# Get Started in <10 Minutes Welcome to Fiddler! Choose your integration path based on what you want to accomplish. Each quick start gets you up and running in 10-20 minutes. *** ## ๐ŸŽฏ What Do You Want to Do? ### ๐Ÿค– Monitor AI Agents & Multi-Step Workflows **Best for:** Applications using LangGraph, Strands, or custom agentic frameworks Your AI agents make complex decisions across multiple steps. Monitor the complete workflow from initial reasoning to final response. **Choose your framework:** | Framework | Integration | Time | Quick Start | | ------------------------- | -------------------- | ------ | -------------------------------------------------------------------------------------- | | **LangGraph / LangChain** | Auto-instrumentation | 10 min | [LangGraph SDK โ†’](/developers/agentic-ai-monitoring/langgraph-sdk-quick-start.md) | | **Strands Agents** | Native integration | 10 min | [Strands Agents SDK โ†’](/developers/agentic-ai-monitoring/strands-agent-quick-start.md) | | **Custom / Other** | OpenTelemetry | 15 min | [OpenTelemetry โ†’](/developers/agentic-ai-monitoring/opentelemetry-quick-start.md) | **What you'll monitor:** * Agent decision-making and tool selection * Multi-step reasoning chains * LLM calls with inputs/outputs * Tool usage and external API calls * Error propagation and recovery *** ### ๐Ÿ’ฌ Monitor Simple LLM Applications **Best for:** Single-shot LLM inference, chatbots, simple RAG systems You're using LLMs for straightforward tasks like Q\&A, content generation, or basic chat interfaces. **Quick Start:** [Simple LLM Monitoring โ†’](/developers/llm-monitoring/simple-llm-monitoring.md) โฑ๏ธ 10 min **What you'll monitor:** * LLM prompts and completions * Token usage and costs * Response latency * Quality metrics (toxicity, PII, sentiment) * Embedding visualizations *** ### ๐Ÿ“Š Monitor Traditional ML Models **Best for:** Scikit-learn, XGBoost, TensorFlow, PyTorch models in production You have traditional ML models (classification, regression, ranking) deployed and need to track their performance. **Quick Start:** [Simple ML Monitoring โ†’](/developers/ml-monitoring/simple-ml-monitoring.md) โฑ๏ธ 10 min **What you'll monitor:** * Model performance (accuracy, precision, recall) * Data drift and distribution shifts * Feature importance * Prediction analytics * Custom business metrics *** ### ๐Ÿงช Evaluate & Test LLM Applications **Best for:** Pre-deployment testing, A/B testing, regression testing You want to systematically evaluate LLM quality before deployment or compare different prompts/models. **Quick Start:** [Experiments โ†’](/developers/experiments/experiments-quick-start.md) โฑ๏ธ 15 min **What you'll evaluate:** * Response accuracy and relevance * Semantic similarity * Custom domain-specific metrics * Safety and bias * RAG-specific metrics (faithfulness, context relevance) *** ### ๐Ÿ›ก๏ธ Add Safety Guardrails **Best for:** Protecting LLM applications from harmful content, PII leaks, and hallucinations You need real-time protection to prevent your LLM from generating harmful, sensitive, or incorrect content. **Quick Start:** [Guardrails โ†’](/developers/guardrails/guardrails-quick-start.md) โฑ๏ธ 10 min **What you'll protect against:** * Harmful and toxic content * PII leaks (emails, SSNs, credit cards) * Hallucinations and unsupported claims * Jailbreak attempts * Content policy violations *** ## ๐Ÿค” Not Sure Where to Start? ### If you're building with AI agents: Start with [**Agentic Observability**](/developers/agentic-ai-monitoring/langgraph-sdk-quick-start.md) - it covers everything you need for multi-step workflows. ### If you're using LLMs for simple tasks: Start with [**Simple LLM Monitoring**](/developers/llm-monitoring/simple-llm-monitoring.md) - perfect for chat, Q\&A, and content generation. ### If you have traditional ML models: Start with [**Simple ML Monitoring**](/developers/ml-monitoring/simple-ml-monitoring.md) - track performance and drift for any ML model. ### If you want to test before deploying: Start with [**Experiments**](/developers/experiments/experiments-quick-start.md) - build confidence with systematic testing. ### If you need to protect your users: Start with [**Guardrails**](/developers/guardrails/guardrails-quick-start.md) - add safety checks in minutes. *** ## ๐Ÿš€ Quick Comparison | Use Case | Monitoring Type | Best Quick Start | Time | | ------------------------------------------------ | --------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------ | | Multi-agent systems, complex workflows | Agentic | [LangGraph](/developers/agentic-ai-monitoring/langgraph-sdk-quick-start.md) / [Strands](/developers/agentic-ai-monitoring/strands-agent-quick-start.md) / [OTeL](/developers/agentic-ai-monitoring/opentelemetry-quick-start.md) | 10 min | | Simple chatbots, Q\&A, content generation | LLM | [Simple LLM Monitoring](/developers/llm-monitoring/simple-llm-monitoring.md) | 15 min | | Classification, regression, ranking models | ML | [Simple ML Monitoring](/developers/ml-monitoring/simple-ml-monitoring.md) | 10 min | | Pre-deployment testing, A/B testing | Experiments | [Experiments](/developers/experiments/experiments-quick-start.md) | 15 min | | Safety, PII protection, hallucination prevention | Guardrails | [Guardrails](/developers/guardrails/guardrails-quick-start.md) | 10 min | *** ## ๐Ÿ“š After Your Quick Start Once you've completed a quick start: 1. **Explore the UI** - View your dashboards, metrics, and insights 2. **Set Up Alerts** - Get notified when issues occur 3. **Customize Metrics** - Add domain-specific monitoring 4. **Read Advanced Guides** - Deep dive into specific features 5. **Join the Community** - Get help and share best practices *** ## ๐Ÿ’ก Pro Tips * **Start Simple**: Pick one quick start, complete it fully, then expand * **Use Notebooks**: Most quick starts have Colab notebooks for hands-on learning * **Test Data First**: Use sample data before connecting production systems * **Monitor + Evaluate**: Combine monitoring with evaluation for comprehensive coverage * **Layer Guardrails**: Add safety checks on both inputs and outputs *** ## Need Help? * **Documentation**: Browse our [complete documentation](https://docs.fiddler.ai/) * **Getting Started Guides**: Read conceptual overviews for [Agentic](/getting-started/agentic-monitoring.md), [LLM](/getting-started/llm-monitoring.md), [ML](/getting-started/ml-observability.md), [Experiments](/getting-started/experiments.md), or [Guardrails](/getting-started/guardrails.md) * **Support**: Contact your Fiddler team or *** --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.fiddler.ai/developers/platform/get-started-in-less-than-10-minutes.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.