Technical guides and step-by-step instructions on building cloud-native applications, engineering thought leadership, and scaling Agentic AI.
Build production-ready long-horizon planning agents in LangGraph that write structured task plans to files, persist state across sessions, spawn subagent children, and resume exactly where they left off after crashes.
Build real-time streaming agent interfaces in LangChain using astream_events(). Learn to differentiate token streams, tool call events, and node transitions — then render them in a live dashboard for multi-agent workflows.
Master LangChain's context engineering toolkit: use trim_messages() to enforce token budgets, implement conversation summarization to fight context rot, and allocate context window space across system prompts, history, and content.
Master LangChain's prompt engineering stack: build reusable ChatPromptTemplates, extract structured JSON with Pydantic via with_structured_output(), and add auto-retry output parsers that self-correct on validation failures.
Build collaborative multi-agent systems in LangGraph with supervisor routing and shared TypedDict state channels. Learn when to use multi-agent architecture vs. a single agent, how to prevent state conflicts, and wire up specialist agents for real business workflows.
LangGraph tutorial 2025: design stateful AI agent workflows as explicit directed graphs. Learn typed state, conditional routing, cyclic execution, and MemorySaver checkpointers with full Python code examples.