Every template and handler message in internal/web goes through a JSON
catalogue under internal/web/locales/<code>/, with English as the fallback.
The language is the person's choice (a cookie set by /_lang/{code}), else
Accept-Language, else landing.language. Tests refuse a key some language
lacks, a translation with other arguments than English, and template text
left in English.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Conflicts: docs.go (manual, OpenAPI and suites all kept), modelserve Job
(trace and eval both kept). The eval routes are added to the OpenAPI route
table, update and promote documented with their optional body, and the
document regenerated.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Conflicts: config imports and docgen sources (both kept); agent.pb.go
regenerated with buf from the merged proto (MIG devices and trace context).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A suite is a set of cases in YAML or JSON lines, each scored by exact,
contains, regex, a number within a tolerance, a JSON Schema subset, or a
judge model with a rubric; weighed into one score against a threshold.
internal/bench/eval runs one on the control plane through Router.Replay, as
the gateway's own requests, and keeps every run and its cases in the store
(migration 0028: eval_runs, eval_cases).
- llmgw ctl eval MODEL --suite FILE|NAME, --history, --run, --suites; exits
non-zero below the score. Suites by name from control.eval_dir, or the
two built in from deploy/eval (arithmetic, json-format).
- model update and promote take --eval SUITE --min-score N, or a model's
standing gate (ctl model gate, eval: in the catalog): a promote asks the
canary first and leaves it below the score; an update tries the new
weights as a canary given no share, then promotes or removes it.
- The console's model menu: ask a suite with its progress, its runs, its
gate.
- The audit middleware no longer hands a handler a body cut off at 64 KiB.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
terraform-provider-llmgw/ is a module of its own, built on
terraform-plugin-framework, so the gateway does not carry its dependencies.
llmgw_model is a model added at runtime: scaling, class, owner and adapters
change in place, anything else replaces it; the definition is kept as
written rather than as the catalog filled it in. llmgw_quota and
llmgw_guard_attachment keep the scope as written beside the ID it resolved
to, and import by that ID. Data sources llmgw_models and llmgw_nodes.
The client lists every endpoint it calls and refuses any other; a test
checks each, and every field it sends and reads, against
docs/admin-api.openapi.json, so a change to the admin API that breaks the
provider fails here. Acceptance tests run Terraform against an admin API in
memory when TF_ACC is set. CI vets and tests the module.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A reconcile pass that does something is a trace, with placement (where it
went, or every candidate turned down), scale-up/down, drain, stop, eviction,
preemption, reaping and restarts beneath it. A replica's start is a span open
from placement until it serves; the start command carries its context in a
new ControlMessage.trace field, and the agent continues the trace through
the weights fetch, engine launch, rendezvous and readiness wait. Stops,
drains, adoption and engine installs are traced on the node, model jobs are
a trace with a span per step linked to the replica's start, and agent.yaml
takes the same tracing block as server.yaml. Off, every span is the no-op one
and commands carry nothing.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Engine trtllm runs trtllm-serve on one node, through a launcher of
llmgw's that demands the replica's key and turns on the Prometheus
endpoint its queue and cache gauges are read from; the router reads it
at /prometheus/metrics. Brought by the operator (engines.trtllm.python),
not installed; checked by ctl engine-check trtllm. Written against the
documented CLI, server and metrics and tested with stand-ins: not yet
run on a datacenter GPU.
mirror import-ollama copies a model's GGUF, and a vision model's
projector, from Ollama's directory into the mirror, checked against the
digest Ollama named each blob by; mirror import does the same for any
local file. The console's Mirror view imports from Ollama with I.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Discovery reports a card in MIG mode as its instances instead of the whole
card, on both paths: nvidia-smi (-L names them, -q -x gives their memory,
ids and processes) and NVML (GetMigMode, GetMaxMigDeviceCount,
GetMigDeviceHandleByIndex, behind an interface so it is tested with a fake).
Each instance has its MIG UUID, its own memory, and a class from the card's
and the profile: a100-80g-mig-3g.40gb. Placement numbers instances
1000 + 100 x card + instance and every view shows them as card/migN.
A replica on MIG runs on exactly one instance: a process opens one MIG
device and instances have no NVLink between them, so tp, pp, gpus or nodes
above one on a MIG class are refused in catalog validation and again in
placement, and the planner offers one instance or none. The agent gives
the engine CUDA_VISIBLE_DEVICES=MIG-<uuid> (no Vulkan device variable on
MIG). Memory and processes are per instance; heat, power and wear the
card's; utilisation is left out rather than reported as zero. A card in MIG
mode with nothing carved stays out of placement with the reason, and
llmgw doctor reports each card's mode and instances and warns of the states
in which nothing can run. ctl nodes, ctl status, the console and the plan
count devices by class. docs/install.md has a MIG section.
Written against fakes of nvidia-smi and NVML output; not yet run on MIG
hardware.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
llmgw keys cache --project P --ttl 1h [--any] | --off, and Cache answers… on
the console's project menu. The same request — project, endpoint, canonical
model and the body without stream, stream_options, user, metadata, store and
service_tier, keys sorted — is answered from the gateway's memory for the
TTL. Only reproducible answers (temperature 0 or a seed; embeddings, rerank,
score) unless --any; only a whole 200 from the model asked for, not a
fallback's, a canary's, or one a guardrail changed.
Looked up after the limits and the input guardrails; the output policy
judges a reused answer; no_store and stored conversations bypass it.
Streamed answers are kept put back together and streamed back as chunks.
x-llmgw-cache: hit|miss|bypass, Age on a hit, Cache-Control: no-cache or
x-llmgw-cache: no to bypass. A hit is recorded with no tokens and no cost,
marked cache_hit (migration 0027) and shown by ctl request. An LRU bounded
by router.cache.max_bytes and max_entry_bytes; llmgw_cache_requests_total,
llmgw_cache_bytes, llmgw_cache_entries.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The manual the binary carries, rendered on the client port: a sidebar of
the documents in the console's reading order (now one list, llmgw.Manual,
for both), heading anchors, links between documents kept in the portal and
links to files outside the binary sent to the repository, no script.
Public by default; landing.docs_sign_in sends visitors to sign in first.
docs/clients.md shows the official OpenAI and Anthropic SDKs against
llmgw in Python, TypeScript, Go and curl, with llmgw's headers; the
portal's index, the landing page and the account page link to it.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
