add usage data to chat backend

src/agent.py

@@ -197,6 +197,18 @@ async def async_response_stream(
                             sample_data=str(potential_tool_fields)[:500]
                         )
 
+                # Detect response.completed event and log usage
+                if isinstance(chunk_data, dict) and chunk_data.get("type") == "response.completed":
+                    response_data = chunk_data.get("response", {})
+                    usage = response_data.get("usage")
+                    if usage:
+                        logger.info(
+                            "Stream usage data",
+                            input_tokens=usage.get("input_tokens"),
+                            output_tokens=usage.get("output_tokens"),
+                            total_tokens=usage.get("total_tokens"),
+                        )
+
                 # Middleware: Detect implicit tool calls and inject standardized events
                 # This helps Granite 3.3 8b and other models that don't emit standard markers
                 if isinstance(chunk_data, dict) and not detected_tool_call:
@@ -487,6 +499,7 @@ async def async_chat_stream(
 
     full_response = ""
     response_id = None
+    usage_data = None
     async for chunk in async_stream(
         async_client,
         prompt,
@@ -506,6 +519,10 @@ async def async_chat_stream(
                 response_id = chunk_data["id"]
             elif "response_id" in chunk_data:
                 response_id = chunk_data["response_id"]
+            # Capture usage from response.completed event
+            if chunk_data.get("type") == "response.completed":
+                response_obj = chunk_data.get("response", {})
+                usage_data = response_obj.get("usage")
         except:
             pass
         yield chunk
@@ -518,6 +535,9 @@ async def async_chat_stream(
             "response_id": response_id,
             "timestamp": datetime.now(),
         }
+        # Store usage data if available (from response.completed event)
+        if usage_data:
+            assistant_message["response_data"] = {"usage": usage_data}
         conversation_state["messages"].append(assistant_message)
 
         # Store the conversation thread with its response_id
@@ -676,6 +696,7 @@ async def async_langflow_chat_stream(
 
     full_response = ""
     response_id = None
+    usage_data = None
     collected_chunks = []  # Store all chunks for function call data
 
     async for chunk in async_stream(
@@ -700,6 +721,10 @@ async def async_langflow_chat_stream(
                 response_id = chunk_data["id"]
             elif "response_id" in chunk_data:
                 response_id = chunk_data["response_id"]
+            # Capture usage from response.completed event
+            if chunk_data.get("type") == "response.completed":
+                response_obj = chunk_data.get("response", {})
+                usage_data = response_obj.get("usage")
         except:
             pass
         yield chunk
@@ -713,6 +738,9 @@ async def async_langflow_chat_stream(
             "timestamp": datetime.now(),
             "chunks": collected_chunks,  # Store complete chunk data for function calls
         }
+        # Store usage data if available (from response.completed event)
+        if usage_data:
+            assistant_message["response_data"] = {"usage": usage_data}
         conversation_state["messages"].append(assistant_message)
 
         # Store the conversation thread with its response_id

src/api/v1/chat.py

@@ -239,11 +239,16 @@ async def chat_get_endpoint(request: Request, chat_service, session_manager):
         # Transform to public API format
         messages = []
         for msg in conversation.get("messages", []):
-            messages.append({
+            message_data = {
                 "role": msg.get("role"),
                 "content": msg.get("content"),
                 "timestamp": msg.get("timestamp"),
-            })
+            }
+            # Include token usage if available (from Responses API)
+            usage = msg.get("response_data", {}).get("usage") if isinstance(msg.get("response_data"), dict) else None
+            if usage:
+                message_data["usage"] = usage
+            messages.append(message_data)
 
         response_data = {
             "chat_id": conversation.get("response_id"),