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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
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.
client_skills: Optional[Iterable[ClientSkill]]
Client-side skills available in the environment. These are rendered in the system prompt’s available skills section alongside agent-scoped skills from MemFS.
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.
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.
input: Optional[Union[str, Iterable[InputUnionMember1], null]]
Syntactic sugar for a single user message. Equivalent to messages=[{‘role’: ‘user’, ‘content’: input}].
Iterable[InputUnionMember1]
class ImageContent: …
class ToolCallContent: …
class ReasoningContent: …
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 ImageContent: …
class ToolCallContent: …
class ReasoningContent: …
class ApprovalCreate: …
Input to approve or deny a tool call request
approvals: Optional[List[Approval]]
The list of approval responses
class ToolReturn: …
tool_return: Union[List[ToolReturnUnionMember0], str]
The tool return value - either a string or list of content parts (text/image)
List[ToolReturnUnionMember0]
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
tool_return: Union[List[ToolReturnUnionMember0], str]
The tool return value - either a string or list of content parts (text/image)
List[ToolReturnUnionMember0]
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.
override_system: Optional[str]
Optional per-request system prompt override. When set, this is passed directly to the underlying LLM request and bypasses the persisted/compiled system message for that request.
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.
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.
request_config: Optional[RequestConfig]
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
}