diff --git a/python/sglang/srt/entrypoints/engine.py b/python/sglang/srt/entrypoints/engine.py
index 63a4d3ac4..535935654 100644
--- a/python/sglang/srt/entrypoints/engine.py
+++ b/python/sglang/srt/entrypoints/engine.py
@@ -285,6 +285,21 @@ class Engine(EngineBase):
         ret = loop.run_until_complete(generator.__anext__())
         return ret
 
+    async def async_encode(
+        self,
+        prompt: Union[str, List[str], List[Dict], List[List[Dict]]],
+        image_data: Optional[Union[List[str], str]] = None,
+    ) -> Dict:
+        """
+        Asynchronous version of encode method.
+
+        The arguments of this function is the same as `sglang/srt/managers/io_struct.py::EmbeddingReqInput`.
+        Please refer to `EmbeddingReqInput` for the documentation.
+        """
+        obj = EmbeddingReqInput(text=prompt, image_data=image_data)
+        generator = self.tokenizer_manager.generate_request(obj, None)
+        return await generator.__anext__()
+
     def shutdown(self):
         """Shutdown the engine"""
         kill_process_tree(os.getpid(), include_parent=False)
diff --git a/test/srt/test_srt_engine.py b/test/srt/test_srt_engine.py
index 672344c63..d6f5ac685 100644
--- a/test/srt/test_srt_engine.py
+++ b/test/srt/test_srt_engine.py
@@ -185,6 +185,35 @@ class TestSRTEngine(CustomTestCase):
         result = throughput_test(server_args=server_args, bench_args=bench_args)
         self.assertGreater(result["total_throughput"], 3000)
 
+    def test_8_engine_async_encode_consistency(self):
+        prompt = "Today is a sunny day and I like"
+        model_path = DEFAULT_SMALL_EMBEDDING_MODEL_NAME_FOR_TEST
+
+        engine = sgl.Engine(
+            model_path=model_path,
+            is_embedding=True,
+            random_seed=42,
+            disable_radix_cache=True,
+        )
+
+        # Get sync and async embeddings
+        out1 = torch.tensor(engine.encode(prompt)["embedding"])
+        loop = asyncio.get_event_loop()
+        out2 = torch.tensor(
+            loop.run_until_complete(engine.async_encode(prompt))["embedding"]
+        )
+
+        engine.shutdown()
+
+        print("\n==== Shapes ====")
+        print(f"sync shape: {out1.shape}")
+        print(f"async shape: {out2.shape}")
+
+        self.assertTrue(
+            torch.allclose(out1, out2, atol=1e-5, rtol=1e-3),
+            "Sync and async embeddings are not equal within tolerance",
+        )
+
 
 if __name__ == "__main__":
     unittest.main()