diff --git a/src/multi_swarm/dashboard/aquarium.py b/src/multi_swarm/dashboard/aquarium.py
index a94ba13..817f13e 100644
--- a/src/multi_swarm/dashboard/aquarium.py
+++ b/src/multi_swarm/dashboard/aquarium.py
@@ -1,8 +1,9 @@
 """Aquarium 2D visualization helpers.
 
-Builds a list of fish records from a merged DataFrame (evaluations + genomes)
-and renders a self-contained HTML/JS canvas animation, embeddable in Streamlit
-via ``streamlit.components.v1.html``.
+Builds fish records (with full genome attributes + ancestor lineage) and
+renders a self-contained HTML/JS canvas animation, embeddable in Streamlit
+via ``streamlit.components.v1.html``. Includes a click handler that opens
+an info panel showing genome details and BFS ancestor levels.
 """
 
 from __future__ import annotations
@@ -25,50 +26,6 @@ STYLE_COLORS: dict[str, str] = {
 DEFAULT_COLOR: str = "#94a3b8"
 
 
-def build_fish_dataset(merged: pd.DataFrame, max_fish: int = 30) -> list[dict[str, Any]]:
-    """Build a list of fish records from a merged evaluations+genomes DataFrame.
-
-    Expects columns: ``genome_id``, ``fitness``, ``cognitive_style``, ``n_trades``,
-    ``dsr``. Rows with NaN ``fitness`` are dropped. Result is sorted by fitness
-    descending and capped at ``max_fish`` entries.
-    """
-    if merged.empty:
-        return []
-
-    cols_needed = ["genome_id", "fitness", "cognitive_style", "n_trades", "dsr"]
-    available = [c for c in cols_needed if c in merged.columns]
-    df = merged[available].copy()
-
-    if "fitness" not in df.columns:
-        return []
-
-    df = df.dropna(subset=["fitness"])
-    df = df.sort_values("fitness", ascending=False).head(max_fish)
-
-    fish: list[dict[str, Any]] = []
-    for _, row in df.iterrows():
-        gid = str(row.get("genome_id", ""))
-        fitness_val = float(row.get("fitness", 0.0))
-        if math.isnan(fitness_val):
-            fitness_val = 0.0
-        style_raw = row.get("cognitive_style", None)
-        style = str(style_raw) if style_raw is not None and not _is_nan(style_raw) else "unknown"
-        n_trades_raw = row.get("n_trades", 0)
-        n_trades = int(n_trades_raw) if not _is_nan(n_trades_raw) else 0
-        dsr_raw = row.get("dsr", 0.0)
-        dsr = float(dsr_raw) if not _is_nan(dsr_raw) else 0.0
-        fish.append(
-            {
-                "id": gid,
-                "fitness": fitness_val,
-                "cognitive_style": style,
-                "n_trades": n_trades,
-                "dsr": dsr,
-            }
-        )
-    return fish
-
-
 def _is_nan(v: Any) -> bool:
     try:
         return bool(pd.isna(v))
@@ -76,17 +33,236 @@ def _is_nan(v: Any) -> bool:
         return False
 
 
+def _safe_float(v: Any, default: float = 0.0) -> float:
+    if v is None or _is_nan(v):
+        return default
+    try:
+        return float(v)
+    except (TypeError, ValueError):
+        return default
+
+
+def _safe_int(v: Any, default: int = 0) -> int:
+    if v is None or _is_nan(v):
+        return default
+    try:
+        return int(v)
+    except (TypeError, ValueError):
+        return default
+
+
+def _safe_str(v: Any, default: str = "") -> str:
+    if v is None or _is_nan(v):
+        return default
+    return str(v)
+
+
+def _safe_list(v: Any) -> list[Any]:
+    if v is None:
+        return []
+    if isinstance(v, list):
+        return list(v)
+    # pandas may store python lists in object cells; if it's e.g. a numpy array,
+    # falling back to list() is fine. NaN scalar is excluded by _is_nan.
+    if _is_nan(v):
+        return []
+    try:
+        return list(v)
+    except TypeError:
+        return []
+
+
+def build_lineage_index(
+    genomes_df: pd.DataFrame, evals_df: pd.DataFrame
+) -> dict[str, dict[str, Any]]:
+    """Build ``{genome_id: attrs}`` for every genome in the run.
+
+    ``genomes_df`` must come from ``genomes_df(repo, run_id)`` (no gen filter):
+    columns include ``id``, ``generation_idx``, ``system_prompt``,
+    ``feature_access``, ``temperature``, ``top_p``, ``model_tier``,
+    ``lookback_window``, ``cognitive_style``, ``parent_ids``, ``generation``.
+
+    ``evals_df`` must come from ``evaluations_df(repo, run_id)``: columns
+    include ``genome_id``, ``fitness``, ``dsr``, ``sharpe``, ``max_dd``,
+    ``n_trades``.
+    """
+    if genomes_df.empty:
+        return {}
+
+    if evals_df is None or evals_df.empty:
+        merged = genomes_df.copy()
+        for col in ("fitness", "dsr", "sharpe", "max_dd", "n_trades"):
+            if col not in merged.columns:
+                merged[col] = 0.0 if col != "n_trades" else 0
+    else:
+        merged = genomes_df.merge(
+            evals_df,
+            left_on="id",
+            right_on="genome_id",
+            how="left",
+            suffixes=("", "_eval"),
+        )
+
+    index: dict[str, dict[str, Any]] = {}
+    for _, row in merged.iterrows():
+        gid = _safe_str(row.get("id"))
+        if not gid:
+            continue
+        # ``generation`` is the genome's evolutionary generation (from payload).
+        # If absent, fall back to ``generation_idx`` (column added by the
+        # repository). Defensive: both may be missing in edge cases.
+        gen_val: Any = row.get("generation")
+        if gen_val is None or _is_nan(gen_val):
+            gen_val = row.get("generation_idx", 0)
+        index[gid] = {
+            "id": gid,
+            "generation": _safe_int(gen_val, 0),
+            "fitness": _safe_float(row.get("fitness"), 0.0),
+            "dsr": _safe_float(row.get("dsr"), 0.0),
+            "sharpe": _safe_float(row.get("sharpe"), 0.0),
+            "max_dd": _safe_float(row.get("max_dd"), 0.0),
+            "n_trades": _safe_int(row.get("n_trades"), 0),
+            "cognitive_style": _safe_str(row.get("cognitive_style"), ""),
+            "system_prompt": _safe_str(row.get("system_prompt"), ""),
+            "temperature": _safe_float(row.get("temperature"), 0.0),
+            "lookback_window": _safe_int(row.get("lookback_window"), 0),
+            "feature_access": _safe_list(row.get("feature_access")),
+            "model_tier": _safe_str(row.get("model_tier"), ""),
+            "parent_ids": _safe_list(row.get("parent_ids")),
+        }
+    return index
+
+
+def trace_ancestors(
+    genome_id: str,
+    lineage_index: dict[str, dict[str, Any]],
+    max_levels: int = 5,
+) -> list[list[dict[str, Any]]]:
+    """BFS over ``parent_ids`` returning levels of ancestors.
+
+    ``levels[0]`` = direct parents, ``levels[1]`` = grandparents, etc. Each
+    entry is a small dict (no ``system_prompt``, to keep JSON payload light):
+    ``{id, generation, fitness, cognitive_style}``. Cycles are guarded via a
+    ``seen`` set; missing parents (not in this run) are stubbed with sentinel
+    values so the lineage display still renders the relationship.
+    """
+    levels: list[list[dict[str, Any]]] = []
+    root = lineage_index.get(genome_id, {})
+    current_ids: list[str] = list(root.get("parent_ids", []))
+    seen: set[str] = {genome_id}
+    for _ in range(max_levels):
+        if not current_ids:
+            break
+        level_entries: list[dict[str, Any]] = []
+        next_ids: list[str] = []
+        for pid in current_ids:
+            if pid in seen:
+                continue
+            seen.add(pid)
+            entry = lineage_index.get(pid)
+            if entry is None:
+                level_entries.append(
+                    {
+                        "id": pid,
+                        "generation": -1,
+                        "fitness": 0.0,
+                        "cognitive_style": "",
+                    }
+                )
+                continue
+            level_entries.append(
+                {
+                    "id": entry["id"],
+                    "generation": entry["generation"],
+                    "fitness": entry["fitness"],
+                    "cognitive_style": entry["cognitive_style"],
+                }
+            )
+            next_ids.extend(entry.get("parent_ids", []))
+        if not level_entries:
+            break
+        levels.append(level_entries)
+        current_ids = next_ids
+    return levels
+
+
+def build_fish_dataset(
+    active_df: pd.DataFrame,
+    lineage_index: dict[str, dict[str, Any]] | None = None,
+    max_lineage_levels: int = 5,
+) -> list[dict[str, Any]]:
+    """Build full fish records for each active genome.
+
+    For every row in ``active_df`` the matching entry in ``lineage_index`` is
+    looked up by ``genome_id`` (or ``id``) and attached together with the BFS
+    ancestor levels. Rows whose id is not in the index are skipped.
+
+    Backward-compat: if ``lineage_index`` is ``None`` (legacy call site, e.g.
+    test fixtures with simple merged DataFrames) we synthesize a minimal
+    lineage from ``active_df`` itself so the function still returns useful
+    fish records.
+    """
+    if active_df.empty:
+        return []
+
+    if lineage_index is None:
+        # Legacy path: build a tiny index from the active df only.
+        synth: dict[str, dict[str, Any]] = {}
+        for _, row in active_df.iterrows():
+            gid = _safe_str(row.get("genome_id") or row.get("id"))
+            if not gid:
+                continue
+            fitness_val = _safe_float(row.get("fitness"), float("nan"))
+            if math.isnan(fitness_val):
+                continue
+            synth[gid] = {
+                "id": gid,
+                "generation": _safe_int(row.get("generation"), 0),
+                "fitness": fitness_val,
+                "dsr": _safe_float(row.get("dsr"), 0.0),
+                "sharpe": _safe_float(row.get("sharpe"), 0.0),
+                "max_dd": _safe_float(row.get("max_dd"), 0.0),
+                "n_trades": _safe_int(row.get("n_trades"), 0),
+                "cognitive_style": _safe_str(row.get("cognitive_style"), "unknown"),
+                "system_prompt": _safe_str(row.get("system_prompt"), ""),
+                "temperature": _safe_float(row.get("temperature"), 0.0),
+                "lookback_window": _safe_int(row.get("lookback_window"), 0),
+                "feature_access": _safe_list(row.get("feature_access")),
+                "model_tier": _safe_str(row.get("model_tier"), ""),
+                "parent_ids": _safe_list(row.get("parent_ids")),
+            }
+        lineage_index = synth
+
+    fish: list[dict[str, Any]] = []
+    for _, row in active_df.iterrows():
+        gid = _safe_str(row.get("genome_id") or row.get("id"))
+        if not gid:
+            continue
+        attrs = lineage_index.get(gid)
+        if attrs is None:
+            continue
+        if math.isnan(attrs.get("fitness", 0.0)):
+            continue
+        ancestors = trace_ancestors(gid, lineage_index, max_lineage_levels)
+        record = {**attrs, "ancestors": ancestors}
+        fish.append(record)
+    return fish
+
+
 def build_aquarium_html(
     fish: list[dict[str, Any]],
     canvas_w: int = 1000,
     canvas_h: int = 600,
-    show_labels: bool = False,
 ) -> str:
-    """Build the self-contained HTML/JS string for the aquarium canvas."""
+    """Build the self-contained HTML/JS string for the aquarium canvas.
+
+    The output embeds a click handler: tapping a fish opens an info panel
+    (top-right of the canvas) showing its genome attributes and BFS ancestor
+    levels. Labels are no longer rendered on the canvas itself.
+    """
     fish_json = json.dumps(fish)
     palette_json = json.dumps(STYLE_COLORS)
     default_color = DEFAULT_COLOR
-    show_labels_js = "true" if show_labels else "false"
 
     # All braces inside <style>/<script> are escaped to literals using {{ }}
     # so we can use Python f-string substitution for the few JSON payloads.
@@ -95,14 +271,40 @@ def build_aquarium_html(
   <canvas id="aquarium" width="{canvas_w}" height="{canvas_h}"
           style="width:100%;height:{canvas_h}px;border-radius:12px;
                  background:linear-gradient(180deg,#0a2540 0%,#1a4d80 100%);
-                 display:block;"></canvas>
+                 display:block;cursor:pointer;"></canvas>
+  <div id="fish-info-panel" style="
+    position:absolute;
+    top:12px;
+    right:12px;
+    width:340px;
+    max-height:580px;
+    overflow-y:auto;
+    background:rgba(8,16,32,0.92);
+    color:#e2e8f0;
+    border-radius:10px;
+    padding:14px 16px;
+    font-family:system-ui,-apple-system,sans-serif;
+    font-size:12px;
+    line-height:1.5;
+    border:1px solid rgba(255,255,255,0.1);
+    backdrop-filter:blur(6px);
+    -webkit-backdrop-filter:blur(6px);
+    display:none;
+    z-index:10;
+  ">
+    <div id="fish-info-content"></div>
+    <button id="fish-info-close" style="
+      position:absolute;top:8px;right:10px;
+      background:transparent;color:#94a3b8;border:none;
+      cursor:pointer;font-size:16px;
+    ">&times;</button>
+  </div>
 </div>
 <script>
 (function() {{
   const FISH_DATA = {fish_json};
   const STYLE_COLORS = {palette_json};
   const DEFAULT_COLOR = {json.dumps(default_color)};
-  const SHOW_LABELS = {show_labels_js};
 
   const canvas = document.getElementById('aquarium');
   if (!canvas) return;
@@ -110,6 +312,15 @@ def build_aquarium_html(
   const W = canvas.width;
   const H = canvas.height;
 
+  const panel = document.getElementById('fish-info-panel');
+  const panelContent = document.getElementById('fish-info-content');
+  const closeBtn = document.getElementById('fish-info-close');
+  if (closeBtn) {{
+    closeBtn.addEventListener('click', function() {{
+      panel.style.display = 'none';
+    }});
+  }}
+
   // Normalize fitness for sizing.
   let maxFit = 0.0;
   for (const f of FISH_DATA) {{
@@ -128,15 +339,14 @@ def build_aquarium_html(
     return STYLE_COLORS[style] || DEFAULT_COLOR;
   }}
 
-  // Init fish state.
-  const fish = FISH_DATA.map((f, idx) => {{
+  // Init fish state. Each entry keeps a reference to the original data dict
+  // so the click handler can show full attributes + ancestors.
+  const fishState = FISH_DATA.map(function(f, idx) {{
     const r = radiusFor(f.fitness);
     return {{
-      id: f.id,
-      fitness: f.fitness,
-      style: f.cognitive_style,
+      data: f,
       color: colorFor(f.cognitive_style),
-      r: r,
+      radius: r,
       x: Math.random() * (W - 2 * r) + r,
       y: Math.random() * (H - 2 * r) + r,
       vx: (Math.random() - 0.5) * 1.5,
@@ -147,12 +357,12 @@ def build_aquarium_html(
 
   // Bubbles for ambience.
   const N_BUBBLES = 25;
-  const bubbles = Array.from({{length: N_BUBBLES}}, () => ({{
+  const bubbles = Array.from({{length: N_BUBBLES}}, function() {{ return {{
     x: Math.random() * W,
     y: Math.random() * H,
     r: 1 + Math.random() * 3,
     vy: 0.3 + Math.random() * 0.7,
-  }}));
+  }}; }});
 
   function drawBubble(b) {{
     ctx.beginPath();
@@ -177,55 +387,46 @@ def build_aquarium_html(
 
     // Halo for top-3 fish.
     if (f.rank < 3) {{
-      const grad = ctx.createRadialGradient(0, 0, f.r * 0.5, 0, 0, f.r * 2.0);
+      const grad = ctx.createRadialGradient(0, 0, f.radius * 0.5, 0, 0, f.radius * 2.0);
       grad.addColorStop(0, f.color + 'aa');
       grad.addColorStop(1, f.color + '00');
       ctx.fillStyle = grad;
       ctx.beginPath();
-      ctx.arc(0, 0, f.r * 2.0, 0, Math.PI * 2);
+      ctx.arc(0, 0, f.radius * 2.0, 0, Math.PI * 2);
       ctx.fill();
     }}
 
     // Body (ellipse).
     ctx.fillStyle = f.color;
     ctx.beginPath();
-    ctx.ellipse(0, 0, f.r, f.r * 0.6, 0, 0, Math.PI * 2);
+    ctx.ellipse(0, 0, f.radius, f.radius * 0.6, 0, 0, Math.PI * 2);
     ctx.fill();
 
-    // Tail (triangle pointing left, body extends right).
+    // Tail.
     ctx.beginPath();
-    ctx.moveTo(-f.r, 0);
-    ctx.lineTo(-f.r * 1.6, -f.r * 0.5);
-    ctx.lineTo(-f.r * 1.6, f.r * 0.5);
+    ctx.moveTo(-f.radius, 0);
+    ctx.lineTo(-f.radius * 1.6, -f.radius * 0.5);
+    ctx.lineTo(-f.radius * 1.6, f.radius * 0.5);
     ctx.closePath();
     ctx.fill();
 
     // Eye.
     ctx.fillStyle = '#ffffff';
     ctx.beginPath();
-    ctx.arc(f.r * 0.45, -f.r * 0.15, Math.max(1.5, f.r * 0.12), 0, Math.PI * 2);
+    ctx.arc(f.radius * 0.45, -f.radius * 0.15, Math.max(1.5, f.radius * 0.12), 0, Math.PI * 2);
     ctx.fill();
     ctx.fillStyle = '#1a1a1a';
     ctx.beginPath();
-    ctx.arc(f.r * 0.50, -f.r * 0.15, Math.max(0.8, f.r * 0.06), 0, Math.PI * 2);
+    ctx.arc(f.radius * 0.50, -f.radius * 0.15, Math.max(0.8, f.radius * 0.06), 0, Math.PI * 2);
     ctx.fill();
 
     ctx.restore();
-
-    if (SHOW_LABELS && f.id) {{
-      ctx.fillStyle = 'rgba(255,255,255,0.85)';
-      ctx.font = '10px sans-serif';
-      ctx.textAlign = 'center';
-      ctx.fillText(String(f.id).slice(0, 6), f.x, f.y - f.r - 4);
-    }}
   }}
 
   function updateFish(f) {{
-    // Random perturbation (organic motion).
     f.vx += (Math.random() - 0.5) * 0.05;
     f.vy += (Math.random() - 0.5) * 0.05;
 
-    // Clamp speed.
     const speed = Math.hypot(f.vx, f.vy);
     const maxSpeed = 1.5;
     if (speed > maxSpeed) {{
@@ -236,17 +437,15 @@ def build_aquarium_html(
     f.x += f.vx;
     f.y += f.vy;
 
-    // Bounce on edges.
-    if (f.x < f.r) {{ f.x = f.r; f.vx = -f.vx; }}
-    if (f.x > W - f.r) {{ f.x = W - f.r; f.vx = -f.vx; }}
-    if (f.y < f.r) {{ f.y = f.r; f.vy = -f.vy; }}
-    if (f.y > H - f.r) {{ f.y = H - f.r; f.vy = -f.vy; }}
+    if (f.x < f.radius) {{ f.x = f.radius; f.vx = -f.vx; }}
+    if (f.x > W - f.radius) {{ f.x = W - f.radius; f.vx = -f.vx; }}
+    if (f.y < f.radius) {{ f.y = f.radius; f.vy = -f.vy; }}
+    if (f.y > H - f.radius) {{ f.y = H - f.radius; f.vy = -f.vy; }}
   }}
 
   function frame() {{
     ctx.clearRect(0, 0, W, H);
 
-    // Subtle current lines.
     ctx.strokeStyle = 'rgba(255,255,255,0.04)';
     ctx.lineWidth = 1;
     for (let i = 0; i < 6; i++) {{
@@ -261,14 +460,124 @@ def build_aquarium_html(
       updateBubble(b);
       drawBubble(b);
     }}
-    for (const f of fish) {{
+    for (const f of fishState) {{
       updateFish(f);
       drawFish(f);
     }}
     requestAnimationFrame(frame);
   }}
 
-  if (fish.length === 0) {{
+  // CLICK HANDLER: hit-test in canvas pixel space (account for CSS scaling).
+  canvas.addEventListener('click', function(e) {{
+    const rect = canvas.getBoundingClientRect();
+    const scaleX = canvas.width / rect.width;
+    const scaleY = canvas.height / rect.height;
+    const cx = (e.clientX - rect.left) * scaleX;
+    const cy = (e.clientY - rect.top) * scaleY;
+    let best = null;
+    let bestDist = Infinity;
+    for (const f of fishState) {{
+      const dx = cx - f.x;
+      const dy = cy - f.y;
+      const d = Math.sqrt(dx * dx + dy * dy);
+      const hit = Math.max(f.radius + 6, 14);
+      if (d < hit && d < bestDist) {{
+        best = f;
+        bestDist = d;
+      }}
+    }}
+    if (best) showFishInfo(best);
+  }});
+
+  const ROW_STYLE = 'display:flex;justify-content:space-between;'
+                  + 'padding:2px 0;border-bottom:1px solid rgba(255,255,255,0.05);';
+  const PROMPT_STYLE = 'margin-top:10px;padding:8px;'
+                     + 'background:rgba(255,255,255,0.04);border-radius:6px;'
+                     + 'font-size:11px;font-style:italic;color:#cbd5e1;';
+  const ANC_HEAD_STYLE = 'margin:14px 0 6px 0;color:#94a3b8;'
+                       + 'text-transform:uppercase;font-size:10px;letter-spacing:1px;';
+  const ANC_ROW_STYLE = 'display:flex;align-items:center;padding:4px 6px;'
+                      + 'margin-bottom:2px;background:rgba(255,255,255,0.03);'
+                      + 'border-radius:4px;border-left:3px solid ';
+  const NO_ANC_STYLE = 'margin-top:10px;font-size:10px;color:#64748b;';
+  const DASH = '\\u2014';
+
+  function metricRow(label, value) {{
+    return '<div style="' + ROW_STYLE + '">'
+         + '<span style="color:#94a3b8;">' + label + '</span>'
+         + '<span style="color:#e2e8f0;">' + value + '</span></div>';
+  }}
+
+  function escapeHtml(s) {{
+    const div = document.createElement('div');
+    div.appendChild(document.createTextNode(String(s)));
+    return div.innerHTML;
+  }}
+
+  function fmt(v, dp) {{
+    if (v === null || v === undefined || typeof v !== 'number' || isNaN(v)) {{
+      return DASH;
+    }}
+    return v.toFixed(dp);
+  }}
+
+  function showFishInfo(fish) {{
+    const data = fish.data;
+    const styleColor = STYLE_COLORS[data.cognitive_style] || DEFAULT_COLOR;
+    let html = '';
+    const idShort = String(data.id || '').slice(0, 8);
+    html += '<h4 style="margin:0 0 10px 0;color:' + styleColor + ';">';
+    html += escapeHtml(idShort)
+         + ' <span style="color:#94a3b8;font-weight:normal;font-size:11px;">'
+         + 'gen ' + escapeHtml(data.generation) + '</span>';
+    html += '</h4>';
+    html += metricRow('fitness', fmt(data.fitness, 3));
+    html += metricRow('DSR', fmt(data.dsr, 3));
+    html += metricRow('Sharpe', fmt(data.sharpe, 3));
+    html += metricRow('max DD', fmt(data.max_dd, 3));
+    const trades = data.n_trades == null ? 0 : data.n_trades;
+    html += metricRow('trades', escapeHtml(trades));
+    html += metricRow('style', escapeHtml(data.cognitive_style || DASH));
+    html += metricRow('tier', escapeHtml(data.model_tier || DASH));
+    html += metricRow('temp', fmt(data.temperature, 2));
+    const lookback = data.lookback_window == null ? DASH : data.lookback_window;
+    html += metricRow('lookback', escapeHtml(lookback));
+    const feats = (data.feature_access || []).join(', ');
+    html += metricRow('features', escapeHtml(feats || DASH));
+    if (data.system_prompt) {{
+      html += '<div style="' + PROMPT_STYLE + '">'
+           + escapeHtml(data.system_prompt) + '</div>';
+    }}
+    if (data.ancestors && data.ancestors.length > 0) {{
+      html += '<h5 style="' + ANC_HEAD_STYLE + '">Discendenza</h5>';
+      data.ancestors.forEach(function(level, idx) {{
+        html += '<div style="margin-bottom:8px;">';
+        html += '<div style="font-size:10px;color:#64748b;margin-bottom:4px;">'
+             + 'Gen N\\u2212' + (idx + 1) + '</div>';
+        level.forEach(function(ancestor) {{
+          const c = STYLE_COLORS[ancestor.cognitive_style] || DEFAULT_COLOR;
+          const aShort = String(ancestor.id || '').slice(0, 8);
+          html += '<div style="' + ANC_ROW_STYLE + c + ';">';
+          html += '<code style="color:' + c + ';font-size:10px;">'
+               + escapeHtml(aShort) + '</code>';
+          const af = ancestor.fitness;
+          const fitTxt = (typeof af === 'number' && !isNaN(af))
+                       ? af.toFixed(2) : DASH;
+          html += '<span style="margin-left:auto;color:#94a3b8;font-size:10px;">'
+               + 'fit ' + fitTxt + '</span>';
+          html += '</div>';
+        }});
+        html += '</div>';
+      }});
+    }} else {{
+      html += '<div style="' + NO_ANC_STYLE + '">'
+           + 'Genoma di prima generazione (no ancestors)</div>';
+    }}
+    panelContent.innerHTML = html;
+    panel.style.display = 'block';
+  }}
+
+  if (fishState.length === 0) {{
     ctx.fillStyle = 'rgba(255,255,255,0.7)';
     ctx.font = '16px sans-serif';
     ctx.textAlign = 'center';
diff --git a/src/multi_swarm/dashboard/pages/04_aquarium.py b/src/multi_swarm/dashboard/pages/04_aquarium.py
index a52ac94..d7efeda 100644
--- a/src/multi_swarm/dashboard/pages/04_aquarium.py
+++ b/src/multi_swarm/dashboard/pages/04_aquarium.py
@@ -7,10 +7,10 @@ from multi_swarm.dashboard.aquarium import (
     STYLE_COLORS,
     build_aquarium_html,
     build_fish_dataset,
+    build_lineage_index,
 )
 from multi_swarm.dashboard.data import (
     evaluations_df,
-    generations_df,
     genomes_df,
     get_repo,
     list_runs_df,
@@ -18,8 +18,8 @@ from multi_swarm.dashboard.data import (
 
 st.title("Aquarium 2D")
 st.caption(
-    "Ogni genoma è un pesce: dimensione proporzionale alla fitness, "
-    "colore per cognitive_style."
+    "Pesci colorati per stile cognitivo, dimensione proporzionale a fitness. "
+    "Click su un pesce per dettaglio + discendenza."
 )
 
 db_path = st.session_state.get("db_path", "./runs.db")
@@ -27,52 +27,55 @@ repo = get_repo(db_path)
 
 runs = list_runs_df(repo)
 if runs.empty:
-    st.info("Nessuna run.")
+    st.info("Nessuna run nel database.")
     st.stop()
 
-selected = st.selectbox("Run", runs["id"].tolist())
+selected_run = st.selectbox("Run", runs["id"].tolist())
 
-gens = generations_df(repo, selected)
-gen_options: list[int | None] = [None]
-if not gens.empty:
-    gen_options.extend(sorted(gens["generation_idx"].unique().tolist()))
+# Fetch ALL genomes of the run (no gen filter): needed to build the lineage
+# index across generations. The active set is filtered afterwards.
+all_genomes = genomes_df(repo, selected_run)
+all_evals = evaluations_df(repo, selected_run)
 
-def _fmt_gen(v: int | None) -> str:
-    return "tutte le generazioni" if v is None else f"gen {v}"
-
-# Default to latest gen if available.
-default_idx = len(gen_options) - 1 if len(gen_options) > 1 else 0
-gen_choice = st.selectbox(
-    "Generazione", gen_options, index=default_idx, format_func=_fmt_gen
-)
-
-with st.sidebar:
-    st.subheader("Aquarium controls")
-    max_fish = st.slider("Max pesci", min_value=5, max_value=100, value=30, step=1)
-    show_labels = st.toggle("Mostra ID genoma", value=False)
-    if st.button("Refresh"):
-        st.rerun()
-
-evals = evaluations_df(repo, selected)
-genomes = genomes_df(repo, selected, generation_idx=gen_choice)
-
-if evals.empty or genomes.empty:
-    st.warning("Nessun dato disponibile per questa run/generazione.")
+if all_genomes.empty:
+    st.warning("Nessun genoma per questa run.")
     st.stop()
 
-merged = evals.merge(
-    genomes, left_on="genome_id", right_on="id", how="inner", suffixes=("", "_g")
+available_gens = sorted(all_genomes["generation_idx"].unique().tolist())
+selected_gen = st.selectbox(
+    "Generazione",
+    available_gens,
+    index=len(available_gens) - 1,  # default ultima
 )
-if merged.empty:
-    st.warning("Nessuna evaluation associata ai genomi della generazione scelta.")
+
+active_genomes = all_genomes[all_genomes["generation_idx"] == selected_gen]
+active_evals = (
+    all_evals[all_evals["genome_id"].isin(active_genomes["id"])]
+    if not all_evals.empty
+    else all_evals
+)
+if not active_evals.empty:
+    active_merged = active_genomes.merge(
+        active_evals,
+        left_on="id",
+        right_on="genome_id",
+        how="left",
+        suffixes=("", "_eval"),
+    )
+else:
+    active_merged = active_genomes.copy()
+    active_merged["genome_id"] = active_merged["id"]
+
+lineage = build_lineage_index(all_genomes, all_evals)
+fish = build_fish_dataset(active_merged, lineage, max_lineage_levels=5)
+
+if not fish:
+    st.warning("Nessun agente attivo in questa generazione.")
     st.stop()
 
-fish = build_fish_dataset(merged, max_fish=max_fish)
-st.write(f"Visualizzati {len(fish)} pesci (top per fitness).")
+st.caption(f"{len(fish)} agenti in generazione {selected_gen}")
 
-html_str = build_aquarium_html(
-    fish, canvas_w=1000, canvas_h=600, show_labels=show_labels
-)
+html_str = build_aquarium_html(fish, canvas_w=1000, canvas_h=600)
 components.html(html_str, height=620, scrolling=False)
 
 with st.expander("Legenda colori"):
diff --git a/tests/integration/test_streamlit_smoke.py b/tests/integration/test_streamlit_smoke.py
index 475fa7b..483408f 100644
--- a/tests/integration/test_streamlit_smoke.py
+++ b/tests/integration/test_streamlit_smoke.py
@@ -16,7 +16,7 @@ def test_dashboard_data_helpers_signatures():
     assert hasattr(data, "genomes_df")
 
 
-def test_aquarium_helper_builds_html():
+def test_aquarium_helper_builds_html_with_click_handler():
     from multi_swarm.dashboard.aquarium import build_aquarium_html
 
     fish = [
@@ -26,40 +26,55 @@ def test_aquarium_helper_builds_html():
             "cognitive_style": "physicist",
             "n_trades": 30,
             "dsr": 0.7,
-        },
-        {
-            "id": "def456",
-            "fitness": 0.0,
-            "cognitive_style": "biologist",
-            "n_trades": 0,
-            "dsr": 0.0,
-        },
+            "sharpe": 1.2,
+            "max_dd": 0.1,
+            "system_prompt": "test",
+            "temperature": 0.9,
+            "lookback_window": 200,
+            "feature_access": ["close"],
+            "model_tier": "C",
+            "generation": 1,
+            "parent_ids": [],
+            "ancestors": [],
+        }
     ]
-    html = build_aquarium_html(fish, canvas_w=800, canvas_h=400, show_labels=True)
+    html = build_aquarium_html(fish, canvas_w=800, canvas_h=400)
     assert "canvas" in html
-    assert "abc123" in html
-    assert "physicist" in html or "4cc9f0" in html
+    assert "abc123" in html  # fish id present in JSON payload
+    assert "addEventListener('click'" in html
+    assert "fish-info-panel" in html
+    assert "showFishInfo" in html
+    assert "Discendenza" in html
     assert "requestAnimationFrame" in html
 
 
-def test_aquarium_build_fish_dataset_sorts_and_caps():
+def test_aquarium_build_fish_dataset_legacy_path():
     from multi_swarm.dashboard.aquarium import build_fish_dataset
 
     df = pd.DataFrame(
         [
-            {"genome_id": "low", "fitness": 0.1, "cognitive_style": "physicist",
-             "n_trades": 1, "dsr": 0.0},
-            {"genome_id": "high", "fitness": 0.9, "cognitive_style": "biologist",
-             "n_trades": 10, "dsr": 0.5},
-            {"genome_id": "mid", "fitness": 0.5, "cognitive_style": "engineer",
-             "n_trades": 5, "dsr": 0.2},
+            {
+                "genome_id": "low",
+                "fitness": 0.1,
+                "cognitive_style": "physicist",
+                "n_trades": 1,
+                "dsr": 0.0,
+            },
+            {
+                "genome_id": "high",
+                "fitness": 0.9,
+                "cognitive_style": "biologist",
+                "n_trades": 10,
+                "dsr": 0.5,
+            },
         ]
     )
-    out = build_fish_dataset(df, max_fish=2)
-    assert len(out) == 2
-    assert out[0]["id"] == "high"
-    assert out[1]["id"] == "mid"
-    assert out[0]["cognitive_style"] == "biologist"
+    out = build_fish_dataset(df)
+    ids = {f["id"] for f in out}
+    assert ids == {"low", "high"}
+    high = next(f for f in out if f["id"] == "high")
+    assert high["cognitive_style"] == "biologist"
+    assert high["ancestors"] == []
 
 
 def test_aquarium_build_fish_dataset_drops_nan_fitness():
@@ -67,10 +82,20 @@ def test_aquarium_build_fish_dataset_drops_nan_fitness():
 
     df = pd.DataFrame(
         [
-            {"genome_id": "ok", "fitness": 0.4, "cognitive_style": "historian",
-             "n_trades": 2, "dsr": 0.1},
-            {"genome_id": "bad", "fitness": float("nan"), "cognitive_style": "ecologist",
-             "n_trades": 0, "dsr": 0.0},
+            {
+                "genome_id": "ok",
+                "fitness": 0.4,
+                "cognitive_style": "historian",
+                "n_trades": 2,
+                "dsr": 0.1,
+            },
+            {
+                "genome_id": "bad",
+                "fitness": float("nan"),
+                "cognitive_style": "ecologist",
+                "n_trades": 0,
+                "dsr": 0.0,
+            },
         ]
     )
     out = build_fish_dataset(df)
@@ -85,3 +110,201 @@ def test_aquarium_empty_input_returns_empty():
     html = build_aquarium_html([], canvas_w=400, canvas_h=200)
     assert "canvas" in html
     assert "Acquario vuoto" in html
+
+
+def test_build_lineage_index_returns_dict_keyed_by_id():
+    from multi_swarm.dashboard.aquarium import build_lineage_index
+
+    genomes = pd.DataFrame(
+        [
+            {
+                "id": "g1",
+                "generation_idx": 0,
+                "generation": 0,
+                "system_prompt": "x",
+                "feature_access": ["close"],
+                "temperature": 0.9,
+                "top_p": 0.95,
+                "model_tier": "C",
+                "lookback_window": 100,
+                "cognitive_style": "physicist",
+                "parent_ids": [],
+            },
+            {
+                "id": "g2",
+                "generation_idx": 1,
+                "generation": 1,
+                "system_prompt": "y",
+                "feature_access": ["close", "volume"],
+                "temperature": 1.0,
+                "top_p": 0.95,
+                "model_tier": "C",
+                "lookback_window": 200,
+                "cognitive_style": "biologist",
+                "parent_ids": ["g1"],
+            },
+        ]
+    )
+    evals = pd.DataFrame(
+        [
+            {
+                "genome_id": "g1",
+                "fitness": 0.5,
+                "dsr": 0.6,
+                "sharpe": 1.2,
+                "max_dd": 0.1,
+                "n_trades": 30,
+                "parse_error": None,
+                "raw_text": "",
+            },
+            {
+                "genome_id": "g2",
+                "fitness": 0.7,
+                "dsr": 0.8,
+                "sharpe": 1.5,
+                "max_dd": 0.05,
+                "n_trades": 40,
+                "parse_error": None,
+                "raw_text": "",
+            },
+        ]
+    )
+    idx = build_lineage_index(genomes, evals)
+    assert "g1" in idx and "g2" in idx
+    assert idx["g2"]["parent_ids"] == ["g1"]
+    assert idx["g2"]["fitness"] == 0.7
+    assert idx["g1"]["cognitive_style"] == "physicist"
+    assert idx["g2"]["feature_access"] == ["close", "volume"]
+
+
+def test_trace_ancestors_walks_levels():
+    from multi_swarm.dashboard.aquarium import trace_ancestors
+
+    idx = {
+        "child": {
+            "id": "child",
+            "parent_ids": ["p1", "p2"],
+            "fitness": 0.8,
+            "generation": 2,
+            "cognitive_style": "physicist",
+        },
+        "p1": {
+            "id": "p1",
+            "parent_ids": ["gp1"],
+            "fitness": 0.5,
+            "generation": 1,
+            "cognitive_style": "biologist",
+        },
+        "p2": {
+            "id": "p2",
+            "parent_ids": [],
+            "fitness": 0.3,
+            "generation": 1,
+            "cognitive_style": "engineer",
+        },
+        "gp1": {
+            "id": "gp1",
+            "parent_ids": [],
+            "fitness": 0.2,
+            "generation": 0,
+            "cognitive_style": "historian",
+        },
+    }
+    levels = trace_ancestors("child", idx, max_levels=5)
+    assert len(levels) == 2
+    assert {a["id"] for a in levels[0]} == {"p1", "p2"}
+    assert {a["id"] for a in levels[1]} == {"gp1"}
+
+
+def test_trace_ancestors_handles_cycles():
+    from multi_swarm.dashboard.aquarium import trace_ancestors
+
+    # Pathological cycle: a <-> b. Should terminate cleanly.
+    idx = {
+        "a": {
+            "id": "a",
+            "parent_ids": ["b"],
+            "fitness": 0.1,
+            "generation": 1,
+            "cognitive_style": "physicist",
+        },
+        "b": {
+            "id": "b",
+            "parent_ids": ["a"],
+            "fitness": 0.2,
+            "generation": 0,
+            "cognitive_style": "biologist",
+        },
+    }
+    levels = trace_ancestors("a", idx, max_levels=5)
+    # a -> b at level 0; b's only parent is a, already seen -> stop.
+    assert len(levels) == 1
+    assert levels[0][0]["id"] == "b"
+
+
+def test_trace_ancestors_no_parents_returns_empty():
+    from multi_swarm.dashboard.aquarium import trace_ancestors
+
+    idx = {
+        "solo": {
+            "id": "solo",
+            "parent_ids": [],
+            "fitness": 0.4,
+            "generation": 0,
+            "cognitive_style": "engineer",
+        },
+    }
+    assert trace_ancestors("solo", idx) == []
+
+
+def test_build_fish_dataset_attaches_ancestors():
+    from multi_swarm.dashboard.aquarium import build_fish_dataset, build_lineage_index
+
+    genomes = pd.DataFrame(
+        [
+            {
+                "id": "p",
+                "generation_idx": 0,
+                "generation": 0,
+                "system_prompt": "p",
+                "feature_access": ["close"],
+                "temperature": 0.8,
+                "top_p": 0.9,
+                "model_tier": "C",
+                "lookback_window": 100,
+                "cognitive_style": "physicist",
+                "parent_ids": [],
+            },
+            {
+                "id": "c",
+                "generation_idx": 1,
+                "generation": 1,
+                "system_prompt": "c",
+                "feature_access": ["close"],
+                "temperature": 0.8,
+                "top_p": 0.9,
+                "model_tier": "C",
+                "lookback_window": 120,
+                "cognitive_style": "biologist",
+                "parent_ids": ["p"],
+            },
+        ]
+    )
+    evals = pd.DataFrame(
+        [
+            {"genome_id": "p", "fitness": 0.3, "dsr": 0.0, "sharpe": 0.0,
+             "max_dd": 0.0, "n_trades": 0},
+            {"genome_id": "c", "fitness": 0.6, "dsr": 0.0, "sharpe": 0.0,
+             "max_dd": 0.0, "n_trades": 0},
+        ]
+    )
+    lineage = build_lineage_index(genomes, evals)
+
+    active = genomes[genomes["generation_idx"] == 1].merge(
+        evals, left_on="id", right_on="genome_id", how="left"
+    )
+    fish = build_fish_dataset(active, lineage)
+    assert len(fish) == 1
+    assert fish[0]["id"] == "c"
+    assert len(fish[0]["ancestors"]) == 1
+    assert fish[0]["ancestors"][0][0]["id"] == "p"