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