# Comparing Production Agent Frameworks: LangGraph vs AutoGen vs CrewAI

Category: Agent Orchestration
Stack: LangGraph, CrewAI, AutoGen, Semantic Kernel

## Overview
Architectural trade-offs between graph-based state machines, role-playing agents, and conversational routing.

## The Problem
Autonomous agent frameworks often lack deterministic state management, resulting in infinite loops and untraceable state divergence.

## Production Rule
For production enterprise workflows, choose graph-based state machines (LangGraph) over open-ended conversational routing. Explicit cyclic graphs allow state checkpointing and human-in-the-loop overrides.

## Code / Specification
File: `langgraph_state_machine.py`

```framework benchmark
from typing import TypedDict
from langgraph.graph import StateGraph, END

class AgentState(TypedDict):
    task: str
    plan: list[str]
    code: str
    test_results: str
    iterations: int

workflow = StateGraph(AgentState)
workflow.add_node("planner", plan_task)
workflow.add_node("executor", write_code)
workflow.add_node("evaluator", run_tests)

workflow.add_edge("planner", "executor")
workflow.add_edge("executor", "evaluator")
workflow.add_conditional_edges("evaluator", should_continue, {
    "retry": "executor",
    "complete": END
})
```