Trace an agent

This page shows you how to enable Cloud Trace on your agent and view traces to analyze query response times and executed operations.

A trace is a timeline of requests as your agent responds to each query. For example, the following Gantt chart shows a sample trace from a LangchainAgent:

 

The first row in the Gantt chart is for the trace. A trace is composed of individual spans, which represent a single unit of work, like a function call or an interaction with an LLM, with the first span representing the overall request. Each span provides details about a specific operation, such as the operation's name, start and end times, and any relevant attributes, within the request. For example, the following JSON shows a single span that represents a call to a large language model (LLM):

  {
    "name": "llm",
    "context": {
        "trace_id": "ed7b336d-e71a-46f0-a334-5f2e87cb6cfc",
        "span_id": "ad67332a-38bd-428e-9f62-538ba2fa90d4"
    },
    "span_kind": "LLM",
    "parent_id": "f89ebb7c-10f6-4bf8-8a74-57324d2556ef",
    "start_time": "2023-09-07T12:54:47.597121-06:00",
    "end_time": "2023-09-07T12:54:49.321811-06:00",
    "status_code": "OK",
    "status_message": "",
    "attributes": {
        "llm.input_messages": [
            {
                "message.role": "system",
                "message.content": "You are an expert Q&A system that is trusted around the world.\nAlways answer the query using the provided context information, and not prior knowledge.\nSome rules to follow:\n1. Never directly reference the given context in your answer.\n2. Avoid statements like 'Based on the context, ...' or 'The context information ...' or anything along those lines."
            },
            {
                "message.role": "user",
                "message.content": "Hello?"
            }
        ],
        "output.value": "assistant: Yes I am here",
        "output.mime_type": "text/plain"
    },
    "events": [],
  }

Note: The format of the trace(s) and span(s) depends on the instrumentation option you go with. The example span is experimental and subject to change so you shouldn't rely on the format to be stable for now. For details, see the Semantic Conventions for Generative AI systems being developed in OpenTelemetry.

For details, see the Cloud Trace documentation on Traces and spans and Trace context.

Write traces for an agent

To write traces for an agent:

LangchainAgent

To enable tracing for LangchainAgent, specify enable_tracing=True when you develop a LangChain agent. For example:

from vertexai.preview.reasoning_engines import LangchainAgent

agent = LangchainAgent(
    model=model,                # Required.
    tools=[get_exchange_rate],  # Optional.
    enable_tracing=True,        # [New] Optional.
)

LanggraphAgent

To enable tracing for LanggraphAgent, specify enable_tracing=True when you develop a LangGraph agent. For example:

from vertexai.preview.reasoning_engines import LanggraphAgent

agent = LanggraphAgent(
    model=model,                # Required.
    tools=[get_exchange_rate],  # Optional.
    enable_tracing=True,        # [New] Optional.
)

Custom

To enable tracing for custom agents, visit Tracing using OpenTelemetry for details.

This will export traces to Cloud Trace under the project in Set up your Google Cloud project.

View traces for an agent

Once enabled, you can find the traces in Trace Explorer.

Go to the Trace Explorer

To learn more, see the Cloud Trace documentation.

Quotas and limits

Some attribute values might get truncated when they reach quota limits. For more information, see Cloud Trace Quota.

Pricing

Cloud Trace has a free tier. For more information, see Cloud Trace Pricing.