agentaxon 0.1.0

Lightweight SDK for tracking AI agent costs and reliability with AgentAxon

AgentAxon Python SDK

Lightweight SDK for tracking AI agent costs, latency, and reliability with AgentAxon.

Installation

pip install agentaxon

Quick Start

from agentaxon import AgentAxon
import openai
import os

# 1. Initialize (do this once in your app)
ap = AgentAxon(
    api_key=os.environ.get("AGENTAXON_API_KEY"),
    endpoint="https://your-agentaxon-domain.com/api/events" # Your deployed dashboard URL
)

# 2. Wrap any AI call with the @ap.track decorator
@ap.track(provider="openai", agent_name="customer-support-bot")
def ask_bot(question):
    return openai.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": question}]
    )

# 3. Call your function as normal
response = ask_bot("How do I upgrade my plan?")

How it Works

The @ap.track decorator automatically intercepts the response from the LLM provider, extracts the input_tokens and output_tokens, measures the latency, and pushes the event to your AgentAxon dashboard in a background thread so it never slows down your application.

If an exception occurs during the API call, AgentAxon catches it, logs the error against the agent_name, and re-raises the exception.

Supported Providers

• openai (Extracts tokens from standard OpenAI completions)
• anthropic (Extracts tokens from standard Anthropic messages)
• google (Extracts tokens from Gemini GenerationResponse)

Advanced Usage (Manual Tracking)

If you aren't using one of the supported providers or you want to track costs manually, you can use ap.log_event():

ap.log_event(
    provider="custom-llm",
    model="llama-3",
    agent_name="local-agent",
    input_tokens=150,
    output_tokens=45,
    latency_ms=1200,
    status="success"
)