## List Steps For Run `runs.steps.list(strrun_id, StepListParams**kwargs) -> SyncArrayPage[Step]` **get** `/v1/runs/{run_id}/steps` Get steps associated with a run with filtering options. ### Parameters - `run_id: str` - `after: Optional[str]` Cursor for pagination (step ID). Returns results relative to this ID in the specified sort order. Expected format: 'step-' - `before: Optional[str]` Cursor for pagination (step ID). Returns results relative to this ID in the specified sort order. Expected format: 'step-' - `limit: Optional[int]` Maximum number of messages to return - `order: Optional[Literal["asc", "desc"]]` Sort order for steps 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 - `class Step: …` - `id: str` The id of the step. Assigned by the database. - `agent_id: Optional[str]` The ID of the agent that performed the step. - `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 during this step. - `completion_tokens_details: Optional[Dict[str, object]]` Detailed completion token breakdown (e.g., reasoning_tokens). - `context_window_limit: Optional[int]` The context window limit configured for this step. - `error_data: Optional[Dict[str, object]]` Error details including message, traceback, and additional context - `error_type: Optional[str]` The type/class of the error that occurred - `feedback: Optional[Literal["positive", "negative"]]` The feedback for this step. Must be either 'positive' or 'negative'. - `"positive"` - `"negative"` - `messages: Optional[List[InternalMessage]]` The messages generated during this step. Deprecated: use `GET /v1/steps/{step_id}/messages` endpoint instead - `id: str` The human-friendly ID of the Message - `role: MessageRole` The role of the participant. - `"assistant"` - `"user"` - `"tool"` - `"function"` - `"system"` - `"approval"` - `"summary"` - `agent_id: Optional[str]` The unique identifier of the agent. - `approval_request_id: Optional[str]` The id of the approval request if this message is associated with a tool call request. - `approvals: Optional[List[Approval]]` The list of approvals for this message. - `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 ApprovalLettaSchemasMessageToolReturnOutput: …` - `status: Literal["success", "error"]` The status of the tool call - `"success"` - `"error"` - `func_response: Optional[Union[str, List[ApprovalLettaSchemasMessageToolReturnOutputFuncResponseUnionMember1], null]]` The function response - either a string or list of content parts (text/image) - `str` - `List[ApprovalLettaSchemasMessageToolReturnOutputFuncResponseUnionMember1]` - `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"` - `stderr: Optional[List[str]]` Captured stderr from the tool invocation - `stdout: Optional[List[str]]` Captured stdout (e.g. prints, logs) from the tool invocation - `tool_call_id: Optional[object]` The ID for the tool call - `approve: Optional[bool]` Whether tool call is approved. - `batch_item_id: Optional[str]` The id of the LLMBatchItem that this message is associated with - `content: Optional[List[Content]]` The content of the message. - `class TextContent: …` - `class ImageContent: …` - `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 ContentSummarizedReasoningContent: …` The style of reasoning content returned by the OpenAI Responses API - `id: str` The unique identifier for this reasoning step. - `summary: List[ContentSummarizedReasoningContentSummary]` 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"` - `conversation_id: Optional[str]` The conversation this message belongs to - `created_at: Optional[datetime]` The timestamp when the object was created. - `created_by_id: Optional[str]` The id of the user that made this object. - `denial_reason: Optional[str]` The reason the tool call request was denied. - `group_id: Optional[str]` The multi-agent group that the message was sent in - `is_err: Optional[bool]` Whether this message is part of an error step. Used only for debugging purposes. - `last_updated_by_id: Optional[str]` The id of the user that made this object. - `model: Optional[str]` The model used to make the function call. - `name: Optional[str]` For role user/assistant: the (optional) name of the participant. For role tool/function: the name of the function called. - `otid: Optional[str]` The offline threading id associated with this message - `run_id: Optional[str]` The id of the run that this message was created in. - `sender_id: Optional[str]` The id of the sender of the message, can be an identity id or agent id - `step_id: Optional[str]` The id of the step that this message was created in. - `tool_call_id: Optional[str]` The ID of the tool call. Only applicable for role tool. - `tool_calls: Optional[List[ToolCall]]` The list of tool calls requested. Only applicable for role assistant. - `id: str` - `function: ToolCallFunction` The function that the model called. - `arguments: str` - `name: str` - `type: Literal["function"]` - `"function"` - `tool_returns: Optional[List[ToolReturn]]` Tool execution return information for prior tool calls - `status: Literal["success", "error"]` The status of the tool call - `"success"` - `"error"` - `func_response: Optional[Union[str, List[ToolReturnFuncResponseUnionMember1], null]]` The function response - either a string or list of content parts (text/image) - `str` - `List[ToolReturnFuncResponseUnionMember1]` - `class TextContent: …` - `class ImageContent: …` - `stderr: Optional[List[str]]` Captured stderr from the tool invocation - `stdout: Optional[List[str]]` Captured stdout (e.g. prints, logs) from the tool invocation - `tool_call_id: Optional[object]` The ID for the tool call - `updated_at: Optional[datetime]` The timestamp when the object was last updated. - `model: Optional[str]` The name of the model used for this step. - `model_endpoint: Optional[str]` The model endpoint url used for this step. - `model_handle: Optional[str]` The model handle (e.g., 'openai/gpt-4o-mini') used for this step. - `origin: Optional[str]` The surface that this agent step was initiated from. - `project_id: Optional[str]` The project that the agent that executed this step belongs to (cloud only). - `prompt_tokens: Optional[int]` The number of tokens in the prompt during this step. - `prompt_tokens_details: Optional[Dict[str, object]]` Detailed prompt token breakdown (e.g., cached_tokens, cache_read_tokens, cache_creation_tokens). - `provider_category: Optional[str]` The category of the provider used for this step. - `provider_id: Optional[str]` The unique identifier of the provider that was configured for this step - `provider_name: Optional[str]` The name of the provider used for this step. - `reasoning_tokens: Optional[int]` The number of reasoning/thinking tokens generated. None if not reported by provider. - `request_id: Optional[str]` The API request log ID from cloud-api for correlating steps with API requests. - `run_id: Optional[str]` The unique identifier of the run that this step belongs to. Only included for async calls. - `status: Optional[Literal["pending", "success", "failed", "cancelled"]]` Status of a step execution - `"pending"` - `"success"` - `"failed"` - `"cancelled"` - `stop_reason: Optional[StopReasonType]` The stop reason associated with the step. - `"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"` - `tags: Optional[List[str]]` Metadata tags. - `tid: Optional[str]` The unique identifier of the transaction that processed this step. - `total_tokens: Optional[int]` The total number of tokens processed by the agent during this step. - `trace_id: Optional[str]` The trace id of the agent step. ### 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.runs.steps.list( run_id="run_id", ) page = page.items[0] print(page.id) ``` #### Response ```json [ { "id": "id", "agent_id": "agent_id", "cache_write_tokens": 0, "cached_input_tokens": 0, "completion_tokens": 0, "completion_tokens_details": { "foo": "bar" }, "context_window_limit": 0, "error_data": { "foo": "bar" }, "error_type": "error_type", "feedback": "positive", "messages": [ { "id": "message-123e4567-e89b-12d3-a456-426614174000", "role": "assistant", "agent_id": "agent_id", "approval_request_id": "approval_request_id", "approvals": [ { "approve": true, "tool_call_id": "tool_call_id", "reason": "reason", "type": "approval" } ], "approve": true, "batch_item_id": "batch_item_id", "content": [ { "text": "text", "signature": "signature", "type": "text" } ], "conversation_id": "conversation_id", "created_at": "2019-12-27T18:11:19.117Z", "created_by_id": "created_by_id", "denial_reason": "denial_reason", "group_id": "group_id", "is_err": true, "last_updated_by_id": "last_updated_by_id", "model": "model", "name": "name", "otid": "otid", "run_id": "run_id", "sender_id": "sender_id", "step_id": "step_id", "tool_call_id": "tool_call_id", "tool_calls": [ { "id": "id", "function": { "arguments": "arguments", "name": "name" }, "type": "function" } ], "tool_returns": [ { "status": "success", "func_response": "string", "stderr": [ "string" ], "stdout": [ "string" ], "tool_call_id": {} } ], "updated_at": "2019-12-27T18:11:19.117Z" } ], "model": "model", "model_endpoint": "model_endpoint", "model_handle": "model_handle", "origin": "origin", "project_id": "project_id", "prompt_tokens": 0, "prompt_tokens_details": { "foo": "bar" }, "provider_category": "provider_category", "provider_id": "provider_id", "provider_name": "provider_name", "reasoning_tokens": 0, "request_id": "request_id", "run_id": "run_id", "status": "pending", "stop_reason": "end_turn", "tags": [ "string" ], "tid": "tid", "total_tokens": 0, "trace_id": "trace_id" } ] ```