* test: drop the cwd-relative sys.path.insert calls from the test suite
TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.
Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.
Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.
* test: drop the duplicate imports the sys.path sweep exposed to F811
* test(pre-call-utils): restore the os import the new bedrock tests need
Python keeps only the last binding for a name, so when a file defines the same
test twice the earlier one is unreachable. pytest cannot collect a function that
no longer exists, so nothing reports it and the file still looks like it covers
the scenario.
These ten are cases where the two definitions have different bodies, meaning a
real test was replaced rather than duplicated. Each is renamed to say what it
actually covers, which makes it reachable again:
- test_gemini_frequency_penalty: the dead copy checks the parameter is listed in
get_supported_openai_params for vertex_ai; the survivor checks get_optional_params
maps a value for gemini. Different function and different provider.
- test_async_log_success_event_adds_to_queue and the failure variant: the dead
copies run without mocking asyncio.create_task, so they exercise the real task
path the survivors mock out.
- test_async_send_batch_triggers_tasks: the dead copy asserts send is not awaited
directly; the survivor asserts create_task was called.
- test_model_id_in_required_metrics: the dead copy checks the model_id label on
twelve further metrics the survivor dropped.
- test_anthropic_messages_pt_file_block_preserves_cache_control: the dead copy
passes model and llm_provider explicitly and uses real base64 PDF content.
- test_translate_streaming_openai_chunk_to_anthropic_with_thinking: the dead copy
covers thinking_delta; the survivor covers signature_delta.
- test_client_initialization and test_client_without_api_key: the dead copies
assert the resource clients are wired with the right base URL and key; the
survivors only construct the object.
- test_client_initialization_strips_trailing_slash: the dead copy constructs
ModelsManagementClient directly rather than going through Client.
Verification: collecting the seven touched files gives 401 node IDs before and
411 after, the ten new names and nothing else, with nothing lost. All ten pass.
Running the touched files in full gives 299 passed, and test_optional_params.py
goes from 111 passed to 112.
Two further shadowed definitions were left alone rather than renamed: the dead
copies of test_prompt_caching and test_cost_calculator_with_base_model_with_router
have no assertions at all, one being a bare pass and the other a lone import, so
restoring them would add tests that cannot fail.
* fix(bedrock): normalize Messages system role and adaptive-thinking for Claude Invoke
* style(bedrock): use builtin generics in new Invoke helpers to clear UP006 gate
* fix(bedrock): honor explicit thinking budget_tokens=0 in clear_thinking conversion
The clear_thinking_20251015 -> adaptive conversion resolved the thinking
budget with `thinking.get("budget_tokens") or BEDROCK_MIN_THINKING_BUDGET_TOKENS`,
which treats a caller-supplied `budget_tokens=0` as missing and silently
substitutes the Bedrock minimum. Resolve the budget with an explicit
`is not None` check so an explicit 0 is honored.
* fix(bedrock): gate Fable 5 into clear_thinking adaptive injection on Invoke
_ensure_thinking_for_clear_thinking_context_management returns early when
_supports_extended_thinking_on_bedrock(model) is False, so the adaptive-thinking
injection never runs for models absent from that gate. Opus 4.8 slips through on
the incidental "opus-4" substring, but Fable 5 had no matching pattern, so a
clear_thinking_20251015 request on Fable 5 reached Bedrock with an unsupported
context-management edit and no thinking field; the exact 400 this path exists to
prevent. Add the fable-5 patterns to the gate so Fable 5 (mapped ids and unmapped
aliases) gets thinking.type=adaptive + output_config.effort like the other
adaptive models.
Extend the adaptive-injection regression test to cover Fable 5 (a mapped id and
an unmapped alias) so it fails without the gate entry, and add focused coverage
for the budget->effort tiers, the disabled/enabled/adaptive thinking branches,
output_config.effort preservation, and list/dict system-role normalization.
Also normalize the Invoke transformation module and its test to line-length 88
so ruff format --check (CI format-check) passes.
* refactor(anthropic): make supports_adaptive_thinking flag authoritative for thinking detection
Replace the per-version name helpers (_is_claude_4_6/4_7/4_8_model,
_is_claude_fable_5_model) with cost-map-flag-first detection. _is_adaptive_thinking_model
now reads supports_adaptive_thinking from the model cost map and falls back to a single
generalized family-version regex (_claude_version_at_least(model, 4, 6)) only when a model
is unmapped, instead of hard-coding each new Claude release.
Wire supports_adaptive_thinking through ProviderSpecificModelInfo and ModelInfo so the cost
map flag actually surfaces at lookup time. Reroute the Bedrock Invoke extended-thinking gate
and the two anthropic/chat/transformation.py call sites through _is_adaptive_thinking_model.
Known gap left to the fallback_generalizations work (#29718): unmapped Fable 5 aliases have
no parseable minor version, so they defer to the cost map and are not detected until a mapped
entry or a generalization rule exists. Covered by an explicit regression test.
* refactor(anthropic): drop name-based version fallback; resolve adaptive thinking from cost map only
The prior commit kept a regex (_claude_version_at_least) as a fallback when an id
resolved to no cost-map entry. Remove it: _is_adaptive_thinking_model now reads
supports_adaptive_thinking and nothing else, so "which Claude versions think
adaptively" lives entirely in the model cost map, and a new adaptive release is a
JSON edit rather than a Python edit.
To keep the flag authoritative across the id forms the Bedrock Invoke and anthropic
paths actually see, backfill supports_adaptive_thinking=true on every adaptive Claude
entry that was missing it (Opus 4.6/4.7 and Sonnet 4.6 across region/provider aliases)
in both the root and bundled cost maps, and generalize _model_map_lookup_candidates to
normalize an id to its base cost-map key: strip a Bedrock version suffix (-v1:0 fully,
or just the :0 inference-profile minor so the -v1-keyed 4.6 entries resolve), strip a
dated-release suffix (-20260219), and rewrite a dotted family version (4.6 -> 4-6).
This is id normalization feeding the lookup, not capability-by-name.
Tests load the PR-local cost map (the flags are not on main until merge) and cover each
normalization path plus the unmapped-alias deferral to fallback_generalizations (#29718).
* refactor(reasoning_effort): single-source effort<->thinking-budget mappings
Route every reasoning_effort <-> thinking-budget conversion through the DEFAULT_REASONING_EFFORT_*_THINKING_BUDGET constants so the numbers stay in sync across providers. The five constants are now 2000/5000/10000/20000/40000
Add reasoning_effort_from_thinking_budget() in litellm_core_utils/reasoning_effort_utils.py and route the three OpenAI-style forward maps (anthropic adapters, responses adapters, hosted_vllm) through it. The bedrock invoke and experimental messages adaptive maps now reference the constants directly; the only behavior change is the xhigh threshold moving from 24000 to 20000. Reverse maps and the cross-provider test grid read the same constants
* test(reasoning_effort): lift budget-mode max_tokens above the new high budget
The single-sourced DEFAULT_REASONING_EFFORT_*_THINKING_BUDGET thresholds moved
high from 4096 to 10000. The live reasoning_effort grid sends budget-mode
requests with max_tokens=8192, so reasoning_effort=high now produces
budget_tokens=10000 > max_tokens and every provider returns 'max_tokens must be
greater than thinking.budget_tokens'. Derive a shared BUDGET_MODE_MAX_TOKENS
(2x the high budget) for the spec and the request builder so the ceiling always
clears the largest 200-expected tier. Also resolve the inherited base
test_reasoning_effort assertion off the same high-budget constant instead of the
stale 4096 literal so it tracks the source of truth.
* fix(reasoning_effort): keep effort<->budget thresholds at pre-PR values
The single-sourcing refactor moved the shared effort<->budget thresholds up
(low 1024->2000, medium 2048->5000, high 4096->10000, xhigh 8192->20000,
max 16384->40000). That silently changes the effort->budget direction: a caller
who sets reasoning_effort together with a max_tokens that used to sit above the
old per-tier budget but below the new one now trips the provider's
"max_tokens must be greater than thinking.budget_tokens" 400. It spans every
backend that derives a budget from an effort (Anthropic, Gemini/Vertex,
hosted vLLM), not just Bedrock.
Restore the constants to their pre-PR values while keeping every backend reading
from the shared DEFAULT_REASONING_EFFORT_*_THINKING_BUDGET constants, so the
mapping stays single-sourced without the behavior change. Tests that pinned the
raised thresholds now derive their boundaries from the same constants.
* test(reasoning_effort): derive high effort->budget assertions from the shared constant
The cross-provider translation tests pinned reasoning_effort="high" to a literal
budget_tokens=10000, the raised value. Point them at
DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET so they track the single source
instead of a magic number.
* fix(anthropic): resolve adaptive flag for combined dated+versioned Bedrock ids
The model-map candidate normalization applied each suffix strip independently to
the original id, so the real Bedrock shape "<base>-<YYYYMMDD>-v1:0" never reduced
to its base cost-map key: stripping the version left the date, and the
dated-suffix regex is anchored to the end so it could not fire while the version
was still present. An adaptive Claude model invoked by its full dated+versioned
id (e.g. us.anthropic.claude-sonnet-4-6-20251101-v1:0) therefore resolved to
supports_adaptive_thinking=null and was treated as non-adaptive, reaching Bedrock
with the rejected thinking.type=enabled shape, the exact 400 this path prevents.
Add a composed normalization that rewrites the dotted family version, then peels
the -vN:rev version suffix, then the -YYYYMMDD dated suffix, so the combined form
resolves to its base key. Regression tests pin the combined suffix on sonnet-4-6
and opus-4-8 across provider/region prefixes.
* fix(reasoning_effort): align budget<->effort tests with reverted constants and format common_utils
The constant revert restored the effort<->budget thresholds to their pre-PR
values (1024/2048/4096/8192/16384) and single-sourced the reverse
budget->effort ladder through reasoning_effort_from_thinking_budget, but
several tests still pinned the briefly-raised values and the old hardcoded
reverse buckets, so the "All Other Providers" shard failed
Derive the anthropic chat effort->budget assertions from the shared
DEFAULT_REASONING_EFFORT_*_THINKING_BUDGET constants, and update the
experimental pass-through and responses adapter expectations to the
single-sourced reverse ladder (budget 1024 -> low, 5000 -> high)
Also run ruff format --line-length 88 over anthropic/common_utils.py so the
CI format-check, which checks the whole changed file, passes
* test: modernize models used in CircleCI e2e test suites
Replaces obsolete models (gpt-4o, gpt-4o-mini, gpt-3.5-turbo,
claude-3-5-sonnet-20240620, claude-sonnet-4-20250514) with current
equivalents across the e2e_openai_endpoints and
proxy_e2e_anthropic_messages_tests CircleCI jobs.
- gpt-4o -> gpt-5.5 (responses API e2e tests)
- gpt-4o-mini -> gpt-5-mini (websocket responses, oai_misc_config)
- gpt-4o-mini-2024-07-18 -> gpt-4.1-mini-2025-04-14 (fine-tuning,
still actively fine-tunable)
- gpt-4 / gpt-3.5-turbo target_model_names example -> gpt-5.5 /
gpt-5-mini
- bedrock claude-3-5-sonnet-20240620 batch entry -> haiku-4-5-20251001
(also aligning oai_misc_config model_name with what
test_bedrock_batches_api.py actually requests)
- bedrock claude-sonnet-4-20250514 (deprecated, retires 2026-06-15)
-> claude-sonnet-4-5-20250929
* test: point bedrock-claude-sonnet-4 alias at Sonnet 4.6, not 4.5
Greptile/Cursor flagged that after the previous commit, the
bedrock-claude-sonnet-4 alias collided with bedrock-claude-sonnet-4.5
(both pointed to claude-sonnet-4-5-20250929). Rename to
bedrock-claude-sonnet-4.6 and point it at the Sonnet 4.6 Bedrock ID
(us.anthropic.claude-sonnet-4-6, already in the litellm model
registry) so the alias name matches the underlying model version.
* test: modernize models across remaining CI-mounted configs & tests
Expands the modernization sweep to all CircleCI-mounted proxy configs
and to test directories where the model literal is a fixture/route key
(not the test's subject).
Config changes:
- proxy_server_config.yaml: bump gpt-3.5-turbo / gpt-3.5-turbo-1106 /
gpt-4o / gemini-1.5-flash / dall-e-3 underlying models; rename
gpt-3.5-turbo-end-user-test alias to gpt-5-mini-end-user-test; bump
text-embedding-ada-002 underlying to text-embedding-3-small. User-
facing aliases (gpt-3.5-turbo, gpt-4, text-embedding-ada-002, etc.)
preserved for backward compatibility with tests.
- simple_config.yaml, otel_test_config.yaml, spend_tracking_config.yaml:
bump gpt-3.5-turbo underlying to gpt-5-mini.
- pass_through_config.yaml: claude-3-5-sonnet / claude-3-7-sonnet /
claude-3-haiku entries replaced with claude-sonnet-4-5 / claude-
haiku-4-5 / claude-opus-4-7.
- oai_misc_config.yaml: align alias name with the gpt-5-mini rename.
Test changes (proactive: claude-sonnet-4-20250514 / claude-opus-4-
20250514 retire 2026-06-15):
- tests/llm_translation/test_anthropic_completion.py: bump 3 references
+ paired Vertex AI ID to claude-sonnet-4-5.
- tests/llm_translation/test_optional_params.py: bump 2 references.
- tests/pass_through_unit_tests/test_anthropic_messages_passthrough.py
and test_bedrock_anthropic_messages_test.py: bump router fixtures
using the deprecated model IDs.
- tests/pass_through_unit_tests/base_anthropic_messages_tool_search_test.py:
modernize docstring examples.
- tests/test_end_users.py: update references to renamed alias.
* test: modernize placeholder model literals in router_unit_tests
Mass replace_all on fixture/placeholder model literals across the
router_unit_tests/ suite (model name is a routing key / label, not the
test subject). Sub-agent sweep so far — additional commits will follow
for logging_callback_tests/, enterprise/, top-level tests/test_*.py,
and other CI-mounted dirs.
Mappings applied:
- gpt-3.5-turbo -> gpt-5-mini
- gpt-4 (bare) -> gpt-5.5
- gpt-4o (bare) -> gpt-5
- text-embedding-ada-002 -> text-embedding-3-small
- claude-3-sonnet-20240229 / claude-3-opus-20240229 /
claude-3-haiku-20240307 / claude-3-5-sonnet-20240620 ->
claude-sonnet-4-5-20250929 / claude-opus-4-7 /
claude-haiku-4-5-20251001 as appropriate
Explicitly preserved:
- gpt-4o-mini-* variants (transcribe, tts, etc.) where they're current
- gpt-4-turbo / gpt-4-vision-preview / gpt-4-0613 (subject literals)
- JSONL batch body literals
- Mock LLM response model fields (must match upstream)
- Fake/mock identifiers
* test: modernize placeholder model literals across remaining CI suites
Sub-agent sweep across logging_callback_tests/, guardrails_tests/,
enterprise/, pass_through_unit_tests/, otel_tests/,
llm_responses_api_testing/, batches_tests/, spend_tracking_tests/,
litellm_utils_tests/, unified_google_tests/, and a few top-level
tests/test_*.py files where the model literal is a fixture or
placeholder (router model_list, mock standard logging payload, mock
callback data) rather than the test's subject.
Mappings applied (see scope notes below):
- gpt-3.5-turbo -> gpt-5-mini
- gpt-4 (bare) -> gpt-5.5
- gpt-4o (bare) -> gpt-5.5 (corrected from initial gpt-5 — bare gpt-5
is not a valid OpenAI alias; only gpt-5.5 / gpt-5.4 / gpt-5.2-codex
/ gpt-5-mini exist)
- gpt-4o-mini (bare) -> gpt-5-mini
- text-embedding-ada-002 -> text-embedding-3-small
- claude-3-sonnet-20240229 -> claude-sonnet-4-5-20250929
- claude-3-opus-20240229 -> claude-opus-4-7
- claude-3-haiku-20240307 -> claude-haiku-4-5-20251001
- claude-3-5-sonnet-20240620/20241022 -> claude-sonnet-4-5-20250929
- claude-3-7-sonnet-20250219 -> claude-sonnet-4-6
- gemini-1.5-flash -> gemini-2.5-flash
- gemini-1.5-pro -> gemini-2.5-pro
Explicitly preserved (not modernized):
- llm_translation/ tests where model is the SUBJECT (provider-specific
translation/transformation logic). Only the deprecated 20250514
references were already bumped in a prior commit.
- Cost-calc / tokenizer subject tests in test_utils.py (skip-ranges
documented by the sub-agent).
- Bedrock model IDs in test_health_check.py path-stripping tests.
- JSONL batch request bodies and mock LLM response bodies (must match
upstream literal).
- Langfuse expected-request-body JSON fixtures (cost values are exact-
match-asserted; changing the model would shift response_cost).
- gpt-3.5-turbo-instruct (text-completion endpoint; no modern OpenAI
equivalent).
- Top-level tests calling the proxy through user-facing aliases
(gpt-3.5-turbo, gpt-4, text-embedding-ada-002, dall-e-3) — aliases
in proxy_server_config.yaml stay; only the underlying model was
bumped.
- tests/test_gpt5_azure_temperature_support.py (the test's whole point
is model-name handling).
- Fake / mock / openai/fake identifiers.
Notable side fixes:
- test_spend_accuracy_tests.py: UPSTREAM_MODEL now matches what
spend_tracking_config.yaml's proxy actually routes to (gpt-5-mini),
resolving a latent inconsistency.
- proxy_server_config.yaml: bare `gpt-5` alias renamed to `gpt-5.5`
(bare gpt-5 is not a valid OpenAI alias).
- test_batches_logging_unit_tests.py: explicit_models list entries
kept distinct (gpt-5-mini + gpt-5.5) after bulk rename.
* test: fix CI failures from model modernization sweep
CI surfaced 4 categories of regression from the bulk modernization:
1. Azure deployment names are customer-specific. Reverted:
- tests/litellm_utils_tests/test_health_check.py: azure/text-
embedding-3-small -> azure/text-embedding-ada-002 (the CI Azure
account does not have a text-embedding-3-small deployment).
- tests/logging_callback_tests/test_custom_callback_router.py:
same revert for two router fixtures driving aembedding.
2. gpt-5 family does not accept temperature != 1. Tests that pass a
custom temperature swapped from gpt-5-mini to gpt-4.1-mini (modern
non-reasoning OpenAI mini that still accepts temperature/logprobs):
- tests/logging_callback_tests/test_datadog.py
- tests/logging_callback_tests/test_langsmith_unit_test.py
- tests/logging_callback_tests/test_otel_logging.py
3. proxy_server_config.yaml's gpt-3.5-turbo-large alias was routing to
gpt-5.5 (a reasoning model that rejects logprobs). The proxy test
tests/test_openai_endpoints.py::test_chat_completion_streaming
exercises logprobs/top_logprobs through that alias. Bumped the
underlying model to gpt-4.1 (non-reasoning, still modern).
4. tests/logging_callback_tests/test_gcs_pub_sub.py asserts against a
pinned JSON fixture (gcs_pub_sub_body/spend_logs_payload.json) with
hardcoded model="gpt-4o" and a model-specific spend value. Reverted
the litellm.acompletion calls in the test to model="gpt-4o" so the
fixture's exact-match assertions still hold.
5. tests/pass_through_unit_tests/test_anthropic_messages_passthrough.py:
anthropic.messages.create routing to openai/gpt-5-mini returned an
empty content[0] with max_tokens=100 (reasoning-token consumption).
Swapped to openai/gpt-4.1-mini.
* test: fix Assistants API model + 2 cursor[bot] review nits
1. pass_through_unit_tests/test_custom_logger_passthrough.py: gpt-5.5
isn't accepted by the /v1/assistants endpoint
("unsupported_model"). Switch to gpt-4.1-mini (modern, Assistants-
API-supported, non-reasoning).
2. example_config_yaml/pass_through_config.yaml: the previous sweep
bumped the claude-3-7-sonnet alias to claude-opus-4-7, which is a
tier change (Sonnet -> Opus). Map to claude-sonnet-4-6 to keep the
Sonnet tier intact. (Cursor bugbot review.)
3. example_config_yaml/simple_config.yaml: model_name was left as
gpt-3.5-turbo while the underlying was bumped to gpt-5-mini, which
muddles the "simple" example. Make both sides gpt-5-mini so the
most basic example is a straight 1:1 mapping again. (Cursor bugbot
review.)
* fix: revert gpt-4/gpt-3.5-turbo alias underlying to non-reasoning models
tests/test_openai_endpoints.py::test_completion calls the proxy alias
"gpt-4" with temperature=0, and other tests call gpt-3.5-turbo with
custom temperature / logprobs / the legacy /v1/completions endpoint.
The earlier modernization mapped both aliases to gpt-5.5 / gpt-5-mini,
which are reasoning models that reject temperature != 1 and don't
expose /v1/completions. Map the aliases to gpt-4.1 / gpt-4.1-mini
(modern non-reasoning OpenAI models) instead — keeps user-facing
aliases preserved while picking a current underlying that still
supports the parameters/endpoints the tests exercise.
AWS Bedrock has reached end-of-life for `claude-3-7-sonnet-20250219-v1:0`,
returning 404s with "This model version has reached the end of its life."
Update test references to `claude-sonnet-4-5-20250929-v1:0` (same capability
surface: thinking, tools, prompt caching, PDF input, vision, computer use).
The bedrock/invoke pass-through tests stay on Sonnet 3.5 since Sonnet 4.5
is converse-only on Bedrock.
* feat(proxy): add NO_OPENAPI env var to disable /openapi.json endpoint (#25696)
* feat(proxy): add NO_OPENAPI env var to disable /openapi.json endpoint - Fixes#25538
* test(proxy): add tests for _get_openapi_url
---------
Co-authored-by: Progressive-engg <lov.kumari55@gmail.com>
* feat(prometheus): add api_provider label to spend metric (#25693)
* feat(prometheus): add api_provider label to spend metric
Add `api_provider` to `litellm_spend_metric` labels so users can
build Grafana dashboards that break down spend by cloud provider
(e.g. bedrock, anthropic, openai, azure, vertex_ai).
The `api_provider` label already exists in UserAPIKeyLabelValues and
is populated from `standard_logging_payload["custom_llm_provider"]`,
but was not included in the spend metric's label list.
* add api_provider to requests metric + add test
Address review feedback:
- Add api_provider to litellm_requests_metric too (same call-site as
spend metric, keeps label sets in sync)
- Add test_api_provider_in_spend_and_requests_metrics following the
existing pattern in test_prometheus_labels.py
* fix: ensure `litellm_metadata` is attached to `pre_call` guardrail to align with `post_call` guardrail (#25641)
* fix: ensure `litellm_metadata` is attached to pre_call to align with post_call
* refactor: remove unused BaseTranslation._ensure_litellm_metadata
* refactor: module level imports for ensure_litellm_metadata and CodeQL
* fix: update based off of Codex comment
* revert: undo usage of `_guardrail_litellm_metadata`
* feat: add pricing entry for openrouter/google/gemini-3.1-flash-lite-preview (#25610)
* fix(bedrock): skip synthetic tool injection for json_object with no schema (#25740)
When response_format={"type": "json_object"} is sent without a JSON
schema, _create_json_tool_call_for_response_format builds a tool with an
empty schema (properties: {}). The model follows the empty schema and
returns {} instead of the actual JSON the caller asked for.
This patch:
- Skips synthetic json_tool_call injection when no schema is provided.
The model already returns JSON when the prompt asks for it.
- Fixes finish_reason: after _filter_json_mode_tools strips all
synthetic tool calls, finish_reason stays "tool_calls" instead of
"stop". Callers (like the OpenAI SDK) misinterpret this as a pending
tool invocation.
json_schema requests with an explicit schema are unchanged.
Co-authored-by: Claude <noreply@anthropic.com>
* fix(utils): allowed_openai_params must not forward unset params as None
`_apply_openai_param_overrides` iterated `allowed_openai_params` and
unconditionally wrote `optional_params[param] = non_default_params.pop(param, None)`
for each entry. If the caller listed a param name but did not actually
send that param in the request, the pop returned `None` and `None` was
still written to `optional_params`. The openai SDK then rejected it as
a top-level kwarg:
AsyncCompletions.create() got an unexpected keyword argument 'enable_thinking'
Reproducer (from #25697):
allowed_openai_params = ["chat_template_kwargs", "enable_thinking"]
body = {"chat_template_kwargs": {"enable_thinking": False}}
Here `enable_thinking` is only present nested inside
`chat_template_kwargs`, so the helper should forward
`chat_template_kwargs` and leave `enable_thinking` alone. Instead it
wrote `optional_params["enable_thinking"] = None`.
Fix: only forward a param if it was actually present in
`non_default_params`. Behavior is unchanged for the happy path (param
sent → still forwarded), and the explicit `None` leakage is gone.
Adds a regression test exercising the helper in isolation so the test
does not depend on any provider-specific `map_openai_params` plumbing.
Fixes#25697
---------
Co-authored-by: lovek629 <59618812+lovek629@users.noreply.github.com>
Co-authored-by: Progressive-engg <lov.kumari55@gmail.com>
Co-authored-by: Ori Kotek <ori.k@codium.ai>
Co-authored-by: Alexander Grattan <51346343+agrattan0820@users.noreply.github.com>
Co-authored-by: Mohana Siddhartha Chivukula <103447836+iamsiddhu3007@users.noreply.github.com>
Co-authored-by: Amiram Mizne <amiramm@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
OpenAI retired o1-mini, o1-preview, gpt-4-0314, and gpt-4-32k from the model
cost map. Google renamed gemini-2.5-flash-image-preview to gemini-2.5-flash-image.
Updated tests to use current model names.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The OpenAI `store` parameter (used for storing completions for
distillation/evals) was missing from `OPENAI_CHAT_COMPLETION_PARAMS`.
This caused it to be unrecognized by `get_standard_openai_params()` and
the `litellm_proxy` provider config. It also meant that code paths using
this list (rather than `DEFAULT_CHAT_COMPLETION_PARAM_VALUES`) would
treat `store` as a provider-specific parameter and forward it to
non-OpenAI providers like Anthropic, resulting in:
"store: Extra inputs are not permitted"
Fixes#19700
* Add openai metadata filed in the request
* Add docs related to openai metadata
* Add utils
* test_completion_openai_metadata[True]
* Added support for though signature for gemini 3 in responses api (#16872)
* Added support for though signature for gemini 3
* Update docs with all supported endpoints and cost tracking
* Added config based routing support for batches and files
* fix lint errors
* Litellm anthropic image url support (#16868)
* Add image as url support to anthropic
* fix mypy errors
* fix tests
* Fix: Populate spend_logs_metadata in batch and files endpoints (#16921)
* Add spend-logs-metadata to the metadata
* Add tests for spend logs metadata in batches
* use better names
* Remove support for penalty param for gemini 3 (#16907)
* Remove support for penalty param
* remove halucinated model names
* fix mypy/test errors
* fix tests
* fix too many lines error
* fix too many lines error
* Add config for cicd test case
* Fix final tests
* fix batch tests
* fix batch tests
- Fix URL construction in Gemini image generation to strip 'gemini/' prefix
- Google AI API expects base model name without the prefix
- Update model references and pricing information for consistency
- Remove outdated image generation pricing entries
Fixes issue where models like 'gemini/imagen-4.0-fast-generate-preview-06-06'
were being rejected by the Google AI API due to incorrect URL formatting.
* fix(main.py): fix async retryer
Fixes https://github.com/BerriAI/litellm/issues/12830
* fix(forward_clientside_headers_by_model_group.py): filter out 'content-type' from forwardable headers
clientside content-type != proxy content type, can cause requests to hang
* test(tests/): update tests
* fix(main.py): use processed non-default-params as standard input params for langfuse
Fixes https://github.com/BerriAI/litellm/issues/11072
Fixes https://github.com/BerriAI/litellm/issues/11096
* fix(main.py): rename variable to be more accurate
* test(test_langfuse_e2e_test.py): add router unit test for langfuse e2e testing
Prevent https://github.com/BerriAI/litellm/issues/11072 from happening again
* build: update lock
* fix(utils.py): refactor optional params function
make it easier to get the standardized non default params
* fix(utils.py): improve process non default params function
* fix(main.py): include provider specific params in processed non default params used in logging
ensures user can see any provider specific params on langfuse
ensures user can see any provider specific params on langfus e
* Support pdf url's to openai (#10640)
* fix(gpt_transformation.py): support pdf url input to openai
pass as base64 as openai doesn't support image url's
* fix(openai.py): support async message transformation
allows async get request to convert url to base64
* fix(gpt_transformation.py): fix linting errrors and use common components across sync + async flows
* fix: fix linting errors
* fix(openai.py): pop correct var
* Fix sagemaker chat calls - content length error (#10607)
* fix(sagemaker_chat/): support passing dynamic aws params
previously being ignored
* refactor(sagemaker/chat): more refactoring
* fix(sagemaker_chat/): make sure streaming is correctly handled post-refactor
* refactor: more refactoring to support using signed json str
* fix(sagemaker/chat): working sync streaming post refactor
* fix(sagemaker/chat): support async streaming post refactor
* fix(llm_http_handler.py): await async function
* fix: remove print statements
* test: update test
* test: update test
* fix(llm_http_handler.py): retain passing in data as json str
* test: update test
* fix(base_model_iterator.py): fix linting error
* test: test auth
* fix: fix linting error
* test: update test
* test: update translation test
* fix(gpt_transformation.py): handle awaitable/non-awaitable object
* fix: handle async flow for message transformation on openai compatible api's
* test: cleanup testing
* test: update test
* test(test_router.py): use model with higher quota
* test: simplify test
* test: update test
* fix(azure/common_utils.py): check for azure tenant id, client id, client secret in env var
Fixes https://github.com/BerriAI/litellm/issues/9598#issuecomment-2801966027
* fix(azure/gpt_transformation.py): fix passing response_format to azure when api year = 2025
Fixes https://github.com/BerriAI/litellm/issues/9703
* test: monkeypatch azure api version in test
* test: update testing
* test: fix test
* test: update test
* docs(config_settings.md): document env vars
* fix(litellm_proxy/chat/transformation.py): support 'thinking' param
Fixes https://github.com/BerriAI/litellm/issues/9380
* feat(azure/gpt_transformation.py): add azure audio model support
Closes https://github.com/BerriAI/litellm/issues/6305
* fix(utils.py): use provider_config in common functions
* fix(utils.py): add missing provider configs to get_chat_provider_config
* test: fix test
* fix: fix path
* feat(utils.py): make bedrock invoke nova config baseconfig compatible
* fix: fix linting errors
* fix(azure_ai/transformation.py): remove buggy optional param filtering for azure ai
Removes incorrect check for support tool choice when calling azure ai - prevented calling models with response_format unless on litell model cost map
* fix(amazon_cohere_transformation.py): fix bedrock invoke cohere transformation to inherit from coherechatconfig
* test: fix azure ai tool choice mapping
* fix: fix model cost map to add 'supports_tool_choice' to cohere models
* fix(get_supported_openai_params.py): check if custom llm provider in llm providers
* fix(get_supported_openai_params.py): fix llm provider in list check
* fix: fix ruff check errors
* fix: support defs when calling bedrock nova
* fix(factory.py): fix test
* fix(anthropic_claude3_transformation.py): fix amazon anthropic claude 3 tool calling transformation on invoke route
move to using anthropic config as base
* fix(utils.py): expose anthropic config via providerconfigmanager
* fix(llm_http_handler.py): support json mode on async completion calls
* fix(invoke_handler/make_call): support json mode for anthropic called via bedrock invoke
* fix(anthropic/): handle 'response_format: {"type": "text"}` + migrate amazon claude 3 invoke config to inherit from anthropic config
Prevents error when passing in 'response_format: {"type": "text"}
* test: fix test
* fix(utils.py): fix base invoke provider check
* fix(anthropic_claude3_transformation.py): don't pass 'stream' param
* fix: fix linting errors
* fix(converse_transformation.py): handle response_format type=text for converse
* fix(o_series_transformation.py): fix optional param check for o-series models
o3-mini and o-1 do not support parallel tool calling
* fix(utils.py): support 'drop_params' for 'thinking' param across models
allows switching to older claude versions (or non-anthropic models) and param to be safely dropped
* fix: fix passing thinking param in optional params
allows dropping thinking_param where not applicable
* test: update old model
* fix(utils.py): fix linting errors
* fix(main.py): add param to acompletion
* fix(azure/chat/gpt_transformation.py): add 'prediction' as a support azure param
Closes https://github.com/BerriAI/litellm/issues/8500
* build(model_prices_and_context_window.json): add new 'gemini-2.0-pro-exp-02-05' model
* style: cleanup invalid json trailing commma
* feat(utils.py): support passing 'tokenizer_config' to register_prompt_template
enables passing complete tokenizer config of model to litellm
Allows calling deepseek on bedrock with the correct prompt template
* fix(utils.py): fix register_prompt_template for custom model names
* test(test_prompt_factory.py): fix test
* test(test_completion.py): add e2e test for bedrock invoke deepseek ft model
* feat(base_invoke_transformation.py): support hf_model_name param for bedrock invoke calls
enables proxy admin to set base model for ft bedrock deepseek model
* feat(bedrock/invoke): support deepseek_r1 route for bedrock
makes it easy to apply the right chat template to that call
* feat(constants.py): store deepseek r1 chat template - allow user to get correct response from deepseek r1 without extra work
* test(test_completion.py): add e2e mock test for bedrock deepseek
* docs(bedrock.md): document new deepseek_r1 route for bedrock
allows us to use the right config
* fix(exception_mapping_utils.py): catch read operation timeout
* fix(utils.py): fix vertex ai optional param handling
don't pass max retries to unsupported route
Fixes https://github.com/BerriAI/litellm/issues/8254
* fix(get_supported_openai_params.py): fix linting error
* fix(get_supported_openai_params.py): default to openai-like spec
* test: fix test
* fix: fix linting error
* Improved wildcard route handling on `/models` and `/model_group/info` (#8473)
* fix(model_checks.py): update returning known model from wildcard to filter based on given model prefix
ensures wildcard route - `vertex_ai/gemini-*` just returns known vertex_ai/gemini- models
* test(test_proxy_utils.py): add unit testing for new 'get_known_models_from_wildcard' helper
* test(test_models.py): add e2e testing for `/model_group/info` endpoint
* feat(prometheus.py): support tracking total requests by user_email on prometheus
adds initial support for tracking total requests by user_email
* test(test_prometheus.py): add testing to ensure user email is always tracked
* test: update testing for new prometheus metric
* test(test_prometheus_unit_tests.py): add user email to total proxy metric
* test: update tests
* test: fix spend tests
* test: fix test
* fix(pagerduty.py): fix linting error
* (Bug fix) - Using `include_usage` for /completions requests + unit testing (#8484)
* pass stream options (#8419)
* test_completion_streaming_usage_metrics
* test_text_completion_include_usage
---------
Co-authored-by: Kaushik Deka <55996465+Kaushikdkrikhanu@users.noreply.github.com>
* fix naming docker stable release
* build(model_prices_and_context_window.json): handle azure model update
* docs(token_auth.md): clarify scopes can be a list or comma separated string
* docs: fix docs
* add sonar pricings (#8476)
* add sonar pricings
* Update model_prices_and_context_window.json
* Update model_prices_and_context_window.json
* Update model_prices_and_context_window_backup.json
* update load testing script
* fix test_async_router_context_window_fallback
* pplx - fix supports tool choice openai param (#8496)
* fix prom check startup (#8492)
* test_async_router_context_window_fallback
* ci(config.yml): mark daily docker builds with `-nightly` (#8499)
Resolves https://github.com/BerriAI/litellm/discussions/8495
* (Redis Cluster) - Fixes for using redis cluster + pipeline (#8442)
* update RedisCluster creation
* update RedisClusterCache
* add redis ClusterCache
* update async_set_cache_pipeline
* cleanup redis cluster usage
* fix redis pipeline
* test_init_async_client_returns_same_instance
* fix redis cluster
* update mypy_path
* fix init_redis_cluster
* remove stub
* test redis commit
* ClusterPipeline
* fix import
* RedisCluster import
* fix redis cluster
* Potential fix for code scanning alert no. 2129: Clear-text logging of sensitive information
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
* fix naming of redis cluster integration
* test_redis_caching_ttl_pipeline
* fix async_set_cache_pipeline
---------
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
* Litellm UI stable version 02 12 2025 (#8497)
* fix(key_management_endpoints.py): fix `/key/list` to include `return_full_object` as a top-level query param
Allows user to specify they want the keys as a list of objects
* refactor(key_list.tsx): initial refactor of key table in user dashboard
offloads key filtering logic to backend api
prevents common error of user not being able to see their keys
* fix(key_management_endpoints.py): allow internal user to query `/key/list` to see their keys
* fix(key_management_endpoints.py): add validation checks and filtering to `/key/list` endpoint
allow internal user to see their keys. not anybody else's
* fix(view_key_table.tsx): fix issue where internal user could not see default team keys
* fix: fix linting error
* fix: fix linting error
* fix: fix linting error
* fix: fix linting error
* fix: fix linting error
* fix: fix linting error
* fix: fix linting error
* test_supports_tool_choice
* test_async_router_context_window_fallback
* fix: fix test (#8501)
* Litellm dev 02 12 2025 p1 (#8494)
* Resolves https://github.com/BerriAI/litellm/issues/6625 (#8459)
- enables no auth for SMTP
Signed-off-by: Regli Daniel <daniel.regli1@sanitas.com>
* add sonar pricings (#8476)
* add sonar pricings
* Update model_prices_and_context_window.json
* Update model_prices_and_context_window.json
* Update model_prices_and_context_window_backup.json
* test: fix test
---------
Signed-off-by: Regli Daniel <daniel.regli1@sanitas.com>
Co-authored-by: Dani Regli <1daniregli@gmail.com>
Co-authored-by: Lucca Zenóbio <luccazen@gmail.com>
* test: fix test
* UI Fixes p2 (#8502)
* refactor(admin.tsx): cleanup add new admin flow
removes buggy flow. Ensures just 1 simple way to add users / update roles.
* fix(user_search_modal.tsx): ensure 'add member' button is always visible
* fix(edit_membership.tsx): ensure 'save changes' button always visible
* fix(internal_user_endpoints.py): ensure user in org can be deleted
Fixes issue where user couldn't be deleted if they were a member of an org
* fix: fix linting error
* add phoenix docs for observability integration (#8522)
* Add files via upload
* Update arize_integration.md
* Update arize_integration.md
* add Phoenix docs
* Added custom_attributes to additional_keys which can be sent to athina (#8518)
* (UI) fix log details page (#8524)
* rollback changes to view logs page
* ui new build
* add interface for prefetch
* fix spread operation
* fix max size for request view page
* clean up table
* ui fix column on request logs page
* ui new build
* Add UI Support for Admins to Call /cache/ping and View Cache Analytics (#8475) (#8519)
* [Bug] UI: Newly created key does not display on the View Key Page (#8039)
- Fixed issue where all keys appeared blank for admin users.
- Implemented filtering of data via team settings to ensure all keys are displayed correctly.
* Fix:
- Updated the validator to allow model editing when `keyTeam.team_alias === "Default Team"`.
- Ensured other teams still follow the original validation rules.
* - added some classes in global.css
- added text wrap in output of request,response and metadata in index.tsx
- fixed styles of table in table.tsx
* - added full payload when we open single log entry
- added Combined Info Card in index.tsx
* fix: keys not showing on refresh for internal user
* merge
* main merge
* cache page
* ca remove
* terms change
* fix:places caching inside exp
---------
Signed-off-by: Regli Daniel <daniel.regli1@sanitas.com>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: Kaushik Deka <55996465+Kaushikdkrikhanu@users.noreply.github.com>
Co-authored-by: Lucca Zenóbio <luccazen@gmail.com>
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
Co-authored-by: Dani Regli <1daniregli@gmail.com>
Co-authored-by: exiao <exiao@users.noreply.github.com>
Co-authored-by: vivek-athina <153479827+vivek-athina@users.noreply.github.com>
Co-authored-by: Taha Ali <123803932+tahaali-dev@users.noreply.github.com>
* fix(gemini/): support gemini 'frequency_penalty' and 'presence_penalty'
Closes https://github.com/BerriAI/litellm/issues/7748
* feat(proxy_server.py): new env var to disable prisma health check on startup
* test: fix test
* refactor(utils.py): migrate amazon titan config to base config
* refactor(utils.py): refactor bedrock meta invoke model translation to use base config
* refactor(utils.py): move bedrock ai21 to base config
* refactor(utils.py): move bedrock cohere to base config
* refactor(utils.py): move bedrock mistral to use base config
* refactor(utils.py): move all provider optional param translations to using a config
* docs(clientside_auth.md): clarify how to pass vertex region to litellm proxy
* fix(utils.py): handle scenario where custom llm provider is none / empty
* fix: fix get config
* test(test_otel_load_tests.py): widen perf margin
* fix(utils.py): fix get provider config check to handle custom llm's
* fix(utils.py): fix check
* fix(proxy_server.py): only update k,v pair if v is not empty/null
Fixes https://github.com/BerriAI/litellm/issues/6787
* test(test_router.py): cleanup duplicate calls
* test: add new test stream options drop params test
* test: update optional params / stream options test to test for vertex ai mistral route specifically
Addresses https://github.com/BerriAI/litellm/issues/7309
* fix(proxy_server.py): fix linting errors
* fix: fix linting errors
* fix use new format for Cohere config
* fix base llm http handler
* Litellm code qa common config (#7116)
* feat(base_llm): initial commit for common base config class
Addresses code qa critique https://github.com/andrewyng/aisuite/issues/113#issuecomment-2512369132
* feat(base_llm/): add transform request/response abstract methods to base config class
---------
Co-authored-by: Krrish Dholakia <krrishdholakia@gmail.com>
* use base transform helpers
* use base_llm_http_handler for cohere
* working cohere using base llm handler
* add async cohere chat completion support on base handler
* fix completion code
* working sync cohere stream
* add async support cohere_chat
* fix types get_model_response_iterator
* async / sync tests cohere
* feat cohere using base llm class
* fix linting errors
* fix _abc error
* add cohere params to transformation
* remove old cohere file
* fix type error
* fix merge conflicts
* fix cohere merge conflicts
* fix linting error
* fix litellm.llms.custom_httpx.http_handler.HTTPHandler.post
* fix passing cohere specific params
---------
Co-authored-by: Krrish Dholakia <krrishdholakia@gmail.com>
* Fix Vertex AI function calling invoke: use JSON format instead of protobuf text format. (#6702)
* test: test tool_call conversion when arguments is empty dict
Fixes https://github.com/BerriAI/litellm/issues/6833
* fix(openai_like/handler.py): return more descriptive error message
Fixes https://github.com/BerriAI/litellm/issues/6812
* test: skip overloaded model
* docs(anthropic.md): update anthropic docs to show how to route to any new model
* feat(groq/): fake stream when 'response_format' param is passed
Groq doesn't support streaming when response_format is set
* feat(groq/): add response_format support for groq
Closes https://github.com/BerriAI/litellm/issues/6845
* fix(o1_handler.py): remove fake streaming for o1
Closes https://github.com/BerriAI/litellm/issues/6801
* build(model_prices_and_context_window.json): add groq llama3.2b model pricing
Closes https://github.com/BerriAI/litellm/issues/6807
* fix(utils.py): fix handling ollama response format param
Fixes https://github.com/BerriAI/litellm/issues/6848#issuecomment-2491215485
* docs(sidebars.js): refactor chat endpoint placement
* fix: fix linting errors
* test: fix test
* test: fix test
* fix(openai_like/handler): handle max retries
* fix(streaming_handler.py): fix streaming check for openai-compatible providers
* test: update test
* test: correctly handle model is overloaded error
* test: update test
* test: fix test
* test: mark flaky test
---------
Co-authored-by: Guowang Li <Guowang@users.noreply.github.com>
* fix(anthropic/chat/transformation.py): add json schema as values: json_schema
fixes passing pydantic obj to anthropic
Fixes https://github.com/BerriAI/litellm/issues/6766
* (feat): Add timestamp_granularities parameter to transcription API (#6457)
* Add timestamp_granularities parameter to transcription API
* add param to the local test
* fix(databricks/chat.py): handle max_retries optional param handling for openai-like calls
Fixes issue with calling finetuned vertex ai models via databricks route
* build(ui/): add team admins via proxy ui
* fix: fix linting error
* test: fix test
* docs(vertex.md): refactor docs
* test: handle overloaded anthropic model error
* test: remove duplicate test
* test: fix test
* test: update test to handle model overloaded error
---------
Co-authored-by: Show <35062952+BrunooShow@users.noreply.github.com>
