mirror add-peer, run on the mirror's machine, sets up a mirror on
another over ssh, kept in step with this one: llmgw there if it has
none, mirror init --peer, this mirror told of it and allowed to be read
by it, and mirror.peers in the cluster's settings. The setup screens
offer one on either database machine, as server init --second-mirror.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A repository with neither GGUF nor safetensors (by the registry's summary
or its tags) is hidden under "runs here", which is ticked by default, and
counted with the architectures llama.cpp cannot load; unticked it is
listed. An open repository's pickle files are not offered, only counted,
and a repository of pickles only says to look for another version.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Python pickles (.bin, .pt, .pth, .ckpt, .pkl) run whatever they hold when
loaded, as the engine's user, on the node; that is how the malicious models
found on Hugging Face work. The node refuses such a source, or a mirror set
holding one, before fetching anything. A model added with one, or given
one, is refused, while one already in the catalog still loads and is
refused by its node with the same words. vLLM and SGLang downloading a
model by its id get --load-format safetensors. The mirror page marks the
file. allow_pickle: true on a model lets it through on purpose, carried to
the node in EngineProfile.allow_pickle.
BACKLOG M lists the rest: checksums against Hugging Face on every path,
its own scan, trusted publishers, engines without the internet, an eval
before an untrusted model serves.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
llmgw keys cache --similar 0.95 also gives a chat question the answer to
one like it: the last user message embedded by router.cache.embedding_model
(asked on the gateway's account, as a judge is, within 2 s), compared by
cosine with the questions answered in the same conversation — system
prompt, earlier turns, tools and sampling the same. The exact cache is
asked first; an exact hit embeds nothing. Said as x-llmgw-cache: similar
with x-llmgw-cache-similarity, charged as a hit.
The vectors live with their answers in the gateway's memory, counted in
max_bytes and evicted with them, not in a pgvector table as BACKLOG I1
planned: the cache keeps answer text out of the store on purpose. A
question over 2,000 characters is reused only when the same. Migration
0051 holds the setting. BACKLOG I1.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
cached_per_1k in a price charges the prompt tokens the answer counts as
cached_tokens at their own rate; zero keeps charging them as prompt tokens.
The count reaches the cost and not a column: CostMicros holds what it
changed. Cached beyond the prompt is clamped, an engine's miscount rather
than a refund. BACKLOG L5.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
The router keeps each endpoint's time to first token, smoothed over its last
streamed answers, and the chat profile scores by it beside session, queue,
KV and inflight: the fastest endpoint 1, one twice as slow 0.5, one not yet
measured 1 so a new replica is tried. Load still sees a slow drain from the
engine's own counters; this sees what answers actually took. BACKLOG L4.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
docs/kong.md: a plain route or ai-proxy with llmgw as the upstream, the
client's llmgw key passed on so per-team budgets hold, Kong as a provider
behind llmgw, and Kong 3.9's streamed answers counted without completion
tokens. Closes BACKLOG L9.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
A site may be required to use a particular guardrail: Azure Content Safety,
Lakera, NeMo, a classifier of its own. A policy's services asks one over
HTTP — a body template given the text, header values from files read each
call, dotted paths to where the answer says unsafe and why — on input,
output or both, to block or flag. On input it reads the latest user turn
before the judge; on output the whole answer once at its end. A service
that does not answer is what the policy's fail says. It is told the text
only, never who sent it.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
With restore: true on a guardrail policy's input, what redact rewrote as
placeholders comes back in the answer: the model reads "[EMAIL_1]", the
client reads the address it sent. A streamed placeholder split across
events is held until it is whole, and what is held is sent at the end.
The swap runs before the output filter, so the output rules judge the
answer as the client reads it and a placeholder they make of a value the
model invented is never taken for the request's own. The map lives in the
Verdict for the request only; nothing stores, logs or traces it.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
An inference request and each attempt at an engine carry gen_ai.*: the
operation, the requested and answering model, max_tokens, temperature and
top_p when the client set them, the session as the conversation id, the
input, output and cached tokens, and on an attempt the engine that answered.
A customer's Grafana, Jaeger or Langfuse then shows llmgw's calls as model
calls. No prompt or answer text is ever put in a span, and a test says so.
BACKLOG gains section L: what is worth taking from Kong's AI Gateway, of
which this is the first.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
The errors other packages give a page (store, catalog, mirror) are
shown in its language; a percentage is written as the language writes
it (%93 in Turkish); the footer's time in its date form; and a stale
form is refused with a sentence rather than "bad request".
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A tool's answer is read by the caller's policies as the prompt it
becomes, so an instruction planted in a ticket is withheld and personal
data redacted; its arguments are read before they leave. ctl mcp add
--rate bounds each caller's calls a minute to a server (migration 0050),
and five failures in a row take a server out for a minute, its tools
off the list.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
ctl guard judge check, and POST /api/v1/guard/judges/check, time three
judgments of a 280-token prompt, then a burst at once, against the
policies' wait: ok, tight or slow, about how many requests a second can
be judged, and what to do. server init runs it once the judge is up and
says a machine too slow before the next steps; the wait is not changed
without asking. BACKLOG reordered by what goes wrong where installed.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
llmgw ctl mcp add|list|remove|test and /api/v1/mcp/servers keep the
servers; a token given with --secret-file is kept in the store, sealed,
and named by reference. keys limits --tools, and the console's limits
form, grant tools as patterns. Documented in install.md and clients.md.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The startup timeout runs from when a node's download last moved, so a
model of hundreds of gigabytes on a slow mirror is not failed while it
arrives. ctl model serve --env sets engine variables (NCCL_DEBUG=INFO)
on every node, and a layout given by hand keeps expert parallelism for a
model of experts. docs/large-models.md: how a model is split, what each
way needs of a card, and a checklist for bringing one up on a fabric.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The setup screen asks for up to two judge machines after the store's
two, and server init takes --judge-node for each. Each is added as a
node labelled llmgw.role: judge, and the judge's profile selects that
label, with cores by init's rule over those machines. A judge beside a
busy control plane refused 38 of 60 requests under load; alone, 2.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A judge a policy names and the catalog lacked was served with 3 GB, no
cores and llama.cpp's prompt cache; on a busy node its judgments missed
their budget and fail: closed refused the requests. It now gets init's
2 GB and cores, and every judge modelserve serves runs with
--cache-ram 0.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
auth.ldap checks a username and password against the domain controllers
over LDAPS or StartTLS: found with a service account, checked by binding
as the person. Groups, nested ones included, give staff roles and places
in the tree. Accounts are tied to the objectGUID; the leader reads the
directory every 15 minutes, suspends whom it disabled or removed and
reinstates them after, and refuses a sync that would suspend most.
Entra ID sends no groups for someone in more than 200, only where to
fetch them; that was read as none and took an administrator's role away.
It is now left as it was, and logged. An account made by SCIM or OIDC
can be reinstated again.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
An external model takes api_key_files, each a file and an optional weight,
in place of api_key_file. Each key is an endpoint of its own
(external:MODEL#1, #2), so the router's scoring, retries and breakers work
per key. A key answering 429 is passed over while its breaker is open. A
key answering 401 is passed over too, and external_key_refused is sent,
naming the endpoint and never the key; with a single key the 401 is still
the client's. A key whose file cannot be read is left out, and usage
records which key answered by its endpoint.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
An engine that answers at once and the gateway in front of it, in one
process on loopback, given the same load straight and through the gateway:
bare, with authentication and accounting, and with the guardrails' pattern
checks. It reports p50 and p99, what the gateway added, and a rough CPU
per thousand requests.
On an Apple M5 at 100 to 2,000 requests a second (10 s a step, 72,000
requests, none failed), the gateway added 0.05–0.42 ms at p50 and at most
0.49 ms at p99. That is now in the README, with how it was measured and
why the CPU figure is rough. CI runs a one-second version.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A v0.14.6 install on fewer than eight cores refused every request until
its judge was given cores by hand, and upgrading did not change that. The
judge keeper, on the leader, now finds a judge whose entry is exactly what
init before v0.14.7 wrote: one llama.cpp CPU profile, an 8192 context and
nothing else, 2 or 4 GB, none to two cores. It gives the judge 2 GB,
--cache-ram 0 and cores by init's rule through modelsvc.SetProfiles, then
restarts its ready replicas. The replica count is left as it is. A judge
whose entry differs is left alone, and the log says so once.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
On a 4-core single-machine install, `ctl model resources` was run on the
judge (to two cores) and on the starter (to none), then both restarted.
The judge's restart was refused: its replacement fit on no node even with
its old replica stopped, because the starter still held its two cores. A
restart now waits for other restarts under way (queued, retiring or
draining) when their going would make room, up to DrainGrace.
A drained replica was also kept until its model had enough live replicas,
and a replica being restarted that still answers did not count. So with
replicas restarted one after another, each drained one waited out the
five-minute grace, holding its room.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
From a comparison with Bifrost: answers to similar questions, the
customer's MCP servers behind llmgw, the gateway's overhead measured, and
several keys for one outside provider. H9's /mcp item is done.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
One job at a time, a 3D job waiting for the card held up another model's
job that could have run (seen on the PoC, BACKLOG H7). The leader's worker
claims jobs while fewer than eight run, passing over the models that have
one running, and starts each in its own goroutine; one ending wakes it for
the next. Lease and leader-only rules are as they were.
BACKLOG: H5–H7 marked, H8 (presets) and H9 (what the PoC taught) written.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Stable Fast 3D's texture_baker and uv_unwrapper are C++/CUDA packages no
index has; on the PoC they were built and installed into ComfyUI's
environment by hand. nodes[].build names such directories, and the gate
builds each as a wheel with ComfyUI's Python, for the compute capability
torch reports for the replica's own card, keeps it on the node by the
node's commit, the architecture and the directory, and installs it
beside the replica with the node's requirements. A copy in ComfyUI's
environment does not pin it. A node with no CUDA toolkit says so.
comfyui.huggingface_repo shows the replica's weights to a node that
loads them by repository name, in the engines' Hugging Face cache, so a
gated repository needs no token on the node.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A catalog entry meant for many sites cannot know their GPU class names:
the PoC has a Quadro RTX 6000, a customer an A100, another an L40S. A
profile may now say gpu: {min_compute_capability, min_memory_gb} in
place of gpu_class, and stands for every class in the cluster that meets
it, named <profile>@<class>, at its own priority; placement still sees
exact classes. ProfileByName and EngineProfile resolve such a name to
the profile on that class, so every caller that maps a replica back to
its profile works unchanged. A ComfyUI profile may carry its own
workflow, checkpoint and nodes, and with its own source an fp8 workflow
and weights go to cards with fp8 and fp16 ones to Turing.
A profile by need is left out of placement until the cluster's cards are
known: with no class it would have read as a CPU profile.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A model's catalog entry now says what its workflow takes and makes:
comfyui.inputs, each a placeholder of its name with a type (text, int,
float, bool, choice, image, mask, audio, video, file), bounds, choices
and a default; and comfyui.output, which gains video, audio and files
beside pictures and meshes. POST /v1/workflows/runs with {model, inputs}
checks a request against them before a job is written — a required input
left out, an unknown one, a number out of bounds, a song sent as a
picture, another owner's file id are each refused with what was wrong —
and the job worker replays it to a replica, where the files go into
ComfyUI's input directory, the workflow is filled and run, and the first
file of the model's output kind is the job's result. /v1/models lists
each ComfyUI model's workflow, so a client can build its form.
A PNG result carries the licence in an iTXt chunk, as a GLB does in
asset.extras. A file whose server named no type is typed by its name
before its bytes, so a WAV is audio/wav, not audio/wave.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A ComfyUI model may name an environment: the models of one environment
agree on every custom node they both name and on every package range,
so one replica can carry all of them and ComfyUI moves between their
weights itself rather than the card being handed over with a cold start
each time. The catalog refuses an environment that cannot share a
Python, naming both models; Catalog.Environments gives each one's
models, the union of their nodes and their weight sources, for
placement to build on.
A licence gains an id (SPDX where there is one), whether its output may
be used commercially, the regions it excludes, a revenue cap and the
attribution it asks for, so a person's or a site's choice of licences
can be matched without reading prose.
BACKLOG gains section H: ComfyUI as a platform.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Stable Fast 3D's own save node hands back the GLB as base64 in its UI
output, which the gate does not read. LLMGWSaveMesh writes each mesh to
output/ as a GLB and names it, as a picture's save node does. The
prepare node also gives the object's mask now, which SF3D's sampler
takes.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A finished job kept the last word heard from ComfyUI, the node running;
it is now said done before the job is marked completed.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The catalog had one source a model, so an embedding model served by vLLM
from its repository on a card and by llama.cpp from a GGUF on CPUs had its
entry swapped by hand halfway through the metallurgy import. A profile's
source is what its engine loads and placement looks for; the model's is
the default, and what updates and canaries move. ctl model add checks
every source, the GGUF check reads each llama.cpp profile's, and ctl
weights collects none a profile loads. An empty source leaves a profile's
fingerprint as it was, so running replicas do not read as stale.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
POST /v1/3d/generations answers 202 with a job; GET says its status,
progress and result file; cancel stops it in the engine's queue too. The
request is kept as sent and replayed by whichever gateway holds the lease
through the path a client's request takes, so it is charged and judged
like any request (tagged job=<id>), and a model still loading is waited
out for as long as the job has. Migration 0044. Nothing makes 3D models
yet; the door refuses every model until the ComfyUI adapter learns meshes.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Section G of the backlog: a jobs table of its own with a leased worker,
progress read from ComfyUI's websocket by the gate, the ComfyUI adapter
widened from pictures to meshes with custom-node installs, and the pages.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Each kept its own hour of quiet per event, so a budget spent on a team
both control planes served was announced by each. An event is now claimed
in the store before it is sent (notify_sent, migration 0043; one statement,
so two asking at once cannot both win), and only the claimer sends it. A
store that cannot be asked sends it anyway.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The cookie that carries a sign-in across the provider was signed with a
key each process made for itself, so with two control planes behind a
balancer a callback reaching the other one was refused as tampered — half
of all sign-ins. The key is now the store's oidc-state secret, sealed like
the others and the same on every control plane.
Changelog: the fixes since v0.14.2.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The leading control plane tells the mirror over its API (PUT /v1/firewall,
kept as .firewall.json beside the models and applied by the same path unit
control planes use), and keeps the state in the store's cluster_state for
members that run no llmgw, which a root timer reads from the member's own
copy. The mirror also lets in the network of every node and control plane
the leader knows. Tried on the PoC: off, on and a network added, on every
kind of machine.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
llmgw_org, llmgw_team and llmgw_project rename and move in place;
llmgw_key holds the key in a sensitive secret and is made again when
revoked or expired outside Terraform; llmgw_membership puts a person in
the tree. Tried with OpenTofu against the fake admin API, drift and
import included.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
What llmgw org, keys and users did against the store on a control plane
can now be done remotely and by automation: the tree read, added to,
renamed, moved and emptied; keys issued (the key in the answer, once),
listed and revoked, with the scopes' lifetime limits; people put in and
taken out of the tree, their keys in projects they no longer reach
revoked with it.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A search asking for more than 10 passages got at most 40 candidates from
HNSW, pgvector's default ef_search. The full-text arm scored every chunk
holding a word of the question before limiting, over the whole
organization's GIN index; it now takes candidates by ts_rank from the
collection's own partial index and scores those. New collections index
halfvec on pgvector 0.7 and later (up to 4000 dimensions).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
An adapter now has an owner (org, team, project, user or key): only callers
there see it, and to anyone else it is a model that does not exist, absent
from /v1/models too. A training job's owner defaults to the one collection
its pairs come from, must be able to read every collection they come from,
and pairs from a knowledge base are never made public. ctl train publish
keeps the GGUF or PEFT files on the mirror, which accepts uploads now, and
sets the owned adapter after the usual architecture check.
The knowledge bases' cache_salt follows the collections searched rather
than the organization, so two teams of one organization no longer share
cached passages.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
