Llm

The initial wave of the Generative AI revolution was built almost entirely on the back of massive cloud APIs. Developers flocked to OpenAI, Anthropic, and Google, trading data sovereignty and high operational costs for the convenience of state-of-the-art inference. However, a significant architectural shift is underway. As open-source models like Llama 3, Mistral, and Phi-3 approach the performance of their proprietary counterparts, enterprises and developers are moving toward Local LLM Orchestration. This shift from “Cloud-First” to “Edge-Intelligence” isn’t just about saving money—it’s about privacy, latency, and the creation of resilient, offline-capable systems. ...

Table of Contents Introduction Understanding Agentic Workflows: Core Concepts Setting Up Your Development Environment Building Your First Agent: The ReAct Pattern Tool Integration and Function Calling Memory Systems for Stateful Agents Multi-Agent Orchestration Patterns Error Handling and Reliability Patterns Observability and Debugging Agentic Systems Production Deployment Strategies Advanced Patterns: Graph-Based Workflows Security and Safety Considerations Performance Optimization Techniques Conclusion Top 10 Resources Introduction Agentic workflows represent the next evolution in AI application development. Unlike traditional request-response systems, agents autonomously plan, execute, and adapt their actions to achieve complex goals. In 2026, the landscape has matured significantly—LLM providers offer robust function calling, frameworks have standardized on proven patterns, and production deployments are increasingly common. ...

llama.cpp is a lightweight, high-performance C/C++ library for running large language models (LLMs) locally on diverse hardware, from CPUs to GPUs, enabling efficient inference without heavy dependencies.[7] This detailed guide covers everything from setup and building to advanced usage, Python integration, and optimization techniques, drawing from official documentation and community tutorials. Whether you’re a developer deploying models on edge devices or an enthusiast running LLMs on a laptop, llama.cpp democratizes AI by prioritizing minimal setup and state-of-the-art performance.[7] ...

Introduction Tool calling (also called function calling or plugins) is the capability that turns large language models from text predictors into general-purpose controllers for software. Instead of only generating natural language, an LLM can: Decide when to call a tool (e.g., “get_weather”, “run_sql_query”) Decide which tool to call Construct arguments for that tool Use the result of the tool to continue its reasoning or response This post is a deep dive into the anatomy of tool calling: the moving parts, how they interact, what can go wrong, and how to design reliable systems on top of them. ...

FastAPI’s speed, async capabilities, and automatic API documentation make it ideal for building production-grade APIs serving Large Language Models (LLMs). This guide details best practices for deploying scalable, secure FastAPI applications handling LLM inference, streaming responses, and high-throughput requests.[1][3][5] LLM APIs often face unique challenges: high memory usage, long inference times, streaming outputs, and massive payloads. We’ll cover project structure, async optimization, security, deployment, and LLM-specific patterns like token streaming and caching. ...