Red Hat

Boston MA 02210 USA hchen@redhat.com

Verizon

Richardson TX USA luay.jalil@verizon.com

Red Hat

New York NY USA ncocker@redhat.com

Internet Research Task Force Network Management Research Group AI inference distributed systems inference API multi-provider agentic AI RBAC rate limiting This document specifies extensions for multi-provider distributed AI inference using the widely-adopted OpenAI Responses API as the reference interface standard. These extensions enable provider diversity, load balancing, failover, and capability negotiation in distributed inference environments while maintaining full backward compatibility with existing implementations. The extensions do not require changes to standard API usage patterns or existing client applications. By treating the OpenAI Responses API as a de facto standard interface (similar to how HTTP serves as a standard protocol), these extensions provide an optional enhancement layer for multi-provider orchestration, intelligent routing, and distributed inference capabilities. The approach preserves the familiar API interface that developers already know and use, while enabling seamless integration across multiple AI inference providers without vendor lock-in. This revision (-01) adds identity-based authorization, role-based access control (RBAC), and rate limiting extensions for secure multi-tenant deployments. Added three subsections to Section 6 (Extension Headers): authorization identity headers for JWT/OAuth integration, an RBAC framework for tiered model access, and rate limiting with RPM/TPM support. Minor updates to Problem Statement, Design Principles, Security Considerations, and IANA Considerations to reflect the new capabilities.

The OpenAI Responses API has emerged as a de facto standard interface for agentic AI applications, with widespread adoption across the industry. Many providers now offer compatible endpoints, creating a rich ecosystem of inference services. This document treats the OpenAI Responses API as a reference standard interface (analogous to how HTTP serves as a standard protocol), rather than as a vendor-specific implementation. However, applications that want to leverage multiple providers face significant challenges in orchestrating distributed inference, handling provider failures, and optimizing resource utilization across heterogeneous environments. This document specifies vendor-neutral extensions that enable multi-provider AI inference orchestration while maintaining the familiar API interface. The extensions allow applications to leverage the best models and tools from multiple providers without vendor lock-in. The approach uses "auto" parameters and extension headers to enable intelligent provider selection, capability mapping, and distributed inference coordination. The extensions are designed as optional HTTP headers and response fields that enhance the reference API with multi-provider capabilities while ensuring that existing applications continue to work unchanged. The key principle is compatibility-first: any application that works with the reference API interface will continue to work with these extensions, while applications that choose to use the extensions gain access to advanced multi-provider features like intelligent routing, automatic failover, and distributed load balancing. The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in BCP 14 when, and only when, they appear in all capitals, as shown here.

OpenAI Responses API: The reference API specification for agentic AI inference , designed for hackathon-friendly rapid prototyping and widely adopted across the industry as a de facto standard. Multi-Provider Router: A service that extends the reference API with multi-provider orchestration capabilities while maintaining full compatibility. Provider Pool: A collection of compatible inference services that can be orchestrated by the multi-provider router. Multi-Vendor Compatibility: The ability to seamlessly integrate and route requests across multiple AI inference providers while maintaining a consistent interface. Extension Headers: Optional HTTP/HTTPS headers that provide multi-provider functionality without affecting standard API behavior. Distributed Inference: The orchestration of AI inference requests across multiple providers to achieve better performance, reliability, and resource utilization. Transport Protocol: All API endpoints support both HTTP and HTTPS protocols. HTTPS SHOULD be used for production deployments to ensure confidentiality and integrity of inference requests and responses.

While the OpenAI API provides an excellent standard interface for AI inference, several challenges arise when deploying at scale across multiple providers: 1. Provider Lock-in: Applications typically connect to a single provider, creating dependency on that provider's availability, pricing, and capabilities. 2. Limited Failover: When a provider experiences issues, applications have no automatic mechanism to failover to alternative providers while maintaining session continuity. 3. Suboptimal Resource Utilization: Different providers excel in different scenarios (cost, latency, specialized models), but applications cannot easily leverage these strengths dynamically. 4. Operational Complexity: Managing multiple provider connections, API keys, and routing logic adds significant complexity to application development and operations. 5. Inconsistent Capabilities: While providers offer OpenAI-compatible APIs, they may have different model names, capabilities, and limitations that applications must handle manually. 6. Multi-Tenancy Requirements: Production deployments require user authentication, authorization, and usage governance across multiple tenants with different access levels and rate limits. These extensions address these challenges while preserving the simplicity and familiarity of the OpenAI API that developers rely on.

The extensions are designed according to the following principles: 1. Multi-Vendor Support: Enable seamless integration across multiple AI inference providers without vendor lock-in. Applications can leverage the best capabilities from different providers within a unified interface. 2. Opt-in Enhancement: Multi-provider features are enabled only when clients explicitly request them through extension headers. Default behavior remains unchanged. 3. Transparent Operation: When multi-provider features are enabled, the complexity of provider orchestration is hidden from the client. Responses maintain standard OpenAI API format. 4. Graceful Degradation: If multi-provider features are unavailable or fail, the system falls back to standard single-provider behavior. 5. Standard Compliance: All extensions use standard HTTP mechanisms and do not require proprietary protocols or non-standard API modifications. 6. Security and Multi-Tenancy: Authentication and authorization integrate with standard identity frameworks (JWT, OAuth) without requiring new authentication mechanisms.

The OpenAI Responses API supports several parameters that benefit from vendor-neutral "auto" values, enabling seamless multi-provider orchestration. The following parameters are enhanced with auto-selection capabilities:

Key Responses API parameters that require provider-specific mapping: Vendor-Neutral Parameters

Parameter	Auto Value	Router Behavior
model	"auto"	Maps to optimal provider-specific model based on task
tools	"auto"	Selects appropriate tools from provider's available toolkit
tool_choice	"auto"	Lets provider decide when to use tools based on context
reasoning	"auto"	Maps to reasoning-capable models or simulates reasoning for multi-vendor compatibility
max_completion_tokens	"auto"	Calculates optimal token limit based on task complexity
response_format	provider-adaptive	Adapts format requirements to provider capabilities

When model is set to "auto", the router uses these criteria for selection: 1. Task Classification: Analyzes the request to determine task type (reasoning, coding, creative, analytical, etc.) 2. Provider Capabilities: Matches task requirements to provider strengths and available models 3. Performance Requirements: Considers latency, cost, and quality constraints from extension headers 4. Context Awareness: Maintains conversation context and provider affinity when beneficial 5. Authorization Context: Considers user identity and role-based access policies when selecting models and providers

When tools is set to "auto", the router implements intelligent tool selection: 1. Tool Category Mapping: Maps generic tool categories (web-search, code-execution, image-generation) to provider-specific tools 2. Capability Discovery: Dynamically discovers available tools from each provider and their capabilities 3. Context-Aware Selection: Chooses tools based on conversation context and task requirements 4. Cross-Provider Orchestration: Coordinates tool usage across multiple providers when beneficial

When reasoning is set to "auto", the router intelligently handles providers with different reasoning capabilities: 1. Native Reasoning Models: Routes to providers with dedicated reasoning models (o1, o1-mini, etc.) 2. Reasoning-Enhanced Models: Uses models optimized for logical thinking and step-by-step analysis 3. Simulated Reasoning: For providers without native reasoning, implements reasoning through structured prompting and chain-of-thought techniques 4. Fallback Strategies: Gracefully degrades to best-available reasoning approximation when native reasoning is unavailable

3x=15->x=5" } ] } ] } ]]>

Fallback to Simulated Reasoning Example:

3x=15->x=5" } ] } ] } ]]>

Auto parameters in the request body are the primary mechanism for enabling multi-vendor capabilities. Extension headers provide supplementary information to assist the router in making optimal decisions: Primary Control: Auto parameters ("model": "auto", "tools": "auto", "reasoning": "auto") trigger multi-vendor selection. Decision Assistance: Headers provide hints, constraints, and preferences to guide the auto-selection process. Synchronization Rules: 1. If auto parameter is NOT "auto" but headers suggest multi-provider behavior, the router SHOULD honor the explicit parameter value and ignore conflicting headers. 2. If auto parameter is "auto" but X-AI-Multi-Provider is "disabled", the router MUST treat the parameter as a regular non-auto value and route to a single default provider. 3. If auto parameter is "auto" and X-AI-Multi-Provider is "enabled" (or absent but other multi-provider headers are present), the router SHOULD use headers as decision assistance. 4. If headers contain conflicting information (e.g., X-AI-Routing-Strategy: "cost" but X-AI-Quality-Threshold: 0.95), the router SHOULD prioritize explicit constraints (quality threshold) over optimization strategies (cost).

The multi-provider extensions are implemented through optional HTTP headers that clients can include in standard OpenAI Responses API requests. These headers provide hints and preferences for auto-selection and multi-provider orchestration.

The following headers can be included in requests to enable multi-provider features: Multi-Provider Assistance Headers

Header	Values	Description
X-AI-Multi-Provider	enabled | disabled	Enable multi-provider orchestration (master switch)
X-AI-Provider-Pool	CSV of provider IDs	Constrain auto-selection to specific providers
X-AI-Routing-Strategy	cost | latency | quality | capability-first	Optimization strategy for auto-parameter decisions
X-AI-Task-Hint	reasoning | coding | creative | analytical | multimodal	Task type hint to assist model auto-selection
X-AI-Tool-Categories	CSV of tool categories	Preferred tool categories to assist tools auto-selection
X-AI-Reasoning-Preference	native | enhanced | simulated	Reasoning approach preference to assist reasoning auto-selection
X-AI-Quality-Threshold	0.0 - 1.0	Minimum quality threshold for auto-selected providers
X-AI-Max-Latency	milliseconds	Maximum acceptable latency for auto-selected providers
X-AI-Cost-Limit	USD per request	Maximum cost limit for auto-selected providers
X-AI-Failover-Policy	none | automatic | manual	Failover behavior when auto-selected providers fail

When multi-provider features are active, responses include additional headers providing transparency into the routing decisions: Auto-Selection Response Headers

Header	Description
X-AI-Provider-Used	ID of the provider selected by auto-routing
X-AI-Model-Mapped	Provider-specific model mapped from "auto"
X-AI-Auto-Selection	JSON object with auto-selection decisions
X-AI-Tool-Mapping	JSON object showing tool category to provider mapping
X-AI-Auto-Decisions	JSON object with all auto-parameter resolutions
X-AI-Alternatives-Considered	JSON array of alternative providers/models considered
X-AI-Selection-Confidence	Confidence score (0.0 - 1.0) for auto-selection

Synchronization Examples: Example 1: Proper Sync (Headers Assist Auto Parameters)

= 0.9 ]]>

Example 2: Conflict Resolution (Explicit Parameter Wins)

Example 3: Multi-Provider Disabled Override

Multi-provider routers in production deployments often operate behind authentication gateways that inject identity information into request headers. This section defines optional headers for conveying authenticated user identity to enable authorization-aware routing. Authorization Identity Headers

Header	Values	Description
X-Authz-User-Id	User identifier	Authenticated user ID (e.g., JWT 'sub' claim)
X-Authz-User-Groups	CSV of group names	User's group memberships (e.g., JWT 'groups' claim)
X-Authz-User-Roles	CSV of role names	User's assigned roles

These headers follow conventions used by common authentication frameworks including JWT , OAuth 2.0 , and Kubernetes-style RBAC systems. The specific header names MAY vary by deployment; the router SHOULD support configurable header mappings.

The X-AI-Authz-Applied response header indicates whether authorization policies were considered in routing. The X-AI-User-Role header provides transparency about which role was matched for auditing purposes.

Role-based access control (RBAC) enables multi-tenant deployments where different users or groups receive different levels of service. The RBAC framework consists of: 1. Role Bindings: Map users and groups to roles (e.g., "admin", "premium_user", "free_user"). 2. Routing Decisions: Associate roles with model access policies, provider selection rules, and capability restrictions. 3. Priority Ordering: Evaluate routing decisions by priority to handle overlapping role assignments.

A free-tier user (X-Authz-User-Groups: free-tier) sending the same request would receive X-AI-RBAC-Role: free_user and be routed to qwen-7b-instruct with reasoning disabled. RBAC policies can combine with task classification, enabling context-aware authorization (e.g., premium users get advanced models only for complex queries).

Multi-provider routers SHOULD implement rate limiting to protect infrastructure and ensure fair resource allocation across tenants. Rate Limit Response Headers

Header	Values	Description
X-RateLimit-Limit	Integer	Total requests allowed per time window
X-RateLimit-Remaining	Integer	Remaining requests in current window
X-RateLimit-Reset	Unix timestamp	When rate limit window resets
X-RateLimit-Retry-After	Seconds	Time to wait before retrying (429 only)
X-TokenLimit-Limit	Integer	Total tokens allowed per window (TPM)
X-TokenLimit-Remaining	Integer	Remaining tokens in current window

Rate limiting can be applied at multiple levels: request-based (RPM), token-based (TPM), model-specific, and per-user/group. Routers MAY implement rate limiting through an external Rate Limit Service (e.g., Envoy RLS via gRPC), a local in-process limiter using sliding window counters, or a hybrid chain using first-deny semantics.

The router SHOULD support fail-open and fail-closed modes. In fail-closed mode (default), rate limiter errors reject requests to prevent bypass during outages. In fail-open mode, errors allow requests through, prioritizing availability.

The following examples demonstrate how the extensions work with OpenAI's Responses API while providing multi-provider capabilities through auto-model and auto-tool selection.

Using the OpenAI Responses API with auto-model selection for vendor-neutral multi-provider routing:

The router automatically maps generic tool requests to provider-specific implementations while maintaining Responses API compatibility:

The Responses API enables vendor-neutral parameter handling through auto-selection, allowing seamless provider switching:

The extensions maintain full compatibility with OpenAI Responses API streaming while providing auto-selection capabilities.

Server-sent events streaming works with auto-model selection, with provider routing happening before stream initiation:

Tool calling with auto-selection maps generic tool requests to provider-specific implementations seamlessly:

The OpenAI Responses API with auto-selection enables dynamic provider routing based on performance requirements and real-time characteristics.

Applications can specify performance requirements through extension headers while using standard Responses API calls:

Auto-selection can balance quality and performance requirements:

The extensions maintain conversation continuity during provider failures through persistent ID tracking and state preservation using standard OpenAI conversation patterns.

| | | | | | Route + | | | | | Store | | | | |---------->| | | | | | | Store | | | | conv-001 | |<----------| | | | conv-001 | | | | |<---------| | | | | | | | | | Continue | | | | |--------->| | | | | | Route + | | | | | Load | | | | |---------->| | | | | X TIMEOUT | | | | | | | | | FAILOVER | | | | | Detect + | | Retrieve | | Retrieve | | conv-001 | | State | | | | | | | | | | Route B + | | | | | Full St | | | | |---------------------->| | | | | | Store | | | | conv-001 | |<----------------------| | | conv-001 | | | | | FAILOVER | | | | |<---------| | | | ]]>

Multi-turn conversations maintain context automatically during failover:

The extensions handle providers with different security requirements and compliance levels through security-aware auto-selection.

| | | | | Evaluate | | | | Security | | | |----------->| | | | | Check | | | | Compliance | | | | Hi|Med|Lo | | | | | | |<-----------| | | | Security | | | | Matched | | | | | | | | Route to | | | | Compliant | | | |------------------------>| | | | | | |<------------------------| | Response +| | | | Sec Info | | | |<----------| | | ]]>

Medical data processing with strict compliance requirements:

Financial workflow with mixed sensitivity levels using auto-selection:

The extensions implement sophisticated failover strategies that handle performance degradation and cascading failures while maintaining OpenAI API compatibility.

| | | | | | | Mon | | | | | | Start| | | | | |----->| | | | | | | Check | | | | | | Health| | | | | |------>| | | | | | |Degrade| | | | |<------| | | | | | | | | | | Route| | | | | | to B | | | | | |--------------------->| | | | | | | | | | | Mon | | | | | | Prov B| | | | | |-------------->| | | | | | Overload | | | |<--------------| | | | | | | | | |GRACEFUL | | | | |DEGRADE | | | | |Route C | | | | |----------------------------->| | | | | | | | |<-----------------------------| | Resp | | | | | |<-----| | | | | ]]>

Auto-selection with graceful degradation during system stress:

Complex workflows can branch and merge while maintaining conversation state through standard OpenAI message arrays and extension headers.

Multi-branch workflow with state inheritance using conversation arrays:

The multi-provider extensions can be implemented as a proxy layer that sits between clients and provider endpoints, or as enhanced provider implementations that support multi-provider orchestration.

The multi-provider router consists of several key components: 1. Header Parser: Extracts multi-provider preferences from request headers while preserving standard OpenAI API structure. 2. Provider Registry: Maintains information about available providers, their capabilities, current status, and performance metrics. 3. Routing Engine: Implements provider selection algorithms based on client preferences, provider capabilities, and real-time performance data. 4. Request Translator: Adapts requests to provider-specific requirements while maintaining OpenAI API compatibility. 5. Response Normalizer: Ensures all responses conform to standard OpenAI API format regardless of the underlying provider. 6. Failover Manager: Handles provider failures and implements retry logic with alternative providers. 7. RBAC Engine: Evaluates role bindings and authorization policies to determine permitted models and providers for each authenticated user. 8. Rate Limiter: Enforces request and token rate limits per user, group, and model using local or external rate limiting services.

The extensions provide strong backward compatibility guarantees: 1. API Compatibility: All standard OpenAI API endpoints, request formats, and response formats remain unchanged. Existing applications work without modification. 2. Default Behavior: Requests without extension headers behave identically to standard OpenAI API calls, typically routing to a default provider. 3. Error Handling: Error responses maintain standard OpenAI API error format and codes, ensuring existing error handling logic continues to work. 4. Authentication: Standard OpenAI API authentication mechanisms (API keys, bearer tokens) are preserved and work unchanged. 5. Rate Limiting: Rate limiting headers and behavior remain compatible with OpenAI API standards. 6. Optional Extensions: Authorization, RBAC, and rate limiting features are optional enhancements that do not affect clients unaware of these capabilities.

Multi-provider routing introduces several security considerations: Credential Management: The router must securely manage credentials for multiple providers while ensuring that client credentials are not exposed to inappropriate providers. Data Privacy: Request data may be processed by different providers with varying privacy policies. The router should provide mechanisms to restrict certain providers based on data sensitivity. Audit Logging: Multi-provider routing decisions should be logged for security auditing and compliance purposes. Provider Trust: The router must validate provider certificates and ensure secure communication channels to all providers. Identity Header Security: Identity headers (X-Authz-User-Id, etc.) MUST only be accepted from trusted authentication gateways. The router SHOULD strip these headers from client requests and only trust them when injected by the authentication layer to prevent authentication bypass. RBAC Policy Security: Role binding configurations should be protected with appropriate access controls. Misconfigured RBAC policies could grant unauthorized access to premium models or providers. Rate Limit Bypass Prevention: In fail-closed mode (recommended for production), rate limiter failures SHOULD reject requests to prevent bypass. Fail-open mode should only be used when availability requirements outweigh rate limit enforcement.

This document requests registration of the following HTTP header fields in the "Message Headers" registry: Request Headers (Decision Assistance):
- X-AI-Multi-Provider
- X-AI-Provider-Pool
- X-AI-Routing-Strategy
- X-AI-Task-Hint
- X-AI-Tool-Categories
- X-AI-Reasoning-Preference
- X-AI-Quality-Threshold
- X-AI-Max-Latency
- X-AI-Cost-Limit
- X-AI-Failover-Policy

Request Headers (Authorization):
- X-Authz-User-Id
- X-Authz-User-Groups
- X-Authz-User-Roles

Response Headers (Transparency):
- X-AI-Provider-Used
- X-AI-Model-Mapped
- X-AI-Auto-Selection
- X-AI-Tool-Mapping
- X-AI-Auto-Decisions
- X-AI-Failover-Occurred
- X-AI-Selection-Confidence
- X-AI-Authz-Applied
- X-AI-User-Role
- X-AI-RBAC-Role

Response Headers (Rate Limiting):
- X-RateLimit-Limit
- X-RateLimit-Remaining
- X-RateLimit-Reset
- X-RateLimit-Retry-After
- X-TokenLimit-Limit
- X-TokenLimit-Remaining

OpenAI Envoy Proxy

The authors thank the OpenAI team for creating the foundational API standard that enables this ecosystem, and the broader AI community for adopting OpenAI-compatible interfaces that make multi-provider orchestration possible. Thanks to the Envoy community for the Rate Limit Service specification and the Kubernetes community for RBAC design patterns that informed the authorization framework.

This document includes comprehensive implementation examples throughout the main sections demonstrating: - Auto-model selection with vendor-neutral routing (Section 4)
- Auto-tool selection and provider mapping (Section 4)
- Performance-based routing with latency and quality constraints (Section 6)
- Security-aware provider selection for compliance (Section 7)
- Multi-turn failover with persistent state tracking (Section 5)
- Workflow branching and state inheritance patterns (Section 8)
- Identity-based authorization with JWT integration (Section 6)
- RBAC-aware routing for multi-tenant deployments (Section 6)
- Rate limiting with RPM/TPM budgets (Section 6) Each example includes complete HTTP/HTTPS request-response pairs showing both the standard OpenAI Responses API format and the optional multi-provider extension headers. The examples are designed to be hackathon-friendly and can be directly adapted for rapid prototyping and production deployment.