Build the system, not just the demo.
A curated library for understanding agent architecture, graph systems, feedback loops, evaluation, and the engineering work that gets AI into production.
Follow the thread that matters.
Search across topics, tools, authors, and formats. Filter by a track when you want a more focused path.
OpenAI
A Practical Guide to Building AI Agents
A grounded framework for deciding when to use agents, choosing tools and models, structuring orchestration, and adding guardrails.
Anthropic
Building Effective Agents
Patterns for prompt chaining, routing, parallelization, orchestrator-worker systems, evaluators, and autonomous agents.
Hugging Face
AI Agents Course
A free, hands-on path through agent fundamentals, frameworks, agentic RAG, observability, evaluation, and a final project.
OpenAI
OpenAI Agents SDK
Developer documentation for agent loops, handoffs, guardrails, sessions, tracing, tools, and realtime agents in Python.
LangChain
LangGraph Overview
The core concepts behind durable, stateful agent graphs with persistence, streaming, interrupts, and human oversight.
Microsoft
GraphRAG: Getting Started
An end-to-end introduction to indexing unstructured text into a knowledge graph and querying it with local and global search.
NetworkX
NetworkX Introduction
A practical reference for graph types, nodes, edges, attributes, algorithms, traversal, analysis, and graph data structures.
Neo4j
Graph Data Science Manual
Production-oriented documentation for graph projections, algorithms, machine learning pipelines, and graph embeddings.
LangChain
Workflows and Agents in LangGraph
Examples of deterministic workflows and dynamic agent loops, including tool execution and conditional control flow.
arXiv
ReAct: Synergizing Reasoning and Acting
The foundational paper on interleaving reasoning traces with actions and observations so language models can interact with tools.
arXiv
Reflexion: Language Agents with Verbal Reinforcement Learning
A research pattern where agents reflect on feedback, store verbal lessons, and improve behavior across repeated attempts.
Lilian Weng
LLM Powered Autonomous Agents
A visual technical survey of planning, memory, tool use, self-reflection, and the practical limitations of agent systems.
DeepLearning.AI
Evaluating AI Agents
A short video course on tracing agent behavior, evaluating trajectories, building structured graders, and monitoring quality.
Full Stack Deep Learning
Large Language Models Bootcamp
Recorded lectures on prompt engineering, augmented language models, LLMOps, deployment, product design, and UX.
Andrej Karpathy
Neural Networks: Zero to Hero
A code-first video series that builds neural networks from basic backpropagation through language models and GPT.
PyTorch
Introduction to PyTorch
A video-backed tutorial covering tensors, model construction, autograd, datasets, training, and inference with PyTorch.
Rules of Machine Learning
Google's practical guidance for metrics, infrastructure, simple baselines, training-serving consistency, and production iteration.
Deep Learning Tuning Playbook
A systematic guide to choosing objectives, designing experiments, debugging training, and tuning deep learning models.
PyTorch
Learn the Basics with PyTorch
A complete beginner workflow from tensors and datasets through model building, optimization, and saving trained models.
Made With ML
MLOps Course
A project-based course covering data preparation, training, tracking, serving, testing, versioning, CI/CD, and monitoring.
Hugging Face
Hugging Face LLM Course
A practical course on transformers, tokenizers, datasets, fine-tuning, model sharing, demos, and advanced LLM topics.