When a proxy fronts Claude Code (which always sends `output_config.effort`)
at a pre-4.5 Anthropic model — haiku-3, sonnet-3.5, opus-3, sonnet-4 — the
forwarded knob causes a forced 400 the client can't fix. Gating a strip
behind the existing `drop_params` flag lets operators opt into silent
fixup once and stop worrying about per-model param hygiene.
Default (`drop_params=False`) still forwards and surfaces the provider's
error, preserving the strict, debuggable contract from #27074.
Per https://platform.claude.com/docs/en/build-with-claude/effort the
supporting set is Opus 4.5+, Sonnet 4.6+, and Mythos Preview; everything
else is dropped (with a verbose_logger warning so the strip is visible).
Recognition uses model-name patterns plus a fallback to any
`supports_*_reasoning_effort` flag in the model map for forward
compatibility with new entries.
https://claude.ai/code/session_01WjHq31rvXT6xYNdVmSJvRp
Follow-up bugs surfaced by the QA sweep on PR #27039
(https://github.com/BerriAI/litellm/pull/27039#issuecomment-4363363610).
1. Stop stripping output_config.effort on Bedrock + Vertex adaptive routes.
- Vertex AI Claude 4.6/4.7 accepts output_config.effort on rawPredict
(verified end-to-end against us-east5 / global). The strip helper now
no-ops for effort.
- Bedrock Converse routes output_config into additionalModelRequestFields
for anthropic base models so the requested adaptive tier (low/medium/
high/xhigh/max) actually reaches the wire instead of all collapsing to
identical thinking.
- Bedrock Invoke chat transformation (AmazonAnthropicClaudeConfig) stops
popping output_config from the post-AnthropicConfig request body.
- Bedrock Invoke /v1/messages allowlist (BedrockInvokeAnthropicMessagesRequest)
now lists output_config so the runtime allowlist filter forwards it.
2. Validate effort across Bedrock Converse so 'disabled' / 'invalid' / '' /
unsupported tiers (xhigh/max on Sonnet 4.6 or budget-mode 4.5 models)
surface as a clean 400 BadRequestError instead of 500.
3. ValueError -> BadRequestError throughout (AnthropicConfig.map_openai_params,
_apply_output_config, AmazonConverseConfig._handle_reasoning_effort_parameter).
Empty-string effort is now rejected (was silently passing the
'if effort and ...' short-circuit).
4. Floor reasoning_effort='minimal' at the Anthropic provider minimum
(1024 budget_tokens) via new ANTHROPIC_MIN_THINKING_BUDGET_TOKENS so it's
a usable tier on direct Anthropic / Azure AI Anthropic / Vertex AI Anthropic /
Bedrock Invoke (all of which 400 below 1024).
5. model_prices: dedupe duplicate supports_max_reasoning_effort key on
claude-opus-4-7 / claude-opus-4-7-20260416.
Adds regression tests across all five affected paths; existing tests asserting
the silent-strip behavior were updated to reflect the new pass-through and
clean 400 surfaces.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
Setting reasoning_effort="none" on Anthropic chat models (direct, Bedrock
Invoke, Bedrock Converse, Vertex AI Anthropic, Azure AI Anthropic) crashed
LiteLLM with:
litellm.APIConnectionError: 'NoneType' object has no attribute 'get'
Both the Anthropic chat transformation and Bedrock Converse called
``AnthropicConfig._map_reasoning_effort`` and assigned the ``None`` it returns
for ``"none"`` directly to ``optional_params["thinking"]``. Downstream
``is_thinking_enabled`` then did ``optional_params["thinking"].get("type")``
and crashed.
Pop ``thinking`` (and on Claude 4.6/4.7, ``output_config``) instead of
assigning ``None``, restoring the documented contract that
``reasoning_effort="none"`` means "do not enable thinking". This also
prevents downstream Anthropic 400s ("thinking: Input should be an object",
"output_config.effort: Input should be ...") if the bug were ever masked.
Verified end-to-end against the live Anthropic API and Bedrock Converse
on claude-opus-4-{5,6,7} and claude-sonnet-4-6, plus Bedrock Invoke for
Claude 4.5/4.6. Vertex AI Anthropic and Azure AI Anthropic inherit the
fixed ``map_openai_params`` from ``AnthropicConfig`` and need no further
changes.
Adds litellm.disable_vertex_batch_output_transformation (default False).
When True, afile_content returns raw Vertex predictions.jsonl untouched
so users that parse candidates/modelVersion directly are not broken.
The method was overwriting logging_obj.optional_params, logging_obj.model,
and logging_obj.start_time on the caller's Logging instance. When invoked
from llm_http_handler.py's generic framework path, the framework's own
logging_obj (which already went through pre_call) had its properties
clobbered, causing model and start_time to reflect the last batch line's
values rather than the original call context.
Fix: create a fresh local Logging instance for the per-line transformation
instead of mutating the incoming logging_obj. The caller's object is now
left entirely untouched regardless of whether a logging_obj was passed in
or not.
Regression tests added to verify model, start_time, and optional_params
are not mutated on the caller's logging_obj.
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
Two SSRF findings were OPEN with no in-flight fix; both are closed
now using narrow defenses that key off existing trust boundaries.
VERIA-6 (Milvus ``litellm_embedding_config``):
``is_request_body_safe`` already blocks ``api_base`` / ``api_key`` /
``langfuse_host`` / ``s3_endpoint_url`` / etc. at the *root* of the
request body, gated by an admin opt-in (``allow_client_side_credentials``
or per-deployment ``configurable_clientside_auth_params``). The bug is
that the Milvus vector-store transformer unpacks
``litellm_embedding_config`` into ``litellm.embedding(**embedding_config)``,
so a caller can smuggle the same banned params in via nesting and bypass
the check. Fix: ``is_request_body_safe`` now recurses into a known list
of nested-config dicts (``litellm_embedding_config`` for now) and applies
the same banned-param check with the same admin opt-in. Admin-side
vector-store config flows through ``litellm_params`` rather than the
request body, so it's unaffected.
VERIA-51 (polling URLs returned by upstream APIs):
Azure DALL-E 2, Azure Document Intelligence, and Black Forest Labs
all blindly fetched a polling URL returned by the upstream and
attached the operator's API key to the request. A compromised upstream
or a future API contract change could redirect credentials anywhere.
New ``url_utils.assert_same_origin(candidate, expected)`` helper checks
scheme, host (case-insensitive), and port (with default-port
normalization). Applied at all five polling sites: Azure DALL-E
sync+async, Azure DI sync+async, BFL image generation sync+async, BFL
image edit sync+async. Cross-origin polling URLs now raise rather than
forward credentials. The Azure DALL-E ``Expected 'status' in response``
exception no longer reflects the raw response body — that path turned
Blind SSRF into Full-Read SSRF for the limited window before the
origin check fully closed it.
Tests: 7 ``assert_same_origin`` unit tests, 6 ``is_request_body_safe``
nested-config tests, 5 polling-site rejection tests + 1 same-origin
sanity check.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Route publisher/model ids (e.g. xai/grok) to .../endpoints/openapi; keep model in JSON body
- Add model_prices keys for vertex_ai/openai/xai/grok-*
- Document xAI Grok on vertex_partner (aligned with GPT-OSS)
- Add tests for create_vertex_url and body-model heuristic
Made-with: Cursor
Trailing slashes on custom API base examples cause double-slash in
get_complete_url. Also fixes inconsistent list indentation in
test_crusoe_models_configuration.
Crusoe's vLLM-based endpoint accepts max_tokens, not max_completion_tokens.
Without this mapping, callers using the OpenAI-standard param would get errors.
- Remove trailing slash from docs Base URL to match providers.json
- Wrap model_cost mutations in try/finally to prevent test state leakage
- Add missing __init__.py to crusoe test package
Replace hand-written CrusoeChatConfig class and manual registrations
across constants.py, __init__.py, get_llm_provider_logic.py, and
_lazy_imports_registry.py with a single entry in
litellm/llms/openai_like/providers.json, consistent with the
recommended pattern for OpenAI-compatible providers.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(gemini): handle Gemini Files API URIs without fetching
Fixes#24907
When a file is uploaded via the Gemini Files API, the returned URI
(https://generativelanguage.googleapis.com/v1beta/files/...) starts
with 'https://' and hits the generic HTTPS handler in
_process_gemini_media(). That handler calls
_get_image_mime_type_from_url() which tries to fetch the URL — but
Gemini Files API URLs return 403 when accessed directly, causing:
'Unable to determine mime type for file_id: ...'
Fix: add an early elif that matches Gemini Files API URLs and passes
them through as file_data without trying to fetch the URL. When an
explicit format is provided it's included; otherwise the Gemini API
infers the MIME type from its stored metadata.
Exactly matches the fix direction suggested by the issue reporter
(rodriciru).
* fix: anchor Gemini Files API URL check with startswith
Address greptile P2: replace `in` substring check with `startswith`
to prevent query-string injection bypass (e.g.
`https://evil.com/?ref=https://generativelanguage...`).
Also adds trailing slash to match only valid file URIs.
---------
Co-authored-by: voidborne-d <voidborne-d@users.noreply.github.com>
service_tier (priority/flex) was not forwarded to generic_cost_per_token
for azure and azure_ai providers, so tier-specific pricing was ignored
and standard pricing was always returned. Other providers (openai,
bedrock, gemini, vertex_ai) already pass it correctly.
The async/sync delete_response_api_handler always passed json=data into
httpx.delete, where data is {} from the transformer. httpx serializes that
to a 2-byte body. The Azure Responses DELETE endpoint now rejects any
request body with code: unexpected_body, breaking
test_basic_openai_responses_delete_endpoint on the llm_responses_api_testing
job. Build the kwargs dict and only set json= when data is truthy.
Add unit tests that patch httpx.delete and assert json/data are not in the
captured kwargs for the Azure DELETE path (sync and async).
``variant`` is user-controlled (passed through from
``litellm.video_content(variant=...)``) and was interpolated raw into
the URL query string. A value like ``thumbnail&extra=1`` would inject
additional query parameters into the upstream request — the same
class of issue this PR's path-segment encoding addresses. Wrap the
value in ``quote(value, safe="")`` so ``&`` / ``=`` / ``#`` cannot
terminate the ``variant`` value or open a new parameter.
Adds a regression test asserting that a malicious ``thumbnail&extra=1``
ends up percent-encoded in the URL, and that the legitimate
``thumbnail`` value still round-trips cleanly.
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Vertex rejects array schemas without an items field
(GenerateContentRequest.tools[*].function_declarations[*].parameters...items: missing field).
This happened for tool params containing anyOf branches like {"type": "array"}
with no items, including the case where convert_anyof_null_to_nullable
strips an empty items entry. Default missing items to {"type": "object"}
in process_items so the same default applies to bare arrays and
arrays nested inside anyOf.
Cover the full litellm.rerank()/arerank() path with HTTP mocked, asserting
metadata.requester_metadata reaches the Discovery Engine :rank body as
userLabels (and stays absent when no metadata is set). Catches plumbing
regressions that unit tests on transform_rerank_request alone would miss.
Resolves merge conflict in tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
by keeping both the new bedrock tool-result file/document tests and the
transform_response body-leak regression test.
Also addresses Greptile P2 comment: when BedrockImageProcessor returns a
block with neither 'image' nor 'document' keys on the tool-result path
(image_url and file content types), log a warning instead of silently
dropping the block.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
