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13 in-depth python articles on setup, real workflows, and production patterns — written for builders shipping with AI.
Your agents work on your laptop. Now what? This final guide covers everything you need to ship agents that run reliably in production: Docker containerisation, VPS vs serverless, structured logging, health checks, cost controls, and the monitoring setup that wakes you up before your users notice something broke.
MarkItDown is Microsoft's open-source Python library that converts PDFs, Word docs, Excel, PowerPoint, and 12+ formats to clean Markdown for LLM pipelines. 139K GitHub stars, 82% F1 accuracy, zero GPU required. Full setup guide with benchmarks.
In Part 1 you built the loop. Now give it real tools: web search, code execution, and file writing - plus the error recovery patterns that separate demo agents from production ones. Copy-paste Python, no frameworks.
Most tutorials start with code. This one starts with the right mental model. Learn exactly what an AI agent is (and isn't), how it differs from a chatbot, and the four components every agent needs - with Python examples you can run today.
Your agent forgets everything the moment a session ends. This guide covers the three memory patterns every AI agent builder needs: in-context, external file, and vector database - with Python code for each.
Your single agent can now use tools and remember things. But one agent has a ceiling. This guide covers the three orchestration patterns that scale agents from demo to real work: pipeline, supervisor/worker, and fan-out. Python code for each.
Level 1 runs a linter. Level 5 ships features end-to-end with no human in the loop. This guide breaks down each level with real examples, code, and the tools used at each stage — so you know exactly where to start.
What AI agents are, how the agent loop works, frameworks vs raw API, memory, multi-agent systems, and MCP. The 2026 builder's roadmap.
MCP is the protocol that lets Claude, Cursor, and any LLM call your own tools, databases, and APIs. This guide explains how it works and walks you through building a real MCP server from scratch in Python.
A complete tutorial on building an AI agent from scratch in Python — no LangChain, no framework. Just the Anthropic SDK, a tool-use loop, and ~60 lines of code that you fully understand and control.
LangChain has 80K stars. CrewAI has 20K. The raw Anthropic/OpenAI SDK is 60 lines. Which should you build your AI agent on? After shipping production agents in all three, here is the honest decision framework.
Build a working multi-agent system in Python — a coordinator agent delegates to specialized workers, handles failures, and synthesizes results. Complete code, real examples, no framework lock-in.
Data engineering is 80% boilerplate. Claude Code writes SQL migrations, scaffolds dbt models, builds Airflow DAGs, and debugs slow queries. Here's how to wire it into your stack.