Guides
CrewAI vs AutoGen: Which Multi-Agent Framework
CrewAI structures agents around fixed roles and a defined process; AutoGen is built around open-ended agent conversation — here’s the real difference
Haystack vs LangChain: Which for RAG
Haystack is built specifically for production-grade retrieval and search pipelines; LangChain is a broader framework that can also build RAG systems
LangChain vs LlamaIndex: What’s the Difference
LangChain is a general LLM-application framework; LlamaIndex is focused specifically on data ingestion and retrieval — here’s when each one actually fits
What Is Hugging Face?
How the Hugging Face Hub, Transformers library, and Spaces fit together, and why it’s become the default starting point for working with open models in Python
What Is MarkItDown? Docs to Markdown Explained
Microsoft’s tool for converting Office documents, PDFs, and other files into clean Markdown — and why that matters for feeding documents to an LLM
What Is the OpenAI Python Library?
The official Python client for the OpenAI API — chat completions, embeddings, streaming, and function calling — and how it fits with Instructor and Pydantic AI
What Is Smolagents? Hugging Face’s Agent Library
A minimal, code-first agent library from Hugging Face — how it differs from heavier frameworks like LangChain, and when its simplicity is the point
What Is Mojo? Chris Lattner’s New AI Language Explained
Mojo combines Python syntax with systems-level performance for GPU/CPU workloads — what it actually is, now that it’s hit 1.0 and gone open source
What Is Ollama? Running LLMs Locally Explained
Ollama runs open-weight LLMs on your own machine with a simple CLI and local API — what it actually does, and why you’d use it over a hosted API
Instructor vs Pydantic AI: What’s the Difference
Instructor and Pydantic AI both promise structured, typed LLM output using Pydantic — here’s what actually separates a narrow tool from a full agent framework
LangChain vs LangGraph: What’s Actually Different
Why LangGraph exists alongside LangChain, built by the same team, and when to reach for one over the other
Python AI Agent Frameworks Compared
How LangGraph, CrewAI, AutoGen, and smolagents actually differ, and which one fits a new multi-agent Python project
What Is MCP (Model Context Protocol)? A Python Guide
What the Model Context Protocol actually standardizes, how it differs from LangChain or DSPy, and a basic FastMCP server example
DSPy Explained: Programming, Not Prompting, Language Models
What DSPy is, how it differs from hand-writing prompts, and a basic example of defining a module and letting DSPy optimize it
Resources
Mojo
Python-syntax systems language for GPU/CPU performance on AI workloads, from Swift/LLVM architect Chris Lattner — hit 1.0 and went open source in August 2026
CrewAI
Python framework for orchestrating role-based multi-agent teams that collaborate to complete complex tasks
LangChain
The most widely used Python framework for building LLM-powered applications, with 700+ integrations and tools for agents, RAG, and chains
LangGraph
Framework for building stateful, multi-step AI agent workflows as graphs — ideal for complex reasoning and tool-use pipelines
Ollama
Run large language models locally with a simple Python API — supports Llama, Mistral, Gemma, and dozens more open-source models
Pydantic
Data validation and settings management using Python type annotations — the cornerstone of FastAPI and the modern AI stack
Pydantic AI
Type-safe Python framework for building production AI agents, built by the Pydantic team with structured outputs and dependency injection