Tokens count

[MindsightsAI]

How to determine tokens usage ?

[joaomdmoura]

You could use callbacks now, but I want to add a better way to report on it adding this as feature accepted

[bishwarupdas]

@joaomdmoura Any update on this feature? Need this.

btw, Awesome job!

[anandhuh1234]

crew = Crew(
agents=[agent1, agent2],
tasks=[task1, task2],.....

print(crew.usage_metrics)

[jackhayter]

I believe I found a bug in the Agent class's set_agent_executor method where the callbacks are added, which make this token count observation possible. I found that the TokenCalcHandler was being called 3 separate times for each interaction with the LLM when only one was expected. This was causing the usage to be over-reported.

By ensuring that only a single instance of TokenCalcHandler can be appended to the callbacks of the LLM, I was able to verify that only a single usage callback was triggered. While my understanding of the internals of this library may be limited, I believe that this fix may be useful. If someone can verify the hypothesis or current behaviour, I'd be happy to open a PR.

I've opened a PR for this, but would like some help verifying this hypothesis please. Thanks everyone!

Here's the modified method:

@model_validator(mode="after")
def set_agent_executor(self) -> "Agent":
    """set agent executor is set."""
    if hasattr(self.llm, "model_name"):
        token_handler = TokenCalcHandler(self.llm.model_name, self._token_process)

        # Ensure self.llm.callbacks is a list
        if not isinstance(self.llm.callbacks, list):
            self.llm.callbacks = []

        # Check if an instance of TokenCalcHandler already exists in the list
        if not any(isinstance(handler, TokenCalcHandler) for handler in self.llm.callbacks):
            self.llm.callbacks.append(token_handler)

    if not self.agent_executor:
        if not self.cache_handler:
            self.cache_handler = CacheHandler()
        self.set_cache_handler(self.cache_handler)
    return self