LLM Smart Proxy: A common boundary for model routing and fallback
CreatorPublic project · 2026
A public project documenting multi-provider request routing, capability-aware selection, fallback, discovery and context compaction behind an OpenAI-compatible interface.
Context & constraints
Working across model providers creates integration and routing decisions: which capability fits a request, what happens when a provider fails, and how to work with different context and interface requirements. LLM Smart Proxy explores a common application-facing boundary around those concerns. It complements orchestration work by focusing on model/provider access rather than the division of engineering tasks.
- Providers and models differ in capabilities and interface behavior.
- A routing layer needs to make fallback and selection behavior understandable to the application using it.
- Context limits and compaction introduce a quality tradeoff: a smaller context can lose useful information.
- Compatibility bridges and provider discovery evolve as external interfaces change.
My contribution
- Created a public multi-provider proxy project with an OpenAI-compatible API and a documented Responses bridge.
- Documented capability groups, dynamic scoring and request routing for model/provider selection.
- Included automatic failover and discovery in the documented design.
- Documented context compaction and quality-check capabilities as part of the proxy’s behavior.
- Published the project for direct inspection of its routing and integration approach.
Technology context OpenAI-compatible API · Responses bridge · Multi-provider routing · Capability discovery · Failover · Context compaction
Proxy responsibilities
The public project’s documented design groups the application boundary, routing responsibilities and provider integration.
Components & responsibilities
- Application / agent client
- Makes a model request through a common interface.
- Compatible API boundary
- OpenAI-compatible API and Responses bridge.
- Selection & routing
- Capability groups, discovery and dynamic scoring.
- Context & quality controls
- Documented compaction and quality-check capabilities.
- Model providers
- Provider integration and automatic failover.
Connections & flow
- Application / agent client to Compatible API boundaryModel request
- Compatible API boundary to Selection & routingRoute by capabilities
- Selection & routing to Context & quality controlsContext/quality handling
- Context & quality controls to Model providersProvider request & fallback
- Model providers to Compatible API boundaryCompatible response
Conceptual view of the project’s documented responsibilities; arrows do not imply a fixed request sequence.
Decisions & trade-offs
Offer a common application-facing interface
The application can express a request through a familiar API boundary while provider concerns are handled behind it.
Trade-off: Compatibility work must account for differences between providers and changing interfaces.
Route by capabilities and include fallback
Selection can consider what a request needs and provide a path when a provider is unavailable.
Trade-off: Fallback can change response behavior, so routing choices need clear controls and quality checks.
Include context compaction in the design
Long interactions need a strategy for constrained context windows.
Trade-off: Compaction reduces context size while risking loss of information that may matter later.
Outcome & operation
Published a project exploring model-provider integration, routing, fallback and context handling, with source and design documentation available on GitHub.
Notes & references
- Public repository
- The project and its design documentation are available on GitHub.