diff --git a/llama_stack/providers/inline/agents/meta_reference/agent_instance.py b/llama_stack/providers/inline/agents/meta_reference/agent_instance.py
index eca7364d7..d2441a9e5 100644
--- a/llama_stack/providers/inline/agents/meta_reference/agent_instance.py
+++ b/llama_stack/providers/inline/agents/meta_reference/agent_instance.py
@@ -476,9 +476,6 @@ class ChatAgent(ShieldRunnerMixin):
                 )
                 span.set_attribute("output", retrieved_context)
                 span.set_attribute("tool_name", MEMORY_QUERY_TOOL)
-                if retrieved_context:
-                    last_message = input_messages[-1]
-                    last_message.context = retrieved_context
 
         output_attachments = []
 
diff --git a/tests/client-sdk/agents/test_agents.py b/tests/client-sdk/agents/test_agents.py
index 4a8fdd36a..016c1f5e2 100644
--- a/tests/client-sdk/agents/test_agents.py
+++ b/tests/client-sdk/agents/test_agents.py
@@ -312,3 +312,69 @@ def test_rag_agent(llama_stack_client, agent_config):
         logs = [str(log) for log in EventLogger().log(response) if log is not None]
         logs_str = "".join(logs)
         assert "Tool:query_from_memory" in logs_str
+
+
+def test_rag_and_code_agent(llama_stack_client, agent_config):
+    urls = ["chat.rst"]
+    documents = [
+        Document(
+            document_id=f"num-{i}",
+            content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
+            mime_type="text/plain",
+            metadata={},
+        )
+        for i, url in enumerate(urls)
+    ]
+    vector_db_id = "test-vector-db"
+    llama_stack_client.vector_dbs.register(
+        vector_db_id=vector_db_id,
+        embedding_model="all-MiniLM-L6-v2",
+        embedding_dimension=384,
+    )
+    llama_stack_client.tool_runtime.rag_tool.insert(
+        documents=documents,
+        vector_db_id=vector_db_id,
+        chunk_size_in_tokens=512,
+    )
+    agent_config = {
+        **agent_config,
+        "toolgroups": [
+            dict(
+                name="builtin::rag",
+                args={"vector_db_ids": [vector_db_id]},
+            ),
+            "builtin::code_interpreter",
+        ],
+    }
+    agent = Agent(llama_stack_client, agent_config)
+    session_id = agent.create_session("test-session")
+    inflation_doc = Document(
+        document_id="test_csv",
+        content="https://raw.githubusercontent.com/meta-llama/llama-stack-apps/main/examples/resources/inflation.csv",
+        mime_type="text/csv",
+        metadata={},
+    )
+    user_prompts = [
+        (
+            "What are the top 5 topics that were explained? Only list succinct bullet points.",
+            documents,
+            "query_from_memory",
+        ),
+        ("What is the average yearly inflation?", [inflation_doc], "code_interpreter"),
+    ]
+
+    for prompt, docs, tool_name in user_prompts:
+        print(f"User> {prompt}")
+        response = agent.create_turn(
+            messages=[{"role": "user", "content": prompt}],
+            session_id=session_id,
+            documents=docs,
+        )
+        logs = []
+        for log in EventLogger().log(response):
+            logs.append(str(log))
+            log.print()
+
+        # logs = [str(log) for log in EventLogger().log(response) if log is not None]
+        logs_str = "".join(logs)
+        assert f"Tool:{tool_name}" in logs_str