diff --git a/.github/workflows/send-message-integration-tests.yaml b/.github/workflows/send-message-integration-tests.yaml
index e578bad4..c66336a3 100644
--- a/.github/workflows/send-message-integration-tests.yaml
+++ b/.github/workflows/send-message-integration-tests.yaml
@@ -41,6 +41,15 @@ jobs:
           --health-interval 10s
           --health-timeout 5s
           --health-retries 5
+      redis:
+        image: redis:7
+        ports:
+          - 6379:6379
+        options: >-
+          --health-cmd "redis-cli ping"
+          --health-interval 5s
+          --health-timeout 5s
+          --health-retries 10
 
     steps:
       # Ensure secrets don't leak
@@ -138,6 +147,8 @@ jobs:
           LETTA_PG_PASSWORD: postgres
           LETTA_PG_DB: postgres
           LETTA_PG_HOST: localhost
+          LETTA_REDIS_HOST: localhost
+          LETTA_REDIS_PORT: 6379
           LETTA_SERVER_PASS: test_server_token
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
diff --git a/tests/integration_test_send_message.py b/tests/integration_test_send_message.py
index acf2b733..47731fd1 100644
--- a/tests/integration_test_send_message.py
+++ b/tests/integration_test_send_message.py
@@ -1132,6 +1132,127 @@ def test_token_streaming_agent_loop_error(
     assert len(messages_from_db) == 0
 
 
+@pytest.mark.parametrize(
+    "llm_config",
+    TESTED_LLM_CONFIGS,
+    ids=[c.model for c in TESTED_LLM_CONFIGS],
+)
+def test_background_token_streaming_greeting_with_assistant_message(
+    disable_e2b_api_key: Any,
+    client: Letta,
+    agent_state: AgentState,
+    llm_config: LLMConfig,
+) -> None:
+    """
+    Tests sending a streaming message with a synchronous client.
+    Checks that each chunk in the stream has the correct message types.
+    """
+    last_message = client.agents.messages.list(agent_id=agent_state.id, limit=1)
+    agent_state = client.agents.modify(agent_id=agent_state.id, llm_config=llm_config)
+    # Use longer message for Anthropic models to test if they stream in chunks
+    if llm_config.model_endpoint_type == "anthropic":
+        messages_to_send = USER_MESSAGE_FORCE_LONG_REPLY
+    else:
+        messages_to_send = USER_MESSAGE_FORCE_REPLY
+    response = client.agents.messages.create_stream(
+        agent_id=agent_state.id,
+        messages=messages_to_send,
+        stream_tokens=True,
+        background=True,
+    )
+    messages = accumulate_chunks(
+        list(response), verify_token_streaming=(llm_config.model_endpoint_type in ["anthropic", "openai", "bedrock"])
+    )
+    assert_greeting_with_assistant_message_response(messages, streaming=True, token_streaming=True, llm_config=llm_config)
+    messages_from_db = client.agents.messages.list(agent_id=agent_state.id, after=last_message[0].id)
+    assert_greeting_with_assistant_message_response(messages_from_db, from_db=True, llm_config=llm_config)
+
+
+@pytest.mark.parametrize(
+    "llm_config",
+    TESTED_LLM_CONFIGS,
+    ids=[c.model for c in TESTED_LLM_CONFIGS],
+)
+def test_background_token_streaming_greeting_without_assistant_message(
+    disable_e2b_api_key: Any,
+    client: Letta,
+    agent_state: AgentState,
+    llm_config: LLMConfig,
+) -> None:
+    """
+    Tests sending a streaming message with a synchronous client.
+    Checks that each chunk in the stream has the correct message types.
+    """
+    last_message = client.agents.messages.list(agent_id=agent_state.id, limit=1)
+    agent_state = client.agents.modify(agent_id=agent_state.id, llm_config=llm_config)
+    # Use longer message for Anthropic models to force chunking
+    if llm_config.model_endpoint_type == "anthropic":
+        messages_to_send = USER_MESSAGE_FORCE_LONG_REPLY
+    else:
+        messages_to_send = USER_MESSAGE_FORCE_REPLY
+    response = client.agents.messages.create_stream(
+        agent_id=agent_state.id,
+        messages=messages_to_send,
+        use_assistant_message=False,
+        stream_tokens=True,
+        background=True,
+    )
+    messages = accumulate_chunks(
+        list(response), verify_token_streaming=(llm_config.model_endpoint_type in ["anthropic", "openai", "bedrock"])
+    )
+    assert_greeting_without_assistant_message_response(messages, streaming=True, token_streaming=True, llm_config=llm_config)
+    messages_from_db = client.agents.messages.list(agent_id=agent_state.id, after=last_message[0].id, use_assistant_message=False)
+    assert_greeting_without_assistant_message_response(messages_from_db, from_db=True, llm_config=llm_config)
+
+
+@pytest.mark.parametrize(
+    "llm_config",
+    TESTED_LLM_CONFIGS,
+    ids=[c.model for c in TESTED_LLM_CONFIGS],
+)
+def test_background_token_streaming_tool_call(
+    disable_e2b_api_key: Any,
+    client: Letta,
+    agent_state: AgentState,
+    llm_config: LLMConfig,
+) -> None:
+    """
+    Tests sending a streaming message with a synchronous client.
+    Checks that each chunk in the stream has the correct message types.
+    """
+    # get the config filename
+    config_filename = None
+    for filename in filenames:
+        config = get_llm_config(filename)
+        if config.model_dump() == llm_config.model_dump():
+            config_filename = filename
+            break
+
+    # skip if this is a limited model
+    if not config_filename or config_filename in limited_configs:
+        pytest.skip(f"Skipping test for limited model {llm_config.model}")
+
+    last_message = client.agents.messages.list(agent_id=agent_state.id, limit=1)
+    agent_state = client.agents.modify(agent_id=agent_state.id, llm_config=llm_config)
+    # Use longer message for Anthropic models to force chunking
+    if llm_config.model_endpoint_type == "anthropic":
+        messages_to_send = USER_MESSAGE_ROLL_DICE_LONG
+    else:
+        messages_to_send = USER_MESSAGE_ROLL_DICE
+    response = client.agents.messages.create_stream(
+        agent_id=agent_state.id,
+        messages=messages_to_send,
+        stream_tokens=True,
+        background=True,
+    )
+    messages = accumulate_chunks(
+        list(response), verify_token_streaming=(llm_config.model_endpoint_type in ["anthropic", "openai", "bedrock"])
+    )
+    assert_tool_call_response(messages, streaming=True, llm_config=llm_config)
+    messages_from_db = client.agents.messages.list(agent_id=agent_state.id, after=last_message[0].id)
+    assert_tool_call_response(messages_from_db, from_db=True, llm_config=llm_config)
+
+
 def wait_for_run_completion(client: Letta, run_id: str, timeout: float = 30.0, interval: float = 0.5) -> Run:
     start = time.time()
     while True: