Adaptive LLM Gateway
The gateway treats model selection as a policy and reliability problem with explicit gates, scoring, health checks, retries, fallbacks, and escalation.
Status
Public mock-first V1
Capabilities
Model routing · Policy design · Observability
The problem
A keyword router cannot reliably balance privacy, context limits, task capability, latency, cost, provider health, and failure recovery.
What I worked on
The gateway scores candidates across nine signals, applies hard gates for requirements such as privacy and context, executes bounded provider attempts, records telemetry, and returns structured escalation when no safe candidate succeeds.
Decisions and tradeoffs
Use hard gates for non-negotiable requirements
Privacy violations, context overflow, missing required capabilities, and open circuits remove a model from consideration.
Explain routing decisions
Ranked candidates include reason codes and decision traces so model selection can be inspected.
Escalate cleanly when execution fails
The gateway returns a structured failure state when the candidate set is empty or every provider attempt fails.
Evidence and scope
Provider catalogs, traces, and evaluation fixtures are synthetic. The repository makes no live benchmark or production reliability claim.
What this shows
Policy design, model operations, cost and latency reasoning, and explicit failure handling around AI infrastructure.
Implementation details
Key subsystems
- Nine-signal candidate scoring
- Privacy and capability hard gates
- Health-aware retries and fallback chains
- Telemetry and offline evaluation harness