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feat: add detailed token metrics tracking for agents and tasks

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This commit is contained in:
Devasy Patel
2025-12-20 18:08:52 +05:30
parent be70a04153
commit 56b538c37c
4 changed files with 243 additions and 15 deletions
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175
lib/crewai/src/crewai/crew.py
View File

@@ -203,6 +203,10 @@ class Crew(FlowTrackable, BaseModel):
default=None,
description="Metrics for the LLM usage during all tasks execution.",
)
workflow_token_metrics: Any | None = Field(
default=None,
description="Detailed per-agent and per-task token metrics.",
)
manager_llm: str | InstanceOf[BaseLLM] | Any | None = Field(
description="Language model that will run the agent.", default=None
)
@@ -1155,12 +1159,22 @@ class Crew(FlowTrackable, BaseModel):
task_outputs = self._process_async_tasks(futures, was_replayed)
futures.clear()
# Capture token usage before task execution
tokens_before = self._get_agent_token_usage(exec_data.agent)
context = self._get_context(task, task_outputs)
task_output = task.execute_sync(
agent=exec_data.agent,
context=context,
tools=exec_data.tools,
)
# Capture token usage after task execution and attach to task output
tokens_after = self._get_agent_token_usage(exec_data.agent)
task_output = self._attach_task_token_metrics(
task_output, task, exec_data.agent, tokens_before, tokens_after
)
task_outputs.append(task_output)
self._process_task_result(task, task_output)
self._store_execution_log(task, task_output, task_index, was_replayed)
@@ -1401,6 +1415,7 @@ class Crew(FlowTrackable, BaseModel):
json_dict=final_task_output.json_dict,
tasks_output=task_outputs,
token_usage=self.token_usage,
token_metrics=getattr(self, 'workflow_token_metrics', None),
)
def _process_async_tasks(
@@ -1616,35 +1631,115 @@ class Crew(FlowTrackable, BaseModel):
def calculate_usage_metrics(self) -> UsageMetrics:
"""Calculates and returns the usage metrics."""
from crewai.types.usage_metrics import (
AgentTokenMetrics,
WorkflowTokenMetrics,
)
total_usage_metrics = UsageMetrics()
# Preserve existing workflow_token_metrics if it exists (has per_task data)
if hasattr(self, 'workflow_token_metrics') and self.workflow_token_metrics:
workflow_metrics = self.workflow_token_metrics
else:
workflow_metrics = WorkflowTokenMetrics()
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
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
)
workflow_metrics.per_agent[agent_role] = agent_metrics
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 and hasattr(self.manager_agent, "_token_process"):
token_sum = self.manager_agent._token_process.get_summary()
total_usage_metrics.add_usage_metrics(token_sum)
if self.manager_agent:
manager_role = getattr(self.manager_agent, 'role', 'Manager Agent')
manager_id = str(getattr(self.manager_agent, 'id', ''))
if hasattr(self.manager_agent, "_token_process"):
token_sum = self.manager_agent._token_process.get_summary()
total_usage_metrics.add_usage_metrics(token_sum)
# Create per-agent metrics for manager
manager_metrics = AgentTokenMetrics(
agent_name=manager_role,
agent_id=manager_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[manager_role] = manager_metrics
if (
self.manager_agent
and hasattr(self.manager_agent, "llm")
and hasattr(self.manager_agent.llm, "get_token_usage_summary")
):
if isinstance(self.manager_agent.llm, BaseLLM):
llm_usage = self.manager_agent.llm.get_token_usage_summary()
else:
llm_usage = self.manager_agent.llm._token_process.get_summary()
if (
hasattr(self.manager_agent, "llm")
and hasattr(self.manager_agent.llm, "get_token_usage_summary")
):
if isinstance(self.manager_agent.llm, BaseLLM):
llm_usage = self.manager_agent.llm.get_token_usage_summary()
else:
llm_usage = self.manager_agent.llm._token_process.get_summary()
total_usage_metrics.add_usage_metrics(llm_usage)
total_usage_metrics.add_usage_metrics(llm_usage)
# Update or create manager metrics
if manager_role in workflow_metrics.per_agent:
workflow_metrics.per_agent[manager_role].total_tokens += llm_usage.total_tokens
workflow_metrics.per_agent[manager_role].prompt_tokens += llm_usage.prompt_tokens
workflow_metrics.per_agent[manager_role].cached_prompt_tokens += llm_usage.cached_prompt_tokens
workflow_metrics.per_agent[manager_role].completion_tokens += llm_usage.completion_tokens
workflow_metrics.per_agent[manager_role].successful_requests += llm_usage.successful_requests
else:
manager_metrics = AgentTokenMetrics(
agent_name=manager_role,
agent_id=manager_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
)
workflow_metrics.per_agent[manager_role] = manager_metrics
# Set workflow-level totals
workflow_metrics.total_tokens = total_usage_metrics.total_tokens
workflow_metrics.prompt_tokens = total_usage_metrics.prompt_tokens
workflow_metrics.cached_prompt_tokens = total_usage_metrics.cached_prompt_tokens
workflow_metrics.completion_tokens = total_usage_metrics.completion_tokens
workflow_metrics.successful_requests = total_usage_metrics.successful_requests
# Store workflow metrics (preserving per_task data)
self.workflow_token_metrics = workflow_metrics
self.usage_metrics = total_usage_metrics
return total_usage_metrics
@@ -1918,3 +2013,55 @@ To enable tracing, do any one of these:
padding=(1, 2),
)
console.print(panel)
def _get_agent_token_usage(self, agent: BaseAgent | None) -> UsageMetrics:
"""Get current token usage for an agent."""
if not agent:
return UsageMetrics()
if isinstance(agent.llm, BaseLLM):
return agent.llm.get_token_usage_summary()
elif hasattr(agent, "_token_process"):
return agent._token_process.get_summary()
return UsageMetrics()
def _attach_task_token_metrics(
self,
task_output: TaskOutput,
task: Task,
agent: BaseAgent | None,
tokens_before: UsageMetrics,
tokens_after: UsageMetrics
) -> TaskOutput:
"""Attach per-task token metrics to the task output."""
from crewai.types.usage_metrics import TaskTokenMetrics
if not agent:
return task_output
# Calculate the delta (tokens used by this specific task)
task_tokens = TaskTokenMetrics(
task_name=getattr(task, 'name', None) or task.description[:50],
task_id=str(getattr(task, 'id', '')),
agent_name=getattr(agent, 'role', 'Unknown Agent'),
total_tokens=tokens_after.total_tokens - tokens_before.total_tokens,
prompt_tokens=tokens_after.prompt_tokens - tokens_before.prompt_tokens,
cached_prompt_tokens=tokens_after.cached_prompt_tokens - tokens_before.cached_prompt_tokens,
completion_tokens=tokens_after.completion_tokens - tokens_before.completion_tokens,
successful_requests=tokens_after.successful_requests - tokens_before.successful_requests
)
# Attach to task output
task_output.usage_metrics = task_tokens
# Store in workflow metrics
if not hasattr(self, 'workflow_token_metrics') or self.workflow_token_metrics is None:
from crewai.types.usage_metrics import WorkflowTokenMetrics
self.workflow_token_metrics = WorkflowTokenMetrics()
task_key = f"{task_tokens.task_name}_{task_tokens.agent_name}"
self.workflow_token_metrics.per_task[task_key] = task_tokens
return task_output

6
lib/crewai/src/crewai/crews/crew_output.py
View File

@@ -7,7 +7,7 @@ from pydantic import BaseModel, Field
from crewai.tasks.output_format import OutputFormat
from crewai.tasks.task_output import TaskOutput
from crewai.types.usage_metrics import UsageMetrics
from crewai.types.usage_metrics import UsageMetrics, WorkflowTokenMetrics
class CrewOutput(BaseModel):
@@ -26,6 +26,10 @@ class CrewOutput(BaseModel):
token_usage: UsageMetrics = Field(
description="Processed token summary", default_factory=UsageMetrics
)
token_metrics: WorkflowTokenMetrics | None = Field(
description="Detailed per-agent and per-task token metrics",
default=None
)
@property
def json(self) -> str | None: # type: ignore[override]

6
lib/crewai/src/crewai/tasks/task_output.py
View File

@@ -6,6 +6,7 @@ from typing import Any
from pydantic import BaseModel, Field, model_validator
from crewai.tasks.output_format import OutputFormat
from crewai.types.usage_metrics import TaskTokenMetrics
from crewai.utilities.types import LLMMessage
@@ -22,6 +23,7 @@ class TaskOutput(BaseModel):
json_dict: JSON dictionary output of the task
agent: Agent that executed the task
output_format: Output format of the task (JSON, PYDANTIC, or RAW)
usage_metrics: Token usage metrics for this specific task
"""
description: str = Field(description="Description of the task")
@@ -42,6 +44,10 @@ class TaskOutput(BaseModel):
description="Output format of the task", default=OutputFormat.RAW
)
messages: list[LLMMessage] = Field(description="Messages of the task", default=[])
usage_metrics: TaskTokenMetrics | None = Field(
description="Token usage metrics for this task",
default=None
)
@model_validator(mode="after")
def set_summary(self):

71
lib/crewai/src/crewai/types/usage_metrics.py
View File

@@ -44,3 +44,74 @@ class UsageMetrics(BaseModel):
self.cached_prompt_tokens += usage_metrics.cached_prompt_tokens
self.completion_tokens += usage_metrics.completion_tokens
self.successful_requests += usage_metrics.successful_requests
class AgentTokenMetrics(BaseModel):
"""Token usage metrics for a specific agent.
Attributes:
agent_name: Name/role of the agent
agent_id: Unique identifier for the agent
total_tokens: Total tokens used by this agent
prompt_tokens: Prompt tokens used by this agent
completion_tokens: Completion tokens used by this agent
successful_requests: Number of successful LLM requests
"""
agent_name: str = Field(description="Name/role of the agent")
agent_id: str | None = Field(default=None, description="Unique identifier for the agent")
total_tokens: int = Field(default=0, description="Total tokens used by this agent")
prompt_tokens: int = Field(default=0, description="Prompt tokens used by this agent")
cached_prompt_tokens: int = Field(default=0, description="Cached prompt tokens used by this agent")
completion_tokens: int = Field(default=0, description="Completion tokens used by this agent")
successful_requests: int = Field(default=0, description="Number of successful LLM requests")
class TaskTokenMetrics(BaseModel):
"""Token usage metrics for a specific task.
Attributes:
task_name: Name of the task
task_id: Unique identifier for the task
agent_name: Name of the agent that executed the task
total_tokens: Total tokens used for this task
prompt_tokens: Prompt tokens used for this task
completion_tokens: Completion tokens used for this task
successful_requests: Number of successful LLM requests
"""
task_name: str = Field(description="Name of the task")
task_id: str | None = Field(default=None, description="Unique identifier for the task")
agent_name: str = Field(description="Name of the agent that executed the task")
total_tokens: int = Field(default=0, description="Total tokens used for this task")
prompt_tokens: int = Field(default=0, description="Prompt tokens used for this task")
cached_prompt_tokens: int = Field(default=0, description="Cached prompt tokens used for this task")
completion_tokens: int = Field(default=0, description="Completion tokens used for this task")
successful_requests: int = Field(default=0, description="Number of successful LLM requests")
class WorkflowTokenMetrics(BaseModel):
"""Complete token usage metrics for a crew workflow.
Attributes:
total_tokens: Total tokens used across entire workflow
prompt_tokens: Total prompt tokens used
completion_tokens: Total completion tokens used
successful_requests: Total successful requests
per_agent: Dictionary mapping agent names to their token metrics
per_task: Dictionary mapping task names to their token metrics
"""
total_tokens: int = Field(default=0, description="Total tokens used across entire workflow")
prompt_tokens: int = Field(default=0, description="Total prompt tokens used")
cached_prompt_tokens: int = Field(default=0, description="Total cached prompt tokens used")
completion_tokens: int = Field(default=0, description="Total completion tokens used")
successful_requests: int = Field(default=0, description="Total successful requests")
per_agent: dict[str, AgentTokenMetrics] = Field(
default_factory=dict,
description="Token metrics per agent"
)
per_task: dict[str, TaskTokenMetrics] = Field(
default_factory=dict,
description="Token metrics per task"
)