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| Author | SHA1 | Message | Date | |
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yuneng-jiang
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61e705a5c9
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Merge pull request #31392 from BerriAI/litellm_reconcile_main_into_staging_v2
chore(ci): reconcile main into internal_staging to unblock promotion (#31384) |
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Mateo Wang
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bc0cb24606
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fix(cost): restore per-query Gemini 3.x web search billing (#31363)
* fix(cost): restore per-query Gemini 3.x web search billing * fix(cost): adopt resolved provider in web search prefix fallback The provider-prefix fallback in _handle_web_search_cost re-resolved model_info from the model's prefix but kept the original custom_llm_provider for routing. A non-Gemini "/"-containing model whose initial lookup failed (e.g. openrouter/google/gemini-3.1-flash-lite, which carries no web search pricing) was therefore re-resolved and then fed into the vertex_ai Gemini calculator, which charged its $0.035 per_prompt default. Adopt the provider from the re-resolved model_info so the cost is always routed and priced with the model that was actually resolved. Tests now derive the expected per-query and per-prompt web search costs from the loaded cost map instead of pinning literals, and add a regression asserting a non-Gemini prefixed model with no web search pricing is not mis-charged via this fallback. * refactor(types): narrow web_search_billing_unit to a Literal Only "per_query" and "per_prompt" are meaningful for this field, so a Literal narrows the type at call sites (an unknown billing unit becomes a type error) and matches the existing Literal-typed mode field on the same TypedDict, instead of leaving it as a coarse str. * test(cost): isolate local cost map mutation behind a monkeypatch fixture The Gemini web search billing tests set LITELLM_LOCAL_MODEL_COST_MAP and reassigned litellm.model_cost without teardown, leaking that global state into later tests. Move both into a local_model_cost_map fixture using monkeypatch.setenv / monkeypatch.setattr so they auto-restore. |
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Sameer Kankute
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133da06aa3
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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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||
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Sameer Kankute
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687a62e561
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fix(cli): mint per-session agent credential on lite login (#31072)
* fix(cli): mint per-session agent credential on lite login
The `lite login` command was producing a shared UI session token that broke agent use in three ways: a $0.25 budget cap (from max_ui_session_budget) that killed agent sessions in minutes, a fixed identity "cli-jwt-token" shared across every user preventing per-session spend attribution, and auth gated behind EXPERIMENTAL_UI_LOGIN so the token was rejected on default deployments.
This fixes all three. Each login now generates a unique cli-session-{uuid} token with no per-key budget cap (enforced via shared team/user counters instead), and the decrypt path activates for any non-sk- token without requiring EXPERIMENTAL_UI_LOGIN.
* fix(cli): address review feedback on EXPERIMENTAL_UI_LOGIN gate and e2e test
Restore EXPERIMENTAL_UI_LOGIN=false as an explicit opt-out: operators who set it to false keep the old boundary; unset (new default) and true both attempt NaCl decryption, which fails closed for non-blob tokens.
In the e2e test: replace the silent Redis fallback with pytest.skip so a missing Redis instance is explicit rather than silently degrading to a directly-minted token. Write the seeded flow back as JSON (proxy reads it via json.loads on cache fetch) instead of Python repr, and build the updated flow immutably.
* fix(key-management): cap CLI session token delegation budget to team ceiling
A CLI session token intentionally carries max_budget=None to avoid a per-session LLM spend cap. The key-generation delegation check (GHSA-q775-qw9r-2r4g) previously skipped non-admin callers with max_budget=None, treating them as having unlimited delegation authority. This allowed any internal user with a lite login session to mint virtual keys with arbitrary budgets.
Adds is_session_token=True to UserAPIKeyAuth for CLI session tokens and uses the caller's team budget as the delegation ceiling in that case, so the effective limit is min(requested_budget, team.max_budget) rather than unbounded.
* chore: regenerate dashboard OpenAPI types
The is_session_token field added to UserAPIKeyAuth cascades to the
dashboard schema. Regenerate types from the updated OpenAPI spec.
* fix(key-management): block personal key budget delegation from CLI session tokens
When team_table is None (personal key, no team_id in request), the personal key
has no team-budget enforcement at request time. A session token therefore cannot
delegate any explicit max_budget for a personal key -- that would open a budget
bypass path. Block the request with a clear 400 directing the caller to use a
team_id instead.
* test(auth): add unit coverage for non-admin CLI session token production path
* fix(type-check): use model_validate in _return_user_api_key_auth_obj to fix reportArgumentType gate
UserAPIKeyAuth(**user_api_key_kwargs) spread triggers a basedpyright
reportArgumentType error for each named field in UserAPIKeyAuth because
the dict's inferred value type (str | Span | LitellmUserRoles | Unknown)
is not assignable to each field's specific type. Adding is_session_token:
bool introduced +2 more such errors, breaching the gate cap.
model_validate accepts an untyped dict without per-field argument checking,
which eliminates the +2 new errors and also ratchets down the pre-existing
333 errors at those call sites. basedpyright-code-budget.json is updated
to reflect the new lower baseline (1814, down from 1934).
* fix(type-check): ratchet down reportArgumentType baseline only
The previous lint-budget-update captured all baselines from the local
environment, raising many ceilings vs the merge-base and failing the
non-gating budget_ratchet_check. Restore staging's values for every
rule and only lower reportArgumentType (1934 -> 1814) to reflect the
reduction from switching to model_validate in _return_user_api_key_auth_obj.
* fix(auth): set max_budget on CLI session token to enforce max_ui_session_budget
CLI session tokens were missing max_budget, so _virtual_key_max_budget_check
had no per-session ceiling to enforce. Operators relying on max_ui_session_budget
could be bypassed for the full token lifetime. Mirrors the existing UI token path.
* revert(auth): remove max_ui_session_budget from CLI session token
max_ui_session_budget defaults to $0.25 and is sized for the UI chat
pane (10-min sessions). CLI sessions are 24-hour tokens for real work;
capping them at that ceiling would throttle users under their actual
user/team budget. Budget enforcement for CLI sessions is via the shared
user and team counters as originally intended.
* fix(auth): cap CLI session at max_ui_session_budget only when user and team have no budget
When neither the user nor their team has a budget configured, CLI sessions
were fully uncapped. The poll endpoint now looks up the real user and team
objects from DB; if both have no max_budget, it passes litellm.max_ui_session_budget
as the token's per-key ceiling. Users or teams that already have a budget
configured are unaffected and continue to rely on the shared counters.
* fix(auth): fix black formatting and update test mock for cli_poll_key budget lookup
The get_user_object and get_team_object async calls in cli_poll_key were
not mocked in the existing test, causing MagicMock await errors. Patch
both functions at the auth_checks module level. Also apply black formatting
to ui_sso.py which CI rejected.
* fix(auth): skip fallback budget cap when team lookup fails for cli session token
* test(auth): pin cli session budget cap to user/team budget presence
The session_max_budget fallback in cli_poll_key only applied
max_ui_session_budget when neither the user nor the resolved team had a
budget. The existing coverage exercised only the team-lookup-failure
branch. Add two regression tests: a user with a configured budget must
not receive the fallback cap, and a session with no user and no team
budget must fall back to max_ui_session_budget. Mutating either guard
out of the branch now fails these tests.
* fix: remove CLI poll session budget cap
* revert(auth): restore CLI session fallback budget cap
Bugbot autofix (
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||
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Sameer Kankute
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2b496bc7f7
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fix(proxy): restore wildcard expansion in /v1/model/info (#31444) | ||
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michelligabriele
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5a47948a3a
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fix(bedrock_guardrails): select latest user message by original role in apply_guardrail (#30482)
* fix(bedrock_guardrails): select latest user message by original role in apply_guardrail (#23476) * test(bedrock_guardrails): cover masking write-back through unified handler (#23476) * fix(bedrock_guardrails): guard masked write-back on unresolved slice, not length * chore(bedrock_guardrails): use builtin generics and extract write-back helper to satisfy strict ruff gate |
||
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Yuneng Jiang
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93aca51251
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Merge branch 'litellm_internal_staging' of github.com:BerriAI/litellm into litellm_/cranky-hamilton-21b5d0 | ||
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Yuneng Jiang
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4a6f0dbd8c
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fix(ui): size Request Logs table columns so it scrolls instead of overflowing
Tremor's Table forwards className to a wrapper div rather than the inner table element, so the table-fixed class never reached the table and it stayed table-layout: auto. Across 16 whitespace-nowrap columns that expanded the table far past the viewport Give each spend-logs column an explicit pixel size and drive the table width from getCenterTotalSize(), matching the Virtual Keys table. The shared DataTable applies this only when columns declare sizes, so the other consumers keep their existing fluid layout |
||
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Yuneng Jiang
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432d99f3ee
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test(pass-through): grant allowed_passthrough_routes so langfuse auth=true test reaches rpm path
#29256 made auth=true pass-through routes deny-by-default unless the key/team has allowed_passthrough_routes configured, but this integration test was not updated. The test key had no allowlist, so the auth=true parametrizations (rpm_limit=0 -> expect 429, rpm_limit=2 -> expect 207) now hit the 403 gate in auth before reaching the rpm/forwarding logic they mean to exercise. Grant the test key allowed_passthrough_routes for /api/public/ingestion so it clears the gate. Also removes a latent order-dependency: the case only passed locally when an earlier (auth=false) parametrization registered the route first; under worker isolation (CI xdist) it failed with 403. |
||
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tin-berri
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52e5b3ae98
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build(docker): build the Admin UI from source in a build-platform-pinned stage (#31130)
The monolith images shipped whatever UI bundle was committed to litellm/proxy/_experimental/out, so refreshing the UI for a release meant running build_ui.sh out of band and committing the regenerated bundle. Add a ui-builder stage to all three monolith Dockerfiles (root, database, non_root) that compiles the Next.js static export from this exact source and replaces the committed bundle before the final uv sync. The stage is pinned with FROM --platform=$BUILDPLATFORM so the architecture-independent static export compiles once on the native builder even in a multi-arch (linux/amd64,linux/arm64) build, rather than once per target arch under QEMU emulation. The destination is cleared before the COPY because COPY merges directories and would otherwise leave the committed bundle's content-hashed chunks behind alongside the fresh ones. build_admin_ui.sh still runs afterward so the enterprise custom-color override is preserved. The UI base image is pinned by digest to match LITELLM_BUILD_IMAGE, LITELLM_RUNTIME_IMAGE and UV_IMAGE, and .dockerignore now excludes the local .next/out so a developer's build artifacts never enter the context. |
||
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Yassin Kortam
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248389c276
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fix(router): surface clean RateLimitError on mid-stream 429 with no fallbacks (#31298)
When a streaming request hits a mid-stream 429 the streaming handler wraps it in the internal MidStreamFallbackError so the router can attempt fallbacks. With no fallbacks configured, async_function_with_fallbacks_common_utils falls through to re-raising that wrapper, which the streaming iterators caught and re-raised verbatim, so the client received MidStreamFallbackError (an internal type) rather than a clean RateLimitError (429). When the fallback path produces a MidStreamFallbackError that carries an original_exception (i.e. no fallback handled it), the iterators now raise that underlying provider exception instead of the wrapper, chained with from. Users with fallbacks are unaffected since their path never reaches this branch. Applied consistently to the chat async, chat sync, and responses streaming iterators. Resolves LIT-3503 Fixes #26015 |
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Yassin Kortam
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29c254d3d3
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fix(vertex): stop O(n^2) re-parse of accumulated Gemini stream JSON (#31297)
handle_accumulated_json_chunk re-ran json.loads on the entire accumulated buffer after every fragment. For a streaming response fragmented across many chunks that is O(n^2) total work in a single GIL-holding C call, so a large enough Gemini response freezes the asyncio event loop for seconds, liveness probes time out, and the proxy pod gets killed and restarted. A complete Gemini stream value is a JSON object or array, so the buffer can only become parseable once its last non-whitespace byte can close one. Gate the json.loads attempt on that, which makes the common fragmented-response case parse roughly once instead of once per fragment. An 8MB payload drops from a 6.9s event-loop freeze to ~0.3s with identical parsed output. Resolves LIT-3503 Fixes #26181 |
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Sameer Kankute
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e5da5a3b6d
|
fix(proxy): skip model override when response has no model field (#31183)
* fix(proxy): skip OpenAI model override for search responses Search responses omit a model field by spec but still set model on the request for routing, which caused noisy errors and dict injection. * fix(proxy): drop redundant search-specific model override skip The silent return for responses without a model field already covers SearchResponse objects; remove the extra search type check. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(proxy): skip model override for dict responses without model key Dict-shaped responses (e.g. search) must not get a spurious model field injected when they never had one; only override when model is present. Co-authored-by: Cursor <cursoragent@cursor.com> * test(proxy): cover swallowed setattr failure in model override --------- Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
||
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Sameer Kankute
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062d8ceeed
|
fix(vertex_ai): prevent stale Vertex bearer token causing /v1/messages 401 after token expiry (#31276)
* fix(vertex_ai): prevent stale Vertex bearer token causing /v1/messages 401 after token expiry Router shallow-copies litellm_params so extra_headers is a shared reference. The chat/completions path was calling headers.update() on that shared dict, persisting the Vertex OAuth bearer. After ~1 h the token expired and /v1/messages kept reusing it (skipping refresh due to Authorization-already-present guard). - Build a new headers dict in the Claude partner-models completion path instead of mutating the shared extra_headers object. - Always call _ensure_access_token() in validate_anthropic_messages_environment regardless of an existing Authorization header; the token cache makes this cheap. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(vertex_ai): copy headers in validate_anthropic_messages_environment to prevent shared-dict mutation Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: Cursor <cursoragent@cursor.com> |
||
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Sameer Kankute
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7eacdd5258
|
chore: litellm oss staging 250626 (#31305)
* fix(anthropic): support Bearer auth for custom api_base endpoints (Fixes #30926) * style: format common_utils.py with black * fix(anthropic): extract api_base from litellm_params in batches/files validate_environment * fix(anthropic): scope Bearer key check to custom api_base endpoints * fix(streaming): reset Anthropic message_start cursor (output_tokens=1) when no message_delta arrives The Anthropic streaming protocol emits `message_start.usage.output_tokens=1` as a placeholder cursor; the real cumulative output count only arrives in the final `message_delta` event. When a stream is cancelled before `message_delta` lands (common for thinking models on long-tail prompts), ChunkProcessor._calculate_usage_per_chunk's last-wins accumulator left completion_tokens stuck at 1. Because 1 is truthy, the `completion_tokens or token_counter(text=...)` fallback in calculate_usage() never fired, and requests were billed for 1 output token even when several thousand tokens of text had actually streamed. Fix: track whether any chunk's completion_tokens exceeded 1 (saw_non_cursor_completion). If the only update we saw was the cursor, reset completion_tokens to 0 so the text-based fallback estimates from the real completion content. Legitimate 1-token completions (model returns "Yes." etc.) are unaffected in practice — token_counter on a 1-token completion_output also yields ~1, so billing stays approximately correct. Tests: - TestAnthropicCursorBug (6 cases) — pins the post-fix behavior - TestNonAnthropicStreamingIntact (2 cases) — guards against regression on providers without the cursor pattern All 8 new tests pass; 9 existing streaming_chunk_builder_utils tests still pass. * fix(streaming): scope cursor reset to anthropic provider + recognize message_delta arrival Addresses both Greptile P2 threads on PR #30420: CLASS A — Anthropic-specific heuristic was applied globally ============================================================ The `completion_tokens == 1 and not saw_non_cursor_completion` reset lived in provider-neutral `streaming_chunk_builder_utils.py`. Any non-Anthropic provider that legitimately reports completion_tokens=1 in a single usage chunk (perfectly normal for short OpenAI / Bedrock / Vertex single-token replies with stream_options.include_usage=true) would have its value silently rewritten to 0 and re-billed via token_counter — producing a different number than what the provider actually charged. Fix: gate the reset on `custom_llm_provider == "anthropic"`, resolved from the first chunk's `_hidden_params` (the same field set by streaming_handler.py:722 on the live path). Unknown / missing provider is treated as non-Anthropic and skips the reset, so newer providers and custom plugins are also safe by default. CLASS B — `saw_non_cursor_completion` missed legitimate single-token replies ============================================================ Previous condition was `usage_chunk_dict["completion_tokens"] > 1`, which never fires for an Anthropic stream where the model legitimately emits exactly one output token (e.g., "Yes."). Anthropic still sends message_start (output_tokens=1, the cursor) AND message_delta (output_tokens=1, the real value) — same value, but two distinct usage events. The old check couldn't tell that apart from a cancelled stream where only message_start landed. Fix: track `completion_usage_updates` and flip `saw_non_cursor_completion` when EITHER (1) the value exceeds 1 (definitely not a placeholder), OR (2) we've seen >=2 completion-bearing usage events (positive evidence that message_delta arrived). Cancelled cursor-only streams still have exactly one event and still hit the reset; cache chunks with completion_tokens=0 don't count toward the threshold. Tests ============================================================ - _make_chunk now sets `_hidden_params["custom_llm_provider"]` (default "anthropic") so the gate is exercised by every existing test — none of them needed assertion changes besides the legitimate-single- token case, which now expects exactly 1 (was a fuzzy 0..3 range). - New: test_anthropic_cache_only_chunks_after_message_start_still_resets - New: test_non_anthropic_provider_completion_tokens_one_not_reset - New: test_unknown_provider_completion_tokens_one_not_reset 11/11 tests pass. * chore: add Co-authored-by trailer for attribution Co-authored-by: songkuan-zheng <songkuan-zheng@users.noreply.github.com> * fix(anthropic): preserve messages cache usage * style(anthropic): format messages cache usage helper * fix(anthropic): accept integral float cache token counts Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * fix(anthropic): accept integral float cache token counts * test(anthropic): cover cache usage edge cases * fix(gemini): preserve thoughtSignature for server-side tool responses When Gemini API returns toolCall and toolResponse parts, they might have different thoughtSignatures. Previously, LiteLLM merged them into a single dict, overwriting the response's thoughtSignature with the call's. This fix extracts them separately and re-injects them correctly. TAG=agy CONV=755b21d0-3200-40bc-bd1a-bb58a378a9a6 * fix(gemini): address PR comments on thoughtSignature handling - Fix orphan-response thoughtSignature regression by copying thought_signature to response_thought_signature - Add missing assertions in existing tests - Add new unit tests for orphan-response signature handling TAG=agy CONV=755b21d0-3200-40bc-bd1a-bb58a378a9a6 * feat(mcp): include server alias and server_id in mcp_info response - Add alias and server_id fields to mcp_info object in /mcp-rest/tools/list endpoint - Update rest_endpoints.py to surface alias from server config - Add test coverage in test_mcp_server.py and test_rest_endpoints.py Fixes #31015 * fix(proxy): reject non-finite spend via validate_finite_spend A NaN/-inf spend would bypass spend >= max_budget enforcement. Add a shared finite-value guard, defined above the litellm.proxy.* imports to avoid the module-level cyclic-import warning. * fix(proxy): require admin for any /key/update spend, reject non-finite Gate the admin check on the presence of `spend` (not a value diff): the DB spend lags the live cross-pod counter, so an "unchanged" spend on the non-admin path let a key owner / team member overwrite the live counter below real usage. Also reject NaN/+-inf spend before the DB write. * fix(proxy): invalidate spend counter on /user/update spend change A direct spend change on /user/update wrote the DB row but left the warm cross-pod counter at the stale value, so enforcement kept reading the old spend. Invalidate spend:user:{user_id} after the write (reseed-from-DB), and reject non-finite spend before the write. * fix(cache): route Bedrock semantic-cache sync embedding through the Router (#28244) The semantic cache's embedding model is a proxy Router alias whose AWS credentials (aws_role_name, aws_session_name) live only in the Router deployment's litellm_params. The sync embedding paths called litellm.embedding() directly, bypassing the Router, so they could neither resolve the alias nor assume the configured role; cross-account Bedrock semantic caching failed with "bedrock:InvokeModel is not authorized". On Redis this surfaced at proxy startup because redisvl's CustomTextVectorizer eagerly fires a dimension-probe embedding during cache construction, while llm_router is still None. Fix A: make the sync paths mirror the already-correct async paths. A shared, dependency-injected helper (litellm/caching/_embedding_router.py) decides whether to route through llm_router.embedding(...) when the model is a Router deployment, else fall back to direct litellm.embedding(...). Redis and qdrant sync set_cache/get_cache now precompute the embedding and pass vector= to the backend, exactly as the async astore/acheck already do. Both async _get_async_embedding methods are unified onto the same helper and now forward the caller's full metadata instead of a hand-picked subset. Fix B (Redis only): defer redisvl index construction from __init__ into a lazy, memoized llmcache property, so the dimension-probe embedding fires on first cache use, after llm_router is wired. A failed build is not memoized, so a transient outage recovers on the next request. Known limitation: resolve_embedding_router gates on an exact model-name match (same as the shipped async path); wildcard/alias/team-public routes still fall back to direct embedding. Tracked as a follow-up. * fix(cache): harden embedding-router and shrink Any surface (review) Address review feedback on the semantic-cache aws-role fix (#28244): - resolve_embedding_router now skips deployment entries missing model_name instead of raising KeyError on a malformed model_list (Greptile P2); add a regression test that fails on the old direct-key access. - Replace the `**kwargs: Any` passthrough on the four cache _get_embedding / _get_async_embedding helpers with an explicit, typed `metadata: Optional[Dict[str, Any]] = None` parameter. The helpers only ever consumed kwargs["metadata"], so this is behavior-preserving, makes the forwarded field obvious at the call site, and removes three bare-Any annotations (keeps the strict-rule ANN401 budget within ceiling). - Note in _build_llmcache that redisvl's dimension-probe embedding adds one extra billable embedding on the first cache request (Greptile P2). * fix(bedrock_mantle): correct responses routing for openai.gpt-5.x models Dashboard Test Connection for bedrock_mantle/openai.gpt-5.4 and openai.gpt-5.5 was failing with maximum recursion depth errors and "model does not exist" Route detection in the bedrock provider matched route tokens by plain substring, so the bedrock_mantle/ prefix was mistaken for the mantle/ invoke route and the body model was rewritten to bedrock_openai.gpt-5.5; route tokens now only match at a path-segment boundary so the bare model name is preserved A responses-mode model whose provider has no responses config bounced forever between the responses API and chat completions; the responses to completion fallback now tags its call so completion() does not bridge back, breaking the loop The Test Connection endpoint hardcoded the test mode to chat, which disabled mode auto-detection for responses-only models; the default is now None so the mode is detected from model capabilities acompletion() now drops a duplicate acompletion kwarg before building the partial and treats model_info=None as an empty dict to avoid a NoneType crash * test(bedrock_mantle): cover route guard and bridge flag; fix reportArgumentType regression Adds the regression coverage codecov flagged on the two responses to completion bridge guard lines and the bedrock route-prefix helper. The handler tests drive both the sync and async fallback paths with litellm.completion and litellm.acompletion mocked, and assert the forwarded kwargs carry _skip_responses_api_bridge=True, so dropping either flag line fails the suite. The common_utils tests assert that bedrock_mantle/openai.gpt-5.x no longer resolves to the mantle route while the genuine mantle/ and bedrock/mantle/ ids still do, exercising both branches of _model_has_route_prefix. Also aligns update_messages_with_model_file_ids model_id to Optional[str], matching its Responses API sibling, so the defensive model_info fallback no longer introduces a new reportArgumentType in completion(); the file-id lookup narrows model_id before the dict get * chore(ui): sync generated OpenAPI types for optional test_connection mode The test_model_connection mode body param default changed from chat to None so the mode is auto-detected from model capabilities, which makes the field optional in the proxy OpenAPI spec. Regenerate the committed schema so the dashboard types match: mode becomes optional and the description and default JSDoc follow the spec, keeping the Check UI API Types Sync gate green * refactor(bedrock): match all explicit route prefixes at path-segment boundary Migrates the remaining substring route checks to the existing _model_has_route_prefix helper so every explicit route token matches only as a leading path segment, consistent with get_bedrock_route and the mantle route. Covers _explicit_converse_route, _explicit_claude_platform_route, _explicit_invoke_route, _explicit_agent_route, _explicit_agentcore_route, _explicit_converse_like_route, _explicit_async_invoke_route and _explicit_openai_route. This also stops invoke/ from substring-matching async_invoke/. Route precedence and order are unchanged, and a note on the segment invariant is added to the helper docstring * test(bedrock): cover explicit route prefix segment matching Exercises all eight migrated _explicit_*_route helpers (converse, converse_like, invoke, async_invoke, agent, agentcore, claude_platform, openai) directly: each matches its token as a leading path segment and rejects the token glued to a preceding segment, so reverting any method to the old substring check fails the suite. Also asserts invoke/ no longer matches async_invoke/ models, the concrete improvement of the segment-boundary migration * test(proxy): assert negative spend is allowed (one-time grant use-case) Negative spend is intentionally permitted so admins can grant extra allowance for the current budget period only, without raising the recurring budget ceiling. Cover it explicitly in validate_finite_spend and via the /user/update invalidation test. * fix(google_genai): forward native generateContent top-level fields Google's native generateContent REST body carries safetySettings, toolConfig, cachedContent and labels at the top level as siblings of generationConfig. The proxy's :generateContent endpoint spread them into agenerate_content as loose kwargs and then dropped them, so callers had to wrap them in extra_body for them to take effect; safetySettings, for instance, was silently ignored The provider config now exposes the native top-level field names and setup_generate_content_call collects whichever are present, merging them into the outgoing request body through the existing extra_body merge so they reach Google verbatim. An explicit extra_body still wins on conflict. The sync generate_content_stream path now also forwards systemInstruction, matching the other three entry points Fixes #12671 Claude-Session: https://claude.ai/code/session_016MFtMXokCjT8u6mvyASudK * fix(proxy): resolve env refs for DB-stored models * fix(proxy): restrict DB env ref resolution * fix(proxy): block team DB env ref resolution * fix(lint): resolve ANN401/UP045/C901 strict-gate violations - Replace Optional[X] with X | None (UP045) in 8 files - Replace Any return/param types with concrete types or object (ANN401) - Extract _make_api_key_auth_header helper to reduce get_anthropic_headers complexity below C901 threshold (17 → 14) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(anthropic): preserve x-api-key for custom endpoints; opt-in Bearer via prefix Users who pass a key already prefixed with "Bearer " get Authorization: Bearer. All other keys continue to use x-api-key, preserving backward compatibility with custom api_base endpoints that expect x-api-key rather than Authorization. Also consolidates get_auth_header to reuse _make_api_key_auth_header helper, eliminating the duplicated custom-endpoint routing logic. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * revert(anthropic): restore Bearer routing for non-sk-ant- keys on custom api_base The backwards-compat change broke existing tests that verify the intentional Bearer-for-custom-base behavior (Fixes #30926). Restore original logic while keeping the _make_api_key_auth_header helper for code deduplication. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(anthropic): gate Bearer-for-custom-base behind use_bearer_for_custom_base flag Previously the auth-header switch from x-api-key to Authorization: Bearer applied unconditionally for non-sk-ant- keys on a custom api_base, silently breaking existing deployments that proxied to gateways expecting x-api-key. Introduce use_bearer_for_custom_base: bool = False on _make_api_key_auth_header, get_anthropic_headers, and get_auth_header. validate_environment reads it from litellm_params so callers can opt in per-model without any API surface change. Tests updated to pass use_bearer_for_custom_base=True where Bearer behavior is asserted. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(redis): apply namespace prefix in delete_cache and async_delete_cache (#29981) DEL was the only Redis cache operation that skipped check_and_fix_namespace, so it targeted the raw SHA256 hash (e.g. 3997c4...) rather than the namespaced key (litellm:3997c4...). This caused two problems: a Redis NOPERM error on deployments with an ACL restricting DEL to the litellm:* pattern, and a silent no-op on all other deployments since the un-prefixed key was never stored. * style(anthropic): reformat common_utils.py with Black (--target-version py312) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix: preserve cache metadata and spend counters * style: apply ruff format to streaming_iterator.py * refactor: reduce complexity of usage/spend helpers to satisfy strict ruff gate Extract Anthropic message_start cursor reset into _reset_anthropic_cursor_completion_tokens and the cross-pod spend-counter invalidation into _invalidate_user_spend_counter_if_changed, keeping both _calculate_usage_per_chunk and _update_single_user_helper under the max-complexity ceiling. Use builtin generics in the new signatures so no new UP006 violations are introduced. Behavior unchanged. --------- Co-authored-by: rupak-eng <rupakji99@gmail.com> Co-authored-by: songkuan-zheng <252822057+songkuan-zheng@users.noreply.github.com> Co-authored-by: songkuan-zheng <songkuan-zheng@users.noreply.github.com> Co-authored-by: Kannan Priyadharshan <kpd2204@gmail.com> Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Co-authored-by: Marco Georgaklis <mgeorgaklis@google.com> Co-authored-by: Anjaiah Methuku <anjaiahspr@gmail.com> Co-authored-by: Andrii Butko <booandrew23@gmail.com> Co-authored-by: Kent <kingdooo@gmail.com> Co-authored-by: kunal2002 <k.nayyar2002@gmail.com> Co-authored-by: Ali Khan <alirazakhan.offi@gmail.com> Co-authored-by: jesco-absolut <team@srswti.com> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Co-authored-by: Matt Hill <mhill@dataminr.com> Co-authored-by: Cursor Agent <cursoragent@cursor.com> |
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yucheng-berri
|
7680cedf42
|
test(logging): regression coverage for streaming /v1/messages OpenAI Responses spend logs (#31388)
* test(logging): cover streaming /v1/messages OpenAI Responses spend logs The #28595 fix added unit tests that call _handle_anthropic_messages_response_logging directly, but nothing exercises the streaming wiring that actually regressed: a streaming /v1/messages call cross-routed to the OpenAI Responses backend whose success handler took the no-op async_log_stream_event path and dropped the SpendLogs row. Add an end-to-end test that drives litellm.anthropic_messages(stream=True) with a mocked upstream Responses SSE and asserts async_log_success_event fires with non-zero cost and call_type anthropic_messages, plus a key-gated live counterpart. * test(logging): exercise stream deltas and assert single success log Address review on the streaming bridge regression test: emit output_item.added plus text deltas before response.completed so it covers mid-stream delta handling rather than only end-of-stream success logging, assert at least one content_block_delta surfaces, restore litellm.callbacks via monkeypatch instead of leaking global state, and assert async_log_success_event fires exactly once. * test(logging): drop live network test from mock-only suite Greptile flagged that tests/test_litellm only permits mock tests; network calls belong in tests/e2e. Remove the key-gated live counterpart and keep the deterministic mocked test as the regression guard. The live verification stays in the PR description as the proof of fix. |
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Mateo Wang
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1a4009caf4
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chore: remove CI section (#31376)
We now require all checks |
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Mateo Wang
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6e3540856c
|
fix(vertex): preserve Gemini Embedding 2 usageMetadata for cost tracking (#31354)
* fix(vertex): preserve Gemini Embedding 2 usageMetadata for cost tracking * style(vertex): apply ruff format to batch_embed_content_transformation * fix(vertex): bill files/ image refs in Gemini embedContent at per-image rate Resolved files/... references whose mime type is an image were not detected by _is_image_element, so image_count stayed 0 and generic_cost_per_token fell back to the text token rate instead of input_cost_per_image. Thread the resolved_files mapping into the usage builder so resolved image references are counted and billed per image. Also modernize the _flatten_input return annotation to satisfy the ruff UP006 strict gate. * fix(vertex): bill Gemini embedding audio per-second and stop video+audio double-billing Audio-only embedContent responses set audio_tokens, but generic_cost_per_token only charges audio via input_cost_per_audio_token. gemini-embedding-2 prices audio via input_cost_per_audio_per_second, so spend stayed at $0. Plumb a new audio_length_seconds field through PromptTokensDetailsWrapper, parse it in _parse_prompt_tokens_details, and bill it from _calculate_input_cost. The vertex embedding transformation derives audio_length_seconds from audio_tokens using the documented 32 tokens/sec Gemini rate. The 1-token text floor that protects video billing only fired when no other modality was billable, but audio presence flipped that flag, leaving text_tokens at zero for video+audio responses. generic_cost_per_token then rewrote text_tokens to prompt_tokens minus audio_tokens (the video token count), charging video tokens as text on top of the per-second video cost. The rewrite trigger is text_tokens == 0 and image_count == 0; align the floor with that trigger and ignore audio_tokens. --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> |
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Shivam Rawat
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eb15fe667d |
refactor(passthrough): address multipart review feedback
Build form_data_dict in one pass with groupby instead of rescanning form_items per field name, and assert on the files list directly in the boundary regression test so repeated field names are not collapsed by dict(). Co-authored-by: Cursor <cursoragent@cursor.com> |
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Yuneng Jiang
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e25f5a3309
|
Merge main into litellm_internal_staging (reconcile OCR hotfix history)
Records main as an ancestor of internal_staging so the staging->main promotion (#31384) merges cleanly. Resolves in staging's favor; changes 0 files (staging already supersedes every main-side hotfix). MUST be merged as a real merge commit (not squash/rebase) or the link to main is lost. |
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yuneng-jiang
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997c7a2676
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chore(ci): main into internal_staging (reconcile OCR hotfix history; unblocks #31384) (#31390)
* 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 * feat: make rust OCR async-first * docs: clarify rust provider call flow * docs: clarify OCR provider transform contract * docs: note Tokio route contract * fix: address OCR bridge review comments * docs: bound rust OCR HTTP exception * feat: generate rust providers from registry * chore: move rust provider registry into core * chore: source rust providers from endpoint registry * fix: satisfy OCR lint budget * fix: reduce OCR basedpyright argument errors * fix: address OCR greptile feedback * fix: align rust OCR request preparation * fix: resolve OCR CodeQL alerts * fix: avoid duplicate Rust OCR authorization header * ci: rerun CircleCI --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: Yassin Kortam <yassin@berri.ai> Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Co-authored-by: Krrish Dholakia <krrish+github@berri.ai> Co-authored-by: Ishaan Jaff <ishaan@berri.ai> Co-authored-by: ishaan-berri <155045088+ishaan-berri@users.noreply.github.com> |
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Shivam Rawat
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ef0785881a |
fix(passthrough): forward all multipart files with repeated field names
Passthrough multipart uploads used form.items() and a files dict, so only the last file under a repeated field name reached the upstream. Read multi_items() and send httpx a list of file tuples instead. Co-authored-by: Cursor <cursoragent@cursor.com> |
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ryan-crabbe-berri
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f16af8853b
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feat(mcp): opt-in least-privilege default for team key MCP access (#31380)
* feat(mcp): add require_key_mcp_access_defined to stop keys inheriting team MCP servers By default a virtual key that grants no MCP servers of its own inherits its team's full MCP server list. The new general_settings flag require_key_mcp_access_defined (default false) flips this so the team list acts purely as a ceiling: a key reaches only the servers it grants explicitly (or via an access group), and inherits none. This mirrors the existing require_end_user_mcp_access_defined setting. The default is unchanged, so existing deployments keep today's behavior until they opt in. The no-mcp-servers sentinel and key access-group grants are unaffected. * docs(mcp): note require_key_mcp_access_defined effect in resolver docstring |
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ishaan-berri
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bdafc9a008
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feat(ocr): thin Rust OCR Python bridge (#31368)
* feat(ocr): thin Rust OCR Python bridge * refactor(rust): group provider routing helpers |
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Mateo Wang
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6cc9ea2538
|
fix(cost-map): retarget mistral-medium-latest to Medium 3.5 and add date-pinned aliases (#31373)
* fix(cost-map): retarget mistral-medium-latest to Medium 3.5 and add date-pinned aliases Mistral repointed the rolling mistral-medium-latest alias from Medium 3.1 to Medium 3.5, but the static cost map still carried Medium 3.1 specs, showing wrong pricing/context in the model hub and undercharging spend by about 3.75x (LIT-3883). Update mistral/mistral-medium-latest to Medium 3.5 ($1.50/$7.50 per 1M, 256K context, reasoning + vision), add the bare date-pinned aliases mistral/mistral-medium-2604 (Medium 3.5) and mistral/mistral-medium-2508 (Medium 3.1) that match Mistral's real API model ids, and add supports_reasoning to mistral/mistral-medium-3-5. Apply every change to both model_prices_and_context_window.json and the bundled litellm/model_prices_and_context_window_backup.json so the two stay in sync, and extend the regression tests to lock the resolved get_model_info values and the main/backup parity for all touched models. * test(cost-map): force local cost map in mistral-medium-latest resolution test get_model_info reads litellm.model_cost, which is fetched from the remote main branch at import time when LITELLM_LOCAL_MODEL_COST_MAP is unset. Until this PR lands on main, that remote map still carries the pre-merge Medium 3.1 pricing, so the assertion was only passing when the remote fetch happened to fail and fell back to the bundled backup. Force the local cost map (the same fixture pattern the other get_model_info tests use) so the alias resolution is verified deterministically against the in-repo file. |
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yuneng-jiang
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97008bad29
|
chore(deps): bump deps (#31377)
* bump: version 0.1.43 → 0.1.44 * uv lock |
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yucheng-berri
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9203488578
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feat(spend): store litellm_call_id on spend logs for DB-to-trace correlation (#31344)
* feat(spend): store litellm_call_id on spend logs for DB-to-trace correlation Successful spend logs keyed request_id to the provider response id while tracing uses x-litellm-call-id, so a DB row could not be correlated with its trace; this only worked for failures, where request_id already fell back to the call id. Add a nullable litellm_call_id column to LiteLLM_SpendLogs, populate it in get_logging_payload, and surface it in the spend logs read endpoints so correlation works both directions for successful calls Fixes LIT-3868 * chore: sync schema.prisma copies from root * test(spend): cover cache-hit and missing-response-id paths for litellm_call_id Lock the intended behavior surfaced in review: on a cache hit request_id gets the uniqueness suffix while litellm_call_id stays the raw call id, and when the provider returns no id request_id falls back to the call id so both columns match. Both assertions fail when the populate line is reverted * test(spend): ignore litellm_call_id in spend logs payload comparisons get_logging_payload now always writes litellm_call_id, so the full-payload comparisons in test_spend_management_endpoints.py saw an unexpected key and failed. litellm_call_id is a per-request runtime uuid like request_id, which is already ignored, so add it to ignored_keys * test(logging): ignore litellm_call_id in gcs pubsub spend logs comparison The gcs pubsub spend logs payload comparison flags any key present in the actual payload but absent from the golden snapshot. get_logging_payload now always emits litellm_call_id, a per-request runtime uuid like request_id which is already ignored, so add it to ignored_keys * refactor(spend): store litellm_call_id in spend log metadata, drop column Switch DB-to-trace correlation off a dedicated column and onto the existing metadata JSON, avoiding a schema migration entirely. litellm_call_id is now written into spend log metadata (already selected and re-hydrated on the read paths) instead of a new LiteLLM_SpendLogs column, so the three schema.prisma copies and the migration are reverted and the read SELECTs go back to their original form. Correlation is queryable via metadata->>'litellm_call_id' Trade-off: an unindexed JSON lookup rather than an indexed column; acceptable for this use case and removes all migration risk * refactor(spend): thread litellm_call_id into _get_spend_logs_metadata Set litellm_call_id beside the other computed metadata values inside _get_spend_logs_metadata rather than mutating clean_metadata back in the caller, matching how applied_guardrails, cost_breakdown and the rest are threaded. No behavior change; the value still comes from kwargs with a litellm_params fallback --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> |
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Yuneng Jiang
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2c8cd6ad4d
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uv lock | ||
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yucheng-berri
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71ee1a852a
|
fix(proxy/client): redact api key from key/info client error messages (#31342)
* fix(proxy/client): redact api key from key/info client error messages
The keys management client builds GET /key/info?key=<key> and lets the
requests HTTPError propagate. str(HTTPError) renders the failing request URL
verbatim ("... for url: .../key/info?key=sk-..."), so any caller that logs the
exception leaks the full key; the 401 branch leaked the same way through
UnauthorizedError(str(orig_exception))
Redact both branches with the existing redact_secrets helper so the
secret-bearing query param is scrubbed to ?REDACTED while the status code,
reason, and response object are preserved. Server-side responses already mask
the key, so this closes the remaining client-side surface
* fix: preserve key info unauthorized response
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
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Yuneng Jiang
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0669d1b3cb
|
bump: version 0.1.43 → 0.1.44 | ||
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Mateo Wang
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c5833a9d70
|
fix: inverted rule in CLAUDE.md (#31370) | ||
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Mateo Wang
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e0e920d80e
|
feat(mistral): support Mistral OCR 4 (mistral-ocr-4-0) (#31353)
* feat(mistral): support Mistral OCR 4 (mistral-ocr-4-0) Add the mistral/mistral-ocr-4-0 model to the cost map and reprice mistral/mistral-ocr-latest, which now resolves to OCR 4 server-side, at $4 / 1000 pages. Add the include_blocks param so callers can request OCR 4's paragraph-level bounding boxes and typed content blocks. OCR 4's new per-page response fields (blocks, confidence_scores, tables, hyperlinks, header, footer) already pass through transform_ocr_response via the extra="allow" config on OCRPage; add a regression test pinning that behavior alongside cost and param coverage. * fix(mistral): revert unverified OCR 4 annotation_cost_per_page bump Mistral's published OCR 4 pricing lists $4/1000 pages for the API and no separate annotation rate; the $5/1000 figure is the distinct Document AI (Studio) tier. The earlier 0.003 -> 0.005 bump on annotation_cost_per_page had no cited source, and ocr_cost() never reads that field (it bills off ocr_cost_per_page), so the value is documentation-only. Revert annotation_cost_per_page to the existing 0.003 convention for both mistral-ocr-latest and mistral-ocr-4-0, keeping only the verified, tested ocr_cost_per_page: 0.004 change. * fix(mistral): set OCR 4 annotation_cost_per_page to verified $5/1000 rate Verified against Mistral's authoritative sources: the pricing page, the OCR 4 announcement, and the ocr-4-0 model card all list OCR 4 at $4/1000 pages for basic OCR and $5/1000 for annotated pages (Document AI). The $5/1000 figure is the annotated-pages rate, which is exactly what annotation_cost_per_page encodes, mirroring the original OCR entry's 0.001 basic / 0.003 annotated split. Restore annotation_cost_per_page to 0.005 for mistral-ocr-latest and mistral-ocr-4-0; the earlier revert to 0.003 was based on an incomplete reading that treated Document AI as a separate product. ocr_cost_per_page stays 0.004, which is the value billed by ocr_cost(). * fix(mistral-rust): include_blocks in Rust OCR supported params --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> |
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Mateo Wang
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b7f28bd89f
|
feat(aiml): add openai/gpt-image-2 image model (#31323)
* feat(aiml): add openai/gpt-image-2 image model Adds aiml/openai/gpt-image-2 to the cost map and teaches AimlImageGenerationConfig to route OpenAI-style image models through the upstream OpenAI request schema instead of the AI/ML flux schema. Without this, size, n, and response_format would be remapped to image_size/num_images/output_format, which the gpt-image-2 endpoint on api.aimlapi.com does not accept. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * chore(aiml): note gpt-image-2 flat-rate pricing basis; apply ruff format Documents in the cost-map notes that output_cost_per_image is AI/ML's published medium-quality rate, billed as a flat per-image price like the other aiml image entries. Reformats the touched files under the repo's ruff formatter (migrated from black in #31317). * fix(aiml): drop /v1/images/edits from gpt-image-2 supported_endpoints LiteLLM only implements an image generation transformer for AIML, so listing /v1/images/edits overclaimed support. Align with every other aiml image entry, which lists only /v1/images/generations. * style(aiml): format transformation.py at line-length 88 The repo formats litellm/ with ruff at line-length 88 (Makefile/CI call sites), while ruff.toml's global 120 only governs E501/import sorting. Reformat the transformer to 88 so make format-check / CI lint pass, and restore the test files to their original layout since tests/ is not part of the auto-formatted tree. --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> |
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yucheng-berri
|
e64cec5add
|
ci(image-scan): add Grype image scan for OS + library CVEs (#31151)
* ci(image-scan): add Grype image scan for OS + library CVEs Builds each of the 6 Dockerfiles via a matrix and scans the resulting image with Grype (pinned v0.114.0, sha256 verified), failing on fixable HIGH or CRITICAL across both OS/apk and language packages. This catches the layer osv-scan is structurally blind to (Wolfi/apk OS packages and vendored deps like prisma's node engine), which is the structural reason the openssl CVE slipped past CI and a customer's image scanner flagged it. Skipped on fork PRs so an outside contributor cannot run arbitrary code on our hosted runner via a malicious Dockerfile RUN line. The same pattern is used by guard-fork-dependencies.yml. Grype runs as a pinned binary with a verified checksum, so there is no mutable-tag GitHub Action in the dependency chain and no vendor credentials in the scan job. The job uses read-only contents permissions and an empty top-level permissions block. * ci(image-scan): scan only Dockerfile.non_root (rootless target) All Dockerfile variants share the same wolfi base and apk set today, so a single scan of Dockerfile.non_root gives the same OS-layer coverage at one-sixth the build cost. Dockerfile.non_root is the rootless variant we ship (USER 65534), so the scan tracks the image customers actually run. Matrix-scan if the variants ever diverge. * ci: retrigger checks (proxy_pass_through_endpoint_tests flaked on prior run) |
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Mateo Wang
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0a92734691
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fix: clarify further that customer names shouldn't be made public (#31365)
* fix: make it clearer that customer names should not be put in PR descriptions * fix: revise the policy to be stricter |
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milan-berri
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7ffce15766
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Add GA pricing for gemini-3-pro-image and gemini-3.1-flash-image. (#30022)
Fixes #29794. Adds bare, gemini/, and vertex_ai/ entries copied from preview models so proxy cost tracking works for GA model names. Co-authored-by: Cursor <cursoragent@cursor.com> |
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Yassin Kortam
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01035499da
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fix(cache): apply Redis namespace to all key operations (#31288)
The namespace configured under cache_params was only applied to get/set/
increment paths. Operations that take keys through other code paths (the Lua
scripts registered via async_register_script, delete, scan_iter, rpush, lpop,
get_ttl, and the sync increment_cache) hit raw keys. With a namespace set, the
rate limiter ({key}:tokens/requests/window), pod-lock release, and budget
limiters wrote keys outside the configured prefix, breaking multi-tenant key
isolation and leaving those operations reading keys the namespaced writes never
created.
check_and_fix_namespace is now applied uniformly across every key-taking
RedisCache operation. It is a no-op when no namespace is configured, so
deployments without a namespace are unaffected. The prefix is prepended ahead of
any {hash-tag}, so Redis Cluster slotting is preserved.
Resolves LIT-3374
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ishaan-berri
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62f93a3343
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feat: add Rust OCR providers (#31272)
* feat: port OCR providers to Rust gateway * chore(deps): update langgraph checkpoint lock * ci: scope ruff format check to changed files * ci: fix OCR lint and patch coverage * fix(ocr): block mapped IPv6 fetch targets * test(ocr): include rust bridge coverage in OCR shard * ci: rerun responses shard |
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Mateo Wang
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92d0788da2
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chore(lint): widen ANN slack to 10% of baseline and drop PLR0913 from the strict gate (#31335)
* chore(lint): widen ruff budget slack to 10% of baseline for high-volume ANN rules and PLR0913 Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * chore(lint): drop PLR0913 from strict gate to roll out rules gradually Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * fix(lint): ratchet-guard rising baselines even when slack is cut to mask them Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> |
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ishaan-berri
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a2d04ccdbb
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ci: harden cargo fetches during maturin builds (#31348) | ||
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Mateo Wang
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f98e935504
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chore: gitignore rust bridge build artifacts (#31349)
Ignore the compiled, platform-specific Rust extension output (litellm/rust_bridge/_native*.so/.pyd) and the litellm-rust/target/ build dir so local maturin/cargo builds don't show up as untracked files. Also drop the two stale self-referential .gitignore entries; .gitignore is tracked, so ignoring it did nothing except add confusion. Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> |
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ishaan-berri
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d8ef1da49d
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feat: package Rust OCR bridge in LiteLLM wheel (#31267)
* feat: package rust ocr bridge in litellm wheel * Install Rust in Windows CircleCI job * Address Rust wheel review feedback * Pin Windows rustup installer hash |
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yucheng-berri
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a545c493d7
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fix(otel): hashable scope for _emit_once when guardrail_mode is list (#31262)
* fix(otel): hashable scope for _emit_once when guardrail_mode is list `_emit_once` keys `spans_logged` by `(class, id, *scope)`. When a guardrail entry's `guardrail_mode` arrives as a `List[GuardrailEventHooks]` (the shape Presidio expands to with `output_parse_pii: true`, and the shape `event_hook` carries for any `mode: [...]` in config), the tuple contains a list and `spans_logged.get(dedupe_key)` raises `TypeError: unhashable type: 'list'`. On the post-call path this fires inside the logging callback and is swallowed; the request returns 200 but the OTEL `guardrail` span is silently dropped. On the blocking path the same error surfaces as HTTP 500. Adds `_freeze_for_dedupe`, a small recursive normalizer that turns lists and tuples into tuples, sets into frozensets, dicts into frozensets of `(key, value)` pairs, and falls back to `repr` for arbitrary unhashables. Applied inside `_emit_once` before the dict lookup, so all three callsites are protected without touching the guardrail-specific callsite. Helper assumes acyclic input; `guardrail_mode` values are built fresh from config (str enums, lists of str enums, TypedDict of str/list-of-str), so no cycle can arise in practice. Regression tests in `TestOpenTelemetrySpanDedupe` cover the list crash, distinct-list-scope collision, dict and set scope parts, and an end-to-end `_create_guardrail_span` exercise that confirms exactly one `guardrail` span is emitted across repeated lifecycle entrypoints. Each new test fails on a reverted helper (4/4 mutation kill) * fix(otel): cap _freeze_for_dedupe recursion depth and ignore in recursive detector CI's recursive_detector blocks new recursive functions in litellm/ unless they are in the allowlist with a documented bound. Cap the helper at 16 levels and return repr(value) past the cap; this is well past the realistic depth of guardrail_mode (1-3 levels) and means a future caller passing a cyclic container can no longer push the proxy logging path into a RecursionError. Add a regression test that exercises the cycle path. * refactor(otel): annotate _freeze_for_dedupe return as a HashableScope union Per review feedback from @mateo-berri: replace the loose `-> object` annotation with a recursive `HashableScope` union (str | int | float | bool | bytes | None | Tuple[HashableScope, ...] | FrozenSet[HashableScope]) so the helper's contract is visible at the signature. Replace the `try/except hash(value); return value` passthrough with an explicit isinstance check over the hashable-scalar types so the type checker can narrow without requiring `cast(Hashable, value)` on the return. Symmetric: dict keys also flow through the freezer (a TypedDict key is already a string in practice, so behaviorally identical). All 16 regression tests still pass; mutation kill behavior preserved * fix: avoid explicit casting --------- Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com> |
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Mateo Wang
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17bfd415ae
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chore: migrate Python formatter from black to ruff format (#31317) | ||
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Mateo Wang
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6db55e0aa5
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feat(mcp): add mcp_xff_num_trusted_hops to harden X-Forwarded-For client IP resolution (#31257)
* feat(mcp): add mcp_xff_num_trusted_hops to harden XFF client IP resolution MCP per-server IP access control reads the client IP from X-Forwarded-For and trusts the leftmost entry. Behind an append-style proxy or load balancer (AWS ALB, nginx with $proxy_add_x_forwarded_for, HAProxy, Envoy, Cloudflare), a client can prepend an arbitrary value to the header, so the leftmost entry is attacker-controllable even when the direct peer is a trusted proxy. An attacker can therefore spoof an internal IP and reach servers marked available_on_public_internet=false. This adds an optional mcp_xff_num_trusted_hops general setting modelled on Envoy's xff_num_trusted_hops. When set to N, the client IP is read N entries from the right of the chain (where N is the number of trusted appending proxies in front of the gateway) instead of the leftmost value, so any entries a client prepends are ignored. It composes with mcp_trusted_proxy_ranges, which still validates the direct peer, and only takes effect once that check passes; without a validated direct peer the gateway keeps failing closed, so hop counting cannot be abused by a direct-to-pod attacker. The chain must contain at least N valid entries or resolution fails closed. Default is unset, preserving existing behaviour. * chore(ui): regenerate dashboard schema for mcp_xff_num_trusted_hops * fix(mcp): warn when mcp_xff_num_trusted_hops is below the minimum A 0 or negative value is silently treated as disabled, which could leave an operator believing they enabled append-style X-Forwarded-For hardening while client IP resolution stays on the spoofable leftmost value. Emit a warning, consistent with how the module already surfaces invalid CIDR config, so the misconfiguration is visible in logs. * fix(mcp): reject mcp_xff_num_trusted_hops < 1 at config-parse time Add a ge=1 bound to the ConfigGeneralSettings field so the update_config_general_settings path rejects 0 and negative values with a clear validation error instead of accepting them, and self-documents the valid range. The runtime warning stays as defense-in-depth for raw-dict config that bypasses model validation. * style(mcp): black-format ip_address_utils.py * fix(mcp): fail closed when mcp_xff_num_trusted_hops is set but invalid A present-but-invalid mcp_xff_num_trusted_hops (non-integer, or below 1) previously made _resolve_num_trusted_hops return None, which the caller treated identically to "unset" and silently fell back to the legacy leftmost X-Forwarded-For value. An operator who set the value to harden client IP resolution but typo'd it would get weaker security than before, with no fail-closed signal. Model the setting as a tagged union (_HopCountUnset, _HopCountInvalid, _HopCount) so the three states are distinct: unset keeps the legacy path, a valid count drives hop-counting, and an invalid value fails closed (returns "") instead of reverting to the spoofable leftmost address. The caller matches on the union exhaustively. Add a parametrized regression test asserting get_mcp_client_ip returns "" for 0, -1, "abc", and 1.5 even with a spoofed internal leftmost entry, and update the resolver unit tests for the new return type. |
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michelligabriele
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0a8a87afe0
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fix(streaming): word-sliced cache replay for stream=true cache hits (#30216)
* fix(streaming): word-sliced cache replay for stream=true cache hits * fix(streaming): align mypy and replay happy-path test with word-sliced cache replay * fix(streaming): short-circuit whitespace-only content in cache replay splitter * fix(streaming): emit tool_calls/function_call only on first replay slice * refactor(streaming): drop dead delattr guard in cache replay A non-None usage on the replay base object always lives in __pydantic_extra__ (it is attached via setattr earlier in the same function), so delattr can never raise here; the try/except AttributeError that silently swallowed a failure was dead defensive code that could only ever hide a real regression, so it is removed in both the async and sync generators. Also switches the new replay annotations from typing.List to the builtin list to satisfy the strict ruff UP006 gate and drops the unused PLR0915 noqa directives (the rule is not enabled in this repo's ruff config, so RUF100 flagged them). * fix(streaming): drop carried-over metadata from later cache replay slices The word-sliced cache replay deep-copies the full ModelResponseStream per slice, so reasoning_content, thinking_blocks, logprobs, enhancements, annotations and the rest of the per-message metadata rode on every slice, not just the first. Downstream handlers that accumulate streamed deltas would collect each one once per slice, e.g. duplicating a cached reasoning trace N times on a stream=true cache hit. Later slices are now rebuilt as a content-only delta with choice-level logprobs and enhancements stripped, so the whole metadata class stays on the first slice. Adds async (logprobs) and sync (reasoning_content/thinking_blocks/logprobs/ enhancements, plus annotations) regression tests --------- Co-authored-by: Mateo <277851410+mateo-berri@users.noreply.github.com> |
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Sameer Kankute
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c712c20d0f
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fix(ci): point OSS contributor workflows to litellm_oss_staging (#31270)
* fix(ci): point OSS contributor workflows to litellm_oss_staging Workflow triggers and guard error messages incorrectly referenced litellm_oss_branch; update them to the branch we actually use for external contributions. * fix(ci): include test-rust.yml in litellm_oss_staging rename Missed test-rust.yml when updating OSS contributor target branch references. Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: Cursor <cursoragent@cursor.com> |
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tin-berri
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0f5603895c
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fix(mcp): challenge delegate-auth OAuth servers with upstream resource_metadata (#31255)
An oauth2 MCP server with delegate_auth_to_upstream=true never prompted the user to sign in. On an unauthenticated initialize the gateway answered locally (200, no tools) and emitted no WWW-Authenticate, so clients like Claude Desktop either connected empty or hit "OAuth probe timeout after 10000ms". #30124 added a bare `continue` in _raise_preemptive_401_for_unauthenticated_servers to stop sending LiteLLM's gateway authorization_uri challenge for delegate-auth servers, expecting the upstream to emit its own challenge. On initialize the gateway never probes upstream, so no challenge ever reached the client. Replace the `continue` with a preemptive 401 carrying the proxied resource_metadata (RFC 9728) challenge, the same form passthrough servers and MCPUpstreamAuthError already use. This keeps #29770 fixed (still no authorization_uri) while restoring the upstream PKCE sign-in prompt. |
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tin-berri
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f426912ba1
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fix(mcp): resolve toolset tools by the server's known prefix (#31254)
* fix(mcp): resolve toolset tools by the server's known prefix
Toolsets store {server_id, bare tool_name} and reconcile that against the
live prefixed tool name at list time. The reconciliation chopped the live
name at the first MCP_TOOL_PREFIX_SEPARATOR with no server context, so a
server whose prefix contains the separator (a hyphenated alias, or the
UUID server_id used as the prefix when a server has no alias) had its
tools silently dropped from /toolset/<name>/mcp while listing fine
everywhere else. Strip the exact known prefix for the tool's server_id
instead of guessing the boundary, on both the resolve and filter sides
Also render toolset tools as {server-prefix}-{tool} in the dashboard
picker result and chips; this is display only, the persisted record
stays {server_id, bare tool_name}
Resolves LIT-3419
* test(mcp): add focused unit tests for strip_known_server_prefix
Cover the LIT-3419 cases directly on the helper with real MCPServer
objects: clean prefix round-trip, hyphenated alias, UUID server_id
fallback, unprefixed passthrough, and the server=None legacy fallback
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Mateo Wang
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9c41077786
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fix(mcp): warn loudly when X-Forwarded-For is present but use_x_forwarded_for is off (#31266)
* fix(mcp): warn loudly when X-Forwarded-For is present but use_x_forwarded_for is off When a request carries an X-Forwarded-For header but use_x_forwarded_for is unset, get_mcp_client_ip silently falls back to the direct peer's IP (the load balancer / reverse proxy). That peer almost always sits inside mcp_internal_ip_ranges, so the 'Internal network only' (available_on_public_internet: false) restriction trusts every external caller as internal and effectively exposes those servers. Emit a one-shot loud error pointing the operator at use_x_forwarded_for instead of hard-failing: on a deployment with no load balancer, a crafted X-Forwarded-For header must not be able to take the service down, and a one-shot log keeps a flood of crafted headers from spamming the logs. * fix(mcp): re-arm XFF-disabled warning on config change and harden test assertion Address PR review: tie the one-shot warning flag to the observed use_x_forwarded_for value so it re-arms whenever the setting is seen enabled, restoring the diagnostic on a later rollback to disabled. Also assert against str(call_args) so the test survives a positional-to-keyword logger refactor. |