25,910 stars and you're still guessing how your LLM performs. Langfuse tracks metrics, manages prompts, and evaluates models, all in one platform. What’s your current LLM setup? #LLM #Langfuse #Observability #Developers #OpenSource
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#ai #llm #systemdesign #machinelearning #engineering #langfuse | DLLMs
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@langfuse @tomasz_antas @altalogy Catching the launch live from the office. These are the good days. 😍
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Langfuse | LinkedIn
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The real challenge in AI today isn’t just building an agent—it’s scaling it reliably in production. An AI agent that works in a demo often breaks when handling large, real-world workloads. Why? Because scaling requires a layered architecture with multiple interdependent components. Here’s a breakdown of the 8 essential building blocks for scalable AI agents: 𝟭. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 Frameworks like LangGraph (scalable task graphs), CrewAI (role-based agents), and Autogen (multi-ag
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Here's The Detailed AI Agents Roadmap With resources 🚀 Save this before you loose it! 🔖 1. Python Basics: FastAPI, Async, Pydantic API management Testing (Unit & Integration) SQLAlchemy� 2. LLMs: Performance vs Cost Token estimation� 3. RAG (Retrieval-Augmented Generation): Data collection & chunking Hybrid retrieval (Keyword Embedding) Vector DB� 4. Frameworks: LangGraph (preferred) CrewAI �5. Agent Orchestration & Design: Multi-agent workflows Tool calling & reasoning loops 6. Tools & Monito
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