Why LangGraph changes everything
Sequential LLM pipelines quickly hit their limits when dealing with complex business workflows: persistent state management, conditional branching, validation loops, parallel tool calls. LangGraph introduces a directed graph paradigm where each node is a processing step and each edge is a state transition — potentially conditional. Unlike LCEL, the graph can contain cycles, which enables reflective architectures (reflect → act → observe → reflect).
Core primitives
A StateGraph rests on three elements. Nodes: pure (or async) functions that receive the current state and return a state patch. Edges: simple transitions or conditional functions that return the next node name. Shared state: a typed TypedDict or dataclass that persists throughout the workflow and acts as the contract between nodes. State typing discipline is the first guarantee of production robustness.
Three multi-agent patterns
The Supervisor pattern: an orchestrator node receives the task, selects the appropriate specialist agent via an LLM router, and consolidates results before responding. The Hierarchical pattern: supervisors can themselves delegate to sub-agents, enabling tree-structured organizations for highly complex tasks. The Parallel pattern: multiple agents execute via the Send() primitive, with their results converging into an aggregation node — particularly effective for multi-source document retrieval.
Production advice
Instrument every node with LangSmith from day one — debugging a complex graph without traceability is a nightmare. Define per-node timeouts and an escape_hatch node that breaks cycles when an iteration counter exceeds a threshold. Persist state with a checkpointer (SQLite in dev, PostgreSQL in prod) to enable resumption after human interruptions or network errors. Version your graphs: a node change can silently break untested downstream paths.