LangGraph for Developers: From Zero to Hero

Trace the evolution of ai from symbolic ai to agentic workflows, highlighting bottlenecks in hardware, data, and logic, and the shift from rule-based systems to transformer-based general purpose reasoning.

Explore agentic AI, its four core capabilities—autonomy, planning, tool use, and self-correction—plus how multi-step, tool-enabled workflows and five levels transform LLMs into capable agents.

Discover the benefits of agentic AI, including autonomy at scale, goal-driven intelligence, adaptive decision-making, and multi-step reasoning that boost productivity and enable tool orchestration.

Explore agentic design patterns—reflection, tool use, planning, and multi-agent collaboration—that let an LLM iterate, reflect on errors, use tools, plan steps, and coordinate multiple agents for better results.

obtain and manage an llm service provider api key with OpenAI, including account setup, adding credits, creating or revoking keys, and configuring the base llm layer for knowledge agents.

Explore core concepts of LangGraph, including state, nodes, and edges, and learn how reducers apply partial updates within super steps for branching, looping, and long running workflows.

Navigate control flow in LangGraph by building a chat-style graph that appends messages via a reducer, tracks messages and step, and uses conditional edges for routing and loops.

Understand short-term memory as thread-level persistence and long-term memory via a store abstraction, with subgraphs enabling reusable multi-agent architectures in LangGraph.

Explore the functional API in LangGraph for developers, with entry points and tasks. Contrast it with the graph API and its support for persistence, memory, interrupts, and streaming.

Create an API endpoint to expose an AI agent, configure a POST route, load env vars, initialize a LangChain agent with a system prompt, and test via Swagger UI.