Stateful Agents with LangGraph

Build cyclical, stateful AI agent workflows using nodes, edges, and state persistence with LangGraph.

Abstract Algorithms

Jul 2, 2026·1 min read·Intermediate

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Quick Take

While linear chains (input -> LLM -> output) are great for simple tasks, agentic workflows require cycles (e.g., run tool, evaluate result, decide to run another tool). LangGraph models these cycles u

While linear chains (input -> LLM -> output) are great for simple tasks, agentic workflows require cycles (e.g., run tool, evaluate result, decide to run another tool).

LangGraph models these cycles using a directed state graph with state persistence.

📊 Cyclical Agent Graph

  Start ──► [ Call LLM Node ] ──► [ Decide Edge ] ──(Done)──► End
                 ▲                     │
                 │                (Need Tool)
                 │                     ▼
                 └────────────── [ Execute Tool Node ]

State: A shared memory schema passed between nodes. Each node returns updates to this state.
Nodes: Standard Python/JavaScript functions (e.g., calling an LLM or querying a database).
Edges: Define routing. Conditional edges evaluate current state to decide which node to call next.

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