Made the cost info easier to read

autogen/agentchat/utils.py

@@ -26,14 +26,14 @@ def consolidate_chat_info(chat_info, uniform_sender=None) -> None:
             ), "llm client must be set in either the recipient or sender when summary_method is reflection_with_llm."
 
 
-def gather_usage_summary(agents: List[Agent]) -> Tuple[Dict[str, any], Dict[str, any]]:
+def gather_usage_summary(agents: List[Agent]) -> Dict[Dict[str, any], Dict[str, any]]:
     r"""Gather usage summary from all agents.
 
     Args:
         agents: (list): List of agents.
 
     Returns:
-        tuple: (total_usage_summary, actual_usage_summary)
+        dictionary: (total_usage_summary, actual_usage_summary)
 
     Example:
 
@@ -77,7 +77,7 @@ def aggregate_summary(usage_summary: Dict[str, Any], agent_summary: Dict[str, An
             aggregate_summary(total_usage_summary, agent.client.total_usage_summary)
             aggregate_summary(actual_usage_summary, agent.client.actual_usage_summary)
 
-    return total_usage_summary, actual_usage_summary
+    return {"total_usage_summary": total_usage_summary, "actual_usage_summary": actual_usage_summary}
 
 
 def parse_tags_from_content(tag: str, content: Union[str, List[Dict[str, Any]]]) -> List[Dict[str, Dict[str, str]]]:

autogen/code_utils.py

@@ -40,7 +40,7 @@
 
 
 def content_str(content: Union[str, List[Union[UserMessageTextContentPart, UserMessageImageContentPart]], None]) -> str:
-    """Converts the `content` field of an OpenAI merssage into a string format.
+    """Converts the `content` field of an OpenAI message into a string format.
 
     This function processes content that may be a string, a list of mixed text and image URLs, or None,
     and converts it into a string. Text is directly appended to the result string, while image URLs are

test/agentchat/test_agent_usage.py

@@ -62,11 +62,11 @@ def test_gathering():
         "gpt-4": {"cost": 0.3, "prompt_tokens": 100, "completion_tokens": 200, "total_tokens": 300},
     }
 
-    total_usage, _ = gather_usage_summary([assistant1, assistant2, assistant3])
+    total_usage = gather_usage_summary([assistant1, assistant2, assistant3])
 
-    assert round(total_usage["total_cost"], 8) == 0.6
-    assert round(total_usage["gpt-35-turbo"]["cost"], 8) == 0.3
-    assert round(total_usage["gpt-4"]["cost"], 8) == 0.3
+    assert round(total_usage["total_usage_summary"]["total_cost"], 8) == 0.6
+    assert round(total_usage["total_usage_summary"]["gpt-35-turbo"]["cost"], 8) == 0.3
+    assert round(total_usage["total_usage_summary"]["gpt-4"]["cost"], 8) == 0.3
 
     # test when agent doesn't have client
     user_proxy = UserProxyAgent(
@@ -77,7 +77,8 @@ def test_gathering():
         default_auto_reply="That's all. Thank you.",
     )
 
-    total_usage, acutal_usage = gather_usage_summary([user_proxy])
+    total_usage = gather_usage_summary([user_proxy])
+    total_usage_summary = total_usage["total_usage_summary"]
 
 
 @pytest.mark.skipif(skip, reason="openai not installed OR requested to skip")