> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.privategpt.dev/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.privategpt.dev/_mcp/server.

# Initiate Async Chat Stream

POST /v1/messages/async
Content-Type: application/json

Initiate an asynchronous chat completion stream.

This endpoint starts an asynchronous chat completion process that streams
events. Unlike synchronous chat, this endpoint returns immediately
with a message_id that can be used to observe the stream progress.

Key Features:
* Asynchronous Processing: Non-blocking request handling with immediate response
* Stream Observation: Use returned message_id to observe real-time events
* Works exactly like synchronous chat, but in an async manner

Notes:
* Optional message_id query parameter for custom stream identification
* Stream events follow the same format as synchronous chat responses
* Stream status can be monitored via status endpoint

Reference: https://docs.privategpt.dev/api-reference/async-messages/chat-messages-v-1-messages-async-post

## Request

### Query parameters

- `message_id` (string, optional, nullable) — Optional custom identifier for the stream. If not provided, a unique ID will be generated automatically.

### Body (application/json)

This endpoint expects a ChatBody.

- `model` (string, required, default: default) — Model identifier or alias.
- `messages` (list of MessageInput, required) — Conversation messages for the request.
- `max_tokens` (integer, required) — Maximum number of tokens to generate in the response.
- `system` (list of System, optional) — System prompt input. Accepts str, list[str], System, list[System], or null. It is normalized internally to list[System].
- `tools` (list of ToolSpecBody, optional, nullable) — Optional tool definitions.
- `thinking` (Thinking, optional) — Thinking configuration.
- `tool_choice` (ToolChoice, optional) — Tool selection policy.
- `output_config` (OutputConfigInput, optional) — Optional output configuration options.
- `cache_control` (ChatBodyCacheControl, optional, nullable) — Optional request-level cache control.
- `stream` (boolean, optional, default: false) — Whether to stream the response back to the client.
- `tool_context` (list of ChatBodyToolContextItems, optional, nullable) — Context to provide to the tools, such as documents, databases connection strings, or data relevant to tool usage.
- `mcp_servers` (list of McpServerConfig, optional) — List of MCP servers to use for tool retrieval. Each server can have its own configuration.
- `container` (string, optional, nullable) — Container identifier for reuse across requests.
- `mounts` (list of MountEntry, optional, nullable) — Mount entries requesting files or folders to be available inside the sandbox.
- `response_format` (ResponseFormat, optional) — Deprecated response format. Use output_config.format instead.
- `priority` (integer, optional, nullable) — Priority of the request, used for prioritizing responses.
- `seed` (integer, optional, nullable) — Random seed for reproducibility.
- `min_p` (double, optional, nullable) — Minimum probability threshold for token selection. Tokens with probability below this value are filtered out.
- `top_p` (double, optional) — Nucleus sampling parameter. Only tokens with cumulative probability up to this value are considered.
- `temperature` (double, optional) — Controls randomness in generation. Higher values make output more random, lower values more deterministic.
- `top_k` (integer, optional) — Limits token selection to the top K most likely tokens at each step.
- `repetition_penalty` (double, optional, nullable) — Penalty applied to tokens that have already appeared in the sequence to reduce repetition.
- `presence_penalty` (double, optional, nullable) — Penalty applied based on whether a token has appeared in the text, encouraging topic diversity.
- `frequency_penalty` (double, optional, nullable) — Penalty applied based on how frequently a token appears in the text, reducing repetitive content.
- `stop_sequences` (list of string, optional) — Custom stop sequences that stop generation when matched.
- `metadata` (CompletionMetadata, optional) — Request metadata (for example, user_id).
- `service_tier` (enum, optional, default: auto) — Service tier preference (for example, "auto" or "standard_only").
  - Allowed values: `auto`, `standard_only`
- `inference_geo` (string, optional, nullable) — Geographic region hint for inference processing.
- `correlation_id` (string, optional, nullable) — Correlation ID for tracking the request across systems.
- `maximum_loaded_skills` (integer, optional, nullable) — Optional cap for concurrently loaded skills in a conversation. When exceeded, the oldest loaded skill is evicted.

## Response

### 200

Chat stream initiated successfully

- `message_id` (string, required) — Unique identifier for the initiated stream
- `status` (enum, required) — Initial status of the stream (typically 'pending')
  - Allowed values: `pending`, `processing`, `completed`, `failed`, `cancelled`, `error`
- `message` (string, optional, default: Request initiated successfully) — Confirmation message for successful stream initiation

## Errors

### 401 Unauthorized Error

Unauthorized

- `any`

### 422 Unprocessable Entity Error

Validation Error - Invalid request parameters

- `detail` (OpenApiValidationErrorResponseDetail, required) — Error detail — a string message or a list of validation error objects.

## Types

### MessageInput

Input message for AI conversations.

- `role` (enum, required) — The role of the message sender
  - Allowed values: `system`, `user`, `assistant`
- `content` (MessageInputContent, required) — The message content

### System

System message configuration for AI behavior and prompting.

- `text` (string, optional, nullable) — System prompt to use for the chat completion
- `citations` (Citations, optional) — Citation configuration for source attribution in responses
- `extensions` (list of enum, optional) — Set of enabled extensions
  - Allowed values: `zylon`
- `blob_visibility` (enum, optional) — Controls how blobs are exposed: binary (raw data), internal (private S3 URI), or public (public S3 URL)
  - Allowed values: `binary`, `internal`, `public`
- `prompt` (PromptConfig, optional) — Controls which platform-level prompt features are injected.
- `use_default_prompt` (boolean, optional, default: false, deprecated) — Deprecated: legacy toggle for built-in default prompt injection. Use system.prompt to control per-category prompt injection.

### ToolSpecBody

Definition for a tool the client can call.

- `name` (string, required) — Unique name identifier for the tool
- `type` (string, optional, nullable) — Type of the tool, use to identify internal tools database_query_v1 or semantic_search_v1
- `description` (string, optional, nullable) — Human-readable description of what the tool does
- `inputSchema` (map from string to any, optional, nullable) — JSON schema defining the input parameters the tool accepts
- `context` (list of ToolSpecBodyContextItems, optional, nullable) — Additional context or metadata for the tool
- `deferLoading` (boolean, optional, default: false) — When true, hide this tool from the model until at least one skill is loaded in the current conversation.
- `instructions` (string, optional, nullable) — Optional instructions injected into the system prompt when this tool is available. For internal tools a default template is used; providing a value here overrides that default. Set to an empty string to disable.

### Thinking

Configuration for AI reasoning and step-by-step thinking capabilities.

- `enabled` (boolean, optional, default: false) — Enable reasoning capabilities for the model, allowing it to think step-by-step
- `effort` (enum, optional, nullable, deprecated) — Deprecated. Use output_config.effort instead. Kept for backward compatibility with legacy clients.
  - Allowed values: `low`, `medium`, `high`, `max`, `xhigh`

### ToolChoice

Configuration for tool selection behavior during AI interactions.

- `type` (enum, optional, default: auto) — Tool selection strategy
  - Allowed values: `auto`, `any`, `tool`, `none`
- `name` (string, optional, nullable) — Name of the tool to use if not auto-selecting
- `disable_parallel_tool_use` (boolean, optional, default: false) — When true, prevents the AI from using multiple tools simultaneously
- `validation_mode` (enum, optional, default: lazy) — Tool validation mode. 'eager' validates tool calls before execution, 'lazy' validates if tool call is made.
  - Allowed values: `eager`, `lazy`

### OutputConfigInput

Output configuration shared across Anthropic-compatible request models.

- `effort` (enum, optional, nullable) — Reasoning effort level for output generation.
  - Allowed values: `low`, `medium`, `high`, `max`, `xhigh`
- `format` (JsonObjectFormat, optional, nullable) — Optional structured output format schema.

### ChatBodyCacheControl

Optional request-level cache control.

- `type`: `ephemeral` (CacheControlEphemeral)
  - `ttl` (enum, optional, default: 5m) — ttl field.
    - Allowed values: `5m`, `1h`

### ChatBodyToolContextItems

- `type`: `file` (FileArtifact)
  - `value` (string, required) — Base64 encoded file content
- `type`: `ingested_artifact` (IngestedArtifact)
  - `context_filter` (ContextFilter, required) — Already processed ContextFilter
- `type`: `skill` (SkillArtifact)
  - `skill_filter` (SkillFilter, required) — Skill filter declaring the collection and optional skill_or_version_ids whitelist.
- `type`: `sql_database` (SqlDatabaseArtifact)
  - `connection_string` (string, required) — SQL database connection string
  - `description` (string, optional, default: ) — Optional description of the database
  - `enable_functions` (boolean, optional, default: true) — Whether to include functions in the inspection
  - `enable_procedures` (boolean, optional, default: true) — Whether to include stored procedures in the inspection
  - `enable_tables` (boolean, optional, default: true) — Whether to include tables in the inspection
  - `enable_views` (boolean, optional, default: true) — Whether to include views in the inspection
  - `schemas` (list of string, optional, nullable) — List of specific schemas to include, if None includes all non-system schemas
  - `ssl` (boolean, optional, default: false) — Whether to use SSL for the database connection
- `type`: `text` (TextArtifact)
  - `value` (string, required) — Plain text content
- `type`: `uri` (UriArtifact)
  - `value` (string, required) — URI to download from

### McpServerConfig

Configuration for the MCP server.

- `url` (string, required) — The URL of the MCP server to connect to.
- `name` (string, optional, nullable, default: mcp) — A name for the MCP server configuration.
- `authorization_token` (string, optional, nullable) — The authorization token to use when connecting to the MCP server.
- `tool_configuration` (McpServerToolConfig, optional) — Configuration for tool filtering from the MCP server

### MountEntry

Declares a file or folder to mount into the sandbox. Part of the chat request body (``mounts`` field). Declares *what* content should be visible inside the sandbox and *where* it should appear. Content is located one of two ways: - ``uri`` (preferred): a content origin (``s3://...``, ``https://...``, ``data:...`` or a local disk path). The mount is lazy — content is fetched only when the backing folder is absent or empty. - ``namespace``/``scope``/``path``: a reference into a registered filesystem namespace, resolved to the exact host file/folder.

- `namespace` (string, required) — Registered namespace name (e.g. 'session', 'skills', or a custom namespace).
- `scope` (string, required) — Opaque scope id within the namespace (e.g. thread-id).
- `path` (string, required) — Relative path within the scope.
- `target` (string, required) — Absolute container path where the content should be visible.
- `mode` (enum, optional, default: rw) — Access mode.
  - Allowed values: `rw`, `ro`
- `etag` (string, optional, nullable) — Optional content checksum.
- `uri` (string, optional, nullable) — Content origin: s3://, https://, data: or a local disk path. When set, the mount is lazy — content is fetched only if the backing folder is absent or empty.

### ResponseFormat

Deprecated response format model. Use JsonObjectFormat.

- `type` (enum, optional) — Response format type
  - Allowed values: `text`, `json_schema`
- `json_schema` (map from string to any, optional, nullable) — JSON schema definition when type is 'json_schema'

### CompletionMetadata

CompletionMetadata schema.

- `user_id` (string, optional, nullable) — Opaque user identifier for request attribution.

### OpenApiValidationErrorResponseDetail

Error detail — a string message or a list of validation error objects.

### MessageInputContent

The message content

### Citations

Configuration for citation generation in AI responses.

- `enabled` (boolean, optional, default: false) — Enable citations in responses
- `known_citations` (list of ZylonCitation, optional, nullable) — List of known citations to use in the response

### PromptConfig

Controls which platform-level prompt features are injected. These flags represent optional AI features adding internal instructions to the system prompt. All flags default to ``False`` — opt-in explicitly.

- `tools` (boolean, optional, default: false) — Enable per-tool instruction injection for all available tools.
- `citations` (boolean, optional, default: false) — Enable citation formatting guidelines injection.
- `thinking` (boolean, optional, default: false) — Enable thinking/reasoning guidelines when thinking is enabled.
- `code_execution` (boolean, optional, default: false) — Enable code execution environment instructions (filesystem layout, available paths) when any code execution tool is present.
- `skills` (boolean, optional, default: false) — Enable skill management instructions (when to load/unload skills, workflow guidance) when any skill management tool is present.

### ToolSpecBodyContextItems

- `type`: `file` (FileArtifact)
  - `value` (string, required) — Base64 encoded file content
- `type`: `ingested_artifact` (IngestedArtifact)
  - `context_filter` (ContextFilter, required) — Already processed ContextFilter
- `type`: `skill` (SkillArtifact)
  - `skill_filter` (SkillFilter, required) — Skill filter declaring the collection and optional skill_or_version_ids whitelist.
- `type`: `sql_database` (SqlDatabaseArtifact)
  - `connection_string` (string, required) — SQL database connection string
  - `description` (string, optional, default: ) — Optional description of the database
  - `enable_functions` (boolean, optional, default: true) — Whether to include functions in the inspection
  - `enable_procedures` (boolean, optional, default: true) — Whether to include stored procedures in the inspection
  - `enable_tables` (boolean, optional, default: true) — Whether to include tables in the inspection
  - `enable_views` (boolean, optional, default: true) — Whether to include views in the inspection
  - `schemas` (list of string, optional, nullable) — List of specific schemas to include, if None includes all non-system schemas
  - `ssl` (boolean, optional, default: false) — Whether to use SSL for the database connection
- `type`: `text` (TextArtifact)
  - `value` (string, required) — Plain text content
- `type`: `uri` (UriArtifact)
  - `value` (string, required) — URI to download from

### JsonObjectFormat

Structured JSON object format compatible with Anthropic output_config.format.

- `type` ("json_schema", required) — Output format type. Always "json_schema".
- `schema` (map from string to any, required) — JSON schema used to constrain the model output.

### ContextFilter

Filter by collection, artifacts and metadata in the ingested context. The main filter is the collection the context is part of. All artifacts ids are ensured to be part of the context. If any of the ids is not usable in the context (i.e. doesn't exist), the related task will fail. The metadata filter will look for all context matching the metadata, if any, and add it to the context. The filter is a dictionary of key-value pairs. The key is the metadata key, and the value is the metadata value. If both artifacts and metadata filters are provided, the result will be the intersection of the two filters.

- `collection` (string, optional, default: pgpt_collection) — The name of the collection to filter on.
- `artifacts` (list of string, optional, nullable) — Artifacts ids to filter on.
- `metadata_filter` (list of map from string to any, optional, nullable) — Metadata filter to apply on the context.

### SkillFilter

Collection-scoped filter used to resolve active skills and versions.

- `collection` (string, required) — Tenant collection boundary used to recover skills.
- `skill_or_version_ids` (list of string, optional, nullable) — Optional whitelist of identifiers inside collection. Each item may be a skill id (resolved to latest version) or a skill version id.

### McpServerToolConfig

Configuration for tool filtering from the MCP server.

- `enabled` (boolean, optional, default: true) — Enable tool filtering for the MCP server.
- `allowed_tools` (list of string, optional, nullable) — List of enabled tools to filter from the MCP server.

### ZylonCitation

ZylonCitation represents a citation in the Zylon format.

- `id` (string, optional, nullable) — Unique identifier for the citation
- `index` (integer, optional, nullable) — Index of the citation in the document
- `artifact_id` (string, optional, nullable) — Identifier of the artifact associated with the citation
- `source_id` (string, optional, nullable) — Identifier of the source document from which the citation is derived

## Examples

### Stream initiated successfully

**Request**

```json
{
  "model": "default",
  "messages": [
    {
      "role": "user",
      "content": "How do you fry an egg? Choose the best method."
    }
  ],
  "max_tokens": 1000,
  "system": [
    {
      "text": "You are a helpful cooking assistant. Provide clear, step-by-step instructions.",
      "citations": {
        "enabled": true
      }
    }
  ],
  "tools": [
    {
      "name": "egg_fryer",
      "description": "A tool to fry eggs with precise temperature control",
      "input_schema": {
        "properties": {
          "temperature": {
            "description": "Temperature in degrees Celsius",
            "type": "number"
          },
          "time": {
            "description": "Time in minutes to fry the egg",
            "type": "number"
          }
        },
        "required": [
          "temperature",
          "time"
        ],
        "type": "object"
      }
    }
  ],
  "thinking": {
    "enabled": false
  },
  "tool_choice": {
    "type": "auto",
    "disable_parallel_tool_use": false
  },
  "stream": false,
  "response_format": {
    "type": "text"
  },
  "temperature": 0.7
}
```

**Response**

```json
{
  "message_id": "msg_async_12345",
  "status": "pending",
  "message": "Request initiated successfully"
}
```

**SDK Code**

```python Stream initiated successfully
import requests

url = "https://api.example.com/v1/messages/async"

payload = {
    "model": "default",
    "messages": [
        {
            "role": "user",
            "content": "How do you fry an egg? Choose the best method."
        }
    ],
    "max_tokens": 1000,
    "system": [
        {
            "text": "You are a helpful cooking assistant. Provide clear, step-by-step instructions.",
            "citations": { "enabled": True }
        }
    ],
    "tools": [
        {
            "name": "egg_fryer",
            "description": "A tool to fry eggs with precise temperature control",
            "input_schema": {
                "properties": {
                    "temperature": {
                        "description": "Temperature in degrees Celsius",
                        "type": "number"
                    },
                    "time": {
                        "description": "Time in minutes to fry the egg",
                        "type": "number"
                    }
                },
                "required": ["temperature", "time"],
                "type": "object"
            }
        }
    ],
    "thinking": { "enabled": False },
    "tool_choice": {
        "type": "auto",
        "disable_parallel_tool_use": False
    },
    "stream": False,
    "response_format": { "type": "text" },
    "temperature": 0.7
}
headers = {"Content-Type": "application/json"}

response = requests.post(url, json=payload, headers=headers)

print(response.json())
```

```javascript Stream initiated successfully
const url = 'https://api.example.com/v1/messages/async';
const options = {
  method: 'POST',
  headers: {'Content-Type': 'application/json'},
  body: '{"model":"default","messages":[{"role":"user","content":"How do you fry an egg? Choose the best method."}],"max_tokens":1000,"system":[{"text":"You are a helpful cooking assistant. Provide clear, step-by-step instructions.","citations":{"enabled":true}}],"tools":[{"name":"egg_fryer","description":"A tool to fry eggs with precise temperature control","input_schema":{"properties":{"temperature":{"description":"Temperature in degrees Celsius","type":"number"},"time":{"description":"Time in minutes to fry the egg","type":"number"}},"required":["temperature","time"],"type":"object"}}],"thinking":{"enabled":false},"tool_choice":{"type":"auto","disable_parallel_tool_use":false},"stream":false,"response_format":{"type":"text"},"temperature":0.7}'
};

try {
  const response = await fetch(url, options);
  const data = await response.json();
  console.log(data);
} catch (error) {
  console.error(error);
}
```

```go Stream initiated successfully
package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.example.com/v1/messages/async"

	payload := strings.NewReader("{\n  \"model\": \"default\",\n  \"messages\": [\n    {\n      \"role\": \"user\",\n      \"content\": \"How do you fry an egg? Choose the best method.\"\n    }\n  ],\n  \"max_tokens\": 1000,\n  \"system\": [\n    {\n      \"text\": \"You are a helpful cooking assistant. Provide clear, step-by-step instructions.\",\n      \"citations\": {\n        \"enabled\": true\n      }\n    }\n  ],\n  \"tools\": [\n    {\n      \"name\": \"egg_fryer\",\n      \"description\": \"A tool to fry eggs with precise temperature control\",\n      \"input_schema\": {\n        \"properties\": {\n          \"temperature\": {\n            \"description\": \"Temperature in degrees Celsius\",\n            \"type\": \"number\"\n          },\n          \"time\": {\n            \"description\": \"Time in minutes to fry the egg\",\n            \"type\": \"number\"\n          }\n        },\n        \"required\": [\n          \"temperature\",\n          \"time\"\n        ],\n        \"type\": \"object\"\n      }\n    }\n  ],\n  \"thinking\": {\n    \"enabled\": false\n  },\n  \"tool_choice\": {\n    \"type\": \"auto\",\n    \"disable_parallel_tool_use\": false\n  },\n  \"stream\": false,\n  \"response_format\": {\n    \"type\": \"text\"\n  },\n  \"temperature\": 0.7\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(res)
	fmt.Println(string(body))

}
```

```ruby Stream initiated successfully
require 'uri'
require 'net/http'

url = URI("https://api.example.com/v1/messages/async")

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

request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n  \"model\": \"default\",\n  \"messages\": [\n    {\n      \"role\": \"user\",\n      \"content\": \"How do you fry an egg? Choose the best method.\"\n    }\n  ],\n  \"max_tokens\": 1000,\n  \"system\": [\n    {\n      \"text\": \"You are a helpful cooking assistant. Provide clear, step-by-step instructions.\",\n      \"citations\": {\n        \"enabled\": true\n      }\n    }\n  ],\n  \"tools\": [\n    {\n      \"name\": \"egg_fryer\",\n      \"description\": \"A tool to fry eggs with precise temperature control\",\n      \"input_schema\": {\n        \"properties\": {\n          \"temperature\": {\n            \"description\": \"Temperature in degrees Celsius\",\n            \"type\": \"number\"\n          },\n          \"time\": {\n            \"description\": \"Time in minutes to fry the egg\",\n            \"type\": \"number\"\n          }\n        },\n        \"required\": [\n          \"temperature\",\n          \"time\"\n        ],\n        \"type\": \"object\"\n      }\n    }\n  ],\n  \"thinking\": {\n    \"enabled\": false\n  },\n  \"tool_choice\": {\n    \"type\": \"auto\",\n    \"disable_parallel_tool_use\": false\n  },\n  \"stream\": false,\n  \"response_format\": {\n    \"type\": \"text\"\n  },\n  \"temperature\": 0.7\n}"

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

```java Stream initiated successfully
import com.mashape.unirest.http.HttpResponse;
import com.mashape.unirest.http.Unirest;

HttpResponse<String> response = Unirest.post("https://api.example.com/v1/messages/async")
  .header("Content-Type", "application/json")
  .body("{\n  \"model\": \"default\",\n  \"messages\": [\n    {\n      \"role\": \"user\",\n      \"content\": \"How do you fry an egg? Choose the best method.\"\n    }\n  ],\n  \"max_tokens\": 1000,\n  \"system\": [\n    {\n      \"text\": \"You are a helpful cooking assistant. Provide clear, step-by-step instructions.\",\n      \"citations\": {\n        \"enabled\": true\n      }\n    }\n  ],\n  \"tools\": [\n    {\n      \"name\": \"egg_fryer\",\n      \"description\": \"A tool to fry eggs with precise temperature control\",\n      \"input_schema\": {\n        \"properties\": {\n          \"temperature\": {\n            \"description\": \"Temperature in degrees Celsius\",\n            \"type\": \"number\"\n          },\n          \"time\": {\n            \"description\": \"Time in minutes to fry the egg\",\n            \"type\": \"number\"\n          }\n        },\n        \"required\": [\n          \"temperature\",\n          \"time\"\n        ],\n        \"type\": \"object\"\n      }\n    }\n  ],\n  \"thinking\": {\n    \"enabled\": false\n  },\n  \"tool_choice\": {\n    \"type\": \"auto\",\n    \"disable_parallel_tool_use\": false\n  },\n  \"stream\": false,\n  \"response_format\": {\n    \"type\": \"text\"\n  },\n  \"temperature\": 0.7\n}")
  .asString();
```

```php Stream initiated successfully
<?php
require_once('vendor/autoload.php');

$client = new \GuzzleHttp\Client();

$response = $client->request('POST', 'https://api.example.com/v1/messages/async', [
  'body' => '{
  "model": "default",
  "messages": [
    {
      "role": "user",
      "content": "How do you fry an egg? Choose the best method."
    }
  ],
  "max_tokens": 1000,
  "system": [
    {
      "text": "You are a helpful cooking assistant. Provide clear, step-by-step instructions.",
      "citations": {
        "enabled": true
      }
    }
  ],
  "tools": [
    {
      "name": "egg_fryer",
      "description": "A tool to fry eggs with precise temperature control",
      "input_schema": {
        "properties": {
          "temperature": {
            "description": "Temperature in degrees Celsius",
            "type": "number"
          },
          "time": {
            "description": "Time in minutes to fry the egg",
            "type": "number"
          }
        },
        "required": [
          "temperature",
          "time"
        ],
        "type": "object"
      }
    }
  ],
  "thinking": {
    "enabled": false
  },
  "tool_choice": {
    "type": "auto",
    "disable_parallel_tool_use": false
  },
  "stream": false,
  "response_format": {
    "type": "text"
  },
  "temperature": 0.7
}',
  'headers' => [
    'Content-Type' => 'application/json',
  ],
]);

echo $response->getBody();
```

```csharp Stream initiated successfully
using RestSharp;

var client = new RestClient("https://api.example.com/v1/messages/async");
var request = new RestRequest(Method.POST);
request.AddHeader("Content-Type", "application/json");
request.AddParameter("application/json", "{\n  \"model\": \"default\",\n  \"messages\": [\n    {\n      \"role\": \"user\",\n      \"content\": \"How do you fry an egg? Choose the best method.\"\n    }\n  ],\n  \"max_tokens\": 1000,\n  \"system\": [\n    {\n      \"text\": \"You are a helpful cooking assistant. Provide clear, step-by-step instructions.\",\n      \"citations\": {\n        \"enabled\": true\n      }\n    }\n  ],\n  \"tools\": [\n    {\n      \"name\": \"egg_fryer\",\n      \"description\": \"A tool to fry eggs with precise temperature control\",\n      \"input_schema\": {\n        \"properties\": {\n          \"temperature\": {\n            \"description\": \"Temperature in degrees Celsius\",\n            \"type\": \"number\"\n          },\n          \"time\": {\n            \"description\": \"Time in minutes to fry the egg\",\n            \"type\": \"number\"\n          }\n        },\n        \"required\": [\n          \"temperature\",\n          \"time\"\n        ],\n        \"type\": \"object\"\n      }\n    }\n  ],\n  \"thinking\": {\n    \"enabled\": false\n  },\n  \"tool_choice\": {\n    \"type\": \"auto\",\n    \"disable_parallel_tool_use\": false\n  },\n  \"stream\": false,\n  \"response_format\": {\n    \"type\": \"text\"\n  },\n  \"temperature\": 0.7\n}", ParameterType.RequestBody);
IRestResponse response = client.Execute(request);
```

```swift Stream initiated successfully
import Foundation

let headers = ["Content-Type": "application/json"]
let parameters = [
  "model": "default",
  "messages": [
    [
      "role": "user",
      "content": "How do you fry an egg? Choose the best method."
    ]
  ],
  "max_tokens": 1000,
  "system": [
    [
      "text": "You are a helpful cooking assistant. Provide clear, step-by-step instructions.",
      "citations": ["enabled": true]
    ]
  ],
  "tools": [
    [
      "name": "egg_fryer",
      "description": "A tool to fry eggs with precise temperature control",
      "input_schema": [
        "properties": [
          "temperature": [
            "description": "Temperature in degrees Celsius",
            "type": "number"
          ],
          "time": [
            "description": "Time in minutes to fry the egg",
            "type": "number"
          ]
        ],
        "required": ["temperature", "time"],
        "type": "object"
      ]
    ]
  ],
  "thinking": ["enabled": false],
  "tool_choice": [
    "type": "auto",
    "disable_parallel_tool_use": false
  ],
  "stream": false,
  "response_format": ["type": "text"],
  "temperature": 0.7
] as [String : Any]

let postData = JSONSerialization.data(withJSONObject: parameters, options: [])

let request = NSMutableURLRequest(url: NSURL(string: "https://api.example.com/v1/messages/async")! as URL,
                                        cachePolicy: .useProtocolCachePolicy,
                                    timeoutInterval: 10.0)
request.httpMethod = "POST"
request.allHTTPHeaderFields = headers
request.httpBody = postData as Data

let session = URLSession.shared
let dataTask = session.dataTask(with: request as URLRequest, completionHandler: { (data, response, error) -> Void in
  if (error != nil) {
    print(error as Any)
  } else {
    let httpResponse = response as? HTTPURLResponse
    print(httpResponse)
  }
})

dataTask.resume()
```

### Stream initiated with custom message_id

**Request**

```json
{
  "model": "default",
  "messages": [
    {
      "role": "user",
      "content": "How do you fry an egg? Choose the best method."
    }
  ],
  "max_tokens": 1000,
  "system": [
    {
      "text": "You are a helpful cooking assistant. Provide clear, step-by-step instructions.",
      "citations": {
        "enabled": true
      }
    }
  ],
  "tools": [
    {
      "name": "egg_fryer",
      "description": "A tool to fry eggs with precise temperature control",
      "input_schema": {
        "properties": {
          "temperature": {
            "description": "Temperature in degrees Celsius",
            "type": "number"
          },
          "time": {
            "description": "Time in minutes to fry the egg",
            "type": "number"
          }
        },
        "required": [
          "temperature",
          "time"
        ],
        "type": "object"
      }
    }
  ],
  "thinking": {
    "enabled": false
  },
  "tool_choice": {
    "type": "auto",
    "disable_parallel_tool_use": false
  },
  "stream": false,
  "response_format": {
    "type": "text"
  },
  "temperature": 0.7
}
```

**Response**

```json
{
  "message_id": "custom_msg_67890",
  "status": "pending",
  "message": "Request initiated successfully"
}
```

**SDK Code**

```python Stream initiated with custom message_id
import requests

url = "https://api.example.com/v1/messages/async"

payload = {
    "model": "default",
    "messages": [
        {
            "role": "user",
            "content": "How do you fry an egg? Choose the best method."
        }
    ],
    "max_tokens": 1000,
    "system": [
        {
            "text": "You are a helpful cooking assistant. Provide clear, step-by-step instructions.",
            "citations": { "enabled": True }
        }
    ],
    "tools": [
        {
            "name": "egg_fryer",
            "description": "A tool to fry eggs with precise temperature control",
            "input_schema": {
                "properties": {
                    "temperature": {
                        "description": "Temperature in degrees Celsius",
                        "type": "number"
                    },
                    "time": {
                        "description": "Time in minutes to fry the egg",
                        "type": "number"
                    }
                },
                "required": ["temperature", "time"],
                "type": "object"
            }
        }
    ],
    "thinking": { "enabled": False },
    "tool_choice": {
        "type": "auto",
        "disable_parallel_tool_use": False
    },
    "stream": False,
    "response_format": { "type": "text" },
    "temperature": 0.7
}
headers = {"Content-Type": "application/json"}

response = requests.post(url, json=payload, headers=headers)

print(response.json())
```

```javascript Stream initiated with custom message_id
const url = 'https://api.example.com/v1/messages/async';
const options = {
  method: 'POST',
  headers: {'Content-Type': 'application/json'},
  body: '{"model":"default","messages":[{"role":"user","content":"How do you fry an egg? Choose the best method."}],"max_tokens":1000,"system":[{"text":"You are a helpful cooking assistant. Provide clear, step-by-step instructions.","citations":{"enabled":true}}],"tools":[{"name":"egg_fryer","description":"A tool to fry eggs with precise temperature control","input_schema":{"properties":{"temperature":{"description":"Temperature in degrees Celsius","type":"number"},"time":{"description":"Time in minutes to fry the egg","type":"number"}},"required":["temperature","time"],"type":"object"}}],"thinking":{"enabled":false},"tool_choice":{"type":"auto","disable_parallel_tool_use":false},"stream":false,"response_format":{"type":"text"},"temperature":0.7}'
};

try {
  const response = await fetch(url, options);
  const data = await response.json();
  console.log(data);
} catch (error) {
  console.error(error);
}
```

```go Stream initiated with custom message_id
package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.example.com/v1/messages/async"

	payload := strings.NewReader("{\n  \"model\": \"default\",\n  \"messages\": [\n    {\n      \"role\": \"user\",\n      \"content\": \"How do you fry an egg? Choose the best method.\"\n    }\n  ],\n  \"max_tokens\": 1000,\n  \"system\": [\n    {\n      \"text\": \"You are a helpful cooking assistant. Provide clear, step-by-step instructions.\",\n      \"citations\": {\n        \"enabled\": true\n      }\n    }\n  ],\n  \"tools\": [\n    {\n      \"name\": \"egg_fryer\",\n      \"description\": \"A tool to fry eggs with precise temperature control\",\n      \"input_schema\": {\n        \"properties\": {\n          \"temperature\": {\n            \"description\": \"Temperature in degrees Celsius\",\n            \"type\": \"number\"\n          },\n          \"time\": {\n            \"description\": \"Time in minutes to fry the egg\",\n            \"type\": \"number\"\n          }\n        },\n        \"required\": [\n          \"temperature\",\n          \"time\"\n        ],\n        \"type\": \"object\"\n      }\n    }\n  ],\n  \"thinking\": {\n    \"enabled\": false\n  },\n  \"tool_choice\": {\n    \"type\": \"auto\",\n    \"disable_parallel_tool_use\": false\n  },\n  \"stream\": false,\n  \"response_format\": {\n    \"type\": \"text\"\n  },\n  \"temperature\": 0.7\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(res)
	fmt.Println(string(body))

}
```

```ruby Stream initiated with custom message_id
require 'uri'
require 'net/http'

url = URI("https://api.example.com/v1/messages/async")

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

request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n  \"model\": \"default\",\n  \"messages\": [\n    {\n      \"role\": \"user\",\n      \"content\": \"How do you fry an egg? Choose the best method.\"\n    }\n  ],\n  \"max_tokens\": 1000,\n  \"system\": [\n    {\n      \"text\": \"You are a helpful cooking assistant. Provide clear, step-by-step instructions.\",\n      \"citations\": {\n        \"enabled\": true\n      }\n    }\n  ],\n  \"tools\": [\n    {\n      \"name\": \"egg_fryer\",\n      \"description\": \"A tool to fry eggs with precise temperature control\",\n      \"input_schema\": {\n        \"properties\": {\n          \"temperature\": {\n            \"description\": \"Temperature in degrees Celsius\",\n            \"type\": \"number\"\n          },\n          \"time\": {\n            \"description\": \"Time in minutes to fry the egg\",\n            \"type\": \"number\"\n          }\n        },\n        \"required\": [\n          \"temperature\",\n          \"time\"\n        ],\n        \"type\": \"object\"\n      }\n    }\n  ],\n  \"thinking\": {\n    \"enabled\": false\n  },\n  \"tool_choice\": {\n    \"type\": \"auto\",\n    \"disable_parallel_tool_use\": false\n  },\n  \"stream\": false,\n  \"response_format\": {\n    \"type\": \"text\"\n  },\n  \"temperature\": 0.7\n}"

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

```java Stream initiated with custom message_id
import com.mashape.unirest.http.HttpResponse;
import com.mashape.unirest.http.Unirest;

HttpResponse<String> response = Unirest.post("https://api.example.com/v1/messages/async")
  .header("Content-Type", "application/json")
  .body("{\n  \"model\": \"default\",\n  \"messages\": [\n    {\n      \"role\": \"user\",\n      \"content\": \"How do you fry an egg? Choose the best method.\"\n    }\n  ],\n  \"max_tokens\": 1000,\n  \"system\": [\n    {\n      \"text\": \"You are a helpful cooking assistant. Provide clear, step-by-step instructions.\",\n      \"citations\": {\n        \"enabled\": true\n      }\n    }\n  ],\n  \"tools\": [\n    {\n      \"name\": \"egg_fryer\",\n      \"description\": \"A tool to fry eggs with precise temperature control\",\n      \"input_schema\": {\n        \"properties\": {\n          \"temperature\": {\n            \"description\": \"Temperature in degrees Celsius\",\n            \"type\": \"number\"\n          },\n          \"time\": {\n            \"description\": \"Time in minutes to fry the egg\",\n            \"type\": \"number\"\n          }\n        },\n        \"required\": [\n          \"temperature\",\n          \"time\"\n        ],\n        \"type\": \"object\"\n      }\n    }\n  ],\n  \"thinking\": {\n    \"enabled\": false\n  },\n  \"tool_choice\": {\n    \"type\": \"auto\",\n    \"disable_parallel_tool_use\": false\n  },\n  \"stream\": false,\n  \"response_format\": {\n    \"type\": \"text\"\n  },\n  \"temperature\": 0.7\n}")
  .asString();
```

```php Stream initiated with custom message_id
<?php
require_once('vendor/autoload.php');

$client = new \GuzzleHttp\Client();

$response = $client->request('POST', 'https://api.example.com/v1/messages/async', [
  'body' => '{
  "model": "default",
  "messages": [
    {
      "role": "user",
      "content": "How do you fry an egg? Choose the best method."
    }
  ],
  "max_tokens": 1000,
  "system": [
    {
      "text": "You are a helpful cooking assistant. Provide clear, step-by-step instructions.",
      "citations": {
        "enabled": true
      }
    }
  ],
  "tools": [
    {
      "name": "egg_fryer",
      "description": "A tool to fry eggs with precise temperature control",
      "input_schema": {
        "properties": {
          "temperature": {
            "description": "Temperature in degrees Celsius",
            "type": "number"
          },
          "time": {
            "description": "Time in minutes to fry the egg",
            "type": "number"
          }
        },
        "required": [
          "temperature",
          "time"
        ],
        "type": "object"
      }
    }
  ],
  "thinking": {
    "enabled": false
  },
  "tool_choice": {
    "type": "auto",
    "disable_parallel_tool_use": false
  },
  "stream": false,
  "response_format": {
    "type": "text"
  },
  "temperature": 0.7
}',
  'headers' => [
    'Content-Type' => 'application/json',
  ],
]);

echo $response->getBody();
```

```csharp Stream initiated with custom message_id
using RestSharp;

var client = new RestClient("https://api.example.com/v1/messages/async");
var request = new RestRequest(Method.POST);
request.AddHeader("Content-Type", "application/json");
request.AddParameter("application/json", "{\n  \"model\": \"default\",\n  \"messages\": [\n    {\n      \"role\": \"user\",\n      \"content\": \"How do you fry an egg? Choose the best method.\"\n    }\n  ],\n  \"max_tokens\": 1000,\n  \"system\": [\n    {\n      \"text\": \"You are a helpful cooking assistant. Provide clear, step-by-step instructions.\",\n      \"citations\": {\n        \"enabled\": true\n      }\n    }\n  ],\n  \"tools\": [\n    {\n      \"name\": \"egg_fryer\",\n      \"description\": \"A tool to fry eggs with precise temperature control\",\n      \"input_schema\": {\n        \"properties\": {\n          \"temperature\": {\n            \"description\": \"Temperature in degrees Celsius\",\n            \"type\": \"number\"\n          },\n          \"time\": {\n            \"description\": \"Time in minutes to fry the egg\",\n            \"type\": \"number\"\n          }\n        },\n        \"required\": [\n          \"temperature\",\n          \"time\"\n        ],\n        \"type\": \"object\"\n      }\n    }\n  ],\n  \"thinking\": {\n    \"enabled\": false\n  },\n  \"tool_choice\": {\n    \"type\": \"auto\",\n    \"disable_parallel_tool_use\": false\n  },\n  \"stream\": false,\n  \"response_format\": {\n    \"type\": \"text\"\n  },\n  \"temperature\": 0.7\n}", ParameterType.RequestBody);
IRestResponse response = client.Execute(request);
```

```swift Stream initiated with custom message_id
import Foundation

let headers = ["Content-Type": "application/json"]
let parameters = [
  "model": "default",
  "messages": [
    [
      "role": "user",
      "content": "How do you fry an egg? Choose the best method."
    ]
  ],
  "max_tokens": 1000,
  "system": [
    [
      "text": "You are a helpful cooking assistant. Provide clear, step-by-step instructions.",
      "citations": ["enabled": true]
    ]
  ],
  "tools": [
    [
      "name": "egg_fryer",
      "description": "A tool to fry eggs with precise temperature control",
      "input_schema": [
        "properties": [
          "temperature": [
            "description": "Temperature in degrees Celsius",
            "type": "number"
          ],
          "time": [
            "description": "Time in minutes to fry the egg",
            "type": "number"
          ]
        ],
        "required": ["temperature", "time"],
        "type": "object"
      ]
    ]
  ],
  "thinking": ["enabled": false],
  "tool_choice": [
    "type": "auto",
    "disable_parallel_tool_use": false
  ],
  "stream": false,
  "response_format": ["type": "text"],
  "temperature": 0.7
] as [String : Any]

let postData = JSONSerialization.data(withJSONObject: parameters, options: [])

let request = NSMutableURLRequest(url: NSURL(string: "https://api.example.com/v1/messages/async")! as URL,
                                        cachePolicy: .useProtocolCachePolicy,
                                    timeoutInterval: 10.0)
request.httpMethod = "POST"
request.allHTTPHeaderFields = headers
request.httpBody = postData as Data

let session = URLSession.shared
let dataTask = session.dataTask(with: request as URLRequest, completionHandler: { (data, response, error) -> Void in
  if (error != nil) {
    print(error as Any)
  } else {
    let httpResponse = response as? HTTPURLResponse
    print(httpResponse)
  }
})

dataTask.resume()
```