Intermediate LevelAI, LLMs, Embeddings & MCP
How do Agentic AI Frameworks (LangGraph, CrewAI, AutoGen) model multi-agent workflows?
❓ Interview Prompt: Compare stateful graph workflows (LangGraph) vs role-based multi-agent crews (CrewAI) vs conversational agents (AutoGen).
Agentic frameworks move beyond single-turn LLM calls by modeling complex tasks as autonomous, multi-step agent loops:
Framework Approaches:
1
LangGraph: Models agent workflows as Stateful Cyclic Graphs. Nodes represent agent code/tools; edges represent conditional decision routing. Supports human-in-the-loop state pausing and time-travel debugging.
2
CrewAI: Role-based architecture assigning specific personas (Researcher, Writer, Reviewer) and delegated tasks to autonomous agent crews.
3
AutoGen: Microsoft framework focused on multi-agent conversational interaction loops.
Code Implementation Example
typescript
// State graph concept in LangGraph
import { StateGraph, END } from '@langchain/langgraph';
interface AgentState { messages: string[]; nextStep: string; }
const workflow = new StateGraph<AgentState>({ channels: { messages: {}, nextStep: {} } });
workflow.addNode("researcher", async (state) => ({ messages: [...state.messages, "Research completed"] }));
workflow.addNode("reviewer", async (state) => ({ messages: [...state.messages, "Review approved"] }));
workflow.setEntryPoint("researcher");
workflow.addConditionalEdges("researcher", (state) => state.nextStep === "approve" ? "reviewer" : END);💡Stateful multi-agent workflow graph structure concept.
🎯 Interview Key Takeaway
LangGraph models agent execution as stateful cyclic graphs with conditional routing edges and human-in-the-loop state persistence.
#Agentic AI#LangGraph#CrewAI#Multi-Agent#Intermediate
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