diff --git a/mem0-ts/src/oss/src/vector_stores/baidu.ts b/mem0-ts/src/oss/src/vector_stores/baidu.ts
index 645910daa..34e54c5fe 100644
--- a/mem0-ts/src/oss/src/vector_stores/baidu.ts
+++ b/mem0-ts/src/oss/src/vector_stores/baidu.ts
@@ -453,7 +453,13 @@ export class BaiduDB implements VectorStore {
     return (response.rows ?? []).map((result) => ({
       id: String(result.row.id),
       payload: resultPayload(result.row),
-      score: result.score,
+      // L2 is a distance (lower = closer); the VectorStore contract wants higher = better.
+      score:
+        this.metricType === "L2"
+          ? result.score != null
+            ? 1 / (1 + result.score)
+            : undefined
+          : result.score,
     }));
   }
 
diff --git a/mem0-ts/src/oss/tests/baidu.test.ts b/mem0-ts/src/oss/tests/baidu.test.ts
index 912758ef9..a2a50013d 100644
--- a/mem0-ts/src/oss/tests/baidu.test.ts
+++ b/mem0-ts/src/oss/tests/baidu.test.ts
@@ -431,9 +431,12 @@ describe("BaiduDB reads", () => {
       ],
     });
 
-    const results = await makeStore(client).search([1, 2, 3], 5, {
-      userId: "alice",
-    });
+    // Non-L2 metrics already return a higher-is-better score and pass through untouched.
+    const results = await makeStore(client, { metricType: "COSINE" }).search(
+      [1, 2, 3],
+      5,
+      { userId: "alice" },
+    );
     expect(results).toEqual([{ id: "m1", payload: { data: "x" }, score: 0.8 }]);
 
     const { request } = client.vectorSearch.mock.calls[0][0];
@@ -445,6 +448,35 @@ describe("BaiduDB reads", () => {
     expect(request.config.params).toEqual({ ef: 200 });
   });
 
+  it("converts an L2 distance into a similarity score (higher = better)", async () => {
+    // Mirrors the Python provider (#6435): 1 / (1 + distance), so closer scores higher.
+    const client = fakeClient();
+    client.vectorSearch.mockResolvedValue({
+      ...OK,
+      rows: [
+        { row: { id: "near", metadata: {} }, score: 0.5 },
+        { row: { id: "far", metadata: {} }, score: 2.0 },
+      ],
+    });
+
+    const results = await makeStore(client).search([1, 2, 3], 2);
+
+    expect(results[0].score).toBeCloseTo(1 / 1.5, 10);
+    expect(results[1].score).toBeCloseTo(1 / 3.0, 10);
+  });
+
+  it("leaves an unscored L2 row's score undefined instead of treating it as the closest match", async () => {
+    const client = fakeClient();
+    client.vectorSearch.mockResolvedValue({
+      ...OK,
+      rows: [{ row: { id: "unscored", metadata: {} } }],
+    });
+
+    const results = await makeStore(client).search([1, 2, 3], 1);
+
+    expect(results[0].score).toBeUndefined();
+  });
+
   it("omits the filter when no filters are supplied", async () => {
     const client = fakeClient();
     client.vectorSearch.mockResolvedValue({ ...OK, rows: [] });