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Validate
Detect and validate ODPS products, ODPC catalogs, ODPG graphs, ODPV vocabulary, and Data Contract references before they move into production workflows.
Agent-Ready Data Product Tooling
The standards toolkit for data product agents
A practical Python SDK and MCP server for loading, validating, explaining, generating, searching, traversing, and publishing Open Data Product artifacts across ODPS, ODPC, ODPG, ODPV, Data Contracts, LLM workflows, and portfolio workspaces.
$ pip install open-data-products
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Built to give AI agents the context to operate
The SDK turns data product standards into executable context. Teams get validation. Developers get Python functions. AI agents get structured outputs, lightweight summaries, bundled retrieval resources, graph reasoning, Data Contract reports, and safe tools for real workflows.
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Explain and summarize
Turn YAML and JSON into readable explanations, structured validation results, references, and small summaries that avoid flooding an agent context window.
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Generate
Use local runtimes or hosted providers to create standards-aware ODPS, ODPC, and ODPG artifacts from business source material.
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Discover resources
Expose bundled schemas, prompt templates, vocabulary records, catalog object records, and graph object records through Python, CLI, and MCP surfaces.
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Reason over graphs
Summarize, traverse, analyze, and extract trusted focus-node context from ODPG relationships, ODPC objects, and ODPV terms.
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Build portfolios
Build, refresh, sync, render, localize, and explain connected ODPC, ODPS, and ODPG portfolio workspaces from real source lanes.
MCP Server Included
Give AI agents the context layer for data product work
The SDK includes Model Context Protocol support so agent hosts can operate on Open Data Products files through safe tools, structured resources, standards-aware context, graph relationships, and LLM-assisted generation.
Agent-ready tools
Expose validation, explanation, reference discovery, summaries, search, traversal, graph reasoning, and product context actions through MCP.
ARWS manifest
Generate an agent manifest with
open-data-products manifest --json for hosts that understand ARWS-style tool discovery.
Local and service workflows
Use the SDK with local LLM runtimes including Ollama, LM Studio, vLLM, llama.cpp, and LocalAI, or hosted providers such as Claude, OpenAI, OpenRouter, and Groq in coding-agent and product engineering workflows.
Designed for local and provider LLM support
The SDK keeps generation workflows provider-aware while staying grounded in the Open Data Products standards family. Choose a local runtime for private or offline work, or connect a hosted provider when managed model access fits better.
Run models on your machine
Use Ollama, LM Studio, vLLM, llama.cpp server, LocalAI, or another OpenAI-compatible local chat server for local generation workflows.
Bring the local model you trust
Work with model families such as Llama, DeepSeek, Qwen, Mistral, Mixtral, Phi, Gemma, Code Llama, StarCoder, Yi, Command R, Falcon, Granite, Nemotron, Vicuna, and WizardLM when exposed by your runtime.
Use managed LLM APIs
Connect service providers such as Claude, OpenAI, OpenRouter, and Groq through generation configuration, then validate generated ODPS, ODPC, ODPG, ODPV, and data contract artifacts before use.
One SDK. Three execution paths.
Use the same standards-aware capabilities from Python, from the terminal, or through an agent host.
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Python API
Call top-level helpers such as load, validate, explain, summarize, resolve references, and list resources.
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Unified CLI
Run human-readable commands by default and add --json for scripts, agents, and CI jobs.
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MCP server
Launch open-data-products serve so compatible agent hosts can call safe SDK tools over stdio.
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ARWS manifest
Render open-data-products manifest --json for agent systems that need a compact tool manifest.
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Guide paths
Move from install and validation into LLM generation, graph reasoning, catalogs, and portfolio workspaces.
Guided paths into the SDK
The GitHub repository includes course-style guides. The website highlights the most useful entry routes instead of listing every lesson at once.
Install, validate, explain
Begin with Python setup, install the package, and validate your first standards file.
Use local or hosted LLMs
Configure generation, keep prompts project-owned, and create reviewable fragments from business notes.
Connect vocabulary, graphs, and catalogs
Search shared terms, build graph context, and assemble ODPC catalogs that humans and agents can review.
Build the complete workspace
Move from separate commands into a connected portfolio with HTML review pages and agent-ready YAML.
Portfolio Builder
Turn source lanes into a connected data product portfolio
The portfolio workflow packages generation, ODPC catalogs, ODPS products, ODPG graphs, review pages, localization, and version history into one practical path for business-facing work.
# Build from business source lanes open-data-products portfolio build \ --objectives source_docs/objectives/ \ --use-cases source_docs/use-cases/ \ --signals source_docs/signals/ \ --products source_docs/products/ \ --output generated/portfolio/ # Refresh, sync, localize, and explain open-data-products portfolio refresh generated/portfolio/ open-data-products portfolio sync generated/portfolio/ open-data-products portfolio localize generated/portfolio/ --languages "fi,sv,ar,vi" open-data-products portfolio explain generated/portfolio/
Designed for the Open Data Products standards family
The SDK gives developers and AI agents one practical interface across product descriptions, ODPC catalogs, ODPG graphs, ODPV vocabulary, and data contract workflows.
Product
Defines the data product, its access, quality, SLA, pricing, support, license, and strategy.
Catalog
Organizes products, objectives, use cases, KPIs, and demand signals at portfolio level.
Graph
Connects products to business value through nodes, edges, and relationship paths.
Vocabulary
Creates shared meaning for humans, platforms, and AI agents working with the standards.
Data Contracts
Supports contract-aware workflows for structure, expectations, quality, access, and product handover.
Start with a clean environment
Install the SDK, check the CLI, then run a human-readable validation command. Add --json when an agent, script, or CI job needs stable machine-readable output.
# Create a project folder mkdir odps-sdk-course cd odps-sdk-course # Create and activate a virtual environment python3 -m venv .venv-sdk source .venv-sdk/bin/activate # Install the SDK pip install open-data-products # Check the command and validate a file open-data-products --help open-data-products validate examples/product.yaml open-data-products explain examples/product.yaml open-data-products summary examples/product.yaml
Reference docs for deeper work
Use the landing page and guides for orientation, then jump into the focused references when you need exact command behavior, API details, or agent-host setup.
API and commands
Use the Python API and CLI references when integrating the SDK into automation, products, or validation pipelines.
LLM and Data Contract workflows
Configure generation providers, copy prompt templates, validate contracts, and produce product-level reports.
MCP, ARWS, and handoff
Connect the SDK to agent hosts, inspect safe tools, and give agents a compact routing file.
Build data product workflows that AI agents understand
Use the SDK as the context and execution layer for AI agents working with data product standards, ODPC catalogs, data contracts, validation, graph traversal, vocabulary helpers, and agent-ready workflows.