* fix(auth): quiet malformed virtual key rejections to stdout
Reduce noisy invalid-api-key error logs by classifying malformed virtual
keys and routing their rejections to stdout as WARNING instead of stderr
as ERROR. Suppressible via LITELLM_LOG=ERROR or log_client_error_tracebacks=true.
Changes:
- auth_utils: is_invalid_virtual_key_error() classifier and marker functions
- auth_exception_handler: log invalid keys as WARNING to child logger before
identity seeding and callbacks, escalate non-401 transforms to ERROR
- user_api_key_auth: websocket early-raise WebSocketException(1008) to avoid
double-logging at HTTP layer
- _logging: child logger verbose_proxy_stdout_logger with no handler/level;
LevelRoutingStreamHandler routes its WARNING records to stdout; handler
setLevel in _turn_on_json() closes JSON config handler level leak
- test_auth_exception_handler: new test case verifying malformed-key logs
at WARNING with marker retention through transformations
Fixes LIT-5362
* fix(auth): classify malformed-key 401 by raise-site marker, not message text
Review round 1 (Greptile P2, veria Low):
- Move the marker attribute name to litellm/constants.py per the shared
sentinel convention
- Stamp the marker on the malformed-key 401 where it is raised and classify
only by it. Message text is caller-influenceable on other 401s (vector
store ids, organization ids are interpolated into their messages), so a
phrase match would let a request body demote an authorization failure to
the quiet log path
- Regression test: a 401 carrying the phrase but not the marker stays at
ERROR on stderr
Replace table tbody and data-slot locators with getByRole, restore prior
public MCP hub entries instead of clearing the whitelist on cleanup, seed
the public agent via the append-semantics per-agent route, and rework
mutable cleanup state into const-scoped try/finally blocks
* fix(ui): keep litellm_credential_name from LiteLLM Params JSON when no credential is selected
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(ui): drop null litellm_credential_name from AddModelPanel payload fixture
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
ProxyExtrasDBManager.spend_logs_is_partitioned() (#38452) silently returns
False when psycopg can't be imported, and psycopg was never added to the
extra_proxy install, so every production image lacks it. Schema
reconciliation then generates the unfiltered primary-key rewrite against a
genuinely partitioned LiteLLM_SpendLogs and Postgres rejects it, exactly the
failure the fix was meant to prevent. Ships psycopg via extra_proxy and logs
a warning when it's still missing instead of failing silently.
At end of drain the pump enqueued the sentinel first and picked the
billing mode from client_detached afterward, so a client that consumed
the sentinel and tore the relay down before the pump resumed (possible
whenever the sentinel enqueue hit a full queue) had its fully delivered
response billed through the teardown path, skipping the proxy's
post-response hook. Bill or park before the sentinel goes out, and let
an unconsumed sentinel fall back to dispatching the parked billing.
Shadow eval jobs previously targeted only virtual keys, so deployments on
pure JWT auth (which present no key at all) could never sample their
traffic. Jobs now carry a typed (target_type, target_id) pair covering
keys, teams, and users; sampling matches the identity every request
resolves to at auth time, so team and user jobs cover JWT traffic with
no client changes.
Resolves LIT-6578
* feat(complexity_router): escalate oversized prompts to a tier that fits before dispatch
The classifier scores complexity and never prompt size, so a long agentic
session whose newest ask is trivial classifies SIMPLE onto a small-window
tier and the provider rejects it with a context-window 400 that nothing
retries. The gate runs after classification on every decision path
(classify tail and session-affinity pin), estimates prompt tokens
including the out-of-band carriers (top-level system, tools,
instructions), and when the decided tier provably cannot hold the prompt
moves the request to the lowest configured tier with a model whose
declared window fits, restricting the pick to fitting models when the
decided tier can keep it. Models with no resolvable window are never
escalated away from or onto, escalated decisions are never written as
session pins, and the decision records context_escalated plus the
original tier in spend logs.
Resolves LIT-6503
* fix(complexity_router): judge groups by smallest window, bound skips by bytes, filter adaptive picks
Review-round rework, one mechanism per finding. A group is judged by its
smallest resolvable deployment window, since the core router picks within
a group with no fit check. The counting skip is gated on UTF-8 byte
length, which BPE token counts can never exceed, so token-dense scripts
cannot slip past it; only a real tokenizer count ever moves a request and
a failed count leaves the placement alone. The fit facts now filter every
adaptive phase including cold start and the tier fallbacks. Window
questions adopt the declared provider and never resolve authenticating
providers, and a router instance without get_model_list degrades the gate
to a no-op. Tests rebuilt on real Router instances resolving deployment
model_info end to end, plus a full-path test through
async_get_available_deployment
A guardrail modify_response verdict on a streaming request only produced a
proper replacement on /v1/messages: the chat completions and Responses API
translations had no build_block_sse_chunks, so the ModifyResponseException
re-raised and surfaced as an in-stream 500 error frame (or a whole-request
500 in buffered mode) instead of the documented 200 replacement.
Implement build_block_sse_chunks for both OpenAI translations: chat emits a
content delta plus a finish_reason content_filter chunk with real usage;
Responses emits the typed event sequence (standalone via
build_synthetic_response_events pre-stream, or an output-item continuation
under the in-progress response id mid-stream) ending in response.completed.
UsersTable overrides DataTable's default noDataMessage with its own
EmptyState, so the row reads "No users found" rather than "No results".
Assert that, and pair it with the seeded user being absent so the check
cannot pass while the filter silently does nothing.
A non-admin switching an existing key's type between the safe preset
buckets (llm_api_routes, info_routes, and empty = full access) got a 403
from the allowed_routes admin gate, because /key/update, unlike
/key/generate and /key/regenerate, had no carve-out for preset-derived
values. Skip the gate only when both the incoming and the stored
allowed_routes consist entirely of safe presets, so clearing an
admin-set custom route restriction still requires proxy admin.
The batch start told the seed which LiteLLM_SpendLogs rows were its own, but
using it as a hard cutoff also dropped rows another pod had already persisted.
Those rows are only repaid by that pod's own increment, so if it died first the
window row stayed permanently under the recorded spend.
The seed now reads both sums in one scan and takes off this batch's own spend,
flooring at the pre-batch total for the case where its log rows have not landed
yet. Redis payloads keep an empty request_ids so a leader from before the field
was dropped can still merge what it pops during a rolling deploy.
Claude-Session: https://claude.ai/code/session_01QvQzYztinxj8ZuD5YxbVdL
Two assertions were checking the wrong thing. The anchoring tests read
getByRole("listbox"), which resolves to SelectPrimitive.List; that sits at
full content height inside the popup that clips and scrolls it, so the box
overlapped the trigger even when nothing visible did. Measure the popup.
The SSO-ID search expected zero rows, but DataTable renders a "No results"
message row when a filter matches nothing, so the count is one. Assert the
empty state the user actually sees.
GigaChat reports prompt_tokens and total_tokens after subtracting cached
tokens (the docs example is prompt_tokens=1, precached_prompt_tokens=37,
total_tokens=5, so the fields are disjoint, not a subset). Map to the
OpenAI convention by adding precached_prompt_tokens back onto prompt and
total while still surfacing it as prompt_tokens_details.cached_tokens.
