NoLag with LangGraph
LangGraph is a framework for building stateful, multi-step agent workflows as graphs. It is excellent at modelling how an agent reasons and moves between steps inside your process. NoLag is complementary: it is the realtime layer that connects agents to each other, to your services, to a UI, and to a human, across processes and machines.
They are not competitors. LangGraph orchestrates the reasoning; NoLag moves the messages.
What each layer does
| LangGraph | NoLag | |
|---|---|---|
| Role | Agent reasoning and control flow | Realtime transport and coordination |
| Scope | Within a process or run | Across agents, services, humans, and UIs |
| Gives you | Graph state, branching, tool steps | Dispatch, shared state, live observation, approval gates, presence |
Why add NoLag under LangGraph
- Dispatch and hand off across workers. Route a task from one agent or process to another over topics, rather than keeping everything in a single run.
- Share state in realtime. Publish intermediate results so other agents, dashboards, or humans see them as they happen.
- Stream decisions to a UI. Send an agent's steps and outputs to a frontend live, without polling.
- Human-in-the-loop approval gates. Pause an action until a person approves it, then resume.
How it fits
Use the NoLag SDK from inside your LangGraph nodes. Where a node produces a result or needs a decision, publish or subscribe on a NoLag topic. The graph keeps owning the control flow; NoLag carries the realtime communication between the moving parts.
Get started
Read the AI agents guide for the coordination patterns, then the 5-minute quick start to connect, subscribe, and publish. SDKs for JavaScript, Python, and Go.