Tool Tracking

Tool Tracking for the Nora Observability Python SDK

Tool Tracking

Function calling and tool executions are automatically tracked. No decorators or manual instrumentation are required beyond initialization.

Tracked Information:

• Tool name

• Arguments passed

• Return value

• Execution time

OpenAI Function Calling

import nora
from openai import OpenAI
import json

nora_client = nora.init(api_key="your-nora-api-key")
client = OpenAI(api_key="your-openai-key")

def get_weather(location, unit="celsius"):
    return {
        "location": location,
        "temperature": 22,
        "unit": unit,
        "condition": "sunny"
    }

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city name, e.g. San Francisco"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"]
                    }
                },
                "required": ["location"]
            }
        }
    }
]

@nora_client.trace_group(name="weather_query")
def ask_weather(question):
    response = client.responses.create(
        model="gpt-5",
        input=[
            {
                "role": "user",
                "content": question
            }
        ],
        tools=tools,
        tool_choice="auto"
    )

    # tool call handling
    if response.output and response.output[0]["type"] == "tool_call":
        tool_call = response.output[0]
        function_name = tool_call["name"]
        function_args = json.loads(tool_call["arguments"])

        if function_name == "get_weather":
            function_response = get_weather(**function_args)

        followup = client.responses.create(
            model="gpt-5",
            input=[
                {
                    "role": "user",
                    "content": question
                },
                {
                    "role": "tool",
                    "tool_call_id": tool_call["id"],
                    "content": json.dumps(function_response)
                }
            ]
        )

        return followup.output_text

    return response.output_text

result = ask_weather("What's the weather like in Tokyo?")
print(result)

Tool Chain Tracking

import nora
from openai import OpenAI

nora_client = nora.init(api_key="your-nora-api-key")
client = OpenAI(api_key="your-openai-key")

@nora_client.trace(span_type="tool", name="SearchDatabase")
def search_database(query):
    return ["Result 1", "Result 2", "Result 3"]

@nora_client.trace(span_type="tool", name="FilterResults")
def filter_results(results, criteria):
    return [r for r in results if criteria in r]

@nora_client.trace(span_type="tool", name="RankResults")
def rank_results(results):
    return sorted(results, reverse=True)

@nora_client.trace_group(name="search_pipeline")
def search_with_tools(user_query):
    results = search_database(user_query)
    filtered = filter_results(results, "Result")
    ranked = rank_results(filtered)

    response = client.responses.create(
        model="gpt-5",
        input=[
            {
                "role": "system",
                "content": f"Search results: {ranked}"
            },
            {
                "role": "user",
                "content": f"Summarize these results for: {user_query}"
            }
        ]
    )

    return response.output_text

result = search_with_tools("Find information about AI")
print(result)