# Messages ## List Conversation Messages `conversations.messages.list(strconversation_id, MessageListParams**kwargs) -> SyncArrayPage[Message]` **get** `/v1/conversations/{conversation_id}/messages` List all messages in a conversation. Returns LettaMessage objects (UserMessage, AssistantMessage, etc.) for all messages in the conversation, with support for cursor-based pagination. **Agent-direct mode**: Pass conversation_id="default" with agent_id parameter to list messages from the agent's default conversation. **Deprecated**: Passing an agent ID as conversation_id still works but will be removed. ### Parameters - `conversation_id: str` The conversation identifier. Can be a conversation ID ('conv-'), 'default' for agent-direct mode (with agent_id parameter), or an agent ID ('agent-') for backwards compatibility (deprecated). - `after: Optional[str]` Cursor for pagination (message ID). Returns results relative to this ID in the specified sort order. Expected format: 'message-' - `agent_id: Optional[str]` Agent ID for agent-direct mode with 'default' conversation - `before: Optional[str]` Cursor for pagination (message ID). Returns results relative to this ID in the specified sort order. Expected format: 'message-' - `group_id: Optional[str]` Group ID to filter messages by. - `include_err: Optional[bool]` Whether to include error messages and error statuses. For debugging purposes only. - `include_return_message_types: Optional[List[MessageType]]` Message types to include in response. When null, all message types are returned. - `"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"` - `limit: Optional[int]` Maximum number of messages to return - `order: Optional[Literal["asc", "desc"]]` Sort order for messages by creation time. 'asc' for oldest first, 'desc' for newest first - `"asc"` - `"desc"` - `order_by: Optional[Literal["created_at"]]` Field to sort by - `"created_at"` ### Returns - `Message` A message generated by the system. Never streamed back on a response, only used for cursor pagination. Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message content (str): The message content sent by the system - `class SystemMessage: …` A message generated by the system. Never streamed back on a response, only used for cursor pagination. Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message content (str): The message content sent by the system - `id: str` - `content: str` The message content sent by the system - `date: datetime` - `is_err: Optional[bool]` - `message_type: Optional[Literal["system_message"]]` The type of the message. - `"system_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` - `class UserMessage: …` A message sent by the user. Never streamed back on a response, only used for cursor pagination. Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message content (Union[str, List[LettaUserMessageContentUnion]]): The message content sent by the user (can be a string or an array of multi-modal content parts) - `id: str` - `content: Union[List[LettaUserMessageContentUnion], str]` The message content sent by the user (can be a string or an array of multi-modal content parts) - `List[LettaUserMessageContentUnion]` - `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. - `"text"` - `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. - `"url"` - `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. - `"base64"` - `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. - `"letta"` - `type: Optional[Literal["image"]]` The type of the message. - `"image"` - `str` - `date: datetime` - `is_err: Optional[bool]` - `message_type: Optional[Literal["user_message"]]` The type of the message. - `"user_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` - `class ReasoningMessage: …` Representation of an agent's internal reasoning. Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message source (Literal["reasoner_model", "non_reasoner_model"]): Whether the reasoning content was generated natively by a reasoner model or derived via prompting reasoning (str): The internal reasoning of the agent signature (Optional[str]): The model-generated signature of the reasoning step - `id: str` - `date: datetime` - `reasoning: str` - `is_err: Optional[bool]` - `message_type: Optional[Literal["reasoning_message"]]` The type of the message. - `"reasoning_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `signature: Optional[str]` - `source: Optional[Literal["reasoner_model", "non_reasoner_model"]]` - `"reasoner_model"` - `"non_reasoner_model"` - `step_id: Optional[str]` - `class HiddenReasoningMessage: …` Representation of an agent's internal reasoning where reasoning content has been hidden from the response. Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message state (Literal["redacted", "omitted"]): Whether the reasoning content was redacted by the provider or simply omitted by the API hidden_reasoning (Optional[str]): The internal reasoning of the agent - `id: str` - `date: datetime` - `state: Literal["redacted", "omitted"]` - `"redacted"` - `"omitted"` - `hidden_reasoning: Optional[str]` - `is_err: Optional[bool]` - `message_type: Optional[Literal["hidden_reasoning_message"]]` The type of the message. - `"hidden_reasoning_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` - `class ToolCallMessage: …` A message representing a request to call a tool (generated by the LLM to trigger tool execution). Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message tool_call (Union[ToolCall, ToolCallDelta]): The tool call - `id: str` - `date: datetime` - `tool_call: ToolCall` - `class ToolCall: …` - `arguments: str` - `name: str` - `tool_call_id: str` - `class ToolCallDelta: …` - `arguments: Optional[str]` - `name: Optional[str]` - `tool_call_id: Optional[str]` - `is_err: Optional[bool]` - `message_type: Optional[Literal["tool_call_message"]]` The type of the message. - `"tool_call_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` - `tool_calls: Optional[ToolCalls]` - `List[ToolCall]` - `arguments: str` - `name: str` - `tool_call_id: str` - `class ToolCallDelta: …` - `class ToolReturnMessage: …` A message representing the return value of a tool call (generated by Letta executing the requested tool). Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message tool_return (str): The return value of the tool (deprecated, use tool_returns) status (Literal["success", "error"]): The status of the tool call (deprecated, use tool_returns) tool_call_id (str): A unique identifier for the tool call that generated this message (deprecated, use tool_returns) stdout (Optional[List(str)]): Captured stdout (e.g. prints, logs) from the tool invocation (deprecated, use tool_returns) stderr (Optional[List(str)]): Captured stderr from the tool invocation (deprecated, use tool_returns) tool_returns (Optional[List[ToolReturn]]): List of tool returns for multi-tool support - `id: str` - `date: datetime` - `status: Literal["success", "error"]` - `"success"` - `"error"` - `tool_call_id: str` - `tool_return: str` - `is_err: Optional[bool]` - `message_type: Optional[Literal["tool_return_message"]]` The type of the message. - `"tool_return_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `stderr: Optional[List[str]]` - `stdout: Optional[List[str]]` - `step_id: Optional[str]` - `tool_returns: Optional[List[ToolReturn]]` - `status: Literal["success", "error"]` - `"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) - `List[ToolReturnUnionMember0]` - `class TextContent: …` - `class ImageContent: …` - `str` - `stderr: Optional[List[str]]` - `stdout: Optional[List[str]]` - `type: Optional[Literal["tool"]]` The message type to be created. - `"tool"` - `class AssistantMessage: …` A message sent by the LLM in response to user input. Used in the LLM context. Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message content (Union[str, List[LettaAssistantMessageContentUnion]]): The message content sent by the agent (can be a string or an array of content parts) - `id: str` - `content: Union[List[LettaAssistantMessageContentUnion], str]` The message content sent by the agent (can be a string or an array of content parts) - `List[LettaAssistantMessageContentUnion]` - `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. - `"text"` - `str` - `date: datetime` - `is_err: Optional[bool]` - `message_type: Optional[Literal["assistant_message"]]` The type of the message. - `"assistant_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` - `class ApprovalRequestMessage: …` A message representing a request for approval to call a tool (generated by the LLM to trigger tool execution). Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message tool_call (ToolCall): The tool call - `id: str` - `date: datetime` - `tool_call: ToolCall` The tool call that has been requested by the llm to run - `class ToolCall: …` - `class ToolCallDelta: …` - `is_err: Optional[bool]` - `message_type: Optional[Literal["approval_request_message"]]` The type of the message. - `"approval_request_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` - `tool_calls: Optional[ToolCalls]` The tool calls that have been requested by the llm to run, which are pending approval - `List[ToolCall]` - `arguments: str` - `name: str` - `tool_call_id: str` - `class ToolCallDelta: …` - `class ApprovalResponseMessage: …` A message representing a response form the user indicating whether a tool has been approved to run. Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message approve: (bool) Whether the tool has been approved approval_request_id: The ID of the approval request reason: (Optional[str]) An optional explanation for the provided approval status - `id: str` - `date: datetime` - `approval_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. - `"approval"` - `class ToolReturn: …` - `status: Literal["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) - `stderr: Optional[List[str]]` - `stdout: Optional[List[str]]` - `type: Optional[Literal["tool"]]` The message type to be created. - `approve: Optional[bool]` Whether the tool has been approved - `is_err: Optional[bool]` - `message_type: Optional[Literal["approval_response_message"]]` The type of the message. - `"approval_response_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `reason: Optional[str]` An optional explanation for the provided approval status - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` - `class SummaryMessage: …` A message representing a summary of the conversation. Sent to the LLM as a user or system message depending on the provider. - `id: str` - `date: datetime` - `summary: str` - `compaction_stats: Optional[CompactionStats]` Statistics about a memory compaction operation. - `context_window: int` The model's context window size - `messages_count_after: int` Number of messages after compaction - `messages_count_before: int` Number of messages before compaction - `trigger: str` What triggered the compaction (e.g., 'context_window_exceeded', 'post_step_context_check') - `context_tokens_after: Optional[int]` Token count after compaction (message tokens only, does not include tool definitions) - `context_tokens_before: Optional[int]` Token count before compaction (from LLM usage stats, includes full context sent to LLM) - `is_err: Optional[bool]` - `message_type: Optional[Literal["summary_message"]]` - `"summary_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` - `class EventMessage: …` A message for notifying the developer that an event that has occured (e.g. a compaction). Events are NOT part of the context window. - `id: str` - `date: datetime` - `event_data: Dict[str, object]` - `event_type: Literal["compaction"]` - `"compaction"` - `is_err: Optional[bool]` - `message_type: Optional[Literal["event_message"]]` - `"event_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` ### Example ```python 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 ) page = client.conversations.messages.list( conversation_id="default", ) page = page.items[0] print(page) ``` #### Response ```json [ { "id": "id", "content": "content", "date": "2019-12-27T18:11:19.117Z", "is_err": true, "message_type": "system_message", "name": "name", "otid": "otid", "run_id": "run_id", "sender_id": "sender_id", "seq_id": 0, "step_id": "step_id" } ] ``` ## Send Conversation Message `conversations.messages.create(strconversation_id, MessageCreateParams**kwargs) -> LettaResponse` **post** `/v1/conversations/{conversation_id}/messages` Send a message to a conversation and get a response. This endpoint sends a message to an existing conversation. By default (streaming=true), returns a streaming response (Server-Sent Events). Set streaming=false to get a complete JSON response. **Agent-direct mode**: Pass conversation_id="default" with agent_id in request body to send messages to the agent's default conversation with locking. **Deprecated**: Passing an agent ID as conversation_id still works but will be removed. ### Parameters - `conversation_id: str` The conversation identifier. Can be a conversation ID ('conv-'), 'default' for agent-direct mode (with agent_id parameter), or an agent ID ('agent-') for backwards compatibility (deprecated). - `agent_id: Optional[str]` Agent ID for agent-direct mode with 'default' conversation. Use with conversation_id='default' in the URL path. - `assistant_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. - `assistant_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. - `background: Optional[bool]` Whether to process the request in the background (only used when streaming=true). - `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. - `description: str` Description of what the skill does - `location: str` Path or location hint for the skill (e.g. skills/my-skill/SKILL.md) - `name: str` The name of the skill - `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 - `enable_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_pings: Optional[bool]` Whether to include periodic keepalive ping messages in the stream to prevent connection timeouts (only used when streaming=true). - `include_return_message_types: Optional[List[MessageType]]` Only return specified message types in the response. If `None` (default) returns all messages. - `"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}]. - `str` - `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. - `"text"` - `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. - `"url"` - `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. - `"base64"` - `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. - `"letta"` - `type: Optional[Literal["image"]]` The type of the message. - `"image"` - `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. - `"tool_call"` - `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. - `"tool_return"` - `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. - `"reasoning"` - `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. - `"redacted_reasoning"` - `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. - `"omitted_reasoning"` - `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. - `"summarized_reasoning"` - `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 - `content: Union[List[LettaMessageContentUnion], str]` The content of the message. - `List[LettaMessageContentUnion]` - `class TextContent: …` - `class ImageContent: …` - `class ToolCallContent: …` - `class ToolReturnContent: …` - `class ReasoningContent: …` Sent via the Anthropic Messages API - `class RedactedReasoningContent: …` Sent via the Anthropic Messages API - `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) - `str` - `role: Literal["user", "system", "assistant"]` The role of the participant. - `"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 (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `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. - `"message"` - `class ApprovalCreate: …` Input to approve or deny a tool call request - `approval_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. - `"approval"` - `class ToolReturn: …` - `status: Literal["success", "error"]` - `"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) - `List[ToolReturnUnionMember0]` - `class TextContent: …` - `class ImageContent: …` - `str` - `stderr: Optional[List[str]]` - `stdout: Optional[List[str]]` - `type: Optional[Literal["tool"]]` The message type to be created. - `"tool"` - `approve: Optional[bool]` Whether the tool has been approved - `group_id: Optional[str]` The multi-agent group that the message was sent in - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `reason: Optional[str]` An optional explanation for the provided approval status - `type: Optional[Literal["approval"]]` The message type to be created. - `"approval"` - `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. - `tool_returns: Iterable[ToolReturnParam]` List of tool returns from client-side execution - `status: Literal["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) - `stderr: Optional[List[str]]` - `stdout: Optional[List[str]]` - `type: Optional[Literal["tool"]]` The message type to be created. - `group_id: Optional[str]` The multi-agent group that the message was sent in - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `type: Optional[Literal["tool_return"]]` The message type to be created. - `"tool_return"` - `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. - `return_logprobs: Optional[bool]` If True, returns log probabilities of the output tokens in the response. Useful for RL training. Only supported for OpenAI-compatible providers (including SGLang). - `return_token_ids: Optional[bool]` 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. - `stream_tokens: Optional[bool]` Flag to determine if individual tokens should be streamed, rather than streaming per step (only used when streaming=true). - `streaming: Optional[bool]` If True (default), returns a streaming response (Server-Sent Events). If False, returns a complete JSON response. - `top_logprobs: Optional[int]` Number of most likely tokens to return at each position (0-20). Requires return_logprobs=True. - `use_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. ### Returns - `class LettaResponse: …` Response object from an agent interaction, consisting of the new messages generated by the agent and usage statistics. The type of the returned messages can be either `Message` or `LettaMessage`, depending on what was specified in the request. Attributes: messages (List[Union[Message, LettaMessage]]): The messages returned by the agent. usage (LettaUsageStatistics): The usage statistics - `messages: List[Message]` The messages returned by the agent. - `class SystemMessage: …` A message generated by the system. Never streamed back on a response, only used for cursor pagination. Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message content (str): The message content sent by the system - `id: str` - `content: str` The message content sent by the system - `date: datetime` - `is_err: Optional[bool]` - `message_type: Optional[Literal["system_message"]]` The type of the message. - `"system_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` - `class UserMessage: …` A message sent by the user. Never streamed back on a response, only used for cursor pagination. Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message content (Union[str, List[LettaUserMessageContentUnion]]): The message content sent by the user (can be a string or an array of multi-modal content parts) - `id: str` - `content: Union[List[LettaUserMessageContentUnion], str]` The message content sent by the user (can be a string or an array of multi-modal content parts) - `List[LettaUserMessageContentUnion]` - `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. - `"text"` - `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. - `"url"` - `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. - `"base64"` - `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. - `"letta"` - `type: Optional[Literal["image"]]` The type of the message. - `"image"` - `str` - `date: datetime` - `is_err: Optional[bool]` - `message_type: Optional[Literal["user_message"]]` The type of the message. - `"user_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` - `class ReasoningMessage: …` Representation of an agent's internal reasoning. Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message source (Literal["reasoner_model", "non_reasoner_model"]): Whether the reasoning content was generated natively by a reasoner model or derived via prompting reasoning (str): The internal reasoning of the agent signature (Optional[str]): The model-generated signature of the reasoning step - `id: str` - `date: datetime` - `reasoning: str` - `is_err: Optional[bool]` - `message_type: Optional[Literal["reasoning_message"]]` The type of the message. - `"reasoning_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `signature: Optional[str]` - `source: Optional[Literal["reasoner_model", "non_reasoner_model"]]` - `"reasoner_model"` - `"non_reasoner_model"` - `step_id: Optional[str]` - `class HiddenReasoningMessage: …` Representation of an agent's internal reasoning where reasoning content has been hidden from the response. Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message state (Literal["redacted", "omitted"]): Whether the reasoning content was redacted by the provider or simply omitted by the API hidden_reasoning (Optional[str]): The internal reasoning of the agent - `id: str` - `date: datetime` - `state: Literal["redacted", "omitted"]` - `"redacted"` - `"omitted"` - `hidden_reasoning: Optional[str]` - `is_err: Optional[bool]` - `message_type: Optional[Literal["hidden_reasoning_message"]]` The type of the message. - `"hidden_reasoning_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` - `class ToolCallMessage: …` A message representing a request to call a tool (generated by the LLM to trigger tool execution). Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message tool_call (Union[ToolCall, ToolCallDelta]): The tool call - `id: str` - `date: datetime` - `tool_call: ToolCall` - `class ToolCall: …` - `arguments: str` - `name: str` - `tool_call_id: str` - `class ToolCallDelta: …` - `arguments: Optional[str]` - `name: Optional[str]` - `tool_call_id: Optional[str]` - `is_err: Optional[bool]` - `message_type: Optional[Literal["tool_call_message"]]` The type of the message. - `"tool_call_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` - `tool_calls: Optional[ToolCalls]` - `List[ToolCall]` - `arguments: str` - `name: str` - `tool_call_id: str` - `class ToolCallDelta: …` - `class ToolReturnMessage: …` A message representing the return value of a tool call (generated by Letta executing the requested tool). Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message tool_return (str): The return value of the tool (deprecated, use tool_returns) status (Literal["success", "error"]): The status of the tool call (deprecated, use tool_returns) tool_call_id (str): A unique identifier for the tool call that generated this message (deprecated, use tool_returns) stdout (Optional[List(str)]): Captured stdout (e.g. prints, logs) from the tool invocation (deprecated, use tool_returns) stderr (Optional[List(str)]): Captured stderr from the tool invocation (deprecated, use tool_returns) tool_returns (Optional[List[ToolReturn]]): List of tool returns for multi-tool support - `id: str` - `date: datetime` - `status: Literal["success", "error"]` - `"success"` - `"error"` - `tool_call_id: str` - `tool_return: str` - `is_err: Optional[bool]` - `message_type: Optional[Literal["tool_return_message"]]` The type of the message. - `"tool_return_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `stderr: Optional[List[str]]` - `stdout: Optional[List[str]]` - `step_id: Optional[str]` - `tool_returns: Optional[List[ToolReturn]]` - `status: Literal["success", "error"]` - `"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) - `List[ToolReturnUnionMember0]` - `class TextContent: …` - `class ImageContent: …` - `str` - `stderr: Optional[List[str]]` - `stdout: Optional[List[str]]` - `type: Optional[Literal["tool"]]` The message type to be created. - `"tool"` - `class AssistantMessage: …` A message sent by the LLM in response to user input. Used in the LLM context. Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message content (Union[str, List[LettaAssistantMessageContentUnion]]): The message content sent by the agent (can be a string or an array of content parts) - `id: str` - `content: Union[List[LettaAssistantMessageContentUnion], str]` The message content sent by the agent (can be a string or an array of content parts) - `List[LettaAssistantMessageContentUnion]` - `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. - `"text"` - `str` - `date: datetime` - `is_err: Optional[bool]` - `message_type: Optional[Literal["assistant_message"]]` The type of the message. - `"assistant_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` - `class ApprovalRequestMessage: …` A message representing a request for approval to call a tool (generated by the LLM to trigger tool execution). Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message tool_call (ToolCall): The tool call - `id: str` - `date: datetime` - `tool_call: ToolCall` The tool call that has been requested by the llm to run - `class ToolCall: …` - `class ToolCallDelta: …` - `is_err: Optional[bool]` - `message_type: Optional[Literal["approval_request_message"]]` The type of the message. - `"approval_request_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` - `tool_calls: Optional[ToolCalls]` The tool calls that have been requested by the llm to run, which are pending approval - `List[ToolCall]` - `arguments: str` - `name: str` - `tool_call_id: str` - `class ToolCallDelta: …` - `class ApprovalResponseMessage: …` A message representing a response form the user indicating whether a tool has been approved to run. Args: id (str): The ID of the message date (datetime): The date the message was created in ISO format name (Optional[str]): The name of the sender of the message approve: (bool) Whether the tool has been approved approval_request_id: The ID of the approval request reason: (Optional[str]) An optional explanation for the provided approval status - `id: str` - `date: datetime` - `approval_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. - `"approval"` - `class ToolReturn: …` - `status: Literal["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) - `stderr: Optional[List[str]]` - `stdout: Optional[List[str]]` - `type: Optional[Literal["tool"]]` The message type to be created. - `approve: Optional[bool]` Whether the tool has been approved - `is_err: Optional[bool]` - `message_type: Optional[Literal["approval_response_message"]]` The type of the message. - `"approval_response_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `reason: Optional[str]` An optional explanation for the provided approval status - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` - `class SummaryMessage: …` A message representing a summary of the conversation. Sent to the LLM as a user or system message depending on the provider. - `id: str` - `date: datetime` - `summary: str` - `compaction_stats: Optional[CompactionStats]` Statistics about a memory compaction operation. - `context_window: int` The model's context window size - `messages_count_after: int` Number of messages after compaction - `messages_count_before: int` Number of messages before compaction - `trigger: str` What triggered the compaction (e.g., 'context_window_exceeded', 'post_step_context_check') - `context_tokens_after: Optional[int]` Token count after compaction (message tokens only, does not include tool definitions) - `context_tokens_before: Optional[int]` Token count before compaction (from LLM usage stats, includes full context sent to LLM) - `is_err: Optional[bool]` - `message_type: Optional[Literal["summary_message"]]` - `"summary_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` - `class EventMessage: …` A message for notifying the developer that an event that has occured (e.g. a compaction). Events are NOT part of the context window. - `id: str` - `date: datetime` - `event_data: Dict[str, object]` - `event_type: Literal["compaction"]` - `"compaction"` - `is_err: Optional[bool]` - `message_type: Optional[Literal["event_message"]]` - `"event_message"` - `name: Optional[str]` - `otid: Optional[str]` The offline threading id (OTID). Set by the client to deduplicate requests. Used for idempotency in background streaming mode — each message in a request must have a unique OTID. Retries of the same request should reuse the same OTIDs. - `run_id: Optional[str]` - `sender_id: Optional[str]` - `seq_id: Optional[int]` - `step_id: Optional[str]` - `stop_reason: StopReason` The stop reason from Letta indicating why agent loop stopped execution. - `stop_reason: StopReasonType` The reason why execution stopped. - `"end_turn"` - `"error"` - `"llm_api_error"` - `"invalid_llm_response"` - `"invalid_tool_call"` - `"max_steps"` - `"max_tokens_exceeded"` - `"no_tool_call"` - `"tool_rule"` - `"cancelled"` - `"insufficient_credits"` - `"requires_approval"` - `"context_window_overflow_in_system_prompt"` - `message_type: Optional[Literal["stop_reason"]]` The type of the message. - `"stop_reason"` - `usage: Usage` The usage statistics of the agent. - `cache_write_tokens: Optional[int]` The number of input tokens written to cache (Anthropic only). None if not reported by provider. - `cached_input_tokens: Optional[int]` The number of input tokens served from cache. None if not reported by provider. - `completion_tokens: Optional[int]` The number of tokens generated by the agent. - `context_tokens: Optional[int]` Estimate of tokens currently in the context window. - `message_type: Optional[Literal["usage_statistics"]]` - `"usage_statistics"` - `prompt_tokens: Optional[int]` The number of tokens in the prompt. - `reasoning_tokens: Optional[int]` The number of reasoning/thinking tokens generated. None if not reported by provider. - `run_ids: Optional[List[str]]` The background task run IDs associated with the agent interaction - `step_count: Optional[int]` The number of steps taken by the agent. - `total_tokens: Optional[int]` The total number of tokens processed by the agent. - `logprobs: Optional[Logprobs]` Log probabilities of the output tokens from the last LLM call. Only present if return_logprobs was enabled. - `content: Optional[List[LogprobsContent]]` - `token: str` - `logprob: float` - `top_logprobs: List[LogprobsContentTopLogprob]` - `token: str` - `logprob: float` - `bytes: Optional[List[int]]` - `bytes: Optional[List[int]]` - `refusal: Optional[List[LogprobsRefusal]]` - `token: str` - `logprob: float` - `top_logprobs: List[LogprobsRefusalTopLogprob]` - `token: str` - `logprob: float` - `bytes: Optional[List[int]]` - `bytes: Optional[List[int]]` - `turns: Optional[List[Turn]]` Token data for all LLM generations in multi-turn agent interaction. Includes token IDs and logprobs for each assistant turn, plus tool result content. Only present if return_token_ids was enabled. Used for RL training with loss masking. - `role: Literal["assistant", "tool"]` Role of this turn: 'assistant' for LLM generations (trainable), 'tool' for tool results (non-trainable). - `"assistant"` - `"tool"` - `content: Optional[str]` Text content. For tool turns, client tokenizes this with loss_mask=0. - `output_ids: Optional[List[int]]` Token IDs from SGLang native endpoint. Only present for assistant turns. - `output_token_logprobs: Optional[List[List[object]]]` Logprobs from SGLang: [[logprob, token_id, top_logprob_or_null], ...]. Only present for assistant turns. - `tool_name: Optional[str]` Name of the tool called. Only present for tool turns. ### Example ```python 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 ) for message in client.conversations.messages.create( conversation_id="default", ): print(message) ``` #### Response ```json { "messages": [ { "id": "id", "content": "content", "date": "2019-12-27T18:11:19.117Z", "is_err": true, "message_type": "system_message", "name": "name", "otid": "otid", "run_id": "run_id", "sender_id": "sender_id", "seq_id": 0, "step_id": "step_id" } ], "stop_reason": { "stop_reason": "end_turn", "message_type": "stop_reason" }, "usage": { "cache_write_tokens": 0, "cached_input_tokens": 0, "completion_tokens": 0, "context_tokens": 0, "message_type": "usage_statistics", "prompt_tokens": 0, "reasoning_tokens": 0, "run_ids": [ "string" ], "step_count": 0, "total_tokens": 0 }, "logprobs": { "content": [ { "token": "token", "logprob": 0, "top_logprobs": [ { "token": "token", "logprob": 0, "bytes": [ 0 ] } ], "bytes": [ 0 ] } ], "refusal": [ { "token": "token", "logprob": 0, "top_logprobs": [ { "token": "token", "logprob": 0, "bytes": [ 0 ] } ], "bytes": [ 0 ] } ] }, "turns": [ { "role": "assistant", "content": "content", "output_ids": [ 0 ], "output_token_logprobs": [ [ {} ] ], "tool_name": "tool_name" } ] } ``` ## Retrieve Conversation Stream `conversations.messages.stream(strconversation_id, MessageStreamParams**kwargs) -> object` **post** `/v1/conversations/{conversation_id}/stream` Resume the stream for the most recent active run in a conversation. This endpoint allows you to reconnect to an active background stream for a conversation, enabling recovery from network interruptions. **Agent-direct mode**: Pass conversation_id="default" with agent_id in request body to retrieve the stream for the agent's most recent active run. **Direct run access**: Pass run_id directly to skip run lookup entirely. Useful for recovery from duplicate request 409 errors. **OTID lookup**: Pass otid to look up the run_id from Redis. Useful when you have the otid from a 409 error response. **Deprecated**: Passing an agent ID as conversation_id still works but will be removed. ### Parameters - `conversation_id: str` The conversation identifier. Can be a conversation ID ('conv-'), 'default' for agent-direct mode (with agent_id parameter), or an agent ID ('agent-') for backwards compatibility (deprecated). - `agent_id: Optional[str]` Agent ID for agent-direct mode with 'default' conversation. Use with conversation_id='default' in the URL path. - `batch_size: Optional[int]` Number of entries to read per batch. - `include_pings: Optional[bool]` Whether to include periodic keepalive ping messages in the stream to prevent connection timeouts. - `otid: Optional[str]` Offline threading ID to look up the run_id. Bypasses active run lookup if run_id not provided. - `poll_interval: Optional[float]` Seconds to wait between polls when no new data. - `run_id: Optional[str]` Run ID to stream directly, bypassing run lookup. Use for recovery from duplicate requests. - `starting_after: Optional[int]` Sequence id to use as a cursor for pagination. Response will start streaming after this chunk sequence id ### Returns - `object` ### Example ```python 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 ) for message in client.conversations.messages.stream( conversation_id="default", ): print(message) ``` #### Response ```json {} ``` ## Compact Conversation `conversations.messages.compact(strconversation_id, MessageCompactParams**kwargs) -> CompactionResponse` **post** `/v1/conversations/{conversation_id}/compact` Compact (summarize) a conversation's message history. This endpoint summarizes the in-context messages for a specific conversation, reducing the message count while preserving important context. **Agent-direct mode**: Pass conversation_id="default" with agent_id in request body to compact the agent's default conversation messages. **Deprecated**: Passing an agent ID as conversation_id still works but will be removed. ### Parameters - `conversation_id: str` The conversation identifier. Can be a conversation ID ('conv-'), 'default' for agent-direct mode (with agent_id parameter), or an agent ID ('agent-') for backwards compatibility (deprecated). - `agent_id: Optional[str]` Agent ID for agent-direct mode with 'default' conversation. Use with conversation_id='default' in the URL path. - `compaction_settings: Optional[CompactionSettings]` Configuration for conversation compaction / summarization. Per-model settings (temperature, max tokens, etc.) are derived from the default configuration for that handle. - `clip_chars: Optional[int]` The maximum length of the summary in characters. If none, no clipping is performed. - `mode: Optional[Literal["all", "sliding_window", "self_compact_all", "self_compact_sliding_window"]]` The type of summarization technique use. - `"all"` - `"sliding_window"` - `"self_compact_all"` - `"self_compact_sliding_window"` - `model: Optional[str]` Model handle to use for sliding_window/all summarization (format: provider/model-name). If None, uses lightweight provider-specific defaults. - `model_settings: Optional[CompactionSettingsModelSettings]` Optional model settings used to override defaults for the summarizer model. - `class OpenAIModelSettings: …` - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["openai"]]` The type of the provider. - `"openai"` - `reasoning: Optional[Reasoning]` The reasoning configuration for the model. - `reasoning_effort: Optional[Literal["none", "minimal", "low", 3 more]]` The reasoning effort to use when generating text reasoning models - `"none"` - `"minimal"` - `"low"` - `"medium"` - `"high"` - `"xhigh"` - `response_format: Optional[ResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `type: Optional[Literal["text"]]` The type of the response format. - `"text"` - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `json_schema: Dict[str, object]` The JSON schema of the response. - `type: Optional[Literal["json_schema"]]` The type of the response format. - `"json_schema"` - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `type: Optional[Literal["json_object"]]` The type of the response format. - `"json_object"` - `strict: Optional[bool]` Enable strict mode for tool calling. When true, tool outputs are guaranteed to match JSON schemas. - `temperature: Optional[float]` The temperature of the model. - `class CompactionSettingsModelSettingsSgLangModelSettings: …` SGLang model configuration (OpenAI-compatible runtime with SGLang-specific parsing). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["sglang"]]` The type of the provider. - `"sglang"` - `reasoning: Optional[CompactionSettingsModelSettingsSgLangModelSettingsReasoning]` The reasoning configuration for the model. - `reasoning_effort: Optional[Literal["none", "minimal", "low", 3 more]]` The reasoning effort to use when generating text reasoning models - `"none"` - `"minimal"` - `"low"` - `"medium"` - `"high"` - `"xhigh"` - `response_format: Optional[CompactionSettingsModelSettingsSgLangModelSettingsResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `strict: Optional[bool]` Enable strict mode for tool calling. When true, tool outputs are guaranteed to match JSON schemas. - `temperature: Optional[float]` The temperature of the model. - `tool_call_parser: Optional[str]` SGLang tool call parser name (for example 'glm47', 'qwen25', or 'hermes'). - `class AnthropicModelSettings: …` - `effort: Optional[Literal["low", "medium", "high", 2 more]]` Effort level for supported Anthropic models (controls token spending). 'xhigh' and 'max' are available on Opus 4.6+. Not setting this gives similar performance to 'high'. - `"low"` - `"medium"` - `"high"` - `"xhigh"` - `"max"` - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["anthropic"]]` The type of the provider. - `"anthropic"` - `response_format: Optional[ResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `strict: Optional[bool]` Enable strict mode for tool calling. When true, tool outputs are guaranteed to match JSON schemas. - `temperature: Optional[float]` The temperature of the model. - `thinking: Optional[Thinking]` The thinking configuration for the model. - `budget_tokens: Optional[int]` The maximum number of tokens the model can use for extended thinking. - `type: Optional[Literal["enabled", "disabled"]]` The type of thinking to use. - `"enabled"` - `"disabled"` - `verbosity: Optional[Literal["low", "medium", "high"]]` Soft control for how verbose model output should be, used for GPT-5 models. - `"low"` - `"medium"` - `"high"` - `class GoogleAIModelSettings: …` - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["google_ai"]]` The type of the provider. - `"google_ai"` - `response_schema: Optional[ResponseSchema]` The response schema for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `thinking_config: Optional[ThinkingConfig]` The thinking configuration for the model. - `include_thoughts: Optional[bool]` Whether to include thoughts in the model's response. - `thinking_budget: Optional[int]` The thinking budget for the model. - `class GoogleVertexModelSettings: …` - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["google_vertex"]]` The type of the provider. - `"google_vertex"` - `response_schema: Optional[ResponseSchema]` The response schema for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `thinking_config: Optional[ThinkingConfig]` The thinking configuration for the model. - `include_thoughts: Optional[bool]` Whether to include thoughts in the model's response. - `thinking_budget: Optional[int]` The thinking budget for the model. - `class AzureModelSettings: …` Azure OpenAI model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["azure"]]` The type of the provider. - `"azure"` - `response_format: Optional[ResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `class XaiModelSettings: …` xAI model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["xai"]]` The type of the provider. - `"xai"` - `response_format: Optional[ResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `class CompactionSettingsModelSettingsMoonshotModelSettings: …` Moonshot/Kimi model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["moonshot"]]` The type of the provider. - `"moonshot"` - `response_format: Optional[CompactionSettingsModelSettingsMoonshotModelSettingsResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `strict: Optional[bool]` Enable strict mode for tool calling. When true, tool outputs are guaranteed to match JSON schemas. - `temperature: Optional[float]` The temperature of the model. - `class CompactionSettingsModelSettingsZaiModelSettings: …` Z.ai (ZhipuAI) model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["zai"]]` The type of the provider. - `"zai"` - `response_format: Optional[CompactionSettingsModelSettingsZaiModelSettingsResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `thinking: Optional[CompactionSettingsModelSettingsZaiModelSettingsThinking]` The thinking configuration for GLM-4.5+ models. - `clear_thinking: Optional[bool]` If False, preserved thinking is used (recommended for agents). - `type: Optional[Literal["enabled", "disabled"]]` Whether thinking is enabled or disabled. - `"enabled"` - `"disabled"` - `class CompactionSettingsModelSettingsMoonshotCodingModelSettings: …` Kimi Code model configuration (Anthropic-compatible). - `effort: Optional[Literal["low", "medium", "high", 2 more]]` Effort level for supported Anthropic models (controls token spending). 'xhigh' and 'max' are available on Opus 4.6+. Not setting this gives similar performance to 'high'. - `"low"` - `"medium"` - `"high"` - `"xhigh"` - `"max"` - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["moonshot_coding"]]` The type of the provider. - `"moonshot_coding"` - `response_format: Optional[CompactionSettingsModelSettingsMoonshotCodingModelSettingsResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `strict: Optional[bool]` Enable strict mode for tool calling. When true, tool outputs are guaranteed to match JSON schemas. - `temperature: Optional[float]` The temperature of the model. - `thinking: Optional[CompactionSettingsModelSettingsMoonshotCodingModelSettingsThinking]` The thinking configuration for the model. - `budget_tokens: Optional[int]` The maximum number of tokens the model can use for extended thinking. - `type: Optional[Literal["enabled", "disabled"]]` The type of thinking to use. - `"enabled"` - `"disabled"` - `verbosity: Optional[Literal["low", "medium", "high"]]` Soft control for how verbose model output should be, used for GPT-5 models. - `"low"` - `"medium"` - `"high"` - `class GroqModelSettings: …` Groq model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["groq"]]` The type of the provider. - `"groq"` - `response_format: Optional[ResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `class DeepseekModelSettings: …` Deepseek model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["deepseek"]]` The type of the provider. - `"deepseek"` - `response_format: Optional[ResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `class TogetherModelSettings: …` Together AI model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["together"]]` The type of the provider. - `"together"` - `response_format: Optional[ResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `class BedrockModelSettings: …` AWS Bedrock model configuration. - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["bedrock"]]` The type of the provider. - `"bedrock"` - `response_format: Optional[ResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `class CompactionSettingsModelSettingsBasetenModelSettings: …` Baseten model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["baseten"]]` The type of the provider. - `"baseten"` - `temperature: Optional[float]` The temperature of the model. - `class CompactionSettingsModelSettingsOpenRouterModelSettings: …` OpenRouter model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["openrouter"]]` The type of the provider. - `"openrouter"` - `response_format: Optional[CompactionSettingsModelSettingsOpenRouterModelSettingsResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `class CompactionSettingsModelSettingsChatGptoAuthModelSettings: …` ChatGPT OAuth model configuration (uses ChatGPT backend API). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["chatgpt_oauth"]]` The type of the provider. - `"chatgpt_oauth"` - `reasoning: Optional[CompactionSettingsModelSettingsChatGptoAuthModelSettingsReasoning]` The reasoning configuration for the model. - `reasoning_effort: Optional[Literal["none", "low", "medium", 2 more]]` The reasoning effort level for GPT-5.x and o-series models. - `"none"` - `"low"` - `"medium"` - `"high"` - `"xhigh"` - `temperature: Optional[float]` The temperature of the model. - `prompt: Optional[str]` The prompt to use for summarization. If None, uses mode-specific default. - `prompt_acknowledgement: Optional[bool]` Whether to include an acknowledgement post-prompt (helps prevent non-summary outputs). - `sliding_window_percentage: Optional[float]` The percentage of the context window to keep post-summarization (only used in sliding window modes). ### Returns - `class CompactionResponse: …` - `num_messages_after: int` - `num_messages_before: int` - `summary: str` ### Example ```python 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 ) compaction_response = client.conversations.messages.compact( conversation_id="default", ) print(compaction_response.num_messages_after) ``` #### Response ```json { "num_messages_after": 0, "num_messages_before": 0, "summary": "summary" } ``` ## Domain Types ### Compaction Request - `class CompactionRequest: …` - `compaction_settings: Optional[CompactionSettings]` Configuration for conversation compaction / summarization. Per-model settings (temperature, max tokens, etc.) are derived from the default configuration for that handle. - `clip_chars: Optional[int]` The maximum length of the summary in characters. If none, no clipping is performed. - `mode: Optional[Literal["all", "sliding_window", "self_compact_all", "self_compact_sliding_window"]]` The type of summarization technique use. - `"all"` - `"sliding_window"` - `"self_compact_all"` - `"self_compact_sliding_window"` - `model: Optional[str]` Model handle to use for sliding_window/all summarization (format: provider/model-name). If None, uses lightweight provider-specific defaults. - `model_settings: Optional[CompactionSettingsModelSettings]` Optional model settings used to override defaults for the summarizer model. - `class OpenAIModelSettings: …` - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["openai"]]` The type of the provider. - `"openai"` - `reasoning: Optional[Reasoning]` The reasoning configuration for the model. - `reasoning_effort: Optional[Literal["none", "minimal", "low", 3 more]]` The reasoning effort to use when generating text reasoning models - `"none"` - `"minimal"` - `"low"` - `"medium"` - `"high"` - `"xhigh"` - `response_format: Optional[ResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `type: Optional[Literal["text"]]` The type of the response format. - `"text"` - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `json_schema: Dict[str, object]` The JSON schema of the response. - `type: Optional[Literal["json_schema"]]` The type of the response format. - `"json_schema"` - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `type: Optional[Literal["json_object"]]` The type of the response format. - `"json_object"` - `strict: Optional[bool]` Enable strict mode for tool calling. When true, tool outputs are guaranteed to match JSON schemas. - `temperature: Optional[float]` The temperature of the model. - `class CompactionSettingsModelSettingsSgLangModelSettings: …` SGLang model configuration (OpenAI-compatible runtime with SGLang-specific parsing). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["sglang"]]` The type of the provider. - `"sglang"` - `reasoning: Optional[CompactionSettingsModelSettingsSgLangModelSettingsReasoning]` The reasoning configuration for the model. - `reasoning_effort: Optional[Literal["none", "minimal", "low", 3 more]]` The reasoning effort to use when generating text reasoning models - `"none"` - `"minimal"` - `"low"` - `"medium"` - `"high"` - `"xhigh"` - `response_format: Optional[CompactionSettingsModelSettingsSgLangModelSettingsResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `strict: Optional[bool]` Enable strict mode for tool calling. When true, tool outputs are guaranteed to match JSON schemas. - `temperature: Optional[float]` The temperature of the model. - `tool_call_parser: Optional[str]` SGLang tool call parser name (for example 'glm47', 'qwen25', or 'hermes'). - `class AnthropicModelSettings: …` - `effort: Optional[Literal["low", "medium", "high", 2 more]]` Effort level for supported Anthropic models (controls token spending). 'xhigh' and 'max' are available on Opus 4.6+. Not setting this gives similar performance to 'high'. - `"low"` - `"medium"` - `"high"` - `"xhigh"` - `"max"` - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["anthropic"]]` The type of the provider. - `"anthropic"` - `response_format: Optional[ResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `strict: Optional[bool]` Enable strict mode for tool calling. When true, tool outputs are guaranteed to match JSON schemas. - `temperature: Optional[float]` The temperature of the model. - `thinking: Optional[Thinking]` The thinking configuration for the model. - `budget_tokens: Optional[int]` The maximum number of tokens the model can use for extended thinking. - `type: Optional[Literal["enabled", "disabled"]]` The type of thinking to use. - `"enabled"` - `"disabled"` - `verbosity: Optional[Literal["low", "medium", "high"]]` Soft control for how verbose model output should be, used for GPT-5 models. - `"low"` - `"medium"` - `"high"` - `class GoogleAIModelSettings: …` - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["google_ai"]]` The type of the provider. - `"google_ai"` - `response_schema: Optional[ResponseSchema]` The response schema for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `thinking_config: Optional[ThinkingConfig]` The thinking configuration for the model. - `include_thoughts: Optional[bool]` Whether to include thoughts in the model's response. - `thinking_budget: Optional[int]` The thinking budget for the model. - `class GoogleVertexModelSettings: …` - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["google_vertex"]]` The type of the provider. - `"google_vertex"` - `response_schema: Optional[ResponseSchema]` The response schema for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `thinking_config: Optional[ThinkingConfig]` The thinking configuration for the model. - `include_thoughts: Optional[bool]` Whether to include thoughts in the model's response. - `thinking_budget: Optional[int]` The thinking budget for the model. - `class AzureModelSettings: …` Azure OpenAI model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["azure"]]` The type of the provider. - `"azure"` - `response_format: Optional[ResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `class XaiModelSettings: …` xAI model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["xai"]]` The type of the provider. - `"xai"` - `response_format: Optional[ResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `class CompactionSettingsModelSettingsMoonshotModelSettings: …` Moonshot/Kimi model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["moonshot"]]` The type of the provider. - `"moonshot"` - `response_format: Optional[CompactionSettingsModelSettingsMoonshotModelSettingsResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `strict: Optional[bool]` Enable strict mode for tool calling. When true, tool outputs are guaranteed to match JSON schemas. - `temperature: Optional[float]` The temperature of the model. - `class CompactionSettingsModelSettingsZaiModelSettings: …` Z.ai (ZhipuAI) model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["zai"]]` The type of the provider. - `"zai"` - `response_format: Optional[CompactionSettingsModelSettingsZaiModelSettingsResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `thinking: Optional[CompactionSettingsModelSettingsZaiModelSettingsThinking]` The thinking configuration for GLM-4.5+ models. - `clear_thinking: Optional[bool]` If False, preserved thinking is used (recommended for agents). - `type: Optional[Literal["enabled", "disabled"]]` Whether thinking is enabled or disabled. - `"enabled"` - `"disabled"` - `class CompactionSettingsModelSettingsMoonshotCodingModelSettings: …` Kimi Code model configuration (Anthropic-compatible). - `effort: Optional[Literal["low", "medium", "high", 2 more]]` Effort level for supported Anthropic models (controls token spending). 'xhigh' and 'max' are available on Opus 4.6+. Not setting this gives similar performance to 'high'. - `"low"` - `"medium"` - `"high"` - `"xhigh"` - `"max"` - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["moonshot_coding"]]` The type of the provider. - `"moonshot_coding"` - `response_format: Optional[CompactionSettingsModelSettingsMoonshotCodingModelSettingsResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `strict: Optional[bool]` Enable strict mode for tool calling. When true, tool outputs are guaranteed to match JSON schemas. - `temperature: Optional[float]` The temperature of the model. - `thinking: Optional[CompactionSettingsModelSettingsMoonshotCodingModelSettingsThinking]` The thinking configuration for the model. - `budget_tokens: Optional[int]` The maximum number of tokens the model can use for extended thinking. - `type: Optional[Literal["enabled", "disabled"]]` The type of thinking to use. - `"enabled"` - `"disabled"` - `verbosity: Optional[Literal["low", "medium", "high"]]` Soft control for how verbose model output should be, used for GPT-5 models. - `"low"` - `"medium"` - `"high"` - `class GroqModelSettings: …` Groq model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["groq"]]` The type of the provider. - `"groq"` - `response_format: Optional[ResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `class DeepseekModelSettings: …` Deepseek model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["deepseek"]]` The type of the provider. - `"deepseek"` - `response_format: Optional[ResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `class TogetherModelSettings: …` Together AI model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["together"]]` The type of the provider. - `"together"` - `response_format: Optional[ResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `class BedrockModelSettings: …` AWS Bedrock model configuration. - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["bedrock"]]` The type of the provider. - `"bedrock"` - `response_format: Optional[ResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `class CompactionSettingsModelSettingsBasetenModelSettings: …` Baseten model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["baseten"]]` The type of the provider. - `"baseten"` - `temperature: Optional[float]` The temperature of the model. - `class CompactionSettingsModelSettingsOpenRouterModelSettings: …` OpenRouter model configuration (OpenAI-compatible). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["openrouter"]]` The type of the provider. - `"openrouter"` - `response_format: Optional[CompactionSettingsModelSettingsOpenRouterModelSettingsResponseFormat]` The response format for the model. - `class TextResponseFormat: …` Response format for plain text responses. - `class JsonSchemaResponseFormat: …` Response format for JSON schema-based responses. - `class JsonObjectResponseFormat: …` Response format for JSON object responses. - `temperature: Optional[float]` The temperature of the model. - `class CompactionSettingsModelSettingsChatGptoAuthModelSettings: …` ChatGPT OAuth model configuration (uses ChatGPT backend API). - `max_output_tokens: Optional[int]` The maximum number of tokens the model can generate. - `parallel_tool_calls: Optional[bool]` Whether to enable parallel tool calling. - `provider_type: Optional[Literal["chatgpt_oauth"]]` The type of the provider. - `"chatgpt_oauth"` - `reasoning: Optional[CompactionSettingsModelSettingsChatGptoAuthModelSettingsReasoning]` The reasoning configuration for the model. - `reasoning_effort: Optional[Literal["none", "low", "medium", 2 more]]` The reasoning effort level for GPT-5.x and o-series models. - `"none"` - `"low"` - `"medium"` - `"high"` - `"xhigh"` - `temperature: Optional[float]` The temperature of the model. - `prompt: Optional[str]` The prompt to use for summarization. If None, uses mode-specific default. - `prompt_acknowledgement: Optional[bool]` Whether to include an acknowledgement post-prompt (helps prevent non-summary outputs). - `sliding_window_percentage: Optional[float]` The percentage of the context window to keep post-summarization (only used in sliding window modes). ### Compaction Response - `class CompactionResponse: …` - `num_messages_after: int` - `num_messages_before: int` - `summary: str`