0.1.0

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"
)