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Open in ChatGPT

Messages

List All Messages
client.messages.list(MessageListParams { after, before, limit, order } query?, RequestOptionsoptions?): MessageListResponse { , , , 8 more }
get/v1/messages/
Search All Messages
client.messages.search(MessageSearchParams { query, end_date, limit, 2 more } body, RequestOptionsoptions?): MessageSearchResponse { , , , 8 more }
post/v1/messages/search
ModelsExpand Collapse
MessageSearchRequest { query, end_date, limit, 2 more }
query: string

Text query for full-text search

end_date?: string | null

Filter messages created on or before this date

formatdate-time
limit?: number

Maximum number of results to return

maximum100
minimum1
search_mode?: "vector" | "fts" | "hybrid"

Search mode to use

Accepts one of the following:
"vector"
"fts"
"hybrid"
start_date?: string | null

Filter messages created after this date

formatdate-time
MessageSearchResult { embedded_text, message, rrf_score, 2 more }

Result from a message search operation with scoring details.

embedded_text: string

The embedded content (LLM-friendly)

message: InternalMessage { id, role, agent_id, 21 more }

The raw message object

id: string

The human-friendly ID of the Message

The role of the participant.

Accepts one of the following:
"assistant"
"user"
"tool"
"function"
"system"
"approval"
agent_id?: string | null

The unique identifier of the agent.

approval_request_id?: string | null

The id of the approval request if this message is associated with a tool call request.

approvals?: Array<ApprovalReturn { approve, tool_call_id, reason, type } | LettaSchemasMessageToolReturn { status, func_response, stderr, 2 more } > | null

The list of approvals for this message.

Accepts one of the following:
ApprovalReturn { 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?: string | null

An optional explanation for the provided approval status

type?: "approval"

The message type to be created.

Accepts one of the following:
"approval"
LettaSchemasMessageToolReturn { status, func_response, stderr, 2 more }
status: "success" | "error"

The status of the tool call

Accepts one of the following:
"success"
"error"
func_response?: string | null

The function response string

stderr?: Array<string> | null

Captured stderr from the tool invocation

stdout?: Array<string> | null

Captured stdout (e.g. prints, logs) from the tool invocation

tool_call_id?: unknown

The ID for the tool call

approve?: boolean | null

Whether tool call is approved.

batch_item_id?: string | null

The id of the LLMBatchItem that this message is associated with

content?: Array<TextContent { text, signature, type } | ImageContent { source, type } | ToolCallContent { id, input, name, 2 more } | 5 more> | null

The content of the message.

Accepts one of the following:
TextContent { text, signature, type }
text: string

The text content of the message.

signature?: string | null

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

type?: "text"

The type of the message.

Accepts one of the following:
"text"
ImageContent { source, type }
source: URLImage { url, type } | Base64Image { data, media_type, detail, type } | LettaImage { file_id, data, detail, 2 more }

The source of the image.

Accepts one of the following:
URLImage { url, type }
url: string

The URL of the image.

type?: "url"

The source type for the image.

Accepts one of the following:
"url"
Base64Image { data, media_type, detail, type }
data: string

The base64 encoded image data.

media_type: string

The media type for the image.

detail?: string | null

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

type?: "base64"

The source type for the image.

Accepts one of the following:
"base64"
LettaImage { file_id, data, detail, 2 more }
file_id: string

The unique identifier of the image file persisted in storage.

data?: string | null

The base64 encoded image data.

detail?: string | null

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

media_type?: string | null

The media type for the image.

type?: "letta"

The source type for the image.

Accepts one of the following:
"letta"
type?: "image"

The type of the message.

Accepts one of the following:
"image"
ToolCallContent { id, input, name, 2 more }
id: string

A unique identifier for this specific tool call instance.

input: Record<string, unknown>

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

name: string

The name of the tool being called.

signature?: string | null

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

type?: "tool_call"

Indicates this content represents a tool call event.

Accepts one of the following:
"tool_call"
ToolReturnContent { 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?: "tool_return"

Indicates this content represents a tool return event.

Accepts one of the following:
"tool_return"
ReasoningContent { 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?: string | null

A unique identifier for this reasoning step.

type?: "reasoning"

Indicates this is a reasoning/intermediate step.

Accepts one of the following:
"reasoning"
RedactedReasoningContent { data, type }

Sent via the Anthropic Messages API

data: string

The redacted or filtered intermediate reasoning content.

type?: "redacted_reasoning"

Indicates this is a redacted thinking step.

Accepts one of the following:
"redacted_reasoning"
OmittedReasoningContent { 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?: string | null

A unique identifier for this reasoning step.

type?: "omitted_reasoning"

Indicates this is an omitted reasoning step.

Accepts one of the following:
"omitted_reasoning"
SummarizedReasoningContent { 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<Summary>

Summaries of the reasoning content.

index: number

The index of the summary part.

text: string

The text of the summary part.

encrypted_content?: string

The encrypted reasoning content.

type?: "summarized_reasoning"

Indicates this is a summarized reasoning step.

Accepts one of the following:
"summarized_reasoning"
created_at?: string

The timestamp when the object was created.

formatdate-time
created_by_id?: string | null

The id of the user that made this object.

denial_reason?: string | null

The reason the tool call request was denied.

group_id?: string | null

The multi-agent group that the message was sent in

is_err?: boolean | null

Whether this message is part of an error step. Used only for debugging purposes.

last_updated_by_id?: string | null

The id of the user that made this object.

model?: string | null

The model used to make the function call.

name?: string | null

For role user/assistant: the (optional) name of the participant. For role tool/function: the name of the function called.

otid?: string | null

The offline threading id associated with this message

run_id?: string | null

The id of the run that this message was created in.

sender_id?: string | null

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

step_id?: string | null

The id of the step that this message was created in.

tool_call_id?: string | null

The ID of the tool call. Only applicable for role tool.

tool_calls?: Array<ToolCall> | null

The list of tool calls requested. Only applicable for role assistant.

id: string
function: Function { arguments, name }
arguments: string
name: string
type: "function"
Accepts one of the following:
"function"
tool_returns?: Array<ToolReturn> | null

Tool execution return information for prior tool calls

status: "success" | "error"

The status of the tool call

Accepts one of the following:
"success"
"error"
func_response?: string | null

The function response string

stderr?: Array<string> | null

Captured stderr from the tool invocation

stdout?: Array<string> | null

Captured stdout (e.g. prints, logs) from the tool invocation

tool_call_id?: unknown

The ID for the tool call

updated_at?: string | null

The timestamp when the object was last updated.

formatdate-time
rrf_score: number

Reciprocal Rank Fusion combined score

fts_rank?: number | null

Full-text search rank position if FTS was used

vector_rank?: number | null

Vector search rank position if vector search was used