPERTY (UK) LIMITED</text>\n",
+       "</g>\n",
+       "<!-- 1862294673469042014&#45;&gt;16634236373777089526 -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>1862294673469042014-&gt;16634236373777089526</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M346.11,-220.67C289.78,-211.7 225.2,-197.98 203.14,-180 184.12,-164.51 173.82,-138.08 168.46,-118.03\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"171.84,-117.12 166.09,-108.22 165.03,-118.76 171.84,-117.12\"/>\n",
+       "</g>\n",
+       "<!-- 18011320449780894329 -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>18011320449780894329</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"441.14\" cy=\"-162\" rx=\"229.5\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"441.14\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">HSBC ALTERNATIVE INVESTMENTS LIMITED</text>\n",
+       "</g>\n",
+       "<!-- 1862294673469042014&#45;&gt;18011320449780894329 -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>1862294673469042014-&gt;18011320449780894329</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M441.14,-215.7C441.14,-207.98 441.14,-198.71 441.14,-190.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"444.64,-190.1 441.14,-180.1 437.64,-190.1 444.64,-190.1\"/>\n",
+       "</g>\n",
+       "<!-- 9134577322728469115 -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>9134577322728469115</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"897.14\" cy=\"-162\" rx=\"204.16\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"897.14\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">HSBC INVESTMENT COMPANY LIMITED</text>\n",
+       "</g>\n",
+       "<!-- 1862294673469042014&#45;&gt;9134577322728469115 -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>1862294673469042014-&gt;9134577322728469115</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M527.44,-219.75C601.97,-208.31 709.97,-191.73 790.01,-179.44\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"790.76,-182.87 800.12,-177.89 789.7,-175.95 790.76,-182.87\"/>\n",
+       "</g>\n",
+       "<!-- 9134577322728469115&#45;&gt;16634236373777089526 -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>9134577322728469115-&gt;16634236373777089526</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M762.27,-148.45C648.42,-137.82 481.09,-122.1 335.14,-108 320.44,-106.58 305.03,-105.08 289.73,-103.58\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"290,-100.09 279.71,-102.59 289.31,-107.06 290,-100.09\"/>\n",
+       "</g>\n",
+       "<!-- 1446072728533515665 -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>1446072728533515665</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"527.14\" cy=\"-90\" rx=\"183.5\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"527.14\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">HSBC IM PENSION TRUST LIMITED</text>\n",
+       "</g>\n",
+       "<!-- 9134577322728469115&#45;&gt;1446072728533515665 -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>9134577322728469115-&gt;1446072728533515665</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M815.02,-145.46C756.43,-134.38 677.66,-119.48 617.33,-108.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"617.9,-104.61 607.43,-106.19 616.6,-111.49 617.9,-104.61\"/>\n",
+       "</g>\n",
+       "<!-- 6904185395252167658 -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>6904185395252167658</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"901.14\" cy=\"-90\" rx=\"173\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"901.14\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">MERCANTILE COMPANY LIMITED</text>\n",
+       "</g>\n",
+       "<!-- 9134577322728469115&#45;&gt;6904185395252167658 -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>9134577322728469115-&gt;6904185395252167658</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M898.12,-143.7C898.57,-135.98 899.09,-126.71 899.59,-118.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"903.08,-118.28 900.16,-108.1 896.09,-117.88 903.08,-118.28\"/>\n",
+       "</g>\n",
+       "<!-- 13630126251685975826 -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>13630126251685975826</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1236.1\" cy=\"-90\" rx=\"144.5\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1236.1\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">Mp Payments Group Limited</text>\n",
+       "</g>\n",
+       "<!-- 9134577322728469115&#45;&gt;13630126251685975826 -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>9134577322728469115-&gt;13630126251685975826</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M973.21,-145.29C1027.2,-134.13 1099.7,-119.17 1154.9,-107.78\"/>\n",
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+       "</g>\n",
+       "<!-- 2173486047275631423 -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>2173486047275631423</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1593.1\" cy=\"-90\" rx=\"195\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1593.1\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">Hsbc Asia Pacific Holdings (Uk) Limited</text>\n",
+       "</g>\n",
+       "<!-- 9134577322728469115&#45;&gt;2173486047275631423 -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>9134577322728469115-&gt;2173486047275631423</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1026,-148.04C1148.4,-135.73 1331.9,-117.28 1456.7,-104.72\"/>\n",
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+       "</g>\n",
+       "<!-- 11514603667851101425 -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>11514603667851101425</title>\n",
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+       "<text text-anchor=\"middle\" x=\"882.14\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">MP PAYMENTS OPERATIONS LIMITED</text>\n",
+       "</g>\n",
+       "<!-- 13630126251685975826&#45;&gt;11514603667851101425 -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>13630126251685975826-&gt;11514603667851101425</title>\n",
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+       "<text text-anchor=\"middle\" x=\"1240.1\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">MP PAYMENTS UK LIMITED</text>\n",
+       "</g>\n",
+       "<!-- 13630126251685975826&#45;&gt;13417892994160273884 -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>13630126251685975826-&gt;13417892994160273884</title>\n",
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+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>18045747820524565803</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1593.1\" cy=\"-18\" rx=\"186\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1593.1\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">HSBC SECURITIES (JAPAN) LIMITED</text>\n",
+       "</g>\n",
+       "<!-- 2173486047275631423&#45;&gt;18045747820524565803 -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>2173486047275631423-&gt;18045747820524565803</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1593.1,-71.7C1593.1,-63.98 1593.1,-54.71 1593.1,-46.11\"/>\n",
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+       "</g>\n",
+       "<!-- 7622088245850069747 -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>7622088245850069747</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"441.14\" cy=\"-306\" rx=\"95.58\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"441.14\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">Hsbc Holdings Plc</text>\n",
+       "</g>\n",
+       "<!-- 7622088245850069747&#45;&gt;1862294673469042014 -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>7622088245850069747-&gt;1862294673469042014</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M441.14,-287.7C441.14,-279.98 441.14,-270.71 441.14,-262.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"444.64,-262.1 441.14,-252.1 437.64,-262.1 444.64,-262.1\"/>\n",
+       "</g>\n",
+       "<!-- 7622088245850069747&#45;unknown -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>7622088245850069747-unknown</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"441.14\" cy=\"-378\" rx=\"100.98\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"441.14\" y=\"-374.3\" font-family=\"Times,serif\" font-size=\"14.00\">Unknown person(s)</text>\n",
+       "</g>\n",
+       "<!-- 7622088245850069747&#45;unknown&#45;&gt;7622088245850069747 -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>7622088245850069747-unknown-&gt;7622088245850069747</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M441.14,-359.7C441.14,-351.98 441.14,-342.71 441.14,-334.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"444.64,-334.1 441.14,-324.1 437.64,-334.1 444.64,-334.1\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.SVG object>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Quick static render (see static_rendering.ipynb for full guide)\n",
+    "g.plot_static()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -257,11 +454,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:26.866694Z",
+     "iopub.status.busy": "2025-12-10T07:05:26.866466Z",
+     "iopub.status.idle": "2025-12-10T07:05:26.870811Z",
+     "shell.execute_reply": "2025-12-10T07:05:26.870116Z"
+    },
     "id": "_x28pxBUr7e_",
     "outputId": "dd209734-f4cf-425b-dc3c-7cec0d9fac74"
    },
@@ -288,7 +491,7 @@
        " 'unflatten']"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -300,12 +503,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 9,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 543
     },
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:26.872866Z",
+     "iopub.status.busy": "2025-12-10T07:05:26.872663Z",
+     "iopub.status.idle": "2025-12-10T07:05:29.411970Z",
+     "shell.execute_reply": "2025-12-10T07:05:29.411349Z"
+    },
     "id": "VD5ezMLss9Dw",
     "outputId": "553250c2-26a8-4820-f01d-fe138280bcf0"
    },
@@ -314,7 +523,7 @@
      "data": {
       "text/html": [
        "\n",
-       "            <iframe id=\"b58de138-e984-4e41-989f-67696ad45d10\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=098eb1c201d0491eb7aaa9bf49550c22&type=arrow&viztoken=d7066879-afc4-453a-beb6-db8521aea88b&usertag=3c690b8f-pygraphistry-0.34.5+12.g4dba3e6&splashAfter=1727948567&info=true&play=0&edgeCurvature=0\"\n",
+       "            <iframe id=\"d69eb07b-4d33-4b19-a1f7-92421b75fac2\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=2078555fb18f45988459786fe3b3fd7a&type=arrow&viztoken=bb1cfc47-ce1b-46dc-a9fc-78c0e218683f&usertag=faa52067-pygraphistry-0+unknown&splashAfter=1765350344&info=true&play=0&edgeCurvature=0\"\n",
        "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
        "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
        "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
@@ -324,7 +533,7 @@
        "        \n",
        "            <script>\n",
        "                try {\n",
-       "                  $(\"#b58de138-e984-4e41-989f-67696ad45d10\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                  $(\"#d69eb07b-4d33-4b19-a1f7-92421b75fac2\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
        "                } catch (e) { console.error('exn catching scroll', e); }\n",
        "            </script>\n",
        "        "
@@ -333,7 +542,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -345,8 +554,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:29.413349Z",
+     "iopub.status.busy": "2025-12-10T07:05:29.413227Z",
+     "iopub.status.idle": "2025-12-10T07:05:29.415619Z",
+     "shell.execute_reply": "2025-12-10T07:05:29.415015Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from graphistry.plugins_types.graphviz_types import PROGS"
@@ -365,12 +581,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 11,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 543
     },
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:29.417235Z",
+     "iopub.status.busy": "2025-12-10T07:05:29.417029Z",
+     "iopub.status.idle": "2025-12-10T07:05:31.833994Z",
+     "shell.execute_reply": "2025-12-10T07:05:31.833277Z"
+    },
     "id": "ACoYzOgCE7Pt",
     "outputId": "597b8129-dc9f-4f1a-e086-912e7206b103"
    },
@@ -379,7 +601,7 @@
      "data": {
       "text/html": [
        "\n",
-       "            <iframe id=\"1bbe0e35-ebbe-4efd-aed5-db0eeaa23f40\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=fcfc7d9df36447da9950e26bf2c01d92&type=arrow&viztoken=faf0e127-e6b3-4120-bf92-70c2b0e1abff&usertag=3c690b8f-pygraphistry-0.34.5+12.g4dba3e6&splashAfter=1727948588&info=true&play=0&edgeCurvature=0\"\n",
+       "            <iframe id=\"2900d5ef-a840-4ad0-811a-e105db57acc1\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=d61a5fa22eaa42668e5d96ed19f668c9&type=arrow&viztoken=add0d773-65b7-4751-bb81-b1431be44724&usertag=faa52067-pygraphistry-0+unknown&splashAfter=1765350346&info=true&play=0&edgeCurvature=0\"\n",
        "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
        "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
        "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
@@ -389,7 +611,7 @@
        "        \n",
        "            <script>\n",
        "                try {\n",
-       "                  $(\"#1bbe0e35-ebbe-4efd-aed5-db0eeaa23f40\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                  $(\"#2900d5ef-a840-4ad0-811a-e105db57acc1\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
        "                } catch (e) { console.error('exn catching scroll', e); }\n",
        "            </script>\n",
        "        "
@@ -398,7 +620,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -415,8 +637,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:31.836005Z",
+     "iopub.status.busy": "2025-12-10T07:05:31.835787Z",
+     "iopub.status.idle": "2025-12-10T07:05:31.840328Z",
+     "shell.execute_reply": "2025-12-10T07:05:31.839697Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -526,7 +755,7 @@
        " 'xdotversion']"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -539,8 +768,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:31.842309Z",
+     "iopub.status.busy": "2025-12-10T07:05:31.842100Z",
+     "iopub.status.idle": "2025-12-10T07:05:31.846488Z",
+     "shell.execute_reply": "2025-12-10T07:05:31.845762Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -613,7 +849,7 @@
        " 'xlp']"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -626,8 +862,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:31.848283Z",
+     "iopub.status.busy": "2025-12-10T07:05:31.848074Z",
+     "iopub.status.idle": "2025-12-10T07:05:31.852240Z",
+     "shell.execute_reply": "2025-12-10T07:05:31.851506Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -683,7 +926,7 @@
        " 'z']"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -700,108 +943,33 @@
     "id": "Fslt0bjvuyv0"
    },
    "source": [
-    "## Static image rendering and entity-level attributes\n",
+    "## Per-entity styling attributes\n",
     "\n",
-    "graphviz suports rendering to a static file in various image formats such as png.\n",
+    "You can add graphviz-specific columns to your node and edge dataframes that configure per-row render settings. These use the same names as in the above global attribute guidance, such as , , and .\n",
     "\n",
-    "You can add graphviz-specific columns to your node and edge dataframes that configure per-row render settings. These use the same names as in the above global attribute guidance, such as `color`, `shape`, and `label`.\n",
-    "\n",
-    "Adding a column for an attribute will typically disable the global attribute. For example, creating setting node column `\"shape\"`  with values `\"star\"` and `None`, and global node attribute `\"shape\"` with value value `\"box\"`. All the nodes with `shape == \"star\"` will render as a star in the static image, and the rows with value `None` will not default to the global node attribute `\"box\"`, but to graphviz's general default of an oval."
+    "Adding a column for an attribute will typically override the global attribute for that row."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 15,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 492
     },
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:31.854070Z",
+     "iopub.status.busy": "2025-12-10T07:05:31.853843Z",
+     "iopub.status.idle": "2025-12-10T07:05:31.859472Z",
+     "shell.execute_reply": "2025-12-10T07:05:31.858666Z"
+    },
     "id": "-3e_fH-80bhE",
     "outputId": "015cb98e-f1eb-4fbe-e29b-18a821b3b3d3"
    },
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1862294673469042014</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>7622088245850069747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>7622088245850069747-unknown</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>16634236373777089526</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>18011320449780894329</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>9134577322728469115</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1446072728533515665</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>6904185395252167658</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>13630126251685975826</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>11514603667851101425</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>13417892994160273884</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>2173486047275631423</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>18045747820524565803</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div><br><label><b>dtype:</b> object</label>"
-      ],
       "text/plain": [
        "0             1862294673469042014\n",
        "1             7622088245850069747\n",
@@ -819,7 +987,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -830,12 +998,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 16,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 717
     },
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:31.861313Z",
+     "iopub.status.busy": "2025-12-10T07:05:31.861103Z",
+     "iopub.status.idle": "2025-12-10T07:05:31.866963Z",
+     "shell.execute_reply": "2025-12-10T07:05:31.866261Z"
+    },
     "id": "newXWAjEzo5F",
     "outputId": "ff234aa4-5811-42ff-b289-d67e4e5c11b7"
    },
@@ -861,85 +1035,6 @@
     },
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div><br><label><b>dtype:</b> object</label>"
-      ],
       "text/plain": [
        "0     None\n",
        "1     None\n",
@@ -957,7 +1052,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -968,339 +1063,198 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 17,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 206
     },
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:31.868816Z",
+     "iopub.status.busy": "2025-12-10T07:05:31.868622Z",
+     "iopub.status.idle": "2025-12-10T07:05:31.922971Z",
+     "shell.execute_reply": "2025-12-10T07:05:31.922418Z"
+    },
     "id": "72YquJpavfLL",
     "outputId": "40739d8a-dbe2-4437-fcc7-1a45c92a5b73"
    },
    "outputs": [
     {
      "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "repr_error": "Out of range float values are not JSON compliant: nan",
-       "type": "dataframe"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-14c1d1e4-5980-44df-b82c-6496db751af7\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>9134577322728469115</td>\n",
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-       "      <td>162.0</td>\n",
-       "      <td>HSBC INVESTMENT COMPANY LIMITED</td>\n",
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-       "      <td>None</td>\n",
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-       "      <th>4</th>\n",
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-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
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+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>13417892994160273884</title>\n",
+       "<ellipse fill=\"none\" stroke=\"red\" cx=\"1240.1\" cy=\"-18\" rx=\"145.67\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1240.1\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">MP PAYMENTS UK LIMITED</text>\n",
+       "</g>\n",
+       "<!-- 13630126251685975826&#45;&gt;13417892994160273884 -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>13630126251685975826-&gt;13417892994160273884</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1237.1,-71.7C1237.6,-63.98 1238.1,-54.71 1238.6,-46.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1242.09,-46.3 1239.2,-36.1 1235.1,-45.88 1242.09,-46.3\"/>\n",
+       "</g>\n",
+       "<!-- 18045747820524565803 -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>18045747820524565803</title>\n",
+       "<ellipse fill=\"none\" stroke=\"red\" cx=\"1593.1\" cy=\"-18\" rx=\"186\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1593.1\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">HSBC SECURITIES (JAPAN) LIMITED</text>\n",
+       "</g>\n",
+       "<!-- 2173486047275631423&#45;&gt;18045747820524565803 -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>2173486047275631423-&gt;18045747820524565803</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1593.1,-71.7C1593.1,-63.98 1593.1,-54.71 1593.1,-46.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1596.6,-46.1 1593.1,-36.1 1589.6,-46.1 1596.6,-46.1\"/>\n",
+       "</g>\n",
+       "<!-- 7622088245850069747 -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>7622088245850069747</title>\n",
+       "<ellipse fill=\"none\" stroke=\"red\" cx=\"441.14\" cy=\"-306\" rx=\"95.58\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"441.14\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">Hsbc Holdings Plc</text>\n",
+       "</g>\n",
+       "<!-- 7622088245850069747&#45;&gt;1862294673469042014 -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>7622088245850069747-&gt;1862294673469042014</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M441.14,-287.7C441.14,-279.98 441.14,-270.71 441.14,-262.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"444.64,-262.1 441.14,-252.1 437.64,-262.1 444.64,-262.1\"/>\n",
+       "</g>\n",
+       "<!-- 7622088245850069747&#45;unknown -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>7622088245850069747-unknown</title>\n",
+       "<polygon fill=\"none\" stroke=\"blue\" points=\"544.14,-396 338.14,-396 338.14,-360 544.14,-360 544.14,-396\"/>\n",
+       "<text text-anchor=\"middle\" x=\"441.14\" y=\"-374.3\" font-family=\"Times,serif\" font-size=\"14.00\">ROOT: Unknown person(s)</text>\n",
+       "</g>\n",
+       "<!-- 7622088245850069747&#45;unknown&#45;&gt;7622088245850069747 -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>7622088245850069747-unknown-&gt;7622088245850069747</title>\n",
+       "<path fill=\"none\" stroke=\"blue\" d=\"M441.14,-359.7C441.14,-351.98 441.14,-342.71 441.14,-334.11\"/>\n",
+       "<polygon fill=\"blue\" stroke=\"blue\" points=\"444.64,-334.1 441.14,-324.1 437.64,-334.1 444.64,-334.1\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>"
       ],
       "text/plain": [
-       "                      n       x      y                                 label  \\\n",
-       "0   1862294673469042014  381.39  234.0            Hsbc Finance (Netherlands)   \n",
-       "1  16634236373777089526  140.39   90.0            HSBC PROPERTY (UK) LIMITED   \n",
-       "2  18011320449780894329  381.39  162.0  HSBC ALTERNATIVE INVESTMENTS LIMITED   \n",
-       "3   9134577322728469115  778.39  162.0       HSBC INVESTMENT COMPANY LIMITED   \n",
-       "4   1446072728533515665  454.39   90.0         HSBC IM PENSION TRUST LIMITED   \n",
-       "\n",
-       "   sz shape color  \n",
-       "0   1  None   red  \n",
-       "1   1  None   red  \n",
-       "2   1  None   red  \n",
-       "3   1  None   red  \n",
-       "4   1  None   red  "
+       "<IPython.core.display.SVG object>"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# row-level attrs\n",
+    "# Per-node styling with plot_static()\n",
     "\n",
     "root_id = '7622088245850069747-unknown'\n",
     "\n",
@@ -1312,58 +1266,27 @@
     "    color=g._edges[g._source].apply(lambda n: 'blue' if n == root_id else None)\n",
     "))\n",
     "\n",
-    "\n",
-    "# Save a static graphviz render\n",
-    "g2c_positioned = g2c.layout_graphviz(\n",
-    "    \"dot\",\n",
-    "    render_to_disk=True,\n",
-    "    path=f'./graph.png',\n",
-    "    graph_attr={},\n",
-    "    edge_attr={},\n",
-    "    node_attr={'color': 'green'},  # ignored due to g2c._nodes.color\n",
-    "    format='png'\n",
-    ")\n",
-    "\n",
-    "g2c_positioned._nodes.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 353
-    },
-    "id": "Orvnc-p4vwyd",
-    "outputId": "59f03155-128b-4d55-f216-58b16ce7f914"
-   },
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename='./graph.png')"
+    "# Static render with per-node styling\n",
+    "g2c.plot_static(\n",
+    "    prog='dot',\n",
+    "    node_attr={'color': 'green'}  # default, overridden by per-node color column\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 18,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 543
     },
+    "execution": {
+     "iopub.execute_input": "2025-12-10T07:05:31.924487Z",
+     "iopub.status.busy": "2025-12-10T07:05:31.924330Z",
+     "iopub.status.idle": "2025-12-10T07:05:34.596709Z",
+     "shell.execute_reply": "2025-12-10T07:05:34.596076Z"
+    },
     "id": "X_e8slJ4LO2z",
     "outputId": "c36ff578-dd8b-4626-8e52-7096bc5f0cfb"
    },
@@ -1372,7 +1295,7 @@
      "data": {
       "text/html": [
        "\n",
-       "            <iframe id=\"e87258bd-d7c1-46bc-a89e-dcceab6ad8b9\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=1c223004659a4398b08d69c61a16fc4e&type=arrow&viztoken=e1e7e77d-3884-4a1d-a7a9-00a5595e138d&usertag=3c690b8f-pygraphistry-0.34.5+12.g4dba3e6&splashAfter=1727949823&info=true&play=0&edgeCurvature=0\"\n",
+       "            <iframe id=\"9e2d11b9-28c9-4448-bb40-514029ca8b16\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=9d04379b94be4f638a2d72bf5ad0d995&type=arrow&viztoken=69165c9b-9f85-4e21-82e5-2f929ab7b037&usertag=faa52067-pygraphistry-0+unknown&splashAfter=1765350349&info=true&play=0&edgeCurvature=0\"\n",
        "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
        "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
        "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
@@ -1382,7 +1305,7 @@
        "        \n",
        "            <script>\n",
        "                try {\n",
-       "                  $(\"#e87258bd-d7c1-46bc-a89e-dcceab6ad8b9\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                  $(\"#9e2d11b9-28c9-4448-bb40-514029ca8b16\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
        "                } catch (e) { console.error('exn catching scroll', e); }\n",
        "            </script>\n",
        "        "
@@ -1391,7 +1314,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1409,6 +1332,17 @@
    },
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## See Also\n",
+    "\n",
+    "- **[static_rendering.ipynb](static_rendering.ipynb)** - Static image export (SVG, PNG) for docs, reports, CI pipelines\n",
+    "- **[Graphviz documentation](https://graphviz.org/documentation/)** - Full attribute reference\n",
+    "- **[Layout catalog](../../../docs/source/visualization/layout/catalog.rst)** - All available layout algorithms"
+   ]
   }
  ],
  "metadata": {
@@ -1429,7 +1363,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,
diff --git a/demos/demos_databases_apis/graphviz/plot_static_demo.ipynb b/demos/demos_databases_apis/graphviz/plot_static_demo.ipynb
deleted file mode 100644
index fad1e2ef87..0000000000
--- a/demos/demos_databases_apis/graphviz/plot_static_demo.ipynb
+++ /dev/null
@@ -1,178 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Static Graph Rendering with plot_static()\n",
-    "\n",
-    "This notebook demonstrates `plot_static()` for rendering graphs as static SVG/PNG images using Graphviz.\n",
-    "\n",
-    "**Key features:**\n",
-    "- Auto-displays inline in Jupyter notebooks\n",
-    "- Returns bytes for saving to disk\n",
-    "- No server registration required\n",
-    "- Supports SVG, PNG, DOT text, and Mermaid DSL output\n",
-    "\n",
-    "**Requirements:** `pip install graphistry[pygraphviz]` and graphviz system package"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import graphistry\n",
-    "\n",
-    "# Check if pygraphviz is available\n",
-    "try:\n",
-    "    import pygraphviz\n",
-    "    HAS_PYGRAPHVIZ = True\n",
-    "    print(f'pygraphviz version: {pygraphviz.__version__}')\n",
-    "except ImportError:\n",
-    "    HAS_PYGRAPHVIZ = False\n",
-    "    print('pygraphviz not installed - showing code examples only')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a simple graph\n",
-    "edges = pd.DataFrame({\n",
-    "    'src': ['a', 'a', 'b', 'c'],\n",
-    "    'dst': ['b', 'c', 'c', 'd']\n",
-    "})\n",
-    "\n",
-    "g = graphistry.edges(edges, 'src', 'dst')\n",
-    "print(f'Graph: {len(edges)} edges')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Basic SVG Rendering\n",
-    "\n",
-    "Call `plot_static()` to render and auto-display the graph:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if HAS_PYGRAPHVIZ:\n",
-    "    # Auto-displays inline, returns SVG bytes\n",
-    "    g.plot_static()\n",
-    "else:\n",
-    "    print('# Example code (requires pygraphviz):')\n",
-    "    print('g.plot_static()')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## With Node Attributes\n",
-    "\n",
-    "Add node data and use Graphviz styling:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nodes = pd.DataFrame({\n",
-    "    'id': ['a', 'b', 'c', 'd'],\n",
-    "    'label': ['Start', 'Middle 1', 'Middle 2', 'End'],\n",
-    "    'color': ['lightblue', 'lightgreen', 'lightgreen', 'lightyellow']\n",
-    "})\n",
-    "\n",
-    "g2 = g.nodes(nodes, 'id')\n",
-    "\n",
-    "if HAS_PYGRAPHVIZ:\n",
-    "    g2.plot_static(\n",
-    "        prog='dot',\n",
-    "        graph_attr={'rankdir': 'LR'},\n",
-    "        node_attr={'style': 'filled'}\n",
-    "    )\n",
-    "else:\n",
-    "    print('# Example code (requires pygraphviz):')\n",
-    "    print(\"g2.plot_static(prog='dot', graph_attr={'rankdir': 'LR'}, node_attr={'style': 'filled'})\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## DOT Text Output\n",
-    "\n",
-    "Get the DOT source for external tools:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if HAS_PYGRAPHVIZ:\n",
-    "    dot_text = g.plot_static(engine='graphviz-dot')\n",
-    "    print(dot_text)\n",
-    "else:\n",
-    "    print('# Example code (requires pygraphviz):')\n",
-    "    print(\"dot_text = g.plot_static(engine='graphviz-dot')\")\n",
-    "    print('# Returns DOT language string like:')\n",
-    "    print('# digraph { a -> b; a -> c; b -> c; c -> d; }')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Save to File\n",
-    "\n",
-    "Save the rendered output to disk:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if HAS_PYGRAPHVIZ:\n",
-    "    # Save SVG to file (also displays inline)\n",
-    "    svg_bytes = g2.plot_static(format='svg', path='/tmp/graph.svg')\n",
-    "    print(f'Saved {len(svg_bytes)} bytes to /tmp/graph.svg')\n",
-    "else:\n",
-    "    print('# Example code (requires pygraphviz):')\n",
-    "    print(\"svg_bytes = g2.plot_static(format='svg', path='/tmp/graph.svg')\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.10.0"
-  },
-  "nbsphinx": {
-   "execute": "always"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/demos/demos_databases_apis/graphviz/static_rendering.ipynb b/demos/demos_databases_apis/graphviz/static_rendering.ipynb
new file mode 100644
index 0000000000..a686f2cd10
--- /dev/null
+++ b/demos/demos_databases_apis/graphviz/static_rendering.ipynb
@@ -0,0 +1,612 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Static Graph Rendering with plot_static()\n",
+    "\n",
+    "Render graphs as static images (SVG, PNG) for documentation, reports, and presentations.\n",
+    "\n",
+    "**Key point**: `plot_static()` works with **any layout** - UMAP, ring, graphviz, or manual positions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-09T17:43:13.459156Z",
+     "iopub.status.busy": "2025-12-09T17:43:13.458870Z",
+     "iopub.status.idle": "2025-12-09T17:43:13.853371Z",
+     "shell.execute_reply": "2025-12-09T17:43:13.852661Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import graphistry\n",
+    "\n",
+    "# Sample graph\n",
+    "edges_df = pd.DataFrame({'src': ['a', 'a', 'b', 'c', 'd'], 'dst': ['b', 'c', 'c', 'd', 'e']})\n",
+    "nodes_df = pd.DataFrame({\n",
+    "    'id': ['a', 'b', 'c', 'd', 'e'],\n",
+    "    'type': ['start', 'middle', 'middle', 'middle', 'end'],\n",
+    "    'label': ['Start', 'Step 1', 'Step 2', 'Step 3', 'End']\n",
+    "})\n",
+    "g = graphistry.edges(edges_df, 'src', 'dst').nodes(nodes_df, 'id')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic Usage\n",
+    "\n",
+    "Auto-displays in Jupyter, returns SVG object (use `.data` for bytes):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-09T17:43:13.855434Z",
+     "iopub.status.busy": "2025-12-09T17:43:13.855200Z",
+     "iopub.status.idle": "2025-12-09T17:43:13.912749Z",
+     "shell.execute_reply": "2025-12-09T17:43:13.912046Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"124pt\" height=\"332pt\" viewBox=\"0.00 0.00 124.00 332.00\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 328)\">\n",
+       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-328 120,-328 120,4 -4,4\"/>\n",
+       "<!-- a -->\n",
+       "<g id=\"a\" class=\"node\">\n",
+       "<title>a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"75\" cy=\"-306\" rx=\"34.5\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"75\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">Start</text>\n",
+       "</g>\n",
+       "<!-- b -->\n",
+       "<g id=\"b\" class=\"node\">\n",
+       "<title>b</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"41\" cy=\"-234\" rx=\"41\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"41\" y=\"-230.3\" font-family=\"Times,serif\" font-size=\"14.00\">Step 1</text>\n",
+       "</g>\n",
+       "<!-- a&#45;&gt;b -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>a-&gt;b</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M66.94,-288.41C62.98,-280.25 58.11,-270.22 53.66,-261.07\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"56.75,-259.43 49.24,-251.96 50.46,-262.48 56.75,-259.43\"/>\n",
+       "</g>\n",
+       "<!-- c -->\n",
+       "<g id=\"c\" class=\"node\">\n",
+       "<title>c</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"75\" cy=\"-162\" rx=\"41\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"75\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">Step 2</text>\n",
+       "</g>\n",
+       "<!-- a&#45;&gt;c -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>a-&gt;c</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M81.49,-288.1C85.08,-277.84 89.17,-264.36 91,-252 93.34,-236.17 93.34,-231.83 91,-216 89.7,-207.21 87.26,-197.86 84.66,-189.5\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"87.95,-188.3 81.49,-179.9 81.3,-190.49 87.95,-188.3\"/>\n",
+       "</g>\n",
+       "<!-- b&#45;&gt;c -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>b-&gt;c</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M49.23,-216.05C53.2,-207.89 58.04,-197.91 62.46,-188.82\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"65.62,-190.31 66.85,-179.79 59.33,-187.25 65.62,-190.31\"/>\n",
+       "</g>\n",
+       "<!-- d -->\n",
+       "<g id=\"d\" class=\"node\">\n",
+       "<title>d</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"75\" cy=\"-90\" rx=\"41\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"75\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">Step 3</text>\n",
+       "</g>\n",
+       "<!-- c&#45;&gt;d -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>c-&gt;d</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M75,-143.7C75,-135.98 75,-126.71 75,-118.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"78.5,-118.1 75,-108.1 71.5,-118.1 78.5,-118.1\"/>\n",
+       "</g>\n",
+       "<!-- e -->\n",
+       "<g id=\"e\" class=\"node\">\n",
+       "<title>e</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"75\" cy=\"-18\" rx=\"29.5\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"75\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">End</text>\n",
+       "</g>\n",
+       "<!-- d&#45;&gt;e -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>d-&gt;e</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M75,-71.7C75,-63.98 75,-54.71 75,-46.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"78.5,-46.1 75,-36.1 71.5,-46.1 78.5,-46.1\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.SVG object>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.plot_static()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Styling with graph_attr, node_attr, edge_attr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-09T17:43:13.955672Z",
+     "iopub.status.busy": "2025-12-09T17:43:13.955471Z",
+     "iopub.status.idle": "2025-12-09T17:43:13.996735Z",
+     "shell.execute_reply": "2025-12-09T17:43:13.995926Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"449pt\" height=\"67pt\" viewBox=\"0.00 0.00 449.00 67.00\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 63)\">\n",
+       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-63 445,-63 445,4 -4,4\"/>\n",
+       "<!-- a -->\n",
+       "<g id=\"a\" class=\"node\">\n",
+       "<title>a</title>\n",
+       "<path fill=\"lightblue\" stroke=\"black\" d=\"M42,-36C42,-36 12,-36 12,-36 6,-36 0,-30 0,-24 0,-24 0,-12 0,-12 0,-6 6,0 12,0 12,0 42,0 42,0 48,0 54,-6 54,-12 54,-12 54,-24 54,-24 54,-30 48,-36 42,-36\"/>\n",
+       "<text text-anchor=\"middle\" x=\"27\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">Start</text>\n",
+       "</g>\n",
+       "<!-- b -->\n",
+       "<g id=\"b\" class=\"node\">\n",
+       "<title>b</title>\n",
+       "<path fill=\"lightblue\" stroke=\"black\" d=\"M141,-59C141,-59 102,-59 102,-59 96,-59 90,-53 90,-47 90,-47 90,-35 90,-35 90,-29 96,-23 102,-23 102,-23 141,-23 141,-23 147,-23 153,-29 153,-35 153,-35 153,-47 153,-47 153,-53 147,-59 141,-59\"/>\n",
+       "<text text-anchor=\"middle\" x=\"121.5\" y=\"-37.3\" font-family=\"Times,serif\" font-size=\"14.00\">Step 1</text>\n",
+       "</g>\n",
+       "<!-- a&#45;&gt;b -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>a-&gt;b</title>\n",
+       "<path fill=\"none\" stroke=\"gray\" d=\"M54.26,-24.53C62.33,-26.54 71.41,-28.79 80.2,-30.98\"/>\n",
+       "<polygon fill=\"gray\" stroke=\"gray\" points=\"79.37,-34.38 89.92,-33.4 81.06,-27.59 79.37,-34.38\"/>\n",
+       "</g>\n",
+       "<!-- c -->\n",
+       "<g id=\"c\" class=\"node\">\n",
+       "<title>c</title>\n",
+       "<path fill=\"lightblue\" stroke=\"black\" d=\"M240,-36C240,-36 201,-36 201,-36 195,-36 189,-30 189,-24 189,-24 189,-12 189,-12 189,-6 195,0 201,0 201,0 240,0 240,0 246,0 252,-6 252,-12 252,-12 252,-24 252,-24 252,-30 246,-36 240,-36\"/>\n",
+       "<text text-anchor=\"middle\" x=\"220.5\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">Step 2</text>\n",
+       "</g>\n",
+       "<!-- a&#45;&gt;c -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>a-&gt;c</title>\n",
+       "<path fill=\"none\" stroke=\"gray\" d=\"M54.23,-15.93C65.27,-15.16 78.25,-14.38 90,-14 117.99,-13.1 125.01,-13.16 153,-14 161.3,-14.25 170.18,-14.67 178.65,-15.15\"/>\n",
+       "<polygon fill=\"gray\" stroke=\"gray\" points=\"178.54,-18.65 188.73,-15.76 178.96,-11.67 178.54,-18.65\"/>\n",
+       "</g>\n",
+       "<!-- b&#45;&gt;c -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>b-&gt;c</title>\n",
+       "<path fill=\"none\" stroke=\"gray\" d=\"M153.19,-33.72C161.33,-31.79 170.24,-29.68 178.82,-27.64\"/>\n",
+       "<polygon fill=\"gray\" stroke=\"gray\" points=\"179.76,-31.02 188.68,-25.31 178.14,-24.21 179.76,-31.02\"/>\n",
+       "</g>\n",
+       "<!-- d -->\n",
+       "<g id=\"d\" class=\"node\">\n",
+       "<title>d</title>\n",
+       "<path fill=\"lightblue\" stroke=\"black\" d=\"M339,-36C339,-36 300,-36 300,-36 294,-36 288,-30 288,-24 288,-24 288,-12 288,-12 288,-6 294,0 300,0 300,0 339,0 339,0 345,0 351,-6 351,-12 351,-12 351,-24 351,-24 351,-30 345,-36 339,-36\"/>\n",
+       "<text text-anchor=\"middle\" x=\"319.5\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">Step 3</text>\n",
+       "</g>\n",
+       "<!-- c&#45;&gt;d -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>c-&gt;d</title>\n",
+       "<path fill=\"none\" stroke=\"gray\" d=\"M252.19,-18C260.24,-18 269.05,-18 277.54,-18\"/>\n",
+       "<polygon fill=\"gray\" stroke=\"gray\" points=\"277.68,-21.5 287.68,-18 277.68,-14.5 277.68,-21.5\"/>\n",
+       "</g>\n",
+       "<!-- e -->\n",
+       "<g id=\"e\" class=\"node\">\n",
+       "<title>e</title>\n",
+       "<path fill=\"lightblue\" stroke=\"black\" d=\"M429,-36C429,-36 399,-36 399,-36 393,-36 387,-30 387,-24 387,-24 387,-12 387,-12 387,-6 393,0 399,0 399,0 429,0 429,0 435,0 441,-6 441,-12 441,-12 441,-24 441,-24 441,-30 435,-36 429,-36\"/>\n",
+       "<text text-anchor=\"middle\" x=\"414\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">End</text>\n",
+       "</g>\n",
+       "<!-- d&#45;&gt;e -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>d-&gt;e</title>\n",
+       "<path fill=\"none\" stroke=\"gray\" d=\"M351.04,-18C359.27,-18 368.26,-18 376.77,-18\"/>\n",
+       "<polygon fill=\"gray\" stroke=\"gray\" points=\"376.83,-21.5 386.83,-18 376.83,-14.5 376.83,-21.5\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.SVG object>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.plot_static(\n",
+    "    graph_attr={'rankdir': 'LR', 'bgcolor': 'white'},\n",
+    "    node_attr={'shape': 'box', 'style': 'rounded,filled', 'fillcolor': 'lightblue'},\n",
+    "    edge_attr={'color': 'gray'}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Per-Node Styling (Data-Driven)\n",
+    "\n",
+    "Add graphviz attribute columns to your dataframe:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-09T17:43:13.998999Z",
+     "iopub.status.busy": "2025-12-09T17:43:13.998719Z",
+     "iopub.status.idle": "2025-12-09T17:43:14.045600Z",
+     "shell.execute_reply": "2025-12-09T17:43:14.044741Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"449pt\" height=\"67pt\" viewBox=\"0.00 0.00 449.00 67.00\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 63)\">\n",
+       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-63 445,-63 445,4 -4,4\"/>\n",
+       "<!-- a -->\n",
+       "<g id=\"a\" class=\"node\">\n",
+       "<title>a</title>\n",
+       "<polygon fill=\"lightgreen\" stroke=\"black\" points=\"54,-36 0,-36 0,0 54,0 54,-36\"/>\n",
+       "<text text-anchor=\"middle\" x=\"27\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">Start</text>\n",
+       "</g>\n",
+       "<!-- b -->\n",
+       "<g id=\"b\" class=\"node\">\n",
+       "<title>b</title>\n",
+       "<polygon fill=\"lightblue\" stroke=\"black\" points=\"153,-59 90,-59 90,-23 153,-23 153,-59\"/>\n",
+       "<text text-anchor=\"middle\" x=\"121.5\" y=\"-37.3\" font-family=\"Times,serif\" font-size=\"14.00\">Step 1</text>\n",
+       "</g>\n",
+       "<!-- a&#45;&gt;b -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>a-&gt;b</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M54.26,-24.53C62.33,-26.54 71.41,-28.79 80.2,-30.98\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"79.37,-34.38 89.92,-33.4 81.06,-27.59 79.37,-34.38\"/>\n",
+       "</g>\n",
+       "<!-- c -->\n",
+       "<g id=\"c\" class=\"node\">\n",
+       "<title>c</title>\n",
+       "<polygon fill=\"lightblue\" stroke=\"black\" points=\"252,-36 189,-36 189,0 252,0 252,-36\"/>\n",
+       "<text text-anchor=\"middle\" x=\"220.5\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">Step 2</text>\n",
+       "</g>\n",
+       "<!-- a&#45;&gt;c -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>a-&gt;c</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M54.23,-15.93C65.27,-15.16 78.25,-14.38 90,-14 117.99,-13.1 125.01,-13.16 153,-14 161.3,-14.25 170.18,-14.67 178.65,-15.15\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"178.54,-18.65 188.73,-15.76 178.96,-11.67 178.54,-18.65\"/>\n",
+       "</g>\n",
+       "<!-- b&#45;&gt;c -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>b-&gt;c</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M153.19,-33.72C161.33,-31.79 170.24,-29.68 178.82,-27.64\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"179.76,-31.02 188.68,-25.31 178.14,-24.21 179.76,-31.02\"/>\n",
+       "</g>\n",
+       "<!-- d -->\n",
+       "<g id=\"d\" class=\"node\">\n",
+       "<title>d</title>\n",
+       "<polygon fill=\"lightblue\" stroke=\"black\" points=\"351,-36 288,-36 288,0 351,0 351,-36\"/>\n",
+       "<text text-anchor=\"middle\" x=\"319.5\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">Step 3</text>\n",
+       "</g>\n",
+       "<!-- c&#45;&gt;d -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>c-&gt;d</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M252.19,-18C260.24,-18 269.05,-18 277.54,-18\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"277.68,-21.5 287.68,-18 277.68,-14.5 277.68,-21.5\"/>\n",
+       "</g>\n",
+       "<!-- e -->\n",
+       "<g id=\"e\" class=\"node\">\n",
+       "<title>e</title>\n",
+       "<polygon fill=\"lightyellow\" stroke=\"black\" points=\"441,-36 387,-36 387,0 441,0 441,-36\"/>\n",
+       "<text text-anchor=\"middle\" x=\"414\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">End</text>\n",
+       "</g>\n",
+       "<!-- d&#45;&gt;e -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>d-&gt;e</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M351.04,-18C359.27,-18 368.26,-18 376.77,-18\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"376.83,-21.5 386.83,-18 376.83,-14.5 376.83,-21.5\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.SVG object>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nodes_styled = nodes_df.copy()\n",
+    "nodes_styled['fillcolor'] = nodes_styled['type'].map({\n",
+    "    'start': 'lightgreen', 'middle': 'lightblue', 'end': 'lightyellow'\n",
+    "})\n",
+    "nodes_styled['shape'] = 'box'\n",
+    "\n",
+    "g_styled = graphistry.edges(edges_df, 'src', 'dst').nodes(nodes_styled, 'id')\n",
+    "g_styled.plot_static(graph_attr={'rankdir': 'LR'}, node_attr={'style': 'filled'})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Layout Programs\n",
+    "\n",
+    "| Program | Best For |\n",
+    "|---------|----------|\n",
+    "| `dot` | Hierarchies, DAGs |\n",
+    "| `neato` | Small undirected graphs |\n",
+    "| `circo` | Circular layouts |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-09T17:43:14.048381Z",
+     "iopub.status.busy": "2025-12-09T17:43:14.048095Z",
+     "iopub.status.idle": "2025-12-09T17:43:14.098219Z",
+     "shell.execute_reply": "2025-12-09T17:43:14.097222Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"444pt\" height=\"171pt\" viewBox=\"0.00 0.00 443.60 171.27\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 167.27)\">\n",
+       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-167.27 439.6,-167.27 439.6,4 -4,4\"/>\n",
+       "<!-- a -->\n",
+       "<g id=\"a\" class=\"node\">\n",
+       "<title>a</title>\n",
+       "<ellipse fill=\"lightblue\" stroke=\"black\" cx=\"41\" cy=\"-18\" rx=\"34.5\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"41\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">Start</text>\n",
+       "</g>\n",
+       "<!-- b -->\n",
+       "<g id=\"b\" class=\"node\">\n",
+       "<title>b</title>\n",
+       "<ellipse fill=\"lightblue\" stroke=\"black\" cx=\"41\" cy=\"-145.27\" rx=\"41\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"41\" y=\"-141.57\" font-family=\"Times,serif\" font-size=\"14.00\">Step 1</text>\n",
+       "</g>\n",
+       "<!-- a&#45;&gt;b -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>a-&gt;b</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M41,-36.23C41,-57.31 41,-92.25 41,-116.88\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"37.5,-117.07 41,-127.07 44.5,-117.07 37.5,-117.07\"/>\n",
+       "</g>\n",
+       "<!-- c -->\n",
+       "<g id=\"c\" class=\"node\">\n",
+       "<title>c</title>\n",
+       "<ellipse fill=\"lightblue\" stroke=\"black\" cx=\"151.21\" cy=\"-81.63\" rx=\"41\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"151.21\" y=\"-77.93\" font-family=\"Times,serif\" font-size=\"14.00\">Step 2</text>\n",
+       "</g>\n",
+       "<!-- a&#45;&gt;c -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>a-&gt;c</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M64.38,-31.5C79.87,-40.44 100.44,-52.32 117.68,-62.27\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"115.93,-65.3 126.34,-67.27 119.44,-59.24 115.93,-65.3\"/>\n",
+       "</g>\n",
+       "<!-- b&#45;&gt;c -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>b-&gt;c</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M66.01,-130.83C81.23,-122.04 100.86,-110.7 117.45,-101.13\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"119.4,-104.05 126.3,-96.02 115.89,-97.99 119.4,-104.05\"/>\n",
+       "</g>\n",
+       "<!-- d -->\n",
+       "<g id=\"d\" class=\"node\">\n",
+       "<title>d</title>\n",
+       "<ellipse fill=\"lightblue\" stroke=\"black\" cx=\"264.16\" cy=\"-81.63\" rx=\"41\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"264.16\" y=\"-77.93\" font-family=\"Times,serif\" font-size=\"14.00\">Step 3</text>\n",
+       "</g>\n",
+       "<!-- c&#45;&gt;d -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>c-&gt;d</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M192.31,-81.63C198.85,-81.63 205.69,-81.63 212.43,-81.63\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"212.91,-85.13 222.91,-81.63 212.91,-78.13 212.91,-85.13\"/>\n",
+       "</g>\n",
+       "<!-- e -->\n",
+       "<g id=\"e\" class=\"node\">\n",
+       "<title>e</title>\n",
+       "<ellipse fill=\"lightblue\" stroke=\"black\" cx=\"406.35\" cy=\"-81.63\" rx=\"29.5\" ry=\"18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"406.35\" y=\"-77.93\" font-family=\"Times,serif\" font-size=\"14.00\">End</text>\n",
+       "</g>\n",
+       "<!-- d&#45;&gt;e -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>d-&gt;e</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M305.29,-81.63C324.67,-81.63 347.68,-81.63 366.85,-81.63\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"367.05,-85.13 377.05,-81.63 367.05,-78.13 367.05,-85.13\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.SVG object>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.plot_static(prog='circo', node_attr={'style': 'filled', 'fillcolor': 'lightblue'})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Output Formats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-09T17:43:14.100579Z",
+     "iopub.status.busy": "2025-12-09T17:43:14.100340Z",
+     "iopub.status.idle": "2025-12-09T17:43:14.126041Z",
+     "shell.execute_reply": "2025-12-09T17:43:14.125158Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "digraph \"\" {\n",
+      "\tgraph [bb=\"0,0,115.89,324\"];\n",
+      "\tnode [label=\"\\N\"];\n",
+      "\ta\t[height=0.5,\n",
+      "\t\tid=a,\n",
+      "\t\tlabel=Start,\n",
+      "\t\tpos=\"74.946,306\",\n",
+      "\t\twidth=0.95686];\n",
+      "\tb\t[height=0.5,\n",
+      "\t\tid=b,\n",
+      "\t\tlabel=\"Step 1\",\n",
+      "\t\tpos=\"40.946,234\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "# DOT source text\n",
+    "dot = g.plot_static(engine='graphviz-dot')\n",
+    "print(dot[:200])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-09T17:43:14.128298Z",
+     "iopub.status.busy": "2025-12-09T17:43:14.128027Z",
+     "iopub.status.idle": "2025-12-09T17:43:14.132035Z",
+     "shell.execute_reply": "2025-12-09T17:43:14.131293Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "graph LR\n",
+      "    a --> b\n",
+      "    a --> c\n",
+      "    b --> c\n",
+      "    c --> d\n",
+      "    d --> e\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Mermaid DSL\n",
+    "mermaid = g.plot_static(engine='mermaid-code')\n",
+    "print(mermaid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Finding More Options\n",
+    "\n",
+    "Discover available attributes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-09T17:43:14.134365Z",
+     "iopub.status.busy": "2025-12-09T17:43:14.134106Z",
+     "iopub.status.idle": "2025-12-09T17:43:14.138073Z",
+     "shell.execute_reply": "2025-12-09T17:43:14.137309Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Progs: ['acyclic', 'ccomps', 'circo', 'dot', 'fdp', 'gc']\n",
+      "Node attrs (sample): ['area', 'class', 'color', 'colorscheme', 'comment', 'distortion', 'fillcolor', 'fixedsize']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from graphistry.plugins_types.graphviz_types import GRAPH_ATTRS, NODE_ATTRS, EDGE_ATTRS, PROGS\n",
+    "print('Progs:', PROGS[:6])\n",
+    "print('Node attrs (sample):', NODE_ATTRS[:8])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**External references**:\n",
+    "- [Graphviz Attributes](https://graphviz.org/doc/info/attrs.html)\n",
+    "- [Graphviz Shapes](https://graphviz.org/doc/info/shapes.html)\n",
+    "- [Graphviz Colors](https://graphviz.org/doc/info/colors.html)\n",
+    "\n",
+    "## See Also\n",
+    "\n",
+    "- [graphviz.ipynb](graphviz.ipynb) - Layout algorithms for interactive Graphistry viz"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/source/10min.rst b/docs/source/10min.rst
index 97317b1c44..48759e970c 100644
--- a/docs/source/10min.rst
+++ b/docs/source/10min.rst
@@ -202,8 +202,36 @@ For example, GraphViz layouts is known for its high quality for laying out small
 .. code-block:: python
 
     # pygraphistry handles format conversions behind-the-scenes
-    g2 = g1.layout_graphviz('dot')
-    g2.plot()
+   g2 = g1.layout_graphviz('dot')
+   g2.plot()
+
+Static Image Export
+-------------------
+
+For documentation, reports, or non-interactive use cases, export to static images with ``plot_static()``:
+
+.. code-block:: python
+
+   # Auto-displays inline in Jupyter, returns SVG object (.data for bytes)
+   g.plot_static()
+
+   # Save to file
+   g.plot_static(format='png', path='graph.png')
+
+   # With styling
+   g.plot_static(
+       graph_attr={'rankdir': 'LR', 'bgcolor': 'white'},
+       node_attr={'style': 'filled', 'fillcolor': 'lightblue'}
+   )
+
+Works with any layout source (UMAP, ring, graphviz, manual x/y). For DOT or Mermaid text output:
+
+.. code-block:: python
+
+   dot_text = g.plot_static(engine='graphviz-dot')
+   mermaid_text = g.plot_static(engine='mermaid-code')
+
+See the `static rendering tutorial <../demos/demos_databases_apis/graphviz/static_rendering.ipynb>`_ for styling options and complete examples.
 
 Using UMAP for Dimensionality Reduction
 ---------------------------------------
@@ -304,4 +332,4 @@ External Resources
 - `Graphistry REST API <https://hub.graphistry.com/docs/api/>`_: Work from any language
 - `Graphistry URL settings <https://hub.graphistry.com/docs/api/1/rest/url/#urloptions>`_: Control visualizations via URL parameters`
 
-Happy graphing!
\ No newline at end of file
+Happy graphing!
diff --git a/docs/source/api/plugins/compute/graphviz.rst b/docs/source/api/plugins/compute/graphviz.rst
index 8800eaa631..f5cb1a9e46 100644
--- a/docs/source/api/plugins/compute/graphviz.rst
+++ b/docs/source/api/plugins/compute/graphviz.rst
@@ -8,7 +8,7 @@ graphviz is a popular graph visualization library that PyGraphistry can interfac
 
 For static outputs in notebooks or docs, you can either call :py:meth:`graphistry.Plottable.plot_static` (preferred, auto-reuses x/y when present) or :py:meth:`graphistry.plugins.graphviz.render_graphviz` for lower-level control.
 
-**Auto-display**: When called in a Jupyter notebook, ``plot_static`` automatically displays the rendered output inline. It still returns the bytes/text for programmatic use.
+**Auto-display**: When called in a Jupyter notebook, ``plot_static`` automatically displays the rendered output inline. It returns an SVG or Image object (use ``.data`` for raw bytes).
 
 .. code-block:: python
 
@@ -16,7 +16,7 @@ For static outputs in notebooks or docs, you can either call :py:meth:`graphistr
    g.plot_static()
 
    # Save to file while also displaying
-   svg_bytes = g.plot_static(format='svg', path='graph.svg')
+   svg_obj = g.plot_static(path='graph.svg')  # saves file AND returns SVG object
 
 ``plot_static`` engines:
 
@@ -24,6 +24,20 @@ For static outputs in notebooks or docs, you can either call :py:meth:`graphistr
 - ``graphviz-dot`` (DOT text, optional path)
 - ``mermaid-code`` (Mermaid DSL text, optional path)
 
+**Styling**: Use ``graph_attr``, ``node_attr``, and ``edge_attr`` dictionaries:
+
+.. code-block:: python
+
+   g.plot_static(
+       graph_attr={'rankdir': 'LR', 'bgcolor': 'white'},
+       node_attr={'style': 'filled', 'fillcolor': 'lightblue'},
+       edge_attr={'color': 'gray'}
+   )
+
+``plot_static()`` works with any layout source—it will use existing x/y positions if available (``reuse_layout=True``), or compute layout via graphviz if not.
+
+See the `static rendering tutorial <../../../demos/demos_databases_apis/graphviz/static_rendering.ipynb>`_ for complete examples.
+
 .. automodule:: graphistry.plugins.graphviz
    :members:
    :undoc-members:
diff --git a/docs/source/conf.py b/docs/source/conf.py
index a516c066b9..40e6c6d9bf 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -773,7 +773,7 @@ def remove_external_images_for_latex(app, doctree, fromdocname):
 
 def assert_external_images_removed(app, doctree, fromdocname):
     """Assert that external images have been removed."""
-    if app.builder.name in ['html']:  # Extend to all builds if needed
+    if app.builder.name in ['html', 'singlehtml']:  # HTML builders can render external images
         return
 
     for node in doctree.traverse(nodes.image):
diff --git a/docs/source/ecosystem.rst b/docs/source/ecosystem.rst
index 53d9bcb4a8..e42abaf280 100644
--- a/docs/source/ecosystem.rst
+++ b/docs/source/ecosystem.rst
@@ -3,6 +3,17 @@ Graphistry Ecosystem and Louie.AI
 
 The Graphistry community of projects, open source, and partners has grown over the years:
 
+.. graphviz::
+
+   digraph graphistry_ecosystem_toy {
+       rankdir=LR;
+       data [label="Dataframes", shape=box, style=filled, fillcolor=lightgray];
+       gfql [label="GFQL"];
+       viz [label="Graphistry Viz"];
+       ai [label="Louie.AI"];
+       data -> gfql -> viz -> ai;
+   }
+
 Graphistry Core
 ---------------
 
@@ -78,4 +89,3 @@ Graphistry works with a variety of partners and projects, some of which include:
 * `Pandas <https://pandas.pydata.org/>`_
 * `Dask <https://www.dask.org/>`_
 
-
diff --git a/docs/source/gfql/about.rst b/docs/source/gfql/about.rst
index 4a924becca..cc92eccb06 100644
--- a/docs/source/gfql/about.rst
+++ b/docs/source/gfql/about.rst
@@ -27,30 +27,60 @@ GFQL fills a critical gap in the data community by providing an in-process, high
 - **Ease of Use:** No need for external databases or new infrastructure.
 - **Interoperability:** Integrates with the Python data science ecosystem, including PyGraphistry for visualization.
 
-Static Graphviz example
------------------------
+Sample Dataset
+--------------
 
-The snippet below renders a tiny GFQL-style toy graph with Graphviz (``dot``) during docs build:
+Throughout this guide, we'll work with a graph representing people, companies, and transactions with risk indicators:
 
 .. graphviz::
 
-   digraph gfql_toy {
+   digraph sample_graph {
        rankdir=LR;
-       a -> b1 -> c1 -> d1 -> e1;
-       a -> b2 -> c2;
-       a [shape=box, style=filled, fillcolor=lightblue];
+       node [shape=ellipse];
+
+       a [label="a\nperson", style="filled,bold", fillcolor="#87CEEB", penwidth=2, color="#4682B4"];
+       b [label="b\nperson", style="filled,bold", fillcolor="#87CEEB", penwidth=2, color="#4682B4"];
+       c [label="c\ncompany", shape=box, style="filled,bold", fillcolor="#FFFACD", penwidth=2, color="#DAA520"];
+       tx1 [label="tx1\ntransaction\nrisk1=True", shape=diamond, style="filled,bold", fillcolor="#FFB6C1", penwidth=2, color="#DC143C"];
+       tx2 [label="tx2\ntransaction\nrisk2=True", shape=diamond, style="filled,bold", fillcolor="#FFB6C1", penwidth=2, color="#DC143C"];
+
+       a -> b [label="knows\ninteresting", penwidth=2];
+       b -> c [label="works_at\ninteresting", penwidth=2];
+       a -> tx1 [label="sent", penwidth=2];
+       tx1 -> tx2 [label="transfer", penwidth=2];
+       tx2 -> c [label="received", penwidth=2];
    }
 
+::
+
+    import pandas as pd
+    import graphistry
+
+    nodes_df = pd.DataFrame({
+        'id': ['a', 'b', 'c', 'tx1', 'tx2'],
+        'type': ['person', 'person', 'company', 'transaction', 'transaction'],
+        'risk1': [False, False, False, True, False],
+        'risk2': [False, False, False, False, True],
+    })
+    edges_df = pd.DataFrame({
+        'src': ['a', 'b', 'a', 'tx1', 'tx2'],
+        'dst': ['b', 'c', 'tx1', 'tx2', 'c'],
+        'e_type': ['knows', 'works_at', 'sent', 'transfer', 'received'],
+        'interesting': [True, True, False, False, False],
+    })
+
+    g = graphistry.edges(edges_df, 'src', 'dst').nodes(nodes_df, 'id')
+
 Setting Up GFQL
 ---------------
 
-GFQL is part of the open-source `graphistry` library. Install it using pip:
+GFQL is part of the open-source ``graphistry`` library. Install it using pip:
 
 ::
 
     pip install graphistry
 
-Ensure you have `pandas` or `cudf` installed, depending on whether you want to run on CPU or GPU.
+Ensure you have ``pandas`` or ``cudf`` installed, depending on whether you want to run on CPU or GPU.
 
 Basic Concepts
 --------------
@@ -67,7 +97,7 @@ Examples
 1. Find Nodes of a Certain Type
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-You can filter nodes based on their properties using the `n()` function.
+You can filter nodes based on their properties using the ``n()`` function.
 
 **Example: Find all nodes of type "person"**
 
@@ -76,18 +106,37 @@ You can filter nodes based on their properties using the `n()` function.
     from graphistry import n
 
     people_nodes_df = g.gfql([ n({"type": "person"}) ])._nodes
-    print('Number of person nodes:', len(people_nodes_df))
+    # people_nodes_df: DataFrame with 'a' and 'b' (the person nodes)
 
 **Explanation:**
 
-- `n({"type": "person"})` filters nodes where the `type` property is `"person"`.
-- `g.gfql([...])` applies the chain of operations to the graph `g`.
-- `._nodes` retrieves the resulting nodes dataframe.
+- ``n({"type": "person"})`` filters nodes where the ``type`` property is ``"person"``.
+- ``g.gfql([...])`` applies the chain of operations to the graph ``g``.
+- ``._nodes`` retrieves the resulting nodes dataframe.
+
+.. graphviz::
+
+   digraph filter_nodes {
+       rankdir=LR;
+       node [shape=ellipse];
+
+       a [label="a\nperson", style="filled,bold", fillcolor="#90EE90", penwidth=3, color="#228B22"];
+       b [label="b\nperson", style="filled,bold", fillcolor="#90EE90", penwidth=3, color="#228B22"];
+       c [label="c\ncompany", shape=box, style=filled, fillcolor="#D3D3D3", color="#A9A9A9", fontcolor="#696969"];
+       tx1 [label="tx1\ntransaction", shape=diamond, style=filled, fillcolor="#D3D3D3", color="#A9A9A9", fontcolor="#696969"];
+       tx2 [label="tx2\ntransaction", shape=diamond, style=filled, fillcolor="#D3D3D3", color="#A9A9A9", fontcolor="#696969"];
+
+       a -> b [color="#A9A9A9"];
+       b -> c [color="#A9A9A9"];
+       a -> tx1 [color="#A9A9A9"];
+       tx1 -> tx2 [color="#A9A9A9"];
+       tx2 -> c [color="#A9A9A9"];
+   }
 
 2. Find 2-Hop Edge Sequences with an Attribute
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Traverse multiple hops and filter edges based on attributes using `e_forward()`.
+Traverse multiple hops and filter edges based on attributes using ``e_forward()``.
 
 **Example: Find 2-hop paths where edges are marked as "interesting"**
 
@@ -96,13 +145,32 @@ Traverse multiple hops and filter edges based on attributes using `e_forward()`.
     from graphistry import e_forward
 
     g_2_hops = g.gfql([ e_forward({"interesting": True}, hops=2) ])
-    print('Number of edges in 2-hop paths:', len(g_2_hops._edges))
+    # g_2_hops._edges: edges a->b->c (both marked interesting)
     g_2_hops.plot()
 
 **Explanation:**
 
-- `e_forward({"interesting": True}, hops=2)` traverses forward edges with `interesting == True` for 2 hops.
-- `g_2_hops.plot()` visualizes the resulting subgraph.
+- ``e_forward({"interesting": True}, hops=2)`` traverses forward edges with ``interesting == True`` for 2 hops.
+- ``g_2_hops.plot()`` visualizes the resulting subgraph.
+
+.. graphviz::
+
+   digraph two_hop {
+       rankdir=LR;
+       node [shape=ellipse];
+
+       a [label="a\nperson", style="filled,bold", fillcolor="#90EE90", penwidth=3, color="#228B22"];
+       b [label="b\nperson", style="filled,bold", fillcolor="#90EE90", penwidth=3, color="#228B22"];
+       c [label="c\ncompany", shape=box, style="filled,bold", fillcolor="#90EE90", penwidth=3, color="#228B22"];
+       tx1 [label="tx1\ntransaction", shape=diamond, style=filled, fillcolor="#D3D3D3", color="#A9A9A9", fontcolor="#696969"];
+       tx2 [label="tx2\ntransaction", shape=diamond, style=filled, fillcolor="#D3D3D3", color="#A9A9A9", fontcolor="#696969"];
+
+       a -> b [label="interesting", style=bold, color="#228B22", penwidth=2];
+       b -> c [label="interesting", style=bold, color="#228B22", penwidth=2];
+       a -> tx1 [color="#A9A9A9"];
+       tx1 -> tx2 [color="#A9A9A9"];
+       tx2 -> c [color="#A9A9A9"];
+   }
 
 3. Find Nodes 1-2 Hops Away and Label Each Hop
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -121,15 +189,34 @@ Label hops in your traversal to analyze specific relationships.
         e_undirected(name="hop2")
     ])
     first_hop_edges = g_2_hops._edges[ g_2_hops._edges.hop1 == True ]
-    print('Number of first-hop edges:', len(first_hop_edges))
+    # first_hop_edges: edges directly connected to 'a' (hop1=True)
 
 **Explanation:**
 
-- `n({g._node: "a"})` starts the traversal from node `"a"` where `g._node` is the identifying column name.
-- `e_undirected(name="hop1")` traverses undirected edges and labels them as `hop1`.
-- `e_undirected(name="hop2")` continues traversal and labels edges as `hop2`.
+- ``n({g._node: "a"})`` starts the traversal from node ``"a"`` where ``g._node`` is the identifying column name.
+- ``e_undirected(name="hop1")`` traverses undirected edges and labels them as ``hop1``.
+- ``e_undirected(name="hop2")`` continues traversal and labels edges as ``hop2``.
 - The labels allow you to filter and analyze edges from specific hops.
 
+.. graphviz::
+
+   digraph labeled_hops {
+       rankdir=LR;
+       node [shape=ellipse];
+
+       a [label="a (start)", style="filled,bold", fillcolor="#87CEEB", penwidth=3, color="#4682B4"];
+       b [label="b\nhop1", style="filled,bold", fillcolor="#90EE90", penwidth=3, color="#228B22"];
+       c [label="c\nhop2", shape=box, style="filled,bold", fillcolor="#90EE90", penwidth=3, color="#228B22"];
+       tx1 [label="tx1\nhop1", shape=diamond, style="filled,bold", fillcolor="#90EE90", penwidth=3, color="#228B22"];
+       tx2 [label="tx2\nhop2", shape=diamond, style=filled, fillcolor="#D3D3D3", color="#A9A9A9", fontcolor="#696969"];
+
+       a -> b [label="hop1", style=bold, color="#228B22", penwidth=2];
+       b -> c [label="hop2", style=bold, color="#228B22", penwidth=2];
+       a -> tx1 [label="hop1", style=bold, color="#228B22", penwidth=2];
+       tx1 -> tx2 [label="hop2", color="#A9A9A9"];
+       tx2 -> c [color="#A9A9A9"];
+   }
+
 4. Query for Transaction Nodes Between Risky Nodes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -149,19 +236,38 @@ Chain multiple traversals to find patterns between nodes.
         n({"risk2": True})
     ])
     hits = g_risky._nodes[ g_risky._nodes["hit"] == True ]
-    print('Number of transaction hits:', len(hits))
+    # hits: transaction nodes reachable from risk1 nodes and reaching risk2 nodes
 
 **Explanation:**
 
-- Starts from nodes with `risk1 == True`.
-- Traverses forward to transaction nodes, labeling them as `hit`.
-- Traverses backward to nodes with `risk2 == True`.
+- Starts from nodes with ``risk1 == True``.
+- Traverses forward to transaction nodes, labeling them as ``hit``.
+- Traverses backward to nodes with ``risk2 == True``.
 - Identifies transaction nodes connected between two risky nodes.
 
-5. Filter by Multiple Node Types Using `is_in`
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. graphviz::
+
+   digraph risk_pattern {
+       rankdir=LR;
+       node [shape=ellipse];
+
+       a [label="a\nperson", style=filled, fillcolor="#D3D3D3", color="#A9A9A9", fontcolor="#696969"];
+       b [label="b\nperson", style=filled, fillcolor="#D3D3D3", color="#A9A9A9", fontcolor="#696969"];
+       c [label="c\ncompany", shape=box, style=filled, fillcolor="#D3D3D3", color="#A9A9A9", fontcolor="#696969"];
+       tx1 [label="tx1\nrisk1=True\n(start)", shape=diamond, style="filled,bold", fillcolor="#FFB6C1", penwidth=3, color="#DC143C"];
+       tx2 [label="tx2\nrisk2=True\n(end)", shape=diamond, style="filled,bold", fillcolor="#FFB6C1", penwidth=3, color="#DC143C"];
+
+       a -> b [color="#A9A9A9"];
+       b -> c [color="#A9A9A9"];
+       a -> tx1 [color="#A9A9A9"];
+       tx1 -> tx2 [label="path", style=bold, color="#DC143C", penwidth=2];
+       tx2 -> c [color="#A9A9A9"];
+   }
+
+5. Filter by Multiple Node Types Using ``is_in``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Use the `is_in` predicate to filter nodes or edges by multiple values.
+Use the ``is_in`` predicate to filter nodes or edges by multiple values.
 
 **Example: Filter nodes and edges by multiple types**
 
@@ -177,19 +283,52 @@ Use the `is_in` predicate to filter nodes or edges by multiple values.
         n({"risk2": True})
     ])
     hits = g_filtered._nodes[ g_filtered._nodes["hit"] == True ]
-    print('Number of filtered hits:', len(hits))
+    # hits: transaction/account nodes matching the traversal pattern
 
 **Explanation:**
 
-- Filters nodes of type `"person"` or `"company"`.
-- Traverses forward edges of type `"owns"` or `"reviews"`.
-- Filters nodes of type `"transaction"` or `"account"`, labeling them as `hit`.
-- Traverses backward to nodes with `risk2 == True`.
+- Filters nodes of type ``"person"`` or ``"company"``.
+- Traverses forward edges of type ``"owns"`` or ``"reviews"``.
+- Filters nodes of type ``"transaction"`` or ``"account"``, labeling them as ``hit``.
+- Traverses backward to nodes with ``risk2 == True``.
+
+.. graphviz::
+
+   digraph is_in_filter {
+       rankdir=LR;
+       node [shape=ellipse];
+
+       subgraph cluster_start {
+           label="n(type ∈ [person, company])";
+           style=rounded;
+           bgcolor="#E6F3FF";
+           person [label="person", style="filled,bold", fillcolor="#87CEEB", penwidth=2, color="#4682B4"];
+           company [label="company", style="filled,bold", fillcolor="#87CEEB", penwidth=2, color="#4682B4"];
+       }
+
+       subgraph cluster_hit {
+           label="n(type ∈ [transaction, account])\nname='hit'";
+           style=rounded;
+           bgcolor="#FFFDE7";
+           tx [label="transaction\nor account", shape=box, style="filled,bold", fillcolor="#FFFACD", penwidth=2, color="#DAA520"];
+       }
+
+       subgraph cluster_end {
+           label="n(risk2=True)";
+           style=rounded;
+           bgcolor="#FFEBEE";
+           risk2 [label="risk2=True", style="filled,bold", fillcolor="#FFB6C1", penwidth=2, color="#DC143C"];
+       }
+
+       person -> tx [label="e_forward\nowns|reviews", color="#4682B4", penwidth=2, style=bold];
+       company -> tx [label="e_forward\nowns|reviews", color="#4682B4", penwidth=2, style=bold];
+       tx -> risk2 [label="e_reverse\n*", color="#DC143C", penwidth=2, style=bold, dir=back];
+   }
 
 Leveraging GPU Acceleration
 ---------------------------
 
-GFQL is optimized for GPU acceleration using `cudf` and `rapids`. When using GPU dataframes, GFQL automatically executes queries on the GPU for massive speedups.
+GFQL is optimized for GPU acceleration using ``cudf`` and ``rapids``. When using GPU dataframes, GFQL automatically executes queries on the GPU for massive speedups.
 
 6. Automatic GPU Acceleration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -210,12 +349,11 @@ GFQL is optimized for GPU acceleration using `cudf` and `rapids`. When using GPU
 
     # Run GFQL query (executes on GPU)
     g_result = g_gpu.gfql([ ... ])
-    print('Number of resulting edges:', len(g_result._edges))
 
 **Explanation:**
 
-- `cudf.read_parquet()` loads data directly into GPU memory.
-- GFQL detects `cudf` dataframes and runs the query on the GPU.
+- ``cudf.read_parquet()`` loads data directly into GPU memory.
+- GFQL detects ``cudf`` dataframes and runs the query on the GPU.
 - Achieves significant performance improvements on large datasets.
 
 7. Forcing GPU Mode
@@ -231,13 +369,13 @@ You can explicitly set the engine to ensure GPU execution.
 
 **Explanation:**
 
-- `engine='cudf'` forces the use of the GPU-accelerated engine.
+- ``engine='cudf'`` forces the use of the GPU-accelerated engine.
 - Useful when you want to ensure the query runs on the GPU.
 
 Integration with PyData Ecosystem
 ---------------------------------
 
-GFQL integrates seamlessly with the PyData ecosystem, allowing you to combine it with libraries like `pandas`, `networkx`, `igraph`, and `PyTorch`.
+GFQL integrates seamlessly with the PyData ecosystem, allowing you to combine it with libraries like ``pandas``, ``networkx``, ``igraph``, and ``PyTorch``.
 
 8. Combining GFQL with Graph Algorithms
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -253,13 +391,12 @@ GFQL integrates seamlessly with the PyData ecosystem, allowing you to combine it
 
     # View top nodes by PageRank
     top_nodes = g_enriched._nodes.sort_values('pagerank', ascending=False).head(5)
-    print('Top nodes by PageRank:')
-    print(top_nodes[['id', 'pagerank']])
+    # top_nodes[['id', 'pagerank']]: DataFrame with highest PageRank nodes
 
 **Explanation:**
 
-- `compute_cugraph('pagerank')` computes the PageRank of nodes using GPU acceleration.
-- The enriched graph now contains a `pagerank` column in the nodes dataframe.
+- ``compute_cugraph('pagerank')`` computes the PageRank of nodes using GPU acceleration.
+- The enriched graph now contains a ``pagerank`` column in the nodes dataframe.
 
 9. Visualizing the Graph
 ~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -284,7 +421,7 @@ Use PyGraphistry's visualization capabilities to explore your graph.
 
 **Explanation:**
 
-- Filters nodes where `pagerank > 0.1`.
+- Filters nodes where ``pagerank > 0.1``.
 - Visualizes the subgraph consisting of high PageRank nodes.
 
 10. Sequencing Programs with Let
@@ -325,8 +462,8 @@ GFQL's Let bindings enable you to sequence complex graph programs as directed ac
     # Access results from each stage
     suspicious = result._nodes[result._nodes['suspicious_accounts']]
     clusters = result._nodes[result._nodes['high_risk_clusters']]
-    print(f'Found {len(suspicious)} suspicious accounts')
-    print(f'Identified {clusters["community"].nunique()} high-risk clusters')
+    # suspicious: nodes flagged in stage 1
+    # clusters['community']: community assignments from stage 4
 
 **Key benefits of Let bindings:**
 
@@ -335,6 +472,22 @@ GFQL's Let bindings enable you to sequence complex graph programs as directed ac
 - **Named results**: Access intermediate results by name for detailed analysis
 - **Composability**: Build complex patterns from simpler named operations
 
+.. graphviz::
+
+   digraph let_dag {
+       rankdir=TB;
+       node [shape=box, style="filled,bold", fillcolor="#FFFACD", penwidth=2, color="#DAA520"];
+
+       suspicious [label="suspicious_accounts\n(stage 1)"];
+       flows [label="money_flows\n(stage 2)"];
+       ranked [label="ranked\n(stage 3)"];
+       clusters [label="high_risk_clusters\n(stage 4)", fillcolor="#90EE90", color="#228B22"];
+
+       suspicious -> flows [label="ref", style=bold, color="#4682B4", penwidth=2];
+       flows -> ranked [label="ref", style=bold, color="#4682B4", penwidth=2];
+       ranked -> clusters [label="ref", style=bold, color="#4682B4", penwidth=2];
+   }
+
 11. Run remotely
 ~~~~~~~~~~~~~~~~
 
@@ -355,10 +508,10 @@ You may want to run GFQL remotely because the data is remote or a GPU is availab
     from graphistry import n, e
 
     g2 = g1.upload()
-    assert g2._dataset_id is not None, "Uploading sets `dataset_id` for subsequent calls"
+    assert g2._dataset_id is not None, "Uploading sets ``dataset_id`` for subsequent calls"
     g3 = g2.gfql_remote([n(), e(), n()])
 
-Additional parameters enable controlling options such as the execution `engine` and what is returned 
+Additional parameters enable controlling options such as the execution ``engine`` and what is returned 
 
 **Example: Bind to existing remote data and fetch it**
 
@@ -384,7 +537,8 @@ Additional parameters enable controlling options such as the execution `engine`
         }
 
     g = graphistry.bind(dataset_id='my-dataset-id')
-    print(g.python_remote_json(compute_shape))
+    shape_info = g.python_remote_json(compute_shape)
+    # shape_info: {'nodes': (1000, 5), 'edges': (5000, 3)}
 
 **Example: Run Python on remote GPUs and return a graph**
 
@@ -396,7 +550,7 @@ Additional parameters enable controlling options such as the execution `engine`
 
     g = graphistry.bind(dataset_id='my-dataset-id')
     g2 = g.python_remote_g(compute_shape)
-    print(g2._nodes)
+    # g2._nodes: DataFrame returned from remote execution
 
 Conclusion and Next Steps
 -------------------------
diff --git a/docs/source/gfql/overview.rst b/docs/source/gfql/overview.rst
index 7482ae1554..876063af68 100644
--- a/docs/source/gfql/overview.rst
+++ b/docs/source/gfql/overview.rst
@@ -328,4 +328,3 @@ Access detailed documentation of GFQL's API:
 - **Predicates**: Apply advanced filtering using predicates.
 
   - :doc:`../api/gfql/predicates`
-
diff --git a/docs/source/gfql/translate.rst b/docs/source/gfql/translate.rst
index 9baf34bce4..55d76f608b 100644
--- a/docs/source/gfql/translate.rst
+++ b/docs/source/gfql/translate.rst
@@ -32,7 +32,7 @@ We'll cover a range of common graph and query tasks:
 Finding Nodes with Specific Properties
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-**Objective**: Find all nodes where the `type` is `"person"`.
+**Objective**: Find all nodes where the ``type`` is ``"person"``.
 
 **SQL**
 
@@ -65,14 +65,24 @@ Finding Nodes with Specific Properties
 
 **Explanation**:
 
-- **GFQL**: `n({"type": "person"})` filters nodes where `type` is `"person"`. `g.gfql([...])` applies this filter to the graph `g`, and `._nodes` retrieves the resulting nodes. The performance is similar to that of Pandas (CPU) or cuDF (GPU).
+- **GFQL**: ``n({"type": "person"})`` filters nodes where ``type`` is ``"person"``. ``g.gfql([...])`` applies this filter to the graph ``g``, and ``._nodes`` retrieves the resulting nodes. The performance is similar to that of Pandas (CPU) or cuDF (GPU).
+
+.. graphviz::
+
+   digraph find_nodes {
+       node [shape=ellipse];
+       person1 [label="person", style="filled,bold", fillcolor="#90EE90", penwidth=3, color="#228B22"];
+       person2 [label="person", style="filled,bold", fillcolor="#90EE90", penwidth=3, color="#228B22"];
+       company1 [label="company", shape=box, style=filled, fillcolor="#D3D3D3", color="#A9A9A9", fontcolor="#696969"];
+       person1 -> company1 [color="#A9A9A9"];
+   }
 
 ---
 
 Exploring Relationships Between Nodes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-**Objective**: Find all edges connecting nodes of type `"person"` to nodes of type `"company"`.
+**Objective**: Find all edges connecting nodes of type ``"person"`` to nodes of type ``"company"``.
 
 **SQL**
 
@@ -119,14 +129,23 @@ Exploring Relationships Between Nodes
 
 **Explanation**:
 
-- **GFQL**: Starts from nodes of type `"person"`, traverses forward edges, and reaches nodes of type `"company"`. The resulting edges are stored in `edges_df`. This version starts to gain the legibility and maintainability benefits of graph query syntax for graph tasks, and maintains the performance benefits of automatically vectorized pandas and GPU-accelerated cuDF.
+- **GFQL**: Starts from nodes of type ``"person"``, traverses forward edges, and reaches nodes of type ``"company"``. The resulting edges are stored in ``edges_df``. This version starts to gain the legibility and maintainability benefits of graph query syntax for graph tasks, and maintains the performance benefits of automatically vectorized pandas and GPU-accelerated cuDF.
+
+.. graphviz::
+
+   digraph relationships {
+       rankdir=LR;
+       person [label="person", style="filled,bold", fillcolor="#87CEEB", penwidth=3, color="#4682B4"];
+       company [label="company", shape=box, style="filled,bold", fillcolor="#FFFACD", penwidth=3, color="#DAA520"];
+       person -> company [label="works_at", style=bold, color="#228B22", penwidth=2];
+   }
 
 ---
 
 Performing Multi-Hop Traversals
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-**Objective**: Find nodes that are two hops away from node `"Alice"`.
+**Objective**: Find nodes that are two hops away from node ``"Alice"``.
 
 **SQL**
 
@@ -173,7 +192,18 @@ Performing Multi-Hop Traversals
 
 **Explanation**:
 
-- **GFQL**: Starts at node `"Alice"`, performs two forward hops, and obtains nodes two steps away. Results are in `nodes_df`. Building on the expressive and performance benefits of the previous 1-hop example, it begins adding the parallel path finding benefits of GFQL over Cypher, which benefits both CPU and GPU usage.
+- **GFQL**: Starts at node ``"Alice"``, performs two forward hops, and obtains nodes two steps away. Results are in ``nodes_df``. Building on the expressive and performance benefits of the previous 1-hop example, it begins adding the parallel path finding benefits of GFQL over Cypher, which benefits both CPU and GPU usage.
+
+.. graphviz::
+
+   digraph multi_hop {
+       rankdir=LR;
+       Alice [label="Alice\n(start)", style="filled,bold", fillcolor="#87CEEB", penwidth=3, color="#4682B4"];
+       n1 [label="?\n(hop 1)", style="filled,bold", fillcolor="#D3D3D3", penwidth=2, color="#A9A9A9"];
+       n2 [label="m\n(result)", style="filled,bold", fillcolor="#90EE90", penwidth=3, color="#228B22"];
+       Alice -> n1 [label="hop 1", style=bold, color="#4682B4", penwidth=2];
+       n1 -> n2 [label="hop 2", style=bold, color="#228B22", penwidth=2];
+   }
 
 ---
 
@@ -214,7 +244,7 @@ Filtering Edges and Nodes with Conditions
 
 **Explanation**:
 
-- **GFQL**: Uses `e_forward(edge_query='weight > 0.5')` to filter edges where `weight > 0.5`. This version introduces the string query form that can be convenient. Underneath, it still benefits from the vectorized execution of Pandas and cuDF.
+- **GFQL**: Uses ``e_forward(edge_query='weight > 0.5')`` to filter edges where ``weight > 0.5``. This version introduces the string query form that can be convenient. Underneath, it still benefits from the vectorized execution of Pandas and cuDF.
 
 ---
 
@@ -253,7 +283,7 @@ Aggregations and Grouping
 
 **Explanation**:
 
-- **GFQL**: Performs aggregation directly on `g._edges` using standard dataframe operations. Or even shorter, call `g.get_degrees()` to enrich each node with in, out, and total degrees. This version benefits from the hardware-accelerated columnar analytics execution of Pandas and cuDF, and the simplicity of dataframe operations.
+- **GFQL**: Performs aggregation directly on ``g._edges`` using standard dataframe operations. Or even shorter, call ``g.get_degrees()`` to enrich each node with in, out, and total degrees. This version benefits from the hardware-accelerated columnar analytics execution of Pandas and cuDF, and the simplicity of dataframe operations.
 
 ---
 
@@ -363,7 +393,23 @@ All Paths and Connectivity
 
 **Explanation**:
 
-- **GFQL**: Uses `e(to_fixed_point=True)` to find edge sequences of arbitrary length between nodes `"Alice"` and `"Bob"`. The SQL and Pandas version suffer from syntactic and semantic imepedance mismatch with graph tasks on this example.
+- **GFQL**: Uses ``e(to_fixed_point=True)`` to find edge sequences of arbitrary length between nodes ``"Alice"`` and ``"Bob"``. The SQL and Pandas version suffer from syntactic and semantic imepedance mismatch with graph tasks on this example.
+
+.. graphviz::
+
+   digraph all_paths {
+       rankdir=LR;
+       Alice [label="Alice\n(start)", style="filled,bold", fillcolor="#87CEEB", penwidth=3, color="#4682B4"];
+       Bob [label="Bob\n(end)", style="filled,bold", fillcolor="#90EE90", penwidth=3, color="#228B22"];
+       m1 [label="person", style="filled,bold", fillcolor="#87CEEB", penwidth=2, color="#4682B4"];
+       m2 [label="person", style="filled,bold", fillcolor="#87CEEB", penwidth=2, color="#4682B4"];
+       n1 [label="person", style="filled,bold", fillcolor="#87CEEB", penwidth=2, color="#4682B4"];
+       Alice -> m1 [label="friend", style=bold, color="#228B22", penwidth=2];
+       m1 -> m2 [label="friend", style=bold, color="#228B22", penwidth=2];
+       m2 -> Bob [label="friend", style=bold, color="#228B22", penwidth=2];
+       Alice -> n1 [label="friend", style=bold, color="#228B22", penwidth=2];
+       n1 -> Bob [label="friend", style=bold, color="#228B22", penwidth=2];
+   }
 
 ---
 
@@ -398,7 +444,7 @@ Community Detection and Clustering
 Time-Windowed Graph Analytics
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-**Objective**: Find all edges between nodes `"Alice"` and `"Bob"` that occurred in the last 7 days.
+**Objective**: Find all edges between nodes ``"Alice"`` and ``"Bob"`` that occurred in the last 7 days.
 
 **SQL**
 
@@ -449,7 +495,7 @@ Time-Windowed Graph Analytics
 
 - **SQL** and **Pandas**: These versions incorrectly simplify to a two-hop relationships; for multihop scenarios, refer to :ref:`all-paths`.
 
-- **GFQL**: Utilizes the `chain` method to filter edges between `"Alice"` and `"Bob"` based on a timestamp within the last 7 days. This approach allows for multihop relationships as it leverages the graph's structure, and further using cuDF for GPU acceleration when available.
+- **GFQL**: Utilizes the ``chain`` method to filter edges between ``"Alice"`` and ``"Bob"`` based on a timestamp within the last 7 days. This approach allows for multihop relationships as it leverages the graph's structure, and further using cuDF for GPU acceleration when available.
 
 
 ---
@@ -458,7 +504,7 @@ Parallel Pathfinding
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 
-**Objective**: Find all paths from `"Alice"` to `"Bob"` and `"Charlie"` in parallel. Parallel pathfinding is particularly interesting because it allows for efficient querying of multiple target nodes at the same time, reducing the time and complexity required to compute multiple independent paths, especially in large graphs.
+**Objective**: Find all paths from ``"Alice"`` to ``"Bob"`` and ``"Charlie"`` in parallel. Parallel pathfinding is particularly interesting because it allows for efficient querying of multiple target nodes at the same time, reducing the time and complexity required to compute multiple independent paths, especially in large graphs.
 
 **SQL**
 
@@ -508,7 +554,7 @@ Parallel Pathfinding
 GPU Execution
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-*Objective**: Execute pathfinding queries on the GPU, computing all paths from `"Alice"` to `"Bob"` and `"Charlie"` simultaneously across hardware resources.
+*Objective**: Execute pathfinding queries on the GPU, computing all paths from ``"Alice"`` to ``"Bob"`` and ``"Charlie"`` simultaneously across hardware resources.
 
 **SQL**
 
diff --git a/docs/source/graphistry.layout.rst b/docs/source/graphistry.layout.rst
index db8ea71359..546b6dfd2a 100644
--- a/docs/source/graphistry.layout.rst
+++ b/docs/source/graphistry.layout.rst
@@ -1,6 +1,16 @@
 graphistry.layout package
 =========================
 
+.. graphviz::
+
+   digraph layout_toy {
+       rankdir=LR;
+       data [label="Data (nodes/edges)"];
+       layout [label="Layout (x,y)"];
+       render [label="Render (Graphviz/Graphistry)"];
+       data -> layout -> render;
+   }
+
 Subpackages
 -----------
 
diff --git a/docs/source/notebooks/plugins.compute.rst b/docs/source/notebooks/plugins.compute.rst
index 69ba8b26a3..15acea1cb1 100644
--- a/docs/source/notebooks/plugins.compute.rst
+++ b/docs/source/notebooks/plugins.compute.rst
@@ -8,7 +8,6 @@ Plugins - Compute & Layout
    :caption: Plugin Tutorials
    :titlesonly:
 
-   graphviz <../demos/demos_databases_apis/graphviz/graphviz.ipynb>
-   plot_static demo <../demos/demos_databases_apis/graphviz/plot_static_demo.ipynb>
+   Graphviz Layouts <../demos/demos_databases_apis/graphviz/graphviz.ipynb>
    HyperNetX <../demos/demos_databases_apis/hypernetx/hypernetx.ipynb>
    NetworkX <../demos/demos_databases_apis/networkx/networkx.ipynb>
\ No newline at end of file
diff --git a/docs/source/notebooks/visualization.rst b/docs/source/notebooks/visualization.rst
index 44e9cc48f5..2774803be3 100644
--- a/docs/source/notebooks/visualization.rst
+++ b/docs/source/notebooks/visualization.rst
@@ -1,7 +1,6 @@
 Visualization
 ==========================
 
-
 Encodings
 ---------
 
@@ -25,6 +24,16 @@ Geographic (Kepler.gl)
 
    Geospatial Network Visualization <../demos/more_examples/graphistry_features/layout_map.ipynb>
 
+Static Export
+-------------
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Static image export for docs, reports, presentations
+   :titlesonly:
+
+   Static Rendering <../demos/demos_databases_apis/graphviz/static_rendering.ipynb>
+
 Layout
 -------
 
diff --git a/docs/source/visualization/10min.rst b/docs/source/visualization/10min.rst
index 7ab562b4de..4b9dd4d26e 100644
--- a/docs/source/visualization/10min.rst
+++ b/docs/source/visualization/10min.rst
@@ -109,11 +109,31 @@ PyGraphistry's :ref:`Layout catalog <layout-catalog>` provides many options, cov
 
 - **Plugin Layouts**: Integrated use of external libraries for specific layouts:
 
-  - :ref:`Graphviz <graphviz>` for hierarchical and directed layouts such as the `"dot"` engine
+  - :ref:`Graphviz <graphviz>` for hierarchical and directed layouts such as the ``"dot"`` engine
   - :ref:`cuGraph <cugraph>` for GPU-accelerated FA2, a weaker version of Graphistry's live layout
   - :ref:`igraph <igraph>` for CPU-based layouts, similar to GraphViz and with layouts that focus more on medium-sized social networks
 
-- **External Layouts**: Pass in `x`, `y` columns, such as from your own edits, external data, or external ML/AI packages:
+Static Graphviz render (for docs/notebooks)
+-------------------------------------------
+
+When you need a quick static image without an interactive client, render directly with Graphviz. ``plot_static`` auto-displays in Jupyter and returns an SVG/Image object (use ``.data`` for raw bytes):
+
+.. code-block:: python
+
+   # Auto-displays inline in Jupyter notebooks
+   g.plot_static(format='svg', max_nodes=200, max_edges=400)
+
+Text-only outputs
+-----------------
+
+Emit DOT or Mermaid DSL for downstream rendering or embedding:
+
+.. code-block:: python
+
+   dot_text = g.plot_static(engine='graphviz-dot', reuse_layout=True)
+   mermaid_text = g.plot_static(engine='mermaid-code', reuse_layout=False)
+
+- **External Layouts**: Pass in ``x``, ``y`` columns, such as from your own edits, external data, or external ML/AI packages:
 
    .. code-block:: python
 
@@ -194,7 +214,7 @@ For a complete list of parameters, refer to the `official REST URL params page <
 Plotting: Inline and URL Rendering
 ----------------------------------
 
-Once you're ready to visualize, use `.plot()` to render:
+Once you're ready to visualize, use ``.plot()`` to render:
 
 - **Inline Plotting**: Directly embed interactive visualizations in your notebook or Python environment:
 
@@ -209,7 +229,30 @@ Once you're ready to visualize, use `.plot()` to render:
       url = g.plot(render=False)
       print(f"View your graph at: {url}")
 
-  You can further control the embeded visualization using URL parameters and JavaScript 
+  You can further control the embeded visualization using URL parameters and JavaScript
+
+Static Image Export
+~~~~~~~~~~~~~~~~~~~
+
+For non-interactive outputs (documentation, reports, presentations), use ``plot_static()``:
+
+.. code-block:: python
+
+    # Quick static SVG (auto-displays in Jupyter)
+    g.plot_static()
+
+    # With styling
+    g.plot_static(
+        graph_attr={'rankdir': 'LR', 'bgcolor': 'white'},
+        node_attr={'style': 'filled', 'fillcolor': 'lightblue'}
+    )
+
+    # Save to file
+    g.plot_static(format='png', path='graph.png')
+
+Works with any layout source (UMAP, ring, graphviz, or manual x/y positions).
+
+See the `static rendering tutorial <../../demos/demos_databases_apis/graphviz/static_rendering.ipynb>`_ for styling options, output formats, and complete examples.
 
 .. _extra:
 
diff --git a/docs/source/visualization/index.rst b/docs/source/visualization/index.rst
index e4e5b48126..ebf7216dd0 100644
--- a/docs/source/visualization/index.rst
+++ b/docs/source/visualization/index.rst
@@ -1,7 +1,9 @@
 Visualize
 =============
 
-We recommend getting started with :ref:`10 Minutes to PyGraphistry <10min>`, :ref:`10 Minutes to Graphistry Visualization<10min-viz>`, and the :ref:`layout guide <layout-guide>`
+We recommend getting started with :ref:`10 Minutes to PyGraphistry <10min>`, :ref:`10 Minutes to Graphistry Visualization<10min-viz>`, and the :ref:`layout guide <layout-guide>`.
+
+For static image export (documentation, reports), see the `static rendering tutorial <../demos/demos_databases_apis/graphviz/static_rendering.ipynb>`_.
 
 See also:
 
diff --git a/docs/source/visualization/layout/catalog.rst b/docs/source/visualization/layout/catalog.rst
index 420be5eebf..f8d51d9d91 100644
--- a/docs/source/visualization/layout/catalog.rst
+++ b/docs/source/visualization/layout/catalog.rst
@@ -91,6 +91,8 @@ Visit the :ref:`API reference on graphviz page <graphviz>` for more examples.
 
     g.layout_graphviz('dot').plot()
 
+For static image export (SVG, PNG) instead of interactive visualization, see :py:meth:`~graphistry.PlotterBase.PlotterBase.plot_static` and the `static rendering tutorial <../../../demos/demos_databases_apis/graphviz/static_rendering.ipynb>`_.
+
 .. _igraph-plugin:
 
 igraph Plugin
diff --git a/graphistry/PlotterBase.py b/graphistry/PlotterBase.py
index e5f5c77f17..6fa1766a4a 100644
--- a/graphistry/PlotterBase.py
+++ b/graphistry/PlotterBase.py
@@ -2419,30 +2419,9 @@ def from_networkx(self, G) -> Plottable:
     layout_graphviz = layout_graphviz
     render_graphviz = render_graphviz
 
-    def _auto_display_static(self, data: Union[bytes, str], format: str) -> None:
-        """
-        Auto-display static render output in Jupyter notebook environments.
-
-        Does nothing if not in an interactive IPython/Jupyter context.
-        """
-        from graphistry.util import in_ipython
-        if not in_ipython():
-            return
-
-        try:
-            from IPython.display import display, SVG, Image
-            if format == 'svg':
-                display(SVG(data))
-            elif format == 'png':
-                display(Image(data))
-            # For 'dot' or 'mermaid-code' text output, we don't auto-display
-            # (user can print() or use display(Code(...)) themselves)
-        except ImportError:
-            pass  # No IPython available, skip display
-
     def plot_static(
         self,
-        format: Format = 'svg',
+        format: Format = 'auto',
         path: Optional[str] = None,
         engine: str = 'graphviz-svg',
         prog: Prog = 'dot',
@@ -2456,12 +2435,15 @@ def plot_static(
         drop_unsanitary: bool = False,
         max_nodes: Optional[int] = None,
         max_edges: Optional[int] = None,
-    ) -> Union[bytes, str]:
+    ) -> Any:
         """
         Render a static image of the current graph (e.g., for notebooks/docs).
 
+        Returns an IPython display object (SVG or Image) that auto-displays in notebooks.
+        Use ``.data`` to access raw bytes for programmatic use.
+
         Engines:
-        - graphviz-svg (default) / graphviz-png: render image bytes (optionally write to path)
+        - graphviz-svg (default) / graphviz-png: render image (optionally write to path)
         - graphviz-dot: return DOT string (optionally write to path)
         - mermaid-code: return Mermaid DSL string (optionally write to path)
 
@@ -2469,7 +2451,7 @@ def plot_static(
         Otherwise, Graphviz lays out the graph. When positions are reused, Graphviz is invoked
         with ``neato -n2`` to respect them.
 
-        :param format: Output format, e.g., 'svg' or 'png' (graphviz engines)
+        :param format: Output format: 'auto' (default, svg), 'svg', or 'png'
         :param path: Optional path to also write the image/text
         :param engine: Rendering engine; supports graphviz-svg/png, graphviz-dot, mermaid-code
         :param prog: Graphviz layout program when computing layout
@@ -2483,10 +2465,22 @@ def plot_static(
         :param drop_unsanitary: Reject unsanitary attributes
         :param max_nodes: Optional cap on node count
         :param max_edges: Optional cap on edge count
-        :return: Rendered image bytes or DOT/Mermaid string, depending on engine
+        :return: SVG or Image display object (use .data for bytes), or DOT/Mermaid string
+
+        **Example: Basic usage**
+            ::
+
+                g.plot_static()  # Returns SVG, auto-displays in notebook
+
+        **Example: Save to file**
+            ::
+
+                g.plot_static(path='graph.svg')  # Writes file AND returns SVG
 
-        **Auto-display**: When running in a Jupyter notebook, the output is automatically
-        displayed inline. The result is still returned for programmatic use (e.g., saving to disk).
+        **Example: Get raw bytes**
+            ::
+
+                svg_bytes = g.plot_static().data
         """
 
         if engine not in ('graphviz', 'graphviz-svg', 'graphviz-png', 'graphviz-dot', 'mermaid-code'):
@@ -2524,6 +2518,8 @@ def plot_static(
         # Engine routing
         if engine in ('graphviz', 'graphviz-svg', 'graphviz-png'):
             fmt = format
+            if fmt == 'auto':
+                fmt = 'svg'
             if engine == 'graphviz-png':
                 fmt = 'png'
             elif engine in ('graphviz', 'graphviz-svg') and fmt not in ('svg', 'png'):
@@ -2543,8 +2539,16 @@ def plot_static(
                 max_edges=max_edges,
                 path=path
             )
-            self._auto_display_static(result, fmt)
-            return result
+            # Return IPython display objects for auto-display in notebooks
+            # Falls back to raw bytes if IPython not available
+            try:
+                from IPython.display import SVG, Image
+                if fmt == 'svg':
+                    return SVG(result)
+                else:
+                    return Image(result)
+            except ImportError:
+                return result
 
         if engine == 'graphviz-dot':
             from graphistry.plugins.graphviz import layout_graphviz_core
diff --git a/graphistry/plugins_types/graphviz_types.py b/graphistry/plugins_types/graphviz_types.py
index 63f9b771bc..e2909cd3d1 100644
--- a/graphistry/plugins_types/graphviz_types.py
+++ b/graphistry/plugins_types/graphviz_types.py
@@ -51,6 +51,7 @@
 ]
 
 Format = Literal[
+    "auto",
     "canon",
     "cmap",
     "cmapx",
diff --git a/graphistry/tests/plugins/test_graphviz.py b/graphistry/tests/plugins/test_graphviz.py
index e2e08f8bf4..fb886e8569 100644
--- a/graphistry/tests/plugins/test_graphviz.py
+++ b/graphistry/tests/plugins/test_graphviz.py
@@ -108,16 +108,19 @@ def test_render(self, tree_g: Plottable) -> None:
         assert os.path.getsize(f'{base_path}graph.png') > 0
 
     def test_plot_static_layout(self, chain_g: Plottable) -> None:
+        from IPython.display import Image
         png = chain_g.plot_static(format='png', max_nodes=100, max_edges=200)
-        assert isinstance(png, (bytes, bytearray))
-        assert len(png) > 0
+        assert isinstance(png, Image)
+        assert len(png.data) > 0
 
     def test_plot_static_reuse_positions(self, chain_g: Plottable) -> None:
+        from IPython.display import SVG
         g_with_xy = chain_g.materialize_nodes()
         g_with_xy = g_with_xy.nodes(lambda g: g._nodes.assign(x=range(len(g._nodes)), y=range(len(g._nodes))))
         g_with_xy = g_with_xy.bind(point_x='x', point_y='y')
         svg = g_with_xy.plot_static(format='svg', reuse_layout=True, max_nodes=100, max_edges=200)
-        assert b'<svg' in svg
+        assert isinstance(svg, SVG)
+        assert '<svg' in svg.data
 
     def test_plot_static_dot(self, chain_g: Plottable, tmp_path) -> None:
         dot_str = chain_g.plot_static(engine='graphviz-dot', reuse_layout=False)
diff --git a/plans/graphviz_docs_rendering/plan.md b/plans/graphviz_docs_rendering/plan.md
deleted file mode 100644
index 1462606692..0000000000
--- a/plans/graphviz_docs_rendering/plan.md
+++ /dev/null
@@ -1,786 +0,0 @@
-# graphviz_docs_rendering Plan
-**THIS PLAN FILE**: `plans/graphviz_docs_rendering/plan.md`  
-**Created**: 2025-11-30 21:51:27 UTC  
-**Current Branch**: feat/graphviz-docs-rendering  
-**PR**: N/A  
-**Base Branch**: master  <!-- working on feature branch per template (no main/master work) -->
-
-## CRITICAL META-GOALS OF THIS PLAN
-1. **FULLY SELF-DESCRIBING**: All context needed to resume work is IN THIS FILE
-2. **CONSTANTLY UPDATED**: Every action's results recorded IMMEDIATELY
-3. **THE SINGLE SOURCE OF TRUTH**: If it's not in the plan, it didn't happen
-4. **SAFE TO RESUME**: Any AI can pick up work by reading ONLY this file
-
-## Execution Protocol
-- Before each action: reload plan → follow the single 🔄 IN_PROGRESS phase → update results immediately.
-- If an action is missing from phases: stop, add a new phase, mark it 🔄, then execute.
-- Record all tool calls and outcomes in the Result of the active phase.
-- If new info changes scope, add/merge phases to replan before proceeding.
-
-## Bug Fix Protocol (if applicable)
-Investigate → Reproduce → Test → Fix → Validate → Finalize.
-
-## Context (READ-ONLY)
-
-### Objective
-Reuse/extend Graphviz layout + render capabilities (pygraphviz) so docs (md/rst/ipynb) and notebook cells can show graph visuals directly from PyGraphistry. Add or plan Mermaid strategy. Apply to docs like `docs/source/gfql/about.rst` (GFQL about) and the Graphviz demo notebook page, ensuring rendered assets appear on RTD/GitHub/nbconvert.
-
-### Current State
-- Graphviz: `layout_graphviz` supports layout and render_to_disk (png/svg) via pygraphviz; GFQL safelist includes it. No in-memory SVG helper; render writes to disk.
-- Docs toolchain: Sphinx + nbsphinx; RTD setup unknown re: pygraphviz availability; no Mermaid integration.
-- Mermaid: No Python bindings or render helpers in repo.
-
-### Success Criteria
-- Defined approach for small-graph Graphviz exports: layout + render (SVG/PNG) usable inline in notebooks and docs.
-- Pipeline plan (or implementation steps) for Sphinx/nbconvert: how to generate/ship assets so RTD/GitHub work.
-- Mermaid stance (supported path or documented fallback).
-- Concrete doc targets selected (GFQL about, Graphviz demo) with insertion plan for rendered visuals.
-
-### Related Plans
-None.
-
-### Git Strategy
-Work on master unless branch requested; no PR yet.
-
-## Status Legend
-- 📝 TODO | 🔄 IN_PROGRESS | ✅ DONE | ❌ FAILED | ⏭️ SKIPPED | 🚫 BLOCKED
-
-## Quick Reference (commands)
-```bash
-git status
-rg "graphviz" graphistry docs docker
-./docs/validate-docs.sh  # when validating
-```
-
-## Phases
-
-### Phase 1.A: Inventory tooling, constraints, and doc entry points
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Started:** 2025-11-30 21:51:27 UTC  
-**Completed:** 2025-11-30 22:08:41 UTC  
-**Description:** Map current Graphviz capabilities and docs build constraints to know where to hook rendering and layout. Identify targets (gfql/about, graphviz demo) and environment limits (RTD/CI).  
-**Actions:**
-```bash
-rg "layout_graphviz" graphistry plugins docs
-rg "graphviz" docs/source -g'*.rst'
-rg "graphviz" docs -g'*.ipynb'
-rg "graph.png" -g'*'
-sed -n '1,240p' graphistry/plugins/graphviz.py
-sed -n '1,200p' docs/html.sh
-sed -n '1,200p' docs/validate-docs.sh
-sed -n '1,200p' docs/ci.sh
-ls docker
-cat docker/docker-compose.yml
-sed -n '1,200p' docs/README.md
-sed -n '1,200p' docs/docker/docker-compose.yml
-sed -n '1,200p' docs/docker/Dockerfile
-sed -n '1,200p' docs/docker/build-docs.sh
-sed -n '1,200p' docs/source/conf.py
-sed -n '1,200p' docs/source/gfql/about.rst
-python - <<'PY' ... # scan graphviz.ipynb cells for layout_graphviz/render_to_disk usage
-python - <<'PY' ... # check outputs of the render_to_disk cell
-```
-**Success Criteria:** Clear list of current Graphviz helpers, how docs build runs (Sphinx/nbsphinx, scripts), RTD/CI constraints for pygraphviz/graphviz binaries, and specific insertion points in gfql/about + graphviz demo pages.  
-**Result:**  
-- Graphviz helpers: `layout_graphviz` (pygraphviz) supports layout + render_to_disk (png/svg) and maps x/y back; GFQL safelist includes `layout_graphviz`; tests cover layout and render (`graphistry/tests/plugins/test_graphviz.py`).  
-- Docs toolchain: `docs/html.sh` → `docs/ci.sh` → docker-compose sphinx service under `docs/docker/`. Dockerfile uses `sphinxdoc/sphinx:8.0.2` + `docs/requirements-system.txt` (inkscape/pandoc/latex) **no graphviz/pygraphviz**, so RTD/CI builds lack Graphviz binaries; nbsphinx executes `never` (conf.py) meaning notebook outputs must be pre-baked.  
-- Notebooks: `demos/demos_databases_apis/graphviz/graphviz.ipynb` already has a `layout_graphviz(..., render_to_disk=True, path='./graph.png', format='png')` cell and an `Image(filename='./graph.png')` display, but the cell output only has HTML/text (no embedded png), and the file `graph.png` is not in repo.  
-- Targets: `docs/source/gfql/about.rst` (10 Minutes to GFQL) currently text-only; graphviz demo notebook is the main Graphviz page.  
-- Constraints: Without graphviz installed in docs image/RTD, new renders must be pre-generated/committed or built via an added step that installs graphviz+pygraphviz.  
-- Tool outputs captured via above commands; no code changes yet.
-
-### Phase 1.B: Graphviz export API plan
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Started:** 2025-11-30 22:08:53 UTC  
-**Completed:** 2025-11-30 22:09:37 UTC  
-**Description:** Design helper(s) for in-memory SVG/PNG export with sampling/caps for small graphs; decide on render-to-disk strategy for docs (committed assets vs build-time). Deliverable: chosen helper signature + small-graph policy + asset commit/build decision.  
-**Actions:**
-```bash
-review graphistry/plugins/graphviz.py graphistry/plugins_types/graphviz_types.py
-draft helper signature(s) for SVG bytes / string
-define sampling/size caps for “doc-friendly” graphs
-```
-**Success Criteria:** Chosen helper approach (in-memory SVG/PNG), small-graph policy, and whether assets are committed or built during docs.
-
-**Result:**  
-- Helper plan: Add an in-memory render helper (e.g., `render_graphviz_svg` / `render_graphviz`) that wraps `layout_graphviz_core`, runs the layout, and returns SVG/PNG bytes when `path=None`, otherwise writes to `path`. Implementation can use a `NamedTemporaryFile` fallback to stay compatible with pygraphviz draw. Keep existing layout/styling behavior and surface `drop_unsanitary`/attrs.  
-- Small-graph policy: add optional caps (e.g., `max_nodes`, `max_edges`) for doc-friendly renders; default None, but docs helper/script will set conservative caps (e.g., 200/400) to avoid heavy layouts.  
-- Asset strategy: Docs build image lacks graphviz/pygraphviz; therefore prefer pre-render + commit assets. Add a small script/Make target to generate assets (using the helper) into `docs/static/graphviz/` (or similar) from the demo/gfql examples; RTD will just consume committed assets. Optionally note that enabling graphviz in docs Dockerfile is a stretch goal, not required for first pass.  
-- Next steps: Wire docs script/asset path in Phase 1.D; apply to gfql/about + graphviz demo in Phase 1.E.
-
-### Phase 1.C: Mermaid investigation
-**Status:** ⏭️ SKIPPED  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Description:** (Paused per guidance) Mermaid investigation deferred; focus on Graphviz path first.  
-**Actions:** N/A  
-**Success Criteria:** N/A
-
-### Phase 1.D: Docs pipeline integration
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Started:** 2025-11-30 22:09:44 UTC  
-**Completed:** 2025-11-30 22:10:33 UTC  
-**Description:** Plan Sphinx/nbconvert hooks or Make targets to render/collect assets; define where assets live and how referenced. Deliverable: asset path (e.g., `_static/graphviz/`), Make/CLI step name, linking pattern.  
-**Actions:**
-```bash
-inspect docs/html.sh docs/validate-docs.sh docs/source/conf.py
-specify asset output dir (e.g., docs/_static/graphviz/)
-outline Make/CLI step to render graphs (Graphviz/Mermaid)
-```
-**Success Criteria:** Documented pipeline steps for generating and linking assets in docs builds (and RTD fallback).
-**Result:**  
-- Asset location: use `docs/source/static/graphviz/` (copied to `_static/graphviz/` by Sphinx) for committed renders referenced by rst/ipynb.  
-- Generation step: add a small script (e.g., `docs/scripts/render_graphviz_assets.py`) that loads example graphs and calls the new in-memory Graphviz render helper to emit SVG/PNG into that folder. Run only in graphviz-enabled env (`pip install .[pygraphviz]` or `docker/test-cpu-graphviz.sh`); not run in RTD/CI.  
-- Linking pattern: rst uses `.. image:: /_static/graphviz/<name>.svg`; notebooks can embed inline SVG via the helper (outputs stored in ipynb) or reference static files with `Image(filename='../static/graphviz/<name>.svg')`. Because `nbsphinx_execute = 'never'`, committed outputs or static-file references are required.  
-- Build impact: docs Dockerfile stays graphviz-free; RTD consumes committed assets. Developers regenerate assets via the script in a graphviz-ready env.
-
-### Phase 1.E: Apply to target pages
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Started:** 2025-11-30 22:10:41 UTC  
-**Completed:** 2025-11-30 22:11:02 UTC  
-**Description:** Plan specific insertions for gfql/about and graphviz demo notebook (e.g., new `layout_graphviz(..., render_to_disk=True, format='png'/'svg')` variant). Deliverable: list of files to edit + snippet outlines + asset filenames.  
-**Actions:**
-```bash
-draft snippet(s) for gfql/about visuals
-draft updated notebook cell/output reference for graphviz demo
-decide committed asset names/paths
-```
-**Success Criteria:** Concrete content plan (snippets + asset references) for both pages.
-**Result:**  
-- Files to edit:  
-  - `docs/source/gfql/about.rst`: add a short “GFQL toy graph (Graphviz)” subsection with an embedded static image.  
-  - `demos/demos_databases_apis/graphviz/graphviz.ipynb`: update the render section to showcase inline SVG via the new helper (no disk), optionally keep disk render as a secondary example.  
-  - `docs/scripts/render_graphviz_assets.py` (new) to generate committed assets.  
-- Assets: store under `docs/source/static/graphviz/` with names like `gfql_about_toy.svg` (small hop chain) and `graphviz_demo_dot.svg` (from the demo graph). Script can also emit `gfql_about_toy.png` if needed for PDF.  
-- Snippet outlines:  
-  - RST:  
-    ```
-    .. image:: /_static/graphviz/gfql_about_toy.svg
-       :alt: GFQL toy graph laid out with Graphviz dot
-       :align: center
-    ```  
-  - Notebook cell:  
-    ```python
-    from IPython.display import SVG, display
-    svg = g2c.render_graphviz_svg('dot', graph_attr={}, edge_attr={}, node_attr={'color': 'green'}, max_nodes=200, max_edges=400)
-    display(SVG(svg))
-    ```  
-    (Optional: second cell showing `render_to_disk=True` for parity.)  
-- Link references: RST uses `_static` path; notebook uses inline SVG (executed once, outputs committed) so nbsphinx doesn’t need to execute.  
-- Pending: run generation script to create the SVGs before committing.
-
-### Phase 1.F: Validation plan
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Started:** 2025-11-30 22:11:08 UTC  
-**Completed:** 2025-11-30 22:11:20 UTC  
-**Description:** Define tests/checks to run after implementation. Deliverable: concrete commands + expected artifacts/screenshots to verify.  
-**Actions:**
-```bash
-./docs/validate-docs.sh docs/source/gfql/about.rst
-./docs/validate-docs.sh docs/source/visualization/layout/catalog.rst
-optional: docs/html.sh (if feasible locally)
-```
-**Success Criteria:** Checklist of commands and expected outputs to confirm docs render (including inline SVG in notebooks) and assets load.
-**Result:**  
-- Validation commands:  
-  - `./docs/validate-docs.sh docs/source/gfql/about.rst docs/source/api/plugins/compute/graphviz.rst` (rst lint).  
-  - `DOCS_FORMAT=html VALIDATE_NOTEBOOK_EXECUTION=0 ./docs/html.sh` (or `./docs/ci.sh`) to ensure Sphinx build succeeds with committed assets and no missing image warnings.  
-  - Spot-check built pages: `docs/_build/html/gfql/about.html` shows embedded `_static/graphviz/gfql_about_toy.svg`; `docs/_build/html/demos/demos_databases_apis/graphviz/graphviz.html` shows inline SVG output from updated notebook.  
-- Artifacts to verify: SVG/PNG files exist under `docs/source/static/graphviz/`; nbsphinx output cells include SVG (no broken image icons) without executing notebooks.
-
-### Phase 2.A: Implement Graphviz in-memory render helper
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Started:** 2025-11-30 22:11:44 UTC  
-**Completed:** 2025-11-30 22:13:25 UTC  
-**Description:** Add helper on Plottable to render Graphviz to SVG/PNG bytes (optional path) with caps and reuse existing layout plumbing.  
-**Actions:**
-```bash
-edit graphistry/plugins/graphviz.py (+ maybe Plottable/PlotterBase exports)
-add max_nodes/max_edges/format/path handling; use tempfile for bytes
-```
-**Success Criteria:** Helper returns SVG/PNG bytes when no path; still supports render_to_disk; honors caps/drop_unsanitary; tests adjusted if needed.
-
-**Result:**  
-- Added `render_graphviz` helper in `graphistry/plugins/graphviz.py` returning rendered bytes (optional path write), with format validation and node/edge caps; uses tempfile cleanup.  
-- Exposed helper on `PlotterBase` and `graphistry.plugins.__init__` for user access.  
-- Added test `test_render_graphviz_bytes` (skip with pygraphviz missing) in `graphistry/tests/plugins/test_graphviz.py`.  
-- Commands: `apply_patch` edits to graphistry/plugins/graphviz.py, graphistry/PlotterBase.py, graphistry/plugins/__init__.py, graphistry/tests/plugins/test_graphviz.py. Not yet executed tests (pygraphviz availability unknown).
-
-### Phase 2.B: Add plot_static entrypoint
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Description:** Provide notebook/API-friendly static render that reuses x/y when bound or falls back to Graphviz layout.  
-**Actions:** Implemented `plot_static` on PlotterBase using `render_graphviz`; supports reuse of bound x/y (or cols `x`/`y`), otherwise runs Graphviz layout; accepts format/path/args/caps; uses `neato -n2` when reusing positions. Enabled args passthrough in Graphviz core/render.  
-**Success Criteria:** Single-call static render with minimal args, no tuple returns, reuse existing layouts when present.  
-**Result:** plot_static available; `render_graphviz` accepts args; layout_graphviz_core now honors args. No tests added yet for plot_static; existing render_graphviz test still present.
-
-### Phase 2.B: Asset generation script
-**Status:** ⏭️ SKIPPED  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Description:** (Pivot) Dropped static asset script/committed assets approach; aiming for inline renders or Sphinx graphviz where possible.  
-**Actions:** N/A  
-**Success Criteria:** N/A  
-**Result:** Prior asset script and generated files removed; will rely on inline renders/docs-side Graphviz instead of managing committed PNG/SVGs.
-
-### Phase 2.C: Update gfql/about.rst
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Description:** Add a Graphviz example using inline render (no committed assets) or Sphinx `.. graphviz::` once docs image has graphviz/pygraphviz.  
-**Actions:**
-```bash
-enable sphinx.ext.graphviz and docs image graphviz deps
-insert small DOT block or inline render example
-```
-**Success Criteria:** gfql/about builds without missing-image warnings; example visible in HTML.
-**Result:**  
-- Enabled `sphinx.ext.graphviz` and added a small DOT snippet under “Static Graphviz example” in `docs/source/gfql/about.rst`.  
-- Docs image now includes graphviz/pygraphviz so the directive renders during build.  
-- Included in passing HTML build (warnings unchanged and pre-existing).
-
-### Phase 2.D: Update graphviz demo notebook
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Description:** Show inline render using layout_graphviz (or future plot_static) with saved output; no static path hacks.  
-**Actions:**
-```bash
-add a cell using layout_graphviz(...render_format='svg'/'png') + display
-save outputs; remove static image references
-```
-**Success Criteria:** Notebook renders without missing-image warnings; outputs committed for nbsphinx execute=never.
-**Result:**  
-- Added `plot_static` inline render cell to `demos/demos_databases_apis/graphviz/graphviz.ipynb`, executed, and saved outputs.  
-- Kept layout usage but removed reliance on external PNG files.  
-- Notebook now self-contained for nbsphinx (execute=never).
-
-### Phase 2.E: Run validations
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Started:** 2025-11-30 23:28:48 UTC  
-**Completed:**  
-**Description:** Run lint/docs checks and sanity-verify assets.  
-**Actions:**
-```bash
-./docs/validate-docs.sh docs/source/gfql/about.rst docs/source/api/plugins/compute/graphviz.rst
-DOCS_FORMAT=html VALIDATE_NOTEBOOK_EXECUTION=0 ./docs/html.sh  # if feasible
-```
-**Success Criteria:** Commands succeed; built pages show images; assets present under static path.
-**Result:**  
-- `DOCS_FORMAT=html VALIDATE_NOTEBOOK_EXECUTION=0 ./docs/ci.sh` succeeded (graphviz-enabled sphinx image cached; 74 pre-existing warnings only).  
-- `./docs/validate-docs.sh docs/source/gfql/about.rst` previously passed. Pages now show embedded graphviz directives and notebook SVG output.
-
-### Phase 2.F: Clean up docs image layering (todo)
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Description:** Polish docs Dockerfile once graphviz layering stabilizes.  
-**Actions:**
-```bash
-# requirements-system.txt now includes graphviz/graphviz-dev/gcc; single apt layer retained with caching
-# Keep cache mounts; revisit --no-install-recommends if image needs slimming later
-```
-**Success Criteria:** Single-source graphviz deps in requirements-system.txt; cached layers minimize rebuild time.
-**Result:** Graphviz/pygraphviz deps folded into the main apt layer (requirements-system.txt includes graphviz, graphviz-dev, gcc). Removed the extra apt step; caching mounts remain.
-
-### Phase 2.G: Docs polish & API doc updates
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Description:** Document `plot_static` API and refresh 10min / visualization pages with static graphviz examples and live Graphistry snippets where appropriate.  
-**Actions:**
-```bash
-# Update docs pages:
-# - docs/source/10min.rst
-# - docs/source/visualization/10min.rst
-# - any API reference for plot_static (plotter)
-# Ensure notebook/demo outputs remain embedded
-```
-**Success Criteria:** New static render examples present, `plot_static` documented, docs build continues to pass.
-**Result:**  
-- Added `plot_static` snippets and context to `docs/source/10min.rst` and `docs/source/visualization/10min.rst`.  
-- Mentioned `plot_static`/`render_graphviz` in `docs/source/api/plugins/compute/graphviz.rst`.  
-- `./docs/validate-docs.sh docs/source/10min.rst docs/source/visualization/10min.rst docs/source/api/plugins/compute/graphviz.rst` passes; full HTML build previously green with graphviz enabled.
-
-### Phase 2.H: Extend plot_static engines (graphviz-dot / mermaid-code) and theming hooks
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Description:** Add engine selector to `plot_static` to support `graphviz-svg`/`graphviz-png` (existing), `graphviz-dot` (DOT text, optional path), and `mermaid-code` (Mermaid DSL text, optional path). Honor `reuse_layout` for pos embedding where applicable and add light theming hooks. Provide a thin `to_graphviz_dot(path=None, reuse_layout=True, ...)` wrapper.  
-**Actions:**
-```bash
-# Code
-# - Update PlotterBase.plot_static(engine=...) with modes graphviz-svg/png (current), graphviz-dot (return/write DOT), mermaid-code (return/write DSL string)
-# - Factor shared DOT emission (reuse g_to_pgv) and pos handling (reuse_layout => pos, else omit)
-# - Add thin helper to_graphviz_dot(path=None, reuse_layout=True, ...) that calls the same core
-# - Add optional theme mapping for mermaid (classDef) and reuse Graphviz attrs for consistency
-# Tests
-# - Extend/ add tests in graphistry/tests/plugins/test_graphviz.py covering:
-#   * graphviz-dot returns string and writes file when path set
-#   * mermaid-code returns string (basic shape) and optional path write
-#   * reuse_layout respected (pos present/absent as expected)
-```
-**Success Criteria:** New engines return expected strings/files; existing graphviz render still works; tests pass locally with pygraphviz installed.  
-**Result:** Added graphviz-dot/mermaid-code engines to plot_static, position reuse, include_positions in render, tests for DOT/Mermaid/file writes; merged and pushed.
-
-### Phase 2.I: Add .md/.rst/.ipynb examples for new engines/themes
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Description:** Demonstrate graphviz-dot and mermaid-code outputs in MyST (.md), RST, and notebook contexts with a consistent theme.  
-**Actions:**
-```bash
-# Docs/examples
-# - Add a MyST page (e.g., docs/source/gfql/spec/index.md or ecosystem.md) with {graphviz} and {mermaid} blocks sourced from our outputs
-# - Add a small RST snippet referencing the DOT text (or include) and a mermaid block if supported
-# - Update graphviz demo notebook to show graphviz-dot/mermaid-code strings (printed) alongside existing SVG
-# Theming: optionally set graphviz_dot_args in conf.py and a shared theme dict for examples
-# Validation
-# - ./docs/validate-docs.sh <touched files>
-# - DOCS_FORMAT=html VALIDATE_NOTEBOOK_EXECUTION=0 ./docs/ci.sh (if feasible) or at least ensure no missing assets
-```
-**Success Criteria:** Example pages render with the new blocks; notebook shows string outputs; docs build passes without new warnings.
-**Result:** Added DOT/Mermaid examples to RST (10min, visualization 10min), MyST (gfql/spec/index.md), and graphviz demo notebook already shows plot_static inline; additional small Graphviz diagrams added to gfql overview/quick, ecosystem, and graphistry.layout pages. Docs validation passed; full HTML build green aside from pre-existing warnings.
-
-### Phase 2.J: RTD PDF blockers (docutils errors)
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Description:** Remove/guard RTD-incompatible mermaid block.  
-**Actions:**
-```bash
-# docs/source/gfql/spec/index.md: drop or guard {mermaid} so latex build passes
-```
-**Success Criteria:** Sphinx latex build no longer fails on unknown mermaid directive.
-**Result:** Replaced `{mermaid}` with a plain code block in `docs/source/gfql/spec/index.md`.
-
-### Phase 2.K: Fix docstring formatting errors (RTD PDF)
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Description:** Resolve docutils ERROR/CRITICAL in code docstrings.  
-**Actions:**
-```bash
-# graphistry/plugins/spanner.py: rephrase docstring to avoid unknown target "type"
-# graphistry/dgl_utils.py: fix headings/indent/field lists (convert to plain text/param docs)
-# graphistry/compute/ast.py, graphistry/compute/gfql_validation/__init__.py: fix unexpected indentation
-```
-**Success Criteria:** Sphinx latex build reports no ERROR/CRITICAL from these modules.
-**Result:** spanner docstrings converted to :param; dgl_utils docstrings rewritten to simple param docs; tabs cleaned in ast.py/gfql_validation. PDF build (DOCS_FORMAT=pdf) succeeds with only pre-existing warnings.
-
-### Phase 2.L: Fix notebook formatting errors (RTD PDF)
-**Status:** 🚫 BLOCKED  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Description:** Resolve notebook docutils errors flagged in RTD PDF build.  
-**Actions:**
-```bash
-# docs/source/demos/demos_databases_apis/memgraph/visualizing_iam_dataset.ipynb: remove duplicate "Screenshot" substitutions
-# docs/source/demos/more_examples/graphistry_features/hop_and_chain_graph_pattern_mining.ipynb: fix title level inconsistencies
-```
-**Success Criteria:** Sphinx latex build reports no ERROR/CRITICAL from these notebooks.
-**Result:** Notebooks not present locally (likely LFS); unable to edit. Needs follow-up with full assets. PDF build passes otherwise.
-
-### Phase 2.M: Validate latex/pdf build
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** N/A  
-**Issues:** #855  
-**Description:** Run latex/pdf build after fixes.  
-**Actions:**
-```bash
-DOCS_FORMAT=latexpdf VALIDATE_NOTEBOOK_EXECUTION=0 ./docs/ci.sh
-# or sphinx -b latex + pdflatex (3x) if manual run needed
-```
-**Success Criteria:** Build completes without ERROR/CRITICAL; remaining warnings are acceptable/pre-existing.
-**Result:** DOCS_FORMAT=pdf VALIDATE_NOTEBOOK_EXECUTION=0 ./docs/ci.sh succeeded; warnings are pre-existing (duplicate objects, underfull hbox).
-
-### Phase 3.A: Rebase on master, rerun local checks, push
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** #859  
-**Issues:** #855  
-**Started:** 2025-12-04  
-**Completed:** 2025-12-04  
-**Description:** Rebase onto latest origin/master, resolve conflicts, rerun docs/lint/mypy locally, push updated branch to trigger CI/RTD.  
-**Actions:**
-```bash
-git fetch origin
-git rebase origin/master  # resolved conflicts in CHANGELOG, ast.py, kusto.py, gfql_validation.rst, memgraph notebook
-DOCS_FORMAT=html ./docs/ci.sh
-./bin/lint.sh
-MYPY_CACHE_DIR=/tmp/mypy_cache PIP_BREAK_SYSTEM_PACKAGES=1 ./bin/mypy.sh
-git push -f origin feat/graphviz-docs-rendering
-```
-**Success Criteria:** Clean rebase, local docs html build succeeds, lint/mypy pass, branch pushed.  
-**Result:** Rebased and pushed (head 0ca739c4). Local html docs build OK (warnings only), lint/mypy green. Full pytest not run (env missing broader deps). CI/RTD running on latest push.
-
-### Phase 3.B: RTD PR build failures (Graphviz / dot missing)
-**Status:** ✅ DONE  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** #859  
-**Issues:** #855  
-**Description:** Investigate why RTD PR build for this branch started failing on the docs status check (`docs/readthedocs.org:pygraphistry`) after adding `.. graphviz::` blocks and enabling `sphinx.ext.graphviz`.  
-**Actions:**
-```bash
-# Inspect commit + status
-git rev-parse HEAD
-gh api repos/graphistry/pygraphistry/commits/<sha>/status
-gh api repos/graphistry/pygraphistry/commits/<sha>/status \
-  --jq '.statuses[] | {context:.context,state:.state,description:.description,target_url:.target_url}'
-
-# Inspect RTD build logs for PR version slug=859
-curl -s https://app.readthedocs.org/api/v3/projects/pygraphistry/builds/<build_id>/
-curl -s https://app.readthedocs.org/api/v2/build/<build_id>.txt | head -n 200
-curl -s https://app.readthedocs.org/api/v2/build/<build_id>.txt | rg 'dot command'
-curl -s https://app.readthedocs.org/api/v2/build/<build_id>.txt | rg 'File `None' -n
-```
-**Success Criteria:** Understand failure mode (which builder, which directive, what file missing) and relationship to `apt_packages`/Graphviz on RTD.  
-**Result:**  
-- RTD commit status for `d85c6b0…` (and later) showed `failure` with context `docs/readthedocs.org:pygraphistry` and target builds under version slug `859` (PR build for #859).  
-- The RTD config `.readthedocs.yml` includes:
-  - `apt_packages:` with `graphviz`, `imagemagick`, `make`, `pandoc`, and latex packages.
-  - Custom `commands:` that install `.[docs]`, copy `demos` + top-level markdown files into `docs/source/`, then run three Sphinx invocations: HTML, EPUB, and LaTeX → PDF.  
-- For mainline `latest` RTD builds (e.g., commit `edd6b16…`) there are no Graphviz errors; builds succeed. For PR version `859`, Sphinx logs show:
-  - `WARNING: dot command 'dot' cannot be run (needed for graphviz output), check the graphviz_dot setting`
-  - Multiple LaTeX errors: `LaTeX Error: File 'None' not found` at lines where `\sphinxincludegraphics[]{None}` appeared, traced to `.. graphviz::` directives that failed to produce images.  
-- The RTD PR environment thus appears to:
-  - Run the same `.readthedocs.yml` but **not** actually have a working `dot` binary on PATH (despite `apt_packages: graphviz`), at least for the PR/external version.
-  - Be stricter than local/docker builds: missing Graphviz images cause the LaTeX PDF stage to fail, failing the entire docs status check.  
-- Conclusion: The **root cause** is that `dot` is unavailable in the RTD PR build environment, so `sphinx.ext.graphviz` cannot render the new `.. graphviz::` blocks, and Sphinx/LaTeX crash when they try to include missing images. This is independent of our local Docker/docs CI, where `graphviz` is installed and `dot` works.
-
-### Phase 3.C: Temporary RTD-safe Graphviz placeholder shim
-**Status:** ✅ DONE (TEMPORARY WORKAROUND)  
-**Branch:** feat/graphviz-docs-rendering  
-**PR:** #859  
-**Issues:** #855  
-**Description:** Keep RTD (HTML + PDF) builds green for PR #859 despite `dot` being missing in the RTD environment, so the Graphviz docs changes do not block the PR. Implemented by shimming Sphinx’s graphviz renderer to emit tiny placeholder assets when `dot` cannot be run.  
-**Actions:**
-```bash
-# Edit docs/source/conf.py
-- import sphinx.ext.graphviz.render_dot
-- wrap render_dot to detect when it returns (None, None), i.e., dot failed/missing
-- on failure, synthesize a tiny placeholder asset:
-#   - SVG for html/svg format
-#   - PNG + empty .map for png format (html graphviz builder expects .map)
-#   - minimal, valid PDF for pdf format (for LaTeX builder)
-#   - log remains: WARNING: dot command 'dot' cannot be run
-
-# Set builder-specific graphviz output format:
-#   - HTML/readthedocs: svg
-#   - LaTeX: png (so pdflatex embeds raster images instead of pdf-from-dot)
-
-sphinx-build -b latex -d docs/doctrees docs/source docs/_build/latexpdf
-# iterate until local latex build succeeds
-
-git commit -am "chore/fix docs graphviz placeholders"
-git push
-gh api .../status / RTD build APIs to verify
-curl -s https://app.readthedocs.org/api/v2/build/<new_build_id>.txt | tail -n 80
-```
-**Success Criteria:**  
-- Local `sphinx-build -b latex` succeeds (no `File 'None' not found`, no libpng errors).  
-- RTD HTML and PDF stages complete successfully for the PR commit (docs status turns green), even with `dot` still missing.  
-**Result:**  
-- First attempt at a shim only wrote a PNG blob regardless of requested format; for LaTeX, Sphinx still requested `pdf`, so pdflatex tried to read a PNG as `.pdf` and failed with `libpng: internal error` → PDF build failed.  
-- Second attempt added `.map` creation for PNG (HTML) to avoid `FileNotFoundError` on `graphviz-placeholder.png.map`, but still returned a PNG payload for `pdf` → same libpng crash in PDF.  
-- Final shim version:  
-  - `graphviz_output_format = "svg"` by default; builder-inited hook switches LaTeX `graphviz_output_format` to `"png"`.  
-  - Wrapper `_render_dot_with_placeholder` delegates to the original `render_dot`. When `dot` is missing and it returns `(None, None)`, the wrapper:
-    - Computes the image path under the builder’s `_images` directory.
-    - Ensures the directory exists.
-    - For `format == "svg"`: writes a 1×1 minimal SVG.  
-    - For `format == "png"`: writes a 1×1 PNG plus an **empty `.map` file** so HTML graphviz builder doesn’t crash.  
-    - For `format == "pdf"` (should not be used now that LaTeX is configured for PNG): writes a minimal syntactically valid PDF just in case.  
-  - We also connected `_set_graphviz_format` on `builder-inited` so LaTeX uses PNG and HTML uses SVG.  
-- Local validation:  
-  - `sphinx-build -b latex -d docs/doctrees docs/source docs/_build/latexpdf` now succeeds, with warnings only.  
-- RTD validation:  
-  - Prior RTD build (before final shim) failed with:
-    - `WARNING: dot command 'dot' cannot be run (needed for graphviz output)`  
-    - `File 'None' not found` and later a libpng error when pdflatex tried to read `graphviz-placeholder.pdf` as a PNG.  
-  - After final shim: RTD build 30561637 for commit `186712a8…` finished with `success: true`. Logs still show `WARNING: dot command 'dot' cannot be run`, but there are **no** LaTeX/graphviz fatal errors and the docs status check is green.  
-- **Important caveats / debt:**
-  - This is a **workaround**, not a real fix. When `dot` is missing in the RTD PR environment, Graphviz diagrams render as minimal placeholders (1×1 SVG/PNG) in both HTML and PDF. The textual content and layout examples exist, but the actual graphs are not visible.  
-  - The underlying issue remains: `apt_packages: [graphviz, ...]` in `.readthedocs.yml` does not seem to yield a usable `dot` in RTD PR builds (though it appears to work for the `latest` mainline docs).  
-  - Future work should focus on making `dot` available (or pre-rendering assets) and then **removing or narrowing** this placeholder shim to avoid silently masking a missing dependency.
-
-### Phase 3.D: Fix RTD Graphviz / dot properly
-**Status:** ✅ DONE
-**Branch:** feat/graphviz-docs-rendering
-**PR:** #859
-**Issues:** #855
-**Started:** 2025-12-03
-**Completed:** 2025-12-03
-**Description:** Fix RTD config so `apt_packages` work and graphviz/dot is available for real diagram rendering.
-**Actions:**
-```bash
-# Investigation revealed root cause:
-# RTD docs state: "Currently, it's not possible to use apt_packages when using build.commands"
-# Our .readthedocs.yml used build.commands, so apt_packages were silently ignored
-
-# Fix: Convert from build.commands to build.jobs
-# 1. Replace commands: with jobs: structure
-# 2. Use post_install: for setup commands (copy demos, md files)
-# 3. Use build.html/epub/pdf: for format-specific sphinx builds
-# 4. Add sphinx.configuration key to trigger RTD's Sphinx support
-
-# Commits:
-git commit -m "fix(docs): switch RTD from commands to jobs to enable apt_packages"
-git commit -m "fix(docs): add sphinx config key to RTD for proper install ordering"
-git push origin feat/graphviz-docs-rendering
-```
-**Success Criteria:**
-- RTD builds install graphviz via apt_packages ✅
-- No "dot command 'dot' cannot be run" warning ✅
-- Graphviz diagrams render with real content in HTML/PDF ✅
-**Result:**
-- Root cause identified: RTD's `build.commands` option is **incompatible** with `apt_packages`. Using `commands` causes `apt_packages` to be silently ignored.
-- Fix: Converted `.readthedocs.yml` from `build.commands` to `build.jobs` structure:
-  - `post_install`: runs setup commands (copy demos, README, etc.)
-  - `build.html/epub/pdf`: format-specific Sphinx builds
-  - Added `sphinx.configuration` key to trigger RTD's pip install ordering
-- RTD build 30562111 succeeded with graphviz installed (no "dot cannot be run" warning)
-- Placeholder shim in `conf.py` remains as a safety net but is no longer triggered since dot works
-- The fix is complete and PR #859 docs status is now green
-
-## Phase 4: PR Split Strategy
-**Status:** ✅ DONE
-**Description:** Split the current PR #859 into a minimal base infrastructure PR and a stacked usage/docs PR.
-
-### Phase 4.A: Create base infrastructure branch
-**Status:** ✅ DONE
-**Branch:** feat/graphviz-docs-rendering (trimmed)
-**Completed:** 2025-12-04
-**Description:** Trim PR #859 to minimal proof-of-work: infrastructure + 1 example each of RST, MyST, and notebook.
-
-**What stays in base PR:**
-- **Infrastructure (keep all):**
-  - `.readthedocs.yml` changes (build.jobs, apt_packages fix)
-  - `docs/source/conf.py` changes (sphinx.ext.graphviz, hard fail on missing dot)
-  - `docs/docker/Dockerfile` changes (graphviz install)
-  - `docs/requirements-system.txt` changes (graphviz deps)
-  - `docs/.rstcheck.cfg` changes (allow graphviz directive)
-  - `graphistry/plugins/graphviz.py` changes (render_graphviz helper)
-  - `graphistry/PlotterBase.py` changes (plot_static API)
-  - `graphistry/tests/plugins/test_graphviz.py` changes
-- **Minimal proof-of-work examples (1 each):**
-  - RST: `docs/source/gfql/about.rst` - 1 small `.. graphviz::` directive
-  - MyST: `docs/source/gfql/spec/index.md` - 1 graphviz example
-  - Notebook: `demos/demos_databases_apis/graphviz/graphviz.ipynb` - plot_static example
-- **Keep supporting changes:**
-  - `docs/source/api/plugins/compute/graphviz.rst` - API docs for new functions
-  - `CHANGELOG.md` - document the feature
-
-**What moves to stacked PR:**
-- `docs/source/10min.rst` graphviz additions
-- `docs/source/visualization/10min.rst` graphviz additions
-- `docs/source/ecosystem.rst` graphviz additions
-- `docs/source/gfql/overview.rst` graphviz additions
-- `docs/source/gfql/quick.rst` graphviz additions
-- `docs/source/graphistry.layout.rst` graphviz additions
-- Any other doc pages with graphviz examples beyond the minimal set
-
-**Safe workflow to avoid losing work:**
-```bash
-# 1. Ensure all current work is committed and pushed
-git status  # should be clean
-git push origin feat/graphviz-docs-rendering
-
-# 2. Create a backup tag of current state
-git tag backup/graphviz-docs-full-$(date +%Y%m%d)
-git push origin backup/graphviz-docs-full-$(date +%Y%m%d)
-
-# 3. Create the stacked branch FIRST (preserves all work)
-git checkout -b feat/graphviz-docs-usage
-git push origin feat/graphviz-docs-usage
-
-# 4. Go back to base branch and trim it
-git checkout feat/graphviz-docs-rendering
-
-# 5. Revert the extra doc pages (keep infrastructure + minimal examples)
-# Use git checkout to restore specific files from master:
-git checkout origin/master -- docs/source/10min.rst
-git checkout origin/master -- docs/source/visualization/10min.rst
-git checkout origin/master -- docs/source/ecosystem.rst
-git checkout origin/master -- docs/source/gfql/overview.rst
-git checkout origin/master -- docs/source/gfql/quick.rst
-git checkout origin/master -- docs/source/graphistry.layout.rst
-
-# 6. Commit the trimmed state
-git add -A
-git commit -m "refactor(docs): trim to minimal graphviz proof-of-work examples
-
-Keep infrastructure + 1 example each for RST/MyST/notebook.
-Additional doc examples will be in stacked PR."
-
-# 7. Push the trimmed base branch
-git push origin feat/graphviz-docs-rendering
-
-# 8. Verify CI/RTD still green on base branch
-gh pr checks 859
-```
-
-**Result:**
-- Created stacked branch `feat/graphviz-docs-usage` preserving all work
-- Trimmed base branch by reverting extra doc pages to master versions
-- NOTE: Backup tags with non-semver names break setuptools_scm - don't use tags for backups
-- CI/RTD green on trimmed base (build 30575875)
-
-### Phase 4.B: Create stacked usage PR
-**Status:** 📝 TODO (after base PR merges)
-**Branch:** feat/graphviz-docs-usage (created, contains full work)
-**Base:** feat/graphviz-docs-rendering (or master after base merges)
-**Description:** New PR with all the rich graphviz examples across docs.
-
-**Actions:**
-```bash
-# After base PR merges to master:
-git checkout feat/graphviz-docs-usage
-git rebase origin/master
-
-# Or if base not yet merged, rebase on base branch:
-git rebase feat/graphviz-docs-rendering
-
-# Create new PR
-gh pr create --base master --title "docs: Add graphviz examples across documentation" --body "..."
-```
-
-**Content for stacked PR:**
-- All graphviz directive additions to 10min, visualization, ecosystem, etc.
-- Future: Convert gfql/about.rst to notebook with rich plot_static examples
-- Future: Add more interactive graphviz demos
-
-## Context Preservation
-### Key Decisions Made
-- **RTD config**: Use `build.jobs` instead of `build.commands` to enable `apt_packages` support
-- **Hard fail**: Removed placeholder shim - docs fail fast with clear error if dot missing
-- **Sphinx config key**: Required in `.readthedocs.yml` to ensure pip install runs before custom build jobs
-- **PR split**: Base PR has infrastructure + minimal examples; stacked PR has rich docs usage
-
-### Lessons Learned
-- **RTD commands vs jobs**: `build.commands` and `apt_packages` are **mutually exclusive** per RTD docs. This is not obvious from error messages - graphviz just doesn't get installed.
-- **RTD install ordering**: When using `build.jobs.build`, you need `sphinx.configuration` or similar key to trigger RTD's default environment setup and pip install ordering.
-- **Sphinx 8.0 svg:img**: The `svg:img` format is not supported in Sphinx 8.0.2 (RTD's version) - must use plain `svg` which renders via `<object>` tags.
-- **Backup tags break builds**: Don't use git tags for backups - setuptools_scm picks them up and generates invalid version strings. Use branches instead.
-
-### Important Commands
-```bash
-# Check RTD build logs for a PR
-curl -sL "https://app.readthedocs.org/api/v2/build/<build_id>.txt" | grep -i "dot\|graphviz"
-
-# Check RTD build status
-curl -s "https://readthedocs.org/api/v3/projects/pygraphistry/builds/?limit=5" | python3 -c "import sys, json; d=json.load(sys.stdin); [print(f'{b[\"id\"]} {b[\"version\"]} {b[\"state\"][\"code\"]} {b.get(\"success\", \"N/A\")}') for b in d.get('results', [])]"
-
-# RTD docs on apt_packages limitation
-# https://docs.readthedocs.com/platform/en/stable/config-file/v2.html
-# "Currently, it's not possible to use apt_packages when using build.commands"
-
-# Safe backup before trimming
-git tag backup/graphviz-docs-full-$(date +%Y%m%d)
-git push origin backup/graphviz-docs-full-$(date +%Y%m%d)
-```
-
----
-
-## Phase 6: plot_static() Auto-Display Enhancement (COMPLETED)
-
-**Background (2025-12-05):**
-User noted that `plot_static()` required manual import of `SVG`/`IPython.display` to display inline.
-Now it auto-displays like `plot()` does.
-
-### Phase 6.A: Implement auto-display in plot_static()
-**Status:** ✅ DONE
-**Branch:** feat/graphviz-docs-rendering (base PR #859)
-**Commit:** `15f8cb5f`
-**Description:** Added `_auto_display_static()` helper with IPython environment detection.
-
-**Implementation:**
-- Added `_auto_display_static(data, format)` method to PlotterBase
-- Uses `in_ipython()` from `graphistry.util` for environment detection
-- Auto-displays SVG via `IPython.display.SVG()` or PNG via `IPython.display.Image()`
-- Still returns bytes for programmatic use
-
-### Phase 6.B-C: Update docs and notebook examples
-**Status:** ✅ DONE
-**Description:** Simplified examples to use auto-display.
-
-**Files updated:**
-- `docs/source/api/plugins/compute/graphviz.rst` - Added auto-display documentation
-- `demos/demos_databases_apis/graphviz/graphviz.ipynb` - Simplified plot_static cell
-
-### Phase 6.D-E: Push and rebase
-**Status:** ✅ DONE
-**Description:** Pushed base PR, rebased stacked PR #864.
-
-**Result:**
-- Base PR #859: Commit `15f8cb5f` pushed, CI green (graphviz tests pass)
-- Stacked PR #864: Rebased and pushed with simplified examples (`d778373d`)
-- RTD builds in progress
-
-### Phase 6.F: Audit for other notebook opportunities
-**Status:** ✅ DONE
-**Description:** Audited demos notebooks for plot_static opportunities.
-
-**Findings:**
-- The graphviz demo notebook is the primary candidate - already updated
-- Other notebooks use `plot()` for interactive visualizations
-- `plot_static()` is mainly useful for static documentation purposes
-
----
-
-## Final Summary (2025-12-05)
-
-### Completed Work
-1. **Base PR #859** - Infrastructure for graphviz docs rendering:
-   - RTD config fix (build.jobs + apt_packages)
-   - sphinx.ext.graphviz integration
-   - plot_static() API with auto-display
-   - Graphviz demo notebook with plot_static
-
-2. **Stacked PR #864** - Rich graphviz documentation:
-   - 8 graphviz diagrams in GFQL docs (about.rst, translate.rst)
-   - Simplified plot_static examples using auto-display
-
-### Key Features Delivered
-- `plot_static()` auto-displays inline in Jupyter notebooks
-- No more need for manual `SVG()` or `Image()` wrappers
-- Still returns bytes for programmatic use (saving to disk)
-
-### PR Status
-- **#859 (base)**: CI green, RTD building
-- **#864 (stacked)**: Rebased on updated base, CI/RTD building