@anthropic-ai/sdk/src/resources/messages/messages.ts

The official TypeScript library for the Anthropic API

// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.

import { APIPromise } from '../../core/api-promise';
import { APIResource } from '../../core/resource';
import { Stream } from '../../core/streaming';
import { buildHeaders } from '../../internal/headers';
import { RequestOptions } from '../../internal/request-options';
import { stainlessHelperHeader } from '../../lib/stainless-helper-header';
import { MessageStream } from '../../lib/MessageStream';
import {
  parseMessage,
  type ExtractParsedContentFromParams,
  type ParseableMessageCreateParams,
  type ParsedMessage,
} from '../../lib/parser';
import * as BatchesAPI from './batches';
import {
  BatchCreateParams,
  BatchListParams,
  Batches,
  DeletedMessageBatch,
  MessageBatch,
  MessageBatchCanceledResult,
  MessageBatchErroredResult,
  MessageBatchExpiredResult,
  MessageBatchIndividualResponse,
  MessageBatchRequestCounts,
  MessageBatchResult,
  MessageBatchSucceededResult,
  MessageBatchesPage,
} from './batches';
import * as MessagesAPI from './messages';

import { MODEL_NONSTREAMING_TOKENS } from '../../internal/constants';

export class Messages extends APIResource {
  batches: BatchesAPI.Batches = new BatchesAPI.Batches(this._client);

  /**
   * 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.
   *
   * Learn more about the Messages API in our
   * [user guide](https://docs.claude.com/en/docs/initial-setup)
   *
   * @example
   * ```ts
   * const message = await client.messages.create({
   *   max_tokens: 1024,
   *   messages: [{ content: 'Hello, world', role: 'user' }],
   *   model: 'claude-sonnet-4-5-20250929',
   * });
   * ```
   */
  create(body: MessageCreateParamsNonStreaming, options?: RequestOptions): APIPromise<Message>;
  create(
    body: MessageCreateParamsStreaming,
    options?: RequestOptions,
  ): APIPromise<Stream<RawMessageStreamEvent>>;
  create(
    body: MessageCreateParamsBase,
    options?: RequestOptions,
  ): APIPromise<Stream<RawMessageStreamEvent> | Message>;
  create(
    body: MessageCreateParams,
    options?: RequestOptions,
  ): APIPromise<Message> | APIPromise<Stream<RawMessageStreamEvent>> {
    if (body.model in DEPRECATED_MODELS) {
      console.warn(
        `The model '${body.model}' is deprecated and will reach end-of-life on ${
          DEPRECATED_MODELS[body.model]
        }\nPlease migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`,
      );
    }
    let timeout = (this._client as any)._options.timeout as number | null;
    if (!body.stream && timeout == null) {
      const maxNonstreamingTokens = MODEL_NONSTREAMING_TOKENS[body.model] ?? undefined;
      timeout = this._client.calculateNonstreamingTimeout(body.max_tokens, maxNonstreamingTokens);
    }

    // Collect helper info from tools and messages
    const helperHeader = stainlessHelperHeader(body.tools, body.messages);

    return this._client.post('/v1/messages', {
      body,
      timeout: timeout ?? 600000,
      ...options,
      headers: buildHeaders([helperHeader, options?.headers]),
      stream: body.stream ?? false,
    }) as APIPromise<Message> | APIPromise<Stream<RawMessageStreamEvent>>;
  }

  /**
   * Send a structured list of input messages with text and/or image content, along with an expected `output_config.format` and
   * the response will be automatically parsed and available in the `parsed_output` property of the message.
   *
   * @example
   * ```ts
   * const message = await client.messages.parse({
   *   model: 'claude-sonnet-4-5-20250929',
   *   max_tokens: 1024,
   *   messages: [{ role: 'user', content: 'What is 2+2?' }],
   *   output_config: {
   *     format: zodOutputFormat(z.object({ answer: z.number() })),
   *   },
   * });
   *
   * console.log(message.parsed_output?.answer); // 4
   * ```
   */
  parse<Params extends MessageCreateParamsNonStreaming>(
    params: Params,
    options?: RequestOptions,
  ): APIPromise<ParsedMessage<ExtractParsedContentFromParams<Params>>> {
    return this.create(params, options).then((message) =>
      parseMessage(message, params, { logger: this._client.logger ?? console }),
    ) as APIPromise<ParsedMessage<ExtractParsedContentFromParams<Params>>>;
  }

  /**
   * Create a Message stream.
   *
   * If `output_config.format` is provided with a parseable format (like `zodOutputFormat()`),
   * the final message will include a `parsed_output` property with the parsed content.
   *
   * @example
   * ```ts
   * const stream = client.messages.stream({
   *   model: 'claude-sonnet-4-5-20250929',
   *   max_tokens: 1024,
   *   messages: [{ role: 'user', content: 'What is 2+2?' }],
   *   output_config: {
   *     format: zodOutputFormat(z.object({ answer: z.number() })),
   *   },
   * });
   *
   * const message = await stream.finalMessage();
   * console.log(message.parsed_output?.answer); // 4
   * ```
   */
  stream<Params extends MessageStreamParams>(
    body: Params,
    options?: RequestOptions,
  ): MessageStream<ExtractParsedContentFromParams<Params>> {
    return MessageStream.createMessage<ExtractParsedContentFromParams<Params>>(
      this,
      body as MessageCreateParamsBase,
      options,
      { logger: this._client.logger ?? console },
    );
  }

  /**
   * Count the number of tokens in a Message.
   *
   * The Token Count API can be used to count the number of tokens in a Message,
   * including tools, images, and documents, without creating it.
   *
   * Learn more about token counting in our
   * [user guide](https://docs.claude.com/en/docs/build-with-claude/token-counting)
   *
   * @example
   * ```ts
   * const messageTokensCount =
   *   await client.messages.countTokens({
   *     messages: [{ content: 'string', role: 'user' }],
   *     model: 'claude-opus-4-5-20251101',
   *   });
   * ```
   */
  countTokens(body: MessageCountTokensParams, options?: RequestOptions): APIPromise<MessageTokensCount> {
    return this._client.post('/v1/messages/count_tokens', { body, ...options });
  }
}

export interface Base64ImageSource {
  data: string;

  media_type: 'image/jpeg' | 'image/png' | 'image/gif' | 'image/webp';

  type: 'base64';
}

export interface Base64PDFSource {
  data: string;

  media_type: 'application/pdf';

  type: 'base64';
}

export interface CacheControlEphemeral {
  type: 'ephemeral';

  /**
   * The time-to-live for the cache control breakpoint.
   *
   * This may be one the following values:
   *
   * - `5m`: 5 minutes
   * - `1h`: 1 hour
   *
   * Defaults to `5m`.
   */
  ttl?: '5m' | '1h';
}

export interface CacheCreation {
  /**
   * The number of input tokens used to create the 1 hour cache entry.
   */
  ephemeral_1h_input_tokens: number;

  /**
   * The number of input tokens used to create the 5 minute cache entry.
   */
  ephemeral_5m_input_tokens: number;
}

export interface CitationCharLocation {
  cited_text: string;

  document_index: number;

  document_title: string | null;

  end_char_index: number;

  file_id: string | null;

  start_char_index: number;

  type: 'char_location';
}

export interface CitationCharLocationParam {
  cited_text: string;

  document_index: number;

  document_title: string | null;

  end_char_index: number;

  start_char_index: number;

  type: 'char_location';
}

export interface CitationContentBlockLocation {
  cited_text: string;

  document_index: number;

  document_title: string | null;

  end_block_index: number;

  file_id: string | null;

  start_block_index: number;

  type: 'content_block_location';
}

export interface CitationContentBlockLocationParam {
  cited_text: string;

  document_index: number;

  document_title: string | null;

  end_block_index: number;

  start_block_index: number;

  type: 'content_block_location';
}

export interface CitationPageLocation {
  cited_text: string;

  document_index: number;

  document_title: string | null;

  end_page_number: number;

  file_id: string | null;

  start_page_number: number;

  type: 'page_location';
}

export interface CitationPageLocationParam {
  cited_text: string;

  document_index: number;

  document_title: string | null;

  end_page_number: number;

  start_page_number: number;

  type: 'page_location';
}

export interface CitationSearchResultLocationParam {
  cited_text: string;

  end_block_index: number;

  search_result_index: number;

  source: string;

  start_block_index: number;

  title: string | null;

  type: 'search_result_location';
}

export interface CitationWebSearchResultLocationParam {
  cited_text: string;

  encrypted_index: string;

  title: string | null;

  type: 'web_search_result_location';

  url: string;
}

export interface CitationsConfigParam {
  enabled?: boolean;
}

export interface CitationsDelta {
  citation:
    | CitationCharLocation
    | CitationPageLocation
    | CitationContentBlockLocation
    | CitationsWebSearchResultLocation
    | CitationsSearchResultLocation;

  type: 'citations_delta';
}

export interface CitationsSearchResultLocation {
  cited_text: string;

  end_block_index: number;

  search_result_index: number;

  source: string;

  start_block_index: number;

  title: string | null;

  type: 'search_result_location';
}

export interface CitationsWebSearchResultLocation {
  cited_text: string;

  encrypted_index: string;

  title: string | null;

  type: 'web_search_result_location';

  url: string;
}

export type ContentBlock =
  | TextBlock
  | ThinkingBlock
  | RedactedThinkingBlock
  | ToolUseBlock
  | ServerToolUseBlock
  | WebSearchToolResultBlock;

/**
 * Regular text content.
 */
export type ContentBlockParam =
  | TextBlockParam
  | ImageBlockParam
  | DocumentBlockParam
  | SearchResultBlockParam
  | ThinkingBlockParam
  | RedactedThinkingBlockParam
  | ToolUseBlockParam
  | ToolResultBlockParam
  | ServerToolUseBlockParam
  | WebSearchToolResultBlockParam;

export interface ContentBlockSource {
  content: string | Array<ContentBlockSourceContent>;

  type: 'content';
}

export type ContentBlockSourceContent = TextBlockParam | ImageBlockParam;

export interface DocumentBlockParam {
  source: Base64PDFSource | PlainTextSource | ContentBlockSource | URLPDFSource;

  type: 'document';

  /**
   * Create a cache control breakpoint at this content block.
   */
  cache_control?: CacheControlEphemeral | null;

  citations?: CitationsConfigParam | null;

  context?: string | null;

  title?: string | null;
}

export interface ImageBlockParam {
  source: Base64ImageSource | URLImageSource;

  type: 'image';

  /**
   * Create a cache control breakpoint at this content block.
   */
  cache_control?: CacheControlEphemeral | null;
}

export interface InputJSONDelta {
  partial_json: string;

  type: 'input_json_delta';
}

export interface JSONOutputFormat {
  /**
   * The JSON schema of the format
   */
  schema: { [key: string]: unknown };

  type: 'json_schema';
}

export interface Message {
  /**
   * Unique object identifier.
   *
   * The format and length of IDs may change over time.
   */
  id: string;

  /**
   * 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 `assistant` 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)" }]
   * ```
   */
  content: Array<ContentBlock>;

  /**
   * The model that will complete your prompt.\n\nSee
   * [models](https://docs.anthropic.com/en/docs/models-overview) for additional
   * details and options.
   */
  model: Model;

  /**
   * Conversational role of the generated message.
   *
   * This will always be `"assistant"`.
   */
  role: 'assistant';

  /**
   * 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
   * - `"pause_turn"`: we paused a long-running turn. You may provide the response
   *   back as-is in a subsequent request to let the model continue.
   * - `"refusal"`: when streaming classifiers intervene to handle potential policy
   *   violations
   *
   * 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_reason: StopReason | null;

  /**
   * 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.
   */
  stop_sequence: string | null;

  /**
   * Object type.
   *
   * For Messages, this is always `"message"`.
   */
  type: 'message';

  /**
   * 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.
   *
   * Total input tokens in a request is the summation of `input_tokens`,
   * `cache_creation_input_tokens`, and `cache_read_input_tokens`.
   */
  usage: Usage;
}

export type MessageCountTokensTool =
  | Tool
  | ToolBash20250124
  | ToolTextEditor20250124
  | ToolTextEditor20250429
  | ToolTextEditor20250728
  | WebSearchTool20250305;

export interface MessageDeltaUsage {
  /**
   * The cumulative number of input tokens used to create the cache entry.
   */
  cache_creation_input_tokens: number | null;

  /**
   * The cumulative number of input tokens read from the cache.
   */
  cache_read_input_tokens: number | null;

  /**
   * The cumulative number of input tokens which were used.
   */
  input_tokens: number | null;

  /**
   * The cumulative number of output tokens which were used.
   */
  output_tokens: number;

  /**
   * The number of server tool requests.
   */
  server_tool_use: ServerToolUsage | null;
}

export interface MessageParam {
  content: string | Array<ContentBlockParam>;

  role: 'user' | 'assistant';
}

export interface MessageTokensCount {
  /**
   * The total number of tokens across the provided list of messages, system prompt,
   * and tools.
   */
  input_tokens: number;
}

export interface Metadata {
  /**
   * 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.
   */
  user_id?: string | null;
}

/**
 * The model that will complete your prompt.\n\nSee
 * [models](https://docs.anthropic.com/en/docs/models-overview) for additional
 * details and options.
 */
export type Model =
  | 'claude-opus-4-5-20251101'
  | 'claude-opus-4-5'
  | 'claude-3-7-sonnet-latest'
  | 'claude-3-7-sonnet-20250219'
  | 'claude-3-5-haiku-latest'
  | 'claude-3-5-haiku-20241022'
  | 'claude-haiku-4-5'
  | 'claude-haiku-4-5-20251001'
  | 'claude-sonnet-4-20250514'
  | 'claude-sonnet-4-0'
  | 'claude-4-sonnet-20250514'
  | 'claude-sonnet-4-5'
  | 'claude-sonnet-4-5-20250929'
  | 'claude-opus-4-0'
  | 'claude-opus-4-20250514'
  | 'claude-4-opus-20250514'
  | 'claude-opus-4-1-20250805'
  | 'claude-3-opus-latest'
  | 'claude-3-opus-20240229'
  | 'claude-3-haiku-20240307'
  | (string & {});

export interface OutputConfig {
  /**
   * A schema to specify Claude's output format in responses. See
   * [structured outputs](https://platform.claude.com/docs/en/build-with-claude/structured-outputs)
   */
  format?: JSONOutputFormat | null;
}

const DEPRECATED_MODELS: {
  [K in Model]?: string;
} = {
  'claude-1.3': 'November 6th, 2024',
  'claude-1.3-100k': 'November 6th, 2024',
  'claude-instant-1.1': 'November 6th, 2024',
  'claude-instant-1.1-100k': 'November 6th, 2024',
  'claude-instant-1.2': 'November 6th, 2024',
  'claude-3-sonnet-20240229': 'July 21st, 2025',
  'claude-3-opus-20240229': 'January 5th, 2026',
  'claude-2.1': 'July 21st, 2025',
  'claude-2.0': 'July 21st, 2025',
  'claude-3-7-sonnet-latest': 'February 19th, 2026',
  'claude-3-7-sonnet-20250219': 'February 19th, 2026',
  'claude-3-5-haiku-latest': 'February 19th, 2026',
  'claude-3-5-haiku-20241022': 'February 19th, 2026',
};

export interface PlainTextSource {
  data: string;

  media_type: 'text/plain';

  type: 'text';
}

export type RawContentBlockDelta =
  | TextDelta
  | InputJSONDelta
  | CitationsDelta
  | ThinkingDelta
  | SignatureDelta;

export interface RawContentBlockDeltaEvent {
  delta: RawContentBlockDelta;

  index: number;

  type: 'content_block_delta';
}

export interface RawContentBlockStartEvent {
  content_block:
    | TextBlock
    | ThinkingBlock
    | RedactedThinkingBlock
    | ToolUseBlock
    | ServerToolUseBlock
    | WebSearchToolResultBlock;

  index: number;

  type: 'content_block_start';
}

export interface RawContentBlockStopEvent {
  index: number;

  type: 'content_block_stop';
}

export interface RawMessageDeltaEvent {
  delta: RawMessageDeltaEvent.Delta;

  type: 'message_delta';

  /**
   * 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.
   *
   * Total input tokens in a request is the summation of `input_tokens`,
   * `cache_creation_input_tokens`, and `cache_read_input_tokens`.
   */
  usage: MessageDeltaUsage;
}

export namespace RawMessageDeltaEvent {
  export interface Delta {
    stop_reason: MessagesAPI.StopReason | null;

    stop_sequence: string | null;
  }
}

export interface RawMessageStartEvent {
  message: Message;

  type: 'message_start';
}

export interface RawMessageStopEvent {
  type: 'message_stop';
}

export type RawMessageStreamEvent =
  | RawMessageStartEvent
  | RawMessageDeltaEvent
  | RawMessageStopEvent
  | RawContentBlockStartEvent
  | RawContentBlockDeltaEvent
  | RawContentBlockStopEvent;

export interface RedactedThinkingBlock {
  data: string;

  type: 'redacted_thinking';
}

export interface RedactedThinkingBlockParam {
  data: string;

  type: 'redacted_thinking';
}

export interface SearchResultBlockParam {
  content: Array<TextBlockParam>;

  source: string;

  title: string;

  type: 'search_result';

  /**
   * Create a cache control breakpoint at this content block.
   */
  cache_control?: CacheControlEphemeral | null;

  citations?: CitationsConfigParam;
}

export interface ServerToolUsage {
  /**
   * The number of web search tool requests.
   */
  web_search_requests: number;
}

export interface ServerToolUseBlock {
  id: string;

  input: unknown;

  name: 'web_search';

  type: 'server_tool_use';
}

export interface ServerToolUseBlockParam {
  id: string;

  input: unknown;

  name: 'web_search';

  type: 'server_tool_use';

  /**
   * Create a cache control breakpoint at this content block.
   */
  cache_control?: CacheControlEphemeral | null;
}

export interface SignatureDelta {
  signature: string;

  type: 'signature_delta';
}

export type StopReason = 'end_turn' | 'max_tokens' | 'stop_sequence' | 'tool_use' | 'pause_turn' | 'refusal';

export interface TextBlock {
  /**
   * Citations supporting the text block.
   *
   * The type of citation returned will depend on the type of document being cited.
   * Citing a PDF results in `page_location`, plain text results in `char_location`,
   * and content document results in `content_block_location`.
   */
  citations: Array<TextCitation> | null;

  text: string;

  type: 'text';
}

export interface TextBlockParam {
  text: string;

  type: 'text';

  /**
   * Create a cache control breakpoint at this content block.
   */
  cache_control?: CacheControlEphemeral | null;

  citations?: Array<TextCitationParam> | null;
}

export type TextCitation =
  | CitationCharLocation
  | CitationPageLocation
  | CitationContentBlockLocation
  | CitationsWebSearchResultLocation
  | CitationsSearchResultLocation;

export type TextCitationParam =
  | CitationCharLocationParam
  | CitationPageLocationParam
  | CitationContentBlockLocationParam
  | CitationWebSearchResultLocationParam
  | CitationSearchResultLocationParam;

export interface TextDelta {
  text: string;

  type: 'text_delta';
}

export interface ThinkingBlock {
  signature: string;

  thinking: string;

  type: 'thinking';
}

export interface ThinkingBlockParam {
  signature: string;

  thinking: string;

  type: 'thinking';
}

export interface ThinkingConfigDisabled {
  type: 'disabled';
}

export interface ThinkingConfigEnabled {
  /**
   * Determines how many tokens Claude can use for its internal reasoning process.
   * Larger budgets can enable more thorough analysis for complex problems, improving
   * response quality.
   *
   * Must be ≥1024 and less than `max_tokens`.
   *
   * See
   * [extended thinking](https://docs.claude.com/en/docs/build-with-claude/extended-thinking)
   * for details.
   */
  budget_tokens: number;

  type: 'enabled';
}

/**
 * Configuration for enabling Claude's extended thinking.
 *
 * When enabled, responses include `thinking` content blocks showing Claude's
 * thinking process before the final answer. Requires a minimum budget of 1,024
 * tokens and counts towards your `max_tokens` limit.
 *
 * See
 * [extended thinking](https://docs.claude.com/en/docs/build-with-claude/extended-thinking)
 * for details.
 */
export type ThinkingConfigParam = ThinkingConfigEnabled | ThinkingConfigDisabled;

export interface ThinkingDelta {
  thinking: string;

  type: 'thinking_delta';
}

export interface Tool {
  /**
   * [JSON schema](https://json-schema.org/draft/2020-12) for this tool's input.
   *
   * This defines the shape of the `input` that your tool accepts and that the model
   * will produce.
   */
  input_schema: Tool.InputSchema;

  /**
   * Name of the tool.
   *
   * This is how the tool will be called by the model and in `tool_use` blocks.
   */
  name: string;

  /**
   * Create a cache control breakpoint at this content block.
   */
  cache_control?: CacheControlEphemeral | null;

  /**
   * 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.
   */
  description?: string;

  /**
   * When true, guarantees schema validation on tool names and inputs
   */
  strict?: boolean;

  type?: 'custom' | null;
}

export namespace Tool {
  /**
   * [JSON schema](https://json-schema.org/draft/2020-12) for this tool's input.
   *
   * This defines the shape of the `input` that your tool accepts and that the model
   * will produce.
   */
  export interface InputSchema {
    type: 'object';

    properties?: unknown | null;

    required?: Array<string> | null;

    [k: string]: unknown;
  }
}

export interface ToolBash20250124 {
  /**
   * Name of the tool.
   *
   * This is how the tool will be called by the model and in `tool_use` blocks.
   */
  name: 'bash';

  type: 'bash_20250124';

  /**
   * Create a cache control breakpoint at this content block.
   */
  cache_control?: CacheControlEphemeral | null;

  /**
   * When true, guarantees schema validation on tool names and inputs
   */
  strict?: boolean;
}

/**
 * How the model should use the provided tools. The model can use a specific tool,
 * any available tool, decide by itself, or not use tools at all.
 */
export type ToolChoice = ToolChoiceAuto | ToolChoiceAny | ToolChoiceTool | ToolChoiceNone;

/**
 * The model will use any available tools.
 */
export interface ToolChoiceAny {
  type: 'any';

  /**
   * Whether to disable parallel tool use.
   *
   * Defaults to `false`. If set to `true`, the model will output exactly one tool
   * use.
   */
  disable_parallel_tool_use?: boolean;
}

/**
 * The model will automatically decide whether to use tools.
 */
export interface ToolChoiceAuto {
  type: 'auto';

  /**
   * Whether to disable parallel tool use.
   *
   * Defaults to `false`. If set to `true`, the model will output at most one tool
   * use.
   */
  disable_parallel_tool_use?: boolean;
}

/**
 * The model will not be allowed to use tools.
 */
export interface ToolChoiceNone {
  type: 'none';
}

/**
 * The model will use the specified tool with `tool_choice.name`.
 */
export interface ToolChoiceTool {
  /**
   * The name of the tool to use.
   */
  name: string;

  type: 'tool';

  /**
   * Whether to disable parallel tool use.
   *
   * Defaults to `false`. If set to `true`, the model will output exactly one tool
   * use.
   */
  disable_parallel_tool_use?: boolean;
}

export interface ToolResultBlockParam {
  tool_use_id: string;

  type: 'tool_result';

  /**
   * Create a cache control breakpoint at this content block.
   */
  cache_control?: CacheControlEphemeral | null;

  content?: string | Array<TextBlockParam | ImageBlockParam | SearchResultBlockParam | DocumentBlockParam>;

  is_error?: boolean;
}

export interface ToolTextEditor20250124 {
  /**
   * Name of the tool.
   *
   * This is how the tool will be called by the model and in `tool_use` blocks.
   */
  name: 'str_replace_editor';

  type: 'text_editor_20250124';

  /**
   * Create a cache control breakpoint at this content block.
   */
  cache_control?: CacheControlEphemeral | null;

  /**
   * When true, guarantees schema validation on tool names and inputs
   */
  strict?: boolean;
}

export interface ToolTextEditor20250429 {
  /**
   * Name of the tool.
   *
   * This is how the tool will be called by the model and in `tool_use` blocks.
   */
  name: 'str_replace_based_edit_tool';

  type: 'text_editor_20250429';

  /**
   * Create a cache control breakpoint at this content block.
   */
  cache_control?: CacheControlEphemeral | null;

  /**
   * When true, guarantees schema validation on tool names and inputs
   */
  strict?: boolean;
}

export interface ToolTextEditor20250728 {
  /**
   * Name of the tool.
   *
   * This is how the tool will be called by the model and in `tool_use` blocks.
   */
  name: 'str_replace_based_edit_tool';

  type: 'text_editor_20250728';

  /**
   * Create a cache control breakpoint at this content block.
   */
  cache_control?: CacheControlEphemeral | null;

  /**
   * Maximum number of characters to display when viewing a file. If not specified,
   * defaults to displaying the full file.
   */
  max_characters?: number | null;

  /**
   * When true, guarantees schema validation on tool names and inputs
   */
  strict?: boolean;
}

export type ToolUnion =
  | Tool
  | ToolBash20250124
  | ToolTextEditor20250124
  | ToolTextEditor20250429
  | ToolTextEditor20250728
  | WebSearchTool20250305;

export interface ToolUseBlock {
  id: string;

  input: unknown;

  name: string;

  type: 'tool_use';
}

export interface ToolUseBlockParam {
  id: string;

  input: unknown;

  name: string;

  type: 'tool_use';

  /**
   * Create a cache control breakpoint at this content block.
   */
  cache_control?: CacheControlEphemeral | null;
}

export interface URLImageSource {
  type: 'url';

  url: string;
}

export interface URLPDFSource {
  type: 'url';

  url: string;
}

export interface Usage {
  /**
   * Breakdown of cached tokens by TTL
   */
  cache_creation: CacheCreation | null;

  /**
   * The number of input tokens used to create the cache entry.
   */
  cache_creation_input_tokens: number | null;

  /**
   * The number of input tokens read from the cache.
   */
  cache_read_input_tokens: number | null;

  /**
   * The number of input tokens which were used.
   */
  input_tokens: number;

  /**
   * The number of output tokens which were used.
   */
  output_tokens: number;

  /**
   * The number of server tool requests.
   */
  server_tool_use: ServerToolUsage | null;

  /**
   * If the request used the priority, standard, or batch tier.
   */
  service_tier: 'standard' | 'priority' | 'batch' | null;
}

export interface WebSearchResultBlock {
  encrypted_content: string;

  page_age: string | null;

  title: string;

  type: 'web_search_result';

  url: string;
}

export interface WebSearchResultBlockParam {
  encrypted_content: string;

  title: string;

  type: 'web_search_result';

  url: string;

  page_age?: string | null;
}

export interface WebSearchTool20250305 {
  /**
   * Name of the tool.
   *
   * This is how the tool will be called by the model and in `tool_use` blocks.
   */
  name: 'web_search';

  type: 'web_search_20250305';

  /**
   * If provided, only these domains will be included in results. Cannot be used
   * alongside `blocked_domains`.
   */
  allowed_domains?: Array<string> | null;

  /**
   * If provided, these domains will never appear in results. Cannot be used
   * alongside `allowed_domains`.
   */
  blocked_domains?: Array<string> | null;

  /**
   * Create a cache control breakpoint at this content block.
   */
  cache_control?: CacheControlEphemeral | null;

  /**
   * Maximum number of times the tool can be used in the API request.
   */
  max_uses?: number | null;

  /**
   * When true, guarantees schema validation on tool names and inputs
   */
  strict?: boolean;

  /**
   * Parameters for the user's location. Used to provide more relevant search
   * results.
   */
  user_location?: WebSearchTool20250305.UserLocation | null;
}

export namespace WebSearchTool20250305 {
  /**
   * Parameters for the user's location. Used to provide more relevant search
   * results.
   */
  export interface UserLocation {
    type: 'approximate';

    /**
     * The city of the user.
     */
    city?: string | null;

    /**
     * The two letter
     * [ISO country code](https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2) of the
     * user.
     */
    country?: string | null;

    /**
     * The region of the user.
     */
    region?: string | null;

    /**
     * The [IANA timezone](https://nodatime.org/TimeZones) of the user.
     */
    timezone?: string | null;
  }
}

export interface WebSearchToolRequestError {
  error_code:
    | 'invalid_tool_input'
    | 'unavailable'
    | 'max_uses_exceeded'
    | 'too_many_requests'
    | 'query_too_long'
    | 'request_too_large';

  type: 'web_search_tool_result_error';
}

export interface WebSearchToolResultBlock {
  content: WebSearchToolResultBlockContent;

  tool_use_id: string;

  type: 'web_search_tool_result';
}

export type WebSearchToolResultBlockContent = WebSearchToolResultError | Array<WebSearchResultBlock>;

export interface WebSearchToolResultBlockParam {
  content: WebSearchToolResultBlockParamContent;

  tool_use_id: string;

  type: 'web_search_tool_result';

  /**
   * Create a cache control breakpoint at this content block.
   */
  cache_control?: CacheControlEphemeral | null;
}

export type WebSearchToolResultBlockParamContent =
  | Array<WebSearchResultBlockParam>
  | WebSearchToolRequestError;

export interface WebSearchToolResultError {
  error_code:
    | 'invalid_tool_input'
    | 'unavailable'
    | 'max_uses_exceeded'
    | 'too_many_requests'
    | 'query_too_long'
    | 'request_too_large';

  type: 'web_search_tool_result_error';
}

export type MessageStreamEvent = RawMessageStreamEvent;

export type MessageStartEvent = RawMessageStartEvent;

export type MessageDeltaEvent = RawMessageDeltaEvent;

export type MessageStopEvent = RawMessageStopEvent;

export type ContentBlockStartEvent = RawContentBlockStartEvent;

export type ContentBlockDeltaEvent = RawContentBlockDeltaEvent;

export type ContentBlockStopEvent = RawContentBlockStopEvent;

export type MessageCreateParams = MessageCreateParamsNonStreaming | MessageCreateParamsStreaming;

export interface MessageCreateParamsBase {
  /**
   * 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.claude.com/en/docs/models-overview) for details.
   */
  max_tokens: number;

  /**
   * 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. Consecutive `user` or `assistant` turns
   * in your request will be combined into a single turn.
   *
   * 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.
   *
   * 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" }] }
   * ```
   *
   * See [input examples](https://docs.claude.com/en/api/messages-examples).
   *
   * Note that if you want to include a
   * [system prompt](https://docs.claude.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.
   *
   * There is a limit of 100,000 messages in a single request.
   */
  messages: Array<MessageParam>;

  /**
   * The model that will complete your prompt.\n\nSee
   * [models](https://docs.anthropic.com/en/docs/models-overview) for additional
   * details and options.
   */
  model: Model;

  /**
   * An object describing metadata about the request.
   */
  metadata?: Metadata;

  /**
   * Configuration options for the model's output, such as the output format.
   */
  output_config?: OutputConfig;

  /**
   * Determines whether to use priority capacity (if available) or standard capacity
   * for this request.
   *
   * Anthropic offers different levels of service for your API requests. See
   * [service-tiers](https://docs.claude.com/en/api/service-tiers) for details.
   */
  service_tier?: 'auto' | 'standard_only';

  /**
   * 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.
   */
  stop_sequences?: Array<string>;

  /**
   * Whether to incrementally stream the response using server-sent events.
   *
   * See [streaming](https://docs.claude.com/en/api/messages-streaming) for details.
   */
  stream?: boolean;

  /**
   * 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.claude.com/en/docs/system-prompts).
   */
  system?: string | Array<TextBlockParam>;

  /**
   * 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.
   */
  temperature?: number;

  /**
   * Configuration for enabling Claude's extended thinking.
   *
   * When enabled, responses include `thinking` content blocks showing Claude's
   * thinking process before the final answer. Requires a minimum budget of 1,024
   * tokens and counts towards your `max_tokens` limit.
   *
   * See
   * [extended thinking](https://docs.claude.com/en/docs/build-with-claude/extended-thinking)
   * for details.
   */
  thinking?: ThinkingConfigParam;

  /**
   * How the model should use the provided tools. The model can use a specific tool,
   * any available tool, decide by itself, or not use tools at all.
   */
  tool_choice?: ToolChoice;

  /**
   * 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.
   *
   * There are two types of tools: **client tools** and **server tools**. The
   * behavior described below applies to client tools. For
   * [server tools](https://docs.claude.com/en/docs/agents-and-tools/tool-use/overview#server-tools),
   * see their individual documentation as each has its own behavior (e.g., the
   * [web search tool](https://docs.claude.com/en/docs/agents-and-tools/tool-use/web-search-tool)).
   *
   * 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/draft/2020-12) 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.claude.com/en/docs/tool-use) for more details.
   */
  tools?: Array<ToolUnion>;

  /**
   * 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_k?: 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`.
   */
  top_p?: number;
}

export namespace MessageCreateParams {
  export type MessageCreateParamsNonStreaming = MessagesAPI.MessageCreateParamsNonStreaming;
  export type MessageCreateParamsStreaming = MessagesAPI.MessageCreateParamsStreaming;
}

export interface MessageCreateParamsNonStreaming extends MessageCreateParamsBase {
  /**
   * Whether to incrementally stream the response using server-sent events.
   *
   * See [streaming](https://docs.claude.com/en/api/messages-streaming) for details.
   */
  stream?: false;
}

export interface MessageCreateParamsStreaming extends MessageCreateParamsBase {
  /**
   * Whether to incrementally stream the response using server-sent events.
   *
   * See [streaming](https://docs.claude.com/en/api/messages-streaming) for details.
   */
  stream: true;
}

export type MessageStreamParams = ParseableMessageCreateParams;

export interface MessageCountTokensParams {
  /**
   * 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. Consecutive `user` or `assistant` turns
   * in your request will be combined into a single turn.
   *
   * 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.
   *
   * 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" }] }
   * ```
   *
   * See [input examples](https://docs.claude.com/en/api/messages-examples).
   *
   * Note that if you want to include a
   * [system prompt](https://docs.claude.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.
   *
   * There is a limit of 100,000 messages in a single request.
   */
  messages: Array<MessageParam>;

  /**
   * The model that will complete your prompt.\n\nSee
   * [models](https://docs.anthropic.com/en/docs/models-overview) for additional
   * details and options.
   */
  model: Model;

  /**
   * Configuration options for the model's output, such as the output format.
   */
  output_config?: OutputConfig;

  /**
   * 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.claude.com/en/docs/system-prompts).
   */
  system?: string | Array<TextBlockParam>;

  /**
   * Configuration for enabling Claude's extended thinking.
   *
   * When enabled, responses include `thinking` content blocks showing Claude's
   * thinking process before the final answer. Requires a minimum budget of 1,024
   * tokens and counts towards your `max_tokens` limit.
   *
   * See
   * [extended thinking](https://docs.claude.com/en/docs/build-with-claude/extended-thinking)
   * for details.
   */
  thinking?: ThinkingConfigParam;

  /**
   * How the model should use the provided tools. The model can use a specific tool,
   * any available tool, decide by itself, or not use tools at all.
   */
  tool_choice?: ToolChoice;

  /**
   * 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.
   *
   * There are two types of tools: **client tools** and **server tools**. The
   * behavior described below applies to client tools. For
   * [server tools](https://docs.claude.com/en/docs/agents-and-tools/tool-use/overview#server-tools),
   * see their individual documentation as each has its own behavior (e.g., the
   * [web search tool](https://docs.claude.com/en/docs/agents-and-tools/tool-use/web-search-tool)).
   *
   * 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/draft/2020-12) 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.
   *
   *