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9 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Sameer Kankute
|
133da06aa3
|
chore: litellm oss staging (#31185)
* fix(ui): widen Y-axis gutter on Usage charts so large token/request labels aren't clipped
The Total Tokens Over Time and Total Requests Over Time AreaCharts on the
Usage page used Tremor's default yAxisWidth (~56 px), which is too narrow
once totals pass the hundred-million mark — leading digits of labels like
"100.00M" / "4500.00M" got clipped against the chart edge. The requests
chart was worse: it formatted with toLocaleString(), so billion-scale
request counts produced "1,000,000,000" (13 chars) and overflowed
immediately.
Fix in two places so neither alone has to carry the whole margin:
- activity_metrics.tsx: add yAxisWidth={80} to both AreaCharts, and
switch the requests chart to the shared valueFormatter so it uses the
same compact k/M/B suffixes as the tokens chart.
- value_formatters.tsx: add a >= 1e9 branch to valueFormatter /
valueFormatterSpend that emits a "B" suffix (4.50B, $4.50B), keeping
every formatted label at most 7 chars.
Co-Authored-By: Claude Opus 4 (1M context) <noreply@anthropic.com>
* Update ui/litellm-dashboard/src/components/UsagePage/utils/value_formatters.tsx
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* docs(readme): add Deploy on AWS/GCP with Terraform section
Adds a quickstart for the two published Terraform modules on the public
registry (BerriAI/litellm/aws and BerriAI/litellm/google). Copy-paste
main.tf for each cloud, the one-time GCP Artifact Registry remote-repo
command, and pointers to the registry pages for the full input surface.
Sits inside the Get Started section, between the gateway/SDK table and
Run in Developer Mode -- where someone scanning the README for "how do I
deploy this" will land.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* docs(readme): add 1-click deploy buttons for AWS + GCP
GCP gets the real 1-click: Open in Cloud Shell badge that clones the repo
and walks through `terraform apply` via the existing DeployStack
tutorial (already shipped at terraform/litellm/gcp/examples/default/
TUTORIAL.md). User just picks a project.
AWS gets a soft 1-click: a Launch in AWS CloudShell badge that opens an
in-browser, already-authenticated shell. User runs four commands
(clone + cd + cp tfvars + terraform apply) once inside. There's no
native AWS deeplink that pre-clones a repo + runs a tutorial -- CFN
"Launch Stack" + CodeBuild would be needed for that, and that's a
separate piece of work.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* docs(readme): move AWS + GCP deploy buttons next to Render button
* docs(readme): unify deploy button sizes and badge styles
* docs(readme): bump deploy button height to 48 to match Render/Railway
* docs(readme): bump AWS/GCP badge height to compensate for SVG padding
* docs(readme): bump AWS/GCP badge height to 72
* docs(readme): bump AWS/GCP badge height to 84
* fix(readme): make deploy buttons same height (48px)
https://claude.ai/code/session_01MxQRMHSDXbqJh74rF86UBc
* docs(readme): flag GCP project ID substitution in image_registry
* docs(readme): equalize deploy button heights and fix Cloud Shell button font
GitHub rewrites an image's height attribute to "height: auto; max-height: Npx", which only caps and never stretches, so each image renders at its intrinsic height. The AWS/GCP shields badges are intrinsically 28px while the Render/Railway buttons are 40px, leaving the row uneven regardless of the height="48" we set. Replace the two shields badges with committed 40px PNGs so all four header buttons render at the same 40px.
Also swap the Cloud Shell button from open-btn.svg to open-btn.png. The SVG renders its label as live text with font-family "Roboto, Sans" and no generic fallback; since neither font exists in GitHub's render environment, the text fell back to a serif (Times New Roman). The PNG bakes in the correct typeface.
* docs(readme): collapse Railway deploy anchor to a single line
The Railway button wrapped its img across indented lines, so the anchor contained leading and trailing whitespace. GitHub underlines link content, rendering that whitespace as a small blue underline beside the button. Put the anchor on one line like the other three buttons so there is no inner whitespace to underline.
* Add Claude Fable 5 cost map entries as a data-only hotfix
Backports only the model map changes from #30064 so deployments on
released litellm versions pick up Fable 5 pricing, context window, and
the adaptive thinking flag through the hosted cost map fetch without
upgrading. Includes the supports_sampling_params flag on the 28
Fable 5 / Opus 4.7 / Opus 4.8 entries (ignored by released code, read
by the gating that ships with the next release) and the matching
one-line schema declaration so the map validation test passes.
https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm
* fix: correct context window tokens for GPT-5 Pro and GPT-5.4 Mini/Nano
Three bugs in model_prices_and_context_window.json:
1. gpt-5-pro and gpt-5-pro-2025-10-06: max_input_tokens and max_tokens
were SWAPPED. GPT-5 Pro has a 400K context window (input) with 128K
max output, but the values were set as max_input=128000,
max_tokens=272000. This caused token limit errors when sending
prompts over 128K tokens to GPT-5 Pro.
2. gpt-5.4-mini and gpt-5.4-mini-2026-03-17: max_input_tokens was
272000, but GPT-5.4 Mini shares the same 1,050,000 token context
window as GPT-5.4. This was inconsistent with the azure/ variants
which already correctly had 1,050,000.
3. gpt-5.4-nano and gpt-5.4-nano-2026-03-17: same issue as Mini,
max_input_tokens was 272000 instead of 1,050,000.
Source: OpenAI model documentation and contextwindows.dev which
aggregates official context window sizes.
Fixes #30928 (partially — the issue incorrectly claims gpt-5/gpt-5-mini
should be 400K; their 272K values are correct per OpenAI docs)
* fix: also correct max_output_tokens for gpt-5-pro (272000→128000)
Per reviewer feedback, max_output_tokens was left at 272000 while
max_tokens was corrected to 128000, causing an internal inconsistency.
Both should be 128000 per OpenAI docs.
* fix(cost): price gpt-image generated output tokens as image tokens (#31147)
The OpenAI Images endpoints (/v1/images/generations, /v1/images/edits) return
usage with no output token breakdown — litellm's `ImageUsage` has no
`output_tokens_details` field — so generated-image OUTPUT tokens were priced at
the text rate (`output_cost_per_token`) instead of the image rate
(`output_cost_per_image_token`). For gpt-image-2 that is $10/1M vs $30/1M, a ~3x
undercount on the dominant cost component (image output is ~74% of spend). This
also affects azure gpt-image, which shares this calculator.
The OpenAI gpt-image cost calculator re-implemented usage handling instead of
reusing `calculate_image_response_cost_from_usage`, the shared helper that
azure_ai/gemini/vertex_ai already use. That helper classifies generated output
tokens as image tokens when the provider does not itemize output, and splits
text/image when it does.
Fix: route the ImageUsage path through `calculate_image_response_cost_from_usage`
(pre-transformed chat Usage objects are still costed directly). Adds a regression
test for the no-breakdown ImageUsage case (gpt-image-2).
* fix(bedrock): route application-inference-profile ARNs to converse (#18258) (#31098)
A bare application-inference-profile ARN passed as bedrock/arn:... fell
through to the invoke route, which cannot derive a provider from the
opaque profile id and raised 'Unknown provider=None'. The converse route
needs no provider, so detect these ARNs in get_bedrock_route and route
them to converse, matching the behavior of the already-documented
bedrock/converse/arn:... workaround.
Explicit invoke/ prefixes still win, and they remain a dead end for these
ARNs by design (no provider derivable). System-defined inference-profile
ARNs that embed a known model, and other opaque ARN types
(provisioned-model, imported-model, custom-model-deployment) that are
frequently invoke-only, are deliberately left on their current routes;
tests guard both boundaries.
* fix(moonshot): stop mutating caller messages on tool_choice='required' (#31060)
_add_tool_choice_required_message appended the "select a tool" prompt to
the caller's messages list in place, so transform_request corrupted the
caller's conversation history and appended a duplicate prompt on every
retry. Build and return a new list instead so the call stays idempotent.
Adds a regression test asserting the input messages list is unchanged
across repeated transform_request calls.
Co-authored-by: Wassbdr <wassim.badraoui07@gmail.com>
* fix(transcription): accept fractional usage.seconds in diarized_json responses (#30996)
gpt-4o-transcribe and compatible ASR backends return a diarized_json
response with usage={"type": "duration", "seconds": <float>}, e.g. 295.8.
TranscriptionUsageDurationObject typed seconds as int, so parsing the
response raised a pydantic ValidationError (int_from_float). That error
surfaces as an APIConnectionError which the router treats as retryable, so
it keeps re-calling the upstream (200 every time) until the upstream
rate-limits and returns 429 to the caller.
OpenAI specs this field as a float (see openai SDK UsageDuration.seconds),
so widen seconds to float. With the parse succeeding there is no exception
left to retry, which removes the loop.
Co-authored-by: Neimar Avila <19142978+neimaravila@users.noreply.github.com>
* fix(deepseek): drop non-function tools before chat completions call (#30910)
* fix(deepseek): drop non-function tools before chat completions call
DeepSeek's /chat/completions only accepts tools of type "function".
Requests bridged from /v1/responses can carry responses-API-native tool
types, for example a Codex CLI tool typed "namespace", which DeepSeek
rejects with "unknown variant 'namespace', expected 'function'" so the
whole request fails (issue #30722).
Filter unsupported tool types in the DeepSeek request transform so the
function tools still go through; when nothing callable remains, also drop
the now-dangling tool_choice and parallel_tool_calls
Fixes #30722
* test(deepseek): cover async tool filtering and document tool_choice assumption
Add an async_transform_request regression test so the sync and async tool
filtering paths cannot silently diverge, and document in _drop_unsupported_tools
that only non-function tools are dropped, so a function-named tool_choice always
references a surviving tool
* feat(catalog): add zai/glm-5.1, zai/glm-4.7-flash, openrouter/z-ai/glm-5.1 (#29840)
* feat(ui): surface team budget on key overview when key has no own budget (#30801)
* feat(ui): surface team budget on key overview when key has no own budget
* fix(ui): replace IIFE with derived variable and use find() for team budget display
* fix(anthropic): emit replayable streaming thinking blocks (#31022)
* feat(proxy): read cold-storage prompts back in the logs detail view (#30364)
* feat(proxy): read cold-storage prompts back in the logs detail view
When a deployment offloads prompts and responses to cold storage instead of
Postgres, the spend-log row holds only "{}" placeholders plus a
metadata.cold_storage_object_key pointer, so the UI logs detail drawer showed
nothing. The detail endpoint only read the placeholder columns and never
fetched the object back.
Resolve the payload per row based on actual content, not a config flag: if
Postgres has content, return it; otherwise read the exact stored object key and
fetch from the configured cold storage backend through ColdStorageHandler.
Reading the persisted key is a single GET. The key embeds a microsecond
timestamp that cannot be reconstructed from the millisecond-precision startTime
column, and listing the day's prefix to match on request_id would be too
expensive for this per-open path.
Also teach the detail drawer's pretty-view parser to accept a bare messages
array. The cold storage payload carries the prompt as a top-level messages list
with no proxy_server_request, so without this the output rendered while the
input stayed blank.
ColdStorageHandler gains an optional injected logger so the resolver can be unit
tested without monkeypatching. Postgres-stored prompts are unaffected: the fast
path returns the existing columns and the request-body object still renders the
same way.
* Update litellm/proxy/spend_tracking/spend_management_endpoints.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* test(proxy): cover ColdStorageHandler resolution paths and cold-storage fetch failure
Add unit tests for ColdStorageHandler (injected logger, graceful None when no
logger is configured, and resolution of a configured logger from the callback
registry) and a regression test asserting a cold storage backend exception
degrades to the Postgres values instead of surfacing a 500.
---------
Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix(mavvrik): advance metricsMarker after upload; fix scheduler startup (#31068)
* fix(mavvrik): advance metricsMarker after upload + fix scheduler startup
Two bugs fixed:
1. deliver() never called PATCH /metrics/agent/ai/{connectionId} after a
successful GCS upload, so metricsMarker stayed at 0 and every daily run
re-exported the same dates in an infinite catch-up loop.
Fix: add _update_metrics_marker(date_epoch) called at the end of deliver()
after _upload_to_gcs() succeeds. A 4xx warns but does not raise (the GCS
file is already committed). A 410 raises consistent with the rest of the
destination.
2. init_mavvrik_focus_background_job runs at proxy startup before any LLM call
has triggered lazy instantiation of MavvrikFocusLogger, so it found no
logger instance and silently skipped registering the daily export job.
Fix: if no instance is found but "mavvrik" is in litellm.callbacks, call
_init_custom_logger_compatible_class to force instantiation before
the APScheduler job is registered.
* fix(mavvrik): catch up from earliest window when metricsMarker=0
When the connector is freshly registered, metricsMarker=0 parses to None.
The catch-up block was guarded by `if last_ingested and ...` which skipped
it entirely for None, so only yesterday was exported instead of the full
_MAX_CATCHUP_DAYS window.
Fix: treat None as being _MAX_CATCHUP_DAYS behind (start from earliest_catchup).
The existing > 7 day warning only fires for non-None markers that are old.
* fix(mavvrik): use now as end_time for yesterday's export window
LiteLLM_DailyUserSpend rows for a given date get their updated_at
bumped by the spend flush job throughout the next morning. The core
database query filters on updated_at, so capping end_time at midnight
(yesterday + 1 day) missed any spend rows flushed after midnight.
Fix: pass now (cron fire time) as end_time for the daily "yesterday"
window so all fully-settled rows are captured regardless of when the
flush job ran.
Verified: claude-3-5-sonnet BilledCost went from 0.0 to ~$2.40 per
row in the exported FOCUS CSV.
* fix(mavvrik): also use now as end_time for catch-up windows
* fix(mavvrik_focus): pass required args to _init_custom_logger_compatible_class
Calling it with only logging_integration raised TypeError at proxy startup
because internal_usage_cache and llm_router have no defaults. Also fix test
name to reflect the actual status code (5xx not 4xx) used in the mock.
* ci: retrigger CI run
* feat: pass through optional `instruction` field in the rerank API (vLLM/Qwen3-Reranker) (#30757)
* Add optional `instruction` passthrough to the rerank API
vLLM's /v1/rerank and /v1/score accept an optional top-level `instruction`
field (folded into the model's chat_template_kwargs and consumed by the
chat template — e.g. Qwen3-Reranker). LiteLLM's managed rerank route silently
dropped it: RerankRequest / OptionalRerankParams had no such field, so the
outgoing body was rebuilt without it.
Thread an opt-in `instruction: Optional[str]` through rerank()/arerank(),
get_optional_rerank_params, and the hosted_vllm transformation into the
request body, only when non-None. When callers omit it, model_dump(exclude_none)
drops the field and the outgoing request is byte-for-byte unchanged — fully
backward-compatible. (DeepInfra already forwards `instruction` via
non_default_params; this formalizes the field in the shared types.)
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Address review: thread `instruction` as a typed param + cover rerank_utils
Per PR review (greptile P2 + codecov):
- Make `instruction` a typed, named argument on the rerank provider interface
instead of recovering it from the opaque `non_default_params` blob. Adds
`instruction: Optional[str] = None` to `BaseRerankConfig.map_cohere_rerank_params`
and every provider override, and forwards it explicitly from
`get_optional_rerank_params`. hosted_vllm now reads the named param directly.
It is still also surfaced in `non_default_params` so providers that read it
there (e.g. DeepInfra) keep working now that `rerank()` consumes `instruction`
as a named param rather than leaving it in **kwargs.
- Add get_optional_rerank_params unit tests (present + absent) to cover the
previously-uncovered threading line flagged by codecov.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix: scan rerank `instruction` through request guardrails
The rerank guardrail translation (CohereRerankHandler.process_input_messages)
only scanned `query`, so the newly added `instruction` field reached the
backend model unscanned. Since instruction-aware rerankers (hosted vLLM /
Qwen3-Reranker) fold `instruction` into the prompt, an authenticated caller
could place content there to bypass configured rerank request guardrails.
Generalize the handler to scan every user-controlled text field (`query` and
`instruction`) in one apply_guardrail call and write each sanitized value back
by index. Query-only requests are unchanged (single-element list at index 0);
non-string fields are left untouched. Adds tests covering instruction
scanning, PII masking write-back, and the non-string case.
Addresses the Veria AI security review on PR #30757.
* test: narrow Optional results before len() to satisfy basedpyright budget
The lint gate (basedpyright delta-vs-base budget) flagged one new
reportArgumentType: len(result.results) where results is
List[RerankResponseResult] | None. Assert results is not None first to
narrow the type before len()/indexing.
* fix: read rerank `instruction` from kwargs to satisfy basedpyright budget
The basedpyright delta-vs-base gate flagged one new reportArgumentType: the
Router forwards rerank calls via an untyped `**kwargs` unpack
(`litellm.arerank(**{**data, **kwargs})`), and declaring `instruction` as a
typed named param on the public `rerank`/`arerank` entrypoints made pyright
check that key against `str | None`, adding an error at router.py with no real
safety gain. Read `instruction` from kwargs in `rerank` instead.
It remains fully typed where it matters - threaded as a typed argument through
`get_optional_rerank_params` and each provider's `map_cohere_rerank_params`
(the original Greptile P2 ask). Whole-repo reportArgumentType is back to the
base count (net 0); rerank hosted_vllm + cohere guardrail suites pass; ruff clean.
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(github_copilot): synthesize empty choices at the provider seam (#30929)
Newer Copilot Claude models (opus-4.7, opus-4.8) return responses with
choices=[], either carrying Anthropic-native content blocks or, for the
max_tokens=1 probe Claude Code sends, no content at all. github_copilot
is dispatched through the OpenAI SDK handler, which calls
convert_to_model_response_object directly and never invokes
GithubCopilotConfig.transform_response, so the empty-choices guard there
surfaced as a 500
Instead of synthesizing choices inside the shared
convert_to_model_response_object (which would silently turn empty choices
into a fabricated success for every provider), add a no-op
transform_parsed_response_dict hook on BaseConfig. GithubCopilotConfig
overrides it to synthesize choices from Anthropic-native content, reusing
its existing parsing, and the OpenAI SDK handler routes its parsed
response through the hook before generic conversion. The core utility
keeps treating empty choices as an error for all other providers
Fixes: https://github.com/BerriAI/litellm/issues/30927
Signed-off-by: David J. M. Karlsen <david@davidkarlsen.com>
* fix(router): stop fallback lookups from mutating the router fallbacks config (#30624)
* fix: correct amazon.titan-embed-text-v2 input price to $0.02/1M tokens (#29693)
* fix: correct amazon.titan-embed-text-v2 input price to $0.02/1M tokens
* test: scope local cost map env var with monkeypatch to avoid test pollution
* fix(sensitive_data_masker): fully mask secrets at or below the reveal threshold (#30764)
* fix(sensitive_data_masker): fully mask secrets at or below the reveal threshold
_mask_value did partial reveal by showing the first visible_prefix and last
visible_suffix characters, but for a value whose length was at or below
visible_prefix + visible_suffix (8 by default) it returned the value verbatim.
A value of exactly 8 chars fell through the length guard and computed
masked_length == 0, reconstructing the original string with no mask characters;
anything shorter hit the early return. Either way short credentials were emitted
in plaintext.
mask_dict routes real secrets through this path, so an 8-char-or-shorter redis
password, api key, or token could be written to logs and the UI unmasked. The
sibling helper mask_sensitive_keys already guards this case; _mask_value now does
the same by fully masking any value at or below the threshold.
* fix(sensitive_data_masker): add mask_short_values opt-out for truncation callers
Fully masking short values is the right default for secret masking, but
CooldownCache reuses the masker purely to truncate exception messages to the
first 50 characters, and it relies on short messages being returned readable.
Masking those blanked out short exception text and broke its tests.
Add a mask_short_values flag (default True, secure) and have CooldownCache pass
False so it keeps the truncation behavior, while every secret-masking caller
still gets short values fully masked.
* fix(mcp_debug): opt out of short-value masking to keep diagnostic token preview
MCPDebug uses the masker to preview auth tokens in debug headers and documents
that values of 10 chars or fewer are shown unchanged so token types stay
distinguishable. Pass mask_short_values=False so that diagnostic behavior is
preserved while secret maskers keep masking short values.
* fix(mcp_debug): mask short auth values in debug headers instead of echoing them
Earlier this masker opted out of short-value masking to keep a token preview, but
that echoes short authorization and token values verbatim in debug response
headers, which is the same leak this change is meant to close. Auth material
should never be emitted in full, so mask short values here too; the first/last
character preview still applies to longer tokens. Only CooldownCache keeps the
opt-out, since it truncates exception text rather than masking secrets.
* test(mcp_debug): assert masked short value preserves length
* refactor(fireworks_ai): remove deprecated audio transcriptions endpoint (#30917)
Fireworks AI deprecated audio inference on 2026-06-10
(https://docs.fireworks.ai/updates/changelog#audio-inference-and-image-generation-deprecation).
Live API testing confirms the endpoint is already non-functional: a valid
Fireworks API key receives HTTP 401 "Unauthorized" from
api.fireworks.ai/inference/v1/audio/transcriptions for every request,
regardless of payload. The audio-prod.api.fireworks.ai host referenced in
the test suite returns 401 for every path; the entire host is decommissioned.
Remove the dead FireworksAIAudioTranscriptionConfig class and every
reference to it across the codebase:
- Delete litellm/llms/fireworks_ai/audio_transcription/ directory (17-line
config class that inherited from OpenAIWhisperAudioTranscriptionConfig)
- Remove the Fireworks branch from
ProviderConfigManager.get_provider_audio_transcription_config() in
litellm/utils.py; update the stale comment in
get_optional_params_transcription that referenced fireworks ai
- Remove the FireworksAIAudioTranscriptionConfig entries from
LLM_CONFIG_NAMES and _LLM_CONFIGS_IMPORT_MAP in
litellm/_lazy_imports_registry.py
- Remove the TYPE_CHECKING re-export in litellm/__init__.py
- Remove the transcription branch in the fireworks_ai case of
get_supported_openai_params() in
litellm/litellm_core_utils/get_supported_openai_params.py
- Remove the whisper-v3 and whisper-v3-turbo entries from
model_prices_and_context_window.json and
litellm/model_prices_and_context_window_backup.json (both had
mode: audio_transcription and zero-cost pricing)
- Remove the TestFireworksAIAudioTranscription test class and its
imports from tests/llm_translation/test_fireworks_ai_translation.py
No other provider is affected. The openai_compatible_providers list,
FireworksAIMixin, and the OpenAI Whisper transcription handler all stay
because they are shared with other Fireworks endpoints and other
providers. The provider_endpoints_support.json registry already had
audio_transcriptions set to false for fireworks_ai.
* feat: add darkbloom provider (#30876)
* feat: add darkbloom provider
* fix: document darkbloom provider endpoints
* fix: address darkbloom review feedback
* fix: update darkbloom tool metadata
* fix: fail fast for non-Postgres database URLs (#30883)
* fix(proxy): fail fast on non-PostgreSQL DATABASE_URL instead of hanging on startup
LiteLLM's Prisma datasource is pinned to provider = 'postgresql', so a sqlite:// or mysql:// DATABASE_URL can never connect.
Today that surfaces as an opaque startup stall where the port never binds, and a separate 'DB not connected' 500 on /key/generate when no DATABASE_URL is set at all leaves operators guessing what to configure.
Validate the DATABASE_URL / DIRECT_URL scheme in run_server before any Prisma call and exit with an actionable message naming the unsupported scheme.
Also reword CommonProxyErrors.db_not_connected_error to tell the operator to set DATABASE_URL to a postgresql:// connection string.
Add regression tests covering postgres acceptance and sqlite/mysql/mssql rejection.
* fix: resolve CI failures and proxy DB URL typing issue
* fix(proxy): fail fast on non-PostgreSQL DATABASE_URLs with clear startup errors instead of hanging
* Validate DIRECT_URL alongside DATABASE_URL startup guards
* fix(bedrock): surface modeled HTTP status for mid-stream error events so 5xx is retryable (#24608) (#30946)
* fix(bedrock): surface modeled HTTP status for mid-stream error events (#24608)
* test(bedrock): mid-stream server errors trigger streaming fallback (#24608)
* style(bedrock): black-format stream-error helper (#24608)
* fix(mcp): re-land native tool preservation with typed annotations (#30645)
* fix(mcp): preserve native tools in semantic filter hook with typed annotations
* fix(mcp): tighten _is_mcp_tool Chat Completions shape check
* fix(sambanova): return embeddings supported params instead of dropping them (#30937)
* fix(router): send fallback metadata when streaming (#30914)
When a streaming request triggers a fallback, there was previously no way to
know it happened. This commit addresses this in a few ways:
1. The response now correctly populates the fallback headers
(`x-litellm-attempted-fallbacks`) so callers know a fallback happened.
2. The correct model ID is passed in the streaming chunks.
3. A streaming chunk with the fallback error can be optionally sent back
to the client (opt-in) by passing `include_fallback_errors: true` in
the request.
The format of the fallback errors while streaming is intentionally OpenAI
compatible to not break existing libraries that parse these events. It was
tested with Vercel's AI SDK (ai-sdk.dev). It is also opt-in, so it is not
delieved unexpectedly to callers by default.
* fix(mistral): drop output-only reasoning fields from input messages (#30884)
LiteLLM attaches reasoning_content and thinking_blocks to assistant
responses. Replaying those assistant turns verbatim forwarded the fields
back to Mistral, whose input schema forbids unknown keys, so the whole
request failed with a 422 extra_forbidden and reasoning models became
unusable across multiple turns.
Strip both fields from assistant messages before the request is built, in
a spot that runs ahead of the image/file branch so it applies on every
path. Fixes #30835
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(perplexity): bill search queries at the per-request price, not 1/1000 of it (#30652)
* fix(perplexity): bill search queries at the per-request price, not 1/1000
The fallback cost calculator divided search_context_cost_per_query by
1000, but that field stores the per-request price in USD: sonar is
{low: 0.005, medium: 0.008, high: 0.012}, matching Perplexity's published
$5/$8/$12 per 1,000 requests expressed per request. The gemini cost
calculator reads the same field per request with no division (its
docstring calls it "the per-request cost").
The division understated search cost by 1000x on every Perplexity call
that falls back to manual calculation (i.e. when the API does not return
a pre-computed usage.cost). Use the value directly.
Update the tests that had encoded the /1000 factor in their expectations,
and drop an unused import flagged by ruff in the touched test file.
* test(perplexity): update integration test search-cost expectations to per-request
The integration tests still encoded the old /1000 search-cost factor, so
they failed once the fallback calculator was corrected to bill
search_context_cost_per_query per request. Update the four expected-cost
computations (and the high-volume dollar-value comments) to match.
* test(perplexity): drop unused mock imports flagged by ruff
* fix: include model_access_groups when expanding all-team-models in get_team_models (#30622)
* fix(fireworks_ai): return None for transcription in get_supported_openai_params
Fireworks AI deprecated audio inference on 2026-06-10; the endpoint is
decommissioned. Without an explicit transcription branch, requests with
request_type='transcription' fell through to the else and returned
FireworksAIConfig chat-completion params. Return None instead to signal
the provider does not support transcription.
* fix(proxy): gate include_fallback_errors behind expose_fallback_errors_to_caller setting
Without an operator gate, any authenticated caller could set include_fallback_errors=True,
trigger a fallback, and read raw upstream exception messages from the
x-litellm-fallback-errors header and the litellm-fallback-metadata SSE event.
Strip include_fallback_errors from request data in common_processing_pre_call_logic
when expose_fallback_errors_to_caller is not set, so the router never builds the
error list. Also gate _should_include_fallback_errors on the same setting as a
secondary check for the streaming SSE injection path.
* test(proxy): opt in to expose_fallback_errors_to_caller in streaming SSE test
The operator gate added in
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Sameer Kankute
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d671a09c20
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Litellm oss staging 050626 (#29774)
* Mark xAI models retiring on 2026-05-15 (#28788) Per https://docs.x.ai/developers/migration/may-15-retirement, xAI is retiring the following slugs on 2026-05-15 (auto-redirect to grok-4.3 with various reasoning efforts; callers continuing to use the old slugs will be billed at grok-4.3 pricing): grok-4-1-fast-reasoning{,-latest} -> grok-4.3 (low effort) grok-4-1-fast-non-reasoning{,-latest} -> grok-4.3 (none) grok-4-fast-reasoning -> grok-4.3 (low effort) grok-4-fast-non-reasoning -> grok-4.3 (none) grok-4-0709 -> grok-4.3 (low effort) grok-code-fast-1{,-0825} -> grok-build-0.1 grok-3 -> grok-4.3 (none) Only the direct xai/ slugs are tagged; third-party hosts (azure_ai, oci, vercel_ai_gateway, perplexity/xai) run their own schedules. The grok-3 retirement list explicitly names only the base grok-3 slug — the -mini / -fast / -beta / -latest variants are not listed, so they remain untouched. * feat(moonshot): advertise json_schema response support on live models (#29683) litellm.responses() already routes Moonshot through the responses->chat-completions bridge, and Moonshot honors response_format json_schema on chat completions. The cost-map entries left supports_response_schema unset, so discovery layers that gate on that flag dropped Moonshot from structured-output / responses listings even though the capability works end to end. Set supports_response_schema on the nine models currently live on api.moonshot.ai: kimi-k2.5, kimi-k2.6, the moonshot-v1 8k/32k/128k text and vision-preview variants, and moonshot-v1-auto. Verified against the live API that each honors json_schema and that litellm.responses() returns schema-valid structured output through the bridge. * chore(moonshot): mark models retired from api.moonshot.ai as deprecated (#29685) Thirteen Moonshot/Kimi models in the cost map no longer resolve on api.moonshot.ai (all return 404). Stamp each with its deprecation_date from platform.kimi.ai/docs/models rather than deleting the entries, so historical cost calculation keeps resolving the names while tooling can surface the retirement. Dates: kimi-thinking-preview 2025-11-11; kimi-latest and its 8k/32k/128k context variants 2026-01-28; the kimi-k2 preview/turbo/thinking series 2026-05-25; the moonshot-v1 -0430 snapshots use their own 2024-04-30 snapshot date (Moonshot publishes no discontinuation date for them). * fix(moonshot): drop temperature for reasoning models (kimi-k2.5/k2.6) (#29687) Kimi reasoning models reject every temperature except 1; a request with temperature=0.2 returns "invalid temperature: only 1 is allowed for this model". litellm only clamped temperature into [0.3, 1], so any value below 1 still 400'd. Drop the temperature param entirely for reasoning models (gated on supports_reasoning, the same signal transform_request already uses) so the model default is used; the non-reasoning moonshot-v1 models keep the existing clamp. Co-authored-by: Sameer Kankute <sameer@berri.ai> * feat(mcp): add per-server timeout configuration (#29672) * feat(mcp): add per-server timeout configuration * fix(mcp): address timeout field review comments - use is not None guard instead of or for 0.0 edge case - copy timeout in both LiteLLM_MCPServerTable constructions (health check path + _build_mcp_server_table) - add timeout Float? column to all three schema.prisma files - extend round-trip test to cover _build_mcp_server_table direction - add test for zero timeout not treated as falsy * fix(mcp): forward timeout in _build_temporary_mcp_server_record * fix(mcp): return 504 instead of 500 when per-server timeout fires * test(mcp): add 504 timeout regression test; fix black formatting * Add jp. Bedrock cross-region inference profile for claude-opus-4-7 (#28567) * 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 jp. Bedrock cross-region inference profile for claude-opus-4-7 AWS Bedrock documents jp.anthropic.claude-opus-4-7 alongside the existing us./eu./au./global. profiles for Claude Opus 4.7 (ap-northeast-1 Tokyo / ap-northeast-3 Osaka), but the entry is missing from model_prices_and_context_window.json. Tokyo-region users currently get an "unknown model" error when routing through the JP geo profile. Adds the entry to both the canonical file and the bundled backup, mirroring the recent pattern for sonnet-4-6 (#27831). Pricing matches the other regional profiles (10% premium over base/global). Regression test pins all six documented profiles (base, global, us, eu, au, jp) and asserts pricing parity between jp. and au. variants. Source: https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-opus-4-7.html --------- Co-authored-by: Terrajlz <info@jouleselectrictech.com> Co-authored-by: Bruno Devaux <devaux.br@gmail.com> Co-authored-by: Sameer Kankute <sameer@berri.ai> * feat(soniox): add soniox audio transcription integration (#29508) * feat(openmeter): add OPENMETER_TRUST_REQUEST_USER to prevent forged attribution (#29650) The OpenMeter callback resolves the CloudEvent subject from kwargs["user"] first, then falls back to the key-bound user_api_key_user_id. For multi-tenant proxy deployments, a client can set `"user": "..."` in the request body and cause their usage to be attributed to that arbitrary string — a billing-attribution forgery risk. Adds OPENMETER_TRUST_REQUEST_USER env var (default "true" for backward compatibility). When set to "false", the request-supplied `user` field is ignored and the subject is resolved solely from user_api_key_user_id. Matches the existing env-var-driven config pattern in this file (OPENMETER_API_KEY, OPENMETER_API_ENDPOINT, OPENMETER_EVENT_TYPE). * feat(search): add you_com as a search provider (#28370) * feat(search): add you_com as a search provider Registers You.com Search API as a first-class `search_provider` in the `search_tools` registry, alongside Tavily, Exa, Perplexity, etc. - New adapter: litellm/llms/you_com/search/transformation.py - POSTs to https://ydc-index.io/v1/search - Auth: X-API-Key from YOUCOM_API_KEY (or explicit api_key) - Maps Perplexity unified spec: max_results -> count, search_domain_filter -> include_domains, country -> country - Flattens results.web + results.news into a single SearchResult list; snippet prefers snippets[0], falls back to description; page_age -> date - Registry: SearchProviders.YOU_COM in litellm/types/utils.py and wired into ProviderConfigManager.get_provider_search_config() - Pricing entry: model_prices_and_context_window.json (placeholder $0.0; happy to adjust to maintainers' preferred public number) - Docs: example router config snippet and example proxy yaml updated - Tests: tests/search_tests/test_you_com_search.py - 5 mocked tests (payload shape, domain filter mapping, snippet fallback, news flattening, missing-api-key error) Refs upstream expansion signal: #15942 * review fixups: normalize api_base, lowercase country, scope env-var to test Addresses Greptile inline review comments on #28370: - get_complete_url: strip trailing slashes from api_base *before* the endswith("/v1/search") check, so a custom base like ".../v1/search/" doesn't become ".../v1/search/v1/search". - transform_search_request: .lower() country before sending, matching Tavily's convention so callers using the unified spec form ("US") get consistent behavior across providers. - Tests: replace direct os.environ writes with an autouse monkeypatch fixture so YOUCOM_API_KEY is set per-test and removed afterwards. The missing-key test now uses monkeypatch.delenv. New test asserts the trailing-slash normalization above. Reverts the ARCHITECTURE.md / example yaml edits per the reviewer note that documentation changes belong in the litellm-docs repo. * support keyless free tier (api.you.com/v1/agents/search) as default You.com offers an IP-throttled keyless endpoint that returns the same response shape as the keyed one (~100 queries/day, no signup). This is a significant onboarding lever - mirrors the keyless DuckDuckGo/SearXNG providers already in the search_tools registry. Behavior: - YOUCOM_API_KEY set -> keyed: POST https://ydc-index.io/v1/search (X-API-Key header) - no key -> free: POST https://api.you.com/v1/agents/search (no auth) - YOUCOM_API_BASE override -> honored as-is Tests: - New: test_you_com_search_keyless_free_tier - asserts URL + absence of X-API-Key when no key is configured. - New: test_you_com_search_validate_environment_keyless - asserts the config no longer raises when the key is absent. - Removed: test_you_com_search_raises_without_api_key (the precondition no longer holds). - Existing payload/domain-filter/etc tests still cover keyed mode via the autouse YOUCOM_API_KEY fixture. Verified both endpoints accept POST + return identical JSON shape: results.web[] / results.news[] with title, url, snippets, description, page_age. * register you_com in provider_endpoints_support.json Adding `litellm/llms/you_com/` requires a corresponding entry in provider_endpoints_support.json or the code-quality/check_provider_folders_documented CI check fails. Follows the compact tavily/serper pattern - endpoints: { search: true }. Local run of the check now reports "All 114 provider folders are documented". * move tests under tests/test_litellm/llms/ so CI exercises them The litellm CI workflows scope unit tests to `tests/test_litellm/...` (see test-unit-llm-providers.yml: `tests/test_litellm/llms` path), so tests living under `tests/search_tests/` are never run in CI - which is why codecov reports 0% patch coverage for the new adapter even though the unit tests exist and pass locally. Move test_you_com_search.py into `tests/test_litellm/llms/you_com/` so the test-unit-llm-providers job picks it up. 7/7 tests still pass at the new location. (Sibling search-only providers - tavily, exa_ai, brave, etc. - still live only in `tests/search_tests/` and would benefit from the same move, but that is out of scope for this PR.) * fix(you_com): pin Accept-Encoding: identity to dodge keyless gzip bug The keyless free-tier endpoint (api.you.com/v1/agents/search) advertises Content-Encoding: gzip but returns a body that httpx's decoder rejects with `zlib.error: Error -3 while decompressing data: incorrect header check`, surfacing as litellm.APIConnectionError in user code. curl works because it doesn't request compression by default. Pin Accept-Encoding: identity in validate_environment so the upstream server skips compression entirely. Harmless on the keyed endpoint (ydc-index.io/v1/search) which negotiates content-encoding correctly. The header uses setdefault so a caller-supplied Accept-Encoding still takes precedence. (Server-side bug has been flagged to the You.com team separately - once fixed there, this workaround can be removed.) New unit test: test_you_com_search_pins_identity_accept_encoding. --------- Co-authored-by: Sameer Kankute <sameer@berri.ai> * docs: fix README typo (#29419) Correct clear spelling mistakes in documentation without changing behavior. Confidence: high Scope-risk: narrow Tested: git diff --check; uvx codespell on changed files Not-tested: Full docs build not run; text-only changes * Fix(langfuse): pass httpx_client to Langfuse in langfuse_prompt_management to respect SSL_VERIFY (#29480) * fix(langfuse): pass ssl_verify to Langfuse httpx client * fix_langfuse_ * add unit tests * addressed comments --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> * feat(models): add minimax/MiniMax-M3 to model cost map (#29412) Add MiniMax's new flagship MiniMax-M3 to the native minimax provider: 512K context, 128K max output, native multimodal (supports_vision), reasoning, prompt caching. Pricing (USD/M tokens): input 0.6 / output 2.4 / cache read 0.12. M3 has no active prompt-cache-write tier, so cache_creation_input_token_cost is omitted. Updated both the root model_prices_and_context_window.json (remote source) and the bundled litellm/model_prices_and_context_window_backup.json (local fallback), keeping them in sync. * fix(logging): handle ResponseCompletedEvent in anthropic_messages streaming spend log (#29394) * fix(logging): handle ResponseCompletedEvent in anthropic_messages streaming spend log * fix(logging): extend terminal event handling to ResponseIncompleteEvent and ResponseFailedEvent; fix return type annotation * feat(provider): Add Neosantara provider as OpenAI Compatible (#29646) * Add Neosantara provider * Register Neosantara provider enum * Address Neosantara provider review feedback * Add Neosantara packaged endpoint support --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> * fix: address greptile and veria review feedback - langfuse: guard httpx_client injection behind version check (>= 2.7.3) - soniox: propagate audio_transcription_duration in _hidden_params for spend tracking - soniox: give SONIOX_API_BASE env var priority over caller-supplied api_base - mcp: replace CancelledError catch with asyncio.wait_for + TimeoutError * chore(mcp): add migration for per-server timeout column * fix(test): add tool_use_system_prompt_tokens to model prices schema validator * fix: mcp timeout test uses real asyncio.wait_for timeout; you_com get_complete_url respects resolved api_key * fix: forward resolved api_key into you_com endpoint selection and apply timeout to soniox polling GETs The search flow resolves api_key in validate_environment but never passed it into get_complete_url, so a programmatic api_key (with no YOUCOM_API_KEY in the env) set the X-API-Key header yet still selected the keyless free-tier endpoint. Forward api_key through both the search entrypoint and the http handler so the keyed endpoint is chosen. HTTPHandler.get/AsyncHTTPHandler.get had no timeout parameter, so the Soniox poll and transcript-fetch GETs silently used the client global default instead of the caller timeout. Add a per-request timeout to get() and forward the configured timeout from the Soniox handler. * fix(soniox): price stt-async-v4 per second so transcriptions are billed The handler stores audio_transcription_duration in _hidden_params, but the model carried only token cost fields and the response has no token usage, so the transcription cost path fell through to cost_per_second and returned $0. An authenticated caller could transcribe Soniox audio without decrementing their budget. Switch the entry to output_cost_per_second at Soniox's published $0.10/hour async rate so the stored duration produces a real charge. * fix(langfuse): use a dedicated httpx client for the SDK injection The httpx_client handed to the Langfuse SDK came from _get_httpx_client(), which returns LiteLLM's globally cached HTTPHandler. If Langfuse closed that client on teardown it would invalidate the shared client used by every other LiteLLM HTTP call. Build a dedicated httpx.Client instead, still resolving SSL verification and client certificate from LiteLLM's configuration. * fix(soniox): prefer caller-supplied api_base over SONIOX_API_BASE env var * fix(cohere): support max_completion_tokens on cohere v2 chat (default route) (#29779) * fix(cohere): support max_completion_tokens on cohere v2 chat The default cohere_chat route resolves to CohereV2ChatConfig, which did not list or map max_completion_tokens, so get_optional_params raised UnsupportedParamsError for the standard OpenAI parameter (the modern replacement for the deprecated max_tokens). The v1 config already maps it to cohere's max_tokens; mirror that in v2 and add v2 regression tests. * fix(cohere): make max_completion_tokens take precedence over max_tokens on v2 When both max_tokens and max_completion_tokens are supplied, prefer max_completion_tokens explicitly rather than relying on dict iteration order, and cover both orderings with a regression test. --------- Co-authored-by: Daniel Yudelevich <4537920+yudelevi@users.noreply.github.com> Co-authored-by: hectorc98 <hector.chamorroalvarez@adyen.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: Dan Lemon <dan@danlemon.com> Co-authored-by: Saswat <saswatds@users.noreply.github.com> Co-authored-by: Brian Sparker <brainsparker@users.noreply.github.com> Co-authored-by: Zhao73 <156770117+Zhao73@users.noreply.github.com> Co-authored-by: Urain Ahmad Shah <60431964+urainshah@users.noreply.github.com> Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: kape <168134658+kapelame@users.noreply.github.com> Co-authored-by: danisalvaa <159898202+danisalvaa@users.noreply.github.com> Co-authored-by: Just R <remixingmagelang@gmail.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Co-authored-by: abhay23-AI <abhaytrivedi22@gmail.com> |
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Ishaan Jaffer
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e8461b5b97
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style: run black formatter on files from main merge | ||
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Krish Dholakia
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51cc102c30
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fix(unified_guardrail.py): support during_call event type for unified guardrails (#17514)
* fix(unified_guardrail.py): support during_call event type for unified guardrails allows guardrails overriding apply_guardrails to work 'during_call' * feat(generic_guardrail_api.py): support new 'tool_calls' field for generic guardrail api returns the tool calls emitted by the LLM API to the user * fix(generic_guardrail_api.py): working anthropic /v1/messages tool call response send llm tool calls to guardrail api when called via `/v1/messages` API * fix(responses/): run generic_guardrail_api on responses api tool call responses * fix: fix tests * test: fix tests * fix: fix tests |
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Krish Dholakia
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8edcc4ecc3
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Guardrails API - add streaming support (#17400)
* fix(initial-commit): adding a way to get the right response type based on the api route * feat(unified_guardrail.py): support streaming guardrails * test: update tests * fix: fix linting errors * test: update tests |
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Krish Dholakia
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2bd41dc034
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Guardrails - Responses API, Image Gen, Text completions, Audio transcriptions, Audio Speech, Rerank, Anthropic Messages API support via the unified apply_guardrails function (#15706)
* fix(presidio.py): handle content as a list of texts covers openai + anthropic messages api * fix(presidio.py): safe get messages * test: add unit testing for presidio guardrails * fix(unified_guardrail.py): initial commit * fix(enkryptai.py): implement apply_guardrail to enkrypt guardrail * fix(unified_guardrail.py): support unified guardrail on input * feat(unified_guardrail.py): add post call success hook implementation allows us to just have 1 place to handle llm translation to guardrail api spec * refactor: refactor initial unified guardrail component * refactor: more refactoring * feat(responses/): add guardrails to responses api allows existing guardrails to work for new llm endpoints * docs(adding_guardrail_support.md): document new guardrail endpoint support * test: add unit tests * feat(image_generation/): add guardrail support for image generation endpoint * feat(openai/text_completion): support guardrails on `/v1/completions` API * docs: document guardrails support on new endpoints * docs: clarify when guardrails run * feat(openai/speech): add guardrail support for input * docs(rerank/): add guardrail support on input query * fix: fix ruff check |
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Roman G
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eac3cba44f
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Support for embeddings_by_type Response Format in Bedrock Cohere Embed v1 (#15707)
* feat(cohere): Enhance embedding transformation to support Bedrock's embeddings by type * test(cohere): Add unit tests for embedding transformation responses |
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Ishaan Jaffer
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60fab591db | rename test files | ||
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Krish Dholakia
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ef42461c1e
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Litellm fix GitHub action testing (#11163)
* test: add __init__.py files * refactor: rename test folder to avoid naming conflict * test: update workflows * test: update tests * test: update imports * test: update tests * test: remove unused import * ci(test-litellm.yml): add pytest retry to github workflow * test: fix test |