What is LangGraph?
LangGraph is a framework for building stateful, multi-actor AI applications using graph-based workflows. Built by the LangChain team, it treats agent execution as a directed graph where nodes are functions/actors and edges are conditional transitions. It excels at production-grade workflows requiring human-in-the-loop, cyclic processes, and complex state management across checkpoints.
Key Features
Graph-Based Execution
Define agent workflows as directed graphs with nodes and edges
Stateful Checkpointing
Save and resume workflow state at any point
Human-in-the-Loop
Pause execution for human approval/ input
Conditional Branching
Dynamic path selection based on intermediate results
Pros & Cons
What works
- Best state management — checkpointing and persistence
- Human-in-the-loop — essential for production workflows
- Graph flexibility — any workflow topology possible
- LangChain ecosystem — vast tool and integration library
Watch out for
- Steeper learning curve — graph concepts harder than roles
- Slower to prototype — more setup than CrewAI
- More verbose — graph definitions require more code
- LangChain dependency — some developers prefer standalone
