GenAI Cost Observability for Google's Generative AI.

ai-tokentrace provides a transparent and easy way to track token consumption in your GenAI applications. Whether you're using the standard google-genai SDK or building complex agents with the Google Agent Development Kit (ADK), this library helps you manage costs, optimize performance, and gain deep insights into your model usage.

• 🔍 Automatic Tracking: Seamlessly integrates with google-genai to capture token usage from every API call.
• 🤖 ADK Support: Includes a plugin for the Google Agent Development Kit for effortless agent monitoring.
• 🔌 Multiple Backends: Export data to where you need it:
  • Logging: Simple standard output for development.
  • JSONL: Structured local files for easy analysis.
  • Google Cloud Firestore: Scalable, queryable cloud storage.
  • Google Cloud Pub/Sub: Event-driven pipelines for real-time analytics.
• ⚡ Async Native: Fully non-blocking to keep your applications fast.
• 📊 Rich Metrics: Tracks input/output tokens, thinking tokens, cached content, tool usage, and more.

Install using pip or uv (recommended).

For standard logging or JSONL export:

pip install ai-tokentrace
# or
uv pip install ai-tokentrace

Install with specific extras for Cloud integrations or ADK support:

# For Google Cloud Firestore
uv pip install "ai-tokentrace[firestore]"

# For Google Cloud Pub/Sub
uv pip install "ai-tokentrace[pubsub]"

# For Google ADK support
uv pip install "ai-tokentrace[adk]"

# Install everything
uv pip install "ai-tokentrace[firestore,pubsub,adk]"

Simply wrap your client with TrackedGenaiClient. It works exactly like the standard client but logs all token usage.

import os
from google import genai
from ai_tokentrace import TrackedGenaiClient

# 1. Initialize standard client
client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])

# 2. Wrap with tracking (uses logging by default)
tracked_client = TrackedGenaiClient(client=client)

# 3. Use as normal!
response = tracked_client.models.generate_content(
    model="gemini-2.5-flash",
    contents="Explain quantum computing in 5 words."
)
print(response.text)
# Output: "Complex superposition processes information fast."
# Log: {"timestamp": "...", "model_name": "gemini-2.5-flash", "total_tokens": 15, ...}

Add the TokenTrackingPlugin to your ADK app.

from google.adk.agents import LlmAgent
from google.adk.apps.app import App
from ai_tokentrace.adk import TokenTrackingPlugin

agent = LlmAgent(model="gemini-2.5-flash", ...)

app = App(
    name="my_app",
    root_agent=agent,
    plugins=[TokenTrackingPlugin()]  # Tracks all agent interactions
)

You can configure different backends for storing your token usage data.

Firestore Example:

from ai_tokentrace import TrackedGenaiClient
from ai_tokentrace.services import FirestoreTokenUsageService

service = FirestoreTokenUsageService(collection_name="genai_usage_logs")
tracked_client = TrackedGenaiClient(client=client, service=service)

Pub/Sub Example:

from ai_tokentrace import TrackedGenaiClient
from ai_tokentrace.services import PubSubTokenUsageService

service = PubSubTokenUsageService(topic_id="my-usage-topic", project_id="my-project")
tracked_client = TrackedGenaiClient(client=client, service=service)

Give your agents the ability to see their own token usage!

from ai_tokentrace.services import FirestoreTokenUsageService

service = FirestoreTokenUsageService(...)

# Add the inspection tool to your agent
agent = LlmAgent(
    ...,
    tools=[service.get_inspection_tool()]
)

Check out the examples/ directory for complete, runnable projects:

• google-genai/: Scripts demonstrating sync/async usage, streaming, and different backends.
• adk/: Full ADK applications showing multi-agent tracking, multimodal capabilities, and self-inspection.

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Apache 2.0 - See LICENSE for more details.