LangGraph Agent combines LLMs with a graph-structured memory to build autonomous agents that can remember facts, reason over relationships, and call external functions or tools when needed. Developers define memory schemas as graph nodes and edges, plug in custom tools or APIs, and orchestrate agent workflows through configurable planners and executors. This approach enhances context retention, enables knowledge-driven decision making, and supports dynamic tool invocation in diverse applications.