Quality Review

Multi-agent quality control on top of any extraction primitive: two labelers (different providers) extract independently, a reviewer identifies disagreement, an adjudicator resolves it against the original input. Use this when label quality matters more than throughput.

Files

• basic.py — labeler → reviewer → adjudicator expressed as an agno Workflow: the two labelers run inside Parallel(...), the reviewer diffs their outputs field by field, and a Condition step runs the adjudicator only when the reviewer flags disagreement. Applied to text extraction (the _03_text_extraction/basic.py Contact schema); the same shape composes on top of any image / audio / document extraction cookbook in this directory.

The demo runs two inputs — a clean one where the labelers agree and the adjudication step is skipped, and one with deliberately conflicting details where the adjudicator resolves the disagreement — and prints the full step trail for both.

When to use

• High-value labels where a wrong answer is expensive.
• Building eval / training sets where disagreement is itself signal.
• Regulated workloads where you need an auditable resolution trail.

If you only need single-pass extraction, use the relevant *_extraction/ cookbook. If you need provider ensembling for evals rather than labels, see _17_llm_as_judge/.

Composition

The workflow is Parallel(labeler_a, labeler_b) → reviewer → Condition(adjudicator), with every run persisted to SQLite (tmp/labeling.db) for traceability. Swap the labeler agents and the schema to apply the same quality gate to any other primitive in this directory.

Run

python cookbook/data_labeling/_18_quality_review/basic.py

Requires GOOGLE_API_KEY and ANTHROPIC_API_KEY (the two labelers run on different providers for ensemble diversity).