langgraph-graphs

What problem does it solve?

Orchestrating complex, multi-step AI agents or data processing pipelines often leads to tangled, hard-to-manage code. LangGraph provides a structured way to define stateful workflows, making them robust and observable.

Core Features & Use Cases

• Stateful Workflow Definition: Design AI agent interactions and data flows using nodes, edges, and conditional routing.
• Checkpointing & Persistence: Integrate with checkpointers to save and resume workflow state, enabling human-in-the-loop processes.
• Error Handling & Retries: Implement sophisticated error recovery and retry logic directly within the graph structure.
• Use Case: Create a multi-agent system where a "planner" agent decides the next step, an "executor" agent performs tasks, and a "reviewer" agent provides feedback, with the ability to pause for human approval at critical junctures.

Quick Start

Explain how to define a simple LangGraph StateGraph with two nodes and a conditional edge, using a TypedDict for state.