Learn to build production AI agents with the Claude Agent SDK in Python. Step-by-step guide covering custom tools, safety hooks, subagents, MCP integration, structured outputs, and deployment patterns with working code.
Learn to fine-tune LLMs with LoRA and QLoRA on a single GPU using Python and Hugging Face. Covers dataset prep, training with TRL and PEFT, evaluation, GGUF export, and Ollama deployment.
Learn to test and evaluate AI agents beyond simple output checks. Covers trajectory evaluation, tool use validation with DeepEval and LangChain AgentEvals, golden datasets, and automated CI/CD integration with Python.
Build GraphRAG pipelines in Python using Microsoft GraphRAG and Neo4j. Covers knowledge graph construction, entity extraction, community detection, and retrieval strategies that hit 89-91% accuracy on relational queries where traditional RAG scores only 28-34%.
Build production-ready A2A agents in Python with Google's Agent-to-Agent protocol. Covers Agent Cards, executors, streaming, multi-agent orchestration, and MCP integration with working code examples.
MCP became the standard for AI tool integration in 2026 — and attackers followed. This guide covers the MCP threat landscape and walks through four defensive layers with working Python code: tool verification, authorization middleware, runtime monitoring, and sandboxed execution.
Your AI agent works in dev but crashes in production. Learn how to implement retry with exponential backoff, multi-provider fallback chains, and circuit breakers in Python to build resilient AI agents that handle LLM API failures gracefully.
Learn how to build production-grade AI agents with PydanticAI v1. Covers structured outputs, type-safe tools, dependency injection, MCP integration, and observability with Logfire — all with working code examples.
Learn how to build production-ready MCP servers using FastMCP 3.0 and Python. Covers tools, resources, providers, transforms, database integration, authentication, OpenTelemetry observability, and deployment.
A practical decision framework for choosing between RAG, AI agents, and MCP in production AI systems. Includes performance benchmarks, code examples, cost analysis, and migration patterns.
A hands-on guide to production-grade AI workflow orchestration — covering LangGraph checkpointing, Temporal durable execution with retry policies and Saga compensation, and the two-layer architecture for mission-critical agent pipelines.
Your AI agent forgets everything between conversations. Here's how to fix that with production-ready memory architectures using Mem0, Letta, Zep, LangGraph, and Redis — with real code you can ship today.
Choose your preferred language to explore our content