API Reference

Session Management

`tj.init()`

Configure the SDK. Call once before other functions. For telemetry-only usage, only project_id and trajectory_api_key are required; for trace import, also pass provider and api_key.

tj.init(
    *,
    project_id: str,
    provider: str | None = None,
    api_key: str | None = None,
    workspace_id: str | None = None,
    destination_id: str | None = None,
    trajectory_api_key: str | None = None,
    transforms: list[BaseTransform] | None = None,
    debug: bool = False,
) -> None

Parameter	Type	Default	Description
`project_id`	`str`	required	Your project ID.
`provider`	`str \| None`	`None`	Trace provider (e.g. `"langsmith"`). Omit for telemetry-only / BYO-data usage.
`api_key`	`str \| None`	`None`	Provider API key. Falls back to `LANGSMITH_API_KEY` / `LANGCHAIN_API_KEY` env vars when `provider` is set.
`workspace_id`	`str \| None`	`None`	LangSmith workspace/tenant ID. Required for bulk export.
`destination_id`	`str \| None`	`None`	Bulk export destination ID. Required for live bulk export.
`trajectory_api_key`	`str \| None`	`None`	API key for `upload`, `upload_trace`, `push_events`. Falls back to `TRAJECTORY_API_KEY` env var.
`transforms`	`list[BaseTransform] \| None`	`None`	Transforms applied after building (e.g. PII redaction).
`debug`	`bool`	`False`	Enable debug logging.

Trace Import

`tj.list_conversations()`

List available conversations from the configured provider.

tj.list_conversations(*, limit: int = 50) -> list[ConversationSummary]

`tj.import_conversations()`

Import conversations and return one Trajectory per conversation.

tj.import_conversations(
    conversations: list[str] | list[ConversationSummary] | None = None,
    *,
    bulk: bool = False,
    runs_query: bool = False,
    source: str | None = None,
    limit: int | None = None,
    since: timedelta | datetime | None = None,
) -> list[Trajectory]

Parameter	Type	Default	Description
`conversations`	`list`	`None`	Conversation IDs or `ConversationSummary` objects.
`bulk`	`bool`	`False`	Use bulk export instead of individual API calls.
`runs_query`	`bool`	`False`	Use the runs-query path (mutually exclusive with `bulk`).
`source`	`str \| None`	`None`	Local parquet file path. When `bulk=True` and `source` is omitted, the SDK triggers a live export.
`limit`	`int \| None`	`None`	Max conversations (bulk mode only).
`since`	`timedelta \| datetime \| None`	`None`	Time window for bulk export.

`tj.transform()`

Transform raw provider data into a Trajectory without fetching. Useful for custom pipelines that already have the source payload.

tj.transform(raw_data: dict, *, provider: str = "langsmith", api_key: str = "", project_id: str = "") -> Trajectory

Building Trajectories

See Bring Your Own Data for the full walkthrough.

`tj.build_trajectory_from_messages()`

Build a Trajectory from a flat list of Message objects. Cumulative steps, tool-call metrics, and telemetry fields (content hash, idempotency key) are computed automatically.

tj.build_trajectory_from_messages(
    messages: list[Message],
    *,
    conversation_id: str,
    data_source: str,
    reward: Reward | None = None,
    task_metadata: dict | None = None,
    error: str | None = None,
    start_time: str | None = None,
    end_time: str | None = None,
    termination_reason: TerminationReason | None = None,
    extra_telemetry: dict | None = None,
    trace_id: str | None = None,
    model_id: str | None = None,
) -> Trajectory

task_metadata recognizes num_turns, total_tokens, total_cost, and completion_tokens; other keys are ignored.

`tj.build_trajectory_from_parsed()`

Build a Trajectory from a ParsedConversation. Provider subclasses delegate to this so the ParsedConversation → Trajectory logic has a single implementation.

tj.build_trajectory_from_parsed(parsed: ParsedConversation) -> Trajectory

`tj.build_reward_from_scalar()`

Build a single-component Reward from a numeric score. scaled_value is normalized to [0, 1] using score_range.

tj.build_reward_from_scalar(
    value: float,
    *,
    name: str = "score",
    score_range: tuple[float, float] = (0.0, 1.0),
    weight: float = 1.0,
) -> Reward

Message Recipes

Adapters from common third-party message formats into Message objects. All recipes fail loud on unexpected input — see Bring Your Own Data for examples.

Function	Source format
`tj.messages_from_openai_chat(raw)`	OpenAI ChatCompletion
`tj.messages_from_anthropic_messages(raw)`	Anthropic Messages API
`tj.messages_from_vercel_ai_sdk(raw)`	Vercel AI SDK (UIMessage / CoreMessage)
`tj.messages_from_prompt_response(prompt, response, *, system=None)`	Flat prompt/response strings
`tj.messages_from_role_content_pairs(pairs)`	`[(role, content), ...]` tuples

Helpers

Function	Purpose
`tj.normalize_role(role_str)`	Canonicalize to `system` / `user` / `assistant` / `tool`. Recognizes aliases (`human`, `ai`, `function`, etc.).
`tj.flatten_text_content(content)`	Collapse multi-modal content (string, list of blocks, or dict) to plain text.
`tj.parse_tool_arguments(raw)`	Normalize tool-call arguments (dict, JSON string, or `None`) into a dict.

Trace Workflow

See Linking Events to Trajectories for the workflow.

`tj.start_trace()`

Create a TraceContext for buffering correlated telemetry and trajectory data.

tj.start_trace(trace_id: str | None = None) -> TraceContext

If trace_id is omitted, the SDK generates a UUID4 hex string.

`TraceContext`

Attribute	Type	Description
`trace_id`	`str`	Correlation key shared by all events and trajectories produced through this context.
`events`	`list[TelemetryEvent]`	Buffered events produced via `.event(...)`.

trace.event(event_type, properties=None, *, user_id=None, trajectory_id=None, source="sdk", metadata=None) -> TelemetryEvent Append a TelemetryEvent to trace.events with trace_id and session_id set to trace.trace_id. trace.build_trajectory(messages, *, data_source, conversation_id=None, ...) -> Trajectory Convenience wrapper around build_trajectory_from_messages that injects trace_id and defaults conversation_id to trace_id. Accepts the same kwargs as build_trajectory_from_messages (minus trace_id).

`tj.upload_trace()`

Upload trajectories, stamp matching events with their returned trajectory_id, then push events — atomically.

tj.upload_trace(
    trajectories: Trajectory | list[Trajectory],
    events: list[TelemetryEvent],
    dataset: str,
    organization_id: str | None = None,
    chunk_size: int = 1000,
    max_retries: int = 3,
) -> dict[str, Any]

Each input trajectory must carry a trace_id. Returns a dict with upload, push, events (as actually sent), and unstamped_events (events pushed without a trajectory_id because no match was found). Raises RuntimeError if any trajectory’s upload failed and there are unstamped events tied to its trace_id — no events are pushed in that case.

Upload & Push

`tj.upload()`

Upload trajectories via the Trajectory API.

tj.upload(
    trajectories: Trajectory | list[Trajectory],
    dataset: str,
) -> dict[str, Any]

Returns a dict:

Key	Type	Description
`uploaded`	`int`	Trajectories accepted by the backend.
`skipped`	`int`	Total skipped (client + backend).
`client_skipped`	`int`	Empty trajectories filtered before upload.
`backend_skipped`	`int`	Trajectories rejected by the backend.
`errors`	`list[str]`	Backend error messages.
`trajectories`	`list[dict]`	Per-trajectory items with `request_index`, `trace_id`, `trajectory_id`, `status`, etc. — used to map `trace_id → trajectory_id`.
`batches`	`int`	Number of HTTP batches.
`elapsed_s`	`float`	Wall-clock upload time.

`tj.push_events()`

Push telemetry events to the Trajectory API. Invalid events are dropped with logged warnings.

tj.push_events(
    events: list[TelemetryEvent],
    *,
    organization_id: str | None = None,
    chunk_size: int = 1000,
    max_retries: int = 3,
) -> PushResult

`tj.save()`

Save trajectories to JSON files on disk. Each file is named {output_dir}/{conversation_id}.json.

tj.save(
    trajectories: Trajectory | list[Trajectory],
    output_dir: str,
) -> None

Model ID Helpers

For chaining a Trajectory-served chat completion back to its trajectory id, the backend returns the model id on the response headers.

Constant / Function	Description
`tj.TRAJECTORY_MODEL_ID_HEADER`	Header name: `"x-trajectory-model-id"`.
`tj.model_id_from_chat_completion_headers(headers)`	Extract the model id from a headers mapping. Returns `None` if absent.
`tj.model_id_from_chat_completion_response(response)`	Extract from a response object with a `.headers` attribute.

Primitives

All primitives are frozen dataclasses unless noted.

Trajectory

Field	Type	Description
`task`	`Task`	Conversation metadata.
`steps`	`list[Step]`	Cumulative decision-point snapshots.
`reward`	`Reward \| None`	Aggregate reward signal.
`metrics`	`TrajectoryMetrics \| None`	Content metrics.
`execution_metrics`	`ExecutionMetrics \| None`	Timing data.
`reference_trajectory`	`dict \| None`	Optional reference for comparison.
`telemetry`	`Telemetry \| None`	Source telemetry metadata.
`idx`	`int \| None`	Index within a batch.
`error`	`str \| None`	Error if the conversation failed.
`trace_id`	`str \| None`	Caller-owned correlation key.
`model_id`	`str \| None`	Trajectory model identifier.

Task

Field	Type	Description
`id`	`str \| None`	Task identifier (`{data_source}:{conversation_id}` when built locally).
`data_source`	`str \| None`	Provider name (e.g. `"langsmith"`) or your own source label.
`conversation_id`	`str \| None`	Unique conversation identifier.
`num_turns`	`int \| None`	Number of user-agent turns.
`num_steps`	`int \| None`	Number of steps.
`total_tokens`	`int \| None`	Total tokens consumed.
`total_cost`	`float \| None`	Estimated cost in USD.

Step

Field	Type	Description
`messages`	`list[Message]`	Cumulative messages up to this point.
`reward`	`Reward \| None`	Per-step reward.
`info`	`dict \| None`	Provider-specific metadata.
`trainable_status`	`TrainableStatus`	Training-suitability flag.

Message

Field	Type	Description
`role`	`Role`	`"system"`, `"user"`, `"assistant"`, or `"tool"`.
`content`	`str \| None`	Text content.
`tool_calls`	`list[ToolCall] \| None`	Tool invocations by the assistant.
`tool_response`	`ToolResponse \| None`	Tool execution result.
`tool_definitions`	`list[ToolDefinition] \| None`	Tools available to the model at this point.
`usage`	`dict \| None`	Token usage stats.
`finish_reason`	`str \| None`	`"stop"`, `"tool_calls"`, etc.
`metadata`	`dict \| None`	Provider-specific message metadata.
`reasoning`	`str \| None`	Chain-of-thought content.
`trainable_status`	`TrainableStatus`	Training-suitability flag.

ToolCall

Field	Type	Description
`name`	`str`	Tool name.
`arguments`	`dict`	Arguments passed to the tool.
`id`	`str \| None`	Identifier matching the `ToolResponse`.

ToolResponse

Field	Type	Description
`id`	`str`	Matches the `ToolCall` id.
`name`	`str`	Tool name.
`arguments`	`dict`	Arguments passed.
`response`	`Any \| None`	Return value.
`error`	`str \| None`	Error if the call failed.
`metadata`	`dict \| None`	Provider-specific metadata.

ToolDefinition

Field	Type	Description
`name`	`str`	Tool name.
`description`	`str`	What the tool does.
`parameters`	`dict`	JSON Schema for tool arguments.

Reward & RewardComponent

Field (Reward)	Type	Description
`aggregated_value`	`float \| None`	Top-level reward score.
`aggregation_method`	`str \| None`	How components were combined.
`components`	`list[RewardComponent] \| None`	Per-rubric / per-judge components.

Field (RewardComponent)	Type	Description
`name`	`str`	Component label.
`value`	`float`	Raw value.
`scaled_value`	`float \| None`	Normalized to `[0, 1]`.
`explanation`	`str \| None`	Free-form notes (e.g. judge rationale).
`weight`	`float`	Weight in the aggregate.
`range`	`tuple[float, float] \| None`	Original score range.
`metadata`	`dict \| None`	Provider-specific context.

TelemetryEvent

See Telemetry Events for the full guide.

Field	Type	Default	Description
`event_type`	`str`	required	Dotted event name.
`session_id`	`str`	required	Session grouping.
`properties`	`dict`	`{}`	Free-form payload.
`event_id`	`str`	new UUID4 hex	Idempotency key.
`timestamp`	`str`	now (UTC ISO)	Event time.
`user_id`	`str \| None`	`None`	End-user identifier.
`trajectory_id`	`str \| None`	`None`	Backend trajectory ID once known.
`trace_id`	`str \| None`	`None`	Caller-owned correlation key.
`source`	`str`	`"sdk"`	Where the event originated.
`metadata`	`dict \| None`	`None`	Additional context.

PushResult

Field	Type	Description
`pushed`	`int`	Events accepted by the backend.
`skipped`	`int`	Events dropped client-side as invalid.
`errors`	`list[str]`	One message per invalid event.

Telemetry

Field	Type	Description
`source`	`str`	Telemetry source identifier.
`data`	`dict`	Raw telemetry data (includes `trace_id`, `content_hash`, `idempotency_key` when set).

TrajectoryMetrics & ExecutionMetrics

Field	Type	Description
`steps`	`int \| None`	Total steps.
`tokens_generated`	`int \| None`	Total completion tokens.
`aggregated_reward`	`float \| None`	Aggregate reward value.
`num_tool_calls`	`int \| None`	Total tool invocations.
`num_tool_failures`	`int \| None`	Failed tool calls.
`num_tool_response_none`	`int \| None`	Tool calls with no response.
`tool_error_rate`	`float \| None`	Failure ratio.
`env_time`	`float \| None`	Time in tool execution (seconds).
`llm_time`	`float \| None`	Time waiting for LLM (seconds).
`total_time`	`float \| None`	Wall-clock time (seconds).
`termination_reason`	`TerminationReason \| None`	Why the conversation ended.

ConversationSummary

Field	Type	Description
`conversation_id`	`str`	Unique conversation identifier.
`num_turns`	`int`	Number of turns.
`first_seen`	`str \| None`	ISO timestamp of earliest trace.
`last_seen`	`str \| None`	ISO timestamp of most recent trace.
`root_run_names`	`list[str]`	Names of top-level runs.

Type Aliases

Role = Literal["system", "user", "assistant", "tool"]

TrainableStatus = Literal[
    "trainable", "not_trainable", "superseded", "summarization_boundary"
]

TerminationReason = Literal[
    "TIMEOUT", "ENV_DONE", "MAX_STEPS", "TRUNCATION", "STALE", "ERROR", "NONE"
]

PiiRule = Literal["EMAIL", "PHONE", "CREDIT_CARD", "SSN"]

Getting Started

SDK

Platform

Session Management

`tj.init()`

Trace Import

`tj.list_conversations()`

`tj.import_conversations()`

`tj.transform()`

Building Trajectories

`tj.build_trajectory_from_messages()`

`tj.build_trajectory_from_parsed()`

`tj.build_reward_from_scalar()`

Message Recipes

Helpers

Trace Workflow

`tj.start_trace()`

`TraceContext`

`tj.upload_trace()`

Upload & Push

`tj.upload()`

`tj.push_events()`

`tj.save()`

Model ID Helpers

Primitives

Trajectory

Task

Step

Message

ToolCall

ToolResponse

ToolDefinition

Reward & RewardComponent

TelemetryEvent

PushResult

Telemetry

TrajectoryMetrics & ExecutionMetrics

ConversationSummary

Type Aliases

​Session Management

​tj.init()

​Trace Import

​tj.list_conversations()

​tj.import_conversations()

​tj.transform()

​Building Trajectories

​tj.build_trajectory_from_messages()

​tj.build_trajectory_from_parsed()

​tj.build_reward_from_scalar()

​Message Recipes

​Helpers

​Trace Workflow

​tj.start_trace()

​TraceContext

​tj.upload_trace()

​Upload & Push

​tj.upload()

​tj.push_events()

​tj.save()

​Model ID Helpers

​Primitives

​Trajectory

​Task

​Step

​Message

​ToolCall

​ToolResponse

​ToolDefinition

​Reward & RewardComponent

​TelemetryEvent

​PushResult

​Telemetry

​TrajectoryMetrics & ExecutionMetrics

​ConversationSummary

​Type Aliases

Session Management

`tj.init()`

Trace Import

`tj.list_conversations()`

`tj.import_conversations()`

`tj.transform()`

Building Trajectories

`tj.build_trajectory_from_messages()`

`tj.build_trajectory_from_parsed()`

`tj.build_reward_from_scalar()`

Message Recipes

Helpers

Trace Workflow

`tj.start_trace()`

`TraceContext`

`tj.upload_trace()`

Upload & Push

`tj.upload()`

`tj.push_events()`

`tj.save()`

Model ID Helpers

Primitives

Trajectory

Task

Step

Message

ToolCall

ToolResponse

ToolDefinition

Reward & RewardComponent

TelemetryEvent

PushResult

Telemetry

TrajectoryMetrics & ExecutionMetrics

ConversationSummary

Type Aliases