Agent Tracing

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What is Agent Tracing?

Agent Tracing in Qualifire provides a detailed, end-to-end view of your AI agent’s operations. By leveraging the open standard of OpenTelemetry (OTLP), you can track every step of a complex workflow, from the initial prompt to the final output, including all intermediate LLM calls, tool usage, and decision-making processes.

This powerful observability feature allows you to:

• Debug complex agent behaviors: Pinpoint the exact source of errors, latency, or unexpected outputs.
• Analyze performance: Identify bottlenecks and optimize the performance of your agents.
• Monitor costs: Track token usage and cost for each step in a workflow.
• Ensure reliability: Understand how your agent chains and tool integrations are functioning in production.

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How It Works

Qualifire’s tracing is built on the foundations of OpenTelemetry, an open-source observability framework.

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Integrating Your Application

To get started, you can use our Python SDK which simplifies the process of instrumenting your application and sending trace data to Qualifire.

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1. Installation

First, install the Qualifire client SDK:

pip install qualifire

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2. Configuration

Next, configure the SDK in your application’s entrypoint. This will automatically instrument popular libraries like OpenAI and LangChain to send traces to Qualifire.

from qualifire_tracing import configure_qualifire_tracing

configure_qualifire_tracing(
  gateway_url="https://proxy.qualifire.ai",
  api_key="YOUR_QUALIFIRE_API_KEY"
)

# Your application code here...
# For example, an OpenAI client call:
from openai import OpenAI
client = OpenAI() # This client is now automatically traced

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Explain OpenTelemetry in one sentence."}]
)

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Visualizing Traces

Once your application is instrumented, traces will appear in the Qualifire dashboard. Our UI provides a rich visualization of your traces, including:

• A hierarchical tree view of all spans within a trace.
• Detailed summaries of performance, cost, and governance metrics.
• In-depth analytics for each span, including attributes, events, and linked model invocations.