LangGraph

Overview

LangGraph is a framework for building stateful, multi-agent AI applications. It uses a graph-based approach where nodes represent actions and edges represent transitions, making complex AI workflows visual and manageable.

Key Features

Graph-Based Orchestration

Define your agent workflow as a directed graph. Each node is a function that processes state, and edges define the flow between nodes. This makes complex workflows easier to reason about.

State Management

LangGraph provides built-in state management that persists across graph executions. This is essential for multi-turn conversations, long-running workflows, and agent memory.

Human-in-the-Loop

Built-in support for human approval steps, edits, and feedback loops. Critical for production agent systems where you need human oversight.

Streaming

First-class support for streaming responses, intermediate states, and real-time updates. This is important for user-facing agent applications.

When to Use LangGraph

Use LangGraph when you need complex, stateful AI workflows that go beyond simple chain-of-thought prompting. It excels at multi-agent coordination, workflows with branching logic, and systems that need human oversight. If you just need simple LLM calls, LangGraph is overkill.