LangGraph workflows

LangGraph workflows are frameworks for building stateful, multi-agent, and cyclical orchestration patterns. Unlike linear LangChain chains, LangGraph enables agentic-ai with loops, allowing agents to iteratively refine outputs or perform complex research via structured nodes and edges.

Key Architecture

• Cyclic Graphs: Enables iterative loops necessary for research, reasoning, and error correction.
• State Management: Maintains a persistent, shared state object across all nodes in the graph.
• Multi-agent Systems: Facilitates the orchestration of multiple specialized agents interacting within a unified graph.

Recent Implementations

• Langchain researcher with Gemini 2.5
  • A multi-modal researcher tool utilizing Google Gemini 2.5’s native capabilities.
  • Operates via a user-defined Topic to perform deep-dive investigations and generate complex outputs.
  • Source
• Google Labs Opal
  • An experimental no-code tool designed for describing, creating, and sharing AI mini-applications.
  • Utilizes natural language and visual editing to chain together prompts, models, and tools.
  • Source

Backlinks

• 2026 04 14 Opal Labs Sam Witteveen