What is LangGraph?
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What is LangGraph?
LangGraph is a framework for building stateful, multi-step agent workflows as graphs. Built by the LangChain team, it models agent logic as a directed graph where nodes are computation steps and edges define the flow between them.
Why Graphs?
Most real-world agent workflows aren't simple linear chains. They involve:
- Branching — take different paths based on the model's output
- Looping — retry or iterate until a condition is met
- State — accumulate information across steps
- Human approval — pause and wait for human input before proceeding
Graphs naturally express all of these patterns, making complex workflows explicit and debuggable.
Core Concepts
| Concept | Description |
|---|---|
| State | A typed dictionary that flows through the graph and accumulates data |
| Node | A function that receives state, does work, and returns updated state |
| Edge | A connection between nodes (can be conditional) |
| Graph | The full workflow defined as connected nodes and edges |
| Checkpointer | Persists state so workflows can be paused, resumed, or replayed |
LangGraph vs. LangChain Chains
| Feature | LangChain Chains | LangGraph |
|---|---|---|
| Flow control | Linear (pipe operator) | Arbitrary graphs with cycles |
| State management | Implicit | Explicit typed state |
| Branching | Limited | First-class conditional edges |
| Persistence | Manual | Built-in checkpointing |
| Human-in-the-loop | Not native | First-class support |
| Debugging | Trace-based | Visual graph + state snapshots |
When to Use LangGraph
LangGraph is ideal for:
- Complex agents that need loops, branching, and error recovery
- Multi-step workflows where you need visibility into each step's state
- Human-in-the-loop applications requiring approval gates
- Long-running agents that need to persist state across sessions
What You'll Learn
This roadmap takes you from understanding graph basics to building production-ready agent workflows with persistence, human-in-the-loop, and multi-agent coordination.