Tracing with Graphsignal

Graphsignal provides observability for AI agents and LLM-powered applications. It helps developers ensure AI applications run as expected and users have the best experience.

Graphsignal automatically traces and monitors LlamaIndex. Traces and metrics provide execution details for query, retrieval, and index operations. These insights include prompts, completions, embedding statistics, retrieved nodes, parameters, latency, and exceptions.

When OpenAI APIs are used, Graphsignal provides additional insights such as token counts and costs per deployment, model or any context.

Installation and Setup

Adding Graphsignal tracer is simple, just install and configure it:

pip install graphsignal

import graphsignal

# Provide an API key directly or via GRAPHSIGNAL_API_KEY environment variable
graphsignal.configure(
    api_key="my-api-key", deployment="my-llama-index-app-prod"
)

You can get an API key here.

See the Quick Start guide, Integration guide, and an example app for more information.

Tracing Other Functions

To additionally trace any function or code, you can use a decorator or a context manager:

with graphsignal.start_trace("load-external-data"):
    reader.load_data()

See Python API Reference for complete instructions.

Useful Links

• Tracing and Monitoring LlamaIndex Applications
• Monitor OpenAI API Latency, Tokens, Rate Limits, and More
• OpenAI API Cost Tracking: Analyzing Expenses by Model, Deployment, and Context