a2a-llm-tracker 0.0.29

This package helps you track your llm costs

a2a-llm-tracker

Track LLM usage and costs across providers (OpenAI, Gemini, Anthropic, etc.) from a single place.

Installation

pip install a2a-llm-tracker

Quick Start (Recommended Pattern)

For applications making multiple LLM calls, use a singleton pattern to initialize once and reuse everywhere.

Step 1: Create a tracking module

Create tracking.py in your project:

# tracking.py
from dotenv import load_dotenv
import os
import asyncio
import concurrent.futures

load_dotenv()

_meter = None

def get_meter():
    """Get or initialize the global meter singleton."""
    global _meter
    if _meter is None:
        try:
            from a2a_llm_tracker import init

            client_id = os.getenv("CLIENT_ID", "")
            client_secret = os.getenv("CLIENT_SECRET", "")
            client_server = os.getenv("CLIENT_SERVER", "https://a2aorchestra.com")

            with concurrent.futures.ThreadPoolExecutor() as executor:
                future = executor.submit(
                    asyncio.run,
                    init(client_id, client_secret, "my-app", client_server)
                )
                _meter = future.result(timeout=5)

        except Exception as e:
            print(f"LLM tracking initialization failed: {e}")
            return None
    return _meter

Step 2: Use it anywhere

import os
from openai import OpenAI
from tracking import get_meter

def call_openai(prompt: str):
    client = OpenAI()
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}],
    )

    # Track usage
    try:
        from a2a_llm_tracker import analyze_response, ResponseType

        meter = get_meter()
        agent_id = os.getenv("AGENT_ID")  # Add AGENT_ID to your .env file

        if meter:
            analyze_response(response, ResponseType.OPENAI, meter, agent_id=int(agent_id))
    except Exception as e:
        print("LLM tracking skipped")

    return response

Environment Variables

Set your credentials in .env file or export them:

CLIENT_ID=your_client_id
CLIENT_SECRET=your_client_secret
CLIENT_SERVER=https://a2aorchestra.com  # optional, this is the default
AGENT_ID=my-agent  # optional, for tracking which agent made the call
OPENAI_API_KEY=sk-xxxxx

Query Total Usage & Costs

Retrieve your accumulated costs and token usage from CCS:

import os
import asyncio
from a2a_llm_tracker import init
from a2a_llm_tracker.sources import CCSSource

async def get_total_usage():
    client_id = os.getenv("CLIENT_ID")
    client_secret = os.getenv("CLIENT_SECRET")

    await init(
        client_id=client_id,
        client_secret=client_secret,
        application_name="my-app",
    )

    source = CCSSource(int(client_id))
    total_cost = await source.count_cost()
    total_tokens = await source.count_total_tokens()

    print(f"Total cost: ${total_cost:.4f}")
    print(f"Total tokens: {total_tokens}")

asyncio.run(get_total_usage())

Request Tracking (Multiple LLM Calls per Request)

Track multiple LLM calls as a single request using set_request_id and set_session_id. These work with any framework - no Starlette required.

Basic Usage (Any Framework)

from a2a_llm_tracker import set_request_id, set_session_id, generate_id

def handle_request():
    # Set at the start of each request - all LLM calls get these IDs automatically
    set_request_id(generate_id())
    set_session_id("user-session-123")

    # All LLM calls anywhere in this request share the same IDs
    step_one()
    step_two()
    step_three()

Flask

from flask import Flask, request
from a2a_llm_tracker import set_request_id, set_session_id, generate_id

app = Flask(__name__)

@app.before_request
def before_request():
    set_request_id(request.headers.get("X-Request-ID") or generate_id())
    set_session_id(request.headers.get("X-Session-ID") or generate_id())

Django

# middleware.py
from a2a_llm_tracker import set_request_id, set_session_id, generate_id

class LLMTrackerMiddleware:
    def __init__(self, get_response):
        self.get_response = get_response

    def __call__(self, request):
        set_request_id(request.headers.get("X-Request-ID") or generate_id())
        set_session_id(request.headers.get("X-Session-ID") or generate_id())
        return self.get_response(request)

FastAPI/Starlette (Optional)

If you have Starlette installed, you can use the built-in middleware:

from fastapi import FastAPI
from a2a_llm_tracker import TrackerMiddleware

app = FastAPI()
app.add_middleware(TrackerMiddleware)

TrackerMiddleware now also reads and propagates call-lineage headers when present:

• X-Call-ID
• X-Parent-Call-ID
• X-Sequence-Number

Calling Backend Proxy Agents With Tracking IDs

Use call_agent to call your backend agent URL and automatically forward the current tracking IDs:

• X-Request-ID
• X-Session-ID
• X-Trace-ID

from a2a_llm_tracker import call_agent, set_request_id, set_session_id, set_context

set_request_id("req-123")
set_session_id("sess-123")
set_context(trace_id="trace-123")

response = call_agent(
    "https://my-backend-agent.example.com/run",
    payload={"task": "summarize this"},
)

Async usage:

from a2a_llm_tracker import call_agent_async

response = await call_agent_async(
    "https://my-backend-agent.example.com/run",
    payload={"task": "summarize this"},
)

High-Level A2A/Orchestra Agent Call (Recommended)

Use call_orchestra_agent when you just want to pass text (or parts) and let the SDK:

• build JSON-RPC payload
• handle message/send vs message/stream
• parse response text
• persist returned tracking headers
• auto-generate call lineage headers (X-Call-ID, X-Parent-Call-ID, X-Sequence-Number)
• automatically send bearer auth using CCS token when available (or user_token override)

from a2a_llm_tracker import call_orchestra_agent

result = call_orchestra_agent(
    input_message="Summarize this contract in 3 bullet points.",
    agent_url="https://node.a2aorchestra.com/api/v1/agents/sendmessage/123/.well-known/agent-card.json",
    stream=False,
    debug=True,  # include request headers + payload in result (auth redacted)
)

print(result["success"], result["text"])
print(result["trace_id"], result["session_id"], result["request_id"])
print(result["call_id"], result["parent_call_id"], result["sequence_number"])

Streaming mode (SSE parsed automatically):

result = call_orchestra_agent(
    agent_id="123",  # optional if agent_url already includes agent path
    input_message="Write a short poem.",
    stream=True,
)

print(result["text"])      # aggregated text
print(len(result["events"]))  # parsed SSE events

Override parent linkage manually (optional):

result = call_orchestra_agent(
    agent_id="123",
    input_message="Next step",
    parent_call_id="my-parent-call-id",
)

Multimodal call (text/image/video parts) by passing parts directly:

result = call_orchestra_agent(
    agent_id="123",
    parts=[
        {"kind": "text", "text": "Describe this image"},
        {"kind": "image", "url": "https://example.com/image.jpg"},
    ],
    stream=False,
)

Google ADK Integration

Track LLM usage in Google Agent Development Kit (ADK) agents using the built-in callback:

from google.adk.agents import LlmAgent
from a2a_llm_tracker import create_adk_callback
from tracking import get_meter

meter = get_meter()

agent = LlmAgent(
    name="my_agent",
    model="gemini-2.0-flash",
    instruction="You are a helpful assistant.",
    after_model_callback=create_adk_callback(
        meter=meter,
        agent_id=123,  # Your agent concept ID (integer)
    ),
)

The callback automatically extracts token usage from ADK's LlmResponse.usage_metadata and records it to CCS.

Supported Providers

Provider	ResponseType
OpenAI	ResponseType.OPENAI
Google Gemini	ResponseType.GEMINI
Anthropic	ResponseType.ANTHROPIC
Cohere	ResponseType.COHERE
Mistral	ResponseType.MISTRAL
Groq	ResponseType.GROQ
Together AI	ResponseType.TOGETHER
AWS Bedrock	ResponseType.BEDROCK
Google Vertex AI	ResponseType.VERTEX
Google ADK	ResponseType.ADK

Documentation

Full documentation available on GitHub:

• LiteLLM Wrapper - Auto-tracking via LiteLLM
• CCS Integration - Centralized tracking setup
• Response Analysis - Direct SDK tracking
• Pricing - Custom pricing configuration
• Building - Development and publishing

What This Package Does NOT Do

• Guess exact billing from raw text
• Replace provider SDKs
• Upload data anywhere automatically
• Require a backend or SaaS