diff --git a/contributing/samples/gepa/experiment.py b/contributing/samples/gepa/experiment.py
index f3751206a8..2710c3894c 100644
--- a/contributing/samples/gepa/experiment.py
+++ b/contributing/samples/gepa/experiment.py
@@ -43,7 +43,6 @@
 from tau_bench.types import EnvRunResult
 from tau_bench.types import RunConfig
 import tau_bench_agent as tau_bench_agent_lib
-
 import utils
 
 
diff --git a/contributing/samples/gepa/run_experiment.py b/contributing/samples/gepa/run_experiment.py
index d857da9635..e31db15788 100644
--- a/contributing/samples/gepa/run_experiment.py
+++ b/contributing/samples/gepa/run_experiment.py
@@ -25,7 +25,6 @@
 from absl import flags
 import experiment
 from google.genai import types
-
 import utils
 
 _OUTPUT_DIR = flags.DEFINE_string(
diff --git a/src/google/adk/flows/llm_flows/base_llm_flow.py b/src/google/adk/flows/llm_flows/base_llm_flow.py
index d4af69378d..ac7fe3f4c9 100644
--- a/src/google/adk/flows/llm_flows/base_llm_flow.py
+++ b/src/google/adk/flows/llm_flows/base_llm_flow.py
@@ -381,6 +381,9 @@ def get_author_for_event(llm_response):
                 id=Event.new_id(),
                 invocation_id=invocation_context.invocation_id,
                 author=get_author_for_event(llm_response),
+                custom_metadata=getattr(
+                    invocation_context.run_config, 'custom_metadata', None
+                ),
             )
 
             async with Aclosing(
@@ -495,6 +498,9 @@ async def _run_one_step_async(
         invocation_id=invocation_context.invocation_id,
         author=invocation_context.agent.name,
         branch=invocation_context.branch,
+        custom_metadata=getattr(
+            invocation_context.run_config, 'custom_metadata', None
+        ),
     )
     async with Aclosing(
         self._call_llm_async(
diff --git a/src/google/adk/models/lite_llm.py b/src/google/adk/models/lite_llm.py
index cb6e02304e..2b2938b6bb 100644
--- a/src/google/adk/models/lite_llm.py
+++ b/src/google/adk/models/lite_llm.py
@@ -808,7 +808,11 @@ async def _get_content(
       return _decode_inline_text_data(part.inline_data.data)
 
   content_objects = []
-  for part in parts_list:
+  for part in parts:
+    # Skip thought parts - these are model reasoning and should not be
+    # fed back as input in subsequent turns
+    if getattr(part, "thought", False):
+      continue
     if part.text:
       content_objects.append({
           "type": "text",