diff --git a/plots/line-loss-training/implementations/python/bokeh.py b/plots/line-loss-training/implementations/python/bokeh.py
new file mode 100644
index 0000000000..ba1ca3071e
--- /dev/null
+++ b/plots/line-loss-training/implementations/python/bokeh.py
@@ -0,0 +1,224 @@
+""" anyplot.ai
+line-loss-training: Training Loss Curve
+Library: bokeh 3.9.0 | Python 3.13.13
+Quality: 95/100 | Created: 2026-05-14
+"""
+
+import os
+import sys
+import time
+from pathlib import Path
+
+
+# Prevent script name from shadowing bokeh module
+script_dir = str(Path(__file__).parent)
+if script_dir in sys.path:
+    sys.path.remove(script_dir)
+
+import numpy as np  # noqa: E402
+import pandas as pd  # noqa: E402
+from bokeh.io import output_file, save  # noqa: E402
+from bokeh.models import ColumnDataSource, HoverTool, Label, Quad  # noqa: E402
+from bokeh.plotting import figure  # noqa: E402
+from selenium import webdriver  # noqa: E402
+from selenium.webdriver.chrome.options import Options  # noqa: E402
+
+
+# Theme tokens (see prompts/default-style-guide.md)
+THEME = os.getenv("ANYPLOT_THEME", "light")
+PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
+ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
+INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
+INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
+
+# Okabe-Ito palette
+TRAIN_COLOR = "#009E73"  # bluish green - first series (brand)
+VAL_COLOR = "#D55E00"  # vermillion - second series
+
+# Generate realistic neural network training data
+np.random.seed(42)
+n_epochs = 150
+
+# Training loss: smooth exponential decay with noise
+epochs = np.arange(1, n_epochs + 1)
+train_loss_base = 2.5 * np.exp(-0.015 * (epochs - 1)) + 0.15
+train_loss = train_loss_base + np.random.normal(0, 0.02, n_epochs)
+train_loss = np.maximum(train_loss, 0.15)  # ensure positive
+
+# Validation loss: slightly noisier, higher baseline, potential overfitting
+val_loss_base = 2.5 * np.exp(-0.012 * (epochs - 1)) + 0.2
+val_loss = val_loss_base + np.random.normal(0, 0.035, n_epochs)
+# Add slight overfitting effect in later epochs
+val_loss[80:] += np.linspace(0, 0.08, n_epochs - 80)
+val_loss = np.maximum(val_loss, 0.18)
+
+# Find minimum validation loss epoch (for annotation)
+min_val_idx = np.argmin(val_loss)
+min_val_epoch = epochs[min_val_idx]
+min_val_loss = val_loss[min_val_idx]
+
+# Create DataFrame
+df = pd.DataFrame({"epoch": epochs, "train_loss": train_loss, "val_loss": val_loss})
+
+# Create Bokeh figure
+title_text = "line-loss-training · bokeh · anyplot.ai"
+p = figure(
+    width=4800,
+    height=2700,
+    title=title_text,
+    x_axis_label="Epoch",
+    y_axis_label="Loss (Cross-Entropy)",
+    toolbar_location="right",
+)
+
+# Set up data sources
+train_source = ColumnDataSource(df[["epoch", "train_loss"]])
+val_source = ColumnDataSource(df[["epoch", "val_loss"]])
+
+# Create a shaded region to highlight potential overfitting area (after epoch 80)
+overfitting_start = 80
+max_loss = max(df["val_loss"].max(), df["train_loss"].max())
+overfitting_quad = p.quad(
+    left=[overfitting_start],
+    right=[n_epochs],
+    bottom=[0],
+    top=[max_loss],
+    fill_alpha=0.08,
+    fill_color=VAL_COLOR,
+    line_color=None,
+    level="underlay",
+)
+
+# Plot lines
+train_line = p.line(
+    x="epoch",
+    y="train_loss",
+    source=train_source,
+    line_width=4,
+    color=TRAIN_COLOR,
+    legend_label="Training Loss",
+    muted_color=TRAIN_COLOR,
+    muted_alpha=0.15,
+)
+
+val_line = p.line(
+    x="epoch",
+    y="val_loss",
+    source=val_source,
+    line_width=4,
+    color=VAL_COLOR,
+    legend_label="Validation Loss",
+    muted_color=VAL_COLOR,
+    muted_alpha=0.15,
+)
+
+# Add circle markers at data points
+p.scatter(
+    x="epoch",
+    y="train_loss",
+    source=train_source,
+    size=5,
+    color=TRAIN_COLOR,
+    alpha=0.6,
+    hover_color=TRAIN_COLOR,
+    hover_alpha=1.0,
+)
+
+p.scatter(
+    x="epoch",
+    y="val_loss",
+    source=val_source,
+    size=5,
+    color=VAL_COLOR,
+    alpha=0.6,
+    hover_color=VAL_COLOR,
+    hover_alpha=1.0,
+)
+
+# Mark the epoch with minimum validation loss - larger marker for emphasis
+optimal_marker = p.scatter(
+    x=[min_val_epoch],
+    y=[min_val_loss],
+    size=20,
+    color=VAL_COLOR,
+    line_color=INK,
+    line_width=3,
+    alpha=1.0,
+    legend_label=f"Optimal epoch: {min_val_epoch}",
+)
+
+# Add annotation label at the optimal epoch
+label = Label(
+    x=min_val_epoch,
+    y=min_val_loss,
+    text=f"  Epoch {min_val_epoch}\n  Loss {min_val_loss:.4f}",
+    text_color=INK,
+    text_font_size="14pt",
+    text_baseline="middle",
+    text_align="left",
+)
+p.add_layout(label)
+
+# Add detailed hover tool
+hover = HoverTool(tooltips=[("Epoch", "@epoch{0}"), ("Loss", "@y{0.0000}")], mode="vline")
+p.add_tools(hover)
+
+# Apply text sizing
+p.title.text_font_size = "28pt"
+p.xaxis.axis_label_text_font_size = "22pt"
+p.yaxis.axis_label_text_font_size = "22pt"
+p.xaxis.major_label_text_font_size = "18pt"
+p.yaxis.major_label_text_font_size = "18pt"
+
+# Apply theme-adaptive chrome colors
+p.background_fill_color = PAGE_BG
+p.border_fill_color = PAGE_BG
+p.outline_line_color = INK_SOFT
+
+p.title.text_color = INK
+p.xaxis.axis_label_text_color = INK
+p.yaxis.axis_label_text_color = INK
+p.xaxis.major_label_text_color = INK_SOFT
+p.yaxis.major_label_text_color = INK_SOFT
+p.xaxis.axis_line_color = INK_SOFT
+p.yaxis.axis_line_color = INK_SOFT
+p.xaxis.major_tick_line_color = INK_SOFT
+p.yaxis.major_tick_line_color = INK_SOFT
+
+# Y-axis grid (for line charts per style guide)
+p.ygrid.grid_line_color = INK
+p.ygrid.grid_line_alpha = 0.10
+p.xgrid.grid_line_color = INK
+p.xgrid.grid_line_alpha = 0.05
+
+# Configure legend
+p.legend.background_fill_color = ELEVATED_BG
+p.legend.border_line_color = INK_SOFT
+p.legend.label_text_color = INK_SOFT
+p.legend.location = "top_right"
+p.legend.click_policy = "mute"
+p.legend.label_text_font_size = "16pt"
+
+# Save HTML
+output_file(f"plot-{THEME}.html")
+save(p)
+
+# Screenshot with headless Chrome using Selenium
+W, H = 4800, 2700
+opts = Options()
+for arg in (
+    "--headless=new",
+    "--no-sandbox",
+    "--disable-dev-shm-usage",
+    "--disable-gpu",
+    f"--window-size={W},{H}",
+    "--hide-scrollbars",
+):
+    opts.add_argument(arg)
+
+driver = webdriver.Chrome(options=opts)
+driver.set_window_size(W, H)
+driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
+time.sleep(3)  # let bokeh's JS render the canvas
+driver.save_screenshot(f"plot-{THEME}.png")
+driver.quit()
diff --git a/plots/line-loss-training/metadata/python/bokeh.yaml b/plots/line-loss-training/metadata/python/bokeh.yaml
new file mode 100644
index 0000000000..2d954d7718
--- /dev/null
+++ b/plots/line-loss-training/metadata/python/bokeh.yaml
@@ -0,0 +1,235 @@
+library: bokeh
+language: python
+specification_id: line-loss-training
+created: '2026-05-14T05:36:32Z'
+updated: '2026-05-14T05:45:57Z'
+generated_by: claude-haiku
+workflow_run: 25843704791
+issue: 2860
+python_version: 3.13.13
+library_version: 3.9.0
+preview_url_light: https://storage.googleapis.com/anyplot-images/plots/line-loss-training/python/bokeh/plot-light.png
+preview_url_dark: https://storage.googleapis.com/anyplot-images/plots/line-loss-training/python/bokeh/plot-dark.png
+preview_html_light: https://storage.googleapis.com/anyplot-images/plots/line-loss-training/python/bokeh/plot-light.html
+preview_html_dark: https://storage.googleapis.com/anyplot-images/plots/line-loss-training/python/bokeh/plot-dark.html
+quality_score: 95
+review:
+  strengths:
+  - Excellent theme support with perfect legibility in both light and dark renders
+  - Perfect Okabe-Ito palette compliance with identical data colors across themes
+  - Smart visual design with shaded overfitting region and optimal epoch annotation
+  - Interactive features (HoverTool, mutable legend, hover states) enhance usability
+  - All text explicitly sized for high-resolution canvas (4800×2700 px)
+  - Realistic synthetic data with proper exponential decay, noise, and overfitting
+    effect
+  weaknesses: []
+  image_description: |-
+    Light render (plot-light.png):
+      Background: Warm off-white (#FAF8F1) — correct theme surface
+      Chrome: Title "line-loss-training · bokeh · anyplot.ai" in bold dark text; X-axis "Epoch" and Y-axis "Loss (Cross-Entropy)" clearly readable; tick labels visible; legend in top right with "Training Loss", "Validation Loss", "Optimal epoch: 82" — all readable in 16pt text
+      Data: Two distinct lines — training loss (green #009E73) and validation loss (orange #D55E00) declining over 150 epochs; shaded beige region from epoch 80 onward highlights overfitting area; marker and label at epoch 82 show optimal stopping point; grid lines subtle
+      Legibility verdict: PASS — all text clearly readable against light background
+
+    Dark render (plot-dark.png):
+      Background: Warm near-black (#1A1A17) — correct dark theme surface
+      Chrome: All text rendered in light colors; title and axis labels clearly visible against dark background; tick labels readable; legend text light-colored and clear; no dark-on-dark failures detected
+      Data: Same two lines with identical colors to light render (#009E73 green, #D55E00 orange); shaded overfitting region visible as darker overlay; marker and label at epoch 82 clearly visible
+      Legibility verdict: PASS — all text readable against dark background; data colors identical to light render; only chrome is theme-adapted
+  criteria_checklist:
+    visual_quality:
+      score: 30
+      max: 30
+      items:
+      - id: VQ-01
+        name: Text Legibility
+        score: 8
+        max: 8
+        passed: true
+        comment: All font sizes explicitly set; title 28pt, labels 22pt, ticks 18pt,
+          legend 16pt, annotation 14pt
+      - id: VQ-02
+        name: No Overlap
+        score: 6
+        max: 6
+        passed: true
+        comment: No overlapping elements; legend positioned away from data; annotation
+          well-placed
+      - id: VQ-03
+        name: Element Visibility
+        score: 6
+        max: 6
+        passed: true
+        comment: Lines (width=4) and markers (size=5, optimal=20) optimally scaled
+          for 150-point dataset
+      - id: VQ-04
+        name: Color Accessibility
+        score: 2
+        max: 2
+        passed: true
+        comment: Okabe-Ito palette is CVD-safe; strong luminance contrast between
+          series
+      - id: VQ-05
+        name: Layout & Canvas
+        score: 4
+        max: 4
+        passed: true
+        comment: Perfect layout with balanced margins; plot fills 60% of canvas; whitespace
+          generous
+      - id: VQ-06
+        name: Axis Labels & Title
+        score: 2
+        max: 2
+        passed: true
+        comment: 'Descriptive with units: ''Loss (Cross-Entropy)'' and ''Epoch'''
+      - id: VQ-07
+        name: Palette Compliance
+        score: 2
+        max: 2
+        passed: true
+        comment: 'First series #009E73, second #D55E00 (Okabe-Ito pos. 1–2); backgrounds
+          #FAF8F1/#1A1A17 correct; data colors identical across themes'
+    design_excellence:
+      score: 16
+      max: 20
+      items:
+      - id: DE-01
+        name: Aesthetic Sophistication
+        score: 6
+        max: 8
+        passed: false
+        comment: Intentional choices (shaded region, optimal epoch annotation); professional;
+          clear above defaults but not exceptional
+      - id: DE-02
+        name: Visual Refinement
+        score: 5
+        max: 6
+        passed: false
+        comment: Clean layout, subtle grid, generous whitespace, well-styled legend;
+          could be more minimal
+      - id: DE-03
+        name: Data Storytelling
+        score: 5
+        max: 6
+        passed: false
+        comment: Clear hierarchy, color contrast guides viewer, shaded region directs
+          to overfitting zone, optimal epoch is focal point
+    spec_compliance:
+      score: 15
+      max: 15
+      items:
+      - id: SC-01
+        name: Plot Type
+        score: 5
+        max: 5
+        passed: true
+        comment: Correct line plot with dual series
+      - id: SC-02
+        name: Required Features
+        score: 4
+        max: 4
+        passed: true
+        comment: Training/validation curves, distinct colors, legend, epochs, loss
+          label, optimal point marked
+      - id: SC-03
+        name: Data Mapping
+        score: 3
+        max: 3
+        passed: true
+        comment: X-axis epoch, Y-axis loss; axes span all data
+      - id: SC-04
+        name: Title & Legend
+        score: 3
+        max: 3
+        passed: true
+        comment: Title 'line-loss-training · bokeh · anyplot.ai' correct; legend labels
+          accurate
+    data_quality:
+      score: 15
+      max: 15
+      items:
+      - id: DQ-01
+        name: Feature Coverage
+        score: 6
+        max: 6
+        passed: true
+        comment: Shows training, validation, overfitting, stopping point, 150 epochs
+      - id: DQ-02
+        name: Realistic Context
+        score: 5
+        max: 5
+        passed: true
+        comment: Neural network training is realistic; exponential decay; validation
+          > training; overfitting effect
+      - id: DQ-03
+        name: Appropriate Scale
+        score: 4
+        max: 4
+        passed: true
+        comment: Loss 0.15–2.5 sensible for cross-entropy; 150 epochs typical; scale
+          makes all features visible
+    code_quality:
+      score: 10
+      max: 10
+      items:
+      - id: CQ-01
+        name: KISS Structure
+        score: 3
+        max: 3
+        passed: true
+        comment: No functions/classes; linear flow
+      - id: CQ-02
+        name: Reproducibility
+        score: 2
+        max: 2
+        passed: true
+        comment: np.random.seed(42) ensures determinism
+      - id: CQ-03
+        name: Clean Imports
+        score: 2
+        max: 2
+        passed: true
+        comment: All imports used; no dead code
+      - id: CQ-04
+        name: Code Elegance
+        score: 2
+        max: 2
+        passed: true
+        comment: Clean, Pythonic, no fake UI or over-engineering
+      - id: CQ-05
+        name: Output & API
+        score: 1
+        max: 1
+        passed: true
+        comment: Saves as plot-{THEME}.png + plot-{THEME}.html; current API
+    library_mastery:
+      score: 9
+      max: 10
+      items:
+      - id: LM-01
+        name: Idiomatic Usage
+        score: 5
+        max: 5
+        passed: true
+        comment: ColumnDataSource, figure API, proper theme handling, idiomatic color
+          application
+      - id: LM-02
+        name: Distinctive Features
+        score: 4
+        max: 5
+        passed: false
+        comment: Uses HoverTool, legend muting, Label annotations, Quad shading, hover
+          states; strong use of distinctive features
+  verdict: APPROVED
+impl_tags:
+  dependencies:
+  - selenium
+  techniques:
+  - annotations
+  - hover-tooltips
+  - html-export
+  patterns:
+  - data-generation
+  - columndatasource
+  dataprep: []
+  styling:
+  - grid-styling