diff --git a/litellm/proxy/agent_endpoints/agent_search.py b/litellm/proxy/agent_endpoints/agent_search.py
index c249079ccac..4e62e592261 100644
--- a/litellm/proxy/agent_endpoints/agent_search.py
+++ b/litellm/proxy/agent_endpoints/agent_search.py
@@ -199,17 +199,21 @@ class AgentSearchIndex:
         if not agents:
             return AgentSearchHits(hits=())
         texts: Final = tuple(agent_search_text(agent) for agent in agents)
-        cached: Final = self._vectors.get(embedding_model, _NO_VECTORS)
-        embedded: Final = await _embed_query_and_agents(embed, query, texts, cached)
+        embedded: Final = await _embed_query_and_agents(
+            embed, query, texts, self._vectors.get(embedding_model, _NO_VECTORS)
+        )
         if isinstance(embedded, AgentSearchEmbeddingFailed):
             return embedded
         if not _same_dimension(embedded.query_vector, embedded.vectors, texts):
             return AgentSearchEmbeddingFailed(
                 reason=f"embedding model {embedding_model} returned vectors of mixed dimensions"
             )
-        self._vectors = MappingProxyType(
-            {**self._vectors, embedding_model: MappingProxyType({**cached, **embedded.vectors})}
+        current: Final = self._vectors.get(embedding_model, _NO_VECTORS)
+        dim: Final = len(embedded.query_vector)
+        merged: Final = MappingProxyType(
+            {text: vector for text, vector in chain(current.items(), embedded.vectors.items()) if len(vector) == dim}
         )
+        self._vectors = MappingProxyType({**self._vectors, embedding_model: merged})
         ranked: Final = sorted(
             (
                 AgentSearchHit(agent=agent, score=cosine_similarity(embedded.query_vector, embedded.vectors[text]))
diff --git a/tests/test_litellm/proxy/agent_endpoints/test_agent_search.py b/tests/test_litellm/proxy/agent_endpoints/test_agent_search.py
index 93f29c4a1d2..5f4bc21a05a 100644
--- a/tests/test_litellm/proxy/agent_endpoints/test_agent_search.py
+++ b/tests/test_litellm/proxy/agent_endpoints/test_agent_search.py
@@ -156,6 +156,19 @@ class TestAgentSearchIndex:
             ("language translation", *(agent_search_text(agent) for agent in AGENTS)),
         ]
 
+    @pytest.mark.asyncio
+    async def test_subset_reembed_after_dimension_change_evicts_stale_vectors(self) -> None:
+        index = AgentSearchIndex()
+        await index.search("language translation", AGENTS, top_k=5, embed=FakeEmbedder(), embedding_model="m")
+        narrow = FixedDimensionEmbedder(2)
+        await index.search("language translation", (TRANSLATOR,), top_k=5, embed=narrow, embedding_model="m")
+        broader = FixedDimensionEmbedder(2)
+        outcome = await index.search("language translation", AGENTS, top_k=5, embed=broader, embedding_model="m")
+        assert isinstance(outcome, AgentSearchHits)
+        assert broader.calls == [
+            ("language translation", agent_search_text(SQL_ANALYST), agent_search_text(TRIP_PLANNER)),
+        ]
+
     @pytest.mark.asyncio
     async def test_mixed_dimensions_in_one_batch_become_embedding_failed(self) -> None:
         async def mixed(texts: Sequence[str]) -> Sequence[Vector]: