feat: support approve tool call request (#4333)

letta/agents/helpers.py

@@ -161,6 +161,10 @@ async def _prepare_in_context_messages_no_persist_async(
                 f"Invalid approval request ID. Expected '{current_in_context_messages[-1].id}' "
                 f"but received '{input_messages[0].approval_request_id}'."
             )
+        if input_messages[0].approve:
+            new_in_context_messages = []
+        else:
+            raise NotImplementedError("Deny flow not yet supported")
     else:
         # User is trying to send a regular message
         if current_in_context_messages[-1].role == "approval":
@@ -169,10 +173,10 @@ async def _prepare_in_context_messages_no_persist_async(
                 "Please approve or deny the pending request before continuing."
             )
 
-    # Create a new user message from the input but dont store it yet
-    new_in_context_messages = create_input_messages(
-        input_messages=input_messages, agent_id=agent_state.id, timezone=agent_state.timezone, actor=actor
-    )
+        # Create a new user message from the input but dont store it yet
+        new_in_context_messages = create_input_messages(
+            input_messages=input_messages, agent_id=agent_state.id, timezone=agent_state.timezone, actor=actor
+        )
 
     return current_in_context_messages, new_in_context_messages
 

letta/schemas/message.py

@@ -769,11 +769,11 @@ class Message(BaseMessage):
                 "role": self.role,
             }
 
-        elif self.role == "assistant":
+        elif self.role == "assistant" or self.role == "approval":
             assert self.tool_calls is not None or text_content is not None
             openai_message = {
                 "content": None if (put_inner_thoughts_in_kwargs and self.tool_calls is not None) else text_content,
-                "role": self.role,
+                "role": "assistant",
             }
 
             if self.tool_calls is not None:

letta/schemas/openai/chat_completion_request.py

@@ -50,7 +50,7 @@ def cast_message_to_subtype(m_dict: dict) -> ChatMessage:
         return SystemMessage(**m_dict)
     elif role == "user":
         return UserMessage(**m_dict)
-    elif role == "assistant":
+    elif role == "assistant" or role == "approval":
         return AssistantMessage(**m_dict)
     elif role == "tool":
         return ToolMessage(**m_dict)

letta/server/rest_api/utils.py

@@ -233,34 +233,36 @@ def create_letta_messages_from_llm_response(
     pre_computed_assistant_message_id: Optional[str] = None,
     llm_batch_item_id: Optional[str] = None,
     step_id: str | None = None,
+    is_approval: bool | None = None,
 ) -> List[Message]:
     messages = []
-    # Construct the tool call with the assistant's message
-    # Force set request_heartbeat in tool_args to calculated continue_stepping
-    function_arguments[REQUEST_HEARTBEAT_PARAM] = continue_stepping
-    tool_call = OpenAIToolCall(
-        id=tool_call_id,
-        function=OpenAIFunction(
-            name=function_name,
-            arguments=json.dumps(function_arguments),
-        ),
-        type="function",
-    )
-    # TODO: Use ToolCallContent instead of tool_calls
-    # TODO: This helps preserve ordering
-    assistant_message = Message(
-        role=MessageRole.assistant,
-        content=reasoning_content if reasoning_content else [],
-        agent_id=agent_id,
-        model=model,
-        tool_calls=[tool_call],
-        tool_call_id=tool_call_id,
-        created_at=get_utc_time(),
-        batch_item_id=llm_batch_item_id,
-    )
-    if pre_computed_assistant_message_id:
-        assistant_message.id = pre_computed_assistant_message_id
-    messages.append(assistant_message)
+    if not is_approval:
+        # Construct the tool call with the assistant's message
+        # Force set request_heartbeat in tool_args to calculated continue_stepping
+        function_arguments[REQUEST_HEARTBEAT_PARAM] = continue_stepping
+        tool_call = OpenAIToolCall(
+            id=tool_call_id,
+            function=OpenAIFunction(
+                name=function_name,
+                arguments=json.dumps(function_arguments),
+            ),
+            type="function",
+        )
+        # TODO: Use ToolCallContent instead of tool_calls
+        # TODO: This helps preserve ordering
+        assistant_message = Message(
+            role=MessageRole.assistant,
+            content=reasoning_content if reasoning_content else [],
+            agent_id=agent_id,
+            model=model,
+            tool_calls=[tool_call],
+            tool_call_id=tool_call_id,
+            created_at=get_utc_time(),
+            batch_item_id=llm_batch_item_id,
+        )
+        if pre_computed_assistant_message_id:
+            assistant_message.id = pre_computed_assistant_message_id
+        messages.append(assistant_message)
 
     # TODO: Use ToolReturnContent instead of TextContent
     # TODO: This helps preserve ordering

tests/integration_test_human_in_the_loop.py

@@ -160,6 +160,8 @@ def test_send_message_with_approval_tool(
     assert len(response.messages) == 2
     assert response.messages[0].message_type == "reasoning_message"
     assert response.messages[1].message_type == "approval_request_message"
+    approval_request_id = response.messages[0].id
+    tool_call_id = response.messages[1].tool_call.tool_call_id
 
     # Attempt to send user message - should fail
     with pytest.raises(ApiError, match="Please approve or deny the pending request before continuing"):
@@ -174,3 +176,22 @@ def test_send_message_with_approval_tool(
             agent_id=agent.id,
             messages=[ApprovalCreate(approve=True, approval_request_id="fake_id")],
         )
+
+    response = client.agents.messages.create(
+        agent_id=agent.id,
+        messages=[
+            ApprovalCreate(
+                approve=True,
+                approval_request_id=approval_request_id,
+            ),
+        ],
+    )
+
+    # Basic assertion that we got a response with tool call return
+    assert response.messages is not None
+    assert len(response.messages) == 3
+    assert response.messages[0].message_type == "tool_return_message"
+    assert response.messages[0].tool_call_id == tool_call_id
+    assert response.messages[0].status == "success"
+    assert response.messages[1].message_type == "reasoning_message"
+    assert response.messages[2].message_type == "assistant_message"