ilkerandClaude Opus 5
a3f71aa5ee
CI / test (push) Successful in 1m1s
llmgw server is real now. It fronts a static set of engine replicas with an OpenAI-compatible API, scoring endpoints through the pipeline added in the previous commit. - proxy: SSE relayed event by event with a flush after each, cancellation propagated upstream, and separate deadlines for dial, headers, first token, inter-token idle and total. The idle watchdog is what distinguishes a wedged engine from a slow one. - router: dispatch with retry and model fallback. Retry only happens while nothing has reached the client; after that, splicing two responses together would be worse than the short stream the client already sees. - config: YAML via koanf overlaid on defaults, with LLMGW_ env overrides. Validation rejects duplicate endpoint ids and aliases that shadow a model, because both misroute silently at request time rather than failing loudly. - metrics: requests, duration, TTFT, tokens, plus per-scorer values and per-filter drops on a separate admin listener with health and pprof. Phase 1 exit criterion, measured in test/e2e: 1000 concurrent streams across 3 sim-engines complete at ~6800 req/s; every client disconnect frees the KV blocks on every engine; router overhead p99 is about 0.9 ms against the plan's 2 ms budget. The handlers live in internal/router rather than an internal/router/openai package as the plan's tree sketched: they need the dispatch loop, and splitting them apart would have meant an interface with exactly one implementation. Plan §4 updated to match. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>