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How AI agents actually work — no buzzwords, just code.
How AI Agents Actually Work (In Plain Python)
AI agents are simpler than frameworks make them look. An agent is a function, tools are a dict, and the agent loop is a while loop. Here's how it all fits together.
You Don't Need LangChain (Here's What It Actually Does)
LangChain's AgentExecutor, @tool, and ConversationBufferMemory map to a few lines of plain Python each. Here's the side-by-side breakdown.
LLM Tool Calling Explained (With Python Code)
Tool calling lets LLMs use functions. The LLM returns a structured request, your code executes it. Here's exactly how it works with the OpenAI API.
Build vs Buy: When to Use an AI Agent Framework
Should you use LangChain, CrewAI, or build from scratch? A practical decision framework based on what these tools actually do under the hood.
HuggingFace Tiny Agents vs Building From Scratch
HuggingFace's Tiny Agents builds an MCP agent in 50 lines of JavaScript. AgentCraft teaches you why those 50 lines work — in 10 interactive Python lessons.
Why 40% of AI Agent Projects Will Fail (And How to Avoid It)
Gartner predicts 40% of agentic AI projects will be cancelled by 2027. The root cause isn't the technology — it's the abstraction layer between engineers and what's actually happening.
The Agent Loop Explained: The 5-Line Pattern Behind Every AI Agent
The agent loop is a while loop. Call the LLM, check for tool calls, execute them, repeat. Here's the exact pattern that LangChain's AgentExecutor, CrewAI, and AutoGen all implement.
Every AI Agent Framework Ranked by Real Data (April 2026)
We pulled GitHub stars, forks, PyPI downloads, funding data, and creator profiles for 20 AI agent frameworks. Here's what the numbers actually say — and what they don't.