One bidirectional connect-go stream per node, carrying registration, heartbeats
and shard status up, and start, stop and drain down. The agent dials out: GPU
nodes sit behind firewalls far more often than a control plane does, and an
outbound connection needs no inbound rule on every node in the fleet.
- registration carries the whole hardware inventory, so there is no separate
discovery call and a reconnecting agent refreshes what the scheduler knows.
The fabric labels travel with it; they cannot be inferred from the wire, so
losing them in transit would silently disable multi-node placement.
- agentsvc is both the stream handler and the reconciler's dispatcher, because
it holds the open streams and is therefore the only thing that can deliver a
command to a node.
- the agent validates a profile before launching anything, since an engine that
refuses a profile does so after minutes of loading, then supervises the
process and reports every transition with the engine's output tail on failure.
- reconnects forever with jittered backoff. A control plane restart must not
require touching every GPU node, and a fleet must not reconnect in lockstep
and knock it over again. Engines keep serving throughout, because the router
talks to them directly.
llmgw agent is real now, and llmgw server runs a control plane whenever any
model declares a profile. The static endpoint list stays for router-only runs.
Verified with real binaries as separate processes: the agent registers, a
replica is placed, an engine launches through the vLLM adapter's actual argument
construction, streaming traffic is served, and killing the agent moves the node
through unreachable to lost with placement explaining why nothing can run.
config.Model and catalog.Model were the same thing described twice, so the
router now reads catalog entries directly and the validation rules live in one
place.
Still missing: mTLS. The stream is plaintext HTTP/2 and must stay on a trusted
network until that lands.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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>
ilkerandClaude Opus 5