flowchart TB
    subgraph "用户层"
        USER[用户应用]
        STUDIO[LangGraph Studio]
        CLI[LangGraph CLI]
    end
    
    subgraph "API层"
        PREBUILT[prebuilt<br/>高级API]
        SDKPY[sdk-py<br/>Python SDK]
        SDKJS[sdk-js<br/>JS/TS SDK]
    end
    
    subgraph "核心框架"
        LANGGRAPH[langgraph<br/>核心框架]
        PREGEL[Pregel引擎]
        GRAPH[图构建器]
        RUNTIME[运行时]
    end
    
    subgraph "持久化层"
        CHECKPOINT[checkpoint<br/>检查点接口]
        CPPOSTGRES[checkpoint-postgres]
        CPSQLITE[checkpoint-sqlite]
        STORE[存储系统]
        CACHE[缓存系统]
    end
    
    subgraph "外部服务"
        LLM[LLM模型]
        TOOLS[外部工具]
        DB[(数据库)]
        LANGSMITH[LangSmith]
    end
    
    USER --> PREBUILT
    USER --> SDKPY
    USER --> SDKJS
    STUDIO --> CLI
    CLI --> LANGGRAPH
    
    PREBUILT --> LANGGRAPH
    SDKPY --> LANGGRAPH
    SDKJS --> LANGGRAPH
    
    LANGGRAPH --> PREGEL
    LANGGRAPH --> GRAPH
    LANGGRAPH --> RUNTIME
    LANGGRAPH --> CHECKPOINT
    
    CHECKPOINT --> CPPOSTGRES
    CHECKPOINT --> CPSQLITE
    CHECKPOINT --> STORE
    CHECKPOINT --> CACHE
    
    RUNTIME --> LLM
    RUNTIME --> TOOLS
    CPPOSTGRES --> DB
    CPSQLITE --> DB
    LANGGRAPH --> LANGSMITH

sequenceDiagram
    autonumber
    participant User as 用户
    participant API as API层
    participant Core as 核心引擎
    participant Checkpoint as 检查点系统
    participant LLM as 语言模型
    participant Tools as 外部工具
    participant Store as 存储系统
    
    User->>API: 提交请求(state, config)
    API->>Core: 创建图实例
    Core->>Checkpoint: 加载检查点状态
    Checkpoint->>Store: 查询历史状态
    Store-->>Checkpoint: 返回状态数据
    Checkpoint-->>Core: 恢复图状态
    
    loop 图执行循环
        Core->>Core: 计算可执行节点
        Core->>LLM: 调用语言模型
        LLM-->>Core: 返回响应
        Core->>Tools: 执行工具调用
        Tools-->>Core: 返回工具结果
        Core->>Checkpoint: 保存中间状态
        Checkpoint->>Store: 持久化状态
    end
    
    Core->>API: 返回最终结果
    API-->>User: 响应用户请求

from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

# 定义工具
def get_weather(city: str) -> str:
    """获取指定城市的天气信息"""
    return f"{city}今天天气晴朗，气温25度"

# 创建Agent
model = ChatOpenAI(model="gpt-4")
agent = create_react_agent(
    model=model,
    tools=[get_weather],
    prompt="你是一个有用的助手"
)

# 执行对话
result = agent.invoke({
    "messages": [{"role": "user", "content": "北京天气怎么样？"}]
})
print(result["messages"][-1]["content"])

from langgraph.graph import StateGraph
from langgraph.checkpoint.sqlite import SqliteSaver
from typing import TypedDict, List

class AgentState(TypedDict):
    messages: List[dict]
    current_task: str
    completed_tasks: List[str]

def task_planner(state: AgentState):
    # 任务规划逻辑（此处省略具体实现）
    return {"current_task": "analyze_data"}

def task_executor(state: AgentState):
    # 任务执行逻辑（此处省略具体实现）
    completed = state["completed_tasks"] + [state["current_task"]]
    return {"completed_tasks": completed}

# 构建复杂工作流
workflow = StateGraph(AgentState)
workflow.add_node("planner", task_planner)
workflow.add_node("executor", task_executor)
workflow.add_edge("planner", "executor")
workflow.set_entry_point("planner")

# 配置持久化
checkpointer = SqliteSaver.from_conn_string("agent_state.db")
app = workflow.compile(checkpointer=checkpointer)

# 执行工作流
config = {"configurable": {"thread_id": "workflow_001"}}
result = app.invoke({
    "messages": [{"role": "user", "content": "开始执行任务"}],
    "current_task": "",
    "completed_tasks": []
}, config=config)

from langgraph.checkpoint.postgres import PostgresSaver
from langgraph.prebuilt import create_react_agent
import os

# 生产环境配置
DB_URI = os.getenv("DATABASE_URL", "postgresql://user:pass@localhost/langgraph")
checkpointer = PostgresSaver.from_conn_string(DB_URI)

# 配置监控和限制
agent = create_react_agent(
    model="openai:gpt-4",
    tools=production_tools,
    checkpointer=checkpointer,
    interrupt_before=["tools"],  # 工具执行前需要确认
    debug=False  # 生产环境关闭调试
)

# 配置资源限制
config = {
    "recursion_limit": 50,  # 增加复杂任务的步数限制
    "configurable": {
        "thread_id": f"user_{user_id}",
        "checkpoint_id": None  # 从最新状态开始
    }
}