Open Data Products Python SDK

open-data-products is a Python SDK and CLI for the OpenDataProducts.org standards family: ODPS, ODPC, ODPG, ODPV, and ODPR.

Use it to validate data product documents, build catalogs and graphs, inspect portfolio source intake, run LLM-assisted generation workflows, expose a local MCP server, and give AI agents a consistent standards-aware API.

Install

pip install open-data-products

Optional extras:

# Outlook .msg intake support
pip install "open-data-products[email]"

# Data Contract CLI integration
pip install "open-data-products[contracts]"

# Embedded llama.cpp generation support
pip install "open-data-products[llama-cpp]"

# Development tools
pip install "open-data-products[dev]"

Python 3.8 or newer is required.

What It Provides

Area	Capabilities
Cross-spec API	Detect, load, validate, explain, summarize, and resolve references across ODPS, ODPC, ODPG, and ODPV documents
CLI	Run validation, generation, catalog, graph, vocabulary, portfolio, OKF, contract, resource, manifest, and MCP workflows through open-data-products
Portfolio workflows	Build, refresh, sync, render, localize, explain, and inspect portfolio workspaces from objectives, use cases, signals, and product source lanes
Document intake	Read Markdown, text, YAML, JSON, EML, MSG with the email extra, DOCX, PPTX, PDF, CSV, and XLSX source files for portfolio workflows
Agent surfaces	Run a safe-class stdio MCP server and generate an ARWS-compatible agent manifest
LLM generation	Generate ODPC fragments, ODPG graphs, and ODPS product YAML from source notes using local or hosted providers
Data Contracts	Resolve ODPS contract references, validate external contracts through optional datacontract-cli, extract schemas, check alignment, and generate reports

Quick CLI Examples

Most commands print human-readable output by default. Add --json for CI, scripts, MCP clients, and agents.

Run the SDK through Python:

# Validate and inspect standards documents
python3 -m open_data_products.cli validate examples/product.yaml
python3 -m open_data_products.cli explain examples/product.yaml --json
python3 -m open_data_products.cli refs examples/product.yaml --json
python3 -m open_data_products.cli summary examples/product.yaml

# Discover bundled schemas, prompts, vocabulary records, and guidance
python3 -m open_data_products.cli resources --json
python3 -m open_data_products.cli resources --id generation.prompt.system --json

# Agent surfaces
python3 -m open_data_products.cli manifest --json
python3 -m open_data_products.cli serve

After installation, the console script provides the same commands:

open-data-products validate examples/product.yaml
open-data-products explain examples/product.yaml --json

Portfolio source intake can be inspected without calling an LLM:

python3 -m open_data_products.cli portfolio intake \
  --objectives sources/objectives/ \
  --use-cases sources/use-cases/ \
  --signals sources/signals/ \
  --products sources/products/ \
  --config generation.config.yaml \
  --json

Portfolio build uses the same source lanes and prompt budget controls:

python3 -m open_data_products.cli portfolio build \
  --objectives sources/objectives/ \
  --use-cases sources/use-cases/ \
  --signals sources/signals/ \
  --products sources/products/ \
  --output generated/portfolio/

LLM Generation

Generation defaults to local Ollama-compatible settings, and can also use embedded llama.cpp, OpenAI-compatible local servers, NVIDIA NIM, Claude, and hosted providers configured in generation.config.yaml.

open-data-products config generation --copy-to generation.config.yaml
open-data-products config generation --config generation.config.yaml --check

open-data-products generate \
  --config generation.config.yaml \
  --input source_docs/products/ \
  --kind product-reference \
  --output generated/

Documentation

The full SDK guide that used to live in this README is now here:

• Full SDK guide

Focused user guides:

• Command guide
• Portfolio intake guide
• LLM generation
• Agent surface
• Recipe workflows
• Data Contract workflows
• API reference

Project references:

• Documentation index
• Examples
• Agent handoff

Development

git clone https://github.com/Open-Data-Product-Initiative/odps-python
cd odps-python
pip install -e ".[dev]"
pytest -q

Before publishing a package, verify the PyPI description renders:

python3 -m build
python3 -m twine check dist/*

Acknowledgments

Thanks to the Open Data Product Initiative community, Chris Howard / Kitard for the original odps-python foundation, devlouie for the MCP layer and agent surface, and the Data Contract CLI project for the optional contract execution engine.

License

Apache License 2.0. See LICENSE for details.