diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/lmcache_integration/vllm_v1_adapter.py b/vllm/distributed/kv_transfer/kv_connector/v1/lmcache_integration/vllm_v1_adapter.py
index 3f60fbd6455a2..ad907c75a244b 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/lmcache_integration/vllm_v1_adapter.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/lmcache_integration/vllm_v1_adapter.py
@@ -44,8 +44,8 @@ from vllm.distributed.kv_transfer.kv_connector.v1.lmcache_integration.utils impo
 )
 from vllm.distributed.parallel_state import get_tensor_model_parallel_rank, get_tp_group
 from vllm.sampling_params import SamplingParams
-from vllm.utils import get_kv_cache_torch_dtype
 from vllm.utils.math_utils import cdiv
+from vllm.utils.torch_utils import get_kv_cache_torch_dtype
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.version import __version__ as VLLM_VERSION
 
@@ -389,7 +389,7 @@ class ReqMeta:
 
 
 def need_gpu_interm_buffer(lmcache_config: LMCacheEngineConfig):
-    return lmcache_config.enable_pd
+    return not lmcache_config.enable_pd
 
 
 def _calculate_mtp_layers(vllm_config, model_config):
@@ -403,6 +403,20 @@ def _calculate_mtp_layers(vllm_config, model_config):
             num_mtp_layers = getattr(
                 model_config.hf_config, "num_nextn_predict_layers", 0
             )
+
+        elif vllm_config.speculative_config.use_eagle():
+            try:
+                draft_model_config = vllm_config.speculative_config.draft_model_config
+                num_mtp_layers = draft_model_config.get_num_layers(
+                    vllm_config.parallel_config
+                )
+                logger.info("EAGLE detected %d extra layer(s)", num_mtp_layers)
+            except Exception:
+                logger.info(
+                    "EAGLE detected, but failed to get the number of extra layers"
+                    "falling back to 1"
+                )
+                num_mtp_layers = 1
     return num_mtp_layers
 
 
@@ -1208,6 +1222,10 @@ class LMCacheConnectorV1Impl:
         if the CacheManager this allocated blocks for us.
         """
 
+        # Clear local status in lookup client when a new request is
+        # successfully scheduled.
+        self.lookup_client.clear_lookup_status(request.request_id)
+
         kv_transfer_params = (
             request.kv_transfer_params
             if hasattr(request, "kv_transfer_params")