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feat: enhance per-agent token metrics accuracy by aggregating task data

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Devasy Patel
2025-12-21 18:23:51 +05:30
parent 56b538c37c
commit 85860610e9
1 changed files with 36 additions and 22 deletions
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58
lib/crewai/src/crewai/crew.py
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@@ -1644,42 +1644,56 @@ class Crew(FlowTrackable, BaseModel):
else:
workflow_metrics = WorkflowTokenMetrics()
# Build per-agent metrics from per-task data (more accurate)
# This avoids the cumulative token issue where all agents show the same total
agent_token_sums = {}
if workflow_metrics.per_task:
# Sum up tokens for each agent from their tasks
for task_name, task_metrics in workflow_metrics.per_task.items():
agent_name = task_metrics.agent_name
if agent_name not in agent_token_sums:
agent_token_sums[agent_name] = {
'total_tokens': 0,
'prompt_tokens': 0,
'cached_prompt_tokens': 0,
'completion_tokens': 0,
'successful_requests': 0
}
agent_token_sums[agent_name]['total_tokens'] += task_metrics.total_tokens
agent_token_sums[agent_name]['prompt_tokens'] += task_metrics.prompt_tokens
agent_token_sums[agent_name]['cached_prompt_tokens'] += task_metrics.cached_prompt_tokens
agent_token_sums[agent_name]['completion_tokens'] += task_metrics.completion_tokens
agent_token_sums[agent_name]['successful_requests'] += task_metrics.successful_requests
# Create per-agent metrics from the summed task data
for agent in self.agents:
agent_role = getattr(agent, 'role', 'Unknown Agent')
agent_id = str(getattr(agent, 'id', ''))
if isinstance(agent.llm, BaseLLM):
llm_usage = agent.llm.get_token_usage_summary()
total_usage_metrics.add_usage_metrics(llm_usage)
# Create per-agent metrics
if agent_role in agent_token_sums:
# Use accurate per-task summed data
sums = agent_token_sums[agent_role]
agent_metrics = AgentTokenMetrics(
agent_name=agent_role,
agent_id=agent_id,
total_tokens=llm_usage.total_tokens,
prompt_tokens=llm_usage.prompt_tokens,
cached_prompt_tokens=llm_usage.cached_prompt_tokens,
completion_tokens=llm_usage.completion_tokens,
successful_requests=llm_usage.successful_requests
total_tokens=sums['total_tokens'],
prompt_tokens=sums['prompt_tokens'],
cached_prompt_tokens=sums['cached_prompt_tokens'],
completion_tokens=sums['completion_tokens'],
successful_requests=sums['successful_requests']
)
workflow_metrics.per_agent[agent_role] = agent_metrics
# Still get total usage for overall metrics
if isinstance(agent.llm, BaseLLM):
llm_usage = agent.llm.get_token_usage_summary()
total_usage_metrics.add_usage_metrics(llm_usage)
else:
# fallback litellm
if hasattr(agent, "_token_process"):
token_sum = agent._token_process.get_summary()
total_usage_metrics.add_usage_metrics(token_sum)
# Create per-agent metrics from litellm
agent_metrics = AgentTokenMetrics(
agent_name=agent_role,
agent_id=agent_id,
total_tokens=token_sum.total_tokens,
prompt_tokens=token_sum.prompt_tokens,
cached_prompt_tokens=token_sum.cached_prompt_tokens,
completion_tokens=token_sum.completion_tokens,
successful_requests=token_sum.successful_requests
)
workflow_metrics.per_agent[agent_role] = agent_metrics
if self.manager_agent:
manager_role = getattr(self.manager_agent, 'role', 'Manager Agent')