Add regression tests for the db-fetch paths whose converted construction
lines were uncovered: the auth_checks getters (default end user budget, end
user, team membership, access group, team by alias, org by alias, object
permission, managed vector stores, project), get_all_team_memberships and
list_available_teams in team_endpoints, and the proxy admin user info
helper. Each test feeds a mocked prisma row through the real function and
asserts the validated model's fields, so a bad model_validate conversion on
any of these paths now fails a test instead of only dropping coverage.
Convert pydantic table-model construction from Cls(**row.model_dump())
kwargs-unpacking to Cls.model_validate(...) across the management endpoint
hotspot files (team, key, internal user, scim, model management, spend
tracking, auth checks, proxy_server). Unpacking an untyped dict reports one
Any-typed argument per matched model field, so each converted site clears
10-35 diagnostics while running the exact same pydantic validation.
Conversions were limited to models verified to use pydantic's default
__init__; UserAPIKeyAuth and LiteLLM_VerificationTokenView keep their custom
kwargs-rewriting __init__ and are untouched. Two locally-verified helper
params move from Any to object.
Whole-tree basedpyright, measured against the branch point in the same
environment: reportAny 24,431 -> 22,741 (-1,690), reportArgumentType
2,189 -> 2,136 (-53), reportUnknownArgumentType 34,370 -> 34,067 (-303),
reportExplicitAny 7,285 -> 7,283 (-2); total 154,882 -> 152,834 (-2,048)
with no rule increasing anywhere and no per-file increases. No casts, no
suppressions, no behavior changes. Budgets ratcheted: basedpyright -2,048
across 4 rules, ruff ANN401 -2.
* fix(mcp): resolve call_tool by registry without requiring tool map
Multi-worker reloads put MCP servers in the registry from the DB but do
not re-run tools/list on every process. Gating call_tool on
tool_name_to_mcp_server_name_mapping made cold workers 500 with Tool not
found after another worker had already listed the tool. Treat a registry
match on server id/name/alias as enough; upstream rejects unknown tools
* test(e2e): poll MCP register, tools/list, and tools/call across multi-worker lag
Stage multi-worker gateways only load MCP servers and tool maps on the
process that handled the request. Poll until the server is listed, the
tool appears on tools/list, and tools/call is not a cold-worker 500 so
key-access and Datadog MCP e2e stop racing the LB
* Revert "fix(mcp): resolve call_tool by registry without requiring tool map"
This reverts commit 8b56e51e39.
* test(e2e): tighten MCP multi-worker lag classifier
Only retry tools/call on gateway shapes Tool <name> not found and
server_not_found, not any 500 that mentions tool/server not found, so
upstream failures are not retried until the poll deadline
* test(e2e): drop unit file for MCP lag classifier
The live await_call_tool polls already cover multi-worker lag; a separate
string-match unit module is not worth keeping
(cherry picked from commit c274cf321c)
* fix(mcp): resolve call_tool by registry without requiring tool map
Multi-worker reloads put MCP servers in the registry from the DB but do
not re-run tools/list on every process. Gating call_tool on
tool_name_to_mcp_server_name_mapping made cold workers 500 with Tool not
found after another worker had already listed the tool. Treat a registry
match on server id/name/alias as enough; upstream rejects unknown tools
* test(e2e): poll MCP register, tools/list, and tools/call across multi-worker lag
Stage multi-worker gateways only load MCP servers and tool maps on the
process that handled the request. Poll until the server is listed, the
tool appears on tools/list, and tools/call is not a cold-worker 500 so
key-access and Datadog MCP e2e stop racing the LB
* Revert "fix(mcp): resolve call_tool by registry without requiring tool map"
This reverts commit 8b56e51e39.
* test(e2e): tighten MCP multi-worker lag classifier
Only retry tools/call on gateway shapes Tool <name> not found and
server_not_found, not any 500 that mentions tool/server not found, so
upstream failures are not retried until the poll deadline
* test(e2e): drop unit file for MCP lag classifier
The live await_call_tool polls already cover multi-worker lag; a separate
string-match unit module is not worth keeping
Gemini returns each thoughtSignature on exactly one part. LiteLLM
stores a function-call signature both message-level (thought_signatures)
and on the tool call itself, then re-attached it to BOTH the text part
and the function-call part when serializing history. gemini-3 and newer
models bill every replayed copy as the previous turn's full reasoning
token count, so long agentic sessions doubled their context growth and
hit the 1,048,576-token limit
Only attach a message-level signature to the text part when the same
signature is not already carried by a tool-call part:
- compare signature values instead of boolean presence so a distinct
text-part signature is never dropped
- ignore the gemini-3 dummy-signature fallback during detection so
replaying gemini-2.5 history to a newer model keeps the real text
signature
- count signatures carried by server-side tool invocations so they are
not re-attached to the text part
gemini-2.5 responses (signature on the text part, function call
unsigned) are unaffected: the text signature is preserved as before
An auto-router deployment's litellm_params.model (auto_router/...) is the
discriminator the router loads it by, but the model management endpoints
accepted any client-supplied value verbatim; a doubled or stripped prefix
made router init fail on the next load and ignore_invalid_deployments
silently dropped the deployment. Validate writes that supply
litellm_params.model at all three endpoints against the merged params and
reject incoherent values with an actionable 400. Classification is
extracted to router_utils/auto_router_model_naming.py so the Router
predicates and the validation share one source
Every model-write endpoint returned 200 off the DB write alone; a model the
reload dropped (ignore_invalid_deployments, or a wholesale reload failure)
stayed invisible on every channel at once, which is how the registry-leak
defect went undiagnosed for three weeks. ProxyConfig.add_deployment and
clear_cache now return whether the reload pass completed, and each write
endpoint verifies the rows it wrote are live in this pod's router afterwards,
distinguishing a deliberately environment-inactive model via the same
predicate the Router's own gate uses. The access-group writers return the
mutated id set instead of discarding it
A poll may start with remaining budget and still return after started+timeout
if the transport overruns its clamp. Recheck the first-listing deadline after
the response so a late listing does not open the continuous DB-sync phase
(cherry picked from commit 7ff2bcbf14)
When less than one full poll interval remained in the first-listing budget,
the pre-sleep check returned NotServable without another /v1/models call.
Sleep only min(interval, time left) so a model that becomes listable in the
last seconds of the timeout still gets a clamped final poll
(cherry picked from commit 8439195922)
create_model returned after the first /v1/models hit that listed the model,
so chat could still land on a cold gateway worker (numWorkers>1 / peer pod)
and 400 Invalid model name. Require continuous listing for the product
default add_deployment interval (30s) after first sight so every worker has
synced from the DB; first listing still bounded at 40s
(cherry picked from commit 7d1ee2ff86)
_await_model_servable used poll_timeout (120s), the spend/log read-back
budget. A stuck model reload therefore stalled every suite that creates a
deployment for two minutes before failing
Give create_model a fixed harness middle ground: model_servable_timeout=40s,
polled every 2s, with each /v1/models call capped at 5s and clamped to the
remaining deadline so one slow GET cannot overrun the wait. Happy path still
returns on the first listing. Not derived from proxy general_settings or env
Transport.get accepts an optional per-call timeout for that clamp. Unit tests
cover the deadline arithmetic and clamp without a live proxy
(cherry picked from commit c082a0e648)
* fix(proxy): warm rotate Prisma client for IAM refresh
* fix(proxy): drain Prisma operations during IAM rotation
* fix(proxy): bound the drain wait when retiring a replaced prisma engine
A replaced engine waited indefinitely for its drain tracker to empty.
Hung queries self-release via prisma's 30s default HTTP timeout, but a
transaction whose owner is hard-cancelled before commit/rollback leaks
its drain count forever, keeping the retired engine and its DB
connection pool alive indefinitely; at one rotation per 12 minutes such
engines accumulate. Cap the wait at 90 seconds, which exceeds every
legitimate operation bound (30s HTTP timeout, 60s max interactive
transaction timeout in this codebase), then kill the engine anyway.
Work killed at the deadline degrades to the pre-drain behavior and is
retried by the existing reconnect/backoff layers.
---------
Co-authored-by: ryan-crabbe-berri <ryan@berri.ai>
Scrub aliases on delete only when the deleted deployment's model_name no
longer resolves in the router. A legacy load-balanced team model can have
several deployment rows sharing one internal name; deleting one replica
must not remove aliases that still route to the survivors, in any team
A team's model_aliases can map a public name like gpt-4 to the internal
routing key (model_name_{team_id}_{uuid}) of a team deployment that has
since been deleted, e.g. after replacing per-team duplicates with one
gateway-level model. The pre-call rewrite then sent every request to a
name the router cannot serve, failing with "no healthy deployments for
model_name_..." even though the requested name still resolves at the
gateway level. The rewrite is now skipped when the alias target has no
live deployment in the router
delete_model also skipped the team alias scan for internal-shaped names
on the assumption they can never be alias values, which is exactly the
shape legacy team model aliases have, so deleting a legacy team model
left the stale alias behind. The scan now always runs, and a public
name that still resolves to a live router deployment (e.g. a shared
gateway-level model group) stays in team.models so the delete does not
revoke the team's access to it
The aggregate gateway DCR flow ends in a 303 to the client's loopback
redirect_uri. When the MCP client runs on a browserless machine (EC2,
SSH box, container) the user authorizes from a browser on another
machine, so the 303 dereferences the wrong loopback and the code never
reaches the client.
The connect banner now offers manual delivery for loopback clients: the
finish form posts delivery=manual and /authorize/complete renders the
callback URL on a no-store page instead of redirecting. The user pastes
it into the client (Claude Code v2.1.191+ accepts a pasted callback URL)
or fetches it from the client machine's terminal. Manual codes keep the
same sealing, PKCE binding, and single-use guard, with a 5 minute
expiry instead of 2 to survive the copy-paste hop; the used-code marker
TTL derives from the code's own remaining lifetime so the single-use
property holds for the full 5 minutes. The default redirect path is
unchanged.
Resolves LIT-4863
The cache-hit and paid rows for the two driver calls flush from different
pods on independent update_spend timers, so waiting only for the cache-hit
row can return a half-arrived result set where the paid-row assertion then
fails on an empty list. Requiring both row kinds in the poll predicate lets
the existing deadline absorb the slower flush without weakening any assertion
Anthropic-shaped usage was mapped through the Responses API usage converter, which ignores top-level cache_read_input_tokens/cache_creation_input_tokens, so cache hits on /v1/messages were billed entirely at the uncached input rate
* test(e2e): harden harness and tests against data-plane pod churn
A stage autoscaler scale-down produced a 2s window of ALB 502s that killed six
budget tests on their first management call, and a freshly scaled-up pod that
had not run its 30s DB object sync yet failed two MCP tests and one prometheus
cardinality test. Retry transient gateway errors (502/503/504, connection
errors) once at the shared e2e_http dispatch seam, poll MCP server registration
to the poll deadline instead of asserting a single-shot listing, anchor the MCP
guardrail full-sync wait to the later of the guardrail and server writes, and
turn the prometheus alias poll into a drive-and-scrape convergence loop that
re-sends traffic for missing aliases and unions results across scrapes
* test(e2e): drain request body in retry stub handler so keep-alive reuse cannot misparse leftovers as requests
* revert(e2e): drop the transient-502 retry seam
A raw 502 during a pod scale-down is what a real client sees, so the suite
retrying past it hides an availability gap instead of flagging it. The
gateway-side fix is graceful drain on the deployment; until then the failures
are signal
* test(e2e): cap per-alias driver re-drives in the prometheus cardinality poll
Bounds worst-case provider spend to 4 completions per alias while scrapes keep
polling to the deadline; counters persist on whichever pod served them, so the
cap costs no convergence unless that pod dies
* test(e2e): drop driver re-drives from the prometheus cardinality poll
The per-key cardinality contract is process-local and counters persist on
whichever pod served the driver call, so unioning aliases across free scrape
polls converges without re-sending billable traffic. The residual gap, a pod
dying inside the poll window, is deferred to direct per-pod scraping
* test(e2e): let the ui suite run from a read-only cwd
The playwright suite never executed on stage. It died in globalSetup before a
single test ran, and the reported error was a red herring.
/app/e2e/ui is a read-only filesystem in the packaged e2e image (the image
runner already redirects playwright's own artifacts to TMPDIR for this reason),
but the suite wrote three things relative to cwd: the per-role storageState
files, the failure-screenshot directory, and the html report. Reproduced in the
pod: storageState raises EROFS, mkdir test-results raises ENOENT.
Worse, the catch block that exists to capture a screenshot threw its own ENOENT
while handling a failure, so the real login error was replaced by a filesystem
error. That is why the run looked like a missing directory rather than whatever
actually went wrong.
Route every artifact through ARTIFACT_DIR (E2E_UI_ARTIFACT_DIR, default "." to
keep run_e2e.sh behavior unchanged), make the diagnostic screenshot best-effort
so it can never mask the underlying failure, and point playwright's reporter and
outputDir at the same place so a bare `npx playwright test` works there too.
fixtures/users.ts had its own copy of the five storageState filenames; it now
re-exports the ones from constants so the paths have a single definition.
Verified in the read-only pod: both writes fail before, both succeed after.
85 tests enumerate and tsc --noEmit is clean.
Refs LIT-4821
* fix(e2e): create the ui artifact root before writing into it
storageState() does not create missing parents, and nothing created ARTIFACT_DIR
itself. Pointing E2E_UI_ARTIFACT_DIR at a writable path that did not exist yet
therefore failed with ENOENT on the very first role's snapshot, before any UI
test ran; the same class of failure the artifact-dir change was meant to remove,
just moved one level up.
Reproduced: writing admin.storageState.json into a missing directory raises
ENOENT. My earlier pod verification masked this because the probe called
mkdirSync itself, which the real code path never did.
mkdir the root once at the top of globalSetup, before the login loop. recursive
makes it idempotent, handles nested paths, and keeps the default "." a no-op.
Playwright creates its own outputDir lazily, so globalSetup is the only place
that needs this, and migration.serverRootPath.globalSetup delegates here so it is
covered too.
* test(e2e): skip the mid-conversation cache checks pending LIT-4873
A mid-conversation role="system" reminder invalidates the prompt cache on the
vertex_ai, azure_ai and bedrock_invoke Messages paths. Measured on the reminder
turn, same conversation shape throughout:
direct to api.anthropic.com 7013 read cache preserved
litellm -> anthropic/claude-opus-4-8 7013 read cache preserved
litellm -> vertex_ai/claude-opus-4-8 0 read cache destroyed
and the Vertex control with the same added assistant/user turns but no reminder
reads 7013, so it is the reminder on the non-first-party paths and not the extra
turns. Anthropic keeping the cache rules out provider behavior; litellm's
first-party anthropic path keeping it rules out the shared Messages transform.
That makes these assertions correct and the failure a real billing bug, so the
tests are skipped rather than weakened; the bodies stay intact and must be
restored unchanged with the fix. Registry rows are left in place, so the three
mid_conversation_system.nonstream.cache_hit cells report as uncovered gaps.
Skips are decorators rather than a pytest.skip() inside the shared helper: a
mid-function skip fires only after setup has already registered a real
deployment via /model/new and left the rest of the body unreachable.
Only Vertex was measured end to end. Azure Foundry and Bedrock Invoke are
inferred from matching nightly failures and should be confirmed with the fix.
Refs LIT-4821, LIT-4873
Both specs assert against UI that has since moved, so they fail on selectors
rather than on behavior.
The MCP discovery modal became a shadcn/Base UI dialog when mcp-servers
migrated off antd, so `.ant-modal` no longer matches it; locate it by its
dialog role instead. The create form below it is still an antd Modal and keeps
its existing locator.
The no-team internal user has no keys, and a keyless non-admin is now sent to
/ui/connect on the post-login landing, which has no sidebar. Wait for that
redirect to settle, then navigate to the keys page explicitly; the redirect is
gated on the ?login=success marker that the fresh navigation drops, so the
dashboard sticks and the rest of the test is unchanged.
* test(reasoning-effort-grid): bump cell-count assertion for claude-opus-5
The claude-opus-5 grid entry added in ae81625ee6 raised the Anthropic direct
route to 31 model combos, but test_grid_cell_count still expected 30, so the
suite went red on the tripwire rather than on any behavior change.
* test(openai): swap the retired deep-research model out of the bridge test
OpenAI shut down o3-deep-research and o4-mini-deep-research on 2026-07-23, so
the live call in this test now comes back as a 400 'Model not found'. The test
was never about deep research specifically; the bridge fires on any model whose
cost-map mode is "responses", so it now uses gpt-5.5-pro, the newest
responses-only OpenAI model, and is renamed to say that.
gpt-5.5-pro was confirmed present on the CI account with an authenticated
GET /v1/models before being picked.
