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domino-genai-tracing

Trace GenAI applications in Domino to capture token usage, latency, cost, and errors.

Updated Apr 11, 2026
Search Tags:#tracing#mlflow#llm-evaluation#genai#domino#agent-tracing#domino-run
One-click install
npx skills add https://github.com/ToXMon/tolu --skill domino-genai-tracing-toxmon
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: domino-genai-tracing
Source: https://github.com/ToXMon/tolu/tree/main/agent-zero-backup/workdir/memory-palace/skills/domino/domino-genai-tracing
Command: npx skills add https://github.com/ToXMon/tolu --skill domino-genai-tracing-toxmon

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Domino GenAI Tracing Skill enables end-to-end monitoring and auditing of GenAI workflows running inside Domino Data Lab. It captures token usage, latency, cost, tool calls, and errors, and groups traces into runs for structured analysis. It also supports evaluators (including LLM-as-judge), MLflow integration, and post-hoc human feedback to drive quality improvements.

Core Features & Use Cases

  • Automatic tracing of function calls with @add_tracing and DominoRun aggregation for grouped visibility.
  • Evaluators for scoring outputs (LLM judges, multi-metric evaluators) and human-in-the-loop workflows to guide improvements.
  • Integration with MLflow and framework adapters to visualize traces, compare runs, and audit multi-agent pipelines (classification, QA, RAG).

Quick Start

Instrument your GenAI workflow by wrapping key functions with @add_tracing and then run a DominoRun context to collect traces and evaluations.

Frequently Asked Questions about domino-genai-tracing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I trace GenAI applications in Domino to capture token usage and latency?▼

Trace GenAI applications in Domino by wrapping key functions with the @add_tracing decorator and running them inside a DominoRun context to capture token usage, latency, and cost across runs.

Can I evaluate LLM outputs and apply LLM-as-judge evaluators to traces inside Domino?▼

Yes, you can evaluate LLM outputs using built-in evaluators including LLM-as-judge and multi-metric scoring, then apply post-hoc human feedback to traces to guide quality improvements.

Does Domino GenAI tracing support debugging multi-step agent orchestration and RAG pipelines?▼

Yes, the tracing supports debugging multi-step AI systems by capturing tool calls and errors across agent orchestration and retrieval augmented generation pipelines within Domino environments.

How does MLflow integration work with Domino run tracing for GenAI workflows?▼

MLflow integration allows you to visualize traces, compare grouped runs, and audit multi-agent pipelines by connecting Domino traces with MLflow tracking and framework adapters.

What is the best way to group traces for structured analysis of multi-agent pipelines in Domino?▼

Use the DominoRun context to group traces into runs for structured analysis, providing grouped visibility into classification, QA, and RAG pipeline executions.

Do I need framework adapters to audit multi-agent pipelines with Domino tracing?▼

Framework adapters are supported alongside MLflow integration to visualize and audit multi-agent pipelines, allowing you to compare runs and evaluate outputs within Domino Data Lab.

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