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feat(agent): exclude generate_title node from LLM call and token statistics

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Filter out internal LangGraph 'generate_title' node from LLM call counting and token usage tracking to avoid inflating statistics with internal operations. This ensures metrics only reflect user-facing AI interactions.
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YueGuobin committed 2026-03-05 23:01:41 +08:00
1 parent 511b155da8
commit b8cb0bdcb0
2 files changed
+260 -31
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gns3server/agent/gns3_copilot/agent_service.py
+49 -31
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@@ -308,46 +308,56 @@ class AgentService:
# Track LLM calls and tokens
if event_type == "on_chat_model_start":
llm_calls_count += 1
log.debug("LLM call started, count=%d", llm_calls_count)
# Filter out generate_title node from statistics
langgraph_node = event.get("metadata", {}).get("langgraph_node", "")
if langgraph_node != "generate_title":
llm_calls_count += 1
log.debug("LLM call started, count=%d", llm_calls_count)
else:
log.debug("Skipping LLM call count for internal node: generate_title")
elif event_type == "on_chat_model_end":
# Extract token usage from response metadata
# Try multiple possible locations where token usage might be stored
token_info_found = False
# Filter out generate_title node from token counting
langgraph_node = event.get("metadata", {}).get("langgraph_node", "")
if langgraph_node == "generate_title":
log.debug("Skipping token counting for internal node: generate_title")
else:
# Extract token usage from response metadata
# Try multiple possible locations where token usage might be stored
token_info_found = False
# Method 1: response.usage_metadata
response = data.get("response", {})
if hasattr(response, "usage_metadata"):
usage = response.usage_metadata
if usage:
input_tokens += usage.get("input_tokens", 0)
output_tokens += usage.get("output_tokens", 0)
token_info_found = True
# Method 2: output.usage_metadata
if not token_info_found:
output_msg = data.get("output", {})
if hasattr(output_msg, "usage_metadata"):
usage = output_msg.usage_metadata
# Method 1: response.usage_metadata
response = data.get("response", {})
if hasattr(response, "usage_metadata"):
usage = response.usage_metadata
if usage:
input_tokens += usage.get("input_tokens", 0)
output_tokens += usage.get("output_tokens", 0)
token_info_found = True
# Method 3: Check data directly for token usage fields
if not token_info_found:
if "input_tokens" in data:
input_tokens += data.get("input_tokens", 0)
if "output_tokens" in data:
output_tokens += data.get("output_tokens", 0)
if "input_tokens" in data or "output_tokens" in data:
token_info_found = True
# Method 2: output.usage_metadata
if not token_info_found:
output_msg = data.get("output", {})
if hasattr(output_msg, "usage_metadata"):
usage = output_msg.usage_metadata
if usage:
input_tokens += usage.get("input_tokens", 0)
output_tokens += usage.get("output_tokens", 0)
token_info_found = True
# Count AI response as one message (only once per turn)
if not ai_response_counted:
message_count += 1
ai_response_counted = True
# Method 3: Check data directly for token usage fields
if not token_info_found:
if "input_tokens" in data:
input_tokens += data.get("input_tokens", 0)
if "output_tokens" in data:
output_tokens += data.get("output_tokens", 0)
if "input_tokens" in data or "output_tokens" in data:
token_info_found = True
# Count AI response as one message (only once per turn)
if not ai_response_counted:
message_count += 1
ai_response_counted = True
# Track tool messages
elif event_type == "on_tool_end":
@@ -357,6 +367,14 @@ class AgentService:
# Convert event to chunk for SSE streaming
# Use accumulator for on_chat_model_stream events to handle progressive tool calls
# Filter out internal nodes (generate_title) from streaming to frontend
langgraph_node = event.get("metadata", {}).get("langgraph_node", "")
if langgraph_node == "generate_title":
# Skip all events from the generate_title node (internal use only)
log.debug("Skipping event from internal node: generate_title")
continue
if event_type == "on_chat_model_stream":
chunks = tool_call_accumulator.process_event(event)
for chunk in chunks: