Token / Cost / Usage Tracking with the new ADK (Using Python, not the Dashboard)?

[BrewHog]

Are there any built-in functions to determine cost / use of queries using the ADK? At the moment, I am using the free exp models (gemini-2.0-flash-exp). I need to understand what the costs/usage will be for my flows when put into production.

[osmariofilho]

yes. correct

[BrewHog]

yes. correct

? There is a function to track tokens/cost? Could you elaborate? Thanks

[thetushargoyal]

I have added support for cost in this PR for LiteLLM models. Check it out #198

[weisiang9678]

can you show an example ? i could not get the "usage_metadata" from the llm response.
The only to get the token usage is through LiteLLM ? For my use case, it does not require LiteLLM.
Thanks !

[Shivam-002]

I am also having same question. Looking into the code I didn't find any build-in methods. If anyone knows that how to do it please let us know.

[thetushargoyal]

I have added support for cost in this PR for LiteLLM models. Check it out #198

[Pavan-Bellam]

Has anyone figured out a solution for this?

[thetushargoyal]

I have added support for cost in this PR for LiteLLM models. Check it out #198

[beinoriusju]

LiteLlm has cost counters

[lsimone]

I had the same problem since I wanted to track some costs/usage stats, so I tried to intercept with after_model_callback but no luck.

I finally came up with something similar:

def track_llm_usage(res: ModelResponse | CustomStreamWrapper, model):
            # Log token usage and cost before returning
        try:
            usage = res.get("usage", {})
            prompt_tokens = usage.get("prompt_tokens", 0)
            completion_tokens = usage.get("completion_tokens", 0)
            total_tokens = usage.get("total_tokens", prompt_tokens + completion_tokens)
            
            # Calculate cost if possible
            cost = litellm.completion_cost(completion_response=res) or 0.0
            
            print(f"[acompletion] Model: {model}")
            print(f"[acompletion] Prompt tokens: {prompt_tokens}")
            print(f"[acompletion] Completion tokens: {completion_tokens}")
            print(f"[acompletion] Total tokens: {total_tokens}")
            print(f"[acompletion] Cost: ${cost:.6f}")
            print(f"[acompletion] Time: {(res._response_ms / 1000):.2f}s")
        except Exception as e:
            print(f"Error logging token usage: {e}")
        return res


class KLiteLLMClient(LiteLLMClient):
    async def acompletion(
      self, model, messages, tools, **kwargs
    ):
        return track_llm_usage(await super().acompletion(
            model=model,
            messages=messages,
            tools=tools,
            **kwargs,
        ), model=model)
        
 
    
 root_agent = LlmAgent(
    name="weather_time_agent",
    model=LiteLlm(model="gemini/gemini-2.5-flash-preview-04-17", 
        llm_client=KLiteLLMClient()
    ),
#    [...]
)

hope it helps!

[PaveLuchkov]

litellm._turn_on_debug()
you can paste this in your code, and it will show you following:

"usage":{"prompt_tokens":350,"completion_tokens":418,"total_tokens":768}}

search within terminal using ctrl f "total_tokens". hope that helps

[OhVIton]

If you are using LiteLlm as an agent model, add stream_options={"include_usage": True} to LiteLlm constructor

LiteLlm(
    'azure/gpt-4.1',
    stream_options={"include_usage": True}
)

and you can get usage tokens by Event.usage_metadata

        events = runner.run_async(
            session_id=session.id,
            user_id=session.user_id,
            new_message=self.__messages_to_agent_new_message(messages),
            run_config=RunConfig(streaming_mode=StreamingMode.SSE),
        )
    async for event in events:
        # cache_tokens_details=None cached_content_token_count=None candidates_token_count=None candidates_tokens_details=None prompt_token_count=172 prompt_tokens_details=[ModalityTokenCount(modality=<MediaModality.TEXT: 'TEXT'>, token_count=172)] thoughts_token_count=44 tool_use_prompt_token_count=None tool_use_prompt_tokens_details=None total_token_count=216 traffic_type=None
        print(event.usage_metadata)