api.ts owned the base-vs-origin precedence, the trailing-slash trim and the
base+path join inline. That logic belongs with the rest of base resolution and
was only reachable through a fetch client, so it could not be tested directly.
Extract resolveRequestUrl into resolveApiBase.ts with its own unit tests.
api.ts now only wires the shared resolver into openapi-fetch's Request option.
No behaviour change: same precedence, same trimming, same output.
* chore(build): move the Admin UI toolchain to Node 24
Node 18 and Node 20 both reached end of life (2025-04-30 and 2026-04-30), and
the release images along with every CI lane were still building on them. Node 24
is the current LTS through 2028-04-30, so this moves the four UI build images,
the CircleCI lanes, and the four GitHub Actions workflows onto it
Node 24 also ships npm 11.17, which is the first line that implements the
min-release-age setting this repo already carries in its .npmrc files. On npm 10
the key is parsed and discarded, so the release-age gate has had no effect
regardless of its value. Tightening the dashboard's engines range and turning on
engine-strict makes an unsupported npm fail loudly rather than skip the gate
quietly, and a new step in the UI build workflow probes an impossible cooldown
so an inert setting cannot pass unnoticed again
Node 24's bundled undici tightened its brand check on RequestInit.signal, which
rejects the AbortSignal jsdom installs and broke the two cases in
src/lib/http/api.test.ts that rebase a request onto a runtime base url. Under
jsdom the Request global comes from Node while AbortSignal comes from jsdom;
tests/jsdomFetchEnv.ts delegates to the jsdom environment and then restores
Node's native AbortController and AbortSignal so both come from one realm.
Upgrading jsdom does not address this, as jsdom still does not own Request
The workflows now read ui/litellm-dashboard/.nvmrc instead of repeating a
literal, so the Node version has a single source of truth, and ui/Dockerfile is
pinned by digest to match the other three build images. The lockfile changes are
npm 11 normalising the engines range and dropping optional peer entries it no
longer records
* fix(build): point every Admin UI build script at .nvmrc
The enterprise Docker path was left on Node 18. docker/build_admin_ui.sh runs
only when enterprise/enterprise_ui/enterprise_colors.json is present, which it
never is in the OSS tree, so neither CI nor a default image build reaches it;
it pinned nvm to v18.17.0 and then built the dashboard, which now requires Node
24, so a customized enterprise image would have failed EBADENGINE
All three UI build scripts now resolve the version from
ui/litellm-dashboard/.nvmrc rather than carrying their own pin, so the Node
version has a single home across Docker, CI, and local builds. build_ui.sh was
on v20 and build_ui_custom_path.sh on v18.17.0
Also drops the dependency-cooldown probe from the UI build workflow. The
engines floor plus engine-strict already fails an unsupported npm loudly at
install time, so the probe was redundant, and treating any nonzero exit from a
live registry call as proof of enforcement made it unsound besides
api.ts read globalThis.location when the module loaded, which froze the base
URL at import and pinned its test file to jsdom. The creation-time baseUrl and
the middleware's runtime rebase were also two mechanisms doing overlapping
work, and the rebase hand-copied eleven RequestInit fields on every call.
Pass openapi-fetch's Request option instead, so the constructor applies
whatever getRequestBaseUrl() returns at the moment the request is built.
registerBaseUrlGetter is now the single source of the base URL, rebaseUrl and
rebaseRequest are deleted, and the request is constructed once, so the init
openapi-fetch assembled reaches the platform Request untouched. The abort
signal is no longer copied by hand.
This preserves behaviour rather than approximating it: getProxyBaseUrl() falls
back to location.origin, so the runtime base was never empty in a browser and
the old middleware already rebased every request, discarding the creation-time
value each time.
setupTests.ts gates its DOM-only tail behind a window check; setup files run
for every environment, so that tail previously stopped any node-environment
test file from loading.
api.test.ts now runs under @vitest-environment node with its assertions intact
and no location stub, plus regressions for per-call base resolution and abort
forwarding. api.sameOrigin.test.ts covers the browser fallback to the page
origin, which needs a DOM environment.
Internal users saw the Tool Policies page but its /v1/tool/list call always
returned 401. This adds a single source of truth for which roles may trigger
which UI fetches (utils/capabilities.ts) plus a useCan hook, and wires the
Tool Policies route through it: the nav item, the page, and the query all
read the same capability, so the sidebar hides the entry, deep links render
an admin-only notice, and the query never fires. The tools list call also
moves onto a queryOptions factory
* feat(ui): add admin-configurable user banner
Proxy admins can publish a markdown announcement that renders as a
dismissible banner on every dashboard page for all authenticated users,
editable from Admin Settings > UI Settings without a redeploy. Backed by
new /get/user_banner and /update/user_banner endpoints persisting to the
existing LiteLLM_UISettings table
* fix(ui): re-surface dismissed banner on identical republish
Stamp a server-side revision on every banner update and fold it into
the client dismissal signature, so unpublishing and republishing the
same message reaches users who dismissed the earlier run
* fix(ui): stamp banner revision as an opaque uuid instead of a counter
Two overlapping admin updates could read the same prior revision and
both persist the same incremented value, letting an identical republish
collide with a previously dismissed signature. A server-generated uuid
per update makes every publication identity unique by construction with
no read-modify-write
* refactor(ui): drop the server-side banner cache
Reads go straight to the single-row table; the dashboard already
throttles fetches client-side, so the cache only added staleness
windows under concurrent updates and multiple workers
* refactor(ui): move banner storage behind a domain repository and drop the store_model_in_db gate
UserBannerRepository owns the row shape instead of the endpoint
reaching through the generic .table bridge, and publishing no longer
depends on the unrelated STORE_MODEL_IN_DB flag; a connected database
remains the only requirement
Adds the auto-router as a third optimization driver beside compression and prompt
caching: a summary card, a donut segment, and a series in the savings graph across
both the cumulative and per-day views.
The number is signed, because a switch that thrashes the prompt cache can cost more
than the cheaper rates save and an operator needs to see that. The donut plots only
drivers that saved, since a negative slice has no meaning, while the card and the
range total keep the sign. `usd()` sizes and signs off the magnitude so a small loss
renders as -$0.01 rather than "$-0.00".
The card's popover states the counterfactual and its two consequences: that a switch
pays to re-warm the cache, and that a first turn the router could not identify is
charged that write and therefore under-reported.
* feat(spend): add net auto-router savings to the cost-optimization dashboard
The dashboard credited compression and prompt caching but said nothing about the
optimization that picks the model, so the driver with the largest lever on a bill
was the one an operator could not see.
Savings are the counterfactual: without a router a deployment runs one model, and
it has to be one that can carry the hardest request, so the baseline is the
priciest model in the router's hardest configured tier. A cheap tier is a choice
the router made, not a ceiling it was bounded by. `auto_router_savings_baseline_model`
overrides it for operators who would genuinely have run something else. Both are
provider-qualified before pricing, because a bare name can resolve to a different
vendor's rates or to nothing at all, and a deployment is priced by its `base_model`
where it has one, which is how Azure deployments are priced everywhere else.
Both arms price the request's real usage through `generic_cost_per_token` rather
than re-deriving per-token arithmetic, so tiered rates, ephemeral cache-write tiers
and regional uplifts stay consistent with what was actually billed. `prompt_tokens`
already includes the cache buckets, so charging them again at the input rate would
price the same tokens twice.
Cache state is what makes this hard. The baseline serves every turn, so whether it
had the prompt cached is whether the conversation was already underway. On a
continuing conversation it wrote the prompt earlier and would only read it now, so
this request's write is what switching cost and counts against the saving. On a
first turn nothing was cached for any model, the baseline would have written the
same prompt, and both arms carry the write at their own rates. Charging the write
to both cases understates a first turn to a few percent of its value, and because
the write premium is fixed by prompt size while the saving grows with completion
length, it can render a profitable route as a loss.
That shape is read off the conversation rather than remembered: a second human ask
means an earlier turn was served. No cache, no session id, and no dependence on a
caller sending a session header. It cannot see a switch on a turn the router did
not classify, and it reads a few-shot prompt's synthetic turns as prior
conversation; both err toward charging the write, which under-claims.
The baseline and the shape ride on the existing `routing_decision` record, which is
already carried from the router to the spend log, already classified for redaction,
and already written-or-cleared per attempt. A fallback that re-enters the hook
therefore cannot leave either fact behind to be attributed to a deployment that
never routed, and no new metadata key crosses the trust boundary.
The result is signed. Whether a switch pays off is a race between the rate gap and
the cache-write cost, and a narrow gap loses; flooring at zero would hide exactly
the routing behaviour an operator needs to see. The donut plots only drivers that
saved, while the card and range total keep the sign.
Savings accrue into a new `autorouter_savings_spend` column on the six daily rollup
tables, declared `NotRequired` because rows queued by a pod on the previous release
carry no such key. It is summed by the rollup merge the cross-pod Redis drain also
runs, and carried through the aggregation query, the per-row accumulation and the
response model, so the dashboard reads a value the API actually sends. Tests
enumerate the drivers from the response model itself and assert each is summed,
accumulated, carried and totalled, so one added later cannot be half-wired.
* fix(spend): let the baseline pay for a continuing turn's own growth
`_baseline_usage` moved every cache-creation token into the baseline's read bucket
whenever the conversation was underway. That is right for a switch, where the
baseline never left the model it was on and really would only read, but wrong for a
turn that stayed put: the prompt grew, and the tokens written are that growth. They
are new to every model, so the baseline would have paid to write them too. Forgiving
it that write made the counterfactual cheaper than it was and shrank the reported
saving on ordinary steady-state traffic, by about 2% per turn.
The selected arm was never involved; it has always been priced on the real usage.
The error sat entirely on the baseline.
The condition is that the request read more than it wrote, not that it read anything.
A switch onto a model already holding a small prefix of this prompt still writes most
of it, and that write is the switch's own cost; keying off a nonzero read would have
handed such a request the full rate gap, turning +$0.0056 into +$0.1177. Comparing
the two buckets separates a warm continuation, which reads far more than it writes,
from a cold arrival, which does the reverse, and it leaves the existing invariant
intact: a request reading 0 and one reading 1 both still land in the same place.
* fix(spend): price each arm under the key litellm billed it, and see agent turns
Two ways the savings number read the wrong thing, both from identifying a model by
its name when the name is not what it costs.
The counterfactual was ranked and priced on the public rate for the model a
deployment names. A deployment may not be charged that rate: the router registers
its configured prices under the deployment's own id and deliberately keeps them off
the shared model-name key so deployments sharing a backend model do not pollute each
other. So a hardest-tier deployment configured above its public rate lost the
ranking to a cheaper candidate, and once chosen was priced at a rate nobody pays.
Which key prices a deployment is now `_select_model_name_for_cost_calc`'s decision,
the resolver the real request is billed through, rather than a second rule here that
would have to re-learn that per-second and tiered overrides count, that a partial
override still counts, and that a deployment configured at zero is priced at zero
rather than treated as unpriced.
The arm being subtracted had the same fault and a sharper edge. It priced the spend
log's `model`, which on Azure is the deployment name, absent from the cost map, so
the whole driver silently read zero for that traffic. It no longer re-derives
anything: `model_map_information.model_map_key` is what litellm actually billed the
request under, recorded at request time by that same resolver with `base_model` and
custom pricing already applied.
Separately, the conversation-shape discriminator counted human asks, and an agent
loop can run twenty turns on one of them. Its tool traffic rides `tool_result`
blocks on user turns that flatten to empty text, and `tool` roles that are never
read, so a long agentic conversation looked like its own first turn and was handed
the arithmetic that leaves the cache write on both arms. That is the one direction
this must never fail in, because it inflates. An assistant turn is the direct
evidence that something answered earlier, and it is blind to how the tool plumbing
is spelled on either surface.
* fix(spend): give the cost-key resolver both inputs the selected arm needs
The served model was resolved through one input at a time, and each choice broke the
half the other fixed.
`model_map_key` is the served model already resolved through `base_model`, which is
the only way an Azure deployment name reaches the cost map at all; without it the
selected arm priced a name absent from the map, returned nothing, and the whole
driver silently read zero for that traffic. But it is built without
`router_model_id`, so it never carries a deployment's own price overrides, and a
custom-priced deployment was compared at its public rate while the baseline used the
real override. On a deployment configured well above its public rate that inverted
the answer outright: a route that lost $21.88 reported saving $0.10.
`_select_model_name_for_cost_calc` takes both, so it gets both. Which key prices a
deployment stays its decision rather than a rule restated here.
* fix(spend): same model is only the same cost when it is the same deployment
The short-circuit compared resolved model identity, so two deployments of one model
collapsed to "no switch" and reported zero. They are not the same cost: a deployment
can carry a negotiated rate, and routing from the dear one to the list-price one is a
real saving the dashboard reported as $0.00 against a true $21.93.
Both arms now carry the key litellm prices them under, so the comparison is between
deployments rather than between names.
* refactor(spend): price from resolved rates, not from a name we keep re-resolving
Four review rounds landed on one mechanism: which identifier prices a deployment.
base_model, then the deployment id, then cache-only overrides. Each round added a
clause to a resolution rule that should not exist, and a wrong primitive fails once
per input shape, so each shape arrived as its own finding.
`Router.get_deployment_model_info` already owns this. It merges a deployment's
configured prices over the built-in map, folds in `base_model` defaults for
deployments whose name is not a model, and falls back to the model name when nothing
is overridden. Every shape hand-rolled here (cache-only, partial, per-second, Azure)
was that function re-implemented badly.
`generic_cost_per_token` now accepts already-resolved rates instead of demanding a
name it looks up itself, which is what forced the name-bending in the first place.
Both arms resolve through the owner and pass what they got: the counterfactual by the
deployment the router would have used, the served request by the deployment that
served it. The invented cost-key resolver is gone, and `Baseline` carries a
deployment id rather than a key we chose on litellm's behalf.
Net 64 insertions against 79 deletions.
* test(spend): follow _most_expensive onto the router that prices its candidates
Ranking moved through `Router.get_deployment_model_info`, since what a deployment
costs is the router's answer to give; these four cases were still calling the old
free-function signature.
* fix(spend): rank baseline candidates by what a request costs, not by two rates
"Most expensive" was decided by comparing output rate then input rate. That is a
property of a rate, not of a request: a deployment dearer per output token can be
cheaper per cached token, so the comparison ordered cache-heavy traffic backwards and
recorded the wrong counterfactual.
Candidates are now costed on one reference request through the same engine the
savings themselves use, which leaves cache read and write rates, tiered tables and
every other billing dimension to that engine rather than to another rule restated
here. The reference request is cache-heavy because auto-routed traffic is.
* fix(spend): pick the baseline against the request that ran, not a stand-in for one
Ranking happened in the pre-routing hook, where the request has not executed yet, so
candidates were costed against a hard-coded reference workload: 20k prompt, 19k of it
cached, 1k out. Which candidate is dearest depends on that mix, so a pooled hardest
tier holding a deployment with non-proportional configured rates could be ranked for
a request nothing like the one served.
The mix is known on the spend path, so the ranking belongs there. The routing
decision now carries the tier's candidates rather than a winner already chosen, and
the baseline is resolved against the usage that actually happened. The reference
workload is gone; nothing here assumes a traffic shape any more.
The router is passed in rather than imported from `proxy_server` inside the
computation, so the savings stay a pure function of their arguments and the caller
owns where the router comes from. That also makes the spend path testable without a
running proxy, which the previous shape was not.
* refactor(spend): measure savings against one configured model, not a derived one
The counterfactual was derived per request: enumerate the hardest tier's
deployments, resolve each one's effective pricing, price them all, take the dearest.
That machinery produced a review finding per input shape it had not anticipated,
and every answer it gave was one an operator could have stated in a line of config.
So they state it. `litellm_settings.autorouter_savings_baseline_model` names the
model the traffic would have run on without a router, for every auto-router on the
proxy, and unset means the driver is off rather than a model nobody named being
guessed at. `savings_baseline.py` and its tests are deleted outright, along with the
tier enumeration, the candidate list on the routing decision, and the per-deployment
override that shadowed it.
Cache-state handling is untouched: the baseline is still priced on this request's own
read and write split, so a switch still pays for re-warming the cache and a first
turn still charges the write to both arms.
45 insertions against 482 deletions.
* refactor(router): compute the conversation shape once and pass it down
`_classify_and_route` re-derived it from the messages the hook had already resolved,
so an ordinary routed request walked the turn list twice for one boolean. The hook
computes it and hands it over, which is also where the affinity-hit path already got
it from.
Also moves `_get_llm_router` below the imports it sat among.
* fix(router): drop the dead conversation_continuing parameter off the hook
It was added to `async_pre_routing_hook` by mistake and immediately overwritten by
the value the hook computes, so it never did anything. It also widened a signature
every pre-routing strategy shares with the protocol in `types/router.py`, leaving
this one router diverged from `AutoRouter` and the interface for no reason.
Also records why an unreadable request counts as continuing: no messages is no
evidence a turn was served, so it pays the cache write and under-claims rather than
being handed a first turn's larger saving on nothing.
* fix(spend): charge a baseline its input rate for cache buckets it cannot price
A model with no cache_creation_input_token_cost, which is every OpenAI, Azure and Gemini entry, resolved that rate to 0.0 and carried the whole written prompt for free, so a first turn routed onto a cheaper model reported a loss. Same hole on cache reads. Those tokens are plain input on such a model, so they move into the text bucket.
* refactor(spend): build the daily upsert payloads in one shot
`common_data` and `update_data` were constructed and then appended to: `request_id`
conditionally for tag rows, `endpoint` unconditionally a few lines later. A dict that
grows after its literal cannot be reasoned about by reading the literal, which is the
whole point of building it at once.
The conditional key resolves to a spreadable value before either payload, so both are
single expressions and the tag branch appears once instead of twice.
Not wrapped in MappingProxyType, though it was suggested: these go straight to
prisma, whose query builder branches on `isinstance(value, dict)` to tell a nested
node from a scalar. A mappingproxy is a Mapping but not a dict, so it falls through
to the serializer and raises `TypeError: Type <class 'mappingproxy'> not
serializable` inside the batch upsert, where the surrounding except would log it and
leave the rollups silently unwritten.
* fix(spend): keep the one-shot upsert payloads under the type-discipline budget
Building both payloads as single literals traded a mutation for two dict literals,
and LIT002 counts construction rather than mutation, so the change the review asked
for is the one the gate charges for.
The empty branch is the avoidable half: it is the same value every time, so it moves
to a module constant built once instead of a literal per transaction, and it is a
read-only mapping so none of the call sites that spread it can fill it in later.
"Add keyword rule" seeds a row with no keywords, and the only check that a
rule carried one lived inside getSemanticConfigError, which returns early
when semantic keyword matching is off. Off is the default, so an unfilled
row fell through to serializeKeywordTierRules and was discarded on the way
to the payload; the create reported success and the rule was gone.
The row now reports the gap itself and the submit is withheld while one is
outstanding, on the create form and the edit modal alike, both reading
emptyKeywordTierRuleIndexes so the row named and the row marked cannot
differ. Enter commits a typed keyword: the dropdown is kept closed, which
left antd nothing for Enter to select, and submitting was what used to
supply the blur that saved the word.
The backend already refused such a rule, but only when the router built the
deployment, so a caller that sent one anyway got the row written, dropped on
reload, and a 500. The management write paths now parse the incoming
complexity_router_config with the router's own ComplexityRouterConfig, judged
on the config alone so a patch that writes one without naming a model is
covered too, and reject it with a 400 having persisted nothing.
* feat(team): custom metadata validation hook for team create and update
Operators can point general_settings.custom_team_metadata_validate at an
async Python function that validates team metadata before /team/new,
POST /team/update, and PATCH /team/{team_id} commit their writes. The
hook receives the metadata that will actually be written (the merged
result on PATCH) plus the stored metadata and requester context, and
fails closed: a rejected value returns the function's own message as a
400 while any exception or timeout blocks the write with a configurable
generic message as a 503. Premium-gated like enforced_params.
* fix(team): validate metadata before model alias writes and strip system keys from validator input
Review follow-ups on the team metadata validation hook: run the validator
before the model_aliases table insert so a rejected create leaves no
orphaned model rows, strip system-managed keys from existing_metadata so
the validator sees symmetric input on both fields, and accept class
instances exposing an async __call__ as validators. Adds a three-way
validator implementation matrix (allowlist function, HTTP-service-backed
function, immutability-enforcing class instance) driven through the real
create, update, and patch endpoints, including an HTTP stub service and
outage coverage.
* test(team): run the metadata validation matrix against the DB-backed proxy in CI
Adds the validator matrix to the proxy_store_model_in_db_tests CircleCI
job so every scenario runs full e2e against a Postgres-backed proxy. The
proxy config registers a dispatching validator that routes each request
to one of the three implementations via a metadata key and accepts
anything that does not opt in, keeping the rest of the suite unaffected.
CI starts a stand-in cost center service on the host for the HTTP-backed
implementation, reached from the container via host.docker.internal, and
the outage path targets a closed port to prove the fail-closed 503
without stopping services.
* feat(ui): edit team metadata as key-value pairs in team create and edit forms
The team create and edit forms asked for metadata as a raw JSON blob in a
textarea buried under Additional Settings. Both forms now render a key-value
pair editor directly under the TPM/RPM limit fields, backed by a shared
MetadataKeyValueFields component. Values round-trip losslessly: non-string
values display as JSON and parse back to their typed form on save, and
JSON-ambiguous strings are quoted so their type survives the trip. The edit
form hides UI-managed keys (logging, guardrails, model rate limits, etc.)
that dedicated controls already own and re-add on save.
* fix(ui): explain typed JSON parsing in the team metadata help text
* feat(team): schema-driven metadata fields from team_metadata_schema config
* refactor(team): render schema metadata fields as locked key-value rows, drop allowed_values
* refactor(team): schema fields reduce to key and label, tag-rendered keys, clean rejection toasts
* refactor(ui): prepopulate declared metadata keys as ordinary key-value rows
* fix(team): let non-admin dashboard users read the team metadata schema
* test(proxy): pin timeout wiring, boundary, and error-message contracts for team metadata validation
* fix(proxy): use pooled async httpx client in the e2e team metadata validator example
* refactor(team): satisfy staging lint ratchets inherited by the merge
* fix(ui): hide guardrail review buttons from non-admin users
The team guardrail submissions list rendered Approve/Reject buttons for
non-admin users even though the backend correctly rejected the calls.
Thread userRole from the page through GuardrailsPanel into
TeamGuardrailsTab and gate the row-card and detail-panel review buttons
on isAdmin so the UI matches the backend authorization.
Defense in depth only — the backend remains the source of truth and is
double-gated at both the route admin check and the explicit endpoint
role check.
Refs LIT-2494
* refactor(ui): read userRole from useAuthorized hook instead of prop drilling
Drop the userRole prop chain through GuardrailsPage → GuardrailsPanel →
TeamGuardrailsTab. Each component reads userRole directly from the
useAuthorized hook, matching the pattern used elsewhere in the dashboard.
Tests now mock useAuthorized per case (the same pattern as
top_key_view.test.tsx) instead of passing userRole as a prop.
Refs LIT-2494
* fix(ui): drop userRole prop on GuardrailsPanel call site in src/app/page.tsx
Missed in the earlier refactor — GuardrailsPanel no longer accepts
userRole as a prop (reads from useAuthorized hook), so callers must
not pass it. The build was failing in production type-check.
Refs LIT-2494
* fix(ui): gate guardrail forward-key toggle and header editors on proxy admin
* refactor(ui): remove dead app_admin case from user role formatting
The Pretty view only parsed the Chat Completions shape (messages /
choices[0].message), so any spend log storing the Responses API shape
(input / output) rendered an empty Input card and the literal text
"No response data available" even though the row held the full request
and response. This also hit plain /v1/chat/completions callers, because
litellm may route those over the Responses bridge and then store the
upstream Responses-shaped body.
Parsing now branches on a tagged union covering both shapes, which also
replaces the any-typed key sniffing and the role guessing it relied on.
Adds 61 unit tests on the modules #35694 extracted: 46 on the payload
builder, 15 on the OAuth redirect snapshot. They run in 9ms against 240s
for the 77 full-render tests they partly replace. Nine of nine mutants
were killed when the extracted logic was deliberately broken, so the
speed does not come at the cost of signal.
Deletes six cases across four blocks that rendered the whole modal to
assert one payload key belonging to a field they never touched. Every
test that proves a form field reaches the right payload key stays; those
cover field to form value to payload, which a unit test cannot reach.
Replaces "should not render when user is not an admin", which asserted
the admin title was absent and so passed for the wrong reason: the modal
does render for a non-admin, retitled. registerMCPServer was mocked but
never asserted anywhere, leaving the whole non-admin submission path
uncovered. It now drives a real submit and asserts the call lands there
and never on createMCPServer.
Renames the slow file to CreateMCPServer.integration.test.tsx and
documents the three tiers in the dashboard CLAUDE.md. No production code
changes.
* feat(ui): expose an Auto-Router session affinity toggle
session_affinity on ComplexityRouterConfig defaults to True, and neither the
create form nor the edit modal ever emitted the key, so every auto-router built
in the UI silently pinned each session to its first turn's model for an hour
with no way to see or change that.
Adds an "Advanced: Session Affinity" switch to both surfaces, defaulted on to
match the backend field. Both paths now write the key explicitly instead of
falling through to the backend default, so a stored config states what the
router actually does. A stored config with the key absent hydrates as on, since
those routers are running with affinity enabled today; showing them as off would
report the opposite of reality and persist it on the next save.
* feat(complexity_router): default session affinity off and expose it in the UI
session_affinity defaulted to True and the Auto-Router UI never emitted the
key, so every router built there silently pinned each session to whatever model
its first turn classified into for an hour, refreshed on every hit. There was
no way to see that from the UI and no way to change it without hand-editing
config.yaml.
The default flips to False, so every turn is classified on its own merits and
lands on the cheapest adequate tier. Pinning is now opt-in.
The toggle added in the previous commit follows the field: it renders off, and
both the create tab and the edit modal keep writing the key explicitly, so a
stored config states what the router does instead of inheriting a default that
can move under it.
Behavior change for existing routers: those created before this have no
session_affinity key stored, so they pick up the new default and start
reclassifying every turn. That gives up the provider prompt cache the pin was
preserving, and a multi-turn session can now change model between turns. Set
session_affinity: true to keep the old behavior.
Google Cloud has renamed Vertex AI RAG Engine to "RAG Engine" and
Vertex AI Search to "Agent Search" in its console. Users following our
setup instructions hit a naming mismatch when they cross-reference the
GCP console. Keep "Vertex AI" as the primary term (the generic new
names would make our provider UI ambiguous) and surface the new names
as secondary asides only where users leave the UI for the console.
Resolves LIT-3081
Pulls four modules out of the 1398-line create component, which drops to
896 lines. No behavior changes: CreateMCPServer.test.tsx is untouched and
all 77 of its tests pass against the refactored component, which is the
review contract for this PR.
createServerPayload.ts is a pure form-values-to-payload function whose
failures are a tagged union instead of inline notification calls, so the
transformation is reachable without a DOM. createOAuthUiState.ts owns the
snapshot that survives the OAuth authorize redirect, keeping every
presence guard the inline version had. AwsSigV4Fields and
OpenApiByokFields are the two largest JSX blocks, moved verbatim so they
can be diffed as moves.
The create/edit setToken divergence, the mcpLogoImg export, and the
untyped form-values bag are left alone on purpose; each is a behavior or
cross-file change that does not belong in a move.
Pure rename, no behavior change. create_mcp_server.tsx and its test move
to CreateMCPServer, the two importers and one stale e2e comment follow,
and the local/filename-pascal-case suppression drops now that the file
passes the rule on its own.
The rename is scoped to this one component rather than the whole
directory because three PRs are currently open against its snake_case
siblings; the rest can follow once those land.
The dashboard pins both packages exactly in `overrides`, so the lockfile
stays on whatever those pins say. Move brace-expansion from 5.0.8 to 5.0.9
and postcss from 8.5.22 to 8.5.23, both upstream patch releases, and
regenerate the lockfile.
`npm ci`, `next build`, and the 5888-test vitest suite all pass on the
updated lockfile.
* feat(teams): apply default organization to new teams from default team settings
Adds organization_id to DefaultTeamSSOParams so proxy admins can pick a
default organization in Default Team Settings. new_team applies it before
org validation whenever a team is created without an explicit
organization_id, so API, Admin UI, SCIM, SSO, and team upsert creations
all inherit it and go through the same existence and org-limit checks.
Explicit organization selections win and existing teams are untouched.
The default is validated at save time (PATCH /update/default_team_settings
returns 400 for an unknown org) and at create time, where a missing org now
surfaces as a clean 400 instead of a 500 by routing OrganizationNotFoundError
into the previously dead org_table None guard.
The Admin UI Default Team Settings tab gets a Default Organization row
backed by the shared OrganizationDropdown.
* fix(teams): validate org limits against final team state including defaults
Applies default_team_params and the legacy max_budget fallback before the
organization validation block, so _check_org_team_limits sees the values the
team will actually be persisted with. Also loads the org's budget table in
the lookup; without include_budget_table every budget comparison in
_check_org_team_limits was skipped because litellm_budget_table was None.
* test(proxy_behavior): pin org team limits as enforced on /team/new
The dead-code pins existed to turn red when include_budget_table went
live; that happened, so the scenarios now assert the 400 rejections plus
within-cap acceptance, and the unknown-org pin asserts the handler's 400
instead of the surfaced 500.
Adds a Stream responses checkbox (default on) to the playground Model
Settings popover. When unchecked, chat completions and responses API
requests are sent with stream: false and the full reply renders at
once. The non-streamed result is replayed through the existing
streaming handlers as synthesized chunks/events so MCP events, vector
store results, usage and response ids behave identically in both
modes. TTFT is suppressed when not streaming; total latency now also
reported for the responses API. The toggle is scoped to the chat and
responses endpoints, persists via sessionStorage, and is isolated from
the simplified Agent Builder chat.
Resolves LIT-3251
disable_team_logging cleared only metadata["callback_settings"], but callbacks
registered through POST /team/{team_id}/callback and the Admin UI live in
metadata["logging"], and request-time resolution stops at that slot without
ever reading callback_settings. The endpoint reported success while the team
kept sending request and response data to its third-party destination.
Empty the logging slot alongside the existing callback_settings reset, and
refresh the cached team object so the change applies to keys that are already
in flight rather than at the next cache expiry. The same refresh is added to
add_team_callbacks, which has the symmetric problem of a newly registered
callback staying dormant until the entry expires.
Resolves LIT-5101
* fix(team-callbacks): report API-registered callbacks from GET /team/{team_id}/callback
POST /team/{team_id}/callback writes metadata["logging"] while the GET read
metadata["callback_settings"], so every team configured through the API or the
Admin UI got back an empty list. c620d76fe4 migrated the writer to the new key
and left this reader on the old one.
Resolve the read the same way request-time resolution does in
_get_dynamic_logging_metadata: a logging slot that is present wins outright and
callback_settings stays as the deprecated fallback, so the endpoint reports what
a request would really do rather than the union of both shapes. An empty logging
list therefore reports no callbacks, matching a request that fires none.
Decrypt callback_vars for the response and mask the credential keys. Ciphertext
would be unusable to the caller, and a value encrypted under a key that is no
longer classified as sensitive would otherwise come back as a raw blob.
Resolves LIT-5093
* Update litellm/proxy/management_endpoints/team_callback_endpoints.py
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(team-callbacks): mask callback vars that fail to decrypt
decrypt_callback_vars passes a value through untouched when it cannot be
decrypted, which happens to existing rows after a salt-key rotation. Under a
key that is not classified as sensitive that blob reached the caller as opaque
ciphertext it could not use or tell apart from a real value, so mask anything
still carrying the encrypted prefix.
Raised by Greptile on the first commit.
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
A keyless internal user signing in to the Admin UI was redirected off the
post-login landing to /ui/connect, which renders nothing but the MCP apps panel,
so a plain gateway sign-in ended on an MCP OAuth surface the user never asked
for. The landing now renders the keys dashboard for every role. The key lookup
that existed only to make that routing decision goes with it, along with the
useKeys enabled flag it was the sole caller of and the role-hydration hold that
guarded its one-frame dashboard flash
The gateway DCR consent flow moves the other way. Its /authorize handed the
browser to /ui/chat/integrations, whose layout hard-blocks when enable_chat_ui
is off, which is the default, and client-side redirects to /ui/ without the
query string; that destroys the connect_flow handle and strands the MCP client
until the 600s flow cookie expires. It now lands on /ui/connect, which reads
connect_flow and connect_client, mounts the consent banner and puts the apps
panel in connect mode. /ui/chat/integrations keeps its connect-mode handling
this release so flows sealed before the deploy still finish
Resolves LIT-5104
Resolves LIT-4911
About 35,000 fixes ruff marks safe across 32 rules (UP006/UP045/UP007
modern annotations, UP032 f-strings, SIM114/SIM118, RET501, and
friends), removal of the 1,296 typing imports the rewrite orphaned, and
hand fixes for what the fixers could not see: five star-import
freeloaders of typing names, two F823 late-import annotations, the
/get/config/list introspection crash on types.UnionType, redundant
function-local RoleMappings imports in ui_sso.py that shadowed the
module-level name once the annotation lost its quotes, and one FURB168
tautology.
B009/B010/PIE804/RUF019 are excluded on purpose: their safe fixes
rewrite getattr/setattr/**-splat/key-in-dict escape hatches into forms
basedpyright then rejects (283 new errors measured), so their budgets
stay at base values.
ruff-strict-budget.json drops by 39,579 this commit (39,968 across the
branch) with 28 rules at an actual 0 and 9 more sharply down.
type-discipline-budget.json ratchets LIT002/LIT006/LIT009 down; LIT001
moves to the now-honest total: the checker matches the spelling `set`
but not the alias `Set`, so the 160 typing.Set annotations rewritten to
set[...] were always mutable-set annotations and only now count.
PR #35471 added classifier_context_include_assistant_turns to ComplexityRouterConfig.
It worked through config.yaml and the model API but had no control on the Add Model or
Edit Auto-Router screens, so an operator working from the dashboard could not reach it.
Wires it into the create and edit forms, shown only when the LLM classifier is
selected, matching what #35315 did for the two context-window fields
The create and edit stacks share the rendered control but keep their own serializer,
their own hydration, and their own managed-key set, so the field is added in five
places rather than one. A field wired into only one stack fails in a way neither
serializer unit test can see, since those are handed a form value assembled by hand,
so the edit-modal test drives the real component through open, edit and save
The switch is emitted even when false, because there the operator turning it off is a
choice that has to overwrite a stored true rather than an absent value a truthiness
gate would drop
Live Cursor Ask-mode captures show the shape dialects mix PER LEVEL: the
tool envelope arrives chat-nested while the grammar format inside it is
still Responses-flat, so a normalizer that pattern-matches whole-tool
templates misses every hybrid. The cursor arm now normalizes the envelope
level and the format level independently and idempotently, making it
total over the envelope x format matrix; a parametrized 8-cell test pins
every combination. The reference BYOK bridge was checked and forwards
chat bodies verbatim, so there is no prior art for these hybrids
Cursor Ask mode sends chat bodies whose tools array mixes nested function
tools with flat Responses-style custom tools; the /cursor messages arm now
nests those before delegating, published via the request parsed-body cache.
Core chat parsing gains first-class custom tool call types mirroring the
openai SDK union: a single dict dispatch feeds the provider-dict sinks,
Delta dispatch stops both stream re-parse sites from silently swallowing
custom deltas, the chunk builder accumulates custom input for spend logs,
function-assuming consumers (json-mode gate, multi_tool_use repair,
helicone, lunary) skip custom entries, and the chat-to-responses bridge
flattens nested custom tools to the Responses flat shape
- delegate messages-shaped bodies to the standard chat completions handler
- strip chat-only stream_options before the Responses pipeline
- fix cursor_data_generator signature (request kwarg) and duck-type the
stream gate so router-wrapped streams convert instead of leaking raw
Responses events
- convert custom_tool_call items and events to chat tool_calls in the
streaming and non-streaming paths; remap streamed tool_call indices to
0-based sequential; accumulate raw and pydantic tool calls into one choice
- normalize generic pydantic output items through the raw-dict handler
Both fields select from a server-side search over existing accounts, so a
typed-in address or id never becomes a value. Say so up front rather than
letting the form look like it accepts a new user and fail on submit.
Applies to the organization member modal too, which shares this component.
* fix(ui): show pass through route selections in team/key forms and match team id substrings in team search
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* perf(teams): keep team id search index-friendly with a prefix match
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(teams): keep /v2/team/list search id matching exact by default and add an opt-in prefix mode
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: milan <milan@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Opening a session from the logs table stored no ?session_id (row clicks
called openLog, which deletes it), so session mode was derived from the
clicked row's session_total_count. Rows fetched by the session drawer
come from /spend/logs/session/ui, which does not enrich that field, so
selecting any log inside the session view swapped in an unenriched row
and collapsed the drawer to a single-log Trace view
Row clicks on a multi-call session's row now call openSession, and
selectLog writes ?session_id when the session view is active, so session
mode is anchored in the URL instead of derived from row data
The create button now sits in the tab bar beside the tabs, the way Teams
lays it out, with one divider between them and the rule running the full
width underneath.
Adds a fillHeight mode to DataTable that treats the parent's height as a
ceiling rather than a target, so the table still sizes to its rows and a
short one keeps its footer under the last row, while a long one scrolls
its rows under a sticky header instead of scrolling the page. This replaces
the hardcoded viewport-height caps those tables would otherwise need. Two
details the mode has to fix: the Table primitive's own overflow container
would capture the sticky header, and rows would show through the
semi-transparent header tint.
Matches the Virtual Keys layout: a page header with the wallet icon, the
create button directly beneath it, and the tab bar below that, on the same
page padding Teams and Access Groups use so the table no longer sits against
the window edge.
Reset and Created start hidden, so the table opens on the four columns it
has always shown and the two new ones are opt-in from the Columns menu.
* feat(spend-logs): record when a spend log row is the auto-router's own classifier call
The complexity router's classifier sub-call copies the parent request's metadata
verbatim, so its spend log row carries the caller's key, team and user and is
indistinguishable from traffic the caller actually sent. Nothing on the row says
otherwise: call_type is "acompletion" either way, model_group is overwritten to the
classifier's own model group so the row never looks auto-routed, and routing_decision
is absent exactly as it is on an ordinary request.
Record the fact the system already knows at call time. internal_call_origin is
declared on SpendLogsMetadata, which is the allowlist _get_spend_logs_metadata
projects onto, and stamped in _classifier_call_metadata; both classifier paths
already route through that one function and it feeds the metadata and
litellm_metadata buckets alike, so every request surface is covered at one site.
The key is reserved rather than caller-supplied, so it joins routing_decision in the
untrusted-metadata strip and a caller cannot label their own traffic as router
overhead.
The classifier call also inherited no session identity, so the router minted a fresh
trace id and the row landed in a session of its own. Forwarding the parent's session
puts it in the trace of the request that triggered it, which is where an operator
looks for what the routing cost.
* feat(ui): show which log rows are the auto-router's own classifier calls
A classifier row now carries internal_call_origin and shares its parent's session,
so the session trace lists it beside the request that triggered it. Without a marker
in the sidebar it reads as another call the caller made, which is the confusion this
resolves.
The tag renders only for a recognized origin, so ordinary traffic and any future
origin this build does not know about stay unlabelled rather than being asserted as
classifier calls.
* feat(proxy): add a generic list contract for management/v1 entity lists
Paging, sorting, filtering and search for an entity collection, declared once
as a ListSpec and served by handle_list. The route injects a ListExecutor that
owns its table, so this module never imports Prisma.
The caller's scope is derived from the caller alone and ANDed with whatever
they filtered on, so a query parameter can only narrow what they may read.
This is the shared half of the budgets list; it lands here so the endpoint has
something to register against, and drops out when the framework arrives on its
own branch.
* feat(proxy): add GET /management/v1/budgets
The Budgets page reads /budget/list, which returns the whole table as a bare
array with no way to page, sort or filter it. A customer with enough budgets to
fill the page has no way to find one.
Registers LiteLLM_BudgetTable against the management/v1 list contract: sortable
on budget_id, max_budget, tpm_limit, rpm_limit and created_at, default order
newest-first with budget_id breaking ties, search on budget_id, and filters for
budget_duration, max_budget and created_at. budget_duration is deliberately not
sortable; the column holds "7d"/"30d" strings, so a lexicographic ORDER BY puts
"30d" ahead of "7d".
tpm_limit and rpm_limit are BigInt? in Prisma, so rows validate through a
pydantic model on the way out and serialize as JSON numbers.
A caller without admin view is refused 403 as a problem document rather than
served an empty page. /budget/list is untouched.
* fix(proxy): rework the budgets list onto the merged list contract
PR #35308 landed a different shape than this branch was written against: `where`
is a tuple of frozen predicates rather than a Prisma-shaped mapping, `ListSpec`
carries both the row and the wire type, and `where_sql` / `order_by_sql` render
for a raw-SQL executor. The budgets executor now queries through `query_raw` the
way the spend logs facet does, selecting only the columns it serves.
Also casts datetime binds in `where_sql`. They cross into the query engine as
JSON, so an uncast placeholder arrives as text and Postgres refuses
`timestamp >= text` outright; every `filter[created_at][gte|lte]` was answering
500. The cast reads the bind as an instant and drops it to naive UTC to match
Prisma's TIMESTAMP(3) column, the same one /spend/logs/ui applies.
* refactor(proxy): fold the predicate renderer instead of recursing
recursive_detector flags `_render_all`, and the flag is fair: it recursed once
per predicate, so the stack grew with the number of filters on the request for
no reason. Walking a predicate list is a running bind index, which is a fold.
`_render` still re-enters for `AnyOf`, but its clauses are plain comparisons
built by `?q=`, so that nesting is one level deep and no caller can drive it
deeper.
The management list route now exists, so budgetItem, the list envelope and
the response type come from schema.d.ts instead of being hand-written
against the contract. The optional fields widen accordingly, so the rate
limit and reset cells accept undefined alongside null.
