Build Your First LangGraph Agent: State, Tools, and HITL in 14 Steps
Originally published on AI Tech Connect. Why LangGraph? A Framework Comparison Most agent tutorials show you a simple loop: call the LLM, check if it wants to use a tool, call the tool, call the LLM again, repeat until
Originally published on AI Tech Connect.
Why LangGraph? A Framework Comparison Most agent tutorials show you a simple loop: call the LLM, check if it wants to use a tool, call the tool, call the LLM again, repeat until done. This pattern works perfectly well for single-session, single-task agents. It breaks the moment you need any of the following: conversation state that persists across HTTP requests, the ability to pause mid-execution and ask a human for approval before proceeding, or complex conditional routing where the next step depends on the output of the last. A state-machine agent models execution as a directed graph rather than a flat loop. Nodes perform work; edges define transitions; a shared state object carries data between nodes. This architecture makes the agent's behaviour explicit, testable, and inspectable.β¦
Originally published by Dev.to AI. Aggregated on AIWithGhost for educational purposes β full credit and traffic to the original publisher.