OpenAI Commerce Feed Specs: What Makes It Different from Google, Facebook, and Amazon
Discover OpenAI's unique Commerce Feed Specification and how it differs from traditional shopping feeds. Learn about the specialized fields and capabilities that enable AI-native commerce experiences.
The future of e-commerce is AI-native, and OpenAI’s Commerce Feed Specification represents a fundamental evolution beyond traditional shopping feeds. While Google Shopping, Facebook Catalogs, and Amazon feeds focus on human browsing experiences, OpenAI’s spec is designed for AI agents to autonomously discover, evaluate, and purchase products. Let’s explore what makes this specification unique and why it matters for modern e-commerce.
What Makes OpenAI’s Feed Spec Different?
Traditional shopping feeds like Google Shopping or Facebook Catalogs are optimized for human browsing - they prioritize visual appeal, search rankings, and conversion optimization for users clicking through to product pages. OpenAI’s Commerce Feed Specification, however, is built for AI agents that need comprehensive product understanding to make autonomous purchasing decisions.
This fundamental difference drives unique requirements around data completeness, contextual information, and AI-friendly formatting that traditional feeds simply don’t need.
OpenAI’s Unique Feed Capabilities
AI Agent Control Flags
Unlike any other shopping feed, OpenAI includes explicit controls for AI agent behavior:
| Attribute | Data Type | Supported Values | Description | Example | Requirement | Dependencies | Validation Rules |
|---|---|---|---|---|---|---|---|
| enable_search | Enum | true, false | Controls whether the product can be surfaced in ChatGPT search results. | true | Required | — | Lower-case string |
| enable_checkout | Enum | true, false | Allows direct purchase inside ChatGPT. enable_search must be true in order for enable_checkout to be enabled for the product. | true | Required | — | Lower-case string |
These fields don’t exist in Google Shopping, Facebook, or Amazon feeds because those platforms don’t support autonomous AI purchasing. OpenAI’s spec allows merchants to control exactly which products AI agents can discover and purchase.
Enhanced Product Discovery Fields
OpenAI requires more comprehensive product identification than traditional feeds:
| Attribute | Data Type | Supported Values | Description | Example | Requirement | Dependencies | Validation Rules |
|---|---|---|---|---|---|---|---|
| id | String (alphanumeric) | — | Merchant product ID (unique) | SKU12345 | Required | — | Max 100 chars; must remain stable over time |
| gtin | String (numeric) | GTIN, UPC, ISBN | Universal product identifier | 123456789543 | Recommended | — | 8–14 digits; no dashes or spaces |
| mpn | String (alphanumeric) | — | Manufacturer part number | GPT5 | Required if gtin missing | Required if gtin is absent | Max 70 chars |
| title | String (UTF-8 text) | — | Product title | Men’s Trail Running Shoes Black | Required | — | Max 150 chars; avoid all-caps |
| description | String (UTF-8 text) | — | Full product description | Waterproof trail shoe with cushioned sole… | Required | — | Max 5,000 chars; plain text only |
| link | URL | RFC 1738 | Product detail page URL | https://example.com/product/SKU12345 | Required | — | Must resolve with HTTP 200; HTTPS preferred |
While Google Shopping requires basic identifiers, OpenAI’s spec emphasizes the importance of universal product identifiers (GTINs) and manufacturer part numbers to ensure AI agents can accurately match products across different sources.
Comprehensive Item Information
OpenAI’s specification includes detailed physical characteristics that traditional feeds often skip:
| Attribute | Data Type | Supported Values | Description | Example | Requirement | Dependencies | Validation Rules |
|---|---|---|---|---|---|---|---|
| condition | Enum | new, refurbished, used | Condition of product | new | Required if product condition differs from new | — | Lower-case string |
| product_category | String | Category taxonomy | Category path | Apparel & Accessories > Shoes | Required | — | Use ”>” separator |
| brand | String | — | Product brand | OpenAI | Required for all excluding movies, books, and musical recording brands | — | Max 70 chars |
| material | String | — | Primary material(s) | Leather | Required | — | Max 100 chars |
| dimensions | String | LxWxH unit | Overall dimensions | 12x8x5 in | Optional | — | Units required if provided |
| length | Number + unit | — | Individual dimension | 10 mm | Optional | Provide all three if using individual fields | Units required |
| width | Number + unit | — | Individual dimension | 10 mm | Optional | Provide all three if using individual fields | Units required |
| height | Number + unit | — | Individual dimension | 10 mm | Optional | Provide all three if using individual fields | Units required |
| weight | Number + unit | — | Product weight | 1.5 lb | Required | — | Units required |
The material field is particularly unique to OpenAI’s spec. Google Shopping and Facebook don’t require material information, but AI agents need this data to make informed compatibility and quality assessments.
Advanced Variant Management
OpenAI’s variant system is more sophisticated than traditional feeds:
| Attribute | Data Type | Supported Values | Description | Example | Requirement | Dependencies | Validation Rules |
|---|---|---|---|---|---|---|---|
| item_group_id | String | — | Variant group ID | SHOE123GROUP | Required if variants exist | — | Max 70 chars |
| item_group_title | String (UTF-8 text) | — | Group product title | Men’s Trail Running Shoes | Optional | — | Max 150 chars; avoid all-caps |
| color | String | — | Variant color | Blue | Recommended (apparel) | — | Max 40 chars |
| size | String | — | Variant size | 10 | Recommended (apparel) | — | Max 20 chars |
| size_system | Country code | ISO 3166 | Size system | US | Recommended (apparel) | — | 2-letter country code |
| gender | Enum | male, female, unisex | Gender target | male | Recommended (apparel) | — | Lower-case string |
| offer_id | String | — | Offer ID (SKU+seller+price) | SKU12345-Blue-79.99 | Recommended | — | Unique within feed |
| Custom_variant1_category | String | — | Custom variant dimension 1 | Size_Type | Optional | — | — |
| Custom_variant1_option | String | — | Custom variant 1 option | Petite / Tall / Maternity | Optional | — | — |
| Custom_variant2_category | String | — | Custom variant dimension 2 | Wood_Type | Optional | — | — |
| Custom_variant2_option | String | — | Custom variant 2 option | Oak / Mahogany / Walnut | Optional | — | — |
| Custom_variant3_category | String | — | Custom variant dimension 3 | Cap_Type | Optional | — | — |
| Custom_variant3_option | String | — | Custom variant 3 option | Snapback / Fitted | Optional | — | — |
The custom variant fields (Custom_variant1_category through Custom_variant3_option) are unique to OpenAI’s spec. Traditional feeds like Google Shopping have limited variant support, but OpenAI recognizes that AI agents need to understand complex product relationships and custom attributes.
Detailed Fulfillment Information
OpenAI requires more comprehensive shipping and delivery data than traditional feeds:
| Attribute | Data Type | Supported Values | Description | Example | Requirement | Dependencies | Validation Rules |
|---|---|---|---|---|---|---|---|
| shipping | String | country:region:service_class:price | Shipping method/cost/region | US:CA:Overnight:16.00 USD | Required where applicable | — | Multiple entries allowed; use colon separators |
| delivery_estimate | Date | ISO 8601 | Estimated arrival date | 2025-08-12 | Optional | — | Must be future date |
The structured shipping format with country:region:service_class:price is more detailed than what Google Shopping or Facebook require. AI agents need this granular shipping information to make optimal delivery decisions.
Comprehensive Merchant Information
OpenAI’s merchant requirements are stricter than traditional feeds:
| Attribute | Data Type | Supported Values | Description | Example | Requirement | Dependencies | Validation Rules |
|---|---|---|---|---|---|---|---|
| seller_name | String | — | Seller name | Example Store | Required / Display | — | Max 70 chars |
| seller_url | URL | RFC 1738 | Seller page | https://example.com/store | Required | — | HTTPS preferred |
| seller_privacy_policy | URL | RFC 1738 | Seller-specific policies | https://example.com/privacy | Required, if enabled_checkout is true | — | HTTPS preferred |
| seller_tos | URL | RFC 1738 | Seller-specific terms of service | https://example.com/terms | Required, if enabled_checkout is true | — | HTTPS preferred |
The requirement for privacy policies and terms of service when checkout is enabled reflects OpenAI’s focus on autonomous transactions where users need clear legal protections.
Advanced Returns and Compliance
OpenAI includes fields that traditional feeds often ignore:
| Attribute | Data Type | Supported Values | Description | Example | Requirement | Dependencies | Validation Rules |
|---|---|---|---|---|---|---|---|
| return_policy | URL | RFC 1738 | Return policy URL | https://example.com/returns | Required | — | HTTPS preferred |
| return_window | Integer | Days | Days allowed for return | 30 | Required | — | Positive integer |
| Attribute | Data Type | Supported Values | Description | Example | Requirement | Dependencies | Validation Rules |
|---|---|---|---|---|---|---|---|
| warning / warning_url | String / URL | — | Product disclaimers | Contains lithium battery, or CA Prop 65 warning | Recommended for Checkout | — | If URL, must resolve HTTP 200 |
| age_restriction | Number | — | Minimum purchase age | 21 | Recommended | — | Positive integer |
The warning and age_restriction fields are crucial for AI agents making autonomous purchasing decisions, ensuring compliance with regulations and safety requirements.
Performance and Trust Signals
OpenAI includes detailed performance metrics that enhance AI decision-making:
| Attribute | Data Type | Supported Values | Description | Example | Requirement | Dependencies | Validation Rules |
|---|---|---|---|---|---|---|---|
| popularity_score | Number | — | Popularity indicator | 4.7 | Recommended | — | 0–5 scale or merchant-defined |
| return_rate | Number | Percentage | Return rate | 2% | Recommended | — | 0–100% |
| Attribute | Data Type | Supported Values | Description | Example | Requirement | Dependencies | Validation Rules |
|---|---|---|---|---|---|---|---|
| product_review_count | Integer | — | Number of product reviews | 254 | Recommended | — | Non-negative |
| product_review_rating | Number | — | Average review score | 4.6 | Recommended | — | 0–5 scale |
| store_review_count | Integer | — | Number of brand/store reviews | 2000 | Optional | — | Non-negative |
| store_review_rating | Number | — | Average store rating | 4.8 | Optional | — | 0–5 scale |
| q_and_a | String | — | FAQ content | Q: Is this waterproof? A: Yes | Recommended | — | Plain text |
| raw_review_data | String | — | Raw review payload | — | Recommended | — | May include JSON blob |
The q_and_a and raw_review_data fields are unique to OpenAI’s spec. Traditional feeds don’t include FAQ content or raw review data, but AI agents need this contextual information to answer user questions and make informed decisions.
Advanced Product Relationships
OpenAI’s specification includes sophisticated product relationship modeling:
| Attribute | Data Type | Supported Values | Description | Example | Requirement | Dependencies | Validation Rules |
|---|---|---|---|---|---|---|---|
| related_product_id | String | — | Associated product IDs | SKU67890 | Recommended | — | Comma-separated list allowed |
| relationship_type | Enum | part_of_set, required_part, often_bought_with, substitute, different_brand, accessory | Relationship type | part_of_set | Recommended | — | Lower-case string |
The relationship_type field with values like “required_part”, “often_bought_with”, and “substitute” enables AI agents to understand product relationships and make intelligent recommendations or substitutions.
Geographic Targeting
OpenAI includes sophisticated geo-targeting capabilities:
| Attribute | Data Type | Supported Values | Description | Example | Requirement | Dependencies | Validation Rules |
|---|---|---|---|---|---|---|---|
| geo_price | Number + currency | Region-specific price | Price by region | 79.99 USD (California) | Recommended | — | Must include ISO 4217 currency |
| geo_availability | String | Region-specific availability | Availability per region | in_stock (Texas), out_of_stock (New York) | Recommended | — | Regions must be valid ISO 3166 codes |
This granular geo-targeting is more advanced than what traditional feeds offer, enabling AI agents to provide accurate regional pricing and availability.
Key Differences from Traditional Feeds
Google Shopping vs OpenAI Commerce
Google Shopping focuses on:
- Search optimization and click-through rates
- Basic product information for browsing
- Limited variant support
- Human-friendly descriptions
OpenAI Commerce emphasizes:
- AI agent decision-making capabilities
- Comprehensive product understanding
- Advanced variant relationships
- Autonomous transaction support
Facebook Catalogs vs OpenAI Commerce
Facebook Catalogs prioritize:
- Visual appeal and social engagement
- Basic product data for advertising
- Limited technical specifications
- Conversion optimization
OpenAI Commerce requires:
- Detailed technical specifications
- Comprehensive compliance information
- AI-friendly data formatting
- Autonomous purchasing support
Amazon vs OpenAI Commerce
Amazon’s feed focuses on:
- Marketplace-specific requirements
- Competitive pricing information
- Limited external platform compatibility
- Seller performance metrics
OpenAI Commerce provides:
- Cross-platform compatibility
- AI agent optimization
- Comprehensive product relationships
- Autonomous decision-making support
Why These Differences Matter
For Merchants
Enhanced Product Visibility: OpenAI’s comprehensive data requirements ensure that AI agents have complete product understanding, leading to better matching and higher conversion rates.
Future-Proofing: As AI-native commerce grows, merchants with OpenAI-compatible feeds will have access to new customer acquisition channels through AI platforms.
Improved Data Quality: The detailed requirements force merchants to maintain higher data quality standards, which benefits all their shopping channels.
For AI Agents
Better Decision Making: Comprehensive product information enables AI agents to make more informed purchasing decisions on behalf of users.
Reduced Errors: Detailed specifications and relationship data minimize the risk of incorrect product matches or compatibility issues.
Enhanced User Experience: Rich product data allows AI agents to provide more accurate recommendations and answer user questions effectively.
Preparing for OpenAI Commerce
Data Quality Requirements
OpenAI’s specification demands higher data quality than traditional feeds. Merchants should focus on:
- Completing all required fields with accurate information
- Maintaining consistent product identifiers across variants
- Providing comprehensive technical specifications
- Ensuring all URLs are accessible and up-to-date
Technical Implementation
The feed format supports TSV, CSV, XML, or JSON, with updates accepted every 15 minutes. Merchants must:
- Implement secure HTTPS delivery to OpenAI’s endpoints
- Maintain stable product IDs that don’t change over time
- Provide initial sample feeds for validation before live updates
- Ensure compliance with all validation rules
Strategic Considerations
Merchants should view OpenAI Commerce as a strategic investment in AI-native commerce rather than just another shopping channel. The comprehensive data requirements and AI optimization features position businesses for the future of autonomous shopping.
The Competitive Advantage
Early adoption of OpenAI’s Commerce Feed Specification provides significant advantages:
- First-mover advantage in AI-native commerce
- Enhanced product discoverability through AI agents
- Improved data quality across all shopping channels
- Future-proofed operations for the AI era
As AI agents become more sophisticated and users become comfortable with autonomous purchasing, the merchants who prepare today will thrive tomorrow.
Getting Started with OpenAI Commerce
Step 1: Assess Your Data
Evaluate your current product data against OpenAI’s comprehensive requirements. Identify gaps in technical specifications, compliance information, and relationship data.
Step 2: Optimize Your Feed
Implement the required fields and validation rules. Focus on data completeness and accuracy rather than just meeting minimum requirements.
Step 3: Partner with Experts
Work with platforms like AI Shopping Feeds that understand both traditional shopping feeds and OpenAI’s unique requirements. This ensures seamless integration and optimal performance.
Step 4: Test and Validate
Provide sample feeds to OpenAI for validation before implementing live updates. This ensures compatibility and identifies any issues early.
The Future is AI-Native
OpenAI’s Commerce Feed Specification represents the future of e-commerce - one where AI agents understand products comprehensively and make autonomous purchasing decisions. The detailed requirements and unique capabilities position this specification as the foundation for AI-native commerce.
While traditional feeds focus on human browsing experiences, OpenAI’s spec enables the next generation of conversational and autonomous shopping. The merchants who embrace this evolution today will lead the industry tomorrow.
Why Start Now?
- ✅ Early access to AI-native commerce channels
- ✅ Enhanced data quality across all platforms
- ✅ Future-proofed operations for the AI era
- ✅ Competitive advantage in autonomous shopping
The transition to AI-native commerce is already underway. OpenAI’s Commerce Feed Specification is building the infrastructure to make it happen.
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