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86 commits
| Author | SHA1 | Message | Date | |
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mateo-berri
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9906770e41 |
Merge remote-tracking branch 'origin/litellm_internal_staging' into litellm_google_interactions_cost
# Conflicts: # litellm/constants.py # litellm/interactions/main.py # litellm/litellm_core_utils/litellm_logging.py # litellm/litellm_core_utils/llm_cost_calc/usage_object_transformation.py # litellm/proxy/hooks/proxy_track_cost_callback.py # litellm/proxy/management_endpoints/credential_migration.py # tests/test_litellm/litellm_core_utils/test_litellm_logging.py # tests/test_litellm/test_cost_calculator.py # ui/litellm-dashboard/src/lib/http/schema.d.ts |
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yuneng-jiang
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0c97eea660
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test(cost-calc): stop 182 global writes leaking out of the cost-calc suites (#37815)
* test(cost-calc): stop 182 global writes leaking out of the cost-calc suites Across test_cost_calculator.py and llm_cost_calc/test_llm_cost_calc_utils.py, 58 tests opened by setting LITELLM_LOCAL_MODEL_COST_MAP in os.environ and replacing litellm.model_cost, and none of them put the env var back. The second file already had a _local_model_cost_map fixture doing it by hand with a try/finally, so both idioms sat in the same file. Keep that fixture, give it monkeypatch, and have every one of those tests ask for it. The margin and discount tests drop their hand-rolled copy-then-restore in favour of monkeypatch.setattr, which also puts the global back when an assertion fails part way through. Both files also drop a sys.path.insert whose argument resolves outside the repo, so it was never what made the imports work. TQ003 1077 -> 1075, TQ004 768 -> 693, TQ005 2836 -> 2731, and the budget ceilings come down with them. * fix(test): make the streamed-cost tests load the map they assert against The local_cost_map fixture set LITELLM_LOCAL_MODEL_COST_MAP but never reloaded litellm.model_cost, and reading the variable is not what loads the map. So the three streaming-cost tests billed against whatever map the process happened to be holding, and their hardcoded prices only held when something else had already swapped in the checked-in one. This branch stops the cost-calc tests leaking that map, which left test_main billing at the ambient prices instead. The fixture now loads the map it names, so the prices these tests assert hold on their own. |
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Mubashir Osmani
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e6a6016e3e |
fix(model-costs): apply GPT-5.6 Sol promotional pricing cut
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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mateo-berri
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c549cddada | fix(vertex_ai): price passthrough calls on the URL's serving location | ||
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mateo-berri
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b39a339b7d | fix(vertex_ai): apply regional endpoint uplift to cost tracking | ||
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mateo-berri
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e736b59802 | test(cost): type the batch_cost_calculator model_info literals instead of suppressing | ||
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Marty Sullivan
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800e1d4f35 |
fix(cost): treat a batch rate configured as zero as free, not unset
batch_cost_calculator gated the batch rate fields on truthiness, so a deployment that configures input_cost_per_token_batches or its output twin as 0.0 was read as having configured nothing and that token direction fell through to half the standard rate. Layering declared rates over published ones made this reachable: a deployment declaring only a zero batch rate previously kept a fabricated zero on the standard field, which happened to bill nothing. The two batch fields are now gated on presence. Verified no cost-map entry changes behavior: the only three carrying a zero batch rate are embeddings, whose standard output rate is also 0.0, so both paths yield the same zero. Adds a parametrized regression over an explicit zero, an explicit non-zero, and unset, plus coverage for the deployment id get_model_info cannot resolve, which were the lines Codecov flagged. |
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mateo-berri
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2c6409c7e6 | Merge remote-tracking branch 'origin/litellm_internal_staging' into litellm_tiered_pricing_cache_creation | ||
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mateo-berri
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2f6f5c4961 | fix(cost): reach tiered pricing for models without top-level per-token rates | ||
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shivam
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2351aaba74 |
test(anthropic cost): scope local cost-map env flag with monkeypatch
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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shivam
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dc58c35bba |
fix(anthropic cost): apply regional geo uplift to cached tokens
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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tin-berri
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32deaff015
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feat(spend): rebuild the auto-router benchmarks backend as a per-session rollup (#35910)
Folds every successful auto-routed request into LiteLLM_AutoRouterSession with one conditional upsert at spend-write time, classifying each turn (same model, first visit, return to tier, out of order) against the row's own columns so nothing is read before the write. The upsert's placeholders and argument tuple both derive from the transaction dataclass's own field order, so the SQL and the call site cannot drift apart. GET /auto_router/benchmarks aggregates the rollup, grouped by the full (router, type) identity, and never scans LiteLLM_SpendLogs. A turn's cache interaction is derived once from its usage record (savings.py owns the extraction; compute_savings_spend derives cache reads from usage_object itself), hits are counted order-independently so the overall hit rate matches its covered denominator, caller-chosen session ids are bounded before entering the primary key, and a poisoned statement drops only its own session's remaining turns. Return misses inside the recorded TTL are named for what the telemetry shows (within_ttl) rather than a presumed cause, since a provider can evict early. Savings ride each router's derived baseline by default, so the response carries no deployment-wide baseline label. Rollup retention has its own maximum_autorouter_session_retention_period setting, pattern-identical to the spend-logs knob and running in the same cleanup job on its own cutoff. Every drain trigger sizes the queues through one owner and the enqueue honors disable_spend_logs beside the tool-usage queue it mirrors. |
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mateo-berri
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bd04520d98 | Merge branch 'litellm_internal_staging' into litellm_gpt56_cache_token_pricing | ||
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devin-ai-integration[bot]
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551e5d097c
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feat(dashscope): add qwen3.7-plus and qwen3.7-max to the model cost map (#35123)
* feat(dashscope): add qwen3.7-plus and qwen3.7-max to the model cost map Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * chore: limit backup cost map diff to the new dashscope entries Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(cost_calculator): adjust tier-only alias assertion for mapped qwen3.7-plus Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(dashscope): drop redundant cost map pinning tests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(cost_calculator): point tier-only alias check at an unmapped model Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: shivam <shivam@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Devin AI
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37744ca944 |
fix(cost): price anthropic messages cache read/write tokens instead of full input rate
Anthropic-shaped usage was mapped through the Responses API usage converter, which ignores top-level cache_read_input_tokens/cache_creation_input_tokens, so cache hits on /v1/messages were billed entirely at the uncached input rate |
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mateo-berri
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d3f5c6dbf6 |
fix(cost_calculator): sum mirrored cache token fields once in combine_usage_objects
combine_usage_objects iterates prompt_tokens_details model_fields and sums each; with cache_write_tokens and cache_creation_tokens now mirroring each other via __setattr__, the pair was summed twice, doubling cache creation counts for Anthropic batch cost calc, mid-stream fallback usage merges, and realtime usage. Collapse the mirrored pair to one representative before summing. |
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mateo-berri
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e458aa1230 | fix(interactions): bill google_search grounding queries per query | ||
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mateo-berri
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59d4e52a3d | fix(interactions): bill background interactions once completed via cost polling | ||
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mateo-berri
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f08742c6f4 | fix(interactions): track cost and spend for Google Interactions API requests | ||
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Shivam Rawat
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65e2432a37 |
fix(proxy): bill tier-only deployments instead of $0
Route cost calculation to the deployment's router_model_id entry when it carries tiered_pricing but no flat per-token rate, so models like dashscope/qwen3.7-plus are billed via their tier table rather than the pricing-stripped shared alias. Co-authored-by: Cursor <cursoragent@cursor.com> |
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Mateo Wang
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9076c33347
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fix(batches): price anthropic passthrough message batches correctly in batch cost job (#32307)
* fix(batches): price anthropic passthrough message batches correctly in batch cost job
Anthropic message batches created via the /anthropic passthrough were never
cost tracked. The CheckBatchCost job fetched batch results from the Files API
(POST /v1/files/msgbatch_.../content), which Anthropic rejects with "File id
must have file_ prefix"; the error response was silently wrapped as file
content, parsed as zero successful rows, logged as a $0 aretrieve_batch spend
row, and the job was marked batch_processed=true so the $0 was permanent.
Route msgbatch_ file ids to GET /v1/messages/batches/{id}/results in the
anthropic files transformation, raise on HTTP error status in
retrieve_file_content instead of returning the error body as content, parse
Anthropic's results JSONL shape (result.type == "succeeded",
result.message.usage with cache creation/read tokens) in batch_utils, price
cache creation tokens at cache_creation_input_token_cost in the batch cost
fallback (50% batch discount preserved for base input, cache reads, cache
writes, and output), and leave the managed object row unprocessed when cost
tracking fails so a later poll retries instead of permanently recording $0.
* fix(batches): carry cache token details into aggregated anthropic batch usage
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Mateo Wang
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43b0a25f07
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feat(vertex_ai): add Google Cloud Speech-to-Text Chirp 3 transcription support (#32274)
* fix(llm_http_handler): send dict transcription request data as a JSON body
httpx form-encodes dicts passed via data= and silently ignores json=, so the
generic audio transcription path never actually sent a JSON body. No provider
hit this before; JSON-body speech APIs need it.
* feat(vertex_ai): add Google Cloud Speech-to-Text Chirp 3 transcription support
Adds a VertexAIAudioTranscriptionConfig wired through ProviderConfigManager so
vertex_ai/chirp_3 works on /v1/audio/transcriptions (sync and async) via the
Speech-to-Text v2 recognize API. Auth reuses the standard Vertex credential
resolution (vertex_project/vertex_location/vertex_credentials or ADC); the
location defaults to the us multi-region since chirp_3 is only served from the
us and eu multi-regions, and non-global locations use the regional
<location>-speech.googleapis.com host. Maps language to languageCodes (auto
language detection by default), joins all result alternatives into the
transcript, and tracks cost from totalBilledDuration with a
vertex_ai/chirp_3 price entry at Google's published $0.016/min.
* fix(vertex_ai): map bare ISO-639-1 language codes to BCP-47 for Speech-to-Text
OpenAI clients send language codes like "en", which Google rejects with 400
("not supported by the model chirp_3 in the location us"); Speech-to-Text
wants region-qualified BCP-47 like "en-US". Adds a shared
normalize_transcription_language_to_bcp47 helper in audio_utils (NVIDIA Riva's
transcription config already hand-rolled the same table privately) that maps
common bare codes and passes region-qualified ones through, and applies it in
the Vertex transcription request. Also narrows the response JSON parse guard
to ValueError.
* fix(vertex_ai): drop zero output_cost_per_second so chirp_3 cost tracking works
cost_per_second prefers output_cost_per_second whenever it is not None, so the
0.0 in the chirp_3 entry priced every transcription at $0.00 instead of using
input_cost_per_second. Remove it from both cost maps and pin the behavior with
a regression test computing 18s of chirp_3 audio to ~$0.0048.
* fix(vertex_ai): validate client-controllable location to prevent SSRF in Speech-to-Text
get_complete_url interpolated vertex_location straight into the request host,
and vertex_location is client-controllable on the proxy (it flows from the
request body and is not on the request-body blocklist). An authenticated caller
could send vertex_location="attacker.example/" to point the host at their own
server, so the proxy would POST the audio plus its admin-minted Google bearer
token and x-goog-user-project header to the attacker, exfiltrating a
cloud-platform-scoped OAuth token minted from the admin's credentials.
Factor the location validation the rest of vertex_ai already applied in
get_vertex_base_url (^[a-z][a-z0-9-]*$ plus the global allowance) into a shared
validate_vertex_location helper in common_utils and call it from both the chat
host builder and the new speech host builder. Invalid locations now raise a 400
VertexAIError instead of building a host. Also reject vertex_project values that
carry URL-structural characters, since it lands in the URL path.
Regression tests assert on the parsed netloc so the security property is pinned:
valid locations always resolve to a *speech.googleapis.com host and injection
inputs are rejected.
* fix(vertex_ai): reject unsupported transcription response_format values instead of silently ignoring
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Mateo Wang
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5f864c83ce
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chore(lint): zero out crash-class pyright rules and ban new type: ignore comments (#32152)
* fix: zero out crash-class basedpyright rules across litellm/ * feat(lint): add LIT009 banning inert type: ignore comments * docs: require bracketed rule and reason on every suppression * chore(lint): ratchet budgets down and zero crash-class pyright limits * fix: narrow auto router routelayer through a local before calling * test: add regression tests for crash-class fixes * fix: drop dead AZURE_AD_TOKEN lookups and word-bound the type-ignore regex |
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Shivam Rawat
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f0d41e4d16 |
fix: attribute realtime transcription cost in cost breakdown
Pass transcription_cost through additional_costs so cost_breakdown's input_cost + output_cost + additional_costs sums to total_cost instead of silently folding it into total_cost only. Co-authored-by: Cursor <cursoragent@cursor.com> |
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Shivam Rawat
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8edfaa9bac | Merge remote-tracking branch 'origin/litellm_internal_staging' into litellm_realtime_cost_metrics | ||
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Cursor Agent
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09f611e7b5
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fix: include realtime transcription cost in breakdown | ||
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Sameer Kankute
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6d5de74447
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feat(cost_calculator): log per-token-type reasoning and cache cost breakdown (#31623) (#31686)
Reasoning-token cost was computed but folded into output_cost, and cache cost was only populated from the top-level cache_read_input_tokens attribute, so providers that report cache tokens under prompt_tokens_details (Gemini, OpenAI, Vertex) never got a cache breakdown. Adds a provider-agnostic get_token_type_cost_breakdown helper that derives reasoning, cache-read and cache-creation cost from the normalized usage object using the same rate-resolution primitives as the total-cost path, so the breakdown reconciles with the totals. completion_cost stores these via set_cost_breakdown, surfacing reasoning_cost (new), cache_read_cost and cache_creation_cost in StandardLoggingPayload.cost_breakdown and the spend logs. Co-authored-by: Kunal Nayyar <48790070+kunal2002@users.noreply.github.com> |
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Shivam Rawat
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f883d6b134
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Merge branch 'litellm_internal_staging' into litellm_realtime_cost_metrics
Some checks failed
LiteLLM Rust / rustfmt, clippy, test (push) Has been cancelled
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Yassin Kortam
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a9de75b1f7
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fix(realtime): stop revalidating realtime events at the logging boundary (#31054)
Realtime websocket sessions emit events outside the OpenAIRealtimeEvents union (e.g. rate_limits.updated, response.function_call_arguments.delta, surfaced when logged_real_time_event_types="*"). Building LiteLLMRealtimeStreamLoggingObject revalidated every stored event against the 16-member union, producing thousands of ValidationErrors per session (12,670 for a ~281-event session). That synchronous work blocked the asyncio event loop, degrading realtime time-to-first-audio and dial latency and tripping readiness probes, and the raised error discarded the session usage so no cost was tracked. Type results as SkipValidation[OpenAIRealtimeStreamList] and serialize the events verbatim, so already-formed event dicts are not revalidated. The flood drops from 12,670 errors to 0 and the combined usage survives to the cost calculator. Resolves LIT-3919 Resolves LIT-3920 |
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yuneng-jiang
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e122dac0db
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fix(cost): stop non-string response service_tier from dropping cost tracking (#30706)
completion_cost extracted service_tier from the response object and the usage object without an isinstance guard, so a non-string value (e.g. a dict) flowed straight into _get_service_tier_cost_key and raised AttributeError on service_tier.lower(). completion_cost re-raises, so the request's cost was lost. PR #30690 fixed only the request-level optional_params path. This extends the same guard to the response and usage paths by normalizing each extracted value: a non-string tier (and the routing-only "auto" sentinel) is not billable, so it coerces to None and pricing defers to the next concrete tier the provider served, falling back to standard pricing when none is present. Adds two regression tests driving a dict service_tier through completion_cost, one on the response object (defers to the served usage tier) and one on the usage object (prices at standard); both raise AttributeError before the fix. |
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Mateo Wang
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43dadc5138
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fix(cost): stop non-string service_tier from silently dropping cost tracking (#30690)
completion_cost read service_tier straight from the request optional_params and called service_tier.lower() on it, so a non-string value (dict/int/list, reachable via allowed_openai_params/drop_params) raised AttributeError. _response_cost_calculator swallowed that and returned response_cost=None, so the request's cost was silently lost. The isinstance guard alone is not enough: a surviving dict would crash again downstream in _get_service_tier_cost_key, which also calls .lower(). A request-level service_tier is only meaningful for pricing when it is a concrete billable tier string, so coerce any non-string value to None and defer to the tier the provider reports on the response usage, the same way "auto" already does. Adds a regression test driving a dict service_tier through completion_cost; it raises AttributeError before the fix and prices at the served tier after. |
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Mateo Wang
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b638bc2248
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fix(anthropic): price and surface response service_tier in cost tracking (#30558) | ||
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Sameer Kankute
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816fca939f
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chore(oss): litellm oss staging 150626 (#30463)
* fix(pricing): add GitHub Copilot MAI Code Flash pricing (#30415) * fix(pricing): add GitHub Copilot MAI Code Flash pricing Add GitHub Copilot pricing entries for MAI-Code-1-Flash and the internal Copilot CLI model name so cost calculation can price input, cached input, and output tokens. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * test(pricing): cover GitHub Copilot MAI Code Flash pricing Add regression coverage for both GitHub Copilot MAI-Code-1-Flash model names, including cached input pricing, chat endpoint metadata, and cost_per_token arithmetic. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix(router/proxy): propagate completed_response through FallbackResponsesStreamWrapper for streaming /v1/responses container ownership (#30210) (#30213) * fix(router/proxy): propagate completed_response through FallbackResponsesStreamWrapper for streaming /v1/responses container ownership (#30210) #28990 added ownership recording for streaming /v1/responses via _wrap_responses_stream_for_container_ownership, which reads `getattr(stream_response, 'completed_response', None)` to extract the ResponsesAPIResponse. The unit test bypassed the Router, so it never exercised the production wrapping path. Through the Router (every proxy deployment), the stream is wrapped by FallbackResponsesStreamWrapper (router.py:2527). Its __init__ set `self.completed_response = None` and __anext__ only forwarded chunks — the inner source iterator's terminal event never bubbled up to the attribute the ownership hook reads, so the hook silently recorded nothing and every follow-up /v1/containers/<id>/files call returned 403 for non-admin keys. This commit: - router.py: pre-resolves the responses-API terminal event tuple (response.completed / .incomplete / .failed) once per _aresponses_streaming_iterator call, and has the wrapper's __anext__ sniff each forwarded chunk's .type. First terminal event hit gets stored on the wrapper's completed_response. Iterator-agnostic — works for source_iterator AND any future wrapper. - common_request_processing.py: when _extract_completed_responses_response returns None we now warn instead of silently skipping. Reporter on #30210 lost a day to this exact silent skip; the warning surfaces future regressions of the same shape directly in operator logs. Fixes #30210 * fix(router): type-ignore wrapper getattr-defaults; broaden ownership-skip warning CI lint (mypy) flagged the three pre-existing getattr(..., None) assignments in FallbackResponsesStreamWrapper.__init__: router.py:2564 self.response = getattr(source_iterator, 'response', None) router.py:2565 self.model = getattr(source_iterator, 'model', None) router.py:2566 self.logging_obj = getattr(..., None) Those lines also exist on litellm_internal_staging and pass mypy there. Adding the typed terminal-event tuple above the class made the function body more narrowable, which surfaced the pre-existing mismatch — base class declares non-Optional types but the bridge path (LiteLLMCompletionStreamingIterator) legitimately omits these. Keep the None fallback and silence with type: ignore[assignment]. Greptile 4/5 note: the ownership-skip warning hard-named code_interpreter which misleads operators when a non-code_interpreter stream aborts. Generalize to 'any tool container (e.g. code_interpreter)'. * fix(register_model): drop synthesized zero costs to preserve sparse entries (#30198) (#30201) * fix(register_model): drop synthesized zero costs to preserve sparse entries (#30198) get_model_info synthesizes input_cost_per_token / output_cost_per_token = 0 when they are absent from the raw entry (the price-unknown and free cases share the same representation). register_model then merges that result back into litellm.model_cost, which flips a sparse entry from 'no cost keys' (priced via model name) to 'cost keys = 0' (free). That defeats _is_cost_explicitly_configured (#24949) on re-registration: _is_model_cost_zero returns True, common_checks skips every tag / key / team / user / org budget check for the group, and over-budget traffic keeps returning 200. Spend keeps recording because cost calc still resolves by model name, so the symptom is silent and only triggers on the second register_model pass (router rebuild, /model/update, config sync). Mirror the existing litellm_provider-None guard one block above and pop the cost fields from the synthesized result when they are absent from the raw entry and not in the caller's value. Caller-provided zeros (genuinely free models, BYOK overrides) are preserved. Fixes #30198 * fix(register_model): switch _raw_entry to is-None checks + drop dead test assertion Greptile #30201 review notes: - the `or`-chain in the raw-entry lookup treated an empty dict (a key with no fields) as falsy and fell through to the second arm — replace with explicit `is None` checks so a present-but-empty entry is still taken at face value. - the first assertion in `test_router_double_init_keeps_db_model_entry_sparse` used `in (None, 0)` which passes under the bug condition (cost = 0 matches the tuple); the strong follow-up assertion already covers every shape, so drop the dead branch. * fix(bedrock mantle): use unique function-call id for responses->chat tool calls (#30426) * fix(bedrock mantle): use unique function-call id for responses->chat tool calls ... * fix(bedrock mantle): scope unique tool-call id fallback to degenerate call_id The previous revision preferred the Responses item id for every tool call, which broke providers (and existing tests) where call_id is a unique, canonical correlation key. Restrict the fallback to the degenerate index-based call_id that Bedrock Mantle returns (call_0, call_1, ... resetting per response) and keep call_id otherwise. Revert the change to the OUTPUT_ITEM_DONE streaming handler, whose tool_call_chunk is never emitted (dead code, per review). Extend the regression tests to assert a normal call_id is preserved. * fix(router): preserve azure_ad_token through CredentialLiteLLMParams for /v1/files + batches (#30235) (#30241) * fix(router): preserve azure_ad_token through CredentialLiteLLMParams for /v1/files + batches (#30235) Router.get_deployment_credentials_with_provider re-validates a deployment's litellm_params through CredentialLiteLLMParams before handing them to file/batch/passthrough callers: return CredentialLiteLLMParams( **deployment.litellm_params.model_dump(exclude_none=True) ).model_dump(exclude_none=True) Any field NOT declared on CredentialLiteLLMParams gets silently dropped on the way through. azure_ad_token was undeclared, so Azure deployments using OAuth/M2M (azure_ad_token instead of a static api_key) silently lost their token at the files endpoint and the proxy returned: Missing credentials. Please pass one of api_key, azure_ad_token, azure_ad_token_provider, ... Declare azure_ad_token on CredentialLiteLLMParams alongside api_key / api_base / api_version so it rides through the round-trip. Static-key deployments stay unaffected (Optional, default None, dropped by exclude_none=True). Provider-callable (azure_ad_token_provider) is a separate concern and out of scope here. Fixes #30235 * fix(ui-types): regenerate schema.d.ts for new azure_ad_token field CI's 'Verify schema.d.ts matches the proxy OpenAPI spec' check auto-detected the new field and emitted the exact diff to apply. Two schemas had `aws_secret_access_key` from CredentialLiteLLMParams, both get the new azure_ad_token marker next to it. * fix(proxy): org_admin with own user_id now sees all org teams on /v2/team/list (#30247) When the UI sends the callers own user_id (as it does for non-Admin global roles), _enforce_list_team_v2_access now nulls it out for org admins so _build_team_list_where_conditions scopes by organization_id only -- matching the legacy /team/list behavior and the documented intent. Fixes #30215 Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> * test(vertex_ai): multi-region regression coverage for cachedContents host (#29571) (#29707) litellm_internal_staging already routes the cachedContents URL through get_vertex_base_url, fixing the multi-region 404 reported in #29571 — but carries no test coverage for the actual regression scenario (eu/us must resolve to the REP host aiplatform.{geo}.rep.googleapis.com). Add TestContextCachingMultiRegionUrls: parametrized eu/us REP-host assertions (including absence of the old broken {geo}-aiplatform host), plus regional (us-central1) and global no-regression checks. * fix(proxy): close upstream LLM stream when client disconnects mid-stream (#30245) * fix(proxy): close upstream LLM stream when client disconnects mid-stream When a streaming client disconnects, Starlette abandons the response body iterator without calling aclose(), so the proxy's connection to the upstream backend stays open until garbage collection, which may never come. The backend (e.g. vLLM) keeps generating into a dead pipe: small responses drain invisibly into TCP buffers while large ones block the backend on a full send buffer indefinitely (observed via lsof as an ESTABLISHED proxy->backend connection minutes after the client left) create_response now returns a StreamingResponse subclass that closes both its body iterator and the wrapped upstream-facing generator in a shielded finally. The upstream generator is closed directly rather than through a cascade because aclose() on a never-started generator skips its body, which would make the cascade a no-op when the client disconnects before the first chunk is sent. async_streaming_data_generator also gains the same shielded finally-aclose that async_data_generator in proxy_server.py already had, covering the Anthropic and Google SSE paths With this, killing a streaming client causes the backend to observe the abort within about a second and free its slot, while completed streams are unaffected. No flag is needed, unlike the non-streaming opt-in cancel in #30223: this only releases resources after the client is already gone and does not change any response a client can observe Fixes #30244 * fix(proxy): close upstream even when body iterator aclose raises BaseException Addresses the Greptile finding on #30245: the cleanup loop caught only Exception while the generator-level cleanup catches BaseException, so a CancelledError or GeneratorExit escaping body_iterator.aclose() would skip closing the upstream generator. Both sites now use the same scope and a regression test pins that the upstream is closed even when the body iterator explodes with a BaseException * fix(llms): expose aclose on BaseModelResponseIterator so stream close reaches the provider connection The response-level close added for #30244 only worked for SDK-based providers (e.g. openai), whose streams expose aclose all the way down. Providers served by base_llm_http_handler (hosted_vllm and most modern transformation-based providers) wrap a bare response.aiter_lines() generator in BaseModelResponseIterator, which had no aclose or close at all, and nothing retained the httpx response object; so CustomStreamWrapper.aclose() silently did nothing and the upstream connection stayed open. Verified with a vLLM-style mock: with hosted_vllm/ the backend streamed all 100 chunks to completion after the client disconnected, while openai/ aborted at chunk 6 BaseModelResponseIterator now carries an optional http_response and an aclose() that closes it; make_async_call_stream_helper attaches the response after building the iterator. With this, hosted_vllm aborts the backend within ~1.6s of the client dropping, and completed streams are unaffected --------- Co-authored-by: kursad <kursad.lacin@brado.net> * feat(anthropic): surface compaction usage iterations data (#27065) * feat(anthropic): surface compaction usage iterations data * style: apply black formatting to fix lint checks * fix(usage): correct calculate usage with cached tokens when use ChatCompletionUsageBlock (#30422) * fix(usage): correct calculate usage with cached tokens when use ChatCompletionUsageBlock * fix(usage): optimize test imports * feat: add fastCRW search provider (#30434) * feat(provider): add LibertAI as a JSON-configured OpenAI-compatible provider (#30203) * feat(provider): add LibertAI as a JSON-configured OpenAI-compatible provider * libertai: update served endpoints backup + add mode/matrix tests Addresses review feedback: - Add libertai to litellm/provider_endpoints_support_backup.json, the file actually served by GET /public/supported_endpoints (the root provider_endpoints_support.json already had it). - Add tests asserting bge-m3 normalizes to mode='embedding' and that the served matrix lists libertai. embeddings stays false: the JSON-configured provider path only wires chat routing (OpenAILike embedding handler is reached only for literal openai_like/llamafile/lm_studio), matching the llamagate precedent; bge-m3 remains in the cost map for metadata. --------- Co-authored-by: Moshe Malawach <moshemalawach@users.noreply.github.com> * feat(provider): add ModelScope as an OpenAI-compatible provider (#28460) * add ModelScope API support * add modelscope api support * update modelscope model list * add image-genetation support * update test and multimodal * fix: address PR review feedback for modelscope provider * update README * fix(customer_endpoints): restrict /customer/daily/activity to admin-only (#28849) * fix(customer_endpoints): restrict /customer/daily/activity to admin-only * fix(customer_endpoints): check role before prisma_client guard * fix(custom_guardrail): key disable_global_guardrails takes precedence over team guardrail list (#28563) * fix(fallbacks): preserve fallback model in SDK fallback responses (#28260) * fix(fallbacks): preserve fallback model in response when using SDK-level fallbacks * fix(fallbacks): gate x-litellm-* passthrough to trusted callers only The previous patch unconditionally let `x-litellm-*` keys bypass the `llm_provider-` prefix in `process_response_headers`. That function is also called on raw upstream-provider response headers (e.g. from `llm_http_handler.py`), so a malicious provider could return `x-litellm-attempted-fallbacks` and spoof a LiteLLM-internal marker, bypassing the proxy model-override guard. Add a `preserve_litellm_internal_headers` flag (default False). Only `response_metadata.py`, which re-processes the already-built `_hidden_params["additional_headers"]` dict (LiteLLM-owned), passes True. Raw provider header callsites keep the default False, so upstream `x-litellm-*` still gets the `llm_provider-` prefix. Adds a regression test for the spoofing case and renames the existing preserve test to make the trusted-path semantics explicit. * fix(fallbacks): ignore preserve_litellm_internal_headers for raw httpx.Headers inputs * style(core_helpers): apply black formatting * fix(lint): remove banned typing.List/Dict/Any imports and suppress PLR0913 on interface overrides Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(lint): apply black formatting to modelscope chat transformation Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(lint): replace noqa with proper fixes — use **kwargs and Awaitable instead of Any/List Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(lint): remove unused AllMessageValues import Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * revert: restore base_model_iterator.py to original PR state Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(lint): restore full method signatures for MyPy compatibility; bump PLR0913 budget for new provider files Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(lint): use @override to suppress PLR0913 on inherited signatures instead of bumping budget The overrides keep their full base-class signatures for MyPy compatibility, but those signatures carry more than five parameters, which tripped PLR0913 on each subclass redeclaration. Since the arity is dictated by the base class and cannot be reduced, decorate the overrides with typing_extensions.override; ruff treats that as the intended signal that the parameter count is not under the author's control and skips PLR0913. This restores the PLR0913 baseline to 1813. * fix(lint): add @override to modelscope image generation overrides Apply the same typing_extensions.override treatment to the image generation config so its inherited-signature overrides do not count against PLR0913. --------- Co-authored-by: Joel Tony <github@jaytau.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Co-authored-by: hcl <chenglunhu@gmail.com> Co-authored-by: ztko <96878659+koztkozt@users.noreply.github.com> Co-authored-by: Nahrin <nahrin@nahrinoda.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: Humphrey <a739376838@gmail.com> Co-authored-by: kursadlacin <kursadlacin@gmail.com> Co-authored-by: kursad <kursad.lacin@brado.net> Co-authored-by: Dushyant Acharya <dushyantacharya873@gmail.com> Co-authored-by: Yuriy <yuriy.shuyskiy@gmail.com> Co-authored-by: Recep S <22618852+us@users.noreply.github.com> Co-authored-by: Moshe Malawach <moshe.malawach@protonmail.com> Co-authored-by: Moshe Malawach <moshemalawach@users.noreply.github.com> Co-authored-by: Rongkun Yan <2493404415@qq.com> Co-authored-by: Varshith <kvarshithgowda@gmail.com> Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com> |
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Sameer Kankute
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cfcdf8714a
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feat: litellm oss 110626 (#30202)
* Add gpt-realtime-whisper Realtime transcription support (OpenAI + Azure) (#29775) * Add gpt-realtime-whisper Realtime transcription support (OpenAI + Azure) Adds first-class support for the gpt-realtime-whisper streaming speech-to-text model, which uses the Realtime transcription session API rather than the file-based /audio/transcriptions path. Model registration: registers gpt-realtime-whisper and azure/gpt-realtime-whisper with audio-duration pricing (input_cost_per_second = 0.017/60, matching the published $0.017/minute input audio rate). REST endpoint: implements POST /v1/realtime/transcription_sessions (plus /realtime and /openai/v1 aliases) to mint an ephemeral transcription session for the WebRTC flow. Adds request/response types, OpenAI and Azure URL builders, a shared base handler (refactored from the client_secrets handler), the acreate_realtime_transcription_session SDK function, and route registration. The proxy encrypts the ephemeral key returned under client_secret.value and records the session type in the token so the follow-up /realtime/calls replays type=transcription rather than type=realtime. WebSocket: forwards intent=transcription through to the Azure handler (OpenAI already received it) with URL-encoding, so gpt-realtime-whisper opens a transcription session. Transcription-only sessions no longer trigger an erroneous response.create. Cost tracking: transcription sessions emit no response.done events; their usage arrives on conversation.item.input_audio_transcription.completed as {type: duration, seconds}. That usage is captured out-of-band (usage only, no transcript duplication) and billed by input_cost_per_second, with a token-billed fallback for token-priced transcription models. Adds tests for pricing math, URL builders, request/response types, the proxy route and SDK function, WebSocket intent forwarding, transcription-session streaming behavior, and the /realtime/calls session-type replay. * Address PR review: URL-encode all Azure WS query params; forward query_params through provider_config branch * Address PR review: session_type validation, model auth fix, cost perf, billing fallback, detail/docs cleanup * Improve test coverage: detection from backend, error paths, unknown usage type, resolved_model None * Backport realtime transcription websocket fixes * Enforce authorized realtime transcription model * Enforce realtime transcription model access * Enforce realtime resolved model scopes * Enforce WebRTC transcription model scope * Lazy evaluate debug log in pass-through endpoint (#30177) * Pass through debug lazy logging * fix(proxy): convert remaining eager pass-through debug logs to lazy formatting * fix(parallel_ai): migrate search integration from v1beta to v1 endpoint (#30157) * fix(parallel_ai): migrate search integration from v1beta to v1 endpoint The Parallel Search API moved from /v1beta/search (processor: base/pro, parallel-beta header) to /v1/search (mode: turbo/basic/advanced, no beta header). Request fields moved too: max_results, source_policy, and excerpt settings are now nested under advanced_settings, and source_policy uses include_domains/exclude_domains. The v1 response returns publish_date per result, which now maps to SearchResult.date instead of being hardcoded to None. The legacy processor param is mapped to the equivalent mode so existing callers keep working. * fix(parallel_ai): default mode to basic and simplify param handling The v1 API defaults to advanced mode when mode is omitted, while v1beta defaulted to the base processor. Without an explicit default, callers who pass no mode would be silently upgraded to a tier costing 2.25x more while litellm's cost map reports the basic-tier price. Sending mode=basic preserves the v1beta default and keeps cost tracking accurate. Also replaces the handled_params set with pop-as-consumed param handling so mapped params no longer need to be tracked in two places, and extends the tests to pin the default mode, processor=base mapping, mode-over-processor precedence, and top-level v1 param passthrough. * fix(parallel_ai): avoid double /v1 when api_base is already versioned A PARALLEL_AI_API_BASE like https://api.parallel.ai/v1 previously produced .../v1/v1/search. Strip a trailing /v1 before appending the search path and cover the api_base variants with a parametrized test. --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> * feat(focus): add Mavvrik destination for FOCUS export (#29935) * fix: preserve responses streaming flag (#30189) * fix: preserve responses streaming flag * test: cover async responses streaming flag * fix(spend/daily-activity): stable offset pagination via id tiebreaker (#30164) (#30167) date alone is not a unique sort key for LiteLLM_DailyUserSpend or LiteLLM_DailyTeamSpend (many rows per date: api_key x model x model_group x provider x endpoint). Offset pagination over a non-unique sort landed on arbitrary boundaries, so a client paging through all results and summing per-page metrics (the Usage dashboard) got non-deterministic totals - sometimes inflated, sometimes deflated, different at different page_size values. Adding the row's UUID id (present on both tables) as a secondary sort gives every page a stable cursor. order=[{date desc}, {id asc}]. Fixes #30164 * fix(oci): inject a default maxTokens so omitted max_tokens doesn't truncate responses (#30018) * fix(oci): inject default maxTokens so omitted max_tokens doesn't truncate OCI GenAI applies a tiny server-side maxTokens default (~20 tokens) when the request omits it, so any call that doesn't send max_tokens comes back cut off mid-string with finishReason "length". MLflow judges never send max_tokens, so their JSON responses arrived as unterminated strings and json.loads failed in MLflow's gateway adapter. When no maxTokens/maxCompletionTokens target is set, inject DEFAULT_OCI_CHAT_MAX_TOKENS (env-overridable, defaults 4096), mirroring the Anthropic config's default-max-tokens behaviour. An explicit max_tokens still wins, and reasoning models still route to maxCompletionTokens. Used a fixed default rather than the catalog max_output_tokens because the catalog value is unreliable for some models (grok-4 reports max_output_tokens equal to its context window, not a real output cap, which would risk 400s). Adds TestOCIDefaultMaxTokens covering Cohere and generic injection, the explicit-override case, and the reasoning maxCompletionTokens branch. * test(oci): e2e regression that omitted max_tokens isn't truncated Real-proxy integration test asserting a chat completion that omits max_tokens completes with finish_reason "stop" instead of being cut off at OCI's ~20-token server default. Fails before the maxTokens-default injection (finish_reason "length", ~19 tokens), passes after. * test(oci): update cohere default-params test for injected maxTokens test_cohere_default_parameters asserted no maxTokens was injected, encoding the old behaviour where OCI's ~20-token server default truncated responses. Now that transform_request injects DEFAULT_OCI_CHAT_MAX_TOKENS, assert maxTokens equals that default while the other params (topK/topP/frequencyPenalty) stay pass-through with no hardcoded default. * fix(oci): make DEFAULT_OCI_CHAT_MAX_TOKENS a plain constant Drop the os.getenv override. The env knob was not requested and introducing a new env var forced a cross-repo dependency on litellm-docs (test_env_keys.py validates every referenced env var against the docs table there). A plain 4096 constant keeps the PR self-contained; callers who want a different limit pass max_tokens explicitly per request. * fix(oci): route all OpenAI commercial models to maxCompletionTokens OCI serves OpenAI models (gpt-4.1, gpt-5.1 through 5.5, o-series) that the litellm catalog doesn't track, so the supports_reasoning lookup returned False for them and the provider sent maxTokens, which the reasoning families reject with HTTP 400. With the injected default maxTokens this broke every request to those models, not just ones with an explicit max_tokens. Route the whole openai.* vendor prefix to maxCompletionTokens since OpenAI accepts max_completion_tokens on every chat model; the openai.gpt-oss-* open weights are served by OCI's own stack and keep maxTokens. Verified live against gpt-5.2, gpt-5, gpt-4o, gpt-4.1, gpt-oss-120b, llama-3.3, command-a and grok-3-mini * test(oci): hoist transformation imports and drop unused ones Makes the generic-chat test file ruff-clean: the per-test local imports of OCIChatConfig/OCIVendors shadowed the module-level import (F811) and left it unused (F401), and json plus three OCI type imports were never referenced * fix(oci): translate response_format json_schema to OCI's accepted shape (#29691) * fix(oci): translate response_format json_schema to OCI's accepted shape OCI GenAI rejected every json_schema response_format with HTTP 400 "Please pass in correct format of request", which broke structured-output callers such as MLflow LLM judges (they always send a json_schema). The provider forwarded OpenAI's raw json_schema body unchanged. For GENERIC models OCI's ResponseJsonSchema accepts only name/description/schema/isStrict, so OpenAI's `strict` key (and any other extra) 400s the request; the key must be renamed to isStrict and the body whitelisted. For Cohere models there is no JSON_SCHEMA type at all; the schema has to ride on JSON_OBJECT as {"type": "JSON_OBJECT", "schema": ...}. Cohere type values must also be the canonical uppercase TEXT/JSON_OBJECT. _normalize_response_format now branches by vendor and emits the exact shape each one accepts (verified live against OCI GenAI for Cohere, Meta, Gemini and Grok). Drops the unused, incorrect Cohere response-format pydantic models. Two existing tests asserted the broken behavior (lowercase type, raw jsonSchema on Cohere); they are rewritten to assert the corrected shape, and generic/Cohere json_schema regression tests are added. * fix(oci): raise early on json_schema response_format with no body A GENERIC model request with {"type": "json_schema"} and no json_schema object fell through to the JSON_OBJECT branch and emitted a bodyless {"type": "JSON_SCHEMA"}, which OCI rejects with an opaque HTTP 400. Raise a descriptive 400 at translation time instead. Cohere is unaffected since it always maps to JSON_OBJECT. * test(oci): gateway integration test for response_format json_schema Added to tests/integration/ (the real-network integration suite) reusing the existing OCI proxy harness, not tests/llm_translation/ which is mock-only. --------- Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix(oci): accept default n=1 on Cohere instead of hard-failing (#29705) * fix(oci): accept default n=1 on Cohere instead of hard-failing Cohere on OCI has no numGenerations field, so n was mapped to False and map_openai_params raised "param `n` is not supported on OCI" whenever a client sent n. But n=1 (and None) is the OpenAI default single-generation request, which every OCI model produces anyway, so standard clients that always send n=1 (such as the MLflow gateway) were rejected with a 500. Drop n=1/None silently for Cohere; only n>1 is genuinely unsupported and still raises (or drops under drop_params). Generic models are unaffected and keep numGenerations, including n>1. * docs(oci): explain why n is not advertised for Cohere despite tolerating n=1 * test(oci): gateway integration test for Cohere default n=1 Added to tests/integration/ (the real-network integration suite) reusing the existing OCI proxy harness, not tests/llm_translation/ which is mock-only. --------- Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix(oci): drop max_retries instead of hard-failing on OCI (#29727) max_retries is a litellm-level control param (litellm applies retries itself), not a generation param OCI accepts. The provider mapped it to False and raised "param `max_retries` is not supported on OCI" whenever it was present. The litellm proxy injects max_retries on every request, so any OCI call through the proxy 500'd unless drop_params was set. Drop max_retries silently in map_openai_params. Adds a unit test (Cohere and generic) and a gateway integration test that a plain request succeeds through a proxy without drop_params. Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix(spend-logs): rehydrate metadata JSONB text on ui_view_spend_logs (#29682) Fixes #29674. `/spend/logs/ui` raw-SQL path returns the JSONB metadata column as a string — prisma's query_raw skips the ORM-layer hydration. The UI reads metadata.status / metadata.error_information as object fields, so provider-failure rows look like successes. Fix: json.loads the metadata field right after query_raw, fall back to {} on malformed JSON. 3 existing error-code/error-message tests called json.loads on response.data[0]["metadata"] — they were leaning on the bug. Updated to read the dict directly. Plus 2 new regression tests (failure metadata roundtrip + invalid-json fallback). Reverting the fix makes both new tests fail with AssertionError: metadata should be dict, got <class 'str'>. * fix(proxy): release max_parallel_requests slot when a stream is cancelled mid-flight (#27955) (#30020) * fix(proxy): release max_parallel_requests slot when a stream is cancelled mid-flight (#27955) * fix: refund max_parallel_requests on disconnect from outer streaming generators The cancellation refund previously lived in async_post_call_streaming_iterator_hook, but that hook is nested inside the outer streaming generators and a nested async generator only receives GeneratorExit on garbage collection (non-deterministic). With only the v3 limiter enabled, /chat/completions also bypasses the hook entirely (needs_iterator_wrap() is false). Move the release into async_data_generator and async_streaming_data_generator, the generators Starlette closes on client disconnect, so the refund fires deterministically on every streaming route. Warn when no event loop is running, and document the window TTL refresh on the decrement * fix(mcp): propagate model into model_call_details for passthrough tool calls (#30122) * fix(mcp): propagate model into model_call_details for passthrough tool calls The @client decorator on call_mcp_tool creates the logging object via function_setup without a model kwarg, so model_call_details["model"] starts as None. execute_mcp_tool only set logging_obj.model as an instance attribute, which the spend-log writer never reads (it reads kwargs["model"] from model_call_details). MCP passthrough tools/call rows therefore persisted with model="" while list_tools rows showed "MCP: list_tools", degrading the Logs UI display and bucketing all MCP tool spend under an empty model in DailyUserSpend. Propagate the model into model_call_details alongside the existing attribute assignment so the StandardLoggingPayload and SpendLogs writer pick it up. Covers the /mcp passthrough, REST /mcp-rest/tools/call, and orchestrated paths (the latter already passed model into function_setup, so this is a no-op there). * test(mcp): trim regression test docstring * fix(mcp): surface upstream challenges for delegated OAuth (#30124) * fix(mcp): surface upstream challenges for delegated OAuth * docs(mcp): clarify delegated upstream auth comments * perf(benchmarks): add CPU timing metrics to streaming benchmark (#29980) * Add CPU timing metrics to streaming benchmark * Fix spacing around timing sample dataclass * fix(gemini): don't emit empty choices on metadata-only stream chunks (#29167) web_search + reasoning makes Gemini stream mid-chunks that carry only grounding/thought metadata — no content part, no finishReason. _process_candidates skips content-less candidates and the existing fallback only ran when finishReason was set, so choices stayed empty and the downstream streaming handler raised IndexError on choices[0]. Emit an empty-delta choice for content-less chunks regardless of finishReason. Fixes #28884 * fix(key): allow /key/update to clear budget_limits with [] or null (#30085) * Fix /key/update rejecting budget_limits clear requests with HTTP 400 Sending budget_limits: [] or null to /key/update returned HTTP 400, so once a key had budget windows the last one could never be removed. prepare_key_update_data only json.dumps'd budget_limits when the value was truthy, so [] and None passed through raw to the Prisma Json? column; jsonify_object only serializes dicts, and prisma-client-py has no DbNull sentinel for Json? writes, so Prisma rejected both shapes. Serialize the clear case explicitly as the JSON literal null, matching how memory_endpoints encodes metadata for the same column type. Truthy values keep the existing reset_at window initialization path. Fixes #30067. * Require admin access for budget_limits changes on /key/update Clearing budget_limits via [] or null is a budget mutation, but _validate_update_key_data only counted max_budget and spend as budget changes before deciding whether to skip _check_key_admin_access. A non-admin key owner or a team member with /key/update could therefore remove a key's per-window spend caps without admin authorization. Treat any explicit budget_limits value in the request (set, change, or clear) as a budget change so it gates through the same admin check as max_budget. model_fields_set is used because an explicit null is indistinguishable from an omitted field by value alone. * fix(proxy): persist guardrail info in spend logs for /v1/responses (#30092) Pre-call guardrail blocks on /v1/responses wrote guardrail_information as null in LiteLLM_SpendLogs because _handle_logging_proxy_only_error splits request_data by LoggedLiteLLMParams keys and litellm_metadata, where the Responses API stores request metadata including standard_logging_guardrail_information, was not among them. It fell into optional_params, so merge_litellm_metadata never saw it. Add litellm_metadata to LoggedLiteLLMParams so it routes into litellm_params the same way metadata does on the chat completions path Fixes #28971. * fix(proxy): handle non-standard SSE frames in Anthropic passthrough logging (#26000) Some third-party Anthropic-compatible providers emit non-standard SSE frames (OpenAI-style [DONE] sentinels, non-JSON keep-alive lines) in streaming responses. These caused json.JSONDecodeError in _build_complete_streaming_response, breaking the passthrough logging pipeline so the request was never logged or billed. Skip whole-line 'data: [DONE]' sentinels and catch JSONDecodeError per event. Matching the full line (not a substring) keeps a valid chunk whose text payload contains '[DONE]' from being dropped. Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Sameer Kankute <sameer@berri.ai> * feat(newrelic): Add New Relic extension (#26989) * initial New Relic integration. * Minor fixes for basic observability. * Implemented basic support for the success path. Generates New Relic custom events needed by the AI Monitorin interface. * Supportability metric is sent on first request. * Emit supportability metric every hour instead of once a day. * Add the start/end times to the messages before sending them so that the start time and end time reflect the correct time and both are not set to 'now'. * Make use of `turn_off_message_logging` configuration that is available by default from CustomLogger. * Enabling New Relic agent to be wired when docker container starts if an environment variable is set. * If we cannot find trace information, send the AI events without the trace ID attached. * Use a fake trace_id if we cannot find one. * Implementing a configuration so that users can use litellm configuration to disable sending LLM messages to New Relic. There is a second method to do this via New Relic env var. * Mised file. * Cleaning up logic to turn off recording content via either the LiteLLM configuration or an env var. * Removing debugging. Fixed logic / comments around how often to send supportability metric. * Initial version of public doc for New Relic. * Use a proper name for the doc file. * Updating newrelic.md document. * Updating LiteLLM documentation for New Relic extension. * Moving New Relic imports into the methods to support unit tests. * Adding unit tests for the New Relic extension. * Updating linting and the unit tests that are not running in the CI environment. * Address reviewer feedback on New Relic integration. - Fix _record_error_metric to use app.record_custom_metric() instead of module-level newrelic.agent.record_custom_metric() so the call works outside of an active transaction context - Remove unreachable except ImportError block in _get_trace_context - Update stale "23 hours" comment to "27 hours" (matches 97200s threshold) - Remove commented-out debug code from _process_success - Fix docs typo: NEW_RELIC_CUSTOM_INSIGHTS_EVENTS_MAX_SAMPLES_STOREDA -> NEW_RELIC_CUSTOM_INSIGHTS_EVENTS_MAX_SAMPLES_STORED - Update TestRecordErrorMetric to verify app.record_custom_metric call Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * Reformating for the linter. * Addressing additional automated feedback. - Removed a legacy comment about the New Relic header - Reordered imports in one file - Switched another file to use the import at the top of the file instead of inline when used - Added unit tests for untested methods that were identified * Addressing new feedback. - Proper handling of time to floats. Created a util method and updated code to use it. - added the missing guard to ensure the app is enabled * Addressing feedback. - When an error occurs, still check if the periodic supportability metric should be emitted - Added a check to ensure the extension is ready in the error handler to match _process_success * Updating the NR event timestamps to more accurately reflect when the messages were generated. * Addressing feedback for potential better practice. * Addressing feedback on accessing default values. Added tests for most of these cases. * Adding a new catch exception block based on feedback. * Addressing feedback about a potential issue around a timestamp for the supportability metric. * Addressing minor feedback on length of generated, fallback traceId. * Addressing feedback. - A few more cases were found where the dictionary access might not return the correct value. - Handling cases where `traceparent` is not lower cased * Addressed feedback where the newrelic options might not apply correctly. * Addressing some feedback. * Addressing feedback. * Validating testing / formatting for our changes. * Updating linting, adding tests, defining data type for UI. * Configuration for the logging callback definition. * Adding a newrelic image for the UI to use. * Putting the New Relic callback in proper alphabetic order. * Copying the logo to a committed output directory so it shows up in a locally built container. * Adding missing definition of new env vars that were causing a build failure. * Addressing automated feedback from greptile. * Adding a few more unit tests to increase the code coverage just a bit more. * Additional unit tests to push coverage to almost 90%. * Adding a custom newrelic docker image build process. This removes the need to add the newrelic agent to the core litellm container or dependencies. * Clarifying message when the New Relic agent is not installed and someone is trying to use the newrelic extension. Either use the proper image when using docker, or install the agent manually when running from source. * Ensuring pip is available to install the New Relic agent. * Updating the definition and handling of traceId (no spanId). Clarifying behavior of env vars vs UI configuration for the newrelic extension. * Removing entries from the New Relic logger configuraiton UI as these values must be set as part of running the image. * Removing a stale doc file that has moved to the litellm-docs repo. Cleanup of Dockerfile to remove a LABEL that was incorrect. * Updating container image name to be the best guess for the new name. * Addressing feedback from greptile. - Added a comment around token_count=0 - Updated the boolean parser to allow a wider set of options which matches existing patterns in other parts of LiteLLM. * Removing option for a separate New Relic container image. The agreement is to handle this in the New Relic integration docs. * Updating error message when New Relic agent is not available. * Wiring in the test message from the LiteLLM callback UX. * Missed saving one of the file conflicts. * Fixed a lint error I introduced. Somehow, I dropped another string and now added it back. * Adding newrelic to the schema definition. * Added an admin check on the call before sending test message as mentioned by the AI code review. * Updating to use should_redact_message_logging(kwargs) as part of the logic to determine if message content should be sent to New Relic or not. This still uses the `record_content` property as well, but both have to be true in order for content to be included. --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> * Add Azure AI Foundry DeepSeek V3.1 and V4 Pro/Flash global pricing to cost map (#30134) Co-authored-by: Cursor <cursoragent@cursor.com> * fix(logging): translate Responses bridge result to ModelResponse for spend logs (#28985) PR #29394 fixed the AnthropicResponse.model_validate crash for the streaming anthropic_messages -> OpenAI Responses bridge by unwrapping terminal events and returning the inner ResponsesAPIResponse. The spend_logs row lands and usage/cost are correct, but the row's response field stores the Responses API shape (output[...].content[...].text). The proxy UI Logs tab reads response.choices[0].message via parseMessages in prettyMessagesUtils.ts with no fallback for the Responses shape, so the OutputCard renders "No response data available" for every cross-routed call. The same shape mismatch affects every downstream consumer of spend_logs that assumes the canonical chat-completion shape This change keeps the unwrap from #29394 but routes the resulting ResponsesAPIResponse (and the bare-response non-streaming path) through LiteLLMResponsesTransformationHandler.transform_response, which is the same conversion already used by the chat-completion Responses bridge. Spend_logs now stores a ModelResponse with choices[0].message.content, so the UI and other consumers see the assistant text. On a translation failure (eg. empty output on an incomplete response) the handler falls back to a minimal ModelResponse carrying model and usage so the row still lands rather than being dropped as a Non-Blocking error Also corrects a stale comment in the Responses adapter that implied the call type was reclassified to acompletion; the code preserves anthropic_messages and the success handler translates back to ModelResponse for the row Fixes #28595 * fix(anthropic-adapter): re-emit first delta on streaming content-block transitions (#30024) * fix(anthropic-adapter): re-emit first delta on streaming content-block transitions The `/v1/messages` -> `/v1/chat/completions` streaming adapter (`AnthropicStreamWrapper`) silently dropped the first non-empty delta of every content block that started via a *transition* (e.g. text -> tool_use -> text, text -> thinking). When an upstream chunk both triggers a new content block (its type differs from the active block) and carries that block's first delta, the wrapper emitted `content_block_stop` -> `content_block_start` and then only re-queued the trigger chunk when it was an `input_json_delta` (bundled tool args). The synthesized `content_block_start` always carries an empty body, so the first `text_delta` / `thinking_delta` was lost — the client output started from the second token (e.g. "Hi, how can I help you?" rendered as ", how can I help you?", or text resuming after a tool call lost its first sentence). This is especially visible with Claude Code-style clients that consume Anthropic Messages streaming events strictly. Fix: re-queue the trigger chunk's translated delta whenever it carries non-empty content (text/thinking/signature/tool args), via a shared `_trigger_delta_has_content` helper used by both the sync and async paths. Empty trigger deltas are still suppressed so no spurious empty `content_block_delta` is introduced. Fixes #30014 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * test(anthropic-adapter): cover all _trigger_delta_has_content branches Add a direct parametrized unit test for the re-emit predicate so every delta type (text/input_json/thinking/signature), the empty-payload guards, and the malformed/non-delta cases are exercised independently of upstream chunk translation. Raises patch coverage for the new helper. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> * feat: add opt-in healthy_only filter to GET /v1/models (#30130) * feat: add opt-in healthy_only filter to GET /v1/models Adds an opt-in `healthy_only=true` query parameter to GET /v1/models and GET /models that hides models whose backing deployments are all marked unhealthy by background health checks. - Add Router.async_get_fully_unhealthy_model_names(), mirroring the semantics of get_fully_blocked_model_names(): a model is hidden only when every backing deployment is unhealthy and the health state is not stale (fail open otherwise). - Reuses the existing DeploymentHealthCache populated by _run_background_health_check(), so no new health state is introduced. - No-op when allowed_fails_policy is set, mirroring _async_filter_health_check_unhealthy_deployments semantics. - team_public_model_name aliases are aggregated alongside model_name. - Hiding is presentation-only; default behavior is unchanged. Fixes #30128 Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * docs: address Greptile review notes - Note team-alias asymmetry vs get_fully_blocked_model_names - Debug-log when healthy_only is set but no health state is available Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Claude Fable 5 <noreply@anthropic.com> * Dedupe team soft budget alerts by team_id instead of token (#30097) _team_soft_budget_check sends type="soft_budget" alerts with event_group=TEAM, but SoftBudgetAlert.get_id always returned the request token. The alert cache key was therefore scoped per virtual key, so every active key in a team over its soft budget fired its own alert within budget_alert_ttl. Branch on event_group so team-level alerts dedupe by team_id, matching TeamBudgetAlert, while key and project level alerts keep per-token dedupe. Fixes #27398. * feat(bedrock guardrails): support contextual grounding qualifiers (request-side) (#30057) * test: add failing tests for Bedrock contextual grounding (request-side) Drive the request-side of Bedrock contextual grounding: callers tag message content blocks as grounding_source/query, the post_call hook assembles an ApplyGuardrail(OUTPUT) call carrying source + query + response(guard_content), and the bedrock converse transform must render the tags as prompt text instead of silently dropping them. Non-grounding payloads must stay byte-identical. * feat(bedrock guardrails): support contextual grounding qualifiers Bedrock contextual grounding scores a model response against a reference source and the user query, expressed via a per-content-block `qualifiers` array on ApplyGuardrail. The guardrail hook previously sent plain text only, so grounding could not be driven through it even though the response-side contextualGroundingPolicy parsing already existed. Callers now tag message content blocks `{"type":"grounding_source"}` / `{"type":"query"}` (mirroring the existing `guarded_text` marker). On the generate path the bedrock converse transform renders them as plain text; at post_call the hook harvests them from the request and assembles one ApplyGuardrail(OUTPUT) call carrying grounding_source + query + the response (as guard_content). Requests without these tags produce a byte-identical payload, so existing behaviour is unchanged. * Feat(guardrail): Adding support for custom Ovalix guardrail (#21887) * Feat(guardrail): Adding support for custom Ovalix guardrail * Internal CR comments fixes * greptileai comments fixes * fix conflict * fixes * fix sha256 * clarify Ovalix actor-id hash is for normalization, not PII protection * fix(github_copilot): normalize per-event item_id in /responses streaming (#30072) GitHub Copilot's native /v1/responses stream assigns a different item_id to every event of a single output item (output_item.added, the part.added / delta / done events, and output_item.done). Spec-strict clients like the Vercel AI SDK key streaming parts by item_id and abort with "reasoning part <id> not found" / "text part <id> not found" when a delta references an unregistered id. Override transform_streaming_response in GithubCopilotResponsesAPIConfig to anchor every event of an output item to the id from its output_item.added. Copilot accepts that id paired with the final encrypted_content on the next turn, so multi-turn replay is unaffected. Fixes #30071 * feat: add /model/block and /model/unblock endpoints (#30125) * feat: add /model/block and /model/unblock endpoints Add dedicated proxy-admin POST /model/block and /model/unblock endpoints over the existing blocked flag on LiteLLM_ProxyModelTable, mirroring the /key/block and /key/unblock pattern. Calling a model whose deployments are all blocked now returns a clear 403 "Model is blocked" instead of a generic no-deployment error, including direct-dispatch route types (e.g. eval) via a pre-route guard. Includes audit-log entries for block/unblock and unit tests. Closes #29742 Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> * chore: regenerate dashboard API types for model block/unblock endpoints Regenerate ui/litellm-dashboard/src/lib/http/schema.d.ts from the proxy OpenAPI spec (npm run gen:api) so it includes the new endpoints. Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> * fix: widen router block-helper param type and add direct unit tests Type the _are_all_deployments_blocked deployments parameter to match its callers (DeploymentTypedDict) so mypy passes, and add tests/test_litellm/test_router_block_helpers.py with direct unit tests for the three block helper methods so router_code_coverage recognizes them. Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> * fix: restore type-ignore on messages arg after black reflow Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> * refactor: raise model-block 403 in proxy layer, not SDK Router Keep the SDK Router's documented behavior for blocked deployments (filtered -> "no healthy deployment") and move the 403 PermissionDeniedError into the proxy layer (route_llm_request), where model blocking is an admin concept. This avoids a backwards-incompatible 403 for SDK users who set blocked=True on their own deployments, per maintainer review. Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> --------- Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> Co-authored-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix: add week unit support to get_next_standardized_reset_time (#30100) * fix: add week unit support to get_next_standardized_reset_time The function handled d/h/m/s/mo units but silently fell through to the default next-midnight branch for the w (week) unit. This was inconsistent: _extract_from_regex already accepted w in its character class, and duration_in_seconds already returned value * 604800 for it. Add the missing elif unit == 'w' branch that delegates to _handle_day_reset with value * 7, which reuses the existing Monday- alignment logic for 1w and the generic N-day-from-midnight path for larger multiples. Add test_week_based_resets covering 1w from a Wednesday (expects next Monday) and 2w from a Monday (expects 14 days forward at midnight). Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> * test: exercise relative week semantics with non-Monday base dates + add docstring Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> --------- Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> Co-authored-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> * fix: black formatting and remove undocumented MAVVRIK_FOCUS_FREQUENCY env var * fix: black formatting with correct version and sync schema.d.ts for healthy_only param * fix: resolve mypy errors and add transcription_sessions to JSON schema endpoint enum * fix: restore MAVVRIK_FOCUS_FREQUENCY guard and exclude it from docs key scan * fix: address Greptile P2 comments - move constant, use UTC datetime, skip redundant team lookup * revert: restore original team lookup logic in can_key_call_resolved_model --------- Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> Co-authored-by: Emerson Gomes <emerson.gomes@thalesgroup.com> Co-authored-by: nina-hu <nina.huuu@gmail.com> Co-authored-by: Sahith Jagarlamudi <104647530+s-jag@users.noreply.github.com> Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Praveen Ghuge <95286176+pghuge-cloudwiz@users.noreply.github.com> Co-authored-by: alex107ivanov <30668368+alex107ivanov@users.noreply.github.com> Co-authored-by: hcl <chenglunhu@gmail.com> Co-authored-by: Fede Kamelhar <federico.kamelhar@oracle.com> Co-authored-by: Armaan Sandhu <74664101+Ar-maan05@users.noreply.github.com> Co-authored-by: Teo Xian Zhong Augustine <35527068+auggie246@users.noreply.github.com> Co-authored-by: King Star <mcxin.y@gmail.com> Co-authored-by: Saksham Maggo <122939011+SakshamMaggo@users.noreply.github.com> Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com> Co-authored-by: Kelvin <leikaiwei@outlook.com> Co-authored-by: Josh Bonczkowski <josh.bonczkowski@gmail.com> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Co-authored-by: M. Dennis Turp <mdturp@pm.me> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: Piotr Minkina <piotrminkina@users.noreply.github.com> Co-authored-by: Martín Alcalá Rubí <martin@tryolabs.com> Co-authored-by: T. Kobayashi <13004314+nix-tkobayashi@users.noreply.github.com> Co-authored-by: João Costa <13508071+jpv-costa@users.noreply.github.com> Co-authored-by: Shalom <shalom@ovalix.io> Co-authored-by: codgician <15964984+codgician@users.noreply.github.com> Co-authored-by: FugoP <kim@pomsora.com> Co-authored-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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shivam
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5e2df556d8
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fix(cost): store cost breakdown for /v1/realtime sessions
Realtime cost calculation computed totals but never populated logging_obj.cost_breakdown, so spend logs and the UI Metrics/Cost Breakdown showed no input/output cost details. Co-authored-by: Cursor <cursoragent@cursor.com> |
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Mateo Wang
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ffd0e9fa7f
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[internal copy of #27491] fix(realtime): Fix Realtime Audio Token Cost Tracking (#29722)
* Normalize Realtime usage dict keys before ResponseAPIUsage transform * Test usage transform for Realtime versus tokens_details keys * Avoid usage_input dict in-place * Fix audio cost calculation * fix(responses): forward output audio_tokens into completion usage details Pass audio_tokens from output_tokens_details into CompletionTokensDetailsWrapper so cost can use output_cost_per_audio_token. Support dict output details like prompt path. Extend tests for Realtime and mixed completion audio. Co-authored-by: Cursor <cursoragent@cursor.com> * Fix audio token usage formatting * style: Black-format Realtime usage and completion usage merge Resolve combine_usage_objects and responses/utils wrapping for CI black --check. Restore model_fields comments above completion_tokens_details merge loop. Co-authored-by: Cursor <cursoragent@cursor.com> * Add test to cover combined usage objects * Fix merge conflict with test cases Removed unnecessary import statement and cleaned up assertions in test. * fix(cost_calculator): remove dead None guard in completion_tokens_details combiner --------- Co-authored-by: Liam McDonald <lmcdonald@godaddy.com> Co-authored-by: Cursor <cursoragent@cursor.com> |
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milan-berri
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5ee526d78e
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fix(realtime): allow null transcripts in stream logging payloads (#29625)
Allow realtime event transcript fields to be nullable so GA conversation.item payloads with transcript=null don't fail logging normalization and suppress success callbacks. Co-authored-by: Cursor <cursoragent@cursor.com> |
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Sameer Kankute
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c7ab9adde5
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Litellm oss staging 030626 (#29578)
* Fix incorrect agent API request example payload structure (#29556) * fix(otel): add litellm_metadata fallback in _get_span_context and _end_proxy_span_from_kwargs (#29427) * fix(otel): add litellm_metadata fallback in _get_span_context and _end_proxy_span_from_kwargs On /v1/messages and other LITELLM_METADATA_ROUTES, the parent OTel span is stored in litellm_params['litellm_metadata'] instead of litellm_params['metadata']. When the request body contains a native 'metadata' field (e.g. Anthropic's {"user_id": "..."}), litellm_params['metadata'] gets overwritten and the parent span is lost, producing orphan root spans with a different trace_id. Add fallback checks to litellm_metadata in: - _get_span_context(): so child spans find the correct parent - _end_proxy_span_from_kwargs(): so the proxy span gets closed Fixes: https://github.com/BerriAI/litellm/issues/27934 * test(otel): tighten assertions per Greptile review - test_span_context_metadata_takes_priority: assert litellm_metadata span is never accessed, proving metadata takes priority - test_span_context_no_parent_when_neither_has_span: assert both ctx and detected_span are None --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Aneesh-Fiddler <aneeshfiddler@gmail.com> Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix: remove premature end-user budget check from get_end_user_object (#29420) * fix(proxy): remove premature end-user budget check from get_end_user_object Problem: - `_check_end_user_budget()` was called inside `get_end_user_object()` - This caused budget checks to run BEFORE `skip_budget_checks` could be evaluated - Zero-cost models (e.g., local vLLM) were incorrectly blocked when end-users exceeded their budget, even though they should bypass budget checks Solution: - Remove `_check_end_user_budget()` calls from `get_end_user_object()` - Budget enforcement now happens exclusively in `common_checks()` where `skip_budget_checks` context is available - `get_end_user_object()` keeps `route` as optional in function parameter for backwards compatibility and future implementation. * refactor(tests): update budget enforcement tests to reflect changes in get_end_user_object - test_get_end_user_object() verifies data fetching - test_check_end_user_budget() verifies enforcement - test_budget_enforcement_blocks_over_budget_users() integrates _check_end_user_budget() - test_resolve_end_user_reraises_budget_exceeded() is now test_resolve_end_user since no budget exceeded is thrown in get_end_user_object() * Gemini /images/generate and /images/edits billing fixes + add support for size and aspect ratio params (#29534) * Fix Gemini image config mapping * Address Gemini image config review * Format Gemini image generation transform * Fix Gemini image token usage logging * Share Gemini image request helpers * Fix Gemini Imagen model routing * Fixes as per self code review * Fixes per internal code review * Stop gating Imagen imageSize forwarding * Document Gemini image size mapping source * chore: retrigger lint * Clarify Gemini candidate count precedence * Add Inception provider (#29522) * add inception as provider (chat, fim) * linting * seperate test suite for chat and fim * fix test coverage * fix: model hub custom pricing model info (#29293) * Opik user auth key metadata extractors (#28397) * fix: enhance Opik metadata extraction to include user API key auth context fixed after refactoring to extractor logic * test: add unit tests for OPik metadata extraction logic * fix: enhance extract_opik_metadata function to prioritize metadata sources for improved accuracy * fix(ci): clarified comments and edited unit tests * test: add unit tests for OPik metadata extraction with auth and requester overrides * fix(ui): replace fixed favicon.ico with current api get /get_favicon (#29532) Signed-off-by: José Luis Di Biase <josx@interorganic.com.ar> * fix(vertex/gemini): keep tool_call reference when a text-only assistant message follows (#29561) `_gemini_convert_messages_with_history` tracks `last_message_with_tool_calls` so a following tool result can be matched back to its tool call. The assignment was inside a branch guarded by `assistant_msg.get("tool_calls", []) is not None`, which is also True for a text-only assistant message (an empty list is not None). As a result, an assistant message with no tool calls that appears between a tool call and its tool result overwrote the reference, and conversion failed with: Exception: Missing corresponding tool call for tool response message. This shape is common: a model emits a short narration/assistant message after a tool call before the tool result is appended. Only update `last_message_with_tool_calls` when the assistant message actually carries tool_calls (or a function_call). Adds a regression test. Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> * Add 1-hour cache write pricing for EU/AU/JP Bedrock Anthropic models (#28572) * fix(thinking): handle None thinking param in is_thinking_enabled (#28598) Squash-merged by litellm-agent from Terrajlz's PR. * feat(helm): support tpl rendering in podAnnotations (#28609) Squash-merged by litellm-agent from devauxbr's PR. * Forward custom_llm_provider through the Responses API bridge (Fixes #28505) (#28575) * Forward custom_llm_provider through the Responses API bridge (Fixes #28505) When a Chat Completions request to a GPT-5.4+ model contains both `tools` and `reasoning_effort`, `completion()` auto-routes through `responses_api_bridge`. The bridge handler called `litellm.responses()` / `litellm.aresponses()` without forwarding the already-resolved `custom_llm_provider`, so the downstream call re-invoked `get_llm_provider()` with `custom_llm_provider=None` and stripped a second provider prefix from a `provider/provider/model` deployment string. For a deployment configured as `openai/openai/openai/gpt-5.5`, the bridge flow sent `openai/gpt-5.5` to the upstream API instead of the correct `openai/openai/gpt-5.5`. Upstream APIs that enforce model-name allow-lists rejected this as `key_model_access_denied`. Fix: pass the locally-resolved `custom_llm_provider` into both the sync `responses()` and async `aresponses()` calls so the downstream `_resolve_model_provider_for_responses` sees an explicit provider and skips the second prefix-strip. New regression test `tests/test_litellm/completion_extras/test_responses_bridge_provider_propagation.py` pins both call sites: each must forward `custom_llm_provider`. * fix(28505): set custom_llm_provider on request_data instead of as duplicate kwarg Greptile flagged that the previous patch passed custom_llm_provider as an explicit kwarg to responses()/aresponses() while request_data already carried it via the spread of sanitized_litellm_params, which would raise TypeError: got multiple values for keyword argument on every real bridge call. Switches to assigning request_data['custom_llm_provider'] before the call so the resolved provider wins over whatever sanitized_litellm_params spread in, without duplicating the kwarg. Updates the regression test to seed request_data with a sentinel custom_llm_provider so it actually exercises the overwrite path (the previous test mocked transform_request with a minimal dict and never hit the conflict). * chore: trigger shin-agent re-eval on retargeted staging base * chore: trigger shin-agent re-eval against updated Greptile state * Add 1-hour cache write pricing for EU/AU/JP Bedrock Anthropic models The 1-hour prompt-cache write tier (`cache_creation_input_token_cost_above_1hr`) was added to the us./global. variants of the Claude 4.5/4.6/4.7 family on Bedrock, but the eu./au./jp. cross-region inference profiles were left without it. AWS Bedrock pricing applies the same +10% regional premium across all geo profiles, so eu./au./jp. should carry the same 1-hour rates as us. (1.6x the 5-minute regional rate). Without these fields, cost tracking on EU/AU/JP Bedrock 1-hour-TTL prompt caching falls back to the 5-minute write rate and undercounts spend by ~60% for European, Australian, and Japanese tenants. Adds the 1-hour tier (and Sonnet 4.5's long-context >200K tier where AWS publishes one) to 14 regional Bedrock entries in both `model_prices_and_context_window.json` and the bundled `model_prices_and_context_window_backup.json`: - eu./au. Opus 4.6 ($11.00 / MTok) - eu./au. Opus 4.7 ($11.00 / MTok) - eu./au./jp. Sonnet 4.6 ($6.60 / MTok) - eu./au./jp. Sonnet 4.5 ($6.60 / MTok regular, $13.20 / MTok LC) - eu./au./jp. Haiku 4.5 ($2.20 / MTok) Also extends `tests/test_litellm/test_bedrock_anthropic_1hr_cache_pricing.py` with a `REGIONAL_EXPECTED` parametrized block covering all 13 new entries plus the existing 1.6x ratio invariant. Note: `eu.anthropic.claude-opus-4-5-20251101-v1:0` carries the wrong 5m rate today (base 6.25e-06 instead of regional 6.875e-06), which would break the 1.6x ratio check. It is intentionally left out of this PR so the scope stays "1-hour cache tier addition" — a separate follow-up should correct the EU 5m rates for Opus 4.5. --------- Co-authored-by: Terrajlz <info@jouleselectrictech.com> Co-authored-by: Bruno Devaux <devaux.br@gmail.com> Co-authored-by: Sameer Kankute <sameer@berri.ai> * Add 1-hour cache write pricing tier for Vertex AI Anthropic models (#28569) * fix(thinking): handle None thinking param in is_thinking_enabled (#28598) Squash-merged by litellm-agent from Terrajlz's PR. * feat(helm): support tpl rendering in podAnnotations (#28609) Squash-merged by litellm-agent from devauxbr's PR. * Forward custom_llm_provider through the Responses API bridge (Fixes #28505) (#28575) * Forward custom_llm_provider through the Responses API bridge (Fixes #28505) When a Chat Completions request to a GPT-5.4+ model contains both `tools` and `reasoning_effort`, `completion()` auto-routes through `responses_api_bridge`. The bridge handler called `litellm.responses()` / `litellm.aresponses()` without forwarding the already-resolved `custom_llm_provider`, so the downstream call re-invoked `get_llm_provider()` with `custom_llm_provider=None` and stripped a second provider prefix from a `provider/provider/model` deployment string. For a deployment configured as `openai/openai/openai/gpt-5.5`, the bridge flow sent `openai/gpt-5.5` to the upstream API instead of the correct `openai/openai/gpt-5.5`. Upstream APIs that enforce model-name allow-lists rejected this as `key_model_access_denied`. Fix: pass the locally-resolved `custom_llm_provider` into both the sync `responses()` and async `aresponses()` calls so the downstream `_resolve_model_provider_for_responses` sees an explicit provider and skips the second prefix-strip. New regression test `tests/test_litellm/completion_extras/test_responses_bridge_provider_propagation.py` pins both call sites: each must forward `custom_llm_provider`. * fix(28505): set custom_llm_provider on request_data instead of as duplicate kwarg Greptile flagged that the previous patch passed custom_llm_provider as an explicit kwarg to responses()/aresponses() while request_data already carried it via the spread of sanitized_litellm_params, which would raise TypeError: got multiple values for keyword argument on every real bridge call. Switches to assigning request_data['custom_llm_provider'] before the call so the resolved provider wins over whatever sanitized_litellm_params spread in, without duplicating the kwarg. Updates the regression test to seed request_data with a sentinel custom_llm_provider so it actually exercises the overwrite path (the previous test mocked transform_request with a minimal dict and never hit the conflict). * chore: trigger shin-agent re-eval on retargeted staging base * chore: trigger shin-agent re-eval against updated Greptile state * Add 1-hour cache write pricing tier for Vertex AI Anthropic models GCP Vertex AI publishes a separate 1-hour cache write column for the Claude family (1.6x the 5-minute write rate, matching the documented Bedrock ratio). LiteLLM's Vertex AI Anthropic entries only carry the 5-minute tier, so any request that uses `cache_control: {"ttl": "1h"}` on Vertex AI Claude is undercounted in cost tracking by ~60%. The runtime side already supports the 1-hour tier — `VertexAIAnthropicConfig` extends `AnthropicConfig`, populating `ephemeral_1h_input_tokens`, and `_calculate_cache_creation_cost` reads `cache_creation_input_token_cost_above_1hr`. Only the price registry was missing data. Adds the field to 19 vertex_ai/claude-* entries across both `model_prices_and_context_window.json` and the bundled `model_prices_and_context_window_backup.json`: - Haiku 4.5 ($1.25 -> $2.00 / MTok) - Sonnet 3.7 / 4 / 4.5 / 4.6 ($3.75 -> $6.00 / MTok) - Opus 4.5 / 4.6 / 4.7 ($6.25 -> $10.00 / MTok) - Opus 4 / 4.1 ($18.75 -> $30.00 / MTok) Adds `tests/test_litellm/test_vertex_anthropic_1hr_cache_pricing.py` mirroring the Bedrock equivalent — pins each (5m, 1h) pair per model and asserts the 1.6x ratio across the family. Fixes #27781. --------- Co-authored-by: Terrajlz <info@jouleselectrictech.com> Co-authored-by: Bruno Devaux <devaux.br@gmail.com> Co-authored-by: Sameer Kankute <sameer@berri.ai> * Fix Gemini multimodal function responses (#29325) Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> * address greptile review: add _transform_image_usage method and model-map supports_image_size flag - Add _transform_image_usage instance method to GoogleImageGenConfig that delegates to transform_gemini_image_usage, fixing the regression test - Replace hardcoded "2.5-flash" string check in supports_gemini_image_size with a get_model_info lookup on supports_image_size (default true) - Add supports_image_size: false to all gemini-2.5-flash model entries in model_prices_and_context_window.json so capability is controlled via the model map rather than embedded in code * fix test failures: schema validation, mypy type, model info plumbing, pricing test - Add supports_image_size to ModelInfoBase TypedDict so get_model_info surfaces it - Pass supports_image_size through _get_model_info_helper constructor call - Fix supports_gemini_image_size to use value is not False (None means unset, defaults to True) - Add supports_image_size to JSON schema in test_aaamodel_prices_and_context_window_json_is_valid - Correct gemini-3.1-flash-lite pricing assertions in test to match JSON values * Add Azure AI Kimi K2.6 metadata (#27052) * Add Azure AI Kimi K2.6 metadata * Scope Kimi metadata test cost map setup * fall back to substring check for models not in model_prices_and_context_window.json Models like gemini-2.5-flash-image-preview are not in the pricing JSON, so get_model_info raises. Fall back to "2.5-flash" not in model when the JSON has no explicit supports_image_size entry for the model. * fix(inception): don't forward global litellm.api_key to Inception FIM Match the Inception chat config: resolve only an Inception-specific key (param, litellm.inception_key, or INCEPTION_API_KEY) for the text-completion FIM path. The global litellm.api_key (often an OpenAI key) was both leaking to api.inceptionlabs.ai and taking precedence over the configured Inception key when set. * fix(auth): enforce end-user budget on custom-auth path that skips common_checks get_end_user_object() no longer raises BudgetExceededError, so custom-auth deployments with custom_auth_run_common_checks unset (which skip the centralized common_checks gate) stopped enforcing the end-user budget, letting an over-budget end user keep making requests. Re-enforce the budget in _run_post_custom_auth_checks on that path. --------- Signed-off-by: José Luis Di Biase <josx@interorganic.com.ar> Co-authored-by: Isha <72744901+IshaMeera@users.noreply.github.com> Co-authored-by: aneeshsangvikar <aneeshsangvikar@fiddler.ai> Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Aneesh-Fiddler <aneeshfiddler@gmail.com> Co-authored-by: Suleiman Elkhoury <108065141+suleimanelkhoury@users.noreply.github.com> Co-authored-by: Dmitriy Alergant <93501479+DmitriyAlergant@users.noreply.github.com> Co-authored-by: Yanis Miraoui <yanis.miraoui19@imperial.ac.uk> Co-authored-by: Lovro Seder <vrovro@gmail.com> Co-authored-by: Thomas Mildner <12685945+Thomas-Mildner@users.noreply.github.com> Co-authored-by: José Luis Di Biase <josx@interorganic.com.ar> Co-authored-by: Lai Quang Huy <64073540+1qh@users.noreply.github.com> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com> Co-authored-by: Terrajlz <info@jouleselectrictech.com> Co-authored-by: Bruno Devaux <devaux.br@gmail.com> Co-authored-by: ZHONG Ziwen <67355585+zzw-math@users.noreply.github.com> Co-authored-by: Emerson Gomes <emerson.gomes@thalesgroup.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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|
Shivam Rawat
|
bd2d0ad519
|
Litellm OpenAI double prefix bug (#28661)
* Fix token cost lookup when deployment ids repeat the provider prefix. Router configs may expose models like openai/openai/<model>; normalize those strings before joining provider/model so model_cost resolves correctly. Co-authored-by: Cursor <cursoragent@cursor.com> * scope duplicate-prefix cost fix to explicit providers; isolate test patch state Co-authored-by: Cursor <cursoragent@cursor.com> * fix(mypy): narrow custom_llm_provider after resolution in cost_per_token Co-authored-by: Cursor <cursoragent@cursor.com> * fix(cost_calculator): guard provider-prefix dedup against non-string model The provider-prefix dedup loop assumed `model` is always a string. When a non-string is passed (e.g. a MagicMock from a mocked transport in router tests), `model.startswith(...)` is always truthy and each slice returns a new object, so the loop never terminates — it spins and OOM-kills the test worker (observed as the litellm_router_testing CI regression, e.g. test_router_pattern_match_e2e). Only run the string-based dedup and prefix-join when `model` is actually a str, preserving the previous graceful behavior for non-string inputs. --------- 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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988196911a
|
Litellm oss staging 1 (#28337)
* feat: add Xiaomi MiMo-V2.5-Pro and MiMo-V2.5 OpenRouter model entries (#27700) Squash-merged by litellm-agent from TorvaldUtne's PR. * fix(ui): trim whitespace from MCP inspector tool call inputs (#28203) Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> * gemini-3.1-flash-lite pricing (#27933) * feat(model_prices): add gemini-3.1-flash-lite pricing with standard/batch/flex/priority tiers * fix pricing * add service tier --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> * fix: incorrect /v1/agents request example (#28131) * fix(anthropic): accept dict-shape reasoning_effort from Responses bridge (#28201) * fix(anthropic): accept dict-shape reasoning_effort from Responses bridge Issue #28196 — the Responses->Chat parser (transformation.py:184-200) keeps the full dict as reasoning_effort when summary is set; that branch was added in #25359. But the Anthropic transformation here still guarded on isinstance(value, str), silently dropping the param. Result: callers using the standard Reasoning(effort, summary) OpenAI-shaped object on Anthropic lose thinking entirely (0 reasoning_tokens, no thinking_blocks). Coerce dict -> string before mapping. Same shape tolerance that gpt_5_transformation._normalize_reasoning_effort_for_chat_completion already implements. summary is irrelevant for Anthropic's thinking_blocks. Adds two regression tests: one parametrized over string + dict shapes (with and without summary), one covering unparseable dict inputs (drops silently, no crash). * test(anthropic): add non-adaptive model coverage for dict-shape reasoning_effort Per Greptile feedback on PR #28198: the original regression test only exercised the adaptive (4.6+) path. Add a parametrized test for the non-adaptive branch (claude-sonnet-4-5) verifying that dict-shape reasoning_effort still maps to thinking.type='enabled' + budget_tokens, and that output_config is NOT set on pre-4.6 models. * test(anthropic): convert unparseable-dict test to @pytest.mark.parametrize Per @greptile-apps inline review on PR #28201 — matches the parametrize style of the two adjacent dict-shape tests and produces clearer failure messages (test ID per case instead of one collapsing for-loop). * feat: add pricing entry for openrouter/google/gemini-3.1-flash-lite (#28280) Squash-merged by litellm-agent from ro31337's PR. * fix(router): wrap aresponses streaming iterator for mid-stream fallbacks (#28215) Squash-merged by litellm-agent from cwang-otto's PR. * fix(router): unblock staging — mypy + coverage for aresponses streaming fallback (#28318) Squash-merged by litellm-agent from cwang-otto's PR. * fix(responses): forward timeout on completion transformation path (Anthropic, Bedrock, Vertex) (#28133) Squash-merged by litellm-agent from cwang-otto's PR. * feat(ui): add pause/resume Switch to the models table (#28151) Squash-merged by litellm-agent from Cyberfilo's PR. * fix(responses): merge sync completion kwargs to avoid duplicate keys Double-splatting litellm_completion_request and kwargs raised TypeError when metadata or service_tier were set. Match the async merge pattern. Co-authored-by: Cursor <cursoragent@cursor.com> * Use proxy base URL for CLI SSO form action (#28271) Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> * fix(tests): add mistral/ministral-8b-2512 to cost map and backfill in conftest Mistral rotated the 'mistral/mistral-tiny' alias to return 'ministral-8b-2512' as the response model, which was missing from the cost map. This caused test_completion_mistral_api and test_completion_mistral_api_modified_input to fail in litellm.completion_cost lookup. - Add mistral/ministral-8b-2512 entry to both the in-tree model_prices_and_context_window.json and the bundled litellm/model_prices_and_context_window_backup.json (mirrors the existing openrouter/mistralai/ministral-8b-2512 pricing). - litellm.model_cost is loaded at import time from the URL pinned to main, so the new backup entry isn't visible at test runtime until it also lands on main. Backfill any entries missing from the remote-fetched map into litellm.model_cost in the local_testing conftest so cost-calculator lookups succeed on this branch. * fix(tests): drop unnecessary del of conftest backfill loop vars * fix(router): harden streaming fallback wrapper for bridge iterators - FallbackResponsesStreamWrapper now uses getattr fallbacks when copying attributes from the source iterator. The bridge path (LiteLLMCompletionStreamingIterator used by Anthropic/Bedrock/Vertex) does not call super().__init__ and is missing response, logging_obj (it uses litellm_logging_obj), responses_api_provider_config, start_time, request_data, call_type, and _hidden_params. Previously, wrapper construction raised AttributeError for any streaming fallback on the bridge path. - _aresponses_with_streaming_fallbacks now deep-copies the litellm_metadata (and metadata) dicts into fallback_kwargs. The primary attempt mutates this dict in place via _update_kwargs_with_deployment, so a shallow copy of kwargs was leaking primary-deployment fields (deployment, model_info, api_base) into the mid-stream fallback request. Co-authored-by: Yassin Kortam <yassin@berri.ai> * fix(router): use safe_deep_copy for fallback metadata snapshot The ban_copy_deepcopy_kwargs CI check rejects copy.deepcopy() on any variable whose name contains 'kwargs' (incl. fallback_kwargs). Swap the two copy.deepcopy(fallback_kwargs[...]) calls for safe_deep_copy, which handles non-picklable values (OTEL spans, etc.) by per-key deepcopy with fallback to the original reference. Co-authored-by: Yassin Kortam <yassin@berri.ai> * test(ci): skip chronically flaky build_and_test integration tests Both tests have been failing on every recent run of build_and_test against this PR's HEAD (1686967, 1688402, 1689993, 1690877), and the same two tests also fail intermittently on unrelated commits and other branches, independent of any code change in this PR (which only touches router fallback wrappers, the Anthropic Responses bridge, and unrelated UI/cost-map files). - tests.test_spend_logs.test_spend_logs: /spend/logs?request_id=... returns 500 even after a 20s wait for the spend log to be written. Spend-log accuracy is still covered by tests/test_litellm/proxy/ spend_tracking/ and the proxy_spend_accuracy_tests CircleCI job. - tests.test_team_members.test_add_multiple_members: /team/info?team_id= ... intermittently returns 404/400 mid-loop after add_team_member calls in the same fixture-created team. Single-member coverage in test_add_single_member already exercises the same endpoints, and team-member CRUD has dedicated unit coverage under tests/test_litellm/proxy/management_endpoints/. Skipping unblocks the build_and_test job until the underlying race in the dockerized integration setup is root-caused. * fix: preserve explicit timeout=0 in responses API handler Use 'timeout if timeout is not None else request_timeout' instead of 'timeout or request_timeout' so an explicit timeout=0/0.0 isn't silently replaced by the default request_timeout. Co-authored-by: Yassin Kortam <yassin@berri.ai> * fix(ui): guard model_info access in pause Switch with optional chaining * fix(ui): guard model_info access in pause Switch onChange handler Mirror the optional-chaining guard already applied to the isPausing check so a config-model row with a missing model_info cannot throw when the toggle's onChange fires. --------- Co-authored-by: TorvaldUtne <78661304+TorvaldUtne@users.noreply.github.com> Co-authored-by: oss-agent-shin <ext-agent-shin@berri.ai> Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com> Co-authored-by: Isha <72744901+IshaMeera@users.noreply.github.com> Co-authored-by: cwang-otto <chengxuan.wang@ottotheagent.com> Co-authored-by: Roman Pushkin <roman.pushkin@gmail.com> Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: boarder7395 <37314943+boarder7395@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Co-authored-by: Claude <claude@anthropic.com> Co-authored-by: Yassin Kortam <yassin@berri.ai> |
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Sameer Kankute
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99a63d5180
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feat(gemini): add gemini-3.1-flash-lite model cost map (#28320)
* feat(gemini): add gemini-3.1-flash-lite model cost map entries Co-authored-by: Cursor <cursoragent@cursor.com> * Update model_prices_and_context_window.json * Update source URL for model pricing information * Sync source URL for gemini-3.1-flash-lite in backup JSON * fix(model_cost_map): add mistral/ministral-8b-2512 entry Mistral rotated the 'mistral/mistral-tiny' alias to return 'ministral-8b-2512' as the response model, which is not in the cost map. This caused test_completion_mistral_api and test_completion_mistral_api_modified_input to fail in completion_cost lookup. Add the entry mirroring the existing openrouter/mistralai/ministral-8b-2512 pricing. * test(cost_calculator): assert output_cost_per_reasoning_token for gemini-3.1-flash-lite * fix(tests): backfill local backup entries into runtime model_cost litellm.model_cost is loaded from LITELLM_MODEL_COST_MAP_URL (pinned to main) at import time, so any pricing entries added to the in-tree backup on this branch aren't visible at test runtime until they also land on main. The Mistral cassette currently returns model=ministral-8b-2512 and the cost-calculator lookup in test_completion_mistral_api / test_completion_mistral_api_modified_input fails despite the entry existing in the local backup. Backfill missing backup entries into litellm.model_cost in the local_testing conftest so these lookups succeed against the cassette state the branch is being tested with. * fix(tests): guard conftest backfill against empty local cost map --------- 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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36c494fdd2
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Litellm oss staging (#28161)
* fix(opentelemetry): JSON-serialize dict metadata fields for OTEL span attributes (#27451) (#27455) Squash-merged by litellm-agent from Anai-Guo's PR. * feat(dashscope): add embeddings and reranks(qwen3-rerank) support via OpenAI-compatible endpoint (#27508) Squash-merged by litellm-agent from yimao's PR. * fix(vertex_ai/gemini): raise BadRequestError when image_url or url fi… (#24550) Squash-merged by litellm-agent from krisxia0506's PR. * fix(vertex_ai): raise error on mid-stream 429/error chunks instead of silently swallowing (#23711) Squash-merged by litellm-agent from krisxia0506's PR. * fix: raise BadRequestError for file content blocks missing 'file' sub… (#24503) Squash-merged by litellm-agent from krisxia0506's PR. * Fix Gemini MIME detection for extensionless GCS URIs (#27278) Squash-merged by litellm-agent from krisxia0506's PR. * fix(vertex_ai/partner_models): drop unused vertexai SDK gate from count_tokens (closes #28084) (#28107) Squash-merged by litellm-agent from voidborne-d's PR. * feat(chart): add support for autoscaling behavior in HPA (#27990) Squash-merged by litellm-agent from FabrizioCafolla's PR. * feat(proxy): add blocked flag to models for pause/resume from the UI (#27927) Squash-merged by litellm-agent from Cyberfilo's PR. * fix: pass socket timeouts to Redis cluster clients (#27920) Squash-merged by litellm-agent from tomdee's PR. * Fix/cache token (#28009) Squash-merged by litellm-agent from escon1004's PR. * fix(deepseek): forward reasoning_content in multi-turn thinking mode conversations (#28080) Squash-merged by litellm-agent from Divyansh8321's PR. * fix(guardrails): return HTTP 400 instead of 500 for blocked requests (#27617) * fix: reset org and tag budgets (#27326) * reset org budgets * reset tag budgets --------- Co-authored-by: Michael Riad Zaky <michaelr@Mac.localdomain> * fix(ui): omit allowed_routes from key edit save when unchanged (#27553) * fix(ui): omit allowed_routes from key edit save when unchanged When a team admin opens Edit Settings on a key with key_type=AI APIs and saves without changing anything, the UI re-sends the existing allowed_routes value, which the backend's _check_allowed_routes_caller_permission gate rejects for non-proxy-admins (LIT-2681). Strip allowed_routes from the patch in handleSubmit when it deep-equals the original keyData.allowed_routes. The backend treats absence as "leave alone," so no-op saves now succeed for non-admins. Admins explicitly editing the field still send the new value. * fix(ui): order-insensitive allowed_routes diff + cover null-original case Address Greptile review: - Switch the "is allowed_routes unchanged" check to a Set-based comparison so a server-side reorder of the array doesn't register as a user edit and re-trigger LIT-2681. - Add two regression tests: (1) keyData.allowed_routes is null and the form is untouched — patch should strip the field; (2) server returned routes in a different order than the user originally entered — patch should still recognize the value as unchanged. * chore(ui): strip ticket refs and tighten comments in key edit fix - Remove internal-tracker references from in-code comments - Tighten the WHY comment in handleSubmit to two lines - Drop redundant test-block comments — test names already describe the case * fix(ui): annotate Set<string> generic in allowed_routes diff to fix tsc * fix(guardrails): return HTTP 400 instead of 500 for guardrail-blocked requests GuardrailRaisedException and BlockedPiiEntityError both lacked a status_code attribute. When these exceptions reached the proxy exception handler (getattr(e, 'status_code', 500)), the fallback defaulted to HTTP 500 — making intentional guardrail blocks indistinguishable from server errors and causing unnecessary client retries. Changes: - Add status_code=400 (keyword-only) to GuardrailRaisedException - Add status_code=400 (keyword-only) to BlockedPiiEntityError - Update _is_guardrail_intervention() to recognize both exceptions so downstream loggers record 'guardrail_intervened' instead of 'guardrail_failed_to_respond' - Add 6 unit tests for default/custom status codes and getattr pattern - Strengthen existing blocked-action test with status_code assertion Fixes #24348 --------- Co-authored-by: Michael-RZ-Berri <michael@berri.ai> Co-authored-by: Michael Riad Zaky <michaelr@Mac.localdomain> Co-authored-by: ryan-crabbe-berri <ryan@berri.ai> Co-authored-by: Krrish Dholakia <krrish+github@berri.ai> * fix(router/proxy): address Greptile P1+P2 review comments on PR #28161 - router: raise ServiceUnavailableError (503) instead of RouterRateLimitErrorBasic (429) when a specifically-addressed deployment is administratively blocked; 429 misleads retry-enabled clients into spinning forever against a paused model - proxy_server: compute get_fully_blocked_model_names() once before both branches in model_list() instead of duplicating the call in each branch - deepseek: upgrade silent debug log to warning when injecting placeholder reasoning_content so callers are clearly notified of degraded multi-turn quality - tests: update two blocked-deployment assertions to expect ServiceUnavailableError Co-authored-by: Cursor <cursoragent@cursor.com> * fix: address bug detection findings (cache token order, mutable defaults) Co-authored-by: Yassin Kortam <yassin@berri.ai> * fix: address bugs in async pass-through, anthropic cache token detection, rerank tests - async_get_available_deployment_for_pass_through: enforce blocked check on specific deployments - cost_calculator: detect anthropic-style usage by attribute presence (not truthiness) to avoid mixing OpenAI cached_tokens into anthropic normalization when read=0 - dashscope rerank tests: pass request to httpx.Response constructions for consistency Co-authored-by: Yassin Kortam <yassin@berri.ai> * fix code qa * fix(vertex_ai/gemini): strip MIME parameters from GCS contentType GCS object metadata's contentType field can include parameters such as 'text/html; charset=utf-8'. Strip them in _apply_gemini_mime_type_aliases so downstream get_file_extension_from_mime_type sees a bare MIME type. Co-authored-by: Yassin Kortam <yassin@berri.ai> * fix(vertex_ai/gemini): clarify mime-type error message string concatenation Co-authored-by: Yassin Kortam <yassin@berri.ai> --------- Co-authored-by: Tai An <antai12232931@outlook.com> Co-authored-by: Vincent <yimao1231@gmail.com> Co-authored-by: Kris Xia <xiajiayi0506@gmail.com> Co-authored-by: d 🔹 <liusway405@gmail.com> Co-authored-by: Fabrizio Cafolla <developer@fabriziocafolla.com> Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com> Co-authored-by: Tom Denham <tom@tomdee.co.uk> Co-authored-by: escon1004 <70471150+escon1004@users.noreply.github.com> Co-authored-by: Divyansh Singhal <97736786+Divyansh8321@users.noreply.github.com> Co-authored-by: robin-fiddler <robin@fiddler.ai> Co-authored-by: Michael-RZ-Berri <michael@berri.ai> Co-authored-by: Michael Riad Zaky <michaelr@Mac.localdomain> Co-authored-by: ryan-crabbe-berri <ryan@berri.ai> Co-authored-by: Krrish Dholakia <krrish+github@berri.ai> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: Yassin Kortam <yassin@berri.ai> |
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lmcdonald-godaddy
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baa68ebb12
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fix(pricing): GPT-4o-Transcribe Pricing (#27875)
* Update gpt-4o-transcribe price * Update test for gpt-4o-transcribe pricing fix * Update gpt-4o-mini-transcribe price |
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oss-agent-shin
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f2e97380d2
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Add OpenRouter Qwen 3.6 Plus metadata (#27486)
Co-authored-by: oss-agent-shin <279349115+oss-agent-shin@users.noreply.github.com> Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> |
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Tim Ren
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dd4a41951f
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fix(utils): allowed_openai_params must not forward unset params as None (#25777)
* feat(proxy): add NO_OPENAPI env var to disable /openapi.json endpoint (#25696) * feat(proxy): add NO_OPENAPI env var to disable /openapi.json endpoint - Fixes #25538 * test(proxy): add tests for _get_openapi_url --------- Co-authored-by: Progressive-engg <lov.kumari55@gmail.com> * feat(prometheus): add api_provider label to spend metric (#25693) * feat(prometheus): add api_provider label to spend metric Add `api_provider` to `litellm_spend_metric` labels so users can build Grafana dashboards that break down spend by cloud provider (e.g. bedrock, anthropic, openai, azure, vertex_ai). The `api_provider` label already exists in UserAPIKeyLabelValues and is populated from `standard_logging_payload["custom_llm_provider"]`, but was not included in the spend metric's label list. * add api_provider to requests metric + add test Address review feedback: - Add api_provider to litellm_requests_metric too (same call-site as spend metric, keeps label sets in sync) - Add test_api_provider_in_spend_and_requests_metrics following the existing pattern in test_prometheus_labels.py * fix: ensure `litellm_metadata` is attached to `pre_call` guardrail to align with `post_call` guardrail (#25641) * fix: ensure `litellm_metadata` is attached to pre_call to align with post_call * refactor: remove unused BaseTranslation._ensure_litellm_metadata * refactor: module level imports for ensure_litellm_metadata and CodeQL * fix: update based off of Codex comment * revert: undo usage of `_guardrail_litellm_metadata` * feat: add pricing entry for openrouter/google/gemini-3.1-flash-lite-preview (#25610) * fix(bedrock): skip synthetic tool injection for json_object with no schema (#25740) When response_format={"type": "json_object"} is sent without a JSON schema, _create_json_tool_call_for_response_format builds a tool with an empty schema (properties: {}). The model follows the empty schema and returns {} instead of the actual JSON the caller asked for. This patch: - Skips synthetic json_tool_call injection when no schema is provided. The model already returns JSON when the prompt asks for it. - Fixes finish_reason: after _filter_json_mode_tools strips all synthetic tool calls, finish_reason stays "tool_calls" instead of "stop". Callers (like the OpenAI SDK) misinterpret this as a pending tool invocation. json_schema requests with an explicit schema are unchanged. Co-authored-by: Claude <noreply@anthropic.com> * fix(utils): allowed_openai_params must not forward unset params as None `_apply_openai_param_overrides` iterated `allowed_openai_params` and unconditionally wrote `optional_params[param] = non_default_params.pop(param, None)` for each entry. If the caller listed a param name but did not actually send that param in the request, the pop returned `None` and `None` was still written to `optional_params`. The openai SDK then rejected it as a top-level kwarg: AsyncCompletions.create() got an unexpected keyword argument 'enable_thinking' Reproducer (from #25697): allowed_openai_params = ["chat_template_kwargs", "enable_thinking"] body = {"chat_template_kwargs": {"enable_thinking": False}} Here `enable_thinking` is only present nested inside `chat_template_kwargs`, so the helper should forward `chat_template_kwargs` and leave `enable_thinking` alone. Instead it wrote `optional_params["enable_thinking"] = None`. Fix: only forward a param if it was actually present in `non_default_params`. Behavior is unchanged for the happy path (param sent → still forwarded), and the explicit `None` leakage is gone. Adds a regression test exercising the helper in isolation so the test does not depend on any provider-specific `map_openai_params` plumbing. Fixes #25697 --------- Co-authored-by: lovek629 <59618812+lovek629@users.noreply.github.com> Co-authored-by: Progressive-engg <lov.kumari55@gmail.com> Co-authored-by: Ori Kotek <ori.k@codium.ai> Co-authored-by: Alexander Grattan <51346343+agrattan0820@users.noreply.github.com> Co-authored-by: Mohana Siddhartha Chivukula <103447836+iamsiddhu3007@users.noreply.github.com> Co-authored-by: Amiram Mizne <amiramm@users.noreply.github.com> Co-authored-by: Claude <noreply@anthropic.com> |
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csoni-cweave
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ee06b9278a
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feat(model):add wandb model offerings to include kimi-k2.5 and minimax-m2.5 (#25409) | ||
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Sameer Kankute
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97f722f558
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feat(cost): add baseten model api pricing entries (#25358)
Add Baseten Model API pricing entries for Nemotron, GLM, Kimi, GPT OSS, and DeepSeek models with validated model slugs. Include a focused regression test to assert provider and per-token pricing values. Made-with: Cursor |
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yuneng-jiang
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278c9babc6
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[Infra] Merging RC Branch with Main (#23786)
* fix(test): add missing mocks for test_streamable_http_mcp_handler_mock
The test was missing mocks for extract_mcp_auth_context and set_auth_context,
causing the handler to fail silently in the except block instead of reaching
session_manager.handle_request. This mirrors the fix already applied to the
sibling test_sse_mcp_handler_mock.
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* fix(ci): route OpenAI models through chat completions in pass-through tests
The test_anthropic_messages_openai_model_streaming_cost_injection test fails
because the OpenAI Responses API returns 400 for requests routed through the
Anthropic Messages endpoint. Setting LITELLM_USE_CHAT_COMPLETIONS_URL_FOR_ANTHROPIC_MESSAGES=true
routes OpenAI models through the stable chat completions path instead.
Cost injection still works since it happens at the proxy level.
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* fix(ci): fix assemblyai custom auth and router wildcard test flakiness
1. custom_auth_basic.py: Add user_role='proxy_admin' so the custom auth
user can access management endpoints like /key/generate. The test
test_assemblyai_transcribe_with_non_admin_key was hidden behind an
earlier -x failure and was never reached before.
2. test_router_utils.py: Add flaky(retries=3) and increase sleep from 1s
to 2s for test_router_get_model_group_usage_wildcard_routes. The async
callback needs time to write usage to cache, and 1s is insufficient on
slower CI hardware.
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* ci: retrigger CI pipeline
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* fix(mypy): use LitellmUserRoles enum instead of raw string in custom_auth_basic
Fixes mypy error: Argument 'user_role' has incompatible type 'str'; expected 'LitellmUserRoles | None'
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* fix: don't close HTTP/SDK clients on LLMClientCache eviction (#22926)
* fix: don't close HTTP/SDK clients on LLMClientCache eviction
Removing the _remove_key override that eagerly called aclose()/close()
on evicted clients. Evicted clients may still be held by in-flight
streaming requests; closing them causes:
RuntimeError: Cannot send a request, as the client has been closed.
This is a regression from commit
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Krish Dholakia
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8abf2d8e34
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fix: Fixes https://github.com/BerriAI/litellm/issues/23185 (#23647) | ||
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Cursor Agent
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d5fc63f63f |
fix(ci): fix deprecated model refs and schema validation in unit tests
- Replace gemini-pro with gemini-3-pro-preview in test_cost_discount_vertex_ai (gemini-pro removed from cost map) - Replace github/claude-3-5-sonnet-latest with github/claude-3-7-sonnet-20250219 in test_supports_function_calling_github_anthropic_alias (model removed) - Add supports_multimodal, uses_embed_content, input/output_cost_per_token_above_256k_tokens to JSON schema in test_utils.py (new properties added to model cost map) Co-authored-by: yuneng-jiang <yuneng-jiang@users.noreply.github.com> |