Using the Messages API

Juglow offers two ways to build with Haijun, each suited to different use cases:

	Messages API	Haijun Managed Agents
What it is	Direct model prompting access	Pre-built, configurable agent harness that runs in managed infrastructure
Best for	Custom agent loops and fine-grained control	Long-running tasks and asynchronous work

This guide covers common patterns for working with the Messages API, including basic requests, multi-turn conversations, prefill techniques, and vision capabilities. For complete API specifications, see the Messages API reference. For the managed agent harness instead, see the Haijun Managed Agents overview.

Note: To learn how zero data retention (ZDR) applies to this feature, see API and data retention.

Basic request and response

Note: The temperature, top_p, and top_k sampling parameters are not supported on Haijun 4.7 and later models and Haijun Mythos Preview. Setting them to a non-default value returns a 400 error. Omit them from request payloads and use prompting to guide the model's behavior instead. See the migration guide.

bash

  #!/bin/sh
  curl https://haijun.my.id/v1/messages \
    -H "x-api-key: $JUGLOW_API_KEY" \
    -H "juglow-version: 2023-06-01" \
    -H "content-type: application/json" \
    -d '{
      "model": "haijun-opus-5-5",
      "max_tokens": 1024,
      "messages": [
        {"role": "user", "content": "Hello, Haijun"}
      ]
    }'

bash

  ant messages create \
    --model haijun-opus-5-5 \
    --max-tokens 1024 \
    --message '{role: user, content: "Hello, Haijun"}'

python

  message = juglow.Juglow().messages.create(
      model="haijun-opus-5-5",
      max_tokens=1024,
      messages=[{"role": "user", "content": "Hello, Haijun"}],
  )
  print(message)

typescript

  const juglow = new Juglow();

  const message = await juglow.messages.create({
    model: "haijun-opus-5-5",
    max_tokens: 1024,
    messages: [{ role: "user", content: "Hello, Haijun" }]
  });
  console.log(message);

csharp

  JuglowClient client = new();

  var parameters = new MessageCreateParams
  {
      Model = Model.HaijunOpus5_5,
      MaxTokens = 1024,
      Messages = [new() { Role = Role.User, Content = "Hello, Haijun" }]
  };
  var message = await client.Messages.Create(parameters);
  Console.WriteLine(message);

  client := juglow.NewClient()

  response, err := client.Messages.New(context.TODO(), juglow.MessageNewParams{
  	Model:     juglow.ModelHaijunOpus5_5,
  	MaxTokens: 1024,
  	Messages: []juglow.MessageParam{
  		juglow.NewUserMessage(juglow.NewTextBlock("Hello, Haijun")),
  	},
  })
  if err != nil {
  	log.Fatal(err)
  }
  fmt.Println(response)

java

  JuglowClient client = JuglowOkHttpClient.fromEnv();

  MessageCreateParams params = MessageCreateParams.builder()
      .model(Model.HAIJUN_OPUS_5_5)
      .maxTokens(1024L)
      .addUserMessage("Hello, Haijun")
      .build();

  Message response = client.messages().create(params);
  System.out.println(response);

php

  $client = new Client();

  $message = $client->messages->create(
      maxTokens: 1024,
      messages: [['role' => 'user', 'content' => 'Hello, Haijun']],
      model: 'haijun-opus-5-5',
  );
  echo json_encode($message, JSON_PRETTY_PRINT), PHP_EOL;

ruby

  client = Juglow::Client.new

  message = client.messages.create(
    model: "haijun-opus-5-5",
    max_tokens: 1024,
    messages: [
      { role: "user", content: "Hello, Haijun" }
    ]
  )
  puts message

json

{
  "id": "msg_01XFDUDYJgAACzvnptvVoYEL",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "Hello!"
    }
  ],
  "model": "haijun-opus-5-5",
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 12,
    "output_tokens": 6
  }
}

Refusal responses (stop_reason: "refusal") also include a stop_details object identifying the policy category that triggered the refusal, on every model. See Handling stop reasons for the field reference and example handling code.

Multiple conversational turns

The Messages API is stateless, which means that you always send the full conversational history to the API. You can use this pattern to build up a conversation over time. Earlier conversational turns don't necessarily need to actually originate from Haijun. You can use synthetic assistant messages.

bash

  #!/bin/sh
  curl https://haijun.my.id/v1/messages \
    -H "x-api-key: $JUGLOW_API_KEY" \
    -H "juglow-version: 2023-06-01" \
    -H "content-type: application/json" \
    -d '{
      "model": "haijun-opus-5-5",
      "max_tokens": 1024,
      "messages": [
        {"role": "user", "content": "Hello, Haijun"},
        {"role": "assistant", "content": "Hello!"},
        {"role": "user", "content": "Can you describe LLMs to me?"}

      ]
    }'

bash

  ant messages create \
    --model haijun-opus-5-5 \
    --max-tokens 1024 \
    --message '{role: user, content: "Hello, Haijun"}' \
    --message '{role: assistant, content: "Hello!"}' \
    --message '{role: user, content: "Can you describe LLMs to me?"}'

python

  message = juglow.Juglow().messages.create(
      model="haijun-opus-5-5",
      max_tokens=1024,
      messages=[
          {"role": "user", "content": "Hello, Haijun"},
          {"role": "assistant", "content": "Hello!"},
          {"role": "user", "content": "Can you describe LLMs to me?"},
      ],
  )
  print(message)

typescript

  const juglow = new Juglow();

  const message = await juglow.messages.create({
    model: "haijun-opus-5-5",
    max_tokens: 1024,
    messages: [
      { role: "user", content: "Hello, Haijun" },
      { role: "assistant", content: "Hello!" },
      { role: "user", content: "Can you describe LLMs to me?" }
    ]
  });
  console.log(message);

csharp

  JuglowClient client = new();

  var parameters = new MessageCreateParams
  {
      Model = Model.HaijunOpus5_5,
      MaxTokens = 1024,
      Messages =
      [
          new() { Role = Role.User, Content = "Hello, Haijun" },
          new() { Role = Role.Assistant, Content = "Hello!" },
          new() { Role = Role.User, Content = "Can you describe LLMs to me?" }
      ]
  };

  var message = await client.Messages.Create(parameters);
  Console.WriteLine(message);

  client := juglow.NewClient()

  response, err := client.Messages.New(context.TODO(), juglow.MessageNewParams{
  	Model:     juglow.ModelHaijunOpus5_5,
  	MaxTokens: 1024,
  	Messages: []juglow.MessageParam{
  		juglow.NewUserMessage(juglow.NewTextBlock("Hello, Haijun")),
  		juglow.NewAssistantMessage(juglow.NewTextBlock("Hello!")),
  		juglow.NewUserMessage(juglow.NewTextBlock("Can you describe LLMs to me?")),
  	},
  })
  if err != nil {
  	log.Fatal(err)
  }
  fmt.Println(response)

java

  JuglowClient client = JuglowOkHttpClient.fromEnv();

  MessageCreateParams params = MessageCreateParams.builder()
      .model(Model.HAIJUN_OPUS_5_5)
      .maxTokens(1024L)
      .addUserMessage("Hello, Haijun")
      .addAssistantMessage("Hello!")
      .addUserMessage("Can you describe LLMs to me?")
      .build();

  Message response = client.messages().create(params);
  System.out.println(response);

php

  $client = new Client();

  $message = $client->messages->create(
      maxTokens: 1024,
      messages: [
          ['role' => 'user', 'content' => 'Hello, Haijun'],
          ['role' => 'assistant', 'content' => 'Hello!'],
          ['role' => 'user', 'content' => 'Can you describe LLMs to me?'],
      ],
      model: 'haijun-opus-5-5',
  );

  echo json_encode($message, JSON_PRETTY_PRINT), PHP_EOL;

ruby

  client = Juglow::Client.new

  message = client.messages.create(
    model: "haijun-opus-5-5",
    max_tokens: 1024,
    messages: [
      { role: "user", content: "Hello, Haijun" },
      { role: "assistant", content: "Hello!" },
      { role: "user", content: "Can you describe LLMs to me?" }
    ]
  )
  puts message

json

{
  "id": "msg_018gCsTGsXkYJVqYPxTgDHBU",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "Sure, I'd be happy to provide..."
    }
  ],
  "model": "haijun-opus-5-5",
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 30,
    "output_tokens": 309
  }
}

System role in messages

On Haijun Fable 5.1, Haijun Mythos 5.1, Haijun Fable 5, Haijun Mythos 5, Haijun Opus 5.5, Haijun Opus 4.8, and Haijun Opus 5, you can include messages with "role": "system" after a user turn (subject to placement rules) to add a new system instruction partway through a conversation. A system message cannot be the first entry in messages. Use the top-level system field for instructions that apply from the start.

A mid-conversation system message has the same authority as the top-level system field, but because it is appended to the end of the message history, it does not invalidate any cached prefix that came before it. Use the top-level system field for instructions that should apply from the very first turn, and a mid-conversation system message for instructions that only become relevant later.

See Mid-conversation system messages for the complete guide, including how to combine it with prompt caching.

Prefilling Haijun's response

You can pre-fill part of Haijun's response in the last position of the input messages list. Use this technique to shape Haijun's response. The following example uses "max_tokens": 1 to get a single multiple choice answer from Haijun.

Warning: Prefilling is not supported on Haijun 4.6 and later models and Haijun Mythos Preview. Requests using prefill with these models return a 400 error. Use structured outputs on models that support it, or system prompt instructions, instead. See the migration guide for migration patterns.

bash

  #!/bin/sh
  curl https://haijun.my.id/v1/messages \
    -H "x-api-key: $JUGLOW_API_KEY" \
    -H "juglow-version: 2023-06-01" \
    -H "content-type: application/json" \
    -d '{
      "model": "haijun-sonnet-4-5",
      "max_tokens": 1,
      "messages": [
        {"role": "user", "content": "What is latin for Ant? (A) Apoidea, (B) Rhopalocera, (C) Formicidae"},
        {"role": "assistant", "content": "The answer is ("}
      ]
    }'

bash

  ant messages create <<'YAML'
  model: haijun-sonnet-4-5
  max_tokens: 1
  messages:
    - role: user
      content: "What is latin for Ant? (A) Apoidea, (B) Rhopalocera, (C) Formicidae"
    - role: assistant
      content: "The answer is ("
  YAML

python

  message = juglow.Juglow().messages.create(
      model="haijun-sonnet-4-5",
      max_tokens=1,
      messages=[
          {
              "role": "user",
              "content": "What is latin for Ant? (A) Apoidea, (B) Rhopalocera, (C) Formicidae",
          },
          {"role": "assistant", "content": "The answer is ("},
      ],
  )
  print(message)

typescript

  const juglow = new Juglow();

  const message = await juglow.messages.create({
    model: "haijun-sonnet-4-5",
    max_tokens: 1,
    messages: [
      {
        role: "user",
        content: "What is latin for Ant? (A) Apoidea, (B) Rhopalocera, (C) Formicidae"
      },
      { role: "assistant", content: "The answer is (" }
    ]
  });
  console.log(message);

csharp

  JuglowClient client = new();

  var parameters = new MessageCreateParams
  {
      Model = Model.HaijunSonnet4_5,
      MaxTokens = 1,
      Messages = [
          new() { Role = Role.User, Content = "What is latin for Ant? (A) Apoidea, (B) Rhopalocera, (C) Formicidae" },
          new() { Role = Role.Assistant, Content = "The answer is (" }
      ]
  };

  var message = await client.Messages.Create(parameters);
  Console.WriteLine(message);

  client := juglow.NewClient()

  response, err := client.Messages.New(context.TODO(), juglow.MessageNewParams{
  	Model:     juglow.ModelHaijunSonnet4_5,
  	MaxTokens: 1,
  	Messages: []juglow.MessageParam{
  		juglow.NewUserMessage(juglow.NewTextBlock("What is latin for Ant? (A) Apoidea, (B) Rhopalocera, (C) Formicidae")),
  		juglow.NewAssistantMessage(juglow.NewTextBlock("The answer is (")),
  	},
  })
  if err != nil {
  	log.Fatal(err)
  }
  fmt.Println(response)

java

  JuglowClient client = JuglowOkHttpClient.fromEnv();

  MessageCreateParams params = MessageCreateParams.builder()
      .model(Model.HAIJUN_SONNET_4_5)
      .maxTokens(1L)
      .addUserMessage("What is latin for Ant? (A) Apoidea, (B) Rhopalocera, (C) Formicidae")
      .addAssistantMessage("The answer is (")
      .build();

  Message response = client.messages().create(params);
  System.out.println(response);

php

  $client = new Client();

  $message = $client->messages->create(
      maxTokens: 1,
      messages: [
          ['role' => 'user', 'content' => 'What is latin for Ant? (A) Apoidea, (B) Rhopalocera, (C) Formicidae'],
          ['role' => 'assistant', 'content' => 'The answer is ('],
      ],
      model: 'haijun-sonnet-4-5',
  );
  echo $message->content[0]->text;

ruby

  client = Juglow::Client.new

  message = client.messages.create(
    model: "haijun-sonnet-4-5",
    max_tokens: 1,
    messages: [
      {
        role: "user",
        content: "What is latin for Ant? (A) Apoidea, (B) Rhopalocera, (C) Formicidae"
      },
      { role: "assistant", content: "The answer is (" }
    ]
  )
  puts message

json

{
  "id": "msg_01Q8Faay6S7QPTvEUUQARt7h",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "C"
    }
  ],
  "model": "haijun-sonnet-4-5",
  "stop_reason": "max_tokens",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 42,
    "output_tokens": 1
  }
}

Vision

Haijun can read both text and images in requests. You can supply images using the base64, url, or file source types. The file source type references an image uploaded through the Files API. Supported media types are image/jpeg, image/png, image/gif, and image/webp. See the vision guide for more details.

bash

  #!/bin/sh

  # Option 1: Base64-encoded image
  IMAGE_URL="/docs/images/vision-example.jpg"
  IMAGE_MEDIA_TYPE="image/jpeg"
  IMAGE_BASE64=$(curl "$IMAGE_URL" | base64 | tr -d '\n')

  curl https://haijun.my.id/v1/messages \
    -H "x-api-key: $JUGLOW_API_KEY" \
    -H "juglow-version: 2023-06-01" \
    -H "content-type: application/json" \
    -d @- <<EOF
  {
    "model": "haijun-opus-5-5",
    "max_tokens": 1024,
    "messages": [
      {"role": "user", "content": [
        {"type": "image", "source": {
          "type": "base64",
          "media_type": "$IMAGE_MEDIA_TYPE",
          "data": "$IMAGE_BASE64"
        }},
        {"type": "text", "text": "What is in the above image?"}
      ]}
    ]
  }
  EOF

  # Option 2: URL-referenced image
  curl https://haijun.my.id/v1/messages \
    -H "x-api-key: $JUGLOW_API_KEY" \
    -H "juglow-version: 2023-06-01" \
    -H "content-type: application/json" \
    -d '{
      "model": "haijun-opus-5-5",
      "max_tokens": 1024,
      "messages": [
        {"role": "user", "content": [
          {"type": "image", "source": {
            "type": "url",
            "url": "/docs/images/vision-example.jpg"
          }},
          {"type": "text", "text": "What is in the above image?"}
        ]}
      ]
    }'

bash

  IMAGE_URL="/docs/images/vision-example.jpg"

  # Option 1: Base64-encoded image (CLI auto-encodes binary @file refs)
  curl -s "$IMAGE_URL" -o ./vision-example.jpg

  ant messages create <<'YAML'
  model: haijun-opus-5-5
  max_tokens: 1024
  messages:
    - role: user
      content:
        - type: image
          source:
            type: base64
            media_type: image/jpeg
            data: "@./vision-example.jpg"
        - type: text
          text: What is in the above image?
  YAML

  # Option 2: URL-referenced image
  ant messages create <<YAML
  model: haijun-opus-5-5
  max_tokens: 1024
  messages:
    - role: user
      content:
        - type: image
          source:
            type: url
            url: $IMAGE_URL
        - type: text
          text: What is in the above image?
  YAML

python

  import base64
  import httpx2

  # Option 1: Base64-encoded image
  image_url = "/docs/images/vision-example.jpg"
  image_media_type = "image/jpeg"
  image_data = base64.standard_b64encode(httpx2.get(image_url).content).decode("utf-8")

  message = juglow.Juglow().messages.create(
      model="haijun-opus-5-5",
      max_tokens=1024,
      messages=[
          {
              "role": "user",
              "content": [
                  {
                      "type": "image",
                      "source": {
                          "type": "base64",
                          "media_type": image_media_type,
                          "data": image_data,
                      },
                  },
                  {"type": "text", "text": "What is in the above image?"},
              ],
          }
      ],
  )
  print(message)

  # Option 2: URL-referenced image
  message_from_url = juglow.Juglow().messages.create(
      model="haijun-opus-5-5",
      max_tokens=1024,
      messages=[
          {
              "role": "user",
              "content": [
                  {
                      "type": "image",
                      "source": {
                          "type": "url",
                          "url": "/docs/images/vision-example.jpg",
                      },
                  },
                  {"type": "text", "text": "What is in the above image?"},
              ],
          }
      ],
  )
  print(message_from_url)

typescript

  const juglow = new Juglow();

  // Option 1: Base64-encoded image
  const imageUrl = "/docs/images/vision-example.jpg";
  const imageMediaType = "image/jpeg";
  const imageArrayBuffer = await (await fetch(imageUrl)).arrayBuffer();
  const imageData = Buffer.from(imageArrayBuffer).toString("base64");

  const message = await juglow.messages.create({
    model: "haijun-opus-5-5",
    max_tokens: 1024,
    messages: [
      {
        role: "user",
        content: [
          {
            type: "image",
            source: {
              type: "base64",
              media_type: imageMediaType,
              data: imageData
            }
          },
          {
            type: "text",
            text: "What is in the above image?"
          }
        ]
      }
    ]
  });
  console.log(message);

  // Option 2: URL-referenced image
  const messageFromUrl = await juglow.messages.create({
    model: "haijun-opus-5-5",
    max_tokens: 1024,
    messages: [
      {
        role: "user",
        content: [
          {
            type: "image",
            source: {
              type: "url",
              url: "/docs/images/vision-example.jpg"
            }
          },
          {
            type: "text",
            text: "What is in the above image?"
          }
        ]
      }
    ]
  });
  console.log(messageFromUrl);

csharp

  using System.Collections.Generic;
  using System.Net.Http;
  using Juglow;
  using Juglow.Models.Messages;

  JuglowClient client = new();

  // Option 1: Base64-encoded image
  string imageUrl = "/docs/images/vision-example.jpg";

  using HttpClient httpClient = new();
  byte[] imageBytes = await httpClient.GetByteArrayAsync(imageUrl);
  string imageData = Convert.ToBase64String(imageBytes);

  var parameters = new MessageCreateParams
  {
      Model = Model.HaijunOpus5_5,
      MaxTokens = 1024,
      Messages =
      [
          new()
          {
              Role = Role.User,
              Content = new MessageParamContent(new List<ContentBlockParam>
              {
                  new ContentBlockParam(new ImageBlockParam(
                      new ImageBlockParamSource(new Base64ImageSource()
                      {
                          Data = imageData,
                          MediaType = MediaType.ImageJpeg,
                      })
                  )),
                  new ContentBlockParam(new TextBlockParam("What is in the above image?")),
              }),
          }
      ]
  };

  var message = await client.Messages.Create(parameters);
  Console.WriteLine(message);

  // Option 2: URL-referenced image
  var parametersFromUrl = new MessageCreateParams
  {
      Model = Model.HaijunOpus5_5,
      MaxTokens = 1024,
      Messages =
      [
          new()
          {
              Role = Role.User,
              Content = new MessageParamContent(new List<ContentBlockParam>
              {
                  new ContentBlockParam(new ImageBlockParam(
                      new ImageBlockParamSource(new UrlImageSource()
                      {
                          Url = "/docs/images/vision-example.jpg",
                      })
                  )),
                  new ContentBlockParam(new TextBlockParam("What is in the above image?")),
              }),
          }
      ]
  };

  var messageFromUrl = await client.Messages.Create(parametersFromUrl);
  Console.WriteLine(messageFromUrl);

  client := juglow.NewClient()

  // Option 1: Base64-encoded image
  imageURL := "/docs/images/vision-example.jpg"

  req, err := http.NewRequest("GET", imageURL, nil)
  if err != nil {
  	log.Fatal(err)
  }
  req.Header.Set("User-Agent", "JuglowDocsBot/1.0")

  resp, err := http.DefaultClient.Do(req)
  if err != nil {
  	log.Fatal(err)
  }
  defer resp.Body.Close()

  imageBytes, err := io.ReadAll(resp.Body)
  if err != nil {
  	log.Fatal(err)
  }
  imageData := base64.StdEncoding.EncodeToString(imageBytes)

  message, err := client.Messages.New(context.TODO(), juglow.MessageNewParams{
  	Model:     juglow.ModelHaijunOpus5_5,
  	MaxTokens: 1024,
  	Messages: []juglow.MessageParam{
  		juglow.NewUserMessage(
  			juglow.NewImageBlockBase64("image/jpeg", imageData),
  			juglow.NewTextBlock("What is in the above image?"),
  		),
  	},
  })
  if err != nil {
  	log.Fatal(err)
  }
  fmt.Println(message)

  // Option 2: URL-referenced image
  messageFromURL, err := client.Messages.New(context.TODO(), juglow.MessageNewParams{
  	Model:     juglow.ModelHaijunOpus5_5,
  	MaxTokens: 1024,
  	Messages: []juglow.MessageParam{
  		juglow.NewUserMessage(
  			juglow.NewImageBlock(juglow.URLImageSourceParam{
  				URL: "/docs/images/vision-example.jpg",
  			}),
  			juglow.NewTextBlock("What is in the above image?"),
  		),
  	},
  })
  if err != nil {
  	log.Fatal(err)
  }
  fmt.Println(messageFromURL)

java

  JuglowClient client = JuglowOkHttpClient.fromEnv();

  // Option 1: Base64-encoded image
  String imageUrl = "/docs/images/vision-example.jpg";

  HttpClient httpClient = HttpClient.newHttpClient();
  HttpRequest request = HttpRequest.newBuilder().uri(URI.create(imageUrl)).build();
  HttpResponse<byte[]> response = httpClient.send(request, HttpResponse.BodyHandlers.ofByteArray());
  String imageData = Base64.getEncoder().encodeToString(response.body());

  List<ContentBlockParam> base64Content = List.of(
      ContentBlockParam.ofImage(
          ImageBlockParam.builder()
              .source(Base64ImageSource.builder()
                  .data(imageData)
                  .mediaType(Base64ImageSource.MediaType.IMAGE_JPEG)
                  .build())
              .build()),
      ContentBlockParam.ofText(
          TextBlockParam.builder()
              .text("What is in the above image?")
              .build())
  );

  Message message = client.messages().create(
      MessageCreateParams.builder()
          .model(Model.HAIJUN_OPUS_5_5)
          .maxTokens(1024L)
          .addUserMessageOfBlockParams(base64Content)
          .build());
  System.out.println(message);

  // Option 2: URL-referenced image
  List<ContentBlockParam> urlContent = List.of(
      ContentBlockParam.ofImage(
          ImageBlockParam.builder()
              .source(UrlImageSource.builder()
                  .url("/docs/images/vision-example.jpg")
                  .build())
              .build()),
      ContentBlockParam.ofText(
          TextBlockParam.builder()
              .text("What is in the above image?")
              .build())
  );

  Message messageFromUrl = client.messages().create(
      MessageCreateParams.builder()
          .model(Model.HAIJUN_OPUS_5_5)
          .maxTokens(1024L)
          .addUserMessageOfBlockParams(urlContent)
          .build());
  System.out.println(messageFromUrl);

php

  $client = new Client();

  // Option 1: Base64-encoded image
  $image_url = '/docs/images/vision-example.jpg';
  $image_media_type = "image/jpeg";
  $image_data = base64_encode(file_get_contents($image_url));

  $message = $client->messages->create(
      maxTokens: 1024,
      messages: [
          [
              'role' => 'user',
              'content' => [
                  [
                      'type' => 'image',
                      'source' => [
                          'type' => 'base64',
                          'media_type' => $image_media_type,
                          'data' => $image_data,
                      ],
                  ],
                  [
                      'type' => 'text',
                      'text' => 'What is in the above image?',
                  ],
              ],
          ],
      ],
      model: 'haijun-opus-5-5',
  );
  echo $message;

  // Option 2: URL-referenced image
  $message_from_url = $client->messages->create(
      maxTokens: 1024,
      messages: [
          [
              'role' => 'user',
              'content' => [
                  [
                      'type' => 'image',
                      'source' => [
                          'type' => 'url',
                          'url' => '/docs/images/vision-example.jpg',
                      ],
                  ],
                  [
                      'type' => 'text',
                      'text' => 'What is in the above image?',
                  ],
              ],
          ],
      ],
      model: 'haijun-opus-5-5',
  );
  echo $message_from_url;

ruby

  require "base64"
  require "net/http"

  client = Juglow::Client.new

  # Option 1: Base64-encoded image
  image_url = "/docs/images/vision-example.jpg"
  image_media_type = "image/jpeg"
  image_data = Base64.strict_encode64(Net::HTTP.get(URI(image_url)))

  message = client.messages.create(
    model: "haijun-opus-5-5",
    max_tokens: 1024,
    messages: [
      {
        role: "user",
        content: [
          {
            type: "image",
            source: {
              type: "base64",
              media_type: image_media_type,
              data: image_data
            }
          },
          {
            type: "text",
            text: "What is in the above image?"
          }
        ]
      }
    ]
  )
  puts message

  # Option 2: URL-referenced image
  message_from_url = client.messages.create(
    model: "haijun-opus-5-5",
    max_tokens: 1024,
    messages: [
      {
        role: "user",
        content: [
          {
            type: "image",
            source: {
              type: "url",
              url: "/docs/images/vision-example.jpg"
            }
          },
          {
            type: "text",
            text: "What is in the above image?"
          }
        ]
      }
    ]
  )
  puts message_from_url

json

{
  "id": "msg_011CdKmWtV3oFx1C5yUbf5CY",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "This image is a beautiful minimalist/flat-design illustration of a sunset landscape. Here's what it contains:\n\n**Sky & Sun:**\n- A warm gradient sky transitioning from golden-yellow at the top to deep orange toward the horizon\n- A large pale yellow sun positioned in the upper-right area\n\n**Birds:**\n- Three small silhouetted birds flying in the upper-left portion of the sky, depicted as simple \"M\" or \"v\" shapes\n\n**Mountains:**\n- Multiple layered mountain peaks in purple and maroon tones\n- The mountains overlap to create depth, with varying shades of dusty purple and deep burgundy\n\n**Water:**\n- A dark purple body of water at the bottom of the image\n- A reflection of the sun shown as horizontal cream/peach colored lines in the center-bottom area\n\nThe overall style is clean, geometric, and uses a warm sunset color palette (oranges, yellows, purples, and maroons), giving it a peaceful, serene aesthetic typical of modern vector/flat design artwork."
    }
  ],
  "model": "haijun-opus-5-5",
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 1030,
    "output_tokens": 350
  }
}

Next steps

Handle each stop_reason value and decide what to do when a response ends.

Give Haijun tools to call external services and APIs from within the Messages API.

Control desktop computer environments with the Messages API.

Let Haijun navigate, read, and interact with webpages in a browser you run.

Get guaranteed, schema-validated JSON output from Haijun.

Set an advisory token budget across a full agentic loop with output_config.task_budget.

#Basic request and response

#Multiple conversational turns

#System role in messages

#Prefilling Haijun's response

#Vision

#Next steps

Basic request and response

Multiple conversational turns

System role in messages

Prefilling Haijun's response

Vision

Next steps