> ## Documentation Index > Fetch the complete documentation index at: https://arize-ax.mintlify.site/docs/llms.txt > Use this file to discover all available pages before exploring further. # Tracing > Complete visibility into how your agents and models work # What is Tracing? Tracing captures the full journey of a request as it moves through parts of a system. A *trace* is made up of a series of *spans* — each span corresponds to an individual unit of work such as invoking a model, retrieving data, or running a function. Tracing shows how the request propagated, what components it touched, how long each part took, and what inputs and outputs were involved. Image # Why is Tracing Important? Tracing helps you spot where things take too long, where errors are happening, and which parts of the agent/application need improvement. By capturing detailed metadata such as latency, token counts, and cost, tracing helps you improve system efficiency. # Why Arize AX for Tracing? * Arize created the industry leading [OpenInference Semantic Conventions](https://github.com/Arize-ai/openinference) that is widely adopted by model providers, frameworks, and even observability platforms. Traces work best in the platform in this format. * Native instrumentation for over [30+ model providers, frameworks, and gateways](/ax/integrations). Because we own the instrumentation, we can extend coverage, adapt quickly, and maintain compatibility across frameworks. * Scales to over terabytes of data and billions of spans * Native cost tracking support and can be customized * Agent Visualizations for aggregate analysis # Getting Started **Using a coding agent?** Paste this into your agent to set up tracing automatically: ``` Follow the instructions from https://arize.com/docs/PROMPT.md and ask me questions as needed. ``` [Learn more about agent-assisted setup →](/ax/set-up-with-ai-assistants) There are 3 approaches to getting traces in platform: 1. [Agent-Assisted Setup](/ax/set-up-with-ai-assistants) (Recommended) - *Let your coding agent set up tracing automatically* 2. [Integrations](/ax/observe/tracing/setup/integrations) - *Use a supported integration* 3. [Manual Instrumentation](/ax/observe/tracing/setup/manual-instrumentation) - *Fully customize your traces* # How Does Tracing Work? *Instrumentation* enables your application to emit trace data by automatically or manually wrapping function calls and capturing attributes like inputs, outputs and latency. An exporter transmits the captured spans in the [OpenInference format](https://github.com/Arize-ai/openinference/tree/main) to Arize AX using the OpenTelemetry Protocol (OTLP) over gRPC. Once received, the Arize collector ingests and visualizes the spans in the Arize AX UI. Image # Supported Integrations Using an integration is the easiest, fastest way to get tracing up and running in Arize AX. You get attributes set automatically (following the [OpenInference conventions](https://github.com/Arize-ai/openinference/tree/main)). We continuously add to our list of supported integrations. Visit the full list [here](https://arize.com/docs/ax/integrations). If you don't see your framework, provider or tool yet, you can file a request for us to add it. ## LLM Providers ## Agent Frameworks ## Java