Everything in server.yaml that is not one machine's own is now a versioned
document in the store (cluster_settings), the same on every control plane.
The first start against a store imports the file's cluster keys, with the
baseline guard policy when there is none; after that those keys in a file
are ignored, with a warning.
Each control plane polls the document's version and its own server.yaml's
hash. A change is validated and applied: guard live (Gate.Replace), anything
else by a graceful restart that systemd brings back, standbys first and the
leader 30s later. A version that does not load on a machine is not applied;
at start the newest one that loads is used, so a bad edit cannot keep every
control plane down.
llmgw ctl config show/get/set/unset/edit/history/rollback, the admin API
under /api/v1/settings, and the console's Settings screen, which layers the
store's document over the machine's file, write it. Commands beside the
control plane that read cluster keys (init, node add, server add, backup,
upgrade) read the layered configuration; init --bundle writes offline to
the store once it holds the settings.
A standby that becomes the leader re-reads models, engine choices and
facility holds from the store.
Retires appendGuardIfMissing: the import does it.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A --context-length 32768 --ctx-pool 32768 model got llama.cpp's default four
slots of 8192, and refused what the gateway admitted; --parallel is now
what the pool holds of the context. max_output was 1024 for every model,
which cut an editor's agent off mid-file; it is now a quarter of the
context, from 1024 to 8192. --tools writes capabilities.tools, which the
Continue configuration now reads, as it reads max_output.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
It told Continue every model uses tools and nothing of its context: a model
whose calls come back as text stalled Continue's agent, and Continue sent
prompts far over a CPU replica's 4096 tokens, each refused. tool_use is now
written for a model whose catalog entry says tools, and the context length
with a fitting maxTokens whenever the catalog knows it.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Conflicts with facility-aware placement (config, control, admin API routes,
store test tables) all keep both; the generated reference and OpenAPI
document regenerated.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Conflicts with the LoRA work: vLLM's arguments, the engine profile, router
metrics and the scheduler's request all keep both; agent.pb.go regenerated
from the merged proto; the two test fakes of vLLM given separate names.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
ctl federation add/list/remove/sync keeps peers in the store (migration
0029); their /v1/models are offered here as external models named
PEER/MODEL and kept in step, a model still named by a fallback kept
withdrawn. Peers are checked at /ready, timed, and taken out of routing
while down. Requests to a peer carry x-llmgw-hop and are refused (508) when
they come back to a cluster they passed through; residency and no_store
keep requests off peers; X-Request-Id and, with --send-tags, attribution
tags go along. ctl model fallbacks sets overflow to a peer. Console view,
metrics, admin API routes and docs.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Plan §7.5 tier 2, opt-in per profile with kv_events. The agent starts vLLM
publishing KV cache events to a loopback port it listens on (pure-Go zmq4,
msgpack in both of vLLM's encodings), with sha256_cbor hashing and a fixed
PYTHONHASHSEED; it mirrors each engine's blocks and sends net changes up the
existing stream, with a full copy on every new one and a reset on gaps,
restarts and clears. The control plane hands them to the router, for the
sending node's own replicas only. The router tokenizes through a replica's
/tokenize, hashes the tokens as vLLM does (checked against cbor2/hashlib
golden values), and scores replicas by the share of leading blocks held,
falling back to the approximate tree. New metrics beside
llmgw_prefix_decisions_total. Untested against a running vLLM.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A vLLM model can carry PEFT adapters, registered at start, or with
max_loaded_adapters held at most N per replica: the router loads an adapter
through vLLM's runtime LoRA API when a request first wants it, sends the
adapter's requests to a replica that holds it while that one is not
saturated, loads each cold adapter once, and unloads the least recently used
idle one to make room. llama.cpp keeps loading every adapter at start.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The router read SGLang replicas with vLLM's names, and SGLang was started
without --enable-metrics, so it served no /metrics: every scrape failed as
if unreachable and the replica was routed to blind. It now gets
--enable-metrics and a mapping of its own (sglang:num_running_reqs,
num_queue_reqs, token_usage, the token counters and num_requests_total, as
0.5.21 publishes them). engine-check asks python -m sglang.launch_server
--help instead of Python's own help, and SGLang is given --tp-size rather
than the --tp argparse only takes as an abbreviation.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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>
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>
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>
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>
notify.webhooks sends signed JSON or Slack-compatible messages, queued,
retried, de-duplicated hourly; ctl notify test checks each webhook.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
