From Chaos to Clarity: LLM Tracing with Logfire and PydanticAI скачать в хорошем качестве

From Chaos to Clarity: LLM Tracing with Logfire and PydanticAI

Join AI Dev Skool & Launch Your AI Startup Today! https://skool.com/ai-software-developers is the community for founders, builders, and AI innovators ready to take their projects to the next level. If you're launching an AI startup or working on a side project, stop wasting time on endless tutorials and start focusing on what really matters. Inside AI Dev Skool, you'll: ✅ Get expert guidance on the best AI frameworks ✅ Cut through the hype and go straight to what works ✅ Maximize your time with curated resources and real-world insights ✅ Build strong connections with like-minded developers and founders Our best members actively engage, share, and build—gaining skills while turning ideas into real businesses. If you're serious about AI development and want a shortcut to success, this is the place for you. 🚀 Join now and start building smarter: https://skool.com/ai-software-developers Masterclass: Using PydanticAI for Creating Multi-Agent Flows In this video, we dive into Logfire, a cutting-edge observability platform built on OpenTelemetry and developed by the creators of Pydantic. Designed for cross-language support, Logfire is a powerful tool for monitoring applications in Python and beyond. Masterclass Series: ▶️ Part 1:    • PydanticAI: The Best AI Agent Framework Ha...   ▶️ Part 2:    • From Chaos to Clarity: LLM Tracing with Lo...   ▶️ Part 3:    • 100% Reliable LLM Outputs with Structured ...   ▶️ Part 4:    • Dramatic Improvement! Design Better Agents...   ▶️ Part 5:    • Design Better AI Agents With Function Calling   ▶️ Part 6:    • Transform You Agents with Result Validator...   ▶️ Part 7:    • Improve Agent Scalability with Dependency ...   ▶️ Part 8:    • Build More Reliable Agents with Retries an...   ▶️ Part 9:    • Better Context Retention with Agent Memory...   ▶️ Part 10:    • Building Resilient Agents: Self-Reflection...   ▶️ Part 11:    • Better User Experience with Streaming Outp...   ▶️ Part 12:    • Hidden Model Settings That Will Transform ...   ▶️ Part 13: Multi-Model Agents in PydanticAI: Unlocking Next-Gen AI Capabilities (Developer Tutorial) ▶️ Part 14: Mastering RAG in PydanticAI: Better AI Agents with Real-Time Data (Developer Tutorial) ▶️ Part 15: Masterclass Final Project: AI Resume Writing with Multiple Agents (Developer Tutorial) Why does this matter for AI developers? Large Language Models (LLMs) are notoriously unreliable—often described as "the worst databases." Having a robust tracing and debugging system can save you countless hours of troubleshooting. We’ll start by walking through the configuration of Logfire, explore its key features, and work through 5 practical examples: 📍 A simple Hello, World to get started 📍 Advanced, real-world debugging scenarios Whether you're building AI agents, distributed systems, or anything in between, Logfire can transform the way you monitor and optimize your applications. 🔗 Links & Resources: Skool: https://www.skool.com/ai-software-dev... Code the Revolution: Newsletter - https://aidev9.substack.com/ Discord server:   / discord   PydanticAI: https://ai.pydantic.dev If you enjoy this video, don't forget to like, subscribe, and hit the notification bell for more deep dives into tools for AI and software development. 🚀 Timecodes: 00:00 - Introduction 02:30 - Logfire setup 04:00 - Hello, World! 05:22 - Span 06:44 - Log levels 08:20 - Exceptions 11:16 - Instrumenting 13:26 - Summary #ai #openai #pydantic #ollama #pydanticai #mistral #llm #developer #software #tutorial #genai #llama #local #private #chatgpt #Observability #Logfire #OpenTelemetry #Pydantic #AIDevelopment