openapi: 3.0.3 info: title: Anthropic API description: API Spec for Anthropic API. Please see https://docs.anthropic.com/en/api for more details. version: "1" servers: - url: https://api.anthropic.com/v1 tags: - name: Messages description: Send a structured list of input messages with text and/or image content, and the model will generate the next message in the conversation. paths: /messages: post: operationId: createMessage tags: - Messages summary: Create a Message description: | 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. requestBody: required: true content: application/json: schema: $ref: "#/components/schemas/CreateMessageRequest" responses: "200": description: OK content: application/json: schema: $ref: "#/components/schemas/Message" components: securitySchemes: ApiKeyAuth: type: apiKey in: header name: x-api-key schemas: CreateMessageRequest: type: object description: The request parameters for creating a message. properties: model: title: Model description: | The model that will complete your prompt. See [models](https://docs.anthropic.com/en/docs/models-overview) for additional details and options. example: "claude-3-5-sonnet-20240620" anyOf: - type: string description: The ID of the model to use for this request. - type: string title: Models description: | Available models. Mind that the list may not be exhaustive nor up-to-date. enum: - claude-3-5-sonnet-20240620 - claude-3-haiku-20240307 - claude-3-opus-20240229 - claude-3-sonnet-20240229 - claude-2.0 - claude-2.1 - claude-instant-1.2 messages: type: array description: | Input messages. Our models are trained to operate on alternating `user` and `assistant` 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. See [message content](https://docs.anthropic.com/en/api/messages-content) for details on how to construct valid message objects. 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://docs.anthropic.com/en/api/messages-examples) for more input examples. Note that if you want to include a [system prompt](https://docs.anthropic.com/en/docs/system-prompts), you can use the top-level `system` parameter — there is no `"system"` role for input messages in the Messages API. minItems: 1 items: $ref: '#/components/schemas/Message' max_tokens: type: integer description: | The maximum number of tokens to generate before stopping. Note that our 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://docs.anthropic.com/en/docs/models-overview) for details. metadata: $ref: '#/components/schemas/CreateMessageRequestMetadata' stop_sequences: type: array description: | Custom text sequences that will cause the model to stop generating. Our 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. items: type: string system: type: string description: | System prompt. A system prompt is a way of providing context and instructions to Claude, such as specifying a particular goal or role. See our [guide to system prompts](https://docs.anthropic.com/en/docs/system-prompts). temperature: type: number description: | 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: $ref: '#/components/schemas/ToolChoice' tools: type: array description: | 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 our [guide](https://docs.anthropic.com/en/docs/tool-use) for more details. items: $ref: '#/components/schemas/Tool' top_k: type: integer description: | 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-sample-from-language-models-682bceb97277). Recommended for advanced use cases only. You usually only need to use `temperature`. top_p: type: number description: | 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`. stream: type: boolean default: false description: | Whether to incrementally stream the response using server-sent events. See [streaming](https://docs.anthropic.com/en/api/messages-streaming) for details. required: - model - messages - max_tokens CreateMessageRequestMetadata: type: object description: An object describing metadata about the request. properties: user_id: type: string description: | An external identifier for the user who is associated with the request. This should be a uuid, hash value, or other opaque identifier. Anthropic may use this id to help detect abuse. Do not include any identifying information such as name, email address, or phone number. ToolChoice: type: object description: | How the model should use the provided tools. The model can use a specific tool, any available tool, or decide by itself. - `auto`: allows Claude to decide whether to call any provided tools or not. This is the default value. - `any`: tells Claude that it must use one of the provided tools, but doesn’t force a particular tool. - `tool`: allows us to force Claude to always use a particular tool specified in the `name` field. properties: type: $ref: "#/components/schemas/ToolChoiceType" name: type: string description: The name of the tool to use. required: - type ToolChoiceType: type: string description: | How the model should use the provided tools. The model can use a specific tool, any available tool, or decide by itself. - `auto`: allows Claude to decide whether to call any provided tools or not. This is the default value. - `any`: tells Claude that it must use one of the provided tools, but doesn’t force a particular tool. - `tool`: allows us to force Claude to always use a particular tool specified in the `name` field. enum: - auto - any - tool Message: type: object description: A message in a chat conversation. properties: id: type: string description: | Unique object identifier. The format and length of IDs may change over time. content: description: The content of the message. oneOf: - type: string description: A single text block. - type: array description: An array of content blocks. items: $ref: "#/components/schemas/Block" role: $ref: "#/components/schemas/MessageRole" model: type: string description: The model that handled the request. stop_reason: $ref: "#/components/schemas/StopReason" stop_sequence: type: string description: | 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. type: type: string description: | Object type. For Messages, this is always `"message"`. usage: $ref: "#/components/schemas/Usage" required: - content - role MessageRole: type: string description: The role of the messages author. enum: - user - assistant Tool: type: object description: A tool the model may use. properties: name: type: string description: The name of the tool. Must match the regex `^[a-zA-Z0-9_-]{1,64}$`. description: type: string description: | 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. input_schema: type: object description: | [JSON schema](https://json-schema.org/) for this tool's input. This defines the shape of the `input` that your tool accepts and that the model will produce. additionalProperties: true required: - name - input_schema Block: description: A block of content in a message. oneOf: - $ref: "#/components/schemas/TextBlock" - $ref: "#/components/schemas/ImageBlock" - $ref: "#/components/schemas/ToolUseBlock" - $ref: "#/components/schemas/ToolResultBlock" discriminator: propertyName: type TextBlock: type: object description: A block of text content. properties: text: type: string description: The text content. type: type: string description: The type of content block. default: text required: - text ImageBlock: type: object description: A block of image content. properties: source: $ref: "#/components/schemas/ImageBlockSource" type: type: string description: The type of content block. default: image required: - source ImageBlockSource: type: object description: The source of an image block. properties: data: type: string description: The base64-encoded image data. media_type: type: string description: The media type of the image. enum: - image/jpeg - image/png - image/gif - image/webp type: type: string description: The type of image source. enum: - base64 required: - data - media_type - type ToolUseBlock: type: object description: The tool the model wants to use. properties: id: type: string description: | A unique identifier for this particular tool use block. This will be used to match up the tool results later. example: toolu_01A09q90qw90lq917835lq9 name: type: string description: The name of the tool being used. example: get_weather input: type: object description: An object containing the input being passed to the tool, conforming to the tool’s `input_schema`. additionalProperties: true type: type: string description: The type of content block. default: tool_use required: - id - name - input ToolResultBlock: type: object description: The result of using a tool. properties: tool_use_id: type: string description: The `id` of the tool use request this is a result for. content: description: | The result of the tool, as a string (e.g. `"content": "15 degrees"`) or list of nested content blocks (e.g. `"content": [{"type": "text", "text": "15 degrees"}]`). These content blocks can use the text or image types. oneOf: - type: string description: A single text block. - type: array description: An array of content blocks. items: $ref: "#/components/schemas/Block" is_error: type: boolean description: Set to `true` if the tool execution resulted in an error. type: type: string description: The type of content block. default: tool_result required: - tool_use_id - content StopReason: type: string description: | 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 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. nullable: true enum: - end_turn - max_tokens - stop_sequence - tool_use Usage: type: object description: | Billing and rate-limit usage. Anthropic's API bills and rate-limits by token counts, as tokens represent the underlying cost to our systems. Under the hood, the API transforms requests into a format suitable for the model. The model's output then goes through a parsing stage before becoming an API response. As a result, the token counts in `usage` will not match one-to-one with the exact visible content of an API request or response. For example, `output_tokens` will be non-zero, even for an empty string response from Claude. properties: input_tokens: type: integer description: The number of input tokens which were used. output_tokens: type: integer description: The number of output tokens which were used. required: - input_tokens - output_tokens MessageStreamEvent: type: object description: A event in a streaming conversation. oneOf: - $ref: "#/components/schemas/MessageStartEvent" - $ref: "#/components/schemas/MessageDeltaEvent" - $ref: "#/components/schemas/MessageStopEvent" - $ref: "#/components/schemas/ContentBlockStartEvent" - $ref: "#/components/schemas/ContentBlockDeltaEvent" - $ref: "#/components/schemas/ContentBlockStopEvent" - $ref: "#/components/schemas/PingEvent" discriminator: propertyName: type MessageStreamEventType: type: string description: The type of a streaming event. enum: - message_start - message_delta - message_stop - content_block_start - content_block_delta - content_block_stop - ping MessageStartEvent: type: object description: A start event in a streaming conversation. properties: message: $ref: "#/components/schemas/Message" type: $ref: "#/components/schemas/MessageStreamEventType" required: - message - type MessageDeltaEvent: type: object description: A delta event in a streaming conversation. properties: delta: $ref: "#/components/schemas/MessageDelta" type: $ref: "#/components/schemas/MessageStreamEventType" usage: $ref: "#/components/schemas/MessageDeltaUsage" required: - delta - type - usage MessageDelta: type: object description: A delta in a streaming message. properties: stop_reason: $ref: "#/components/schemas/StopReason" stop_sequence: type: string description: | 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. MessageDeltaUsage: type: object description: | Billing and rate-limit usage. Anthropic's API bills and rate-limits by token counts, as tokens represent the underlying cost to our systems. Under the hood, the API transforms requests into a format suitable for the model. The model's output then goes through a parsing stage before becoming an API response. As a result, the token counts in `usage` will not match one-to-one with the exact visible content of an API request or response. For example, `output_tokens` will be non-zero, even for an empty string response from Claude. properties: output_tokens: type: integer description: The cumulative number of output tokens which were used. required: - output_tokens MessageStopEvent: type: object description: A stop event in a streaming conversation. properties: type: $ref: "#/components/schemas/MessageStreamEventType" required: - type ContentBlockStartEvent: type: object description: A start event in a streaming content block. properties: content_block: $ref: "#/components/schemas/Block" index: type: integer description: The index of the content block. type: $ref: "#/components/schemas/MessageStreamEventType" required: - content_block - index - type ContentBlockDeltaEvent: type: object description: A delta event in a streaming content block. properties: delta: $ref: "#/components/schemas/BlockDelta" index: type: integer description: The index of the content block. type: $ref: "#/components/schemas/MessageStreamEventType" required: - delta - index - type BlockDelta: description: A delta in a streaming message. oneOf: - $ref: "#/components/schemas/TextBlockDelta" - $ref: "#/components/schemas/InputJsonBlockDelta" discriminator: propertyName: type TextBlockDelta: type: object description: A delta in a streaming text block. properties: text: type: string description: The text delta. type: type: string description: The type of content block. default: text_delta required: - text - type InputJsonBlockDelta: type: object description: A delta in a streaming input JSON. properties: partial_json: type: string description: The partial JSON delta. type: type: string description: The type of content block. default: input_json_delta required: - text - type ContentBlockStopEvent: type: object description: A stop event in a streaming content block. properties: index: type: integer description: The index of the content block. type: $ref: "#/components/schemas/MessageStreamEventType" required: - index - type PingEvent: type: object description: A ping event in a streaming conversation. properties: type: $ref: "#/components/schemas/MessageStreamEventType" required: - type security: - ApiKeyAuth: [ ]