Create Message 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-
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.
If True, compaction events emit structured SummaryMessage and EventMessage types. If False (default), compaction messages are not included in the response.
Only return specified message types in the response. If None (default) returns all messages.
input: Optional[Union[str, Iterable[InputUnionMember1], null]]
Syntactic sugar for a single user message. Equivalent to messages=[{'role': 'user', 'content': input}].
Iterable[InputUnionMember1]
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.
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.
class MessageCreate: …
Request to create a message
The content of the message.
List[LettaMessageContentUnion]
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.
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.
role: Literal["user", "system", "assistant"]
The role of the participant.
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
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"]
tool_return: Union[List[ToolReturnUnionMember0], str]
The tool return value - either a string or list of content parts (text/image)
List[ToolReturnUnionMember0]
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.
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.
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.
class MessageToolReturnCreate: …
Submit tool return(s) from client-side tool execution.
This is the preferred way to send tool results back to the agent after client-side tool execution. It is equivalent to sending an ApprovalCreate with tool return approvals, but provides a cleaner API for the common case.
List of tool returns from client-side execution
status: Literal["success", "error"]
tool_return: Union[List[ToolReturnUnionMember0], str]
The tool return value - either a string or list of content parts (text/image)
List[ToolReturnUnionMember0]
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.
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.
type: Optional[Literal["tool"]]
The message type to be created.
type: Optional[Literal["tool_return"]]
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.
If True, returns log probabilities of the output tokens in the response. Useful for RL training. Only supported for OpenAI-compatible providers (including SGLang).
If True, returns token IDs and logprobs for ALL LLM generations in the agent step, not just the last one. Uses SGLang native /generate endpoint. Returns 'turns' field with TurnTokenData for each assistant/tool turn. Required for proper multi-turn RL training with loss masking.
top_logprobs: Optional[int]
Number of most likely tokens to return at each position (0-20). Requires return_logprobs=True.
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.
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.
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.
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.
Only return specified message types in the response. If None (default) returns all messages.
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.
stop_reason: Optional[StopReasonType]
The reason why the run was stopped.
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
}