什么是 langgraph？
langgraph是专为llm应用程序设计的工作流编排框架。其核心原则是：
将复杂任务分解为状态和转换管理状态转换逻辑任务执行过程中各种异常的处理
想想购物：浏览→添加到购物车→结账→付款。 langgraph 帮助我们有效地管理此类工作流程。
核心概念1. 国家
状态就像任务执行中的检查点：
from typing import typeddict, listclass shoppingstate(typeddict): # current state current_step: str # cart items cart_items: list[str] # total amount total_amount: float # user input user_input: strclass shoppinggraph(stategraph): def __init__(self): super().__init__() # define states self.add_node("browse", self.browse_products) self.add_node("add_to_cart", self.add_to_cart) self.add_node("checkout", self.checkout) self.add_node("payment", self.payment)
. 状态转换
状态转换定义任务流的“路线图”：
class shoppingcontroller: def define_transitions(self): # add transition rules self.graph.add_edge("browse", "add_to_cart") self.graph.add_edge("add_to_cart", "browse") self.graph.add_edge("add_to_cart", "checkout") self.graph.add_edge("checkout", "payment") def should_move_to_cart(self, state: shoppingstate) -> bool: """determine if we should transition to cart state""" return "add to cart" in state["user_input"].lower()
3. 状态持久化
为了保证系统的可靠性，我们需要持久化状态信息：
class statemanager: def __init__(self): self.redis_client = redis.redis() def save_state(self, session_id: str, state: dict): """save state to redis""" self.redis_client.set(f"shopping_state:{session_id}",json.dumps(state),ex=3600 # 1 hour expiration ) def load_state(self, session_id: str) -> dict: """load state from redis""" state_data = self.redis_client.get(f"shopping_state:{session_id}") return json.loads(state_data) if state_data else none
4. 错误恢复机制
任何步骤都可能失败，我们需要优雅地处理这些情况：
class errorhandler: def __init__(self): self.max_retries = 3 async def with_retry(self, func, state: dict): """function execution with retry mechanism""" retries = 0 while retries < self.max_retries:try: return await func(state)except exception as e: retries += 1 if retries == self.max_retries: return self.handle_final_error(e, state) await self.handle_retry(e, state, retries) def handle_final_error(self, error, state: dict): """handle final error""" # save error state state["error"] = str(error) # rollback to last stable state return self.rollback_to_last_stable_state(state)
现实示例：智能客户服务系统
让我们看一个实际的例子——智能客服系统：
from langgraph.graph import stategraph, stateclass customerservicestate(typeddict): conversation_history: list[str] current_intent: str user_info: dict resolved: boolclass customerservicegraph(stategraph): def __init__(self): super().__init__() # initialize states self.add_node("greeting", self.greet_customer) self.add_node("understand_intent", self.analyze_intent) self.add_node("handle_query", self.process_query) self.add_node("confirm_resolution", self.check_resolution) async def greet_customer(self, state: state): """greet customer""" response = await self.llm.generate(prompt=f"""conversation history: {state[‘conversation_history’]}task: generate appropriate greetingrequirements:1. maintain professional friendliness . acknowledge returning customers3. ask how to help""" ) state[‘conversation_history’].append(f"assistant: {response}") return state async def analyze_intent(self, state: state): """understand user intent""" response = await self.llm.generate(prompt=f"""conversation history: {state[‘conversation_history’]}task: analyze user intentoutput format:{{ "intent": "refund/inquiry/plaint/other", "confidence": 0.95, "details": "specific description"}}""" ) state[‘current_intent’] = json.loads(response) return state
用法
# Initialize systemgraph = CustomerServiceGraph()state_manager = StateManager()error_handler = ErrorHandler()async def handle_customer_query(user_id: str, message: str): # Load or create state state = state_manager.load_state(user_id) or { "conversation_history": [], "current_intent": None, "user_info": {}, "resolved": False } # Add user message state["conversation_history"].append(f"User: {message}") # Execute state machine flow try: result = await graph.run(state) # Save state state_manager.save_state(user_id, result) return result["conversation_history"][-1] except Exception as e: return await error_handler.with_retry(graph.run,state )
最佳实践
陈述设计原则
保持状态简单明了仅存储必要的信息考虑序列化要求
转换逻辑优化
使用条件转换避免无限循环设置最大步数限制
错误处理策略
实施优雅降级记录详细信息提供回滚机制
性能优化
使用异步操作实现状态缓存控制状态大小
常见陷阱和解决方案
状态爆炸
问题：状态太多导致维护困难解决方案：合并相似的状态，使用状态组合而不是创建新的
死锁情况
问题：循环状态转换导致任务挂起解决方案：添加超时机制和强制退出条件
状态一致性
问题：分布式环境中状态不一致解决方案：使用分布式锁和事务机制
概括
langgraph 状态机为管理复杂的 ai agent 任务流提供了强大的解决方案：
清晰的任务流程管理可靠的状态持久性全面的错误处理灵活的扩展性
以上就是LangGraph 状态机：管理生产中的复杂代理任务流的详细内容，更多请关注范的资源库其它相关文章！
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