> ## Documentation Index
> Fetch the complete documentation index at: https://docs.apimart.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Claude Messages API

>  - Claude Messages API形式と完全互換
- 複数ターンの会話と単一クエリをサポート
- テキストと画像を含むマルチモーダルコンテンツをサポート 

<RequestExample>
  ```bash cURL theme={null}
  curl https://api.apimart.ai/v1/messages \
    -H "x-api-key: $API_KEY" \
    -H "anthropic-version: 2025-10-01" \
    -H "content-type: application/json" \
    -d '{
      "model": "claude-sonnet-4-6",
      "max_tokens": 1024,
      "messages": [
        {"role": "user", "content": "Hello, world"}
      ]
    }'
  ```

  ```python Python theme={null}
  import anthropic

  client = anthropic.Anthropic(
      api_key="YOUR_API_KEY",
      base_url="https://api.apimart.ai"
  )

  message = client.messages.create(
      model="claude-sonnet-4-6",
      max_tokens=1024,
      messages=[
          {"role": "user", "content": "Hello, world"}
      ]
  )

  print(message.content)
  ```

  ```javascript JavaScript theme={null}
  import Anthropic from '@anthropic-ai/sdk';

  const client = new Anthropic({
    apiKey: process.env.API_KEY,
    baseURL: 'https://api.apimart.ai'
  });

  const message = await client.messages.create({
    model: 'claude-sonnet-4-6',
    max_tokens: 1024,
    messages: [
      { role: 'user', content: 'Hello, world' }
    ]
  });

  console.log(message.content);
  ```

  ```go Go theme={null}
  package main

  import (
      "bytes"
      "encoding/json"
      "fmt"
      "io/ioutil"
      "net/http"
      "os"
  )

  func main() {
      url := "https://api.apimart.ai/v1/messages"

      payload := map[string]interface{}{
          "model": "claude-sonnet-4-6",
          "max_tokens": 1024,
          "messages": []map[string]string{
              {
                  "role":    "user",
                  "content": "Hello, world",
              },
          },
      }

      jsonData, _ := json.Marshal(payload)

      req, _ := http.NewRequest("POST", url, bytes.NewBuffer(jsonData))
      req.Header.Set("x-api-key", os.Getenv("API_KEY"))
      req.Header.Set("anthropic-version", "2025-10-01")
      req.Header.Set("Content-Type", "application/json")

      client := &http.Client{}
      resp, err := client.Do(req)
      if err != nil {
          panic(err)
      }
      defer resp.Body.Close()

      body, _ := ioutil.ReadAll(resp.Body)
      fmt.Println(string(body))
  }
  ```

  ```java Java theme={null}
  import java.net.http.HttpClient;
  import java.net.http.HttpRequest;
  import java.net.http.HttpResponse;
  import java.net.URI;

  public class Main {
      public static void main(String[] args) throws Exception {
          String url = "https://api.apimart.ai/v1/messages";
          String apiKey = System.getenv("API_KEY");

          String payload = """
          {
            "model": "claude-sonnet-4-6",
            "max_tokens": 1024,
            "messages": [
              {
                "role": "user",
                "content": "Hello, world"
              }
            ]
          }
          """;

          HttpClient client = HttpClient.newHttpClient();
          HttpRequest request = HttpRequest.newBuilder()
              .uri(URI.create(url))
              .header("x-api-key", apiKey)
              .header("anthropic-version", "2025-10-01")
              .header("Content-Type", "application/json")
              .POST(HttpRequest.BodyPublishers.ofString(payload))
              .build();

          HttpResponse<String> response = client.send(request,
              HttpResponse.BodyHandlers.ofString());

          System.out.println(response.body());
      }
  }
  ```

  ```php PHP theme={null}
  <?php

  $url = "https://api.apimart.ai/v1/messages";
  $apiKey = getenv('API_KEY');

  $payload = [
      "model" => "claude-sonnet-4-6",
      "max_tokens" => 1024,
      "messages" => [
          [
              "role" => "user",
              "content" => "Hello, world"
          ]
      ]
  ];

  $ch = curl_init($url);
  curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
  curl_setopt($ch, CURLOPT_POST, true);
  curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($payload));
  curl_setopt($ch, CURLOPT_HTTPHEADER, [
      "x-api-key: " . $apiKey,
      "anthropic-version: 2025-10-01",
      "Content-Type: application/json"
  ]);

  $response = curl_exec($ch);
  curl_close($ch);

  echo $response;
  ?>
  ```

  ```ruby Ruby theme={null}
  require 'net/http'
  require 'json'
  require 'uri'

  url = URI("https://api.apimart.ai/v1/messages")
  api_key = ENV['API_KEY']

  payload = {
    model: "claude-sonnet-4-6",
    max_tokens: 1024,
    messages: [
      {
        role: "user",
        content: "Hello, world"
      }
    ]
  }

  http = Net::HTTP.new(url.host, url.port)
  http.use_ssl = true

  request = Net::HTTP::Post.new(url)
  request["x-api-key"] = api_key
  request["anthropic-version"] = "2025-10-01"
  request["Content-Type"] = "application/json"
  request.body = payload.to_json

  response = http.request(request)
  puts response.body
  ```

  ```swift Swift theme={null}
  import Foundation

  let url = URL(string: "https://api.apimart.ai/v1/messages")!
  let apiKey = ProcessInfo.processInfo.environment["API_KEY"] ?? ""

  let payload: [String: Any] = [
      "model": "claude-sonnet-4-6",
      "max_tokens": 1024,
      "messages": [
          [
              "role": "user",
              "content": "Hello, world"
          ]
      ]
  ]

  var request = URLRequest(url: url)
  request.httpMethod = "POST"
  request.setValue(apiKey, forHTTPHeaderField: "x-api-key")
  request.setValue("2025-10-01", forHTTPHeaderField: "anthropic-version")
  request.setValue("application/json", forHTTPHeaderField: "Content-Type")
  request.httpBody = try? JSONSerialization.data(withJSONObject: payload)

  let task = URLSession.shared.dataTask(with: request) { data, response, error in
      if let error = error {
          print("Error: \(error)")
          return
      }
      
      if let data = data, let responseString = String(data: data, encoding: .utf8) {
          print(responseString)
      }
  }

  task.resume()
  ```

  ```csharp C# theme={null}
  using System;
  using System.Net.Http;
  using System.Text;
  using System.Threading.Tasks;

  class Program
  {
      static async Task Main(string[] args)
      {
          var url = "https://api.apimart.ai/v1/messages";
          var apiKey = Environment.GetEnvironmentVariable("API_KEY");

          var payload = @"{
              ""model"": ""claude-sonnet-4-6"",
              ""max_tokens"": 1024,
              ""messages"": [
                  {
                      ""role"": ""user"",
                      ""content"": ""Hello, world""
                  }
              ]
          }";

          using var client = new HttpClient();
          client.DefaultRequestHeaders.Add("x-api-key", apiKey);
          client.DefaultRequestHeaders.Add("anthropic-version", "2025-10-01");

          var content = new StringContent(payload, Encoding.UTF8, "application/json");
          var response = await client.PostAsync(url, content);
          var result = await response.Content.ReadAsStringAsync();

          Console.WriteLine(result);
      }
  }
  ```

  ```c C theme={null}
  #include <stdio.h>
  #include <curl/curl.h>
  #include <stdlib.h>

  int main(void) {
      CURL *curl;
      CURLcode res;
      const char *api_key = getenv("API_KEY");

      curl_global_init(CURL_GLOBAL_DEFAULT);
      curl = curl_easy_init();

      if(curl) {
          const char *url = "https://api.apimart.ai/v1/messages";
          const char *payload = "{"
              "\"model\":\"claude-sonnet-4-6\","
              "\"max_tokens\":1024,"
              "\"messages\":[{\"role\":\"user\",\"content\":\"Hello, world\"}]"
          "}";

          char auth_header[256];
          snprintf(auth_header, sizeof(auth_header), "x-api-key: %s", api_key);

          struct curl_slist *headers = NULL;
          headers = curl_slist_append(headers, auth_header);
          headers = curl_slist_append(headers, "anthropic-version: 2025-10-01");
          headers = curl_slist_append(headers, "Content-Type: application/json");

          curl_easy_setopt(curl, CURLOPT_URL, url);
          curl_easy_setopt(curl, CURLOPT_POSTFIELDS, payload);
          curl_easy_setopt(curl, CURLOPT_HTTPHEADER, headers);

          res = curl_easy_perform(curl);

          if(res != CURLE_OK) {
              fprintf(stderr, "curl_easy_perform() failed: %s\n",
                      curl_easy_strerror(res));
          }

          curl_slist_free_all(headers);
          curl_easy_cleanup(curl);
      }

      curl_global_cleanup();
      return 0;
  }
  ```

  ```objectivec Objective-C theme={null}
  #import <Foundation/Foundation.h>

  int main(int argc, const char * argv[]) {
      @autoreleasepool {
          NSURL *url = [NSURL URLWithString:@"https://api.apimart.ai/v1/messages"];
          NSString *apiKey = [NSProcessInfo processInfo].environment[@"API_KEY"];
          
          NSDictionary *payload = @{
              @"model": @"claude-sonnet-4-6",
              @"max_tokens": @1024,
              @"messages": @[
                  @{
                      @"role": @"user",
                      @"content": @"Hello, world"
                  }
              ]
          };
          
          NSError *error;
          NSData *jsonData = [NSJSONSerialization dataWithJSONObject:payload
                                                            options:0
                                                              error:&error];
          
          NSMutableURLRequest *request = [NSMutableURLRequest requestWithURL:url];
          [request setHTTPMethod:@"POST"];
          [request setValue:apiKey forHTTPHeaderField:@"x-api-key"];
          [request setValue:@"2025-10-01" forHTTPHeaderField:@"anthropic-version"];
          [request setValue:@"application/json" forHTTPHeaderField:@"Content-Type"];
          [request setHTTPBody:jsonData];
          
          NSURLSessionDataTask *task = [[NSURLSession sharedSession] 
              dataTaskWithRequest:request
              completionHandler:^(NSData *data, NSURLResponse *response, NSError *error) {
                  if (error) {
                      NSLog(@"Error: %@", error);
                      return;
                  }
                  NSString *result = [[NSString alloc] initWithData:data 
                                                          encoding:NSUTF8StringEncoding];
                  NSLog(@"%@", result);
              }];
          
          [task resume];
          [[NSRunLoop mainRunLoop] run];
      }
      return 0;
  }
  ```

  ```ocaml OCaml theme={null}
  (* Requires cohttp and yojson libraries *)
  open Lwt
  open Cohttp
  open Cohttp_lwt_unix

  let url = "https://api.apimart.ai/v1/messages"
  let api_key = Sys.getenv "API_KEY"

  let payload = {|{
    "model": "claude-sonnet-4-6",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": "Hello, world"
      }
    ]
  }|}

  let () =
    let headers = Header.init ()
      |> fun h -> Header.add h "x-api-key" api_key
      |> fun h -> Header.add h "anthropic-version" "2025-10-01"
      |> fun h -> Header.add h "Content-Type" "application/json"
    in
    let body = Cohttp_lwt.Body.of_string payload in
    
    let response = Client.post ~headers ~body (Uri.of_string url) >>= fun (resp, body) ->
      body |> Cohttp_lwt.Body.to_string >|= fun body_str ->
      print_endline body_str
    in
    Lwt_main.run response
  ```

  ```dart Dart theme={null}
  import 'dart:convert';
  import 'dart:io';
  import 'package:http/http.dart' as http;

  void main() async {
    final url = Uri.parse('https://api.apimart.ai/v1/messages');
    final apiKey = Platform.environment['API_KEY'];
    
    final payload = {
      'model': 'claude-sonnet-4-6',
      'max_tokens': 1024,
      'messages': [
        {
          'role': 'user',
          'content': 'Hello, world'
        }
      ]
    };
    
    final response = await http.post(
      url,
      headers: {
        'x-api-key': apiKey!,
        'anthropic-version': '2025-10-01',
        'Content-Type': 'application/json',
      },
      body: jsonEncode(payload),
    );
    
    print(response.body);
  }
  ```

  ```r R theme={null}
  library(httr)
  library(jsonlite)

  url <- "https://api.apimart.ai/v1/messages"
  api_key <- Sys.getenv("API_KEY")

  payload <- list(
    model = "claude-sonnet-4-6",
    max_tokens = 1024,
    messages = list(
      list(
        role = "user",
        content = "Hello, world"
      )
    )
  )

  response <- POST(
    url,
    add_headers(
      `x-api-key` = api_key,
      `anthropic-version` = "2025-10-01",
      `Content-Type` = "application/json"
    ),
    body = toJSON(payload, auto_unbox = TRUE),
    encode = "raw"
  )

  cat(content(response, "text"))
  ```
</RequestExample>

<ResponseExample>
  ```json 200 theme={null}
  {
    "code": 200,
    "data": {
      "id": "msg_013Zva2CMHLNnXjNJJKqJ2EF",
      "type": "message",
      "role": "assistant",
      "content": [
        {
          "type": "text",
          "text": "こんにちは！私はClaudeです。お会いできて嬉しいです。"
        }
      ],
      "model": "claude-sonnet-4-6",
      "stop_reason": "end_turn",
      "stop_sequence": null,
      "usage": {
        "input_tokens": 12,
        "output_tokens": 18
      }
    }
  }
  ```

  ```json 400 theme={null}
  {
    "type": "error",
    "error": {
      "type": "invalid_request_error",
      "message": "リクエストパラメータが無効です"
    }
  }
  ```

  ```json 401 theme={null}
  {
    "type": "error",
    "error": {
      "type": "authentication_error",
      "message": "無効なAPIキー"
    }
  }
  ```

  ```json 429 theme={null}
  {
    "type": "error",
    "error": {
      "type": "rate_limit_error",
      "message": "レート制限を超過しました"
    }
  }
  ```

  ```json 500 theme={null}
  {
    "type": "error",
    "error": {
      "type": "api_error",
      "message": "サーバー内部エラー"
    }
  }
  ```
</ResponseExample>

## 認証

<ParamField header="x-api-key" type="string" required>
  認証用APIキー

  [APIキー管理ページ](https://apimart.ai/keys)にアクセスしてAPIキーを取得してください

  リクエストヘッダーに追加：

  ```
  x-api-key: YOUR_API_KEY
  ```
</ParamField>

<ParamField header="anthropic-version" type="string" required>
  APIバージョン

  使用するClaude APIバージョンを指定します

  例：`2025-10-01`
</ParamField>

## リクエストボディ

<ParamField body="model" type="string" required default="claude-sonnet-4-6">
  Model name

  * `claude-opus-4-8` - Claude Opus 4.8 flagship model
  * `claude-opus-4-7` - Claude Opus 4.7 flagship model
  * `claude-opus-4-6` - Claude Opus 4.6 flagship model
  * `claude-sonnet-4-6` - Claude Sonnet 4.6 balanced version
  * `claude-opus-4-5-20251101` - Claude Opus 4.5 model
</ParamField>

<ParamField body="messages" type="array" required>
  メッセージのリスト

  モデルが次のレスポンスを生成するためのメッセージの配列。各メッセージには `role` と `content` の2つのフィールドが含まれます。

  **💡 クイック入力（Try itエリア）：**

  1. "+ Add an item" をクリックしてメッセージを追加
  2. `role` に入力：`user`（ユーザーメッセージ）または `assistant`（AI応答、複数ターン会話用）
  3. `content` に入力：話したい内容

  <Expandable title="フィールド詳細">
    <ParamField body="role" type="string" required default="user">
      ロールタイプ

      選択肢：`user`（ユーザーメッセージ）、`assistant`（AI応答、複数ターン会話および事前入力用）

      注：Claude APIではシステムプロンプトは別の `system` パラメータを使用し、messagesには含めません
    </ParamField>

    <ParamField body="content" type="string" required>
      メッセージ内容

      メッセージのテキスト内容
    </ParamField>
  </Expandable>

  **単一のユーザーメッセージ：**

  ```json theme={null}
  [{"role": "user", "content": "こんにちは、Claude"}]
  ```

  **複数ターンの会話：**

  ```json theme={null}
  [
    {"role": "user", "content": "こんにちは。"},
    {"role": "assistant", "content": "こんにちは、私はClaudeです。どのようにお手伝いできますか？"},
    {"role": "user", "content": "LLMを分かりやすく説明していただけますか？"}
  ]
  ```

  **事前入力されたアシスタント応答：**

  ```json theme={null}
  [
    {"role": "user", "content": "太陽のギリシャ語名は？ (A) Sol (B) Helios (C) Sun"},
    {"role": "assistant", "content": "正解は("}
  ]
  ```
</ParamField>

<ParamField body="max_tokens" type="integer">
  生成する最大トークン数

  停止前に生成する最大トークン数。モデルはこの制限に達する前に停止する可能性があります。

  モデルによって最大値が異なります。最小値：1
</ParamField>

<ParamField body="system" type="string | array">
  システムプロンプト

  システムプロンプトはClaudeの役割、性格、目標、指示を設定します。

  **文字列形式：**

  ```json theme={null}
  {
    "system": "あなたはプロフェッショナルなPythonプログラミング講師です"
  }
  ```

  **構造化形式：**

  ```json theme={null}
  {
    "system": [
      {
        "type": "text",
        "text": "あなたはプロフェッショナルなPythonプログラミング講師です"
      }
    ]
  }
  ```
</ParamField>

<ParamField body="temperature" type="number">
  温度パラメータ、範囲は0～1

  出力のランダム性を制御：

  * 低い値（例：0.2）：より決定的、保守的
  * 高い値（例：0.8）：よりランダム、創造的

  デフォルト：1.0
</ParamField>

<ParamField body="top_p" type="number">
  ニュークレアスサンプリングパラメータ、範囲は0～1

  ニュークレアスサンプリングを使用します。`temperature`または`top_p`のいずれかの使用を推奨します。

  デフォルト：1.0
</ParamField>

<ParamField body="top_k" type="integer">
  Top-Kサンプリング

  上位K個のオプションのみからサンプリングし、「ロングテール」の低確率応答を除去します。

  高度なユースケースにのみ推奨。
</ParamField>

<ParamField body="stream" type="boolean">
  ストリーミングを有効化

  `true`の場合、Server-Sent Events（SSE）を使用してレスポンスをストリーミングします。

  デフォルト：false
</ParamField>

<ParamField body="stop_sequences" type="array">
  停止シーケンス

  モデルの生成を停止させるカスタムテキストシーケンス。

  最大4つのシーケンス。

  例：`["\n\nHuman:", "\n\nAssistant:"]`
</ParamField>

<ParamField body="metadata" type="object">
  Metadata

  Metadata object for the request.

  Includes:

  * `user_id`: User identifier
</ParamField>

<ParamField body="tools" type="array">
  Tool definitions

  List of tools the model can use to complete tasks.

  **Function tool example:**

  ```json theme={null}
  {
    "tools": [
      {
        "name": "get_weather",
        "description": "Get the current weather in a given location",
        "input_schema": {
          "type": "object",
          "properties": {
            "location": {
              "type": "string",
              "description": "The city and state, e.g. San Francisco, CA"
            },
            "unit": {
              "type": "string",
              "enum": ["celsius", "fahrenheit"],
              "description": "Temperature unit"
            }
          },
          "required": ["location"]
        }
      }
    ]
  }
  ```

  Supported tool types:

  * Custom function tools
  * Computer use tool (computer\_20241022)
  * Text editor tool (text\_editor\_20241022)
  * Bash tool (bash\_20241022)
</ParamField>

<ParamField body="tool_choice" type="object">
  Tool choice strategy

  Controls how the model uses tools:

  * `{"type": "auto"}`: Auto-decide (default)
  * `{"type": "any"}`: Must use a tool
  * `{"type": "tool", "name": "tool_name"}`: Use specific tool
</ParamField>

## Response

<ResponseField name="id" type="string">
  Unique message identifier

  Example: `"msg_013Zva2CMHLNnXjNJJKqJ2EF"`
</ResponseField>

<ResponseField name="type" type="string">
  Object type

  Always `"message"`
</ResponseField>

<ResponseField name="role" type="string">
  Role

  Always `"assistant"`
</ResponseField>

<ResponseField name="content" type="array">
  Content blocks array

  Content generated by the model, as an array of content blocks.

  **Text content:**

  ```json theme={null}
  [{"type": "text", "text": "Hello! I'm Claude."}]
  ```

  **Tool use:**

  ```json theme={null}
  [
    {
      "type": "tool_use",
      "id": "toolu_01A09q90qw90lq917835lq9",
      "name": "get_weather",
      "input": {"location": "San Francisco, CA", "unit": "celsius"}
    }
  ]
  ```

  Content types:

  * `text`: Text content
  * `tool_use`: Tool invocation
</ResponseField>

<ResponseField name="model" type="string">
  Model that handled the request

  Example: `"claude-sonnet-4-6"`
</ResponseField>

<ResponseField name="stop_reason" type="string">
  Stop reason

  Possible values:

  * `end_turn`: Natural completion
  * `max_tokens`: Reached maximum tokens
  * `stop_sequence`: Hit stop sequence
  * `tool_use`: Invoked a tool
</ResponseField>

<ResponseField name="stop_sequence" type="string | null">
  Stop sequence triggered

  The stop sequence that was generated, if any; otherwise `null`
</ResponseField>

<ResponseField name="usage" type="object">
  Token usage statistics

  <Expandable title="Properties">
    <ResponseField name="input_tokens" type="integer">
      Number of input tokens
    </ResponseField>

    <ResponseField name="output_tokens" type="integer">
      Number of output tokens
    </ResponseField>
  </Expandable>
</ResponseField>

## Usage Examples

### Basic Conversation

```python theme={null}
import anthropic

client = anthropic.Anthropic(
    api_key="YOUR_API_KEY",
    base_url="https://api.apimart.ai"
)

message = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Explain quantum computing basics"}
    ]
)

print(message.content[0].text)
```

### Multi-turn Conversation

```python theme={null}
messages = [
    {"role": "user", "content": "What is machine learning?"},
    {"role": "assistant", "content": "Machine learning is a branch of AI..."},
    {"role": "user", "content": "Can you give a practical example?"}
]

message = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=messages
)
```

### Using System Prompts

```python theme={null}
message = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    system="You are a senior Python developer expert in code review and optimization.",
    messages=[
        {"role": "user", "content": "How to optimize this code?\n\n[code]"}
    ]
)
```

### Streaming Response

```python theme={null}
with client.messages.stream(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Write a short essay about AI"}
    ]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)
```

### Tool Use

```python theme={null}
tools = [
    {
        "name": "get_stock_price",
        "description": "Get real-time stock price",
        "input_schema": {
            "type": "object",
            "properties": {
                "ticker": {
                    "type": "string",
                    "description": "Stock ticker symbol, e.g., AAPL"
                }
            },
            "required": ["ticker"]
        }
    }
]

message = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    tools=tools,
    messages=[
        {"role": "user", "content": "What's Tesla's stock price?"}
    ]
)

# Handle tool calls
if message.stop_reason == "tool_use":
    tool_use = next(block for block in message.content if block.type == "tool_use")
    print(f"Calling tool: {tool_use.name}")
    print(f"Arguments: {tool_use.input}")
```

### Vision Understanding

```python theme={null}
message = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "url",
                        "url": "https://example.com/image.jpg"
                    }
                },
                {
                    "type": "text",
                    "text": "Describe this image"
                }
            ]
        }
    ]
)
```

### Base64 Image

```python theme={null}
import base64

with open("image.jpg", "rb") as image_file:
    image_data = base64.b64encode(image_file.read()).decode("utf-8")

message = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": "image/jpeg",
                        "data": image_data
                    }
                },
                {
                    "type": "text",
                    "text": "Analyze this image"
                }
            ]
        }
    ]
)
```

## Best Practices

### 1. Prompt Engineering

**Clear role definition:**

```python theme={null}
system = """You are an experienced data scientist specializing in:
- Statistical analysis and data visualization
- Machine learning model development
- Python and R programming
Provide professional, accurate advice."""
```

**Structured output:**

```python theme={null}
message = "Please return the analysis results in JSON format with summary, key_findings, and recommendations fields."
```

### 2. Error Handling

```python theme={null}
from anthropic import APIError, RateLimitError

try:
    message = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        messages=[{"role": "user", "content": "Hello"}]
    )
except RateLimitError:
    print("Rate limit exceeded, please retry later")
except APIError as e:
    print(f"API error: {e}")
```

### 3. Token Optimization

```python theme={null}
# Use shorter prompts
messages = [
    {"role": "user", "content": "Summarize key points:\n\n[long text]"}
]

# Limit output length
message = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=500,  # Limit output
    messages=messages
)
```

### 4. Prefilling Responses

```python theme={null}
# Guide model to specific format
messages = [
    {"role": "user", "content": "List 5 Python best practices"},
    {"role": "assistant", "content": "Here are 5 Python best practices:\n\n1."}
]

message = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=messages
)
```

## Streaming Response Handling

### Python Streaming

```python theme={null}
import anthropic

client = anthropic.Anthropic(
    api_key="YOUR_API_KEY",
    base_url="https://api.apimart.ai"
)

with client.messages.stream(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Write a Python decorator example"}
    ]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)
```

### JavaScript Streaming

```javascript theme={null}
import Anthropic from '@anthropic-ai/sdk';

const client = new Anthropic({
  apiKey: process.env.API_KEY,
  baseURL: 'https://api.apimart.ai'
});

const stream = await client.messages.stream({
  model: 'claude-sonnet-4-6',
  max_tokens: 1024,
  messages: [
    { role: 'user', content: 'Write a React component example' }
  ]
});

for await (const chunk of stream) {
  if (chunk.type === 'content_block_delta' && 
      chunk.delta.type === 'text_delta') {
    process.stdout.write(chunk.delta.text);
  }
}
```

## Important Notes

1. **API Key Security**:
   * Store API keys in environment variables
   * Never hardcode keys in source code
   * Rotate keys regularly

2. **Rate Limiting**:
   * Be aware of API rate limits
   * Implement retry mechanisms
   * Use exponential backoff

3. **Token Management**:
   * Monitor token usage
   * Optimize prompt length
   * Use appropriate max\_tokens values

4. **Model Selection**:
   * Opus: Complex tasks, deep thinking required
   * Sonnet: Balanced performance and cost
   * Haiku: Fast response, simple tasks

5. **Content Filtering**:
   * Validate user input
   * Filter sensitive information
   * Implement content moderation