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Documentation Index

Fetch the complete documentation index at: https://docs.agentops.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

The @track_endpoint decorator provides HTTP endpoint tracing for Flask applications with automatic request/response monitoring. It’s designed to work seamlessly with Flask and extends the functionality of the basic @trace decorator.

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Quick Example with OpenAI

Here’s a simple Flask endpoint that generates text using OpenAI: 
from flask import Flask, request
from openai import OpenAI
import agentops

# Initialize AgentOps
agentops.init(
    api_key="your-api-key",
    auto_start_session=False,  # Required for endpoint tracing
)

app = Flask(__name__)
client = OpenAI()

@app.route("/api/generate", methods=["POST"])
@agentops.track_endpoint(
    name="generate_text",
    tags=["ai", "openai"]
)
def generate_text():
    """Generate text using OpenAI"""
    data = request.get_json()
    prompt = data.get("prompt", "Hello!")
    
    # OpenAI call is automatically traced
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
        max_tokens=150
    )
    
    return {
        "text": response.choices[0].message.content,
        "usage": {
            "total_tokens": response.usage.total_tokens
        }
    }

if __name__ == "__main__":
    app.run(debug=True)
The decorator automatically captures:
  • HTTP request data (method, URL, headers, body)
  • HTTP response data (status code, headers, body)
  • OpenAI API calls and their results
  • Any errors that occur during request processing
You can customize tracing with parameters like:
  • name: Custom name for the trace
  • tags: List or dict of tags for categorizing traces
  • capture_request: Whether to capture request data (default: True)
  • capture_response: Whether to capture response data (default: True)