Core Concepts¶

This page covers the small set of concepts you need to integrate Tuor cleanly: traces, review outcomes, payload shapes, and how reviewed data gets back to your system.

Trace¶

A trace is one ordered model trajectory that can be reviewed by a human. It has three payloads you control:

Field	Required	Purpose
`spans`	yes	What happened in order: instructions, user and assistant messages, reasoning, tool requests and responses, and unknown provider data.
`model_output`	yes	What your model produced.
`trace_config`	no	Metadata for traceback: model name, prompt version, run ID, customer/account ID, experiment ID.

Tuor adds review fields:

Field	Meaning
`status`	`pending`, `approved`, `rejected`, or `corrected`.
`corrected_output`	Human-edited output, present after a correction.
`final_output`	The output to trust downstream. It equals `model_output` when approved, `corrected_output` when corrected, and `null` when pending or rejected.
`tags`	Optional labels for routing, filtering, and export cohorts.

Each JSON payload field is capped at 5 MB and 10 nesting levels.

Review outcomes¶

Every trace starts as pending. Reviewers then choose one of three outcomes:

Outcome	Meaning	`final_output`
`approved`	The model output is correct as-is.	`model_output`
`corrected`	A reviewer fixed the output.	`corrected_output`
`rejected`	The output is not usable.	`null`

For most integrations, this is the state machine that matters: send traces while they are pending, then consume approved and corrected traces through webhooks or export.

Span taxonomy¶

spans is a flat array. Its order is the trace order; Tuor does not sort, group, or infer model-call boundaries. Every returned span receives a Tuor-generated id.

Span	Purpose
`message`	An `instruction`, `user`, or `assistant` message containing ordered content blocks.
`reasoning`	Reviewable reasoning text.
`tool_call`	A tool `request` or `response`; an optional `tool_call_id` connects the pair.
`unknown`	Lossless raw JSON for unsupported or malformed span data.

Messages support text, image, document, and unknown blocks. Media blocks describe their base64 source. A document looks like:

{
  "type": "message",
  "role": "user",
  "content": [
    {"type": "text", "value": "Extract the invoice total."},
    {
      "type": "document",
      "source": {
        "type": "base64",
        "media_type": "application/pdf",
        "filename": "invoice.pdf",
        "value": "JVBERi0xLjQK..."
      }
    }
  ]
}

Unknown and malformed values are preserved as unknown at the smallest valid boundary. Tool requests and responses may be incomplete or orphaned; Tuor renders the supplied trajectory without inventing status.

Tuor calls these ordered units spans, but they are not full OpenTelemetry spans and are not OTLP-compatible.

Output shapes¶

Outputs

model_output is structured JSON — the reviewer edits nested object leaves as dot-path form rows. Values can be strings, numbers, booleans, arrays, or nested objects. Each form row gets a sensible control inferred from the value's type, but you can pin a path to a specific typed control by describing its dot path in the trace's output_schema. Three field types are available:

`type`	Reviewer control	Extra keys
`enum`	Fixed-choice label picker, pre-selected to the model's value.	`options`, `multi`, `open`
`boolean`	A true/false toggle.	none
`json`	The value's whole subtree stays one editable JSON block instead of expanding into child rows.	none

`model_output`	`output_schema` (optional)
`{"invoice": {"vendor": "Acme", "total": 1250.45}}`	omitted — controls inferred per field
`{"review": {"sentiment": "positive", "confidence": 0.97}}`	`{"review.sentiment": {"type": "enum", "options": ["positive", "neutral", "negative"]}}`
`{"is_compliant": true, "raw": {"a": 1}}`	`{"is_compliant": {"type": "boolean"}, "raw": {"type": "json"}}`

Paths absent from output_schema are unconstrained JSON and fall back to inferred controls. For enum fields, add "multi": true for multi-label fields and "open": false to reject write-in values at ingest and correction time; boolean rejects non-boolean values, and json accepts any JSON. See Reviewer Blueprints for practical recipes.

Getting results back¶

There are three common patterns:

Pattern	Best for
`trace_config` IDs	Tying every Tuor trace back to your own run, user, ticket, or database row.
Webhooks	Reacting as soon as a reviewer approves, rejects, or corrects a trace.
JSONL export	Batch evals, dashboards, analytics, training-data jobs, or prompt-improvement workflows.

Use trace_config aggressively. For example, include internal_run_id, prompt_version_id, and customer_id when you create a trace. Tuor echoes those values in exports and on the trace object returned by GET /v1/traces/{trace_id}, so most downstream systems do not need a separate mapping table.