- Published on
- •7 min read
Part 4 covers patterns 07–15 — orchestration, safety, memory, RAG, and event-driven agents — plus how to move from runnable examples to production architecture.
Langgraph
All Posts
langgraph (7)
- python (4)
- ai-agents (4)
- design-patterns (3)
- llm (3)
- machine-learning (2)
- clustering (2)
- adjusted-rand-index (2)
- agentic-ai (2)
- multi-agent (2)
- pyspark (2)
- data-engineering (2)
- postgresql (2)
- mcp (1)
- react-agent (1)
- routing (1)
- planning (1)
- human-in-the-loop (1)
- rag (1)
- production (1)
- json (1)
- data-types (1)
- data-wrangling (1)
- dataframes (1)
- go (1)
- golang (1)
- nextjs (1)
- prisma (1)
- rest-api (1)
- langchain (1)
- hexagonal-architecture (1)
- docker (1)
- Published on
- •6 min read
Part 3 covers patterns 01–06 — ReAct, Tool Use, Planning, Prompt Chaining, Routing, and Parallelization — with LangGraph flow diagrams and runnable commands for each.- Published on
- •6 min read
Part 2 explains the three shared scenarios (IT helpdesk, e-commerce, ML pipeline) and the shared runtime that lets every pattern example run with the same CLI, tools, and LangGraph helpers.- Published on
- •6 min read
Part 1 introduces agentic-ai-overview — a hands-on catalog of 15 LangGraph design patterns with three shared scenarios, and explains how to learn agentic AI by comparing patterns on the same domain.- Published on
- •8 min read
Part 4 covers hexagonal architecture, the agent memory model, the background worker, Docker deployment, and the live agent explorer for learning multi-agent systems.