mirror of
https://github.com/agno-agi/agno.git
synced 2026-10-12 02:10:54 +00:00
AshpreetandClaude Opus 4.7
bf88d39df7
## Summary Adds a new `cookbook/data_labeling/` directory built around **one cookbook per core data-labeling primitive**. Each subfolder is a self-contained example with `README.md`, `basic.py`, optional task-meaningful variants, and a `TEST_LOG.md` per the cookbook convention. The 18 workflows, grouped by family: | Family | Cookbooks | |---|---| | Text | `text_classification`, `text_multilabel_classification`, `text_extraction`, `text_span_labeling`, `text_pairwise_preference` | | Image | `image_classification`, `image_extraction`, `image_extraction_to_vectordb`, `image_bounding_boxes` | | Audio | `audio_classification`, `audio_transcription`, `audio_extraction` | | Video | `video_classification`, `video_extraction` | | Document | `document_classification`, `document_extraction` | | Composed | `llm_as_judge`, `quality_review` | Strategic rationale (why labeling, why a primitives-per-cookbook shape) and the implementation plan live in `specs/agno/cookbooks/data-labeling/` (private specs repo). The branch also includes a small refresh of `cookbook/99_docs/` (`sorting_hat.py` / `workbench.py` scratch examples) — unrelated to the data-labeling work but bundled in this PR by request. ## Type of change - [ ] Bug fix - [x] New feature - [ ] Breaking change - [x] Improvement - [ ] Model update - [ ] Other: ## Checklist - [x] Code complies with style guidelines (`cookbook/STYLE_GUIDE.md`) - [x] Ran format/validation scripts (`./scripts/format.sh` and `./scripts/validate.sh`) - [x] Self-review completed - [x] Documentation updated — per-cookbook READMEs explain purpose, when-to-use, run commands - [x] Examples and guides: this PR _is_ examples - [x] Tested in clean environment — every cookbook run end-to-end; see each `TEST_LOG.md` - [ ] Tests added/updated — N/A; cookbook content ### Duplicate and AI-Generated PR Check - [x] Searched existing open PRs; nothing else addresses this - [x] No similar PR exists - [x] This PR was largely AI-generated (Claude Code) under direction; code was reviewed and end-to-end tested before commit ## Additional Notes ### Conventions - `OpenAIResponses(id="gpt-5.5")` is the default for text / image / document workloads - `Gemini(id="gemini-3-flash-preview")` for audio + video (native modality input) - `Claude(id="claude-sonnet-4-5")` for the second labeler in `quality_review/` (provider diversity) - Inline Pydantic schemas; no shared `schemas.py` per cookbook (forkability beats DRY here) - Variants are task-meaningful (e.g. `with_confidence.py`, `multilabel.py`), not provider swaps ### Two test-driven fixes worth flagging - `image_bounding_boxes/with_confidence.py` — gpt-5.5 produced degenerate (all-zero or all-one) boxes without per-field x/y/w/h `description=` strings on the Pydantic model. Added descriptions + expanded coordinate guidance in the instructions; now produces tight boxes. - `document_extraction/with_confidence.py` — OpenAI's strict structured-output mode rejects `Field(..., description=...)` on a field whose type is a referenced sub-model. Dropped the field-level description; explanatory text moved to a code comment. ### Out of scope / deliberate omissions - No batch driver, rate limiter, DLQ, or warehouse export — these are production patterns and don't belong in primitive cookbooks. The earlier `pipeline.py` / `run_batch.py` / `export.py` files are removed; if those patterns get cookbooks later they live under `cookbook/05_production/` (TBD). - No labeling UI / Label Studio integration. - No RLHF training loop (we show the _data shape_ in `text_pairwise_preference`, not the trainer). - `image_extraction_to_vectordb/basic.py` requires `lancedb` (not in `agno[demo]` extras). README documents the install. Worth either folding into the demo extras or shipping a `requirements.in` for that folder — happy to do as a follow-up. 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 4.7 (1M context) <[email protected]>
Build, run, and manage agent platforms.
Introduction
Agno is an SDK for building agent platforms.
- Build agents using any agent framework.
- Run them as production services with tracing, scheduling, and RBAC.
- Manage using a single control plane.
Agno allows you to own your agent stack. Maintain control of your data, context, tools, permissions, memory and human-review loops. Run your platform in your cloud, and manage it using a beautiful UI.
What you can build
Agno can bring any agent to life, here are some examples:
- Coda → A code companion that lives in Slack and works alongside your team.
- Dash → A self-learning data agent that grounds answers in 6 layers of context.
- Scout → A context agent that navigates Slack and Google Drive to answer questions.
- Auto Improving Agent Platform → Build your own agent platform with an auto-improvement loop.
Get started
- Read the docs
- Build your first agent in 20 lines of code.
- Build an auto-improving agent platform managed entirely by claude code.
Features
- Production API. 50+ endpoints with SSE and websockets to build a product on top.
- Storage. Store sessions, memory, knowledge, and traces in your own database.
- 100+ integrations. Integrate with 100+ tools using pre-built toolkits.
- Context Providers. Access live data from Slack, Drive, wikis, MCP, and custom sources.
- Human approval. Pause runs for user confirmation. Block tools that require admin approval.
- Observability. Get monitoring via OpenTelemetry tracing, run history, and audit logs out of the box.
- Security. Get JWT-based RBAC and multi-user, multi-tenant isolation out of the box.
- Interfaces. Expose your agents via Slack, Telegram, WhatsApp, Discord, AG-UI, A2A.
- Scheduling. Cron-based scheduling and background jobs with no external infrastructure.
- Deploy anywhere. Run on any cloud platform that runs containers. Docker, Railway, AWS, GCP.
Use Agno with your coding agent
Two options:
- Add Agno docs as an indexed source. In Cursor: Settings → Indexing & Docs → Add
https://docs.agno.com/llms-full.txt. Also works in VSCode, Windsurf, and similar tools. - Add Agno docs as an MCP server. Add docs.agno.com/mcp to your favourite coding agent.
Read the full guide here.
Community
- (https://x.com/AgnoAgi): follow for releases and demos
- Newsletter: monthly updates on what's shipping
Contributing
See the contributing guide.
Telemetry
Agno logs which model providers are used to prioritize updates. Disable with AGNO_TELEMETRY=false.