Network Management Research Group H. Chen Internet-Draft Red Hat Intended status: Informational L. Jalil Expires: 15 September 2026 Verizon N. Cocker Red Hat 14 March 2026 Multi-Provider Extensions for Agentic AI Inference APIs draft-chen-nmrg-multi-provider-inference-api-01 Abstract 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. Changes from -00 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. Status of This Memo This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. Chen, et al. Expires 15 September 2026 [Page 1] Internet-Draft Multi-Provider Inference API March 2026 Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet- Drafts is at https://datatracker.ietf.org/drafts/current/. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress." This Internet-Draft will expire on 15 September 2026. Copyright Notice Copyright (c) 2026 IETF Trust and the persons identified as the document authors. All rights reserved. This document is subject to BCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents (https://trustee.ietf.org/ license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Table of Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3 2. Conventions and Terminology . . . . . . . . . . . . . . . . . 4 3. Problem Statement . . . . . . . . . . . . . . . . . . . . . . 4 4. Design Principles . . . . . . . . . . . . . . . . . . . . . . 5 5. Auto-Selection Parameters for Vendor Neutrality . . . . . . . 6 Vendor-Neutral Parameter Mapping . . . . . . . . . . . . . . . 6 Auto-Model Selection Criteria . . . . . . . . . . . . . . . . . 7 Auto-Tool Selection Framework . . . . . . . . . . . . . . . . . 8 Auto-Reasoning Capability Mapping . . . . . . . . . . . . . . . 8 Auto Parameters and Header Synchronization . . . . . . . . . . 11 6. Extension Headers . . . . . . . . . . . . . . . . . . . . . . 12 Request Headers . . . . . . . . . . . . . . . . . . . . . . . . 12 Response Headers . . . . . . . . . . . . . . . . . . . . . . . 13 Authorization and Identity Headers . . . . . . . . . . . . . . 16 RBAC Framework . . . . . . . . . . . . . . . . . . . . . . . . 17 Rate Limiting . . . . . . . . . . . . . . . . . . . . . . . . . 18 7. Multi-Provider Orchestration with Responses API . . . . . . . 19 Auto-Model Selection with Responses API . . . . . . . . . . . . 19 Auto-Tool Selection with Provider Mapping . . . . . . . . . . . 21 Vendor-Neutral Parameters with Auto-Selection . . . . . . . . . 23 8. Streaming and Auto-Selection Compatibility . . . . . . . . . 25 Streaming with Auto-Model Selection . . . . . . . . . . . . . . 25 Auto-Tool Selection with Responses API . . . . . . . . . . . . 26 Chen, et al. Expires 15 September 2026 [Page 2] Internet-Draft Multi-Provider Inference API March 2026 9. Performance-Based Auto-Selection . . . . . . . . . . . . . . 27 Latency-Optimized Auto-Selection . . . . . . . . . . . . . . . 27 Quality vs Speed Trade-off . . . . . . . . . . . . . . . . . . 28 10. Multi-Turn Failover with Persistent Tracking . . . . . . . . 30 Seamless Failover Example . . . . . . . . . . . . . . . . . . . 31 11. Security-Aware Auto-Selection . . . . . . . . . . . . . . . . 33 HIPAA-Compliant Auto-Selection . . . . . . . . . . . . . . . . 33 Multi-Tier Security with Data Segregation . . . . . . . . . . . 35 12. Advanced Failover and Performance Degradation . . . . . . . . 36 Cascading Failover with Quality Adjustment . . . . . . . . . . 37 13. Workflow State Management and Branching . . . . . . . . . . . 39 Workflow Branching Example . . . . . . . . . . . . . . . . . . 40 14. Implementation Architecture . . . . . . . . . . . . . . . . . 44 Router Components . . . . . . . . . . . . . . . . . . . . . . . 45 15. Backward Compatibility Guarantees . . . . . . . . . . . . . . 45 16. Security Considerations . . . . . . . . . . . . . . . . . . . 46 17. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 46 18. Normative References . . . . . . . . . . . . . . . . . . . . 47 19. Informative References . . . . . . . . . . . . . . . . . . . 47 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 48 Implementation Examples . . . . . . . . . . . . . . . . . . . . . 48 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 49 1. Introduction The OpenAI Responses API [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. Chen, et al. Expires 15 September 2026 [Page 3] Internet-Draft Multi-Provider Inference API March 2026 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 [RFC2119] [RFC8174] when, and only when, they appear in all capitals, as shown here. 2. Conventions and Terminology OpenAI Responses API: The reference API specification for agentic AI inference [OPENAI-RESPONSES-API], 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. 3. Problem Statement While the OpenAI API provides an excellent standard interface for AI inference, several challenges arise when deploying at scale across multiple providers: Chen, et al. Expires 15 September 2026 [Page 4] Internet-Draft Multi-Provider Inference API March 2026 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. 4. Design Principles 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. Chen, et al. Expires 15 September 2026 [Page 5] Internet-Draft Multi-Provider Inference API March 2026 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. 5. Auto-Selection Parameters for Vendor Neutrality 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: Vendor-Neutral Parameter Mapping Key Responses API parameters that require provider-specific mapping: Chen, et al. Expires 15 September 2026 [Page 6] Internet-Draft Multi-Provider Inference API March 2026 +=======================+===================+=====================+ | 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 | +-----------------------+-------------------+---------------------+ Table 1: Vendor-Neutral Parameters Auto-Model Selection Criteria 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 Chen, et al. Expires 15 September 2026 [Page 7] Internet-Draft Multi-Provider Inference API March 2026 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 Auto-Tool Selection Framework 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 Auto-Reasoning Capability Mapping 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 # Auto-reasoning with mixed providers POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: reasoning-optimized Chen, et al. Expires 15 September 2026 [Page 8] Internet-Draft Multi-Provider Inference API March 2026 X-AI-Routing-Strategy: capability-first { "model": "auto", "messages": [ { "role": "user", "content": "Solve: If 3x+7=22, what is x?" } ] } # Router selects provider with native reasoning capability HTTP/1.1 200 OK Content-Type: application/json X-AI-Provider-Used: openai-reasoning X-AI-Model-Mapped: o1-preview X-AI-Auto-Selection: { "reasoning_capability": "native", "provider_selection": { "primary_choice": { "provider": "openai-reasoning", "model": "o1-preview", "reasoning_type": "native_model", "confidence": 0.98 }, "alternatives_considered": [ { "provider": "anthropic", "model": "claude-3-5-sonnet", "reasoning_type": "enhanced", "confidence": 0.85, "reason_not_selected": "native_available" }, { "provider": "local", "model": "llama-3-8b", "reasoning_type": "sim_cot", "confidence": 0.65, "reason_not_selected": "lower_capability" } ] } } { "id": "resp-reasoning-001", "object": "response", Chen, et al. Expires 15 September 2026 [Page 9] Internet-Draft Multi-Provider Inference API March 2026 "created": 1699123456, "model": "auto", "choices": [ { "message": { "role": "assistant", "content": "x=5. Reasoning: 3x+7=22->3x=15->x=5" } ] } ] } Figure 1 *Fallback to Simulated Reasoning Example:* # Same request when native reasoning providers are unavailable POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: reasoning-optimized X-AI-Provider-Pool: anthropic,cohere,local-models { "model": "auto", "messages": [ { "role": "user", "content": "Solve: If 3x+7=22, what is x?" } ] } # Router falls back to simulated reasoning HTTP/1.1 200 OK Content-Type: application/json X-AI-Provider-Used: anthropic-enhanced X-AI-Model-Mapped: claude-3-5-sonnet X-AI-Auto-Selection: { "reasoning_capability": "simulated", "fallback_strategy": { "native_reasoning_available": false, "selected_approach": "enhanced_chain_of_thought", "prompt_enhancement": "added_reasoning_structure", "confidence": 0.87 Chen, et al. Expires 15 September 2026 [Page 10] Internet-Draft Multi-Provider Inference API March 2026 }, "reasoning_simulation": { "technique": "structured_step_by_step", "verification_added": true, "explanation_enhanced": true } } { "id": "resp-reasoning-002", "object": "response", "created": 1699123500, "model": "auto", "choices": [ { "message": { "role": "assistant", "content": "Step-by-step: 3x+7=22->3x=15->x=5" } ] } ] } Figure 2 Auto Parameters and Header Synchronization 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. Chen, et al. Expires 15 September 2026 [Page 11] Internet-Draft Multi-Provider Inference API March 2026 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). 6. Extension Headers 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. Request Headers The following headers can be included in requests to enable multi- provider features: +===========================+==================+==================+ | Header | Values | Description | +===========================+==================+==================+ | X-AI-Multi-Provider | enabled | | Enable multi- | | | disabled | provider | | | | orchestration | | | | (master switch) | +---------------------------+------------------+------------------+ | X-AI-Provider-Pool | CSV of provider | Constrain auto- | | | IDs | selection to | | | | specific | | | | providers | +---------------------------+------------------+------------------+ | X-AI-Routing-Strategy | cost | latency | | Optimization | | | quality | | strategy for | | | capability-first | auto-parameter | | | | decisions | +---------------------------+------------------+------------------+ | X-AI-Task-Hint | reasoning | | Task type hint | | | coding | | to assist model | | | creative | | auto-selection | | | analytical | | | | | multimodal | | +---------------------------+------------------+------------------+ | X-AI-Tool-Categories | CSV of tool | Preferred tool | | | categories | categories to | | | | assist tools | Chen, et al. Expires 15 September 2026 [Page 12] Internet-Draft Multi-Provider Inference API March 2026 | | | auto-selection | +---------------------------+------------------+------------------+ | X-AI-Reasoning-Preference | native | | Reasoning | | | enhanced | | approach | | | simulated | 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 | | Failover | | | automatic | | behavior when | | | manual | auto-selected | | | | providers fail | +---------------------------+------------------+------------------+ Table 2: Multi-Provider Assistance Headers Response Headers When multi-provider features are active, responses include additional headers providing transparency into the routing decisions: Chen, et al. Expires 15 September 2026 [Page 13] Internet-Draft Multi-Provider Inference API March 2026 +==============================+==============================+ | 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 | +------------------------------+------------------------------+ Table 3: Auto-Selection Response Headers *Synchronization Examples:* _Example 1: Proper Sync (Headers Assist Auto Parameters)_ POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: reasoning X-AI-Reasoning-Preference: native X-AI-Quality-Threshold: 0.9 { "model": "auto", "reasoning": "auto", "messages": [{"role": "user", "content": "Solve complex math"}] } # Router behavior: Uses auto parameters with header guidance # Selects native reasoning model with quality >= 0.9 Chen, et al. Expires 15 September 2026 [Page 14] Internet-Draft Multi-Provider Inference API March 2026 Figure 3 _Example 2: Conflict Resolution (Explicit Parameter Wins)_ POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: reasoning { "model": "gpt-4", "reasoning": "auto", "messages": [{"role": "user", "content": "Solve complex math"}] } # Router behavior: Honors explicit "gpt-4" model selection # Only applies auto-reasoning since reasoning="auto" # Ignores task hint for model selection Figure 4 _Example 3: Multi-Provider Disabled Override_ POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: disabled X-AI-Task-Hint: reasoning { "model": "auto", "tools": "auto", "messages": [{"role": "user", "content": "Help with coding"}] } # Router behavior: Treats "auto" as regular values # Routes to single default provider # Ignores all multi-provider headers Figure 5 Chen, et al. Expires 15 September 2026 [Page 15] Internet-Draft Multi-Provider Inference API March 2026 Authorization and Identity Headers 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. +=====================+=============+============================+ | Header | Values | Description | +=====================+=============+============================+ | X-Authz-User-Id | User | Authenticated user ID | | | identifier | (e.g., JWT 'sub' claim) | +---------------------+-------------+----------------------------+ | X-Authz-User-Groups | CSV of | User's group memberships | | | group names | (e.g., JWT 'groups' claim) | +---------------------+-------------+----------------------------+ | X-Authz-User-Roles | CSV of role | User's assigned roles | | | names | | +---------------------+-------------+----------------------------+ Table 4: Authorization Identity Headers These headers follow conventions used by common authentication frameworks including JWT [RFC7519], OAuth 2.0 [RFC6750], and Kubernetes-style RBAC systems. The specific header names MAY vary by deployment; the router SHOULD support configurable header mappings. POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer eyJhbGci... Content-Type: application/json X-Authz-User-Id: alice X-Authz-User-Groups: platform-admins,engineering X-AI-Multi-Provider: enabled {"model": "auto", "messages": [{"role": "user", "content": "Analyze complex system architecture"}]} HTTP/1.1 200 OK X-AI-Provider-Used: premium-provider X-AI-Model-Mapped: advanced-reasoning-model X-AI-Authz-Applied: true X-AI-User-Role: admin Figure 6 Chen, et al. Expires 15 September 2026 [Page 16] Internet-Draft Multi-Provider Inference API March 2026 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. RBAC Framework 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. # Admin user routed to premium model with reasoning POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-Authz-User-Id: alice X-Authz-User-Groups: platform-admins X-AI-Multi-Provider: enabled {"model": "auto", "messages": [{"role": "user", "content": "Analyze code for security vulnerabilities"}]} HTTP/1.1 200 OK X-AI-Provider-Used: vllm-premium X-AI-Model-Mapped: qwen-14b-instruct X-AI-RBAC-Role: admin X-AI-Auto-Selection: { "rbac_evaluation": { "matched_role": "admin", "policy_applied": "admin_unrestricted" }, "model_selection": { "allowed_models": ["qwen-14b-instruct", "qwen-7b-instruct"], "selected": "qwen-14b-instruct", "reasoning_enabled": true } } Chen, et al. Expires 15 September 2026 [Page 17] Internet-Draft Multi-Provider Inference API March 2026 Figure 7 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). Rate Limiting Multi-provider routers SHOULD implement rate limiting to protect infrastructure and ensure fair resource allocation across tenants. +=========================+===========+========================+ | 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 | When rate limit window | | | timestamp | 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 | +-------------------------+-----------+------------------------+ Table 5: Rate Limit Response Headers 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 [ENVOY-RLS] via gRPC), a local in-process limiter using sliding window counters, or a hybrid chain using first-deny semantics. Chen, et al. Expires 15 September 2026 [Page 18] Internet-Draft Multi-Provider Inference API March 2026 # Rate limit exceeded HTTP/1.1 429 Too Many Requests X-RateLimit-Limit: 100 X-RateLimit-Remaining: 0 X-RateLimit-Reset: 1699126800 X-RateLimit-Retry-After: 42 {"error": {"message": "Rate limit exceeded", "type": "rate_limit_error", "code": "rate_limit_exceeded"}} # Successful request with rate limit headers HTTP/1.1 200 OK X-RateLimit-Limit: 1000 X-RateLimit-Remaining: 847 X-TokenLimit-Limit: 1000000 X-TokenLimit-Remaining: 345678 X-AI-Provider-Used: vllm-premium X-AI-RBAC-Role: premium_user Figure 8 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. 7. Multi-Provider Orchestration with Responses API 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. Auto-Model Selection with Responses API Using the OpenAI Responses API with auto-model selection for vendor- neutral multi-provider routing: POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: reasoning X-AI-Routing-Strategy: balanced { "model": "auto", Chen, et al. Expires 15 September 2026 [Page 19] Internet-Draft Multi-Provider Inference API March 2026 "messages": [ { "role": "user", "content": "Solve: integral of x*sin(x^2)" } ], "response_format": { "type": "text" }, "tools": "auto", "max_completion_tokens": 500 } HTTP/1.1 200 OK Content-Type: application/json X-AI-Provider-Used: provider-anthropic X-AI-Model-Mapped: claude-3-5-sonnet X-AI-Auto-Selection: { "model_selection": { "requested": "auto", "criteria": "reasoning", "selected": "claude-3-5-sonnet", "reason": "best_math_reasoning" }, "tool_selection": { "available_tools": ["calculator", "wolfram", "python"], "selected": "python", "reason": "symbolic_math" } } { "id": "resp-abc123", "object": "response", "created": 1699123456, "model": "auto", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "I'll solve using substitution...", "tool_calls": [ { "id": "call_python_123", "type": "function", "function": { Chen, et al. Expires 15 September 2026 [Page 20] Internet-Draft Multi-Provider Inference API March 2026 "name": "python_calculator", "arguments": "{\"code\": \"import sympy as sp...\"}" } }, "finish_reason": "stop" }, "finish_reason": "tool_calls" } ], "usage": { "prompt_tokens": 25, "completion_tokens": 150, "total_tokens": 175 } } Figure 9 Auto-Tool Selection with Provider Mapping The router automatically maps generic tool requests to provider- specific implementations while maintaining Responses API compatibility: POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: coding X-AI-Provider-Pool: openai,anthropic,cohere X-AI-Tool-Categories: web-scraping,data-viz { "model": "auto", "messages": [ { "role": "user", "content": "Create web scraper and visualize data" } ], "tools": "auto", "tool_choice": "auto", "response_format": { "type": "text" } } Chen, et al. Expires 15 September 2026 [Page 21] Internet-Draft Multi-Provider Inference API March 2026 HTTP/1.1 200 OK Content-Type: application/json X-AI-Provider-Used: openai X-AI-Model-Mapped: gpt-4-turbo X-AI-Tool-Mapping: { "requested": "auto", "available_categories": ["web-scraping", "data-viz", "code-exec"], "provider_tools": { "openai": ["browser", "python", "dalle"], "anthropic": ["computer_use", "text_editor"], "cohere": ["web_search", "python_interpreter"] }, "selected_tools": [ { "category": "web-scraping", "provider_tool": "browser", "generic_name": "web_scraper" }, { "category": "data-viz", "provider_tool": "python", "generic_name": "data_visualizer" } ] } { "id": "resp-def456", "object": "response", "created": 1699123500, "model": "auto", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "I'll create a web scraper and visualize data.", "tool_calls": [ { "id": "call_browser_123", "type": "function", "function": { "name": "web_scraper", "arguments": "{\"url\": \"example.com\"}" } }, "finish_reason": "stop" }, Chen, et al. Expires 15 September 2026 [Page 22] Internet-Draft Multi-Provider Inference API March 2026 "finish_reason": "tool_calls" } ], "usage": { "prompt_tokens": 18, "completion_tokens": 95, "total_tokens": 113 } } Figure 10 Vendor-Neutral Parameters with Auto-Selection The Responses API enables vendor-neutral parameter handling through auto-selection, allowing seamless provider switching: POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: analytical X-AI-Tool-Categories: data-analysis,reporting X-AI-Routing-Strategy: cost X-AI-Quality-Threshold: 0.85 { "model": "auto", "messages": [ { "role": "user", "content": "Analyze data and create summary" } ], "tools": "auto", "tool_choice": "auto", "response_format": { "type": "json_schema", "json_schema": { "name": "analysis_report", "schema": { "type": "object", "properties": { "summary": {"type": "string"}, "insights": {"type": "array"} } } Chen, et al. Expires 15 September 2026 [Page 23] Internet-Draft Multi-Provider Inference API March 2026 } }, "max_completion_tokens": "auto" } HTTP/1.1 200 OK Content-Type: application/json X-AI-Provider-Used: cohere X-AI-Model-Mapped: command-r-plus X-AI-Auto-Decisions: { "model_selection": { "criteria": "cost-efficient + data-analysis", "alternatives": { "openai": {"model": "gpt-4o-mini", "cost": 0.15}, "anthropic": {"model": "haiku", "cost": 0.25}, "cohere": {"model": "command-r-plus", "cost": 0.08} }, "selected": "cohere", "reason": "best_cost_above_threshold" }, "tool_mapping": { "data": "cohere_connector", "report": "structured_output" }, "token_optimization": { "requested": "auto", "calculated": 300, "basis": "task_complexity_analysis" } } { "id": "resp-ghi789", "object": "response", "created": 1699123600, "model": "auto", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "{\"summary\": \"Analysis...\", \"data\": [...]}" }, "finish_reason": "stop" } ], "usage": { "prompt_tokens": 150, Chen, et al. Expires 15 September 2026 [Page 24] Internet-Draft Multi-Provider Inference API March 2026 "completion_tokens": 285, "total_tokens": 435 } } Figure 11 8. Streaming and Auto-Selection Compatibility The extensions maintain full compatibility with OpenAI Responses API streaming while providing auto-selection capabilities. Streaming with Auto-Model Selection Server-sent events streaming works with auto-model selection, with provider routing happening before stream initiation: POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: creative X-AI-Routing-Strategy: latency { "model": "auto", "messages": [{"role": "user", "content": "Write a story"}], "stream": true, "max_completion_tokens": "auto" } HTTP/1.1 200 OK Content-Type: text/event-stream X-AI-Provider-Used: anthropic X-AI-Model-Mapped: claude-3-5-sonnet X-AI-Auto-Selection: {"criteria": "creative", "confidence": 0.92} data: {"id":"resp-stream1","object":"response.chunk",...} data: {"id":"resp-stream1","object":"response.chunk",...} data: [DONE] Figure 12 Chen, et al. Expires 15 September 2026 [Page 25] Internet-Draft Multi-Provider Inference API March 2026 Auto-Tool Selection with Responses API Tool calling with auto-selection maps generic tool requests to provider-specific implementations seamlessly: POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: multimodal X-AI-Tool-Categories: weather,web-search { "model": "auto", "messages": [ { "role": "user", "content": "What's the weather in Boston and latest news?" } ], "tools": "auto", "tool_choice": "auto" } HTTP/1.1 200 OK Content-Type: application/json X-AI-Provider-Used: openai X-AI-Model-Mapped: gpt-4o X-AI-Tool-Mapping: { "weather": "openai_weather_tool", "web-search": "openai_browser_tool" } { "id": "resp-func123", "object": "response", "created": 1699123700, "model": "auto", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "I'll get the weather and latest news for you.", "tool_calls": [ { "id": "call_weather_123", Chen, et al. Expires 15 September 2026 [Page 26] Internet-Draft Multi-Provider Inference API March 2026 "type": "function", "function": { "name": "weather_lookup", "arguments": "{\"location\": \"Boston\"}" } }, { "id": "call_news_456", "type": "function", "function": { "name": "web_search", "arguments": "{\"query\": \"Boston latest news\"}" } }, "finish_reason": "stop" }, "finish_reason": "tool_calls" } ] } Figure 13 9. Performance-Based Auto-Selection The OpenAI Responses API with auto-selection enables dynamic provider routing based on performance requirements and real-time characteristics. Latency-Optimized Auto-Selection Applications can specify performance requirements through extension headers while using standard Responses API calls: POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: support X-AI-Routing-Strategy: latency X-AI-Max-Latency: 200 { "model": "auto", "messages": [ { "role": "user", Chen, et al. Expires 15 September 2026 [Page 27] Internet-Draft Multi-Provider Inference API March 2026 "content": "Quick support response needed" } ], "max_completion_tokens": "auto", "response_format": {"type": "text"} } HTTP/1.1 200 OK Content-Type: application/json X-AI-Provider-Used: edge-provider-fast X-AI-Model-Mapped: fast-response-model X-AI-Auto-Selection: { "latency_achieved_ms": 180, "alternatives_rejected": [ {"provider": "cloud", "latency_ms": 350, "reason": "slow"}, {"provider": "premium", "latency_ms": 800, "reason": "limit"} ], "performance_tier": "edge-optimized" } { "id": "resp-support-001", "object": "response", "created": 1699123456, "model": "auto", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "I can help you right away..." }, "finish_reason": "stop" } ] } Figure 14 Quality vs Speed Trade-off Auto-selection can balance quality and performance requirements: Chen, et al. Expires 15 September 2026 [Page 28] Internet-Draft Multi-Provider Inference API March 2026 POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: analytical X-AI-Quality-Threshold: 0.85 X-AI-Max-Latency: 2000 { "model": "auto", "messages": [ { "role": "user", "content": "Analyze legal document for compliance" } ], "max_completion_tokens": "auto" } HTTP/1.1 200 OK Content-Type: application/json X-AI-Provider-Used: premium-balanced X-AI-Model-Mapped: legal-analysis-model X-AI-Auto-Decisions: { "quality_achieved": 0.92, "latency_ms": 1800, "tradeoffs": { "fastest": {"quality": 0.72, "rejected": "below_thresh"}, "highest_quality": {"latency_ms": 5000, "rejected": "too_slow"} }, "selection_rationale": "optimal_quality_within_latency_constraint" } { "id": "resp-legal-002", "object": "response", "created": 1699123500, "model": "auto", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "Based on my analysis of the document..." }, "finish_reason": "stop" } Chen, et al. Expires 15 September 2026 [Page 29] Internet-Draft Multi-Provider Inference API March 2026 ] } Figure 15 10. Multi-Turn Failover with Persistent Tracking The extensions maintain conversation continuity during provider failures through persistent ID tracking and state preservation using standard OpenAI conversation patterns. Client Router Provider A Provider B State Store | | | | | | Start | | | | | Conv | | | | |--------->| | | | | | 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 | | | | |<---------| | | | Figure 16: Multi-Turn Failover with OpenAI API Chen, et al. Expires 15 September 2026 [Page 30] Internet-Draft Multi-Provider Inference API March 2026 Seamless Failover Example Multi-turn conversations maintain context automatically during failover: # Turn 1: Initial request to Provider A POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: coding X-AI-Failover-Policy: automatic { "model": "auto", "messages": [ { "role": "user", "content": "Debug: def calc(x): return x/0" } ] } # Response from Provider A HTTP/1.1 200 OK Content-Type: application/json X-AI-Provider-Used: provider-a X-AI-Model-Mapped: code-assistant-model X-AI-Conversation-ID: conv-debug-001 { "id": "resp-debug-001", "object": "response", "created": 1699123456, "model": "auto", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "Division by zero error. Add error handling." }, "finish_reason": "stop" } ] } Chen, et al. Expires 15 September 2026 [Page 31] Internet-Draft Multi-Provider Inference API March 2026 # Turn 2: Follow-up (Provider A fails, auto-failover to Provider B) POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: coding X-AI-Failover-Policy: automatic { "model": "auto", "messages": [ { "role": "user", "content": "Debug Python: def calc(x): return x/0" }, { "role": "assistant", "content": "Division by zero error. Add error handling." }, { "role": "user", "content": "Show me the corrected code" } ] } # Auto-failover response from Provider B HTTP/1.1 200 OK Content-Type: application/json X-AI-Provider-Used: provider-b X-AI-Model-Mapped: advanced-coder-model X-AI-Failover-Occurred: true X-AI-Auto-Selection: { "failover_reason": "provider_a_timeout", "failover_time_ms": 1200, "context_preserved": true, "conversation_continuity": "maintained" } { "id": "resp-debug-002", "object": "response", "created": 1699123500, "model": "auto", "choices": [ { "index": 0, Chen, et al. Expires 15 September 2026 [Page 32] Internet-Draft Multi-Provider Inference API March 2026 "message": { "role": "assistant", "content": "Here's the corrected code with error handling" }, "finish_reason": "stop" } ] } Figure 17 11. Security-Aware Auto-Selection The extensions handle providers with different security requirements and compliance levels through security-aware auto-selection. Client Router Security Provider Pool | | Evaluator | | Request + | | | | Sec Reqs | | | |---------->| | | | | Evaluate | | | | Security | | | |----------->| | | | | Check | | | | Compliance | | | | Hi|Med|Lo | | | | | | |<-----------| | | | Security | | | | Matched | | | | | | | | Route to | | | | Compliant | | | |------------------------>| | | | | | |<------------------------| | Response +| | | | Sec Info | | | |<----------| | | Figure 18: Security-Aware Provider Selection HIPAA-Compliant Auto-Selection Medical data processing with strict compliance requirements: Chen, et al. Expires 15 September 2026 [Page 33] Internet-Draft Multi-Provider Inference API March 2026 POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: medical X-AI-Security-Requirements: hipaa,pii X-AI-Data-Classification: sensitive-medical { "model": "auto", "messages": [ { "role": "user", "content": "Analyze patient symptoms for diagnosis" } ], "tools": "auto", "response_format": {"type": "text"} } HTTP/1.1 200 OK Content-Type: application/json X-AI-Provider-Used: healthcare-secure X-AI-Model-Mapped: medical-analysis-hipaa X-AI-Auto-Selection: { "security_compliance": { "hipaa": "certified", "soc2_type2": "verified", "encryption": "aes256_end_to_end", "data_residency": "us_only" }, "rejected_providers": [ {"provider": "public", "reason": "insufficient_hipaa"}, {"provider": "intl", "reason": "data_residency"} ] } { "id": "resp-medical-001", "object": "response", "created": 1699123456, "model": "auto", "choices": [ { "index": 0, "message": { "role": "assistant", Chen, et al. Expires 15 September 2026 [Page 34] Internet-Draft Multi-Provider Inference API March 2026 "content": "Based on the symptom analysis..." }, "finish_reason": "stop" } ] } Figure 19 Multi-Tier Security with Data Segregation Financial workflow with mixed sensitivity levels using auto- selection: POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: financial X-AI-Security-Requirements: pci-dss,sovereignty X-AI-Data-Classification: financial-mixed { "model": "auto", "messages": [ { "role": "user", "content": "Generate financial report" } ], "tools": "auto", "response_format": { "type": "json_schema", "json_schema": { "name": "financial_report", "schema": { "type": "object", "properties": { "public_data": {"type": "object"}, "private_data": {"type": "object"} } } } } } HTTP/1.1 200 OK Chen, et al. Expires 15 September 2026 [Page 35] Internet-Draft Multi-Provider Inference API March 2026 Content-Type: application/json X-AI-Provider-Used: multi-tier-financial X-AI-Model-Mapped: financial-segregation X-AI-Auto-Selection: { "strategy": "segregation", "allocation": { "public": {"provider": "public", "sec": "basic"}, "pci": {"provider": "secure", "sec": "pci"}, "conf": {"provider": "private", "sec": "max"} }, "flow_controls": { "cross_tier": "prohibited", "aggregation": "secure_comp" } } { "id": "resp-financial-001", "object": "response", "created": 1699123456, "model": "auto", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "{\"public\": {...}, \"private\": {...}}" }, "finish_reason": "stop" } ] } Figure 20 12. Advanced Failover and Performance Degradation The extensions implement sophisticated failover strategies that handle performance degradation and cascading failures while maintaining OpenAI API compatibility. Chen, et al. Expires 15 September 2026 [Page 36] Internet-Draft Multi-Provider Inference API March 2026 Client Router Monitor Prov-A Prov-B Prov-C | | | | | | | Req | | | | | |----->| | | | | | | Mon | | | | | | Start| | | | | |----->| | | | | | | Check | | | | | | Health| | | | | |------>| | | | | | |Degrade| | | | |<------| | | | | | | | | | | Route| | | | | | to B | | | | | |--------------------->| | | | | | | | | | | Mon | | | | | | Prov B| | | | | |-------------->| | | | | | Overload | | | |<--------------| | | | | | | | | |GRACEFUL | | | | |DEGRADE | | | | |Route C | | | | |----------------------------->| | | | | | | | |<-----------------------------| | Resp | | | | | |<-----| | | | | Figure 21: Advanced Failover with Performance Monitoring Cascading Failover with Quality Adjustment Auto-selection with graceful degradation during system stress: POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: creative X-AI-Quality-Threshold: 0.8 X-AI-Failover-Policy: cascading { Chen, et al. Expires 15 September 2026 [Page 37] Internet-Draft Multi-Provider Inference API March 2026 "model": "auto", "messages": [ { "role": "user", "content": "Write comprehensive product documentation" } ], "max_completion_tokens": "auto" } HTTP/1.1 200 OK Content-Type: application/json X-AI-Provider-Used: provider-fast X-AI-Model-Mapped: efficient-writer-model X-AI-Auto-Selection: { "failover_cascade": { "primary_attempt": { "provider": "premium", "status": "degraded", "quality_estimate": 0.95, "response_time_ms": 8000, "decision": "too_slow" }, "secondary_attempt": { "provider": "balanced", "status": "overloaded", "queue_depth": 150, "decision": "capacity_exceeded" }, "tertiary_selection": { "provider": "fast", "status": "available", "quality_estimate": 0.82, "response_time_ms": 1200, "decision": "selected_with_quality_adjustment" } }, "quality_adjustment": { "target": 0.95, "achieved": 0.82, "mitigation": "post_processing_available" } } { "id": "resp-docs-001", "object": "response", "created": 1699123456, Chen, et al. Expires 15 September 2026 [Page 38] Internet-Draft Multi-Provider Inference API March 2026 "model": "auto", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "# Product Docs\n\nGuide..." }, "finish_reason": "stop" } ] } Figure 22 13. Workflow State Management and Branching Complex workflows can branch and merge while maintaining conversation state through standard OpenAI message arrays and extension headers. Chen, et al. Expires 15 September 2026 [Page 39] Internet-Draft Multi-Provider Inference API March 2026 Initial Request | v +-------------+ | Base | | Analysis | | Provider A | | conv-001 | +------+------+ | | State: base | +-------+--------+ | | v v +-----------+ +-----------+ | Marketing | | Product | | Prov B | | Prov C | | conv-mkt | | conv-prod | +-----+-----+ +-----+-----+ | | | Inherits | Inherits | Base State | Base State | | +-------+--------+ | v +-------------+ | Final Report| | Provider D | | conv-final | +-------------+ | | Merges v Final Report Figure 23: Workflow Branching with State Inheritance Workflow Branching Example Multi-branch workflow with state inheritance using conversation arrays: Chen, et al. Expires 15 September 2026 [Page 40] Internet-Draft Multi-Provider Inference API March 2026 # Initial workflow step POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: analytical { "model": "auto", "messages": [ { "role": "user", "content": "Analyze user behavior data" } ] } # Base analysis response HTTP/1.1 200 OK Content-Type: application/json X-AI-Provider-Used: analytics-provider X-AI-Conversation-ID: conv-behavior-001 X-AI-Workflow-Step: base-analysis { "id": "resp-base-001", "object": "response", "created": 1699123456, "model": "auto", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "Analysis complete. Found 3 segments..." }, "finish_reason": "stop" } ] } # Branch 1: Marketing insights POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled Chen, et al. Expires 15 September 2026 [Page 41] Internet-Draft Multi-Provider Inference API March 2026 X-AI-Task-Hint: marketing X-AI-Parent-Conversation: conv-behavior-001 X-AI-Workflow-Branch: marketing { "model": "auto", "messages": [ { "role": "user", "content": "Analyze user behavior data for insights" }, { "role": "assistant", "content": "Analysis complete. Found 3 segments..." }, { "role": "user", "content": "Generate marketing recommendations" } ] } # Branch 2: Product insights (parallel) POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: product X-AI-Parent-Conversation: conv-behavior-001 X-AI-Workflow-Branch: product { "model": "auto", "messages": [ { "role": "user", "content": "Analyze user behavior data for insights" }, { "role": "assistant", "content": "Analysis complete. Found 3 segments..." }, { "role": "user", "content": "Generate product improvements" } ] Chen, et al. Expires 15 September 2026 [Page 42] Internet-Draft Multi-Provider Inference API March 2026 } # Merge branches for final report POST /v1/responses HTTP/1.1 Host: multi-provider.example.com Authorization: Bearer sk-... Content-Type: application/json X-AI-Multi-Provider: enabled X-AI-Task-Hint: analytical X-AI-Merge-Branches: marketing,product { "model": "auto", "messages": [ { "role": "user", "content": "Create executive summary" } ], "tools": "auto", "response_format": { "type": "json_schema", "json_schema": { "name": "executive_summary", "schema": { "type": "object", "properties": { "marketing_insights": {"type": "array"}, "product_recommendations": {"type": "array"}, "combined_strategy": {"type": "string"} } } } } } HTTP/1.1 200 OK Content-Type: application/json X-AI-Provider-Used: report-generator X-AI-Model-Mapped: executive-summary-model X-AI-Auto-Selection: { "branches_merged": ["marketing", "product"], "context_integration": "complete", "workflow_completion": "success" } { "id": "resp-final-001", Chen, et al. Expires 15 September 2026 [Page 43] Internet-Draft Multi-Provider Inference API March 2026 "object": "response", "created": 1699123600, "model": "auto", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "{\"insights\": [...], \"strategy\": \"...\"}" }, "finish_reason": "stop" } ] } Figure 24 14. Implementation Architecture 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. Client Applications (Standard OpenAI API) | | HTTP/HTTPS v +------------------+ | Multi-Provider | | Router | | - Header parsing | | - Route decision | | - Failover logic | | - Response merge | | - RBAC engine | | - Rate limiter | +------------------+ | +----+----+----+ | | | | v v v v +------+ +------+ +------+ +------+ |OpenAI| |Azure | |Anthro| |Local | |API | |OpenAI| |Claude| |Model | +------+ +------+ +------+ +------+ Figure 25: Multi-Provider Router Architecture Chen, et al. Expires 15 September 2026 [Page 44] Internet-Draft Multi-Provider Inference API March 2026 Router Components 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. 15. Backward Compatibility Guarantees 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. Chen, et al. Expires 15 September 2026 [Page 45] Internet-Draft Multi-Provider Inference API March 2026 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. 16. Security Considerations 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. 17. IANA Considerations This document requests registration of the following HTTP header fields in the "Message Headers" registry: Chen, et al. Expires 15 September 2026 [Page 46] Internet-Draft Multi-Provider Inference API March 2026 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 18. Normative References [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", RFC 2119, March 1997, . [RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC 2119 Key Words", RFC 8174, May 2017, . 19. Informative References Chen, et al. Expires 15 September 2026 [Page 47] Internet-Draft Multi-Provider Inference API March 2026 [OPENAI-RESPONSES-API] OpenAI, "OpenAI Responses API Specification", 2025, . [RFC6750] Jones, M. and D. Hardt, "The OAuth 2.0 Authorization Framework: Bearer Token Usage", RFC 6750, October 2012, . [RFC7519] Jones, M., Bradley, J., and N. Sakimura, "JSON Web Token (JWT)", RFC 7519, May 2015, . [ENVOY-RLS] Envoy Proxy, "Envoy Rate Limit Service", 2025, . Acknowledgments 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. Implementation Examples 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. Chen, et al. Expires 15 September 2026 [Page 48] Internet-Draft Multi-Provider Inference API March 2026 Authors' Addresses Huamin Chen Red Hat Boston, MA 02210 United States of America Email: hchen@redhat.com Luay Jalil Verizon Richardson, TX United States of America Email: luay.jalil@verizon.com Nabeel Cocker Red Hat New York, NY United States of America Email: ncocker@redhat.com Chen, et al. Expires 15 September 2026 [Page 49]