Checked against the installed llama.cpp (b10991): at its default verbosity
it prints neither the devices it uses nor the layers it offloads, so the
start-up output said nothing and every replica's ON column stayed empty.
The agent now takes compute from nvidia-smi - the GPUs the shard's processes
hold memory on, and how much - or CPU, for every engine, falling back to it
when llama.cpp's output says nothing; adopted engines get it too, and each
session reports it once per shard as a compute-only status. engine-check
runs its GPU token at -lv 4, where llama.cpp does say where its layers went.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
It was sent once, with READY, and kept only in the leader's memory: a
failover or an agent restart lost it. The agent now keeps it with the
shard's state on disk (ShardSpec.Compute, set by Supervisor.SetCompute) and
reports it with each running shard when it registers (RunningShard.compute),
which adoption carries onto the replica.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Seeing a device is not computing on it. For llama.cpp on a machine with
NVIDIA GPUs, engine-check now loads a small GGUF (--model, or the smallest in
the weights cache) with every layer on the GPU, asks for one token, and
reads from llama.cpp's own output where the layers went: a build that
computes on the CPU fails, naming the fix. With no --bin it checks the build
the agent installed under /opt/llmgw/engines.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
llama.cpp's start-up output is read as a shard becomes ready: the devices it
used, its backend, and the layers it offloaded. That goes up with READY in a
new ShardStatus.compute, is kept on the shard, and is shown by the admin
API, ctl replicas (ON) and the console (COMPUTE). A shard given GPUs whose
engine says it found no usable GPU or offloaded no layer is stopped and
failed, even where nvidia-smi cannot be asked.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
firewall.enabled (default true) is a cluster setting, applied live: the
leader sends a Firewall message to every agent when it registers and when
the setting or firewall_networks changes, and the agent (root) takes llmgw's
nft table out or installs it. A control plane writes the state to
/var/lib/llmgw/firewall.json, which llmgw-firewall-sync.path applies as root
with `llmgw firewall sync`; server init and server add install the units,
`llmgw firewall sync --install` on one set up before. node add and server
add put no firewall on a machine while the setting is off.
`llmgw wipe` removes llmgw from a machine after listing what goes and asking
for its name: units, engines' processes, the nft table, /etc/llmgw,
/var/lib/llmgw, the agent's state, the sudo grant, the CA from the trust
store, keepalived's configuration when llmgw wrote it, and the users.
--models, --store (Patroni member, etcd, or the local database) and --all
take more. The console has it under Do.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The console and ctl on a standby showed its own empty view: nodes and
replicas live in the leader's memory. A standby now passes /api/ to the
leader over the agent port, with the cluster's certificates both ways; the
leader answers only a certificate naming a control plane the store lists,
and only while it leads. Control plane certificates are issued for client
use as well, and server add reissues older ones.
An agent's reconnect backoff starts again after a session that worked, so
each failover it lives through no longer makes the next one slower.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A control plane being stopped held every agent's stream for the thirty
seconds of a graceful shutdown, so the standby led within four seconds and
had no nodes for thirty. The agent server is now closed, not drained. A
control plane that loses its lease while it lives on lets its agents go,
rather than keeping nodes the new leader cannot reach.
ctl status and the console's Overview say which control plane leads, which
stand by, and whether the floating address is on the leader. The console
adds a control plane (Do → Add a control plane), asking for the machine and
the floating address. server add says when the mirror would refuse the new
control plane.
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>
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>
Conflicts: server.go imports (both kept); agent.pb.go regenerated with buf
from the merged proto.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A bundle carries what a cluster would download — llmgw's release with its
signed checksums, every llama.cpp build per architecture with GitHub's
checksums, model files pinned and checked against Hugging Face's — into a
directory or a tar whose manifest sha256 is carried apart. Import checks
everything, the release against the signing key, before anything is put in
the mirror. With offline.mirror set, nodes install llama.cpp from the
mirror against checksums the control plane holds and sends over the agent
stream; releases default to the mirror for node add, ctl upgrade and agent
upgrades. server init and the setup form install from a bundle.
The Helm chart runs one control plane with SQLite on a claim or an external
PostgreSQL, Secrets for the agent certificate and admin token, Services,
Ingress and ServiceMonitor; deploy/container/Dockerfile builds its image.
GPU nodes stay outside Kubernetes, as docs/kubernetes.md explains.
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 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>
The supervisor drops to the run_as user (default llmgw-engine) when the
agent is root: its credential and groups, a 0700 home under the work
directory as the working directory, HOME and XDG_CACHE_HOME there. A node
without the user keeps engines as root and logs why. node add creates the
user in the video and render groups.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
env is an allowlist of tuning families less path, plugin, library, address
and credential names (NCCL_*_PLUGIN, CUDA_INJECTION64_PATH and the like
got through the old denylist); llama.cpp's LLAMA_ARG_* are checked as
flags. args refuse --trust-remote-code, --log-file, --slot-save-path,
--tool-parser-plugin, --middleware, --allowed-local-media-path and others
that read, write or load, and what the agent sets itself. Checked in the
catalog and again on the node before an engine starts.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
trusted.pub now holds the release signing key, so self-upgrade, agent
upgrade and node onboarding refuse a release whose checksums are not signed
with it. Onboarding fetched the release on the node and compared it with a
checksum from the same server; the control plane now fetches it for the
node's architecture with release.Fetch, checks signature and checksum, and
hands the bytes over. Release addresses must be https (loopback excepted).
LLMGW_RELEASE_KEY overrides the key only in development builds. The CI
release job fails instead of publishing an unsigned release while a key is
built in.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
model serve takes --projector, fetched beside the weights and loaded by
llama.cpp with --mmproj, and --format safetensors with --gpu-class and --tp,
which fetches the weights with their config and tokenizer and runs them on
vLLM. The console offers both, F switching between GGUF and original
weights. The mirror view is one row per version with its files on Enter.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
server add gives another machine this llmgw, a certificate, the
configuration and store address, lets it into the store through Patroni, and
starts it; --vip keeps a floating address on the leader with keepalived,
following a new /leader endpoint. Nodes learn every control plane from the
leader and notice a dead one by HTTP/2 ping instead of after the kernel's
fifteen minutes. The setup, console, node and init screens are drawn full
screen in one frame: title bar, sidebar, bordered panel, key bar, dialogs.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A card with something else on it when the agent started stayed out of
placement after that process let go of it, until the agent restarted. The
agent now sends what processes other than its own engines hold on each card,
and the control plane replaces the registration verdict with it.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Every minute the agent reads nvidia-smi's full report, its NVLink error
counters and the kernel's XID lines. They are published per card,
shown on the node page and the GPU map, charted in Grafana, and five
alerts watch them.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
They were read once, at agent start, and published unchanged since.
Each sample now carries utilisation, memory and bandwidth in use, the SM
clock and uncorrected ECC errors. The node page charts each card, the
cluster page the fleet by class, Grafana has a GPU row, and three GPU
alerts are added.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Each engine has a list of builds, best first, from what its project
publishes and how each was compiled: llama.cpp CUDA 13.3, CUDA 12.8
(driver 570 for the GPUs it ships as PTX), then Vulkan, never the CPU
build on an NVIDIA node; vLLM CUDA 13, then CUDA 12.9, and for GPUs
older than every build of the version asked for, the newest built for
them (V100: 0.19.1). The node takes the first its hardware meets and
that installs and computes on the GPU, moving on when one does not, and
reports the version it runs. GPU processes are read from nvidia-smi's
full report, so Vulkan engines count as on their GPU.
Refs #2.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
tp on a llama.cpp GPU profile is read as gpus_per_node rather than
refused, and a Hugging Face repository page as a vLLM source as the
repository id, so models written that way are placed after the upgrade
instead of dropped from the catalog. Engines adopted after an upgrade
are checked for computing on their GPUs too.
Refs #2.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Read from vLLM's release pipeline: every x86 build from 0.20.0 has
kernels for compute 7.5 and up, the CUDA 12.9 one included, and 0.19.1
is the last built for 7.0. PyPI's build is CUDA 13 from 0.20, not 0.26.
A V100 asked for a newer vLLM is refused, naming 0.19.1 for
engine_overrides; the CUDA 12.9 fallback is for older drivers on
Turing and newer. The cu129 wheel is found among the release's assets,
since its manylinux tag differs between versions.
Refs #2.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A node that cannot run the CUDA 13 build installs the same version's
CUDA 12.9 build from vLLM's GitHub release, with PyTorch's cu129 torch;
the install computes on the GPU before it counts. The build can be
chosen per node — auto, cu13, cu129, none — on the cluster page, with
ctl engine-build, or over the admin API; the control plane keeps the
choice and tells the node at once.
Refs #2.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The explorer fetches a safetensors set with its config and tokenizer;
the mirror gives out a version's manifest only once nothing is
downloading and every shard its index names is there, with each file's
sha256, which nodes check; mirror add takes several URLs; a repository
page is refused as a source.
Refs #2 (item 9).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
engine_overrides give nodes picked by GPU class, id or labels their own
engine versions; node add --disable-engine keeps one off a node; what
vLLM cannot do on a node is a warning rather than a refused node; the
CUDA requirement follows the version; a hand-named binary is no longer
replaced by an installed one.
Refs #2 (items 4 and 7).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
tp on a llama.cpp profile was a width nothing placed. gpus_per_node is
now the count: placement reserves that many cards on one node and the
engine splits layers across them. tp, pp and nodes are refused with
what to write instead.
Refs #2 (item 8).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
llama.cpp whose CUDA backend cannot load runs on the CPU without an
error. The CUDA build is now asked for its devices when installed, a
shard on GPUs must hold memory on each before it is ready, and
engine-check reports the devices. Shards get CUDA_DEVICE_ORDER=PCI_BUS_ID
so CUDA numbers GPUs as placement does.
Refs #2 (item 2).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
--lora with --lora-init-without-apply leaves an adapter's scale at 1,
and llama-server applies it at that scale to any request without a
"lora" list of its own: every plain request for the model was served
with its adapters on. Found live on qwen2.5-coder-7b-instruct. Each
adapter is now loaded with --lora-scaled FILE:0.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A model's adapters are fetched by each node with its weights and loaded
unapplied at start; a request naming an adapter reaches the model's
replicas with llama.cpp's per-request lora field. Adapter names share the
model namespace, are listed, and follow allowlists on their own. The
router's config seed and pushed catalog now build the name map in one place.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Every agent samples, at most every ten seconds: nvidia-smi's temperature,
HBM temperature, power draw, enforced limit and throttle reasons per GPU;
hwmon's CPU package and board inlet temperatures; and CPU package power
from RAPL's energy counters. Sent with heartbeats, published as metrics
and shown on the node page; unmeasured sensors are absent, never zero.
Observe-only, step 0 of docs/facility-aware-placement.md, which now
describes the agent source and what these readings can and cannot say.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A profile's cpu_cores is reserved out of its node's CPUs by placement, as
memory is, and becomes the engine's thread count. Agents report their CPU
count in registration and heartbeats. New CPU models must declare memory;
model views show reserved against measured memory with warnings, and
ctl model resources sets both for a stored model.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
CPU replicas start on half the machine's CPUs, not all of them: two busy
llama.cpp servers each spinning on all 8 vCPUs fell to 0.8 tok/s apiece,
where at 4 threads they keep 5.5. llamacpp.threads sets it per node.
Inference requests can be traced over OTLP/HTTP (tracing.endpoint), with
admission, schedule and upstream spans and traceparent sent to the engine.
Security: onboarding no longer puts the cluster root in the system trust
store and removes it where it was; the mirror's mutual-TLS API can be
limited to named certificates (--api-client); init.log no longer keeps the
administrator's password; a warning when auth is off on a public listen;
the recorded actor is sanitised and sudo-aware; Ray's dashboard is off, and
the remaining engine gaps are documented.
reconciler.go and agent.go are split by topic, the status mapping of each
failure is tested, and the plan's checklist is brought up to date.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Every upgrade so far has been a script of mine copying a binary to each
machine over ssh, restarting it and polling until it came back — which is
the job, but not something the product could do, and which needed ssh into
every node.
`sudo llmgw ctl upgrade VERSION` on the control plane does it. This machine
first: the release for its platform, checked against the release's
checksums, the old binary kept beside it, llmgw-server restarted, and a wait
until every node that was ready has reconnected. Then each node in turn: the
control plane tells its agent the version over the stream it already holds;
the agent fetches and checks the release itself, swaps it in with a rename,
answers, and exits with status 75 so systemd starts the new binary. Engines
keep running, as for any agent restart, and are adopted again.
A node is done when it is back on the new version, ready, with as many ready
replicas as before; one that is not within the timeout stops the run before
the next node, so a bad release reaches one machine. Nodes already on the
version are skipped, so running it again resumes.
The fetch-and-verify is one package now, shared with onboarding's idea of
what a release version looks like.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A review of v0.10.4-rc.4, and a look at the live network, found:
- Alertmanager listening on every interface: from any node its API took
silences, with no login. Bound to loopback on the monitoring host, and the
monitoring README now installs it that way; Prometheus beside it is its only
client, and one Alertmanager needs no cluster port.
- Login and signup unbounded in memory. Each attempt runs argon2 with 64 MiB,
and nothing limited how many ran at once; the per-address throttle kept one
entry per address ever tried. Hashing now takes one of a few slots and
waits for one otherwise. The throttle expires entries whose window has
passed when it fills, and if it is still full it refuses new addresses:
evicting live ones instead would let a flood of made-up addresses reset the
throttle on the address actually being guessed.
- Metric labels taken from the request. A refused request counted the model
name it asked for, so any key holder could mint a series per request.
Requests are counted under the catalog's name, or "unknown".
- Two in code written this week. A SQLite backup made its snapshot with the
default mode in the listable backup directory, a full copy of the store
readable while the backup ran; it is now made in a private directory, and
the unit sets UMask=0077. And archive links were checked by name only, so
"a -> ." then "a/a/b -> ../.." passed and a file written through them landed
above the install directory, as root. Archives now unpack through os.Root,
and a link may only point at a file beside it — which every llama.cpp
b10991 archive, CPU and CUDA, was checked to follow.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
CI's test job has failed intermittently — two of three runs on some days —
while every run here passed, including six repetitions on one CPU under the
race detector with eight packages at once. The difference is a shared,
loaded Linux runner, and the tests most exposed to it are the ones that
sleep a fixed time and then assert:
- admission: idle admission bounded at 50ms of wall clock against a 1ms
tick, and a queued request checked for after a 50ms sleep;
- scrape: a 10ms scrape timeout, which on a slow runner reports the engine
unreachable and then the violation a second time, in a test that counts
one;
- supervisor: log lines, three lines of output and a removed state file
each checked after a fixed sleep, and an exited worker bounded at 1.5s;
- agent: a heartbeat checked for after four intervals.
Each now waits for its condition up to a deadline of seconds, and the two
wall-clock bounds are set against what they exist to distinguish — the
admission queue's tick raised to ten seconds, the exited worker bounded
below the five-second grace it must not wait out — rather than against how
fast this laptop is.
Without the CI logs this cannot be proven to be the failure; these are the
tests that fail when the machine is slow, and they no longer do.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Engines listen on every address their node has, with no key. Checked on the
live cluster: from a neighbouring node, a request straight to an engine's
port was answered — past the gateway's authentication, quotas and
accounting, by anyone who could reach the node.
Each replica's engine is now started with a key and refuses requests without
it. The key is derived from the replica id with an HMAC over a secret kept
in the store, rather than stored per replica: the router has to send the
right key to replicas this process never started — adopted after a restart,
or placed by the other control plane — and a function of a shared secret
gives that without a table to keep in step. A key taken from one node opens
that replica only. The secret is made on first use and copied with the rest
of the store when moving to PostgreSQL; a new one would lock the router out
of every running engine at the moment of the move.
The key reaches llama.cpp and vLLM through their environment
(LLAMA_API_KEY, VLLM_API_KEY) rather than their command line, which every
user on the node can read; SGLang only takes it as an argument. The agent's
readiness check, the router's proxy and its metrics scrape send it, the
client's own Authorization still never reaches the engine, and `ctl bench`
fetches the key of the replica it is pointed at from the admin API.
Engines started before this keep running without a key until their replica
is next started. With no store there is nowhere to keep the secret, and
engines start without one, with a warning.
Also formats two files that went in unformatted.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The admin listener has no login. Reaching it is meant to mean holding the
control plane, so it binds to loopback and an operator reaches it over an
ssh tunnel — and so does every page open in that operator's browser. The
API decoded JSON whatever the Content-Type said, so a page could POST a
text/plain model definition, no preflight needed. A model's profile env
reached an engine the agent runs as root, unfiltered: LD_PRELOAD in a
definition was code execution on every node. A page that rebound its own
name to 127.0.0.1 could also read pprof's heap, which holds API keys.
The admin listener now answers only when Host is an address or localhost,
refuses requests a browser marks as cross-site (Origin, Sec-Fetch-Site),
and takes changes only as application/json, which no page can send across
origins without a preflight nobody grants. ctl sends that on every change,
bodiless DELETEs included; curl and ctl are otherwise unaffected.
Profile env that changes what runs rather than how — the dynamic loader's
LD_*, PATH, Python's PYTHON* start-up variables, BASH_ENV and their like —
is refused when a model is defined, and dropped again by the engine adapter
before the process starts, whatever sent the profile. None of the live
catalog's models sets any.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
