diff --git a/lib/crewai/src/crewai/memory/encoding_flow.py b/lib/crewai/src/crewai/memory/encoding_flow.py
index 968b439bff..a75faeacc5 100644
--- a/lib/crewai/src/crewai/memory/encoding_flow.py
+++ b/lib/crewai/src/crewai/memory/encoding_flow.py
@@ -118,12 +118,83 @@ def batch_embed(self) -> None:
 
     @listen(batch_embed)
     def intra_batch_dedup(self) -> None:
-        """Drop near-exact duplicates within the batch."""
+        """Drop near-exact duplicates within the batch.
+
+        Computes the pairwise cosine-similarity matrix in one vectorized pass
+        (normalize rows once, then a single ``X @ Xᵀ`` BLAS call) instead of the
+        previous O(n²) loop of pure-Python cosine calls, each of which also
+        recomputed both vector norms from scratch (O(n²·d)). The greedy
+        "first occurrence wins" selection is preserved exactly: item ``j`` is
+        dropped iff some earlier *kept* item is at least ``threshold`` similar.
+        """
         items = list(self.state.items)
         if len(items) <= 1:
             return
 
         threshold = self._config.batch_dedup_threshold
+
+        try:
+            import numpy as np
+        except ImportError:
+            # numpy is a transitive dependency (chromadb, lancedb); if it is
+            # somehow unavailable, fall back to the scalar reference.
+            self._dedup_scalar(items, threshold)
+            return
+
+        # Only items carrying an embedding participate; pre-dropped items are
+        # excluded so they neither get re-dropped nor suppress others — exactly
+        # as the scalar reference skips them.
+        active: list[tuple[int, list[float]]] = [
+            (idx, item.embedding)
+            for idx, item in enumerate(items)
+            if item.embedding and not item.dropped
+        ]
+        if len(active) <= 1:
+            return
+
+        dim = len(active[0][1])
+        if any(len(emb) != dim for _, emb in active):
+            # Ragged embeddings cannot form a matrix; this should not happen for
+            # a single embedder, but fall back to the scalar reference so the
+            # len-mismatch-as-zero-similarity behavior is preserved exactly.
+            # Warn (rather than silently degrade) since it signals an embedder
+            # returning variable-length vectors — an encoding bug worth surfacing.
+            logger.warning(
+                "intra_batch_dedup: ragged embeddings in batch (sample lengths: "
+                "%s); falling back to scalar dedup. This usually means the "
+                "embedder returned variable-length vectors.",
+                [len(emb) for _, emb in active[:5]],
+            )
+            self._dedup_scalar(items, threshold)
+            return
+
+        matrix = np.asarray([emb for _, emb in active], dtype=np.float64)
+        norms = np.linalg.norm(matrix, axis=1)
+        nonzero = norms > 0.0
+        normalized = np.zeros_like(matrix)
+        normalized[nonzero] = matrix[nonzero] / norms[nonzero, None]
+        # Cosine-similarity matrix; zero-norm rows contribute 0.0, matching
+        # _cosine_similarity's zero-norm guard.
+        sims = normalized @ normalized.T
+
+        m = len(active)
+        dropped = np.zeros(m, dtype=bool)
+        for j in range(1, m):
+            # Drop j iff an earlier, still-kept item is near-identical.
+            if bool((~dropped[:j] & (sims[:j, j] >= threshold)).any()):
+                dropped[j] = True
+
+        for local_idx in range(m):
+            if dropped[local_idx]:
+                items[active[local_idx][0]].dropped = True
+                self.state.items_dropped_dedup += 1
+
+    def _dedup_scalar(self, items: list[ItemState], threshold: float) -> None:
+        """Reference O(n²) dedup using scalar cosine similarity.
+
+        Retained as the exact behavioral reference and as a fallback for the
+        (unexpected) ragged-embedding case.
+        """
         n = len(items)
         for j in range(1, n):
             if items[j].dropped or not items[j].embedding:
diff --git a/lib/crewai/tests/memory/test_encoding_flow_dedup.py b/lib/crewai/tests/memory/test_encoding_flow_dedup.py
new file mode 100644
index 0000000000..f6198df60c
--- /dev/null
+++ b/lib/crewai/tests/memory/test_encoding_flow_dedup.py
@@ -0,0 +1,147 @@
+"""Tests for EncodingFlow.intra_batch_dedup (vectorized cosine dedup).
+
+The vectorized implementation must reproduce the exact drop decisions of the
+original scalar O(n^2) algorithm, which is retained as ``_dedup_scalar``.
+"""
+
+from __future__ import annotations
+
+import logging
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+import pytest
+
+from crewai.memory.encoding_flow import EncodingFlow, ItemState
+from crewai.memory.types import MemoryConfig
+
+
+def _make_flow(threshold: float = 0.98) -> EncodingFlow:
+    config = MemoryConfig()
+    config.batch_dedup_threshold = threshold
+    return EncodingFlow(
+        storage=MagicMock(), llm=MagicMock(), embedder=MagicMock(), config=config
+    )
+
+
+def _run_vectorized(items: list[ItemState], threshold: float = 0.98) -> EncodingFlow:
+    flow = _make_flow(threshold)
+    flow.state.items = items
+    flow.intra_batch_dedup()
+    return flow
+
+
+def test_drops_identical_keeps_distinct() -> None:
+    items = [
+        ItemState(content="a", embedding=[0.5] * 8),
+        ItemState(content="b", embedding=[0.5] * 8),  # identical to a -> dropped
+        ItemState(content="c", embedding=[1.0] + [0.0] * 7),  # orthogonal -> kept
+    ]
+    flow = _run_vectorized(items)
+    assert [it.dropped for it in items] == [False, True, False]
+    assert flow.state.items_dropped_dedup == 1
+
+
+def test_first_occurrence_wins() -> None:
+    items = [ItemState(content=str(i), embedding=[0.5] * 8) for i in range(4)]
+    flow = _run_vectorized(items)
+    # Only the first survives; the rest are dropped against it.
+    assert [it.dropped for it in items] == [False, True, True, True]
+    assert flow.state.items_dropped_dedup == 3
+
+
+def test_items_without_embeddings_never_dropped() -> None:
+    items = [
+        ItemState(content="a", embedding=[0.5] * 8),
+        ItemState(content="no-emb", embedding=[]),  # never participates
+        ItemState(content="b", embedding=[0.5] * 8),  # dup of a
+    ]
+    flow = _run_vectorized(items)
+    assert [it.dropped for it in items] == [False, False, True]
+    assert flow.state.items_dropped_dedup == 1
+
+
+def test_pre_dropped_item_is_skipped() -> None:
+    """A pre-dropped item must neither be re-counted nor suppress others."""
+    a = ItemState(content="a", embedding=[0.5] * 8)
+    a.dropped = True  # already dropped upstream
+    items = [
+        a,
+        ItemState(content="b", embedding=[0.5] * 8),  # same vector as the dropped a
+        ItemState(content="c", embedding=[0.5] * 8),  # dup of b
+    ]
+    flow = _run_vectorized(items)
+    # 'a' stays dropped (not re-counted); 'b' becomes the surviving original;
+    # 'c' is dropped against 'b'.
+    assert items[0].dropped is True
+    assert items[1].dropped is False
+    assert items[2].dropped is True
+    assert flow.state.items_dropped_dedup == 1  # only 'c' is a new drop
+
+
+def test_falls_back_to_scalar_when_numpy_unavailable() -> None:
+    """If numpy can't be imported, dedup still works via the scalar path."""
+    items = [
+        ItemState(content="a", embedding=[0.5] * 8),
+        ItemState(content="b", embedding=[0.5] * 8),  # dup -> dropped
+        ItemState(content="c", embedding=[1.0] + [0.0] * 7),  # distinct -> kept
+    ]
+    flow = _make_flow()
+    flow.state.items = items
+    # Make `import numpy` raise ImportError inside intra_batch_dedup.
+    with patch.dict("sys.modules", {"numpy": None}):
+        flow.intra_batch_dedup()
+    assert [it.dropped for it in items] == [False, True, False]
+    assert flow.state.items_dropped_dedup == 1
+
+
+def test_ragged_embeddings_warn_and_fall_back(
+    caplog: pytest.LogCaptureFixture,
+) -> None:
+    """Variable-length embeddings (an encoding bug) must warn and still dedup
+    correctly via the scalar fallback (len mismatch -> 0 similarity)."""
+    items = [
+        ItemState(content="a", embedding=[0.5] * 8),
+        ItemState(content="b", embedding=[0.5] * 8),  # dup of a -> dropped
+        ItemState(content="c", embedding=[0.5] * 4),  # ragged -> never matches
+    ]
+    with caplog.at_level(logging.WARNING, logger="crewai.memory.encoding_flow"):
+        flow = _run_vectorized(items)
+
+    assert any("ragged embeddings" in r.message for r in caplog.records)
+    assert [it.dropped for it in items] == [False, True, False]
+    assert flow.state.items_dropped_dedup == 1
+
+
+def test_matches_scalar_reference_on_clustered_data() -> None:
+    """Vectorized dedup must match the scalar reference exactly on clustered
+    embeddings (intra-cluster ~1.0, inter-cluster low), across many trials.
+    """
+    rng = np.random.default_rng(0)
+    dim = 32
+    threshold = 0.98
+
+    for _ in range(25):
+        n_clusters = int(rng.integers(1, 5))
+        centers = rng.normal(size=(n_clusters, dim))
+        embeddings: list[list[float]] = []
+        for _ in range(int(rng.integers(2, 12))):
+            c = centers[int(rng.integers(0, n_clusters))]
+            vec = c + rng.normal(scale=1e-3, size=dim)  # tiny noise -> sim ~1.0
+            embeddings.append(vec.tolist())
+
+        vec_items = [
+            ItemState(content=str(i), embedding=e) for i, e in enumerate(embeddings)
+        ]
+        scalar_items = [
+            ItemState(content=str(i), embedding=e) for i, e in enumerate(embeddings)
+        ]
+
+        _run_vectorized(vec_items, threshold)
+
+        scalar_flow = _make_flow(threshold)
+        scalar_flow._dedup_scalar(scalar_items, threshold)
+
+        assert [it.dropped for it in vec_items] == [
+            it.dropped for it in scalar_items
+        ]