Skip to content
Letta Platform Letta Platform Letta Docs
Sign up

Create Message Async

agents.messages.create_async(stragent_id, MessageCreateAsyncParams**kwargs) -> Run
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.

Note: Sending multiple concurrent requests to the same agent can lead to undefined behavior. Each agent processes messages sequentially, and concurrent requests may interleave in unexpected ways. Wait for each request to complete before sending the next one. Use separate agents or conversations for parallel processing.

ParametersExpand Collapse
agent_id: str

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

minLength42
maxLength42
Deprecatedassistant_message_tool_kwarg: Optional[str]

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[str]

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

callback_url: Optional[str]

Optional callback URL to POST to when the job completes

client_tools: Optional[Iterable[ClientTool]]

Client-side tools that the agent can call. When the agent calls a client-side tool, execution pauses and returns control to the client to execute the tool and provide the result via a ToolReturn.

name: str

The name of the tool function

description: Optional[str]

Description of what the tool does

parameters: Optional[Dict[str, object]]

JSON Schema for the function parameters

Deprecatedenable_thinking: Optional[str]

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

include_compaction_messages: Optional[bool]

If True, compaction events emit structured SummaryMessage and EventMessage types. If False (default), compaction messages are not included in the response.

include_return_message_types: Optional[List[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"
"summary_message"
"event_message"
input: Optional[Union[str, Iterable[InputUnionMember1], null]]

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

Accepts one of the following:
str
Iterable[InputUnionMember1]
Accepts one of the following:
class TextContent:
text: str

The text content of the message.

signature: Optional[str]

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

type: Optional[Literal["text"]]

The type of the message.

class ImageContent:
source: Source

The source of the image.

Accepts one of the following:
class SourceURLImage:
url: str

The URL of the image.

type: Optional[Literal["url"]]

The source type for the image.

class SourceBase64Image:
data: str

The base64 encoded image data.

media_type: str

The media type for the image.

detail: Optional[str]

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

type: Optional[Literal["base64"]]

The source type for the image.

class SourceLettaImage:
file_id: str

The unique identifier of the image file persisted in storage.

data: Optional[str]

The base64 encoded image data.

detail: Optional[str]

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

media_type: Optional[str]

The media type for the image.

type: Optional[Literal["letta"]]

The source type for the image.

type: Optional[Literal["image"]]

The type of the message.

class ToolCallContent:
id: str

A unique identifier for this specific tool call instance.

input: Dict[str, object]

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

name: str

The name of the tool being called.

signature: Optional[str]

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

type: Optional[Literal["tool_call"]]

Indicates this content represents a tool call event.

class ToolReturnContent:
content: str

The content returned by the tool execution.

is_error: bool

Indicates whether the tool execution resulted in an error.

tool_call_id: str

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

type: Optional[Literal["tool_return"]]

Indicates this content represents a tool return event.

class ReasoningContent:

Sent via the Anthropic Messages API

is_native: bool

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

reasoning: str

The intermediate reasoning or thought process content.

signature: Optional[str]

A unique identifier for this reasoning step.

type: Optional[Literal["reasoning"]]

Indicates this is a reasoning/intermediate step.

class RedactedReasoningContent:

Sent via the Anthropic Messages API

data: str

The redacted or filtered intermediate reasoning content.

type: Optional[Literal["redacted_reasoning"]]

Indicates this is a redacted thinking step.

class OmittedReasoningContent:

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[str]

A unique identifier for this reasoning step.

type: Optional[Literal["omitted_reasoning"]]

Indicates this is an omitted reasoning step.

class InputUnionMember1SummarizedReasoningContent:

The style of reasoning content returned by the OpenAI Responses API

id: str

The unique identifier for this reasoning step.

summary: Iterable[InputUnionMember1SummarizedReasoningContentSummary]

Summaries of the reasoning content.

index: int

The index of the summary part.

text: str

The text of the summary part.

encrypted_content: Optional[str]

The encrypted reasoning content.

type: Optional[Literal["summarized_reasoning"]]

Indicates this is a summarized reasoning step.

max_steps: Optional[int]

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

messages: Optional[Iterable[Message]]

The messages to be sent to the agent.

Accepts one of the following:
class MessageCreate:

Request to create a message

content: Union[List[LettaMessageContentUnion], str]

The content of the message.

Accepts one of the following:
Accepts one of the following:
class TextContent:
text: str

The text content of the message.

signature: Optional[str]

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

type: Optional[Literal["text"]]

The type of the message.

class ImageContent:
source: Source

The source of the image.

Accepts one of the following:
class SourceURLImage:
url: str

The URL of the image.

type: Optional[Literal["url"]]

The source type for the image.

class SourceBase64Image:
data: str

The base64 encoded image data.

media_type: str

The media type for the image.

detail: Optional[str]

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

type: Optional[Literal["base64"]]

The source type for the image.

class SourceLettaImage:
file_id: str

The unique identifier of the image file persisted in storage.

data: Optional[str]

The base64 encoded image data.

detail: Optional[str]

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

media_type: Optional[str]

The media type for the image.

type: Optional[Literal["letta"]]

The source type for the image.

type: Optional[Literal["image"]]

The type of the message.

class ToolCallContent:
id: str

A unique identifier for this specific tool call instance.

input: Dict[str, object]

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

name: str

The name of the tool being called.

signature: Optional[str]

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

type: Optional[Literal["tool_call"]]

Indicates this content represents a tool call event.

class ToolReturnContent:
content: str

The content returned by the tool execution.

is_error: bool

Indicates whether the tool execution resulted in an error.

tool_call_id: str

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

type: Optional[Literal["tool_return"]]

Indicates this content represents a tool return event.

class ReasoningContent:

Sent via the Anthropic Messages API

is_native: bool

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

reasoning: str

The intermediate reasoning or thought process content.

signature: Optional[str]

A unique identifier for this reasoning step.

type: Optional[Literal["reasoning"]]

Indicates this is a reasoning/intermediate step.

class RedactedReasoningContent:

Sent via the Anthropic Messages API

data: str

The redacted or filtered intermediate reasoning content.

type: Optional[Literal["redacted_reasoning"]]

Indicates this is a redacted thinking step.

class OmittedReasoningContent:

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[str]

A unique identifier for this reasoning step.

type: Optional[Literal["omitted_reasoning"]]

Indicates this is an omitted reasoning step.

str
role: Literal["user", "system", "assistant"]

The role of the participant.

Accepts one of the following:
"user"
"system"
"assistant"
batch_item_id: Optional[str]

The id of the LLMBatchItem that this message is associated with

group_id: Optional[str]

The multi-agent group that the message was sent in

name: Optional[str]

The name of the participant.

otid: Optional[str]

The offline threading id associated with this message

sender_id: Optional[str]

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

type: Optional[Literal["message"]]

The message type to be created.

class ApprovalCreate:

Input to approve or deny a tool call request

Deprecatedapproval_request_id: Optional[str]

The message ID of the approval request

approvals: Optional[List[Approval]]

The list of approval responses

Accepts one of the following:
class ApprovalReturn:
approve: bool

Whether the tool has been approved

tool_call_id: str

The ID of the tool call that corresponds to this approval

reason: Optional[str]

An optional explanation for the provided approval status

type: Optional[Literal["approval"]]

The message type to be created.

class ToolReturn:
status: Literal["success", "error"]
Accepts one of the following:
"success"
"error"
tool_call_id: str
tool_return: Union[List[ToolReturnUnionMember0], str]

The tool return value - either a string or list of content parts (text/image)

Accepts one of the following:
List[ToolReturnUnionMember0]
Accepts one of the following:
class TextContent:
text: str

The text content of the message.

signature: Optional[str]

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

type: Optional[Literal["text"]]

The type of the message.

class ImageContent:
source: Source

The source of the image.

Accepts one of the following:
class SourceURLImage:
url: str

The URL of the image.

type: Optional[Literal["url"]]

The source type for the image.

class SourceBase64Image:
data: str

The base64 encoded image data.

media_type: str

The media type for the image.

detail: Optional[str]

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

type: Optional[Literal["base64"]]

The source type for the image.

class SourceLettaImage:
file_id: str

The unique identifier of the image file persisted in storage.

data: Optional[str]

The base64 encoded image data.

detail: Optional[str]

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

media_type: Optional[str]

The media type for the image.

type: Optional[Literal["letta"]]

The source type for the image.

type: Optional[Literal["image"]]

The type of the message.

str
stderr: Optional[List[str]]
stdout: Optional[List[str]]
type: Optional[Literal["tool"]]

The message type to be created.

Deprecatedapprove: Optional[bool]

Whether the tool has been approved

group_id: Optional[str]

The multi-agent group that the message was sent in

Deprecatedreason: Optional[str]

An optional explanation for the provided approval status

type: Optional[Literal["approval"]]

The message type to be created.

override_model: Optional[str]

Model handle to use for this request instead of the agent's default model. This allows sending a message to a different model without changing the agent's configuration.

Deprecateduse_assistant_message: Optional[bool]

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
class Run:

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: str

The human-friendly ID of the Run

agent_id: str

The unique identifier of the agent associated with the run.

background: Optional[bool]

Whether the run was created in background mode.

base_template_id: Optional[str]

The base template ID that the run belongs to.

callback_error: Optional[str]

Optional error message from attempting to POST the callback endpoint.

callback_sent_at: Optional[datetime]

Timestamp when the callback was last attempted.

formatdate-time
callback_status_code: Optional[int]

HTTP status code returned by the callback endpoint.

callback_url: Optional[str]

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

completed_at: Optional[datetime]

The timestamp when the run was completed.

formatdate-time
conversation_id: Optional[str]

The unique identifier of the conversation associated with the run.

created_at: Optional[datetime]

The timestamp when the run was created.

formatdate-time
metadata: Optional[Dict[str, object]]

Additional metadata for the run.

request_config: Optional[RequestConfig]

The request configuration for the run.

assistant_message_tool_kwarg: Optional[str]

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

assistant_message_tool_name: Optional[str]

The name of the designated message tool.

include_return_message_types: Optional[List[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"
"summary_message"
"event_message"
use_assistant_message: Optional[bool]

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

status: Optional[Literal["created", "running", "completed", 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"
"max_tokens_exceeded"
"no_tool_call"
"tool_rule"
"cancelled"
"requires_approval"
"context_window_overflow_in_system_prompt"
total_duration_ns: Optional[int]

Total run duration in nanoseconds

ttft_ns: Optional[int]

Time to first token for a run in nanoseconds

Create Message Async
import os
from letta_client import Letta

client = Letta(
    api_key=os.environ.get("LETTA_API_KEY"),  # This is the default and can be omitted
)
run = client.agents.messages.create_async(
    agent_id="agent-123e4567-e89b-42d3-8456-426614174000",
)
print(run.id)
{
  "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",
  "conversation_id": "conversation_id",
  "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",
  "conversation_id": "conversation_id",
  "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
}