Introduction to Langraph

 Langraph is a powerful framework for building multi-agent workflows using graph based orchestration. It extends LangChain by allowing developers to define workflows as state machines, where each is an agent, tool or function and edges represent transitions based on outputs.

Why Lang Graph stands out:

Deterministic execution: you control the flow precisely - great for audit logging and debugging

Multi-agent support: Easily model agent collaboration, delegation, and feedback loops.

Tool Integration: seemlessly plug in langchain tools, memory and retrievers.

Async and streaming: Supports asynchronous execution and streaming responses.

Visualizable: Graph structure makes it easy to visualize and reason about flows.

Example Use Case:

1, User submits a request

2, Classifier agent routes it to the right department

3, Approval agent checks policy

4, Execution agent triggers automation

5, Audit logger records the flow

In langGraph, each of these would be a node, transitions would be based on outputs like "approved" or "needs_review".

Sample Code Snippet:

const graph = new Graph();

graph.addNode("classify", classifyAgent);

graph.addNode("approve", approvalAgent);

graph.addNode("execute", executionAgent);

graph.addEdge("classify", "approve", (state) => state.route === "finance");

graph.addEdge("approve", "execute", (state) => state.approved === true);

const result = await graph.invoke({ input: "Request to purchase laptop" });

Thanks for Reading!.