Data labeling

Agents for labeling, classification, and synthetic data generation. 28 folders: 75 single-file runnable examples plus the image_search app (81 Python files in all).

Each subfolder holds examples for one theme, containing a basic.py that runs end-to-end, plus variants that add task-meaningful options on top.

Workflows are organized by modality (text, image, audio, video, document) and output shape (classify, extract, rank, span-label). Further patterns (_17_llm_as_judge, _18_quality_review, _19_inter_annotator_agreement) compose on top of any of these, and the synthetic-data workflows (_20-_25) generate and curate training data rather than label existing inputs.

Start with _01_text_classification/basic.py. Every other cookbook mirrors its structure.

Layout

cookbook/data_labeling/
├── README.md
├── <workflow>/
│   ├── README.md
│   ├── basic.py            # smallest readable example
│   ├── <variant>.py        # one file per task-meaningful variant
│   ├── schemas.py          # shared Pydantic types, if any
│   ├── data/               # sample inputs or dataset pointers
│   └── TEST_LOG.md         # run log per the cookbook convention
└── ...

Workflows

Text

• _01_text_classification/: assign one of N labels (sentiment, intent, topic).
• _02_text_multilabel_classification/: assign any subset of N tags, optionally hierarchical.
• _03_text_extraction/: text into a typed Pydantic object (entities, fields, nested structures).
• _04_text_span_labeling/: mark character or token spans (NER, PII detection, claim and evidence highlighting).
• _05_text_pairwise_preference/: rank A vs B against a rubric (RLHF data shape).

Image

• _06_image_classification/: single or multi-label per image.
• _07_image_extraction/: image into a typed object (attributes, OCR fields, captions).
• _09_image_extraction_to_vectordb/: extract, embed, and store for similarity search.
• _08_image_bounding_boxes/: region detection with (x, y, w, h) per object.

Audio

• _10_audio_classification/: clip-level labels (language, speaker, emotion, genre).
• _11_audio_transcription/: speech-to-text with optional diarization and timestamps.
• _12_audio_extraction/: call or meeting recording into a typed object (action items, attendees, decisions).

Video

• _13_video_classification/: clip-level labels.
• _14_video_extraction/: events, scene descriptions, action timestamps.

Document

• _15_document_classification/: invoice, receipt, contract, spec sheet.
• _16_document_extraction/: multipage PDF into a typed object, with line items where relevant.

Composed patterns

These layer on top of any modality.

• _17_llm_as_judge/: score outputs against a rubric. The same machinery as labeling, repurposed for evals.
• _18_quality_review/: labeler, reviewer, adjudicator pipeline applied on top of an extraction primitive.
• _19_inter_annotator_agreement/: raw agreement, Fleiss' kappa, Krippendorff's alpha, and pairwise Cohen's kappa over agent labelers and jury votes, with low-agreement items routed to review.

Synthetic data generation

These emit training data (JSONL with per-row provenance; filtered files print kept/dropped counts) rather than labels.

• _20_instruction_generation/: self-instruct from seeds, typed Evol-Instruct operators, and a topic-tree pipeline emitting SFT chat rows.
• _21_rejection_sampling/: sample K solutions and keep what a programmatic verifier or judge accepts - verified reasoning traces, best-of-n for non-verifiable prompts, and RL prompt selection by pass rate.
• _22_dataset_curation/: the filters - judge quality-gate over JSONL, pure-stdlib MinHash near-dedup, and 13-gram benchmark decontamination.
• _23_critique_and_revision/: constitutional-AI-style draft, critique against a written principle, revise - SFT rows with critique provenance, plus (chosen, rejected) pairs in the exact shape the _05 jury consumes.
• _24_persona_driven_generation/: typed personas condition prompt and gold-answer problem generation, with a measured (not asserted) diversity report.
• _25_tool_call_trajectories/: function-calling SFT data validated against real agno tool schemas, multi-turn user-sim vs tool-executing assistant rollouts, and a judge filter keeping successful trajectories.

Scale and safety

• _26_scale_out/: the N=100k mechanics every other folder inherits - async fan-out with bounded concurrency and measured speedup, checkpointed resume by row id, and token/cost accounting with batch-tier projections.
• _27_safety_labeling/: policy-taxonomy classification with escalation, over-refusal preference pairs in the _05 jury shape, and a persona-generated boundary-probe eval set with a content screen.

Running a cookbook

From the agno repo root, create and activate the demo venv:

./scripts/demo_setup.sh

source .venvs/demo/bin/activate

python cookbook/data_labeling/_01_text_classification/basic.py

Each subfolder's README.md documents its inputs, the model it expects, and any extra dependencies.

The per-cookbook README calls out which model it uses and why.