工具

函数调用

定义自定义工具，使模型在对话过程中可以调用。模型请求调用，您在本地执行，然后返回结果。这 enables 与数据库、API 和任何外部系统的集成。

WARNING

使用流式传输时，函数调用作为一个完整的块返回，而不是跨多个块流式传输。

1. 使用名称、描述和参数的 JSON schema 定义工具
2. 在请求中包含工具
3. 当模型需要外部数据时，返回一个 tool_call
4. 在本地执行函数并返回结果
5. 模型根据您的结果继续处理

快速开始

curl https://api.x.ai/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $XAI_API_KEY" \
  -d '{
  "model": "grok-4.5",
  "input": [
    {"role": "user", "content": "What is the temperature in San Francisco?"}
  ],
  "tools": [
    {
      "type": "function",
      "name": "get_temperature",
      "description": "Get current temperature for a location",
      "parameters": {
        "type": "object",
        "properties": {
          "location": {"type": "string", "description": "City name"},
          "unit": {"type": "string", "enum": ["celsius", "fahrenheit"], "default": "fahrenheit"}
        },
        "required": ["location"]
      }
    }
  ]
}'

import os
import json

from xai_sdk import Client
from xai_sdk.chat import user, tool, tool_result

client = Client(api_key=os.getenv("XAI_API_KEY"))

# Define tools
tools = [
    tool(
        name="get_temperature",
        description="Get current temperature for a location",
        parameters={
            "type": "object",
            "properties": {
                "location": {"type": "string", "description": "City name"},
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"], "default": "fahrenheit"}
            },
            "required": ["location"]
        },
    ),
]

chat = client.chat.create(
    model="grok-4.5",
    tools=tools,
)
chat.append(user("What is the temperature in San Francisco?"))
response = chat.sample()

# Handle tool calls
if response.tool_calls:
    chat.append(response)
    for tc in response.tool_calls:
        args = json.loads(tc.function.arguments)
        # Execute your function
        result = {"location": args["location"], "temperature": 59, "unit": args.get("unit", "fahrenheit")}
        chat.append(tool_result(json.dumps(result)))

    response = chat.sample()

print(response.content)

import os
import json
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("XAI_API_KEY"),
    base_url="https://api.x.ai/v1",
)

tools = [
    {
        "type": "function",
        "name": "get_temperature",
        "description": "Get current temperature for a location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string", "description": "City name"},
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"], "default": "fahrenheit"}
            },
            "required": ["location"]
        },
    },
]

response = client.responses.create(
    model="grok-4.5",
    input=[{"role": "user", "content": "What is the temperature in San Francisco?"}],
    tools=tools,
)

# Handle function calls
for item in response.output:
    if item.type == "function_call":
        args = json.loads(item.arguments)
        result = {"location": args["location"], "temperature": 59, "unit": args.get("unit", "fahrenheit")}

        response = client.responses.create(
            model="grok-4.5",
            input=[{"type": "function_call_output", "call_id": item.call_id, "output": json.dumps(result)}],
            tools=tools,
            previous_response_id=response.id,
        )

for item in response.output:
    if item.type == "message":
        print(item.content[0].text)

import { xai } from '@ai-sdk/xai';
import { streamText, tool, stepCountIs } from 'ai';
import { z } from 'zod';

const result = streamText({
  model: xai.responses('grok-4.5'),
  tools: {
    getTemperature: tool({
      description: 'Get current temperature for a location',
      inputSchema: z.object({
        location: z.string().describe('City name'),
        unit: z.enum(['celsius', 'fahrenheit']).default('fahrenheit'),
      }),
      execute: async ({ location, unit }) => ({
        location,
        temperature: unit === 'fahrenheit' ? 59 : 15,
        unit,
      }),
    }),
  },
  stopWhen: stepCountIs(5),
  prompt: 'What is the temperature in San Francisco?',
});

for await (const chunk of result.fullStream) {
  if (chunk.type === 'text-delta') {
    process.stdout.write(chunk.text);
  }
}

使用 Pydantic 定义工具

使用 Pydantic 模型实现类型安全的参数 schema：

from typing import Literal
from pydantic import BaseModel, Field
from xai_sdk.chat import tool

class TemperatureRequest(BaseModel):
    location: str = Field(description="City and state, e.g. San Francisco, CA")
    unit: Literal["celsius", "fahrenheit"] = Field("fahrenheit", description="Temperature unit")

class CeilingRequest(BaseModel):
    location: str = Field(description="City and state, e.g. San Francisco, CA")

# Generate JSON schema from Pydantic models
tools = [
    tool(
        name="get_temperature",
        description="Get current temperature for a location",
        parameters=TemperatureRequest.model_json_schema(),
    ),
    tool(
        name="get_ceiling",
        description="Get current cloud ceiling for a location",
        parameters=CeilingRequest.model_json_schema(),
    ),
]

from typing import Literal
from pydantic import BaseModel, Field

class TemperatureRequest(BaseModel):
    location: str = Field(description="City and state, e.g. San Francisco, CA")
    unit: Literal["celsius", "fahrenheit"] = Field("fahrenheit", description="Temperature unit")

class CeilingRequest(BaseModel):
    location: str = Field(description="City and state, e.g. San Francisco, CA")

tools = [
    {
        "type": "function",
        "name": "get_temperature",
        "description": "Get current temperature for a location",
        "parameters": TemperatureRequest.model_json_schema(),
    },
    {
        "type": "function",
        "name": "get_ceiling",
        "description": "Get current cloud ceiling for a location",
        "parameters": CeilingRequest.model_json_schema(),
    },
]

处理工具调用

当模型想要使用您的工具时，执行函数并返回结果：

import json

def get_temperature(location: str, unit: str = "fahrenheit") -> dict:
    # In production, call a real weather API
    temp = 59 if unit == "fahrenheit" else 15
    return {"location": location, "temperature": temp, "unit": unit}

def get_ceiling(location: str) -> dict:
    return {"location": location, "ceiling": 15000, "unit": "ft"}

tools_map = {
    "get_temperature": get_temperature,
    "get_ceiling": get_ceiling,
}

chat.append(user("What's the weather in Denver?"))
response = chat.sample()

# Process tool calls
if response.tool_calls:
    chat.append(response)

    for tool_call in response.tool_calls:
        name = tool_call.function.name
        args = json.loads(tool_call.function.arguments)

        result = tools_map[name](**args)
        chat.append(tool_result(json.dumps(result)))

    response = chat.sample()

print(response.content)

import json

def get_temperature(location: str, unit: str = "fahrenheit") -> dict:
    temp = 59 if unit == "fahrenheit" else 15
    return {"location": location, "temperature": temp, "unit": unit}

tools_map = {"get_temperature": get_temperature}

# Process function calls
for item in response.output:
    if item.type == "function_call":
        name = item.name
        args = json.loads(item.arguments)

        if name not in tools_map:
            output = json.dumps({"error": f"Unknown function: {name}"})
        else:
            output = json.dumps(tools_map[name](**args))

        response = client.responses.create(
            model="grok-4.5",
            input=[{"type": "function_call_output", "call_id": item.call_id, "output": output}],
            tools=tools,
            previous_response_id=response.id,
        )

for item in response.output:
    if item.type == "message":
        print(item.content[0].text)

与内置工具结合使用

函数调用与内置的智能体工具协同工作。模型可以使用网络搜索，然后调用您的自定义函数：

from xai_sdk.chat import tool
from xai_sdk.tools import web_search, x_search

tools = [
    web_search(),                    # Built-in: runs on xAI servers
    x_search(),                      # Built-in: runs on xAI servers
    tool(                            # Custom: runs on your side
        name="save_to_database",
        description="Save research results to the database",
        parameters={
            "type": "object",
            "properties": {
                "data": {"type": "string", "description": "Data to save"}
            },
            "required": ["data"]
        },
    ),
]

chat = client.chat.create(
    model="grok-4.5",
    tools=tools,
)

tools = [
    {"type": "web_search"},          # Built-in
    {"type": "x_search"},            # Built-in
    {                                # Custom
        "type": "function",
        "name": "save_to_database",
        "description": "Save research results to the database",
        "parameters": {
            "type": "object",
            "properties": {
                "data": {"type": "string", "description": "Data to save"}
            },
            "required": ["data"]
        },
    },
]

当混合使用工具时：

• 内置工具 在 xAI 服务器上自动执行
• 自定义工具 暂停执行并返回给您处理

有关带工具循环的完整示例，请参阅高级用法。

工具选择

控制模型使用工具的时机：

值	行为
"auto"	模型决定是否调用工具（默认）
"required"	模型必须至少调用一个工具
"none"	禁用工具调用
{"type": "function", "function": {"name": "..."}}	强制使用特定工具

并行函数调用

默认情况下启用并行函数调用 — 模型可以在单个响应中请求多个工具调用。在继续处理之前，请处理所有调用：

# response.tool_calls may contain multiple calls
for tool_call in response.tool_calls:
    result = tools_map[tool_call.function.name](**json.loads(tool_call.function.arguments))
    # Append each result...

在请求中使用 parallel_tool_calls: false 禁用。

工具 Schema 参考

字段	必需	描述
name	是	唯一标识符（每个请求最多 200 个工具）
description	是	工具的功能 — 帮助模型决定何时使用它
parameters	是	定义函数输入的 JSON Schema

参数 Schema

{
  "type": "object",
  "properties": {
    "location": {
      "type": "string",
      "description": "City name"
    },
    "unit": {
      "type": "string",
      "enum": ["celsius", "fahrenheit"],
      "default": "celsius"
    }
  },
  "required": ["location"]
}

parameters schema 的根必须是一个对象 ("type": "object")；将任何其他类型嵌套在 properties 内。当每个分支本身就是一个对象时，根 anyOf 或 oneOf 也适用，让您可以定义接受几种对象变体之一的工具：

{
  "oneOf": [
    {
      "type": "object",
      "properties": {
        "kind": { "const": "email" },
        "address": { "type": "string" }
      },
      "required": ["kind", "address"]
    },
    {
      "type": "object",
      "properties": {
        "kind": { "const": "sms" },
        "phone": { "type": "string" }
      },
      "required": ["kind", "phone"]
    }
  ]
}

WARNING

如果工具的 parameters 根既不是对象也不是对象的联合（例如，标量、数组，或包含非对象分支的 anyOf/oneOf），则无法将其编译为工具调用语法，并且会收到一个命名该工具的 400 错误。

完整的 Vercel AI SDK 示例

Vercel AI SDK 自动处理工具定义、执行以及请求/响应循环：

import { xai } from '@ai-sdk/xai';
import { streamText, tool, stepCountIs } from 'ai';
import { z } from 'zod';

const result = streamText({
  model: xai.responses('grok-4.5'),
  tools: {
    getCurrentTemperature: tool({
      description: 'Get current temperature for a location',
      inputSchema: z.object({
        location: z.string().describe('City and state, e.g. San Francisco, CA'),
        unit: z.enum(['celsius', 'fahrenheit']).default('fahrenheit'),
      }),
      execute: async ({ location, unit }) => ({
        location,
        temperature: unit === 'fahrenheit' ? 59 : 15,
        unit,
      }),
    }),
    getCurrentCeiling: tool({
      description: 'Get current cloud ceiling for a location',
      inputSchema: z.object({
        location: z.string().describe('City and state'),
      }),
      execute: async ({ location }) => ({
        location,
        ceiling: 15000,
        ceiling_type: 'broken',
        unit: 'ft',
      }),
    }),
  },
  stopWhen: stepCountIs(5),
  prompt: "What's the temperature and cloud ceiling in San Francisco?",
});

for await (const chunk of result.fullStream) {
  switch (chunk.type) {
    case 'text-delta':
      process.stdout.write(chunk.text);
      break;
    case 'tool-call':
      console.log(`Tool call: ${chunk.toolName}`, chunk.input);
      break;
    case 'tool-result':
      console.log(`Tool result: ${chunk.toolName}`, chunk.output);
      break;
  }
}