Overview - Fiddler Documentation

Practical guides, tutorials, and reference documentation for building with Fiddler.

⚡ Quick Starts

Get up and running in minutes with step-by-step quick start guides:

Get Started in <10 Minutes - Fastest way to integrate Fiddler
Agentic Monitoring Quick Starts - Monitor AI agents and multi-step workflows
- OpenTelemetry Quick Start (fiddler-otel) or
- LangChain SDK Quick Start (fiddler-langchain) or
- LangGraph SDK Quick Start (fiddler-langgraph) or
- Strands Agent SDK Quick Start (fiddler-strands)
Experiments Quick Start - Evaluate LLM outputs with custom metrics
Guardrails Quick Start - Add safety guardrails to your AI applications

In-depth, hands-on tutorials organized by product area:

Learn how to evaluate and test your LLM applications:

Monitor production LLM applications and AI agents:

Implement safety controls for your AI applications:

Monitor traditional ML models in production:

Use-case oriented guides that demonstrate end-to-end workflows for solving real problems:

RAG Evaluation Fundamentals — Evaluate RAG quality with built-in evaluators
Running RAG Experiments at Scale — Compare pipeline configurations systematically
Building Custom Judge Evaluators — Create domain-specific evaluation criteria
Detecting Hallucinations in RAG — Monitor for hallucinations in production
Monitoring Agentic Content Generation — Quality and brand compliance for content agents

Comprehensive reference documentation for Fiddler’s Python client:

For supported task setup and schema guidance, continue with the Model Onboarding guide and Updating Model Schema.