---
title: "Create a Message"
method: POST
path: "/anthropic/{region}/v1/messages"
tags: ["Messages"]
---

# Create a Message

`POST /anthropic/{region}/v1/messages`

## Request

- Base URL: `https://api.langdock.com`
- URL: `https://api.langdock.com/anthropic/{region}/v1/messages`
- Auth: HTTP bearer

Create a Message.

Send a structured list of input messages with text and/or image content, and the model will generate the next message in the conversation.

The Messages API can be used for either single queries or stateless multi-turn conversations.

## Path parameters

- `region` 'eu' | 'us', required

## Request body

- CreateMessageParams
  - `model` string, required: The model that will complete your prompt. See [models](https://platform.claude.com/docs/en/docs/models-overview) for additional details and options.
  - `messages` InputMessage[], required: Input messages. Anthropic's models are trained to operate on alternating `user` and `agent` conversational turns. When creating a new `Message`, you specify the prior conversational turns with the `messages` parameter, and the model then generates the next `Message` in the conversation. Each input message must be an object with a `role` and `content`. You can specify a single `user`-role message, or you can include multiple `user` and `assistant` messages. The first message must always use the `user` role. If the final message uses the `assistant` role, the response content will continue immediately from the content in that message. This can be used to constrain part of the model's response. Example with a single `user` message: ```json [{"role": "user", "content": "Hello, Claude"}] ``` Example with multiple conversational turns: ```json [ {"role": "user", "content": "Hello there."}, {"role": "assistant", "content": "Hi, I'm Claude. How can I help you?"}, {"role": "user", "content": "Can you explain LLMs in plain English?"}, ] ``` Example with a partially-filled response from Claude: ```json [ {"role": "user", "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun"}, {"role": "assistant", "content": "The best answer is ("}, ] ``` Each input message `content` may be either a single `string` or an array of content blocks, where each block has a specific `type`. Using a `string` for `content` is shorthand for an array of one content block of type `"text"`. The following input messages are equivalent: ```json {"role": "user", "content": "Hello, Claude"} ``` ```json {"role": "user", "content": [{"type": "text", "text": "Hello, Claude"}]} ``` Starting with Claude 3 models, you can also send image content blocks: ```json {"role": "user", "content": [ { "type": "image", "source": { "type": "base64", "media_type": "image/jpeg", "data": "/9j/4AAQSkZJRg...", } }, {"type": "text", "text": "What is in this image?"} ]} ``` We currently support the `base64` source type for images, and the `image/jpeg`, `image/png`, `image/gif`, and `image/webp` media types. See [examples](https://platform.claude.com/docs/en/api/messages-examples#vision) for more input examples. Note that if you want to include a [system prompt](https://platform.claude.com/docs/en/docs/system-prompts), you can use the top-level `system` parameter â€" there is no `"system"` role for input messages in the Messages API.
    - `role` 'user' | 'agent', required
    - `content` union, required
      - string
      - union[]
        - union
          - object
            - `type` 'text', required
            - `text` string, required
            - `cache_control` CacheControlEphemeral: Marks this block as a prompt caching breakpoint. The prompt prefix up to and including this block is cached and reused across requests. If the requested model resolves to a provider route without prompt caching support, the request returns `400 Bad Request`. See the [Anthropic prompt caching guide](https://platform.claude.com/docs/en/build-with-claude/prompt-caching) for details.
              - …
          - object
            - `type` 'image', required
            - `source` Base64ImageSource, required
              - …
            - `cache_control` CacheControlEphemeral: Marks this block as a prompt caching breakpoint. The prompt prefix up to and including this block is cached and reused across requests. If the requested model resolves to a provider route without prompt caching support, the request returns `400 Bad Request`. See the [Anthropic prompt caching guide](https://platform.claude.com/docs/en/build-with-claude/prompt-caching) for details.
              - …
          - object
            - `type` 'tool_use', required
            - `id` string, required
            - `name` string, required
            - `input` object, required
            - `cache_control` CacheControlEphemeral: Marks this block as a prompt caching breakpoint. The prompt prefix up to and including this block is cached and reused across requests. If the requested model resolves to a provider route without prompt caching support, the request returns `400 Bad Request`. See the [Anthropic prompt caching guide](https://platform.claude.com/docs/en/build-with-claude/prompt-caching) for details.
              - …
          - object
            - `type` 'tool_result', required
            - `tool_use_id` string, required
            - `is_error` boolean
            - `content` union
              - …
            - `cache_control` CacheControlEphemeral: Marks this block as a prompt caching breakpoint. The prompt prefix up to and including this block is cached and reused across requests. If the requested model resolves to a provider route without prompt caching support, the request returns `400 Bad Request`. See the [Anthropic prompt caching guide](https://platform.claude.com/docs/en/build-with-claude/prompt-caching) for details.
              - …
  - `max_tokens` integer, required: The maximum number of tokens to generate before stopping. Note that Anthropic's models may stop _before_ reaching this maximum. This parameter only specifies the absolute maximum number of tokens to generate. Different models have different maximum values for this parameter. See [models](https://platform.claude.com/docs/en/docs/models-overview) for details.
  - `stop_sequences` string[]: Custom text sequences that will cause the model to stop generating. Anthropic's models will normally stop when they have naturally completed their turn, which will result in a response `stop_reason` of `"end_turn"`. If you want the model to stop generating when it encounters custom strings of text, you can use the `stop_sequences` parameter. If the model encounters one of the custom sequences, the response `stop_reason` value will be `"stop_sequence"` and the response `stop_sequence` value will contain the matched stop sequence.
  - `stream` boolean: Whether to incrementally stream the response using server-sent events. See [streaming](https://platform.claude.com/docs/en/build-with-claude/streaming) for details.
  - `system` union: System prompt. A system prompt is a way of providing context and instructions to Claude, such as specifying a particular goal or role. See Anthropic's [guide to system prompts](https://platform.claude.com/docs/en/docs/system-prompts).
    - string
    - RequestTextBlock[]
      - `type` 'text', required
      - `text` string, required
      - `cache_control` CacheControlEphemeral: Marks this block as a prompt caching breakpoint. The prompt prefix up to and including this block is cached and reused across requests. If the requested model resolves to a provider route without prompt caching support, the request returns `400 Bad Request`. See the [Anthropic prompt caching guide](https://platform.claude.com/docs/en/build-with-claude/prompt-caching) for details.
        - `type` 'ephemeral', required
  - `temperature` number: Amount of randomness injected into the response. Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0` for analytical / multiple choice, and closer to `1.0` for creative and generative tasks. Note that even with `temperature` of `0.0`, the results will not be fully deterministic.
  - `tool_choice` union: How the model should use the provided tools. The model can use a specific tool, any available tool, or decide by itself.
    - ToolChoiceAuto: The model will automatically decide whether to use tools.
      - `type` 'auto', required
    - ToolChoiceAny: The model will use any available tools.
      - `type` 'any', required
    - ToolChoiceTool: The model will use the specified tool with `tool_choice.name`.
      - `type` 'tool', required
      - `name` string, required: The name of the tool to use.
  - `tools` Tool[]: Definitions of tools that the model may use. If you include `tools` in your API request, the model may return `tool_use` content blocks that represent the model's use of those tools. You can then run those tools using the tool input generated by the model and then optionally return results back to the model using `tool_result` content blocks. Each tool definition includes: * `name`: Name of the tool. * `description`: Optional, but strongly-recommended description of the tool. * `input_schema`: [JSON schema](https://json-schema.org/) for the tool `input` shape that the model will produce in `tool_use` output content blocks. For example, if you defined `tools` as: ```json [ { "name": "get_stock_price", "description": "Get the current stock price for a given ticker symbol.", "input_schema": { "type": "object", "properties": { "ticker": { "type": "string", "description": "The stock ticker symbol, e.g. AAPL for Apple Inc." } }, "required": ["ticker"] } } ] ``` And then asked the model "What's the S&P 500 at today?", the model might produce `tool_use` content blocks in the response like this: ```json [ { "type": "tool_use", "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", "name": "get_stock_price", "input": { "ticker": "^GSPC" } } ] ``` You might then run your `get_stock_price` tool with `{"ticker": "^GSPC"}` as an input, and return the following back to the model in a subsequent `user` message: ```json [ { "type": "tool_result", "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", "content": "259.75 USD" } ] ``` Tools can be used for workflows that include running client-side tools and functions, or more generally whenever you want the model to produce a particular JSON structure of output. See Anthropic's [guide](https://platform.claude.com/docs/en/docs/tool-use) for more details.
    - `description` string: Description of what this tool does. Tool descriptions should be as detailed as possible. The more information that the model has about what the tool is and how to use it, the better it will perform. You can use natural language descriptions to reinforce important aspects of the tool input JSON schema.
    - `name` string, required
    - `input_schema` InputSchema, required
      - `type` 'object', required
      - `properties` object
    - `cache_control` CacheControlEphemeral: Marks this block as a prompt caching breakpoint. The prompt prefix up to and including this block is cached and reused across requests. If the requested model resolves to a provider route without prompt caching support, the request returns `400 Bad Request`. See the [Anthropic prompt caching guide](https://platform.claude.com/docs/en/build-with-claude/prompt-caching) for details.
      - `type` 'ephemeral', required
  - `top_k` integer: Only sample from the top K options for each subsequent token. Used to remove "long tail" low probability responses. [Learn more technical details here](https://towardsdatascience.com/how-to-write-expert-prompts-for-chatgpt-gpt-4-and-other-language-models-23133dc85550/). Recommended for advanced use cases only. You usually only need to use `temperature`.
  - `top_p` number: Use nucleus sampling. In nucleus sampling, we compute the cumulative distribution over all the options for each subsequent token in decreasing probability order and cut it off once it reaches a particular probability specified by `top_p`. You should either alter `temperature` or `top_p`, but not both. Recommended for advanced use cases only. You usually only need to use `temperature`.
  - `thinking` ThinkingConfig: Configuration for enabling Claude's extended thinking. When enabled, responses include `thinking` content blocks showing Claude's reasoning process before the final answer. See [extended thinking](https://platform.claude.com/docs/en/build-with-claude/extended-thinking) for details.
    - `type` 'enabled', required: Must be "enabled" to activate extended thinking.
    - `budget_tokens` integer, required: Maximum number of tokens Claude can use for thinking. Must be ≥1024 and less than max_tokens.

## Response `200`

Message object.

- Message
  - `id` string, required: Unique object identifier. The format and length of IDs may change over time.
  - `type` 'message', required: Object type. For Messages, this is always `"message"`.
  - `role` 'agent', required: Conversational role of the generated message. This will always be `"assistant"`.
  - `content` ContentBlock[], required: Content generated by the model. This is an array of content blocks, each of which has a `type` that determines its shape. Example: ```json [{"type": "text", "text": "Hi, I'm Claude."}] ``` If the request input `messages` ended with an `agent` turn, then the response `content` will continue directly from that last turn. You can use this to constrain the model's output. For example, if the input `messages` were: ```json [ {"role": "user", "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun"}, {"role": "assistant", "content": "The best answer is ("} ] ``` Then the response `content` might be: ```json [{"type": "text", "text": "B)"}] ```
    - union
      - ResponseTextBlock
        - `type` 'text', required
        - `text` string, required
      - ResponseToolUseBlock
        - `type` 'tool_use', required
        - `id` string, required
        - `name` string, required
        - `input` object, required
      - ResponseThinkingBlock: A thinking content block containing Claude's reasoning process when extended thinking is enabled.
        - `type` 'thinking', required: The type of content block.
        - `thinking` string, required: Claude's internal reasoning process.
        - `signature` string: A signature verifying the thinking block's authenticity.
  - `model` string, required: The model that will complete your prompt. See [models](https://platform.claude.com/docs/en/docs/models-overview) for additional details and options.
  - `stop_reason` 'end_turn' | 'max_tokens' | 'stop_sequence' | 'tool_use', required: The reason that we stopped. This may be one the following values: * `"end_turn"`: the model reached a natural stopping point * `"max_tokens"`: we exceeded the requested `max_tokens` or the model's maximum * `"stop_sequence"`: one of your provided custom `stop_sequences` was generated * `"tool_use"`: the model invoked one or more tools In non-streaming mode this value is always non-null. In streaming mode, it is null in the `message_start` event and non-null otherwise.
  - `stop_sequence` string, required: Which custom stop sequence was generated, if any. This value will be a non-null string if one of your custom stop sequences was generated.
  - `usage` Usage, required
    - `input_tokens` integer, required: The number of input tokens which were used.
    - `output_tokens` integer, required: The number of output tokens which were used.
    - `cache_creation_input_tokens` integer: The number of input tokens written to the cache when creating a new cache entry.
    - `cache_read_input_tokens` integer: The number of input tokens read from the cache.

## Other responses

- `504`: The model did not respond in time on a non-streaming request.
- `4XX`: Error response. See Anthropic's [errors documentation](https://platform.claude.com/docs/en/api/errors) for more details.

## Changes

- **2026-11-03** `c62bf90b4013`: 1 info
  - endpoint added

[Operation history](https://skmtc.dev/langdock/apis/langdock-api/changes/anthropic/:region/v1/messages/post.md)

---

[API](https://skmtc.dev/langdock/apis/langdock-api.md) - [All operations](https://skmtc.dev/langdock/apis/langdock-api/llms.txt) - [OpenAPI document](https://skmtc.dev/langdock/apis/langdock-api/revisions/c62bf90b4013?raw)