Obtenir une sortie structurée des agents

import { query } from "@anthropic-ai/claude-agent-sdk";

// Définir la forme des données que vous souhaitez récupérer
const schema = {
  type: "object",
  properties: {
    company_name: { type: "string" },
    founded_year: { type: "number" },
    headquarters: { type: "string" }
  },
  required: ["company_name"]
};

for await (const message of query({
  prompt: "Research Anthropic and provide key company information",
  options: {
    outputFormat: {
      type: "json_schema",
      schema: schema
    }
  }
})) {
  // Le message de résultat contient structured_output avec des données validées
  if (message.type === "result" && message.subtype === "success" && message.structured_output) {
    console.log(message.structured_output);
    // { company_name: "Anthropic", founded_year: 2021, headquarters: "San Francisco, CA" }
  }
}

import asyncio
from claude_agent_sdk import query, ClaudeAgentOptions, ResultMessage

// Définir la forme des données que vous souhaitez récupérer
schema = {
    "type": "object",
    "properties": {
        "company_name": {"type": "string"},
        "founded_year": {"type": "number"},
        "headquarters": {"type": "string"},
    },
    "required": ["company_name"],
}


async def main():
    async for message in query(
        prompt="Research Anthropic and provide key company information",
        options=ClaudeAgentOptions(
            output_format={"type": "json_schema", "schema": schema}
        ),
    ):
        // Le message de résultat contient structured_output avec des données validées
        if isinstance(message, ResultMessage) and message.structured_output:
            print(message.structured_output)
            // {'company_name': 'Anthropic', 'founded_year': 2021, 'headquarters': 'San Francisco, CA'}


asyncio.run(main())

import { z } from "zod";
import { query } from "@anthropic-ai/claude-agent-sdk";

// Définir le schéma avec Zod
const FeaturePlan = z.object({
  feature_name: z.string(),
  summary: z.string(),
  steps: z.array(
    z.object({
      step_number: z.number(),
      description: z.string(),
      estimated_complexity: z.enum(["low", "medium", "high"])
    })
  ),
  risks: z.array(z.string())
});

type FeaturePlan = z.infer<typeof FeaturePlan>;

// Convertir en JSON Schema
const schema = z.toJSONSchema(FeaturePlan);

// Utiliser dans la requête
for await (const message of query({
  prompt:
    "Plan how to add dark mode support to a React app. Break it into implementation steps.",
  options: {
    outputFormat: {
      type: "json_schema",
      schema: schema
    }
  }
})) {
  if (message.type === "result" && message.subtype === "success" && message.structured_output) {
    // Valider et obtenir un résultat entièrement typé
    const parsed = FeaturePlan.safeParse(message.structured_output);
    if (parsed.success) {
      const plan: FeaturePlan = parsed.data;
      console.log(`Feature: ${plan.feature_name}`);
      console.log(`Summary: ${plan.summary}`);
      plan.steps.forEach((step) => {
        console.log(`${step.step_number}. [${step.estimated_complexity}] ${step.description}`);
      });
    }
  }
}

import asyncio
from pydantic import BaseModel
from claude_agent_sdk import query, ClaudeAgentOptions, ResultMessage


class Step(BaseModel):
    step_number: int
    description: str
    estimated_complexity: str  # 'low', 'medium', 'high'


class FeaturePlan(BaseModel):
    feature_name: str
    summary: str
    steps: list[Step]
    risks: list[str]


async def main():
    async for message in query(
        prompt="Plan how to add dark mode support to a React app. Break it into implementation steps.",
        options=ClaudeAgentOptions(
            output_format={
                "type": "json_schema",
                "schema": FeaturePlan.model_json_schema(),
            }
        ),
    ):
        if isinstance(message, ResultMessage) and message.structured_output:
            # Valider et obtenir un résultat entièrement typé
            plan = FeaturePlan.model_validate(message.structured_output)
            print(f"Feature: {plan.feature_name}")
            print(f"Summary: {plan.summary}")
            for step in plan.steps:
                print(
                    f"{step.step_number}. [{step.estimated_complexity}] {step.description}"
                )


asyncio.run(main())

import { query } from "@anthropic-ai/claude-agent-sdk";

// Définir la structure pour l'extraction de TODO
const todoSchema = {
  type: "object",
  properties: {
    todos: {
      type: "array",
      items: {
        type: "object",
        properties: {
          text: { type: "string" },
          file: { type: "string" },
          line: { type: "number" },
          author: { type: "string" },
          date: { type: "string" }
        },
        required: ["text", "file", "line"]
      }
    },
    total_count: { type: "number" }
  },
  required: ["todos", "total_count"]
};

// L'agent utilise Grep pour trouver les TODOs, Bash pour obtenir les informations de git blame
for await (const message of query({
  prompt: "Find all TODO comments in this codebase and identify who added them",
  options: {
    outputFormat: {
      type: "json_schema",
      schema: todoSchema
    }
  }
})) {
  if (message.type === "result" && message.subtype === "success" && message.structured_output) {
    const data = message.structured_output as { total_count: number; todos: Array<{ file: string; line: number; text: string; author?: string; date?: string }> };
    console.log(`Found ${data.total_count} TODOs`);
    data.todos.forEach((todo) => {
      console.log(`${todo.file}:${todo.line} - ${todo.text}`);
      if (todo.author) {
        console.log(`  Added by ${todo.author} on ${todo.date}`);
      }
    });
  }
}

import asyncio
from claude_agent_sdk import query, ClaudeAgentOptions, ResultMessage

# Définir la structure pour l'extraction de TODO
todo_schema = {
    "type": "object",
    "properties": {
        "todos": {
            "type": "array",
            "items": {
                "type": "object",
                "properties": {
                    "text": {"type": "string"},
                    "file": {"type": "string"},
                    "line": {"type": "number"},
                    "author": {"type": "string"},
                    "date": {"type": "string"},
                },
                "required": ["text", "file", "line"],
            },
        },
        "total_count": {"type": "number"},
    },
    "required": ["todos", "total_count"],
}


async def main():
    # L'agent utilise Grep pour trouver les TODOs, Bash pour obtenir les informations de git blame
    async for message in query(
        prompt="Find all TODO comments in this codebase and identify who added them",
        options=ClaudeAgentOptions(
            output_format={"type": "json_schema", "schema": todo_schema}
        ),
    ):
        if isinstance(message, ResultMessage) and message.structured_output:
            data = message.structured_output
            print(f"Found {data['total_count']} TODOs")
            for todo in data["todos"]:
                print(f"{todo['file']}:{todo['line']} - {todo['text']}")
                if "author" in todo:
                    print(f"  Added by {todo['author']} on {todo['date']}")


asyncio.run(main())

for await (const msg of query({
  prompt: "Extract contact info from the document",
  options: {
    outputFormat: {
      type: "json_schema",
      schema: contactSchema
    }
  }
})) {
  if (msg.type === "result") {
    if (msg.subtype === "success" && msg.structured_output) {
      // Utiliser la sortie validée
      console.log(msg.structured_output);
    } else if (msg.subtype === "error_max_structured_output_retries") {
      // Gérer l'échec - réessayer avec une invite plus simple, revenir à non-structuré, etc.
      console.error("Could not produce valid output");
    }
  }
}

async for message in query(
    prompt="Extract contact info from the document",
    options=ClaudeAgentOptions(
        output_format={"type": "json_schema", "schema": contact_schema}
    ),
):
    if isinstance(message, ResultMessage):
        if message.subtype == "success" and message.structured_output:
            # Utiliser la sortie validée
            print(message.structured_output)
        elif message.subtype == "error_max_structured_output_retries":
            # Gérer l'échec
            print("Could not produce valid output")