Master-key rotation re-encrypted only the credentials column and the
litellm_mcpusercredentials table, leaving the new global env_vars values
and the litellm_mcpuserenvvars values_b64 column encrypted under the old
key. After a rotation those values fail to decrypt, so global ${VAR}
headers are forwarded as empty substitutions and every per-user value
reads back as missing (412). Re-encrypt both new columns alongside the
existing ones, skipping undecryptable entries so a corrupt row is
preserved rather than overwritten.
Read env_vars from the exclude_unset-filtered data_dict (like every other
JSON column) so a partial update that omits env_vars can never overwrite the
stored values. Make the reload path's env-var encryption state explicit by
passing env_vars_are_encrypted=True, since raw DB rows are still encrypted
there unlike the already-decrypted records add_server/update_server receive.
The db.py read/write helpers (get_mcp_server, create_mcp_server,
update_mcp_server) decrypt global env var values in place before returning,
while leaving credentials encrypted. add_server/update_server then passed
those records to build_mcp_server_from_table with the default
credentials_are_encrypted=True, which decrypted the global env var values a
second time. Decrypting an already-plaintext value (e.g. "postgresql")
fails and zeroes it, so the registry entry forwarded the raw ${NAME}
placeholder upstream instead of the interpolated secret, and reload's
updated_at-equality reuse kept the broken entry.
Add an env_vars_are_encrypted flag to build_mcp_server_from_table (defaulting
to credentials_are_encrypted) and have add_server/update_server pass
env_vars_are_encrypted=False so global env var values are decrypted exactly
once.
Streaming responses from the proxy (/chat/completions, /v1/messages,
/v1/responses, assistants) all return through create_response() but never
sent the headers that tell an intermediary reverse proxy not to buffer the
SSE stream. nginx with the default proxy_buffering, k8s ingress-nginx, and
Envoy/Istio sidecars therefore hold the whole stream and release it in one
batch, which looks like a broken/buffered stream to the client even though
litellm is yielding chunks incrementally.
Add Cache-Control: no-cache and X-Accel-Buffering: no to every
StreamingResponse create_response() returns, matching what the proxy already
does for its own usage/policy SSE endpoints. Fixes#28384.
Global env var values are always stored encrypted, so a value that no longer
decrypts (typically a rotated LITELLM_SALT_KEY) was being forwarded into
upstream ${NAME} headers as ciphertext with only a debug log. Drop the value
and log a warning so the failure surfaces instead of silently sending ciphertext.
Per-user env var stores now merge over the existing values instead of replacing
them. Per-user credentials are write-only and never shown back, so requiring the
full set on every save forced users to re-enter credentials they could not see
just to change one field. Omitting (or sending empty) a field now keeps its
stored value; DELETE still clears everything. The modal no longer marks
already-set fields as required.
The (user_id, server_id) unique index cannot serve the
delete_many(where={server_id}) orphan cleanup in delete_mcp_server,
since server_id is not the leading column, so it falls back to a full
table scan. Add a server_id index to cover that delete path
The per-user env var cache is process-local, so in multi-worker
deployments a user who stored values on another worker could hit a stale
cached negative and receive a misleading 412 'missing credentials'
response until the entry expired. Before raising MCPMissingUserEnvVarsError
the resolver now re-reads straight from the DB with force_refresh, so a
process-local stale entry can never mask values stored elsewhere; the hot
success path still serves from cache.
Also drops the inaccurate claim that env vars are interpolated into the
server URL from the schema and type docs; only static_headers are
interpolated.
The health check and initialize-instructions prefetch copied
server.static_headers verbatim, so any auth header backed by a
${NAME} global env var was sent upstream as the literal placeholder.
Servers that migrated to the new ${NAME} convention authenticated fine
on real tool calls but flipped to 'unhealthy' in the dashboard. Both
probes now resolve static headers through
_resolve_static_headers_with_env_vars (no user context, best-effort)
so global values are substituted before the connection opens.
Global-scope MCP env vars hold admin-supplied secrets (API keys, passwords) that interpolate into static headers, but their raw value was serialized into the env_vars JSON column in plaintext, so anyone with read access to the database could recover those upstream credentials. Credentials and the per-user values_b64 column are already encrypted; global env var values now match that, encrypted on write in _prepare_mcp_server_data and decrypted when the server is built into the runtime registry and when records are read back for admin views. Per-user placeholder values are not secrets and stay verbatim.
A stored per-user value for a var that the admin later switched to global
scope was still able to override the admin's global value, because the
header merge applied the full user blob over the globals. Filter the user
blob to vars that are currently user-scoped and let admin globals win.
MCPServer.env_vars is stored as List[Dict[str, Any]] (deserialized from
the JSON column), but LiteLLM_MCPServerTable.env_vars is typed as
List[MCPEnvVar]. Passing the raw dicts relied on pydantic coercion at
runtime and tripped mypy's dataclass_transform __init__ check, failing
the lint job. Normalize the dicts into MCPEnvVar models at both
construction sites.
These values interpolate into Static Headers and Auth via ${NAME};
they are not exported into the MCP server's process environment, so
'Environment Variables' overpromised. The backend env_vars field is
unchanged.
#29612 exempts UI/CLI session tokens from the key budget ceiling when they
create a team key, keyed on data.team_id. That value is read after the
default_key_generate_params loop can populate team_id, so on deployments that
set default_key_generate_params.team_id a request the caller did not scope to a
team is treated as a team key and skips the ceiling. Capture _requested_team_id
before defaults run and key the exemption off it, mirroring how
_requested_max_budget is already captured. Requests the caller did not scope to a
team keep the ceiling.
The per-user value column rendered as a plain input, so it read like a static
value shared by every user, defeating the purpose of per-user variables. Add a
persistent "Hint" addon with an explanatory tooltip, lighten the typed text,
and align the row to the top so the addon group no longer sits lower than its
neighbours and the layout stays put when the name field shows a validation
error.
The GET /v1/mcp/server list and health endpoints build LiteLLM_MCPServerTable
from the in-memory registry via _build_mcp_server_table and health_check_server.
Both copied static_headers but dropped env_vars, so the list always returned
env_vars: null. The admin edit form is populated from that list data, so it
loaded an empty env-var list; saving any edit then persisted env_vars: [],
silently wiping the stored variables. With nothing left to interpolate, the
${VAR} static headers were forwarded upstream verbatim as literal text.
Carry env_vars through both conversions, mirroring static_headers. Add
regression tests asserting both paths round-trip name, scope, value, and
description.
ESLint 9 defaults to flat config and eslint-config-next was pinned at 15
while Next is on 16, so eslint only ran with ESLINT_USE_FLAT_CONFIG=false
and next lint is gone on Next 16. Replace .eslintrc.json with a native
flat eslint.config.mjs (config-next 16 ships flat configs, so no
FlatCompat shim is needed), bump eslint-config-next to 16.2.6, add
@eslint/js and typescript-eslint as explicit devDeps for the recommended
rule sets, and point the lint script at eslint directly.
This only makes eslint runnable on modern tooling; it does not wire it
into CI. The same rules carry over (next/core-web-vitals, eslint and
typescript-eslint recommended, prettier, unused-imports)
Allow realtime event transcript fields to be nullable so GA conversation.item payloads with transcript=null don't fail logging normalization and suppress success callbacks.
Co-authored-by: Cursor <cursoragent@cursor.com>
add_mcp_server wrote the new row and then reloaded the entire registry from the
database inside one try block. A single pre-existing malformed row made the
reload raise, so the endpoint returned 500 even though the new server was already
persisted; callers assumed failure and retried, creating duplicate servers.
Split the flow so the database write is the commit point and still 500s on
failure, while the in-memory registry refresh is best-effort and only logged on
error. Add regression tests for both the refresh-fails-after-commit path and the
db-write-fails path
Non-admin users creating a team key through the UI were rejected with
"max_budget cannot exceed the caller's own max_budget (0.25)". The request is
authenticated by a UI/CLI session token whose max_budget is the per-session chat
spend cap (max_ui_session_budget, default $0.25), and the delegated-authority
budget ceiling (GHSA-q775-qw9r-2r4g) treated that cap as a delegation limit.
Skip the ceiling only when a session token creates a team key (data.team_id set);
that key's spend is bounded by the team budget at request time. Personal keys and
every other non-admin caller keep the ceiling, so a session token cannot mint an
arbitrary-budget personal key.
A stale admin secret left in form state after switching an env var's
scope from instance to per-user was forwarded to the backend as the
user-scope value, which is returned unredacted to authorized non-admin
users. Per-user entries carry no admin value, so drop it on submit.
* Fix remaining VCR live-call leaks
* test(vcr): dedupe live-test helpers and drop spurious kwargs
Extract the duplicated isVertexQuotaError/runVertexRequestOrSkip Vertex
quota-skip helpers into tests/pass_through_tests/vertex_test_helpers.js and the
duplicated _skip_live_prompt_caching_test guard into tests/_live_test_helpers.py
so each lives in one place. In test_aarun_thread_litellm, build a separate
message_data carrying role/content for add_message and a thread_data without
them for run_thread/run_thread_stream/get_messages, which no longer receive the
spurious message fields.
* test(overhead): assert mock transport is exercised in non-streaming and stream tests
The single-server and bulk per-user env var status endpoints echoed the
decrypted credential value back to any holder of the user's LiteLLM token,
so a leaked token could exfiltrate the raw upstream secret (e.g. a personal
access token) for use outside the proxy. Drop the value field from
MCPUserEnvVarSpec and the include_values plumbing so the status reports only
whether each credential is_set; users overwrite a field to rotate it. The
fill-in modal no longer pre-populates from the secret and flags already-set
fields instead.
* fix(ci): keep coverage rename green when a parallel node runs no tests
local_testing_part1 and local_testing_part2 run with parallelism 4. When
CircleCI reruns only the failed tests, the failed test lands on a single
node and the other nodes receive an empty bucket, so pytest never writes
coverage.xml or .coverage. The unguarded "mv coverage.xml ..." then exits
1 and turns the whole job red even though the rerun passed; the next
persist_to_workspace step would fail the same way on the missing paths.
Guard the rename so a node with no coverage emits empty placeholders
instead. coverage combine tolerates the empty files, so the downstream
upload-coverage job keeps the real nodes' data intact.
* fix(ci): pre-create test-results in litellm_router_testing for empty-bucket reruns
litellm_router_testing also runs with parallelism 4. On a rerun of only the
failed tests, a node can receive no tests, so the test command never creates
test-results and the final store_test_results step can fail on the missing
path. Pre-create the directory up front, matching what local_testing_part1
and part2 already do and CircleCI's own guidance for parallel reruns.
* test(openai): retry wildcard chat completion on transient OpenAI 500
build_and_test reddened on test_openai_wildcard_chat_completion when the
real gpt-3.5-turbo-0125 call returned an OpenAI 500 ("The server had an
error while processing your request"). The base branch passed the same
call concurrently, so the 500 is an intermittent OpenAI server error, not
a regression. Add the same pytest-retry marker the sibling real-call tests
in this file already use so a transient upstream 500 no longer fails CI.
getMCPUserEnvVars now throws on non-2xx so UserEnvVarsModal reports the
error instead of silently rendering the empty 'no per-user fields' state.
The per-user env-var endpoints now run the access check before the server
lookup so a non-admin cannot tell a missing server (404) apart from one
they lack access to (403), closing a server-id enumeration leak.
PROXY_ADMIN_VIEW_ONLY callers are treated as admin_view by GET /v1/mcp/server
and GET /v1/mcp/server/{id}, so they received unredacted scope=global env var
values, which can carry upstream API keys used in Authorization headers. Only
a full PROXY_ADMIN needs those values (to pre-fill the edit form); read-only
admins now get the same global-secret redaction already applied to non-admin
and restricted virtual-key views.
The single-server DB fetch returned the raw Prisma model, whose JSONB
env_vars deserialize to plain dicts. The non-admin and virtual-key
sanitizers read env_var.scope as an attribute, so GET /v1/mcp/server/{id}
for a server with env_vars raised AttributeError and returned a 500.
Wrap the result like every bulk fetch helper so env_vars become MCPEnvVar
objects before redaction.
* fix duplicate cost callbacks for anthropic streaming pass-through
Two bugs caused _PROXY_track_cost_callback to see stream=True +
complete_streaming_response=None on every streaming pass-through request,
making the dedup guard in dispatch_success_handlers permanently inactive:
1. pass_through_endpoints.py created the Logging object with stream=False
for all requests. _is_assembled_stream_success short-circuits on
self.stream is not True, so has_dispatched_final_stream_success was
never set and any second dispatch went through unchecked.
Fix: set logging_obj.stream = True after stream detection.
2. _create_anthropic_response_logging_payload set complete_streaming_response
inside the try block after litellm.completion_cost(), so a pricing error
caused an early return without setting it on model_call_details.
Fix: set complete_streaming_response before the try block.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix stream
* add stream to logging obj
* test(pass_through): give mock logging object a real model_call_details dict
The anthropic passthrough logging payload now records the assembled
response on model_call_details before cost calculation, which requires
model_call_details to support item assignment. In production it is always
a dict; the existing unit test stubbed the logging object with a bare Mock
whose attribute is not subscriptable, so the new assignment raised
TypeError. Use a real dict to match the production logging object.
* test(pass_through): cover streaming logging-obj stream flag
The streaming branch of pass_through_request that marks the logging object
as streaming (logging_obj.stream and model_call_details["stream"]) had no
unit coverage, so the patch coverage gate flagged it. Add a regression test
that drives a streaming pass-through request through pass_through_request and
asserts the logging object is flagged as a stream before dispatch.
* test(pass_through): cover SSE-response stream flag fallback branch
The auto-detected streaming branch of pass_through_request (when a request
that was not flagged as streaming returns a text/event-stream response) sets
logging_obj.stream and model_call_details["stream"] but had no unit coverage,
so the codecov patch gate failed at 60%. Drive a non-streaming pass-through
request whose upstream response is SSE through pass_through_request and assert
the logging object is flagged as a stream before dispatch.
* fix(pass_through): gate complete_streaming_response on stream flag
perform_redaction only scrubs complete_streaming_response when
model_call_details["stream"] is True. Setting it unconditionally for
non-streaming Anthropic pass-through responses left the assembled
response unredacted in model_call_details, which is handed to logging
callbacks as kwargs when message logging is disabled. Only record it for
actual streaming responses so redaction always applies.
---------
Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat(arize): enrich OpenInference attributes for better span rendering
Pure rendering enhancements to the Arize / Arize Phoenix integration. No
existing attribute keys or values are removed or overwritten; every new
emit is independently try/except-wrapped and fires only when its source
data is present so existing behavior is preserved.
What this adds
- Coerce non-dict response objects (e.g. httpx.Response from passthrough
routes) via JSON decode so id/model/usage extraction stops crashing
with "'Response' object has no attribute 'get'". Dicts and Pydantic
objects with .get pass through unchanged.
- Set OPENINFERENCE_SPAN_KIND defensively early so a downstream failure
can't blank the kind; the original late write (incl. TOOL upgrade) is
preserved.
- Add "passthrough" keyword to _infer_open_inference_span_kind so
allm_passthrough_route / llm_passthrough_route resolve to LLM instead
of UNKNOWN.
- Emit cache token breakdown: LLM_TOKEN_COUNT_PROMPT_DETAILS_CACHE_READ /
_CACHE_WRITE / _AUDIO. Sources covered: OpenAI prompt_tokens_details
and Anthropic / Bedrock cache_{read,creation}_input_tokens.
- Render assistant tool_calls on both input and output messages via
MESSAGE_TOOL_CALLS.* (Pydantic-aware, handles ModelResponse choices).
Tool-result input messages also get MESSAGE_TOOL_CALL_ID and
MESSAGE_NAME.
- Render multimodal list-shaped content via MESSAGE_CONTENTS.* (OpenAI
image_url, Anthropic source.{media_type,data} as data: URI). Legacy
MESSAGE_CONTENT write is unchanged.
- Emit SESSION_ID (end_user_id / trace_id), USER_ID (only when not
already set by optional_params.user or model_params.user), and
litellm.{team_id,team_alias,key_alias} from StandardLoggingPayload
metadata.
- Emit llm.response.cost as float from StandardLoggingPayload.response_cost.
- Bedrock / Anthropic passthrough normalization: extract input from
additional_args.complete_input_dict and output from the coerced
provider response so INPUT_VALUE / OUTPUT_VALUE / LLM_INPUT_MESSAGES /
LLM_OUTPUT_MESSAGES are populated. Only runs when call_type contains
"passthrough" / "pass_through".
Tests
- 15 new unit tests covering each addition plus explicit regression
guards (USER_ID overwrite protection, passthrough normalizer scope,
coerce identity for dicts/.get-bearing objects, no spurious cache
emits).
- Existing test_arize_set_attributes count bumped from 26 to 27 to
account for the additional defensive span.kind write (same value,
written twice).
- tests/test_litellm/integrations/arize/: 70 passed (55 baseline + 15
new). tests/test_litellm/integrations/test_opentelemetry.py: 221
passed.
Co-authored-by: Cursor <cursoragent@cursor.com>
* refactor(arize): collapse additive try/except blocks into _safe_emit helper
The additive attribute emitters all share the same shape: run a callable,
swallow any exception to debug log so it cannot blank the span. Hoisting
that pattern into a single _safe_emit(label, fn, *args, **kwargs) helper
removes 5 repeated try/except blocks. Behavior unchanged; arize test
suite still passes (70/70).
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(arize): emit cost under canonical llm.cost.total key
Arize's "Total Cost" column reads the OpenInference-standard
`llm.cost.total` attribute. The previous custom `llm.response.cost`
key never surfaced in the trace list. Now emits both keys (canonical +
legacy) so renderers + any existing consumers both work.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(arize): keep span.kind=LLM for tool-using completions + render tool_calls in Output
A chat completion that passes `tools=[...]` or returns `tool_calls` is still
an LLM call per the OpenInference spec — TOOL is reserved for actual tool
execution. The previous override demoted these to TOOL, breaking Arize's
LLM-scoped dashboards/evals and skewing token/cost analytics for any
tool-using traffic.
Additionally, when an assistant response had no text content but did
request tool calls, `output.value` was set to the empty string so Arize's
"Output" pane rendered blank. Now serializes the tool_calls into a compact
JSON summary in `output.value` (the structured `MESSAGE_TOOL_CALLS.*`
attributes are still emitted unchanged).
Cleanups:
- extract `_get_tool_calls` and `_normalize_tool_call` helpers,
deduplicating the dict-vs-Pydantic + function-dict logic across
`_set_choice_outputs`, `_emit_message_tool_calls`, and the new
`_summarize_tool_calls_for_output`.
- drop redundant late `OPENINFERENCE_SPAN_KIND` write — the defensive
early write is now the single source of truth.
- remove a dead local re-import of `MessageAttributes`/`SpanAttributes`.
Tests: 73 pass (added regression guard asserting span.kind stays LLM for
completions that pass tools AND return tool_calls; existing call_count
assertion restored to 26).
Co-authored-by: Cursor <cursoragent@cursor.com>
* chore(arize): tighten cleanup — fold _get_tool_calls into _safe_get
Two tiny cleanups, no behavior change:
- collapse `_get_tool_calls` to use `_safe_get`, removing a 7-line
hand-rolled dict-vs-attribute fallback that duplicated existing logic.
- trim the `_set_choice_outputs` tool-call summary comment from 4 lines
to 2 (was over-explaining).
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(arize): address Greptile review — drop session_id=trace_id fallback, remove dead code, fix Black
Three Greptile-flagged issues + the Black formatting CI failure.
1. SESSION_ID no longer falls back to trace_id. Previously every span
without an explicit `user_api_key_end_user_id` would have its
session.id set to the per-request trace_id, which creates one
distinct "session" per request and breaks Arize's Session-grouping
analytics. Now SESSION_ID is emitted only when an explicit end-user
identifier exists, and the trace_id is emitted under its own
`litellm.trace_id` key so spans remain filterable by trace.
2. Removed dead `ArizeOTELAttributes.set_response_output_messages`
override. Confirmed zero callers in the entire repo (the live path
is `_set_choice_outputs` via `_set_response_attributes`). The
override was preexisting dead code, but the expansion of
`_set_choice_outputs` in this PR made the divergence misleading.
3. Removed permanently-dead first branch in cache_write detection.
`_safe_get(prompt_token_details, "cache_creation_tokens")` looks
for a key that neither OpenAI's `prompt_tokens_details` nor
Anthropic's payload ever exposes. Now reads straight off `usage`
for `cache_creation_input_tokens`.
4. Reformatted both files under Black 26.3.1 (the version CI uses
via `uv sync --frozen`). Local previously used 24.10.0.
Tests: 74/74 pass in the arize suite (added
`test_arize_does_not_use_trace_id_as_session_id_fallback`).
Combined arize + opentelemetry suite: 295/295 pass.
End-to-end verified live: tool-call still emits `span.kind=LLM` and
JSON tool_calls in `output.value`; `session.id` is now correctly
unset when no end_user_id is provided; `litellm.trace_id` is
populated; Bedrock passthrough input/output unchanged.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(arize): gate passthrough prompt export on message redaction
- Skip the complete_input_dict bridge in _maybe_normalize_passthrough when
should_redact_message_logging() is true, so enabling redaction no longer
leaks raw passthrough prompts into Arize (Veria security finding).
- Split passthrough input/output rendering into helpers to satisfy PLR0915.
- Remove dead call_type assignment (F841).
Validated live against a Bedrock passthrough proxy exporting to Arize:
non-redacted renders the real prompt on litellm_request; global
turn_off_message_logging yields input.value=redacted-by-litellm with the
raw_gen_ai_request child span suppressed and no SSN/marker leakage.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
* Add support for websocket via codex
* Add model alias and creds support
* fix: skip cost tracking for WS session wrapper call types
The @client decorator on _aresponses_websocket fires async_success_handler
with result=None after the session ends. This triggered cost tracking errors
because standard_logging_object is never built for None results.
Per-turn costs are correctly tracked by individual litellm.aresponses calls
inside the session. The outer session-level logging obj should not attempt
cost tracking.
Fix: skip _aresponses_websocket and _arealtime call types in deployment_callback_on_success,
RouterBudgetLimiting.async_log_success_event, and _PROXY_track_cost_callback.
* fix: address Greptile review comments
Fix JSON injection: use json.dumps instead of f-string interpolation for model name in WS body.
Add 30s timeout for first WS frame to prevent unbounded connection resource tie-up.
Restore per-event model override in streaming_iterator; fall back to connection-level model when event omits it.
Strengthen regression test: inject alias into kwargs via _update_kwargs_with_deployment mock so the test would fail on un-fixed code.
* fix: handle nested response.create format in first-frame model extraction
When ?model= is omitted, the first WS frame can carry the model in either flat
format (first_event["model"]) or nested format (first_event["response"]["model"]).
The flat-only check would silently reject clients using the nested wire format.
Mirrors the same two-format logic in _build_base_call_kwargs.
* fix: don't force connection-level custom_llm_provider on per-event model overrides
If a client sends a different model per response.create turn, litellm needs to
re-resolve the provider from that model string. Forcing the connection-level
custom_llm_provider would silently route the request to the wrong backend.
Only inject custom_llm_provider when the per-event model matches the
connection-level model.
* refactor: extract WS model extraction into testable function
Pull the flat/nested model extraction into _extract_model_from_first_ws_event
so tests import and exercise the real function rather than a copy.
* fix: compare providers not full model strings in _inject_credentials
The model == self.model guard was too strict: same-provider model variants
(e.g., vertex_ai/gemini-2.0 -> vertex_ai/gemini-1.5 on one connection) would
lose custom_llm_provider, breaking routing when a custom api_base is in use.
Compare the provider extracted by get_llm_provider instead, so same-provider
variants still inherit the connection-level provider while cross-provider
overrides let litellm re-resolve.
* style: black formatting
* refactor: extract first-frame model resolution to fix PLR0915 (too many statements)
* Fix responses WebSocket first-frame validation
* fix: classify WS first-frame read errors and clarify cost-skip log
Distinguish client disconnects from server errors when reading the
responses WebSocket first frame, make the cost-tracking skip log message
accurate for session wrappers (which do carry a model), and resolve the
connection-level provider once per session instead of on every
response.create event.
* test: cover WS first-frame read errors and same-provider credential injection
Adds regression tests for the still-uncovered responses WebSocket paths:
the timeout, invalid-JSON and missing-model branches of
_read_ws_model_from_first_frame, plus the provider comparison in
ManagedResponsesWebSocketHandler._same_provider and _inject_credentials
(same-provider model variants keep the connection provider; cross-provider
models re-resolve).
* fix(responses-ws): fall back to explicit custom_llm_provider when connection model is unresolvable
When a WebSocket session is opened with a custom deployment alias that litellm
cannot resolve to a provider, _connection_provider was None, so _same_provider
returned False for every resolvable per-event model and the connection-level
custom_llm_provider was dropped. Use the explicitly-set custom_llm_provider as
the connection provider in that case so same-provider per-event models still
inherit it while genuinely cross-provider models continue to re-resolve.
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(vertex): strip output_config.effort for models that reject it
Haiku 4.5 on Vertex AI does not support output_config.effort and 400s with
"output_config.effort: Extra inputs are not permitted". PR #27074 emptied
VERTEX_UNSUPPORTED_OUTPUT_CONFIG_KEYS so effort would forward for Opus/Sonnet
4.6+, but that made the strip unconditional across every Vertex Anthropic
model, including ones that don't support it. Claude Code injects effort into
its default Messages payload, so `claude --model claude-haiku-4.5` started
failing.
Make the sanitizer model-aware: drop output_config.effort for models that
don't advertise output_config support (or any reasoning effort level) while
forwarding it for those that do. The fix covers both the chat-completion and
Messages pass-through transformation paths since they share the helper.
* chore(vertex): log at debug when dropping unsupported output_config.effort
Operators pointing an unregistered Vertex Claude alias that does support
effort would otherwise see it stripped with no signal. Debug level keeps it
out of normal logs since Claude Code sends effort on every request.
* ci: reproduce default-Windows wheel install to guard MAX_PATH
The existing using_litellm_on_windows job installs the project with
`uv sync`, an editable source install that never copies package files
into a deep site-packages path, so it cannot see the 260-char MAX_PATH
overflow that breaks `pip install litellm` on default Windows. The
content-filter benchmark fixtures have hit that limit three times
(#21941, #22039, #29536), each caught only after release.
This adds a guard to the same job that builds the wheel and installs it
the way an end user would: into a venv whose site-packages prefix is
padded to a realistic worst-case Windows length (~100 chars), then
asserts the install completes and litellm imports. Any packaged path
long enough to bust MAX_PATH at that prefix is reported up front, so the
check is deterministic regardless of the runner's long-path setting,
while the real install also covers failure modes a length heuristic
cannot (half-unpacked packages, reserved names, case collisions).
This commit is the guard only; on the current tree it correctly fails
because nine fixtures still exceed the limit. The rename that brings
them back under it follows on this branch.
* fix(packaging): shorten content-filter benchmark fixtures under MAX_PATH
The 10 content-filter benchmark result fixtures used the legacy
block_{topic}_-_contentfilter_({yaml}).json naming, up to 176 chars
inside the wheel, which busts the Windows 260-char MAX_PATH limit once
extracted under a realistic site-packages prefix and aborts
`pip install litellm` on default Windows.
Rename them to the short {topic}_cf.json scheme that
_save_confusion_results already emits today (it splits the label on the
em-dash and writes f"{topic}_cf"), matching the insults_cf.json and
investment_cf.json files fixed earlier. Re-running the eval suite now
regenerates these same short names rather than recreating the long ones.
This drops the longest packaged path from 176 to 128, so the guard added
in the previous commit goes from red to green with a 32-char margin.
* test(windows): tidy MAX_PATH guard per review
Close the wheel zip via a context manager rather than leaning on
refcount collection, and select the wheel under dist/ by newest mtime so
a stale artifact from an earlier build cannot be tested instead of the
one just produced. Also pin down the venv-depth formula with a short
note: the +2 is the separator joining the venv root to "Lib" plus the
trailing separator before the entry, which lands the simulated
site-packages prefix at exactly 100 chars.
* fix(anthropic/adapter): open thinking block for reasoning_content-only streaming chunks
The /v1/messages streaming content-block classifier (_translate_streaming_openai_chunk_to_anthropic_content_block) only recognized thinking_blocks. OpenAI-compatible reasoning backends (vLLM/SGLang reasoning parsers: DeepSeek-R1, Qwen3, gpt-oss, ...) populate reasoning_content with thinking_blocks=None, so the classifier fell through to a text block. The delta translator already emits thinking_delta for reasoning_content, so those deltas landed inside a text block and Anthropic streaming clients (Claude Code, SDK .stream()) silently dropped the chain-of-thought.
Mirror the reasoning_content fallback already present in the non-stream translator and the streaming delta translator so the classifier opens a thinking block. Adds a focused regression test.
* fix(anthropic/adapter): reach reasoning_content branch when thinking_blocks attr is absent
Delta deletes the thinking_blocks attribute when unset, so the prior nested check was unreachable for reasoning-only chunks (vLLM/SGLang). Make it a sibling elif so the content block is classified as thinking.
* test(proxy): stop component-allowlist test leaking DATABASE_URL into xdist peers
The component-allowlist test pins throwaway DATABASE_URL/LITELLM_MASTER_KEY
values at import time via os.environ so importing proxy_server doesn't need a
live database. Those values persisted for the whole pytest-xdist worker, so a
sibling test sharing the worker (test_key_rotation_e2e's DB-backed E2E case)
saw the leaked sqlite DATABASE_URL, treated it as an available database instead
of skipping, and the Prisma engine rejected the non-postgres URL (P1012 ->
httpx.ConnectError). Restore the prior environment after the import so the
throwaway values never escape the module.
---------
Co-authored-by: Tai An <antai12232931@outlook.com>
The bulk /user-env-vars/status feed only drives the dashboard "N fields
missing" badge, which needs is_set, so it no longer returns the stored
credential values; the single-server endpoint still returns them for the
fill-in modal to pre-populate.
Adds a description input to the admin env-var form for per-user scope so
admins can tell users what to enter; the per-user modal already surfaces
that description as a hint.
* Fix error code and context id injection bug
* Add support for all A2A methods
* Add logging
* address greptile review: relay upstream JSON-RPC errors, move _PASCAL_TO_WIRE to module level, add error path tests
* fix(a2a): run pre_call_hook for tasks/resubscribe SSE path to enforce guardrails
tasks/resubscribe was returning the raw SSE stream without calling proxy_logging_obj.pre_call_hook, silently bypassing any guardrails configured on the agent. This patch calls pre_call_hook before streaming begins and wires post_call_failure_hook into the SSE generator so errors are logged. Adds a regression test verifying the hook is called.
* fix(a2a): use get_async_httpx_client instead of creating httpx clients per request
Creating httpx.AsyncClient instances per-request adds ~500ms latency. Switch _forward_jsonrpc and _forward_jsonrpc_sse to use the shared client from get_async_httpx_client(httpxSpecialProvider.A2A).
* fix(a2a): forward caller identity headers on task ops; validate push notification URL
Two security fixes for task management methods:
1. All task operations (tasks/get, tasks/list, tasks/cancel, tasks/resubscribe, push notification config methods) now forward X-LiteLLM-User-Id and X-LiteLLM-Team-Id headers to the upstream agent, so the agent can scope task access to the authenticated caller.
2. tasks/pushNotificationConfig/set validates the callback URL before forwarding: requires HTTPS and rejects private/loopback/reserved IP ranges and localhost hostnames to prevent SSRF.
* Fix A2A task hook and push URL handling
* fix(a2a): fix mypy type errors for request_id and header_name dict key types
* Fix A2A request id and params forwarding
* Forward trace IDs for A2A task calls
* fix(a2a): strip client-forwarded X-LiteLLM-* headers before applying authenticated identity
A client could send x-a2a-<agent>-x-litellm-user-id in their request and have it forwarded to the upstream agent as an authenticated identity header. Fix: sanitize any X-LiteLLM-* headers from agent_extra_headers before merging, then apply the authenticated identity headers last so they always override client-supplied values.
* Fix A2A SSE fallback JSON-RPC error code
* Fix A2A SSE error id backfill
* fix(a2a): validate both push notification url fields to close SSRF bypass
* fix(a2a): widen request_id annotation to match JSON-RPC id call sites
* fix(a2a): run post-call streaming hook for tasks/resubscribe so agent guardrails apply
tasks/resubscribe returned the raw upstream SSE stream without routing events
through the post-call streaming hook, so output guardrails configured on the
agent were silently skipped for streaming task subscriptions while every other
task method and message/stream applied them. Parse upstream JSON-RPC SSE events
and feed them through async_streaming_data_generator, matching message/stream,
so guardrails inspect the streamed task content. Adds a regression test that
fails when the streamed events bypass the guardrail hook.
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Make the per-user env-var delete idempotent with delete_many so a missing
row is a no-op, and remove the bare except on the clear endpoint that
swallowed real DB failures; a failed delete previously reported success to
the dashboard while the row remained in the database, so the next tool call
would 412 for credentials the user believed they had cleared.
Also use the module-level json instead of a redundant inline import, and
drop the no-op duplicate static_headers/env_vars assignment in the
create-server UI payload.
The per-user env-var endpoints (GET/POST/DELETE /server/{id}/user-env-vars)
fetched the server by id and returned its name, alias, and required-credential
metadata, or persisted/cleared stored values, without checking that the caller
can access that server. A non-admin could query or mutate env-var state for any
server id. Apply the same access gate fetch_mcp_server uses so non-admins are
limited to servers in their allowed set.
* Fix incorrect agent API request example payload structure (#29556)
* fix(otel): add litellm_metadata fallback in _get_span_context and _end_proxy_span_from_kwargs (#29427)
* fix(otel): add litellm_metadata fallback in _get_span_context and _end_proxy_span_from_kwargs
On /v1/messages and other LITELLM_METADATA_ROUTES, the parent OTel span
is stored in litellm_params['litellm_metadata'] instead of
litellm_params['metadata']. When the request body contains a native
'metadata' field (e.g. Anthropic's {"user_id": "..."}),
litellm_params['metadata'] gets overwritten and the parent span is lost,
producing orphan root spans with a different trace_id.
Add fallback checks to litellm_metadata in:
- _get_span_context(): so child spans find the correct parent
- _end_proxy_span_from_kwargs(): so the proxy span gets closed
Fixes: https://github.com/BerriAI/litellm/issues/27934
* test(otel): tighten assertions per Greptile review
- test_span_context_metadata_takes_priority: assert litellm_metadata
span is never accessed, proving metadata takes priority
- test_span_context_no_parent_when_neither_has_span: assert both ctx
and detected_span are None
---------
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Aneesh-Fiddler <aneeshfiddler@gmail.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
* fix: remove premature end-user budget check from get_end_user_object (#29420)
* fix(proxy): remove premature end-user budget check from get_end_user_object
Problem:
- `_check_end_user_budget()` was called inside `get_end_user_object()`
- This caused budget checks to run BEFORE `skip_budget_checks` could be evaluated
- Zero-cost models (e.g., local vLLM) were incorrectly blocked when
end-users exceeded their budget, even though they should bypass budget checks
Solution:
- Remove `_check_end_user_budget()` calls from `get_end_user_object()`
- Budget enforcement now happens exclusively in `common_checks()` where
`skip_budget_checks` context is available
- `get_end_user_object()` keeps `route` as optional in function parameter for backwards compatibility and future implementation.
* refactor(tests): update budget enforcement tests to reflect changes in get_end_user_object
- test_get_end_user_object() verifies data fetching
- test_check_end_user_budget() verifies enforcement
- test_budget_enforcement_blocks_over_budget_users() integrates _check_end_user_budget()
- test_resolve_end_user_reraises_budget_exceeded() is now test_resolve_end_user since no budget exceeded is thrown in get_end_user_object()
* Gemini /images/generate and /images/edits billing fixes + add support for size and aspect ratio params (#29534)
* Fix Gemini image config mapping
* Address Gemini image config review
* Format Gemini image generation transform
* Fix Gemini image token usage logging
* Share Gemini image request helpers
* Fix Gemini Imagen model routing
* Fixes as per self code review
* Fixes per internal code review
* Stop gating Imagen imageSize forwarding
* Document Gemini image size mapping source
* chore: retrigger lint
* Clarify Gemini candidate count precedence
* Add Inception provider (#29522)
* add inception as provider (chat, fim)
* linting
* seperate test suite for chat and fim
* fix test coverage
* fix: model hub custom pricing model info (#29293)
* Opik user auth key metadata extractors (#28397)
* fix: enhance Opik metadata extraction to include user API key auth context fixed after refactoring to extractor logic
* test: add unit tests for OPik metadata extraction logic
* fix: enhance extract_opik_metadata function to prioritize metadata sources for improved accuracy
* fix(ci): clarified comments and edited unit tests
* test: add unit tests for OPik metadata extraction with auth and requester overrides
* fix(ui): replace fixed favicon.ico with current api get /get_favicon (#29532)
Signed-off-by: José Luis Di Biase <josx@interorganic.com.ar>
* fix(vertex/gemini): keep tool_call reference when a text-only assistant message follows (#29561)
`_gemini_convert_messages_with_history` tracks `last_message_with_tool_calls`
so a following tool result can be matched back to its tool call. The assignment
was inside a branch guarded by
`assistant_msg.get("tool_calls", []) is not None`, which is also True for a
text-only assistant message (an empty list is not None). As a result, an
assistant message with no tool calls that appears between a tool call and its
tool result overwrote the reference, and conversion failed with:
Exception: Missing corresponding tool call for tool response message.
This shape is common: a model emits a short narration/assistant message after a
tool call before the tool result is appended.
Only update `last_message_with_tool_calls` when the assistant message actually
carries tool_calls (or a function_call). Adds a regression test.
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
* Add 1-hour cache write pricing for EU/AU/JP Bedrock Anthropic models (#28572)
* fix(thinking): handle None thinking param in is_thinking_enabled (#28598)
Squash-merged by litellm-agent from Terrajlz's PR.
* feat(helm): support tpl rendering in podAnnotations (#28609)
Squash-merged by litellm-agent from devauxbr's PR.
* Forward custom_llm_provider through the Responses API bridge (Fixes#28505) (#28575)
* Forward custom_llm_provider through the Responses API bridge (Fixes#28505)
When a Chat Completions request to a GPT-5.4+ model contains both
`tools` and `reasoning_effort`, `completion()` auto-routes through
`responses_api_bridge`. The bridge handler called
`litellm.responses()` / `litellm.aresponses()` without forwarding the
already-resolved `custom_llm_provider`, so the downstream call
re-invoked `get_llm_provider()` with `custom_llm_provider=None` and
stripped a second provider prefix from a `provider/provider/model`
deployment string.
For a deployment configured as `openai/openai/openai/gpt-5.5`,
the bridge flow sent `openai/gpt-5.5` to the upstream API instead of
the correct `openai/openai/gpt-5.5`. Upstream APIs that enforce
model-name allow-lists rejected this as `key_model_access_denied`.
Fix: pass the locally-resolved `custom_llm_provider` into both the
sync `responses()` and async `aresponses()` calls so the downstream
`_resolve_model_provider_for_responses` sees an explicit provider
and skips the second prefix-strip.
New regression test
`tests/test_litellm/completion_extras/test_responses_bridge_provider_propagation.py`
pins both call sites: each must forward `custom_llm_provider`.
* fix(28505): set custom_llm_provider on request_data instead of as duplicate kwarg
Greptile flagged that the previous patch passed custom_llm_provider as an
explicit kwarg to responses()/aresponses() while request_data already
carried it via the spread of sanitized_litellm_params, which would raise
TypeError: got multiple values for keyword argument on every real bridge
call.
Switches to assigning request_data['custom_llm_provider'] before the call
so the resolved provider wins over whatever sanitized_litellm_params spread
in, without duplicating the kwarg.
Updates the regression test to seed request_data with a sentinel
custom_llm_provider so it actually exercises the overwrite path (the
previous test mocked transform_request with a minimal dict and never hit
the conflict).
* chore: trigger shin-agent re-eval on retargeted staging base
* chore: trigger shin-agent re-eval against updated Greptile state
* Add 1-hour cache write pricing for EU/AU/JP Bedrock Anthropic models
The 1-hour prompt-cache write tier
(`cache_creation_input_token_cost_above_1hr`) was added to the
us./global. variants of the Claude 4.5/4.6/4.7 family on Bedrock, but
the eu./au./jp. cross-region inference profiles were left without it.
AWS Bedrock pricing applies the same +10% regional premium across all
geo profiles, so eu./au./jp. should carry the same 1-hour rates as
us. (1.6x the 5-minute regional rate).
Without these fields, cost tracking on EU/AU/JP Bedrock 1-hour-TTL
prompt caching falls back to the 5-minute write rate and undercounts
spend by ~60% for European, Australian, and Japanese tenants.
Adds the 1-hour tier (and Sonnet 4.5's long-context >200K tier where
AWS publishes one) to 14 regional Bedrock entries in both
`model_prices_and_context_window.json` and the bundled
`model_prices_and_context_window_backup.json`:
- eu./au. Opus 4.6 ($11.00 / MTok)
- eu./au. Opus 4.7 ($11.00 / MTok)
- eu./au./jp. Sonnet 4.6 ($6.60 / MTok)
- eu./au./jp. Sonnet 4.5 ($6.60 / MTok regular, $13.20 / MTok LC)
- eu./au./jp. Haiku 4.5 ($2.20 / MTok)
Also extends `tests/test_litellm/test_bedrock_anthropic_1hr_cache_pricing.py`
with a `REGIONAL_EXPECTED` parametrized block covering all 13 new
entries plus the existing 1.6x ratio invariant.
Note: `eu.anthropic.claude-opus-4-5-20251101-v1:0` carries the
wrong 5m rate today (base 6.25e-06 instead of regional 6.875e-06),
which would break the 1.6x ratio check. It is intentionally left out
of this PR so the scope stays "1-hour cache tier addition" — a
separate follow-up should correct the EU 5m rates for Opus 4.5.
---------
Co-authored-by: Terrajlz <info@jouleselectrictech.com>
Co-authored-by: Bruno Devaux <devaux.br@gmail.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
* Add 1-hour cache write pricing tier for Vertex AI Anthropic models (#28569)
* fix(thinking): handle None thinking param in is_thinking_enabled (#28598)
Squash-merged by litellm-agent from Terrajlz's PR.
* feat(helm): support tpl rendering in podAnnotations (#28609)
Squash-merged by litellm-agent from devauxbr's PR.
* Forward custom_llm_provider through the Responses API bridge (Fixes#28505) (#28575)
* Forward custom_llm_provider through the Responses API bridge (Fixes#28505)
When a Chat Completions request to a GPT-5.4+ model contains both
`tools` and `reasoning_effort`, `completion()` auto-routes through
`responses_api_bridge`. The bridge handler called
`litellm.responses()` / `litellm.aresponses()` without forwarding the
already-resolved `custom_llm_provider`, so the downstream call
re-invoked `get_llm_provider()` with `custom_llm_provider=None` and
stripped a second provider prefix from a `provider/provider/model`
deployment string.
For a deployment configured as `openai/openai/openai/gpt-5.5`,
the bridge flow sent `openai/gpt-5.5` to the upstream API instead of
the correct `openai/openai/gpt-5.5`. Upstream APIs that enforce
model-name allow-lists rejected this as `key_model_access_denied`.
Fix: pass the locally-resolved `custom_llm_provider` into both the
sync `responses()` and async `aresponses()` calls so the downstream
`_resolve_model_provider_for_responses` sees an explicit provider
and skips the second prefix-strip.
New regression test
`tests/test_litellm/completion_extras/test_responses_bridge_provider_propagation.py`
pins both call sites: each must forward `custom_llm_provider`.
* fix(28505): set custom_llm_provider on request_data instead of as duplicate kwarg
Greptile flagged that the previous patch passed custom_llm_provider as an
explicit kwarg to responses()/aresponses() while request_data already
carried it via the spread of sanitized_litellm_params, which would raise
TypeError: got multiple values for keyword argument on every real bridge
call.
Switches to assigning request_data['custom_llm_provider'] before the call
so the resolved provider wins over whatever sanitized_litellm_params spread
in, without duplicating the kwarg.
Updates the regression test to seed request_data with a sentinel
custom_llm_provider so it actually exercises the overwrite path (the
previous test mocked transform_request with a minimal dict and never hit
the conflict).
* chore: trigger shin-agent re-eval on retargeted staging base
* chore: trigger shin-agent re-eval against updated Greptile state
* Add 1-hour cache write pricing tier for Vertex AI Anthropic models
GCP Vertex AI publishes a separate 1-hour cache write column for the
Claude family (1.6x the 5-minute write rate, matching the documented
Bedrock ratio). LiteLLM's Vertex AI Anthropic entries only carry the
5-minute tier, so any request that uses `cache_control: {"ttl": "1h"}`
on Vertex AI Claude is undercounted in cost tracking by ~60%.
The runtime side already supports the 1-hour tier — `VertexAIAnthropicConfig`
extends `AnthropicConfig`, populating `ephemeral_1h_input_tokens`, and
`_calculate_cache_creation_cost` reads `cache_creation_input_token_cost_above_1hr`.
Only the price registry was missing data.
Adds the field to 19 vertex_ai/claude-* entries across both
`model_prices_and_context_window.json` and the bundled
`model_prices_and_context_window_backup.json`:
- Haiku 4.5 ($1.25 -> $2.00 / MTok)
- Sonnet 3.7 / 4 / 4.5 / 4.6 ($3.75 -> $6.00 / MTok)
- Opus 4.5 / 4.6 / 4.7 ($6.25 -> $10.00 / MTok)
- Opus 4 / 4.1 ($18.75 -> $30.00 / MTok)
Adds `tests/test_litellm/test_vertex_anthropic_1hr_cache_pricing.py`
mirroring the Bedrock equivalent — pins each (5m, 1h) pair per model
and asserts the 1.6x ratio across the family.
Fixes#27781.
---------
Co-authored-by: Terrajlz <info@jouleselectrictech.com>
Co-authored-by: Bruno Devaux <devaux.br@gmail.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
* Fix Gemini multimodal function responses (#29325)
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
* address greptile review: add _transform_image_usage method and model-map supports_image_size flag
- Add _transform_image_usage instance method to GoogleImageGenConfig that
delegates to transform_gemini_image_usage, fixing the regression test
- Replace hardcoded "2.5-flash" string check in supports_gemini_image_size
with a get_model_info lookup on supports_image_size (default true)
- Add supports_image_size: false to all gemini-2.5-flash model entries in
model_prices_and_context_window.json so capability is controlled via the
model map rather than embedded in code
* fix test failures: schema validation, mypy type, model info plumbing, pricing test
- Add supports_image_size to ModelInfoBase TypedDict so get_model_info surfaces it
- Pass supports_image_size through _get_model_info_helper constructor call
- Fix supports_gemini_image_size to use value is not False (None means unset, defaults to True)
- Add supports_image_size to JSON schema in test_aaamodel_prices_and_context_window_json_is_valid
- Correct gemini-3.1-flash-lite pricing assertions in test to match JSON values
* Add Azure AI Kimi K2.6 metadata (#27052)
* Add Azure AI Kimi K2.6 metadata
* Scope Kimi metadata test cost map setup
* fall back to substring check for models not in model_prices_and_context_window.json
Models like gemini-2.5-flash-image-preview are not in the pricing JSON,
so get_model_info raises. Fall back to "2.5-flash" not in model when the
JSON has no explicit supports_image_size entry for the model.
* fix(inception): don't forward global litellm.api_key to Inception FIM
Match the Inception chat config: resolve only an Inception-specific key
(param, litellm.inception_key, or INCEPTION_API_KEY) for the text-completion
FIM path. The global litellm.api_key (often an OpenAI key) was both leaking
to api.inceptionlabs.ai and taking precedence over the configured Inception
key when set.
* fix(auth): enforce end-user budget on custom-auth path that skips common_checks
get_end_user_object() no longer raises BudgetExceededError, so custom-auth
deployments with custom_auth_run_common_checks unset (which skip the
centralized common_checks gate) stopped enforcing the end-user budget,
letting an over-budget end user keep making requests. Re-enforce the
budget in _run_post_custom_auth_checks on that path.
---------
Signed-off-by: José Luis Di Biase <josx@interorganic.com.ar>
Co-authored-by: Isha <72744901+IshaMeera@users.noreply.github.com>
Co-authored-by: aneeshsangvikar <aneeshsangvikar@fiddler.ai>
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Aneesh-Fiddler <aneeshfiddler@gmail.com>
Co-authored-by: Suleiman Elkhoury <108065141+suleimanelkhoury@users.noreply.github.com>
Co-authored-by: Dmitriy Alergant <93501479+DmitriyAlergant@users.noreply.github.com>
Co-authored-by: Yanis Miraoui <yanis.miraoui19@imperial.ac.uk>
Co-authored-by: Lovro Seder <vrovro@gmail.com>
Co-authored-by: Thomas Mildner <12685945+Thomas-Mildner@users.noreply.github.com>
Co-authored-by: José Luis Di Biase <josx@interorganic.com.ar>
Co-authored-by: Lai Quang Huy <64073540+1qh@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com>
Co-authored-by: Terrajlz <info@jouleselectrictech.com>
Co-authored-by: Bruno Devaux <devaux.br@gmail.com>
Co-authored-by: ZHONG Ziwen <67355585+zzw-math@users.noreply.github.com>
Co-authored-by: Emerson Gomes <emerson.gomes@thalesgroup.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
A transient DB failure while loading per-user env vars on the tool-call
path was swallowed and turned into a misleading MCPMissingUserEnvVarsError
(412 'set up your credentials'). _load_user_env_vars now propagates DB
errors; the resolver keeps them non-blocking only on the listing path.
Global-scope env var values hold admin-supplied plaintext secrets. They
were returned verbatim inside LiteLLM_MCPServerTable.env_vars to non-admin
and virtual-key callers via the server list/detail endpoints. Both
sanitizers now blank global-scope values while leaving per-user
placeholders intact.
