# 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†’](https://docs.fiddler.ai/developers/agentic-ai-monitoring/langgraph-sdk-quick-start) | | **Strands Agents** | Native integration | 10 min | [Strands Agents SDK โ†’](https://docs.fiddler.ai/developers/agentic-ai-monitoring/strands-agent-quick-start) | | **Custom / Other** | OpenTelemetry | 15 min | [OpenTelemetry โ†’](https://docs.fiddler.ai/developers/agentic-ai-monitoring/opentelemetry-quick-start) | **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†’](https://docs.fiddler.ai/developers/llm-monitoring/simple-llm-monitoring) 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†’](https://docs.fiddler.ai/developers/ml-monitoring/simple-ml-monitoring) 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†’](https://docs.fiddler.ai/developers/experiments/experiments-quick-start) 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†’](https://docs.fiddler.ai/developers/guardrails/guardrails-quick-start) 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**](https://docs.fiddler.ai/developers/agentic-ai-monitoring/langgraph-sdk-quick-start) - it covers everything you need for multi-step workflows. ### If you're using LLMs for simple tasks: Start with [**Simple LLM Monitoring**](https://docs.fiddler.ai/developers/llm-monitoring/simple-llm-monitoring) - perfect for chat, Q\&A, and content generation. ### If you have traditional ML models: Start with [**Simple ML Monitoring**](https://docs.fiddler.ai/developers/ml-monitoring/simple-ml-monitoring) - track performance and drift for any ML model. ### If you want to test before deploying: Start with [**Experiments**](https://docs.fiddler.ai/developers/experiments/experiments-quick-start) - build confidence with systematic testing. ### If you need to protect your users: Start with [**Guardrails**](https://docs.fiddler.ai/developers/guardrails/guardrails-quick-start) - add safety checks in minutes. *** ## ๐Ÿš€ Quick Comparison | Use Case | Monitoring Type | Best Quick Start | Time | | ------------------------------------------------ | --------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------ | | Multi-agent systems, complex workflows | Agentic | [LangGraph](https://docs.fiddler.ai/developers/agentic-ai-monitoring/langgraph-sdk-quick-start) / [Strands](https://docs.fiddler.ai/developers/agentic-ai-monitoring/strands-agent-quick-start) / [OTeL](https://docs.fiddler.ai/developers/agentic-ai-monitoring/opentelemetry-quick-start) | 10 min | | Simple chatbots, Q\&A, content generation | LLM | [Simple LLM Monitoring](https://docs.fiddler.ai/developers/llm-monitoring/simple-llm-monitoring) | 15 min | | Classification, regression, ranking models | ML | [Simple ML Monitoring](https://docs.fiddler.ai/developers/ml-monitoring/simple-ml-monitoring) | 10 min | | Pre-deployment testing, A/B testing | Experiments | [Experiments](https://docs.fiddler.ai/developers/experiments/experiments-quick-start) | 15 min | | Safety, PII protection, hallucination prevention | Guardrails | [Guardrails](https://docs.fiddler.ai/developers/guardrails/guardrails-quick-start) | 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://app.gitbook.com/o/MIMFsmMfRqhAZbzV2AtV/s/82RHcnYWV62fvrxMeeBB/) * **Getting Started Guides**: Read conceptual overviews for [Agentic](https://app.gitbook.com/s/82RHcnYWV62fvrxMeeBB/getting-started/agentic-monitoring), [LLM](https://app.gitbook.com/s/82RHcnYWV62fvrxMeeBB/getting-started/llm-monitoring), [ML](https://app.gitbook.com/s/82RHcnYWV62fvrxMeeBB/getting-started/ml-observability), [Experiments](https://app.gitbook.com/s/82RHcnYWV62fvrxMeeBB/getting-started/experiments), or [Guardrails](https://app.gitbook.com/s/82RHcnYWV62fvrxMeeBB/getting-started/guardrails) * **Support**: Contact your Fiddler team or ***