Create Message Async

post/v1/agents/{agent_id}/messages/async

Asynchronously process a user message and return a run object. The actual processing happens in the background, and the status can be checked using the run ID.

This is "asynchronous" in the sense that it's a background run and explicitly must be fetched by the run ID.

Path ParametersExpand Collapse

agent_id: string

The ID of the agent in the format 'agent-'

minLength42

maxLength42

Body ParametersExpand Collapse

Deprecatedassistant_message_tool_kwarg: optional string

The name of the message argument in the designated message tool. Still supported for legacy agent types, but deprecated for letta_v1_agent onward.

Deprecatedassistant_message_tool_name: optional string

The name of the designated message tool. Still supported for legacy agent types, but deprecated for letta_v1_agent onward.

callback_url: optional string

Optional callback URL to POST to when the job completes

Deprecatedenable_thinking: optional string

If set to True, enables reasoning before responses or tool calls from the agent.

include_return_message_types: optional array of MessageType

Only return specified message types in the response. If None (default) returns all messages.

Accepts one of the following:

"system_message"

"user_message"

"assistant_message"

"reasoning_message"

"hidden_reasoning_message"

"tool_call_message"

"tool_return_message"

"approval_request_message"

"approval_response_message"

input: optional string or array of TextContent { text, signature, type } or ImageContent { source, type } or ToolCallContent { id, input, name, 2 more } or 5 more

Syntactic sugar for a single user message. Equivalent to messages=[{'role': 'user', 'content': input}].

Accepts one of the following:

UnionMember0 = string

UnionMember1 = array of TextContent { text, signature, type } or ImageContent { source, type } or ToolCallContent { id, input, name, 2 more } or 5 more

Accepts one of the following:

TextContent = object { text, signature, type }

text: string

The text content of the message.

signature: optional string

Stores a unique identifier for any reasoning associated with this text content.

type: optional "text"

The type of the message.

Accepts one of the following:

"text"

ImageContent = object { source, type }

source: object { url, type } or object { data, media_type, detail, type } or object { file_id, data, detail, 2 more }

The source of the image.

Accepts one of the following:

URL = object { url, type }

url: string

The URL of the image.

type: optional "url"

The source type for the image.

Accepts one of the following:

"url"

Base64 = object { data, media_type, detail, type }

data: string

The base64 encoded image data.

media_type: string

The media type for the image.

detail: optional string

What level of detail to use when processing and understanding the image (low, high, or auto to let the model decide)

type: optional "base64"

The source type for the image.

Accepts one of the following:

"base64"

Letta = object { file_id, data, detail, 2 more }

file_id: string

The unique identifier of the image file persisted in storage.

data: optional string

The base64 encoded image data.

detail: optional string

What level of detail to use when processing and understanding the image (low, high, or auto to let the model decide)

media_type: optional string

The media type for the image.

type: optional "letta"

The source type for the image.

Accepts one of the following:

"letta"

type: optional "image"

The type of the message.

Accepts one of the following:

"image"

ToolCallContent = object { id, input, name, 2 more }

id: string

A unique identifier for this specific tool call instance.

input: map[unknown]

The parameters being passed to the tool, structured as a dictionary of parameter names to values.

The name of the tool being called.

signature: optional string

Stores a unique identifier for any reasoning associated with this tool call.

type: optional "tool_call"

Indicates this content represents a tool call event.

Accepts one of the following:

"tool_call"

ToolReturnContent = object { content, is_error, tool_call_id, type }

content: string

The content returned by the tool execution.

is_error: boolean

Indicates whether the tool execution resulted in an error.

tool_call_id: string

References the ID of the ToolCallContent that initiated this tool call.

type: optional "tool_return"

Indicates this content represents a tool return event.

Accepts one of the following:

"tool_return"

ReasoningContent = object { is_native, reasoning, signature, type }

Sent via the Anthropic Messages API

is_native: boolean

Whether the reasoning content was generated by a reasoner model that processed this step.

reasoning: string

The intermediate reasoning or thought process content.

signature: optional string

A unique identifier for this reasoning step.

type: optional "reasoning"

Indicates this is a reasoning/intermediate step.

Accepts one of the following:

"reasoning"

RedactedReasoningContent = object { data, type }

Sent via the Anthropic Messages API

data: string

The redacted or filtered intermediate reasoning content.

type: optional "redacted_reasoning"

Indicates this is a redacted thinking step.

Accepts one of the following:

"redacted_reasoning"

OmittedReasoningContent = object { signature, type }

A placeholder for reasoning content we know is present, but isn't returned by the provider (e.g. OpenAI GPT-5 on ChatCompletions)

signature: optional string

A unique identifier for this reasoning step.

type: optional "omitted_reasoning"

Indicates this is an omitted reasoning step.

Accepts one of the following:

"omitted_reasoning"

SummarizedReasoning = object { id, summary, encrypted_content, type }

The style of reasoning content returned by the OpenAI Responses API

id: string

The unique identifier for this reasoning step.

summary: array of object { index, text }

Summaries of the reasoning content.

index: number

The index of the summary part.

text: string

The text of the summary part.

encrypted_content: optional string

The encrypted reasoning content.

type: optional "summarized_reasoning"

Indicates this is a summarized reasoning step.

Accepts one of the following:

"summarized_reasoning"

max_steps: optional number

Maximum number of steps the agent should take to process the request.

messages: optional array of MessageCreate { content, role, batch_item_id, 5 more } or ApprovalCreate { approval_request_id, approvals, approve, 3 more }

The messages to be sent to the agent.

Accepts one of the following:

MessageCreate = object { content, role, batch_item_id, 5 more }

Request to create a message

content: array of LettaMessageContentUnion or string

The content of the message.

Accepts one of the following:

UnionMember0 = array of LettaMessageContentUnion

Accepts one of the following:

TextContent = object { text, signature, type }

text: string

The text content of the message.

signature: optional string

Stores a unique identifier for any reasoning associated with this text content.

type: optional "text"

The type of the message.

Accepts one of the following:

"text"

ImageContent = object { source, type }

source: object { url, type } or object { data, media_type, detail, type } or object { file_id, data, detail, 2 more }

The source of the image.

Accepts one of the following:

URL = object { url, type }

url: string

The URL of the image.

type: optional "url"

The source type for the image.

Accepts one of the following:

"url"

Base64 = object { data, media_type, detail, type }

data: string

The base64 encoded image data.

media_type: string

The media type for the image.

detail: optional string

What level of detail to use when processing and understanding the image (low, high, or auto to let the model decide)

type: optional "base64"

The source type for the image.

Accepts one of the following:

"base64"

Letta = object { file_id, data, detail, 2 more }

file_id: string

The unique identifier of the image file persisted in storage.

data: optional string

The base64 encoded image data.

detail: optional string

What level of detail to use when processing and understanding the image (low, high, or auto to let the model decide)

media_type: optional string

The media type for the image.

type: optional "letta"

The source type for the image.

Accepts one of the following:

"letta"

type: optional "image"

The type of the message.

Accepts one of the following:

"image"

ToolCallContent = object { id, input, name, 2 more }

id: string

A unique identifier for this specific tool call instance.

input: map[unknown]

The parameters being passed to the tool, structured as a dictionary of parameter names to values.

The name of the tool being called.

signature: optional string

Stores a unique identifier for any reasoning associated with this tool call.

type: optional "tool_call"

Indicates this content represents a tool call event.

Accepts one of the following:

"tool_call"

ToolReturnContent = object { content, is_error, tool_call_id, type }

content: string

The content returned by the tool execution.

is_error: boolean

Indicates whether the tool execution resulted in an error.

tool_call_id: string

References the ID of the ToolCallContent that initiated this tool call.

type: optional "tool_return"

Indicates this content represents a tool return event.

Accepts one of the following:

"tool_return"

ReasoningContent = object { is_native, reasoning, signature, type }

Sent via the Anthropic Messages API

is_native: boolean

Whether the reasoning content was generated by a reasoner model that processed this step.

reasoning: string

The intermediate reasoning or thought process content.

signature: optional string

A unique identifier for this reasoning step.

type: optional "reasoning"

Indicates this is a reasoning/intermediate step.

Accepts one of the following:

"reasoning"

RedactedReasoningContent = object { data, type }

Sent via the Anthropic Messages API

data: string

The redacted or filtered intermediate reasoning content.

type: optional "redacted_reasoning"

Indicates this is a redacted thinking step.

Accepts one of the following:

"redacted_reasoning"

OmittedReasoningContent = object { signature, type }

A placeholder for reasoning content we know is present, but isn't returned by the provider (e.g. OpenAI GPT-5 on ChatCompletions)

signature: optional string

A unique identifier for this reasoning step.

type: optional "omitted_reasoning"

Indicates this is an omitted reasoning step.

Accepts one of the following:

"omitted_reasoning"

UnionMember1 = string

role: "user" or "system" or "assistant"

The role of the participant.

Accepts one of the following:

"user"

"system"

"assistant"

batch_item_id: optional string

The id of the LLMBatchItem that this message is associated with

group_id: optional string

The multi-agent group that the message was sent in

The name of the participant.

otid: optional string

The offline threading id associated with this message

sender_id: optional string

The id of the sender of the message, can be an identity id or agent id

type: optional "message"

The message type to be created.

Accepts one of the following:

"message"

ApprovalCreate = object { approval_request_id, approvals, approve, 3 more }

Input to approve or deny a tool call request

Deprecatedapproval_request_id: optional string

The message ID of the approval request

approvals: optional array of ApprovalReturn { approve, tool_call_id, reason, type } or ToolReturn { status, tool_call_id, tool_return, 3 more }

The list of approval responses

Accepts one of the following:

ApprovalReturn = object { approve, tool_call_id, reason, type }

approve: boolean

Whether the tool has been approved

tool_call_id: string

The ID of the tool call that corresponds to this approval

reason: optional string

An optional explanation for the provided approval status

type: optional "approval"

The message type to be created.

Accepts one of the following:

"approval"

ToolReturn = object { status, tool_call_id, tool_return, 3 more }

status: "success" or "error"

Accepts one of the following:

"success"

"error"

tool_call_id: string

tool_return: string

stderr: optional array of string

stdout: optional array of string

type: optional "tool"

The message type to be created.

Accepts one of the following:

"tool"

Deprecatedapprove: optional boolean

Whether the tool has been approved

group_id: optional string

The multi-agent group that the message was sent in

Deprecatedreason: optional string

An optional explanation for the provided approval status

type: optional "approval"

The message type to be created.

Accepts one of the following:

"approval"

Deprecateduse_assistant_message: optional boolean

Whether the server should parse specific tool call arguments (default send_message) as AssistantMessage objects. Still supported for legacy agent types, but deprecated for letta_v1_agent onward.

ReturnsExpand Collapse

Run = object { id, agent_id, background, 13 more }

Representation of a run - a conversation or processing session for an agent. Runs track when agents process messages and maintain the relationship between agents, steps, and messages.

id: string

The human-friendly ID of the Run

agent_id: string

The unique identifier of the agent associated with the run.

background: optional boolean

Whether the run was created in background mode.

base_template_id: optional string

The base template ID that the run belongs to.

callback_error: optional string

Optional error message from attempting to POST the callback endpoint.

callback_sent_at: optional string

Timestamp when the callback was last attempted.

formatdate-time

callback_status_code: optional number

HTTP status code returned by the callback endpoint.

callback_url: optional string

If set, POST to this URL when the run completes.

completed_at: optional string

The timestamp when the run was completed.

formatdate-time

created_at: optional string

The timestamp when the run was created.

formatdate-time

metadata: optional map[unknown]

Additional metadata for the run.

request_config: optional object { assistant_message_tool_kwarg, assistant_message_tool_name, include_return_message_types, use_assistant_message }

The request configuration for the run.

assistant_message_tool_kwarg: optional string

The name of the message argument in the designated message tool.

assistant_message_tool_name: optional string

The name of the designated message tool.

include_return_message_types: optional array of MessageType

Only return specified message types in the response. If None (default) returns all messages.

Accepts one of the following:

"system_message"

"user_message"

"assistant_message"

"reasoning_message"

"hidden_reasoning_message"

"tool_call_message"

"tool_return_message"

"approval_request_message"

"approval_response_message"

use_assistant_message: optional boolean

Whether the server should parse specific tool call arguments (default send_message) as AssistantMessage objects.

status: optional "created" or "running" or "completed" or 2 more

The current status of the run.

Accepts one of the following:

"created"

"running"

"completed"

"failed"

"cancelled"

stop_reason: optional StopReasonType

The reason why the run was stopped.

Accepts one of the following:

"end_turn"

"error"

"llm_api_error"

"invalid_llm_response"

"invalid_tool_call"

"max_steps"

"no_tool_call"

"tool_rule"

"cancelled"

"requires_approval"

total_duration_ns: optional number

Total run duration in nanoseconds

ttft_ns: optional number

Time to first token for a run in nanoseconds

Create Message Async

curl https://api.letta.com/v1/agents/$AGENT_ID/messages/async \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer $LETTA_API_KEY" \
    -d '{}'

{
  "id": "run-123e4567-e89b-12d3-a456-426614174000",
  "agent_id": "agent_id",
  "background": true,
  "base_template_id": "base_template_id",
  "callback_error": "callback_error",
  "callback_sent_at": "2019-12-27T18:11:19.117Z",
  "callback_status_code": 0,
  "callback_url": "callback_url",
  "completed_at": "2019-12-27T18:11:19.117Z",
  "created_at": "2019-12-27T18:11:19.117Z",
  "metadata": {
    "foo": "bar"
  },
  "request_config": {
    "assistant_message_tool_kwarg": "assistant_message_tool_kwarg",
    "assistant_message_tool_name": "assistant_message_tool_name",
    "include_return_message_types": [
      "system_message"
    ],
    "use_assistant_message": true
  },
  "status": "created",
  "stop_reason": "end_turn",
  "total_duration_ns": 0,
  "ttft_ns": 0
}

Returns Examples

{
  "id": "run-123e4567-e89b-12d3-a456-426614174000",
  "agent_id": "agent_id",
  "background": true,
  "base_template_id": "base_template_id",
  "callback_error": "callback_error",
  "callback_sent_at": "2019-12-27T18:11:19.117Z",
  "callback_status_code": 0,
  "callback_url": "callback_url",
  "completed_at": "2019-12-27T18:11:19.117Z",
  "created_at": "2019-12-27T18:11:19.117Z",
  "metadata": {
    "foo": "bar"
  },
  "request_config": {
    "assistant_message_tool_kwarg": "assistant_message_tool_kwarg",
    "assistant_message_tool_name": "assistant_message_tool_name",
    "include_return_message_types": [
      "system_message"
    ],
    "use_assistant_message": true
  },
  "status": "created",
  "stop_reason": "end_turn",
  "total_duration_ns": 0,
  "ttft_ns": 0
}