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1633 commits
| Author | SHA1 | Message | Date | |
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milan-berri
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1b141bc588
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fix(bedrock): decouple STS region from Bedrock aws_region_name (#28245)
* fix(bedrock): decouple STS region from Bedrock aws_region_name STS AssumeRole now resolves signing region from aws_sts_endpoint (parsed host) or AWS_REGION/AWS_DEFAULT_REGION instead of aws_region_name, fixing air-gapped cross-region Bedrock setups and endpoint/signature mismatches. Co-authored-by: Cursor <cursoragent@cursor.com> * test(bedrock): add regression coverage for _build_sts_client_kwargs Parametrize _resolve_sts_region and _build_sts_client_kwargs matrix cases, and assert IRSA/web-identity paths use aligned STS endpoint and region_name. Co-authored-by: Cursor <cursoragent@cursor.com> * refactor(bedrock): tighten STS region helpers and drop redundant web-identity endpoint synthesis Co-authored-by: Cursor <cursoragent@cursor.com> * test(bedrock): cover FIPS, GovCloud, and China STS endpoints Addresses greptile P2: regex sts(?:-fips)? supported sts-fips hosts but was not exercised by the parametrized parse test. Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: Cursor <cursoragent@cursor.com> |
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Krrish Dholakia
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a3c953ed4e
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style: apply black formatting to fix lint CI (LIT-3274) (#28639) (#28641)
* fix(bedrock): strip bedrock/ prefix and URL-encode ARNs in get_bedrock_model_id for invoke path
The invoke path (used by /v1/messages → Anthropic SDK / Claude Code) called
get_bedrock_model_id() which, when falling back to the raw model string, did
not strip the 'bedrock/' routing prefix and did not URL-encode ARNs.
For a model like:
bedrock/arn:aws:bedrock:us-east-1:<ACCOUNT>:inference-profile/global.anthropic...
the URL built was:
/model/bedrock/arn:aws:bedrock:…/invoke-with-response-stream ❌
Bedrock returned a JSON error body. LiteLLM's AWSEventStreamDecoder passed
those bytes into botocore's EventStreamBuffer which expects binary event-stream
framing. Checksum validation failed on the JSON prelude (0x223a7b22 == ':{"')
producing a misleading botocore.eventstream.ChecksumMismatch instead of the
actual Bedrock error.
Fix: strip 'bedrock/' (and 'invoke/') routing prefix from model string, then
URL-encode if the result is an ARN — matching what the converse path already
does in converse_handler.py.
Fixes: LIT-3274
* fix(bedrock): use strip_bedrock_routing_prefix to handle compound prefixes
Address greptile review: the original fix used a loop with break, so
bedrock/invoke/arn:... only stripped bedrock/ leaving invoke/arn:...
which is not an ARN → fell through to .replace('invoke/','',1) →
bare unencoded ARN → same malformed-URL bug.
strip_bedrock_routing_prefix() iterates without break, correctly
stripping bedrock/ then invoke/ in sequence. Also adds test case
for the compound-prefix scenario.
* style: apply black formatting to fix lint CI (LIT-3274)
---------
Co-authored-by: oss-agent-shin <ext-agent-shin@berri.ai>
Co-authored-by: LiteLLM Bot <bot@berri.ai>
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milan-berri
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9600fda2cc
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fix(sagemaker): send native Cohere embed payload to Cohere SageMaker endpoints (#28613)
* fix(sagemaker): use Cohere embed payload for Marketplace endpoints
SageMaker embedding only special-cased Voyage; every other endpoint received
HuggingFace TGI `{"inputs": [...]}`. AWS Marketplace Cohere containers expect
the native Cohere embed payload (`texts`, `input_type`) and reject the HF
shape with `422 EmbedReqV2.inputs is of type string but should be of type
Object`.
Add `SagemakerCohereEmbeddingConfig` that reuses Bedrock/Cohere request and
response transforms, and route SageMaker endpoint names containing `cohere`
or a Cohere embed model fragment (`embed-multilingual`, `embed-english`,
`embed-v3`, `embed-v4`) to it. Supports `input_type`, `dimensions`, and
`encoding_format`. Voyage and HuggingFace SageMaker endpoints are unchanged.
Co-authored-by: Cursor <cursoragent@cursor.com>
* refactor(sagemaker): simplify cohere detection and align with file conventions
- Detect Cohere SageMaker endpoints with a single `"cohere" in model.lower()`
check, mirroring the existing Voyage branch instead of a separate helper
function and marker constant.
- Drop instance caches of sub-configs; instantiate `BedrockCohereEmbeddingConfig`
/ `CohereEmbeddingConfig` per call to match the existing pattern in
`BedrockCohereEmbeddingConfig._transform_request`.
- Match `SagemakerEmbeddingConfig`'s signatures, defaults, and `Any` typing for
`logging_obj`; collapse the input-normalization helper inline.
- Inline `transform_embedding_response` input lookup; no behavior change.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(sagemaker): restore provider-supported embedding params after map
Cohere input_type is advertised in get_supported_openai_params but was
filtered out of non_default_params by OPENAI_EMBEDDING_PARAMS before
map_openai_params ran. Merge supported params from passed_params after
map (same path Greptile flagged). Handle input_type explicitly in
SagemakerCohereEmbeddingConfig.map_openai_params and add an integration
test through get_optional_params_embeddings.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(embeddings): only restore non-OpenAI supported params after map
The post-map restore loop must skip OPENAI_EMBEDDING_PARAMS so mapped
fields (e.g. dimensions -> output_dimension) are not duplicated under
their OpenAI names. Align SageMaker embedding import order with sibling
files and add a regression test for dimensions mapping.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(sagemaker): avoid double post_call on Cohere embedding response
Greptile review on #28613 caught that `CohereEmbeddingConfig._transform_response`
calls `logging_obj.post_call` internally. The SageMaker embedding handler
already calls `post_call` once before invoking the transform, so the Cohere
SageMaker path fired callbacks, cost calculators, and log handlers twice
per request.
Extract the parsing body of `_transform_response` into
`_populate_embedding_response` (pure extract-method, no behavior change
for existing Cohere direct or Bedrock Cohere paths, which keep calling
`_transform_response`). Have `SagemakerCohereEmbeddingConfig` call the
new helper directly so it parses the response without re-logging.
Add a regression test asserting `logging_obj.post_call` is not invoked
by the SageMaker Cohere transform.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
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Sameer Kankute
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e9f0eddbd1
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Litellm oss staging 2 (#28582)
* fix(anthropic): handle empty streaming tool calls (#28549) Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> * [Feature][Bug Fix] Decouple Azure OpenAI Deployment ID from model name via base_model to fix gpt5 model routing (#28490) * feat(azure): decouple deployment ID from model name via base_model Azure OpenAI deployments have arbitrary names (deployment IDs) that may not match the underlying model. Previously, model-type detection (o-series, gpt-5, etc.) relied on substring matching against the deployment name, causing misrouted configs and rejected params when deployment names were non-standard (e.g. 'my-deployment-id' for gpt-5.2). This change extends the existing base_model field to drive model-type detection, config selection, supported param resolution, and param mapping throughout the Azure call path: - _get_azure_config() uses base_model for is_o_series/is_gpt_5 checks - get_provider_chat_config() threads base_model for Azure - get_supported_openai_params() accepts and uses base_model - get_optional_params() accepts base_model and passes it to all Azure config method calls (get_supported_openai_params, map_openai_params) - azure.py completion handler uses base_model for GPT-5 detection - Config internal methods (e.g. is_model_gpt_5_2_model) now receive base_model so features like logprobs are correctly enabled Fully backward compatible - when base_model is unset, behavior is identical. Existing o_series/ and gpt5_series/ prefix workarounds continue to work. Usage in proxy config: model_list: - model_name: my-gpt5 litellm_params: model: azure/my-deployment-id model_info: base_model: azure/gpt-5.2 Fixes: non-standard deployment names like 'prefix-gpt-5.2' rejecting logprobs/top_logprobs despite the underlying model supporting them. * Addressing Greptile comments. * 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(openai-responses): strip Anthropic cache_control from Responses API requests (#28431) Squash-merged by litellm-agent from cwang-otto's PR. * Treat None litellm_provider as wildcard in _check_provider_match (#28523) Squash-merged by litellm-agent from adityasingh2400's PR. * fix greptile * fix: use _azure_detection_model in default Azure branch of get_supported_openai_params Co-authored-by: Yassin Kortam <yassin@berri.ai> * fix(openai-responses): strip cache_control on compact endpoint as well Co-authored-by: Yassin Kortam <yassin@berri.ai> --------- Co-authored-by: Felipe Garé <90070734+FelipeRodriguesGare@users.noreply.github.com> Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: withomasmicrosoft <withomas@microsoft.com> Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com> Co-authored-by: cwang-otto <chengxuan.wang@ottotheagent.com> Co-authored-by: Aditya Singh <60082699+adityasingh2400@users.noreply.github.com> Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Yassin Kortam <yassin@berri.ai> |
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Mateo Wang
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b60d4677cd
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fix(vertex_gemma): strip context_management from request body (#28438)
Vertex AI Gemma's chatCompletions wrapper does not understand the context_management parameter (an Anthropic / OpenAI Responses API concept). When callers route this field to a Gemma deployment (e.g. through allowed_openai_params or proxy passthrough), the upstream endpoint would reject the request with an unknown-field error. Drop context_management in VertexGemmaConfig.transform_request, matching the existing pattern used for stream and stream_options. Adds a direct transform_request unit test plus an acompletion-level test that exercises the realistic allowed_openai_params path. Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> |
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Sameer Kankute
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b7e978a5c3
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Litellm oss staging 04 21 2026 2 (#26569)
* fix(bedrock): use model info lookup for output_config support instead of hardcoded check Replace hardcoded _is_claude_4_6_model() string matching with supports_output_config flag in model_prices_and_context_window.json, accessed via _supports_factory(). This follows the project's established pattern for model capability checks (per AGENTS.md rule #8). Bedrock Invoke now conditionally preserves output_config for models that declare supports_output_config=true (currently Claude 4.6 models), while stripping it for older models to avoid request rejection. Ref: https://github.com/BerriAI/litellm/issues/22797 * fix(vertex_ai): single-flight credential refresh to prevent thundering herd (#26024) * fix(vertex_ai): single-flight credential refresh to prevent thundering herd When GCP credentials expire under high concurrency, all requests simultaneously call credentials.refresh() via asyncify, saturating the 40-thread anyio pool and blocking the proxy for 20+ seconds. This adds: - Per-credential asyncio.Lock in get_access_token_async for single-flight refresh (1 coroutine refreshes, others wait on the lock) - Background refresh when token_state is STALE (usable but near expiry), returning the current token immediately with zero added latency - threading.Lock on the sync get_access_token path - Uses google-auth's TokenState enum (FRESH/STALE/INVALID) instead of reimplementing expiry logic Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: address PR review comments - Use asyncio.create_task() instead of deprecated get_event_loop().create_task() - Track in-flight background refresh tasks to prevent duplicate refreshes when multiple STALE-path callers pass through the lock before the first background task completes - Add token validation in the STALE branch (consistent with FRESH/INVALID) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: lazy-import TokenState to avoid breaking when google-auth is not installed Also extract helper methods to bring get_access_token_async under the PLR0915 statement limit (50). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * chore: apply Black formatting to test file and update uv.lock Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: remove user-provided project_id from log messages (CodeQL log injection) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: avoid leaking token value in error message, log type instead Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * chore: restore uv.lock to match litellm_oss_branch Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: remove project_id from remaining log message (CodeQL log injection) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: remove remaining project_id from log and error messages Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: reuse cached credentials in VertexAIPartnerModels (#26065) * fix: reuse cached credentials in VertexAIPartnerModels instead of creating new VertexLLM per request VertexAIPartnerModels.completion() was creating a throwaway VertexLLM() instance on every call to get an access token, bypassing the credential cache inherited from VertexBase. This caused a fresh token fetch for every single request, adding significant latency overhead. Fix: call super().__init__() to initialize VertexBase's credential cache, and use self._ensure_access_token() instead of a new VertexLLM instance. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: apply same credential caching fix to VertexAIGemmaModels and VertexAIModelGardenModels Same bug as VertexAIPartnerModels: both classes had `pass` in __init__ instead of `super().__init__()`, and created throwaway VertexLLM() instances per request instead of using self._ensure_access_token(). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix(fireworks): add glm-5p1 metadata and parallel_tool_calls (#26069) * fix(chatgpt): preserve responses routing and recover empty output (#25403) (#26219) - preserve existing shared backend `mode` when router deployment registration reuses a provider/model key already in `litellm.model_cost` (prevents alias with `mode: chat` from downgrading shared `chatgpt/gpt-5.4` from `responses` to `chat` and triggering 403s on /v1/chat/completions) - teach the ChatGPT Responses parser to recover `response.output_item.done` entries when `response.completed.output` is empty - add defensive /responses -> /chat/completions bridge fallback that reconstructs output items from raw SSE when `raw_response.output` is empty - regression coverage for shared alias routing, empty completed.output parsing, and SSE bridge recovery Closes #25403 Co-authored-by: afoninsky <andrey.afoninsky@gmail.com> Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(deps): relax core runtime dependency pins from exact == to ranges When litellm migrated from Poetry to uv (PR #24905, v1.83.1), the core dependency specifications in pyproject.toml changed from Poetry bare-version strings (e.g. openai = "2.30.0") to PEP 621 exact pins (openai==2.24.0). Poetry bare-version strings are actually caret ranges (^X.Y.Z == >=X.Y.Z,<X+1), but PEP 621 == is exact. This means every downstream package that installs litellm as a library dependency is now forced to downgrade aiohttp, pydantic, openai, click, and 8 other common packages to exact old versions. Fix: restore range specifiers for the 12 core runtime dependencies. The optional extras (proxy, proxy-runtime, etc.) are consumed primarily by Docker images where exact pins are appropriate and are left unchanged. The uv.lock file continues to provide exact reproducibility for Docker builds and CI. Fixes: #26154 * Add Rubrik as officially-supported guardrail plugin (#25305) * Add Rubrik as officially-supported guardrail plugin Adds tool blocking and batch logging integration with an external Rubrik webhook service. The plugin validates LLM tool calls against a policy service (fail-open on errors) and batch-logs all requests/responses. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Update Rubrik docs: config.yaml as primary, env vars as fallback Restructures the Quick Start to present config.yaml as the recommended approach with tabbed UI, and environment variables as an alternative fallback. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add Rubrik env vars to config_settings reference Fixes documentation validation by adding RUBRIK_API_KEY, RUBRIK_BATCH_SIZE, RUBRIK_SAMPLING_RATE, and RUBRIK_WEBHOOK_URL to the environment settings reference table. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add fallback message when blocking service returns empty explanation Prevents whitespace-only violation message when the tool blocking service blocks tools but returns an empty content field. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(ocr): add Reducto parse OCR support (#26068) * feat(ocr): add Reducto parse OCR support * fix(reducto): address OCR review feedback * chore: refresh uv lockfile * Revert "chore: refresh uv lockfile" This reverts commit |
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Sameer Kankute
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988196911a
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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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fecf212d70
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fix(vertex_ai): omit function_call id on Vertex Gemini 3.5+ tool turns (#28324)
* fix(vertex_ai): omit function_call id on Vertex Gemini 3.5+ tool turns Vertex AI rejects `id` on function_call/function_response parts; only Google AI Studio accepts it for Gemini 3.5+ strict tool matching. Co-authored-by: Cursor <cursoragent@cursor.com> * Update litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * fix(vertex_ai): forward custom_llm_provider in context caching Pass custom_llm_provider through to _gemini_convert_messages_with_history in the context caching path so Gemini 3.5+ tool-call `id` forwarding behaves consistently between cached and non-cached completions on Google AI Studio. Co-authored-by: Claude <claude@anthropic.com> --------- Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: Claude <claude@anthropic.com> |
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Sameer Kankute
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3c3d131f01
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Day 0 support : Gemini 3.5 Flash (#28268)
* Add day 0 support for gemini 3.5 flash * Fix pricing * Fix greptile review * Fix failing test * Fix tests * Fix: revert tool removing logic * fix greptile and test --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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harish-berri
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cff3e0b75e
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refactor(bedrock/sagemaker): switch to lazy loading for response stre… (#28189)
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* refactor(bedrock/sagemaker): switch to lazy loading for response stream shapes
- Replace eager loading of BEDROCK_RESPONSE_STREAM_SHAPE and SAGEMAKER_RESPONSE_STREAM_SHAPE with lazy loading via get_bedrock_response_stream_shape() and get_sagemaker_response_stream_shape() respectively.
- This change optimizes performance by avoiding unnecessary imports and logging warnings unless the response stream shapes are actually needed.
- Update relevant classes and tests to utilize the new lazy loading functions, ensuring consistent behavior across the codebase.
* test(bedrock/sagemaker): add fixtures to clear response stream shape cache
- Introduced `_reset_bedrock_response_stream_shape_cache` and `_reset_sagemaker_response_stream_shape_cache` fixtures to prevent lru_cache leakage between tests in their respective modules.
- Updated tests to utilize these fixtures, ensuring that the response stream shape cache is cleared before and after each test run.
- Added `pytest.importorskip("botocore")` to ensure that tests are skipped if the botocore library is not available.
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Mateo Wang
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761c280a6e
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fix(deepseek): use native /anthropic/v1/messages endpoint and sanitize tools (#28200)
* fix(deepseek): route messages api through anthropic config Add a DeepSeek-specific Anthropic Messages config so deepseek/... models use the native messages endpoint and preserve thinking blocks. Strip Anthropic custom tool type markers that DeepSeek rejects while keeping hosted tool types intact. * fix(deepseek): normalize anthropic messages api base Handle OpenAI-style DeepSeek api_base values ending in /v1 or /v1/messages by stripping those suffixes before adding the /anthropic messages path. * chore(deepseek): format messages transformation * chore(deepseek): add test package markers * fix(deepseek): tighten anthropic url path check and fall back to DEEPSEEK_API_BASE Author: mateo-berri <277851410+mateo-berri@users.noreply.github.com> * fix(tests): normalize smart quotes in realtime guardrail refusal check gpt-realtime nondeterministically returns refusals with Unicode curly apostrophes (e.g. 'I’m sorry, but I can’t assist with that.'), but the safe_markers tuple in test_text_message_blocked_by_guardrail_no_ai_response only contains straight ASCII apostrophes. The substring match then fails even though the response is a clear refusal, flipping CI red. Normalize the AI text to ASCII quotes before the marker check so both straight and curly variants count as safe outcomes. * fix(deepseek): drop redundant anthropic v1/messages endswith check * fix(deepseek): strip /beta suffix in anthropic messages URL normalization Co-authored-by: Yassin Kortam <yassin@berri.ai> --------- Co-authored-by: Felipe Rodrigues Gare Carnielli <felipe.gare@hotmail.com> Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Yassin Kortam <yassin@berri.ai> |
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Mateo Wang
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761ab19209
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fix(bedrock): sanitize batch metadata to prevent Pydantic ValidationError (#28202)
* fix(bedrock): sanitize batch metadata to prevent Pydantic ValidationError Proxy guardrail hooks (Model Armor, OpenAI Moderations) and internal processing inject non-string values (dicts, floats) into the request metadata. When the Bedrock batch handler passes this metadata directly to LiteLLMBatch (which inherits OpenAI's Batch Pydantic model with metadata: Dict[str, str]), Pydantic raises a ValidationError. This causes the router retry loop to re-submit the same Bedrock job multiple times before ultimately failing. Add _get_openai_compatible_batch_metadata() that serializes non-string values to JSON strings via safe_dumps, skips None values and internal logging keys, ensuring the response object always validates. * test(bedrock): add tests for batch metadata sanitization Covers _get_openai_compatible_batch_metadata: string passthrough, dict/float serialization, None/internal key exclusion, and LiteLLMBatch compatibility. --------- Co-authored-by: Noah Nistler <60981020+noahnistler@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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ishaan-berri
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8c6625216b
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fix(bedrock/cohere): send embedding_types as JSON array, not string (#28172)
* fix(bedrock/cohere): wrap embedding_types as list in map_openai_params Bedrock Cohere expects embedding_types as a JSON array but encoding_format was passed through as a raw string, causing: Malformed input request: #/embedding_types: expected type: JSONArray, found: String * test(bedrock/cohere): assert embedding_types is sent as JSON array --------- Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com> |
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ishaan-berri
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f9ba70d357
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fix(bedrock-mantle): use /anthropic/v1/messages path for Mantle endpo… (#27976)
* fix(bedrock-mantle): use /anthropic/v1/messages path for Mantle endpoint (#27943) * docs: add one-line docstring to _disable_debugging (#27894) Squash-merged by litellm-agent from oss-agent-shin's PR. * Add jp. Bedrock cross-region inference profile for claude-sonnet-4-6 (#27831) Squash-merged by litellm-agent from Cyberfilo's PR. * Sanitize empty text content blocks on /v1/messages (#27832) Squash-merged by litellm-agent from Cyberfilo's PR. * fix(bedrock-mantle): use /anthropic/v1/messages path for Mantle endpoint The bedrock-mantle gateway (Claude Mythos Preview) serves the Anthropic Messages API at /anthropic/v1/messages; /v1/messages returns 404 Not Found. Both AmazonMantleConfig (chat/completions caller route) and AmazonMantleMessagesConfig (anthropic-messages caller route) hardcoded the wrong path, so every Mantle request 404'd before reaching the model. Per the Anthropic docs: "[Claude in Amazon Bedrock] uses the Messages API at /anthropic/v1/messages with SSE streaming." https://platform.claude.com/docs/en/api/claude-on-amazon-bedrock Confirmed independently against the live endpoint: /v1/chat/completions -> 200 OK /v1/messages -> 404 Not Found (what litellm used) /anthropic/v1/messages -> 200 OK (Claude only) Adds a regression test asserting both Mantle configs build the /anthropic/v1/messages path, and updates the existing assertions that encoded the wrong path. --------- Co-authored-by: oss-agent-shin <ext-agent-shin@berri.ai> Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com> * fix: sanitize empty text blocks in sync anthropic_messages_handler path Co-authored-by: Yassin Kortam <yassin@berri.ai> --------- Co-authored-by: João Costa <13508071+jpv-costa@users.noreply.github.com> Co-authored-by: oss-agent-shin <ext-agent-shin@berri.ai> Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com> Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Yassin Kortam <yassin@berri.ai> |
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milan-berri
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6274b4c217
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fix(fireworks_ai): strip thinking_blocks from chat messages before Fireworks API call (#27881)
* fix(fireworks_ai): strip thinking_blocks from chat messages before API call Fireworks OpenAI-compatible ChatMessage schema uses additionalProperties:false and rejects Anthropic-style messages[].thinking_blocks (e.g. Claude Code replays), returning invalid_request_error. Remove the field in _transform_messages_helper alongside provider_specific_fields. Adds unit test test_transform_messages_helper_strips_thinking_blocks. Co-authored-by: Cursor <cursoragent@cursor.com> * chore(fireworks_ai): drop inline comments from message sanitization Co-authored-by: Cursor <cursoragent@cursor.com> * docs(fireworks_ai): explain why provider_specific_fields and thinking_blocks are stripped Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: Cursor <cursoragent@cursor.com> |
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ishaan-berri
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b593b88ec6
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Ishaan - May 13th Staging LiteLLM (#27877)
* fix: strip Gemini thought-signature from tool_use.id in non-streaming path; example websearch config (#27873) - adapters/transformation.py: mirror the streaming path and strip the `__thought__<b64>` suffix off `tool_call.id` before building the AnthropicResponseContentBlockToolUse. Base64's `+ / =` characters violate Anthropic's `^[a-zA-Z0-9_-]+$` tool_use.id pattern, so when a conversation that flowed through Gemini is later replayed to an Anthropic-native provider (Bedrock or Anthropic API) the request 400s. - example_config_yaml/websearch_interception_config.yaml: register the interceptor under `callbacks:` not `success_callback:`. `success_callback` does not run pre-request hooks, so the tool-conversion step never fires on `/v1/messages` and the raw `web_search_20250305` tool is forwarded to Bedrock, which 400s. - adds a unit test pinning the non-streaming strip behavior and the surviving `^[a-zA-Z0-9_-]+$` shape of the resulting id. Co-authored-by: oss-agent-shin <279349115+oss-agent-shin@users.noreply.github.com> * Fix/azure image edit auth header (#27863) * fix(azure/image_edit): use api-key header instead of Authorization Bearer Delegate `AzureImageEditConfig.validate_environment` to `BaseAzureLLM._base_validate_azure_environment` so the image-edit route follows the same auth resolution as every other Azure provider: - prefer the Azure-native `api-key` header when an API key is available - fall back to `Authorization: Bearer <azure_ad_token>` only for AAD auth The previous implementation unconditionally set `Authorization: Bearer <api_key>`, which is the OpenAI-direct convention and is rejected by Azure OpenAI / APIM-fronted deployments with `401 Access denied due to missing subscription key`. Adds regression tests covering api_key kwarg, litellm_params.api_key, and the AAD-token fallback path. Co-authored-by: Cursor <cursoragent@cursor.com> * docs(azure/image_edit): pin api-key precedence semantics + add regression test Address review feedback that the move to ``BaseAzureLLM._base_validate_azure_environment`` changed the relative priority of the positional ``api_key`` kwarg vs. ``litellm_params["api_key"]``. The new behavior — ``litellm_params["api_key"]`` wins, positional only fills in when ``litellm_params["api_key"]`` is empty — is intentional and matches every other Azure ``validate_environment``: ``AzureVideosConfig`` uses the exact same merge logic, while ``AzureVectorStoresConfig`` and ``AzureResponsesAPIConfig`` don't accept a positional ``api_key`` at all. The old ``or`` chain (positional wins) was the outlier and was part of the same OpenAI-vs-Azure convention drift that produced the original ``Authorization: Bearer`` bug. The only production caller (``llm_http_handler.image_edit``) sources both values from the same ``litellm_params.api_key``, so this change is behaviorally a no-op there. Document the precedence in the docstring and lock it in with an explicit test so future refactors can't quietly re-invert it. Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: ryan-crabbe-berri <ryan@berri.ai> Co-authored-by: Adam Kirstein <adam.kirstein@disney.com> Co-authored-by: Cursor <cursoragent@cursor.com> * test(azure/image_edit): expect api-key header instead of Authorization Bearer PR #27863 fixed Azure image edit to use the Azure-native api-key header instead of OpenAI's Authorization: Bearer convention, but did not update test_azure_image_edit_litellm_sdk to match. The test still asserted 'Authorization' in headers, which now fails since the new code routes through BaseAzureLLM._base_validate_azure_environment and emits api-key when an api_key is provided. Update the assertion to pin the correct Azure behavior: api-key header present with the resolved key, and no Authorization header. --------- Co-authored-by: oss-agent-shin <ext-agent-shin@berri.ai> Co-authored-by: oss-agent-shin <279349115+oss-agent-shin@users.noreply.github.com> Co-authored-by: Adam Kirstein <107421694+justalittleadam@users.noreply.github.com> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: ryan-crabbe-berri <ryan@berri.ai> Co-authored-by: Adam Kirstein <adam.kirstein@disney.com> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com> |
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Sameer Kankute
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bbeb094d00
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Litellm agent oss staging 05 11 2026 (#27733)
* fix(ollama): Include provider in model list for ollama (#26135) * Include provider in model names for ollama * Fix unit tests * fix(ollama): process both thinking and content in same streaming chunk (#26098) * fix(health_check): skip max_tokens for image_generation mode (#26417) * fix(health_check): skip max_tokens for image_generation mode `_update_litellm_params_for_health_check` injected `max_tokens` for every deployment. OpenAI `/v1/images/generations` strictly rejects unknown fields, so health checks for dall-e-* and gpt-image-1 always failed with `400 "Unknown parameter: 'max_tokens'"` even though the actual image endpoint calls succeed. Skip the `max_tokens` injection when `model_info.mode == "image_generation"`. `messages` still gets injected (downstream `_filter_model_params` already strips it for non-chat handlers). * Switch to allow-list with per-deployment override Per @krrishdholakia review: deny-listing image_generation only re-introduces the same bug for every other non-chat mode (embedding, audio_*, rerank, video_generation, ocr, search, moderation, ...). Replace the single image_generation skip with `_MAX_TOKEN_SUPPORT_MODES = {chat, completion, responses}`. Missing `mode` is treated as chat for backward compatibility. New modes are safe by default. Add `model_info.health_check_supports_max_tokens` as an operator escape hatch — True forces injection on a non-listed deployment (operator wants to bound probe tokens), False suppresses it on a chat-style deployment behind a strict-schema provider. Tests: parametrize over 3 chat-style + 10 non-chat modes, plus override on/off and the no-mode legacy path. * fix(http_handler): handle RequestNotRead in MaskedHTTPStatusError for multipart uploads (#26718) Squash-merged by litellm-agent from dawidkulpa's PR. * fix(ollama): guard against double 'ollama/' prefix in live model listing Greptile flagged that Ollama servers can return names that already start with 'ollama/'. Check the prefix before prepending so we don't produce 'ollama/ollama/...'. Adds a regression test. * Fix Ollama empty reasoning stream chunks Co-authored-by: Yassin Kortam <yassin@berri.ai> --------- Co-authored-by: James Myatt <james@jamesmyatt.co.uk> Co-authored-by: VHash <225398745+vhash0@users.noreply.github.com> Co-authored-by: hayden <sewhan.kim+@a-bly.com> Co-authored-by: dawidkulpa <84176950+dawidkulpa@users.noreply.github.com> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: Claude <claude@anthropic.com> Co-authored-by: Yassin Kortam <yassin@berri.ai> |
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Krrish Dholakia
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0deffd3618
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chore: reject bare str at file-input sinks to prevent local-file read (#27762)
* chore: reject bare str at file-input sinks to prevent local-file read (#27667) Squash-merged by litellm-agent from stuxf's PR. * fix: use os.PathLike in ocr sink and check truthy reasoningSummary for bridge - ocr/main.py: widen Path check to os.PathLike for consistency with other sinks - main.py: bridge condition checks truthiness of reasoning_summary, not just None Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com> * fix: remove unused pathlib.Path import in ocr/main.py --------- Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: ryan-crabbe-berri <ryan@berri.ai> Co-authored-by: stuxf <70670632+stuxf@users.noreply.github.com> Co-authored-by: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com> |
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ryan-crabbe-berri
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63a2d1ddc9
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fix(tests): use canonical litellm_enterprise import path (#27699)
The enterprise package is installed as `litellm_enterprise` (per enterprise/pyproject.toml), but several tests imported it as `enterprise.litellm_enterprise.*` — a path that only resolves because the repo root happens to sit on sys.path, letting Python's implicit namespace package machinery discover `enterprise/` as a directory. This breaks any test runner that relocates source (e.g. the mutation-testing workflow, which copies tests under `mutants/`) and also caused two `patch()` strings to target a module path that does not match what production code imports — meaning those mocks were never actually patching the production module's attribute. Replace `from enterprise.litellm_enterprise.` with the canonical `from litellm_enterprise.` across 6 test files, and fix two `patch()` target strings (and one `sys.modules` patch key in the SSO test) to match. |
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oss-agent-shin
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473cfca969
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Add Bedrock Claude Platform route (#27678)
* Add Claude Platform AWS Bedrock route Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> * Use Bedrock Claude Platform route Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> * Move Claude Platform route under Bedrock Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> * Split Claude Platform messages config Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> * Centralize Claude Platform Bedrock route Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> * Address Claude Platform review feedback Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> --------- 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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Dawei Gu
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0751886680
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feat(batch-job): bedrock batch model invocation job retrieval (#26834)
* feat(bedrock): support retrieve for model-invocation-job batch ARNs `bedrock.retrieve_batch` previously only handled `:async-invoke/` ARNs (Twelve Labs Marengo embeddings). The `:model-invocation-job/` ARNs returned by `CreateModelInvocationJob` (the bulk batch inference API behind `bedrock.create_batch`) fell through and returned a misleading data-plane error, leaving created jobs unretrievable through the LiteLLM batches API. The two ARN families live on different AWS service endpoints (`bedrock-runtime` data plane vs `bedrock` control plane), so they need distinct handlers. This adds: * `BedrockBatchesHandler._handle_model_invocation_job_status` — calls the control plane via boto3 (`bedrock:GetModelInvocationJob`), reusing `BaseAWSLLM.get_credentials` for credential resolution so model_list / env / role-assumption configs continue to apply. The response is reshaped into a `LiteLLMBatch` with the same status mapping `transform_create_batch_response` already uses. * Output-file-URI prediction. Bedrock surfaces the user-supplied `s3OutputDataConfig.s3Uri` *prefix* in `GetModelInvocationJob`, but results actually land at `<prefix>/<job-id>/<basename(input)>.out`. We compute that single-file URI client-side and surface it as `output_file_id`, so OpenAI-style `client.files.content(...)` works without an extra `ListObjectsV2` round-trip. The bare prefix stays in metadata for callers that want the manifest. * Dispatch in `litellm/batches/main.py` for the new ARN family, alongside the existing async-invoke branch. * Unit tests covering ARN parsing, output-URI prediction (incl. edge cases), the full status mapping, region resolution precedence, and failure-message propagation. Note: `request_counts` is intentionally `(0, 0, 0)` — `GetModelInvocationJob` does not report per-record counts; getting accurate numbers requires parsing `manifest.json.out` from the output S3 prefix, which is left to callers. Made-with: Cursor * fix(bedrock): address PR feedback on model-invocation-job retrieve Addresses Greptile P2 findings on #26834: 1. Use the bare job id (not the full ARN) when constructing the `api_base` URL for `pre_call` logging. Passing the full ARN double- counts the `model-invocation-job/` segment and embeds colons in the path, producing misleading log lines. 2. Drop the `or output_prefix` fallback when `_predict_output_file_uri` returns None. A bare prefix is not a downloadable object and surfacing it as `output_file_id` re-creates the very NoSuchKey bug this handler exists to fix. The bare prefix is still preserved in `metadata["output_s3_uri"]` for callers that want to do their own S3 listing or read `manifest.json.out`. `metadata["output_file_uri"]` uses "" rather than None to satisfy the OpenAI Batch metadata schema (`dict[str, str]`); callers should branch on the typed `output_file_id` field instead. Also expands test coverage on the new code path: - new "stay None" regression test for the prediction-fail case - pre_call/post_call logging hook assertions (incl. the bare-id URL) - explicit cancelled_at / expired_at coverage - _to_epoch type-handling matrix and the boto3 ImportError branch - defensive _extract_region_from_bedrock_arn exception path - empty-basename case for _predict_output_file_uri Patch coverage on the changed lines is now 100% (the only remaining uncovered lines in the file belong to the pre-existing `_handle_async_invoke_status` method, which this PR does not touch). Made-with: Cursor * test(bedrock): cover retrieve_batch dispatch for both ARN families Codecov flagged 8 uncovered lines on `litellm/batches/main.py` after this PR refactored the Bedrock dispatch into a single guard with two sub-branches (`async-invoke` + `model-invocation-job`). Existing tests exercised the handlers directly but not the dispatch in `main.py`. Adds `tests/test_litellm/batches/test_retrieve_batch_bedrock_dispatch.py` with 6 mocked tests that exercise `litellm.retrieve_batch` end-to-end for the dispatch logic: - async-invoke ARN routes to `_handle_async_invoke_status` - async-invoke ARN with no region falls back to "us-east-1" (preserves prior behavior on this branch) - model-invocation-job ARN routes to the new `_handle_model_invocation_job_status` handler - model-invocation-job ARN with no region forwards None (so the new handler can sniff region from the ARN itself, rather than getting silently routed to us-east-1) - unrelated bedrock ARN family falls through to the generic provider-config retrieve path (neither special handler invoked) - non-bedrock batch ids skip the bedrock dispatch entirely Both handlers are mocked at the import site so the tests don't hit AWS — the focus here is purely the new dispatch logic in main.py. Co-authored-by: Cursor <cursoragent@cursor.com> * test(bedrock): move retrieve_batch dispatch test to tests/test_litellm/ The dispatch test landed under `tests/test_litellm/batches/`, a new directory that no upstream `test-unit-*.yml` workflow's `test-path` allow-list includes. As a result, the test was never executed in CI and codecov reported `litellm/batches/main.py` patch coverage at 11.11% (8 lines uncovered) — the lines belonging to this PR's dispatch refactor itself. Move the file up one level so it matches the `tests/test_litellm/test_*.py` glob that `test-unit-misc.yml` already runs, and adjust `sys.path.insert` for the new depth. The companion handler tests under `tests/test_litellm/llms/bedrock/batches/test_handler.py` are unaffected — they're picked up by the `llms` directory in `test-unit-llm-providers.yml`. Made-with: Cursor --------- Co-authored-by: Cursor <cursoragent@cursor.com> |
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Sameer Kankute
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5833d3eadd
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Merge pull request #27618 from BerriAI/litellm_reasoning_summary_chat_bridge
fix(openai): route reasoningSummary for gpt-5.4+ chat without tools to Responses API |
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Sameer Kankute
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4c1d91d96f
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fix(anthropic): inject dummy tool without modify_params (#27620)
Anthropic rejects tool_use/tool_result when tools is omitted. Always map and attach the dummy tool in transform_request so CLIs work without litellm.modify_params. - Add unit test for transform_request dummy tool with modify_params off - Adjust parallel function calling integration expectations: Bedrock Converse still requires modify_params for this path Co-authored-by: Cursor <cursoragent@cursor.com> |
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Sameer Kankute
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79618b1c38
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Merge pull request #27658 from BerriAI/litellm_internal_staging
merge main |
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Sameer Kankute
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aa1f57fff8
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fix black and github mock test | ||
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Sameer Kankute
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083d87a396
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Merge branch 'litellm_internal_staging' into shin_agent_oss_staging_05_09_2026 | ||
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Cursor Agent
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1628886f4a
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Fix GPT-5 reasoning summary strip test path | ||
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Cursor Agent
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0ac923c6b6
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Fix GPT-5 reasoning summary alias stripping | ||
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Cursor Agent
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eed6985cd6
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Fix reasoning summary alias stripping | ||
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Sameer Kankute
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ce17e9490f
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Merge branch 'litellm_internal_staging' into litellm_agent_oss_staging_05_06_2026 | ||
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Tai An
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f8b078b749
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fix(bedrock/messages): preserve compact_20260112 context_management on /v1/messages (#27534)
Squash-merged by litellm-agent from Anai-Guo's PR. |
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harish-berri
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a67b7a7e87
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Refactor Bedrock response stream shape handling (#27257)
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* Refactor Bedrock response stream shape handling - Introduced a module-level constant `BEDROCK_RESPONSE_STREAM_SHAPE` to cache the response stream shape, eliminating the need for per-instance caching in `BedrockEventStreamDecoderBase`. - Updated relevant methods to utilize the new constant, improving performance by avoiding redundant loading of the shape. - Added tests to ensure the shape is loaded correctly at import time and is consistent across different modules. - Added a new mock server script for testing Bedrock pass-through functionality. * Refactor response parsing for Bedrock and SageMaker - Improved code readability by formatting the parsing method calls in `AWSEventStreamDecoder` for both Bedrock and SageMaker response stream shapes. - Added blank lines for better separation of code blocks in `invoke_handler.py` and `common_utils.py` to enhance maintainability. * Enhance error handling for Bedrock and SageMaker response stream shape loading - Wrapped the loading logic in `_load_bedrock_response_stream_shape` and `_load_sagemaker_response_stream_shape` with try-except blocks to gracefully handle exceptions. - Added logging to warn when the response stream shape cannot be pre-loaded, ensuring the module imports cleanly. - Updated tests to verify that loading failures return `None` instead of propagating exceptions. * Implement error handling for missing response stream shapes in Bedrock and SageMaker - Added checks in `_parse_message_from_event` methods to raise appropriate errors when `BEDROCK_RESPONSE_STREAM_SHAPE` or `SAGEMAKER_RESPONSE_STREAM_SHAPE` is None, ensuring clearer error reporting. - Updated logging messages to reflect the unavailability of event-stream decoding for both Bedrock and SageMaker. - Enhanced unit tests to verify that the correct exceptions are raised when the response stream shapes are not loaded. |
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ishaan-berri
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aba131d3cf
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fix: Vertex Anthropic streaming status error hangs (#27310)
* Fix streaming HTTP status error hangs Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> * Fix sync streaming HTTP status error hangs Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> * Cap sync streaming error read workers Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> --------- 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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ishaan-berri
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c15718f9d1
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Fix Anthropic streaming reasoning token usage (#27319)
* fix anthropic streaming reasoning token usage Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> * test anthropic streaming reasoning usage end to end Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> * address anthropic reasoning token text split Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> * harden anthropic reasoning usage for mocked tokens Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> --------- 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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oss-pr-review-agent-shin[bot]
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75245de895
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Merge PR #26595 into agent staging branch | ||
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Sameer Kankute
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fd7ff0f269
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fix(hosted_vllm): normalize custom tools for chat completions (#25763)
* fix(hosted_vllm): normalize custom tools for chat completions Convert custom tool definitions into OpenAI function tools before forwarding hosted_vllm chat requests to avoid provider-side validation failures. Add a regression test and include a local curl verification screenshot. Made-with: Cursor * Fix black issue * Fix hosted vllm custom tool schema fallback * fix black --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> |
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Sameer Kankute
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e912e6d4ff
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feat(audio_transcription): add NVIDIA Riva STT provider (#27185)
* feat(audio_transcription): add NVIDIA Riva STT provider Adds nvidia_riva as a new audio transcription provider, supporting both NVCF-hosted and self-hosted Riva ASR deployments via gRPC streaming. - Auto-resamples input audio to 16 kHz mono LINEAR_PCM (soundfile + numpy, audioread fallback) so callers can send any common format. - Maps OpenAI params: language (en -> en-US), response_format (text/json/ verbose_json), timestamp_granularities=["word"] -> enable_word_time_offsets, word offsets converted ms -> s for verbose_json. - Auth: NVCF when nvcf_function_id is set (SSL on by default), self-hosted otherwise (SSL off by default), with explicit use_ssl override. - gRPC errors wrapped via NvidiaRivaException -> litellm exception classes. - Optional deps gated behind [stt-nvidia-riva] extra (nvidia-riva-client, soundfile, audioread, numpy). Co-authored-by: Cursor <cursoragent@cursor.com> * fix(nvidia_riva): address PR review feedback - handler: forward call-level `timeout` to streaming_response_generator (kwarg-detected via inspect for older riva-client compat) so a stalled Riva server cannot block the caller indefinitely. - audio_utils: spill bytes to a tempfile before audioread.audio_open; most audioread backends (FFmpeg, GStreamer) require a real filesystem path and previously raised TypeError on BytesIO, breaking the mp3/m4a fallback path. - audio_utils: prefer soxr / scipy.signal.resample_poly for resampling (anti-aliased polyphase) when installed, falling back to linear only as a last resort. Avoids aliasing on 44.1/48 kHz -> 16 kHz downsamples. - transformation: bare `es` now maps to es-ES (Castilian) instead of es-US, matching BCP-47 conventions. Co-authored-by: Cursor <cursoragent@cursor.com> * chore: trigger CI re-run [stabilize loop 1/3] * Update litellm/llms/nvidia_riva/audio_transcription/transformation.py Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * chore: trigger CI re-run [stabilize loop 1/3] * fix code qa * fix lint * fix mypy * fix mypy * Fix NVIDIA Riva ASR service lookup * Fix NVIDIA Riva transcription payload logging --------- Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: oss-pr-review-agent-shin[bot] <281797381+oss-pr-review-agent-shin[bot]@users.noreply.github.com> Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Krrish Dholakia
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454ce5073f
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fix(anthropic, mcp): sanitize tool names to match Anthropic's [a-zA-Z0-9_-]{1,128} pattern (#26788)
* fix(anthropic, mcp): sanitize tool names to match Anthropic's `^[a-zA-Z0-9_-]{1,128}$`
Tool names with characters like `/` or `.` (commonly produced by the
OpenAPI -> MCP generator from `operationId`s such as
`actions/download-job-logs-for-workflow-run`) caused Anthropic to reject
requests with `tools.N.custom.name: String should match pattern
'^[a-zA-Z0-9_-]{1,128}$'`.
Two layers of fix:
1. Anthropic transformation: build a per-request forward map (original ->
sanitized, disambiguated by suffix on collisions) and a reverse map
(only for names actually rewritten). Forward map is applied to tool
defs, `tool_choice`, and historical assistant tool_calls in messages.
Reverse map is threaded through both the non-streaming and streaming
response paths so callers continue to see their original tool names
in `tool_use` blocks.
2. OpenAPI -> MCP generator: sanitize `operationId` (and the
method+path fallback) at registration time so generated MCP tools are
valid for any strict-name provider, not just Anthropic. The dashboard
preview endpoint applies the same sanitization for parity.
Includes unit tests covering: collision disambiguation between
`foo_bar` and `foo/bar` in the same request, reverse-map only firing
for actually-rewritten names, message rewrite for historical tool_calls,
streaming chunk_parser reverse-mapping, and sanitization of OpenAPI
operationIds plus the preview endpoint output.
Made-with: Cursor
* fix(anthropic): build tool-name maps in transform_request, not optional_params
The previous patch stashed the per-request forward and reverse tool-name
maps under ``optional_params["_anthropic_tool_name_forward_map"]`` and
``optional_params["_anthropic_tool_name_map"]``. ``optional_params`` is
the dict that becomes the JSON body via ``data = {**optional_params}``,
so those internal keys leaked over the wire and Anthropic 400'd with:
_anthropic_tool_name_forward_map: Extra inputs are not permitted
Worse, this meant *every* request whose tool list contained any name with
an invalid character (the exact case the patch was meant to fix) regressed
into a confusing meta-error pointing at LiteLLM's internal map instead of
the offending tool.
Fix: move all tool-name sanitization into ``transform_request``, which is
the single chokepoint already shared by ``AnthropicConfig``,
``AmazonAnthropicConfig`` (Bedrock invoke), ``VertexAIAnthropicConfig``,
and ``AzureAnthropicConfig`` (all call ``super().transform_request`` /
``AnthropicConfig.transform_request(self, ...)``). New static helper
``_sanitize_tool_names_in_request`` walks the already-Anthropic-shaped
``optional_params["tools"]`` (only ``type=="custom"`` entries -- hosted
tool names are reserved by Anthropic and must not be touched), builds
the per-request forward/reverse maps, and applies the forward map in
place to ``tools[*].name`` and ``tool_choice.name``. The reverse map is
stashed exclusively on ``litellm_params`` (which is never serialized to
a provider) under ``_anthropic_tool_name_map`` for the response paths
to consume.
Side effect of this restructure: ``map_openai_params`` is now a pure
OpenAI->Anthropic param translator with no side-channel state, which
matches its contract everywhere else in the codebase.
Tests: replaced the now-incorrect "stashes maps in optional_params"
tests with regressions that assert no underscore-prefixed keys appear
in either ``optional_params`` after ``map_openai_params`` or in the
final ``transform_request`` body. Added end-to-end coverage for:
sanitization in ``transform_request``, ``tool_choice`` rewriting,
historical ``tool_calls`` rewriting in messages, and hosted-tool
passthrough.
Made-with: Cursor
* fix(anthropic): always sanitize empty text content blocks
Anthropic 400s on `{"role": "user", "content": ""}` with:
"messages: text content blocks must be non-empty"
LiteLLM already had `_sanitize_empty_text_content` to rewrite empty text
to a placeholder, but it was gated behind `litellm.modify_params=True`.
With that flag off (default), empty content from upstream agent
frameworks (e.g. pydantic-ai) flowed straight through and tripped the
Anthropic validator.
Fix:
- Always run `_sanitize_empty_text_content` at the top of
`anthropic_messages_pt`, independent of `modify_params`. There is no
way to "pass through" an empty text block, so this is non-optional.
The richer tool-call sanitizations (Cases A/B/D, which actually
mutate conversation structure) remain gated on `modify_params`.
- Extend `_sanitize_empty_text_content` to also handle list-of-blocks
content (`[{"type": "text", "text": ""}]`), not just string content.
Adds 3 regression tests covering string content, list-of-blocks
content, and the no-op case (non-empty messages with modify_params off).
Made-with: Cursor
* fix(anthropic): drop dead tool-name forward-map params, fix mypy + caller-mutation
- remove unused `name_forward_map` param from `_map_tool_choice`,
`_map_tool_helper`, `_map_tools` and the `_apply_anthropic_tool_name_forward`
helper. Production sanitization runs in `_sanitize_tool_names_in_request`
at `transform_request`; these params were never threaded through.
- handler.py: use `ANTHROPIC_TOOL_NAME_REVERSE_MAP_KEY` constant instead of
the hardcoded `"_anthropic_tool_name_map"` string.
- fix mypy `"object" has no attribute "__iter__"` in
`_rewrite_tool_names_in_messages` by guarding `tool_calls` with
`isinstance(..., list)`.
- `_sanitize_tool_names_in_request`: build a new tools list with copy-on-
change entries (and copy `tool_choice` on rewrite) so a caller reusing
the same tool list/dicts across requests doesn't see its inputs
permanently rewritten.
- doc-comment `_build_request_tool_name_maps` clarifying it operates on
OpenAI-format tools (vs `_sanitize_tool_names_in_request` which runs
on Anthropic-format tools post-`_map_tools`).
- tests: drop 3 tests pinning the now-removed param paths; add coverage
for tool_calls + None function_call rewrite and caller-dict immutability.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(mcp): inherit stored credentials in test/tools/list for edit flow
When editing an existing MCP server, the Tool Configuration preview
calls POST /mcp-rest/test/tools/list with server_id but no credentials
(management API redacts them). The endpoint now calls
_inherit_credentials_from_existing_server() so stored bearer tokens
and OAuth2 M2M credentials are loaded from global_mcp_server_manager
automatically — tools load without re-entering credentials.
New servers (no server_id) and requests with explicit credentials are
unaffected (function is a no-op in both cases).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(mcp): show all tools in edit panel, not just allowed tools
Edit flow was passing externalTools (from GET /tools/list, filtered by
allowed_tools) to MCPToolConfiguration, disabling the internal hook.
Remove the external props so the internal hook fires via
POST /test/tools/list, which returns all tools unfiltered. Combined
with the credential inheritance fix, tools load automatically without
re-entering credentials and all tools are visible for re-configuration.
existingAllowedTools still pre-checks previously allowed tools.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Fix order-dependent collision in _build_anthropic_tool_name_maps
Use a two-pass approach: first pre-register all already-valid tool names
in the 'used' set, then sanitize/disambiguate names that need rewriting.
This ensures valid names always have priority regardless of input order,
preventing duplicate tool names on the wire when e.g. 'foo/bar' appears
before 'foo_bar' in the tool list.
Add regression test for the reversed ordering case.
* Fix OpenAPI tool name collision: disambiguate sanitized names with numeric suffixes
sanitize_openapi_tool_name replaces all invalid chars with '_', but when
two operationIds differ only by sanitized characters (e.g. 'foo/list' and
'foo.list' both become 'foo_list'), the second registration silently
overwrites the first in the tool registry.
Add collision disambiguation in register_tools_from_openapi that appends
_2, _3, ... suffixes when a sanitized name is already taken, mirroring
the existing logic in _build_anthropic_tool_name_maps.
* Fix preview endpoint missing collision disambiguation for tool names
Add used_names tracking and _2/_3 suffix disambiguation to
_preview_openapi_tools, matching the logic in register_tools_from_openapi.
Without this, two operationIds that sanitize to the same string (e.g.
'foo/list' and 'foo.list' both becoming 'foo_list') would show duplicate
names in the preview while registration would disambiguate them.
* Align preview HTTP method order with register_tools_from_openapi
The preview endpoint and register_tools_from_openapi both use
order-dependent collision disambiguation (_2, _3 suffixes). When the
iteration order differs, two operations on the same path with sanitized
names that collide get different suffixes in preview vs registration,
so the dashboard shows names that don't match what actually got
registered.
Also adds a regression test that fails on the swapped order.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* Skip duplicate originals in _build_anthropic_tool_name_maps
If the same invalid tool name appeared twice in original_names (e.g.
['foo/bar', 'foo/bar']), the second occurrence overwrote the forward
map entry with a freshly-suffixed name (foo_bar_2), leaving foo_bar
orphaned in 'used' with no reverse mapping. _sanitize_tool_names_in_request
then rewrote both tool entries to foo_bar_2, and Anthropic 400'd on
duplicate tool names.
Skip the rewrite if forward already has the original mapped.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
|
||
|
Sameer Kankute
|
b8635bbc7a
|
feat(realtime): OpenAI Realtime GA support and beta compatibility (#27110)
* feat(realtime): OpenAI Realtime GA support and beta compatibility
- Normalize beta-style session.update to GA for upstream OpenAI; optional GA→beta
event translation when client sends OpenAI-Beta: realtime=v1
- Default upstream WebSocket without OpenAI-Beta; forward header when client opts in
- Extend OpenAI realtime types for GA event names and conversation item shapes
- Relax LiteLLMRealtimeStreamLoggingObject.results to List[Any] for GA events
- Update proxy client_secrets fallback to omit beta header; dashboard RealtimePlayground
- Add unit tests for remap, translation, and beta header helper
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix results
* fix greptile
* Fix mypy issues
* Remove unused class constants _GA_TEXT_DELTA_TYPES and _GA_AUDIO_DELTA_TYPES
These frozensets were defined as class-level constants in realtime_streaming.py
but never referenced anywhere in the codebase. Removing dead code.
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
* fix(realtime): use GA-shaped session.update in guardrail injections
The guardrail VAD injection code sent a beta-style session.update with a
flat turn_detection field:
{"session": {"turn_detection": {"create_response": false}}}
When the upstream OpenAI backend operates in GA mode (no OpenAI-Beta
header forwarded), it requires the nested GA shape:
{"session": {"type": "realtime", "audio": {"input": {"turn_detection": {"create_response": false}}}}}
The _remap_beta_session_to_ga helper was only applied to client-
originated session.update messages in client_ack_messages. Internally-
generated session.updates (sent via _send_to_backend) in two paths:
- _handle_raw_backend_message (raw/no provider_config path, line 518)
- backend_to_client_send_messages provider_config path (line 481)
bypassed the remap, so GA upstreams ignored or rejected them, breaking
audio transcription guardrails for all non-beta clients.
Fix: add _make_disable_auto_response_message() helper that always emits
the correct GA-shaped session.update, and replace both injection sites
with it.
Update existing tests to assert the GA nested shape instead of the old
flat beta shape, and add a new unit test for the helper itself.
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
* Log realtime session type
* Fix beta realtime session payloads
* Fix realtime audio format remapping edge case
* Fix Azure realtime beta session shape
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
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harish-berri
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4fec69dd1e
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refactor(BaseAWSLLM): implement shared IAM cache and static credentia… (#27125)
* refactor(BaseAWSLLM): implement shared IAM cache and static credential caching - Introduced a process-wide shared IAM cache to optimize credential management across instances. - Added a method to handle caching of static credentials, ensuring only long-lived credentials are cached. - Updated the get_credentials method to utilize the new caching mechanism for static credential flows. - Enhanced unit tests to verify the correct behavior of the shared cache and static credential usage. * refactor(BaseAWSLLM): enhance IAM credential caching and update related tests - Improved the process-wide IAM credential caching mechanism to better handle static and AssumeRole credentials. - Renamed the caching method for clarity and updated comments to reflect the new caching behavior. - Added a fixture to ensure the IAM cache is flushed between tests to prevent leakage of cached entries. - Updated unit tests to verify the correct behavior of the shared IAM cache, particularly for static credentials and role assumptions. * refactor(BaseAWSLLM): clarify IAM credential caching behavior and enhance tests - Updated documentation to specify that only static and ambient environment credentials are cached, excluding AssumeRole and other credential types. - Modified the caching logic to ensure that AssumeRole credentials are not stored in the IAM cache, requiring STS calls for each request. - Enhanced unit tests to verify that AssumeRole credentials are not cached and to ensure proper behavior of the IAM cache across different scenarios. * Code Readability improvement for aws auth path * refactor(BaseAWSLLM): enhance IAM credential caching documentation and add tests - Updated comments to clarify the behavior of the in-process IAM credential cache, specifying the TTL for static and ambient credentials. - Added new unit tests to verify the caching behavior for ambient environment credentials across instances and ensure that static access key sessions are constructed only once when cached. - Ensured that temporary session tokens and AWS profiles are not cached, validating the expected behavior through additional tests. * refactor(BaseAWSLLM): improve IAM credential handling and add tests for role assumption - Updated comments to clarify the behavior of IAM credential caching, particularly regarding the handling of ambient credentials and role assumptions. - Enhanced unit tests to verify that the caching mechanism correctly distinguishes between already running roles and new role assumptions, ensuring that cached environment credentials are not reused incorrectly. - Added a new test case to validate the behavior when switching roles, confirming that the system correctly uses AssumeRole when the role changes. |
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Sameer Kankute
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b0edffb883
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Merge pull request #27103 from BerriAI/litellm_azure-deployment-image-body
fix(azure): omit model from deployment image gen and image edit bodies |
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Sameer Kankute
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bb0e4168ad
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refactor(azure): move image gen JSON helper; rename image edit finalize hook
- Add image_generation/http_utils.azure_deployment_image_generation_json_body; call from azure.py (keeps AzureChatCompletion focused on chat). - Rename finalize_image_edit_multipart_data to finalize_image_edit_request_data with docstring covering multipart and JSON POST payloads (review feedback). Co-authored-by: Cursor <cursoragent@cursor.com> |
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yuneng-jiang
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555a8131fe
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Merge pull request #26951 from stuxf/codex/skills-containers-tenant-guard
chore(proxy): tighten resource ownership checks |
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user
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aee064ad37
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Merge remote-tracking branch 'upstream/litellm_internal_staging' into fix/managed-resource-service-account-isolation | ||
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user
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6ce84effe1
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chore: simplify ownership tracking — drop thin stores, in-memory fallback, hand-rolled cache
Substantial reduction (~765 LOC) without changing the security boundary: * Drop ContainerOwnershipStore and LiteLLMSkillsStore — both were one-method-per-Prisma-call wrappers. Inline the calls instead, matching the established pattern in vector_store_endpoints, agent_endpoints, and mcp_server/db.py. * Drop the prisma_client is None in-memory fallback. Production deploys always have Prisma; running ownership-critical paths on a process-local dict is a security footgun in the dev-mode case it was meant to support, and complicates every code path with a branch. Fail-secure: skip recording if Prisma is unavailable, and treat reads as "not found" (admin-only). * Drop the hand-rolled module-level cache. Replace with the existing litellm.caching.in_memory_cache.InMemoryCache, which already has TTL + max-size + eviction tested in its own module. Sentinel string for negative caching since InMemoryCache can't disambiguate "miss" from "cached as None". * Tests: drop coverage for removed code paths (in-memory fallback, hand-rolled cache internals). Keep tests for actual behavior (cache hit-rate, negative caching, owner check, list filtering, identity-less reject, admin bypass). |
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user
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83971a8712 | fix(proxy): normalize managed resource team owner field | ||
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user
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12fe945e7b
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fix: keep skills handler FastAPI-free; fold gcs deny list into the body bouncer
Two cleanups: * ``LiteLLMSkillsHandler.create_skill`` raised ``HTTPException`` for identity-less callers, importing FastAPI from a ``litellm/llms/`` module — that violates the project rule that FastAPI lives only under ``proxy/``. Switch to ``ValueError`` (the same shape the rest of the handler uses for not-found/forbidden) and update the test. * The proxy-auth body bouncer derived its observability ban list from ``_supported_callback_params`` only, missing ``_request_blocked_callback_params`` (where ``gcs_bucket_name`` and ``gcs_path_service_account`` live). Two recently-merged sibling PRs (#27019 added the deny list, #27081 added the test asserting these are rejected at the request body root) crossed without folding them together. Union the GCS deny list into the bouncer's derivation so the single source of truth covers both code paths. |
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user
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777862a018
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Merge remote-tracking branch 'upstream/litellm_internal_staging' into codex/skills-containers-tenant-guard | ||
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user
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758b488326
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fix(ownership): reject identity-less callers instead of sharing a sentinel scope
UNSCOPED_RESOURCE_OWNER_SCOPE collapsed every caller without an identity field (no user_id / team_id / org_id / api_key / token) into a single shared owner — a cross-tenant access primitive: any two such callers could see and delete each other's containers and skills. Drop the sentinel. ``get_primary_resource_owner_scope`` returns ``None`` and ``get_resource_owner_scopes`` returns ``[]`` for identity-less callers. ``record_container_owner`` and ``LiteLLMSkillsHandler.create_skill`` now reject creates from identity-less callers with a 403 instead of stamping the placeholder. Read paths already deny ``owner is None`` correctly so legacy rows (if any) are admin-only. |