agent-atm 1.0.0

Agent Token Manager (agent-atm) - A lightweight, privacy-first LLM token usage metering & quota capping SDK.

Agent Token Manager (agent-atm)

agent-atm is a lightweight, privacy-first Python SDK built to monitor, measure, and cap LLM token consumption natively inside application workflows.

Designed as a high-performance observability and control utility for agentic systems, it plugs seamlessly into any model or agent framework to record precise token metrics, manage nested metadata scopes, and enforce real-time budget quotas over daily, hourly, or minute-level windows.

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✨ Key Features

• Plug-and-Play Observability: An intuitive API designed to feel as familiar as Python's standard logging library.
• Extensible Interface Architecture: Built on clean, developer-friendly abstractions (interfaces for storage managers and tokenizers), making custom integrations simple.
• Privacy-First Guarantee: Zero raw prompt or response text storage. All incoming text is parsed strictly in-memory to calculate token metrics and instantly discarded.
• Flexible Storage Managers: Shipped with a thread-safe InMemoryManager, a robust SQLAlchemyManager for persistent local deployments, and a RemoteHTTPDataManager for distributed telemetry.
• Centralized Telemetry Daemon: A built-in FastAPI server serving as a centralized collector to asynchronously gather token events from distributed client instances.
• Premium Visual Analytics: A modern, interactive dark-mode dashboard to view token trends, audit active configurations, and track live budget allocations.

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🚀 Quick Setup

Get up and running in less than 60 seconds using either the local SDK or a centralized standalone telemetry server.

1. Installation

Install from PyPI:

pip install agent-atm

Or install directly from GitHub:

pip install git+https://github.com/msampathkumar/agent_atm.git

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2. Setup Storage & Record Telemetry

Perfect for Python applications running in-process. Initialize the SQLite storage engine and begin logging request and response telemetry:

import agent_atm as atm

# Initialize local persistent database with in-memory quota caching
atm.init(data_manager="sqlite", db_path="agent_atm.db", default_app_id="customer-bot", quota_cache="memory")

# Record a request event with token count and tags
atm.add_user_request(token_count=32, _additional_metadata_tags=["user-prompt"])

# Record a response event with token count and tags
atm.add_model_response(token_count=120, _additional_metadata_tags=["gemini-response"])

Granular Observability: For token usage analysis, atm allow the recording of context attributes like, model_id, username, session_id, app_id, token_count, list tags (_additional_metadata_tags), and arbitrary key-value config dicts (_additional_metadata_config).

Feature: Direct LLMPayload Dataclass Logging

For advanced configurations, wrap LLM inputs in an explicit LLMPayload object:

from agent_atm.types import LLMPayload

payload = LLMPayload(
    token_count_override=45,
    model_id="example-model",
    event_type="request",  # or "response"
    _additional_metadata_tags=["dev-test"],
    _additional_metadata_config={"node_id": "emea-east-1"}
)
atm.add_user_request(payload)

Feature: Nested Context Scoping

Cascade session IDs, user attributes, and tags cleanly across deeply nested function calls without passing parameters down the stack:

with atm.context(
    session_id="session-abc-123", 
    username="vip-user", 
    _additional_metadata_tags=["production"],
    department="finance" # Custom key-value configs are dynamically captured!
):
    # Seamlessly inherits session_id, username, tags, and department configs
    atm.add_user_request("How does compound interest work?", model_id="gemini-2.5-pro")

Feature: Native Google Gemini Observability

When passing a real google-genai SDK client response, agent-atm automatically extracts precise metrics directly from the native Google usage metadata:

import os
from google import genai
import agent_atm as atm

# 1. Initialize ATM
atm.init(data_manager="sqlite", db_path="usage.db")

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

# 3. Track LLM workflow
with atm.context(session_id="sess-vip-99", username="[e-mail]"):
    prompt = "Draft a professional email response regarding refund query."
    
    # Count and log the request prompt
    atm.add_user_request(prompt, model_id="gemini-2.5-flash")
    
    # Call Gemini
    response = client.models.generate_content(
        model="gemini-2.5-flash",
        contents=prompt,
    )
    
    # Record native response: ATM auto-extracts exact candidate and prompt counts from usage_metadata!
    atm.add_model_response(response, model_id="gemini-2.5-flash")

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3. Pure Web API Style: Central Telemetry Server & curl Commands

Perfect for enterprise microservice environments. Run the agent-atm server standalone and report events from any programming language via standard REST HTTP requests:

Launch the Standalone Telemetry Daemon:

uv run agent_atm --db-path agent_atm.db --host 127.0.0.1 --port 8000

Push Telemetry via curl (fully independent of Python/SDK):

# Log a User Request Event
curl -X POST http://127.0.0.1:8000/api/events \
  -H "Content-Type: application/json" \
  -d '{
    "event_type": "request",
    "token_count": 45,
    "model_id": "gemini-2.5-pro",
    "username": "[e-mail]",
    "session_id": "session-abc-999",
    "app_id": "finance-agent",
    "tags": ["api-call", "production"],
    "config": {"node_id": "aws-east-1"}
  }'

# Log a Model Response Event
curl -X POST http://127.0.0.1:8000/api/events \
  -H "Content-Type: application/json" \
  -d '{
    "event_type": "response",
    "token_count": 180,
    "model_id": "gemini-2.5-pro",
    "username": "[e-mail]",
    "session_id": "session-abc-999",
    "app_id": "finance-agent",
    "tags": ["api-response", "production"]
  }'

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🤖 Native Tokenizer Integrations

For specific families like Google Gemini (google-genai SDK) and Google Gemma (Gemma3Tokenizer), agent-atm provides in-built tokenizer mappings.

Instead of calculating token counts manually, you can pass the raw string content directly and let the SDK compute and track metrics automatically:

# Pass the raw prompt: ATM automatically tokenizes and counts the metrics under the hood!
atm.add_user_request("Explain quantum computing in simple terms.", model_id="gemma-3")

[!TIP] Custom Tokenizer Extensibility: Using another provider? Check our baseline BaseTokenizerIntegration class to see how easy it is to implement a custom tokenizer module.

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🛡️ ATM Controls: Rules, Hooks & Quota Caps

Take complete control of your LLM consumption using reactive quota caps and custom event interceptors:

1. Dynamic Quota Budgeting

Enforce strict token ceilings over minute, hourly, or daily windows per user or app scope. Exceeding a blocking quota raises a TokenQuotaExceeded exception, allowing you to gracefully handle and reject further LLM calls:

# Limit free-tier users to 500 tokens per minute
atm.limits.add(
    scope=atm.Scope(user="free-tier"),
    quota=atm.Quota(minute_limit=500),
    alert_level=atm.AlertLevel.BLOCKING
)

with atm.context(username="free-tier"):
    try:
        # If minute consumption exceeds 500, this raises TokenQuotaExceeded
        atm.add_user_request("Some very long prompt text...", token_count=600)
    except atm.TokenQuotaExceeded as e:
        print(f"Request Blocked: {e}")

2. Pre & Post Hook Interceptors

Register custom hook decorators to validate contexts, mutate event scopes, or trigger asynchronous notifications (like Slack webhooks) around database writes:

@atm.hook("pre")
def pre_save_audit(event):
    # Mutate or validate event metadata BEFORE the database write
    event._additional_metadata_tags.append("audited")

@atm.hook("post")
def slack_alert(event):
    # Trigger non-blocking alerts AFTER the event is successfully written
    if event.token_count > 10000:
        trigger_slack_notification(f"Warning: High token consumption detected: {event.token_count}")

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📊 Real-Time Analytics Dashboard

Start the telemetry metrics daemon to view real-time consumption trend lines, aggregate app metrics, top-consuming users, and live event telemetry logs inside a premium visual dashboard:

uv run agent_atm --db-path agent_atm.db --host 127.0.0.1 --port 8000

Open your web browser to http://127.0.0.1:8000 to access the visual console.

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📖 Additional Resources

• GEMINI.md: Native Gemini & Gemma Tokenizer Integration Handbook.
• CONTRIBUTING.md: Contribution Rules, Virtual Env Setup, and Automated Testing Suite Guide.
• FUTURE.md: TimescaleDB, Distributed Redis Lock, and Remote Buffer scaling roadmaps.