LangGraph Pros & Cons: Honest Review (2026)

Use LangGraph when your workflow needs cycles (loops), conditional branching, persistent state, or human-in-the-loop approval. Simple linear chains don't need LangGraph. If your agent needs to make decisions about what to do next, retry on failure, or maintain state across interactions, LangGraph adds real value.

Partially. LangGraph has its own package and doesn't require LangChain's chains or retrieval abstractions. However, it depends on langchain-core for base types and message formats. You can use raw API calls within nodes, but you're still importing LangChain's foundational types.

Use PostgresSaver for production. Configure it when compiling your graph: graph.compile(checkpointer=PostgresSaver(conn_string)). Every node execution automatically persists the full state. You can resume from any checkpoint by passing its thread_id and checkpoint_id. This also enables human-in-the-loop — pause before a node, wait for approval, then resume.

Implement retry logic through conditional edges — if a node fails, route back to it or to an error handling node. With checkpointing, you can resume from the last successful step after fixing the issue. The framework itself doesn't have built-in retry decorators, but the graph structure makes retry patterns natural.

LangGraph adds minimal computational overhead — the graph execution engine is lightweight Python. The real costs are LLM calls and checkpointing I/O. MemorySaver has negligible overhead; PostgresSaver adds a few milliseconds per checkpoint. For most applications, LLM latency dominates total execution time by 100x.