from pydantic_ai import Agent
from pydantic import BaseModel, Field

class Invoice(BaseModel):
    vendor: str = Field(description="发票供应商名称")
    amount: float = Field(gt=0, description="金额，必须大于0")
    items: list[str] = Field(min_length=1, description="至少一个项目")

invoice_agent = Agent(
    "openai:gpt-4o",
    output_type=Invoice,  # ← 整个Agent的返回值类型被锁定
    system_prompt="你是一个发票解析助手。从用户描述中提取结构化发票信息。"
)

result = await invoice_agent.run("我在星巴克买了一杯拿铁35元和一个三明治28元")
# result.output 的类型在IDE中就是 Invoice，不需要手动断言
print(result.output.vendor)   # "星巴克"
print(result.output.amount)   # 63.0

@invoice_agent.tool
async def lookup_vendor(ctx: RunContext[str], vendor_name: str) -> VendorInfo:
    """查询供应商信息"""
    # vendor_name已经被验证为str，不需要手动检查
    return await db.query_vendor(vendor_name)

import { Agent, Tool } from "@mastra/core";
import { z } from "zod";

// Schema即类型
const WeatherOutput = z.object({
  temperature: z.number(),
  condition: z.enum(["sunny", "cloudy", "rainy", "snowy"]),
  humidity: z.number().min(0).max(100),
});
type Weather = z.infer<typeof WeatherOutput>; // 自动推导

const weatherAgent = new Agent({
  name: "weather-agent",
  instructions: "你是一个天气查询助手",
  output: WeatherOutput,  // ← Agent输出被Schema锁定
  tools: {
    getWeather: new Tool({
      name: "getWeather",
      description: "获取指定城市的天气",
      parameters: z.object({
        city: z.string().describe("城市名称"),
      }),
      execute: async ({ city }) => {
        return await fetchWeatherData(city);
      },
    }),
  },
});

// result的类型是 { temperature: number; condition: "sunny"|...; humidity: number }
const result = await weatherAgent.generate("北京今天天气怎么样？");

use rig::{agent::AgentBuilder, providers::openai};

// AgentBuilder的类型状态编码了构建进度
let agent = AgentBuilder::new(openai::client::Client::new(&api_key))
    .model("gpt-4o")
    .preamble("你是一个Rust专家")
    .temperature(0.7)
    .build(); // ← 如果漏掉 .model()，编译报错

// 工具定义通过 trait 实现
struct CodeSearch;
impl Tool for CodeSearch {
    type Input = SearchQuery;  // ← 输入类型由trait约束
    type Output = Vec<CodeSnippet>; // ← 输出类型由trait约束

    fn name(&self) -> &str { "code_search" }
    fn definitions(&self, _prompt: &str) -> Vec<ToolDefinition> {
        vec![ToolDefinition { /* ... */ }]
    }
    async fn call(&self, input: Self::Input) -> Result<Self::Output> {
        search_codebase(input.query).await
    }
}

// v5: 结构化输出是独立函数
const result = await generateObject({
  model: openai("gpt-4o"),
  schema: z.object({ name: z.string(), age: z.number() }),
  prompt: "生成一个用户",
});

// v6: 结构化输出是文本生成的一个选项
import { generateText, output } from "ai";

const result = await generateText({
  model: openai("gpt-4o"),
  prompt: "生成一个用户",
  output: output.object({
    schema: z.object({ name: z.string(), age: z.number() }),
  }),
});
// result.output.name 类型安全地可访问

from google.adk import Agent, FunctionTool
from pydantic import BaseModel

class TravelPlan(BaseModel):
    destination: str
    budget: float
    days: int
    activities: list[str]

travel_agent = Agent(
    name="travel-planner",
    model="gemini-2.5-pro",
    instruction="你是一个旅行规划助手",
    output_schema=TravelPlan,  # ← 输出被Schema约束
)

# 工具定义也是Schema-First
@travel_agent.tool
def search_flights(destination: str, date: str, budget: float) -> dict:
    """搜索航班信息"""
    return {"flights": [...]}

using Microsoft.AgentFramework;

public record InvoiceData(string Vendor, decimal Amount, List<string> Items);

public class InvoiceAgent : Agent<string, InvoiceData>
{
    public InvoiceAgent() : base(
        name: "invoice-parser",
        model: "gpt-4o",
        instructions: "解析发票信息")
    { }

    [KernelFunction]
    [Description("查询供应商历史记录")]
    public async Task<VendorHistory> LookupVendor(
        [Description("供应商名称")] string vendorName)
    {
        return await _db.GetVendorHistory(vendorName);
    }
}

// 使用时类型完全确定
var agent = new InvoiceAgent();
var result = await agent.InvokeAsync("星巴克消费63元");
// result.Output 的编译期类型是 InvoiceData
Console.WriteLine(result.Output.Vendor); // IDE自动补全

graph TB
    subgraph "编译期类型安全层"
        RIG["Rig (Rust)<br/>trait系统 + 所有权<br/>编译期拒绝非法组合"]
        MAF["Microsoft Agent Framework (C#)<br/>泛型 + record types<br/>编译期输入输出锁定"]
    end

    subgraph "Schema验证层"
        PYD["PydanticAI (Python)<br/>Pydantic BaseModel<br/>运行时验证 + 自动重试"]
        MAS["Mastra (TypeScript)<br/>Zod Schema<br/>全栈类型穿透"]
        VAI["Vercel AI SDK v6 (TypeScript)<br/>Zod + output参数<br/>统一文本/结构化管线"]
    end

    subgraph "API约束层"
        ADK["Google ADK 2.0 (Python)<br/>output_schema参数<br/>Gemini原生优化"]
    end

    RIG --> |"零运行时开销"| PROD["生产部署"]
    MAF --> |"Azure企业集成"| PROD
    PYD --> |"Pydantic生态"| PROD
    MAS --> |"TS全栈"| PROD
    VAI --> |"Next.js生态"| PROD
    ADK --> |"GCP Agent Engine"| PROD

    style RIG fill:#ff6b6b,color:#fff
    style MAF fill:#9b59b6,color:#fff
    style PYD fill:#3498db,color:#fff
    style MAS fill:#2ecc71,color:#fff
    style VAI fill:#000,color:#fff
    style ADK fill:#f39c12,color:#fff
    style PROD fill:#1a1a2e,color:#fff

flowchart LR
    subgraph "PydanticAI"
        P1[LLM返回JSON] --> P2[Pydantic验证]
        P2 --> |"通过"| P3[返回Typed对象]
        P2 --> |"失败"| P4[ModelRetry重试]
    end

    subgraph "Mastra"
        M1[LLM返回JSON] --> M2[Zod.parse]
        M2 --> |"通过"| M3[返回Typed对象]
        M2 --> |"失败"| M5[解析守卫+重试]
    end

    subgraph "Rig"
        R1[LLM返回JSON] --> R2[serde反序列化]
        R2 --> |"通过"| R3[返回Typed结构体]
        R2 --> |"失败"| R4[编译期错误或Result::Err]
    end

    subgraph "Vercel AI SDK v6"
        V1[LLM返回JSON流] --> V2[流式Zod解析]
        V2 --> |"通过"| V3[部分返回Typed片段]
        V2 --> |"失败"| V4[onFinish回调错误处理]
    end

flowchart TD
    START{你的主技术栈？} --> |Python| PY{需要复杂Workflow？}
    START --> |TypeScript| TS{需要全栈一体化？}
    START --> |Rust| RUST{性能是第一优先级？}
    START --> |C#/.NET| CS{企业Azure环境？}

    PY --> |是| LG["LangGraph<br/>（PydanticAI做子Agent）"]
    PY --> |否，纯Agent| PA["PydanticAI<br/>（最快上手）"]
    PY --> |Google Cloud优先| ADK["Google ADK 2.0"]

    TS --> |是| MAS["Mastra<br/>（全栈类型穿透）"]
    TS --> |否，轻量| VAI["Vercel AI SDK v6<br/>（最小依赖）"]

    RUST --> |是| RIG["Rig"]
    RUST --> |否| PA2["换Python + PydanticAI"]

    CS --> |是| MAF["Microsoft Agent Framework"]
    CS --> |否| PA3["换Python + PydanticAI"]

    style START fill:#2c3e50,color:#fff
    style PA fill:#3498db,color:#fff
    style MAS fill:#2ecc71,color:#fff
    style RIG fill:#ff6b6b,color:#fff
    style VAI fill:#000,color:#fff
    style ADK fill:#f39c12,color:#fff
    style MAF fill:#9b59b6,color:#fff