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1545 commits

Author SHA1 Message Date
Sameer Kankute
00f7361f11
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>
(cherry picked from commit 3c3d131f01)
2026-05-20 18:42:35 -07:00
yuneng-jiang
6ff668c7aa
[Infra] Promote internal staging to main (#27245)
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* default requested_model to empty string on litellm-side rejects

* Update litellm/router.py

Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>

* fix: scope key access_group_ids override by team's assigned groups

A team member could set any access_group_ids on their key (e.g. a group
assigned only to a different team) and override the team's model
restriction. Intersect the key's access_group_ids with team_object.access_group_ids
in _key_access_group_grants_model so foreign groups are dropped before
model expansion. Adds a regression test that asserts expansion is never
called for foreign groups.

* [Fix] Proxy: Skip Personal Budget Hook When Reservation Covers Counter

The reservation path (PR #26845) atomically pre-fills `spend:user:{user_id}`
and admits at the strict-`<` boundary. The legacy `_PROXY_MaxBudgetLimiter`
pre-call hook re-reads the same counter with `>=`, so a reservation that
fills the counter to exactly `max_budget` (e.g. a request without a
`max_tokens` cap that falls back to reserving the smallest remaining
headroom) is rejected by the hook even though the reservation already
admitted it.

Skip the hook when the request's active `budget_reservation` covers
`spend:user:{user_id}`. The reservation is the source of truth for that
counter cross-pod; the legacy `>=` path remains in place for requests
without a reservation (e.g. paths that bypass the reservation entirely).

Reproduces as `tests/otel_tests/test_prometheus.py::test_user_budget_metrics`
on a fresh user with `max_budget=10` calling `fake-openai-endpoint` without
`max_tokens`. Adds focused unit coverage in
`tests/test_litellm/proxy/hooks/test_max_budget_limiter.py`.

* harden bedrock file bucket validation

* Fix syntax errors from botched merge in router.py

* Fix Vertex batch output edge cases

* [Fix] RBAC: Drop management_routes Write Fallback for Admin Viewer

Greptile P1: the unsafe-method branch of `_check_proxy_admin_viewer_access`
ended with a blanket `if route in management_routes: return`. That set is a
mix of reads (info/list — handled via the safe-method GET branch above) and
writes. The fallback let Admin Viewer POST to write endpoints not enumerated
in `_ADMIN_VIEWER_BLOCKED_WRITE_ROUTES`, including:
  - /team/block, /team/unblock, /team/permissions_update
  - /jwt/key/mapping/{new,update,delete}
  - /key/bulk_update
  - /key/{key_id}/reset_spend

Remove the fallback. The two remaining allow sets (admin_viewer_routes and
global_spend_tracking_routes) are both read-only, so removal does not affect
the legitimate POST-as-read cases (e.g. /spend/calculate, which is in
spend_tracking_routes ⊂ admin_viewer_routes).

Tests:
  - 8 new parametrized cases pinning each previously-leaking management write
    endpoint to 403 on POST for PROXY_ADMIN_VIEW_ONLY.

* fix(tests): anchor VCR redis cassette key to repo root

`os.path.relpath` with no `start` arg uses the current working
directory, so running pytest from a subdirectory produced a
different Redis key than running from the repo root. CI-recorded
cassettes and locally-replayed runs would silently miss each
other's cache.

Anchor the path to the repo root (derived from `__file__`) so the
key is stable regardless of CWD.

https://claude.ai/code/session_018uCx7pcrkdUJZrCVMaTdPx

* fix: gate key access_group override on group's own assignment

Replaces the previous intersect-with-team.access_group_ids check, which
made the override unreachable in practice (the team-gate fallback already
covered every case the intersection allowed). The override now resolves
each of the key's access_group_ids via get_access_object and accepts the
group only if its assigned_team_ids includes the key's team_id, or its
assigned_key_ids includes the key's token. This fulfills the original ask
(a key can extend a team's allow-list via a group the admin granted to
that team or that specific key) while still rejecting foreign groups
referenced by team members of other teams.

* [Fix] Proxy/Key Management: Honor team_member_permissions /key/list In /key/list Endpoint

When a team grants /key/list via team_member_permissions, non-admin members
should see all keys for that team — same as a team admin. Previously the
classification in list_keys() only checked admin status, so permitted
members fell into the service-account-only path and could not see other
members' personal keys. Routes those members into the full-visibility set.

* Fix access-group bypass via litellm-model fallback path

When _get_all_deployments returns 0 candidates and the litellm-model
fallback branch (_get_deployment_by_litellm_model) finds deployments that
the access-group filter then empties, _access_group_filter_emptied_candidates
remained False (it was captured before that branch ran). The router would
then proceed to default fallbacks; the fallback model could have no
access_groups and short-circuit the filter, silently serving a caller
blocked by access-group restrictions.

Update the flag inside the litellm-model branch when filtering empties a
non-empty candidate set so the default-fallback guard still triggers.

* fix(proxy): redact MCP server URL and headers for non-admin viewers (VERIA-8)

Many MCP integrations (Zapier, etc.) embed an upstream API key
directly in the server URL, e.g.
``https://actions.zapier.com/mcp/<api-key>/sse``. The list and
single-server endpoints were returning the full URL to any
authenticated user — `_redact_mcp_credentials` only stripped the
explicit ``credentials`` field, and `_sanitize_mcp_server_for_virtual_key`
only ran for restricted virtual keys. Non-admin internal users could
read the dashboard, click the unmask toggle, and exfiltrate the raw
token.

Add `_sanitize_mcp_server_for_non_admin` that runs on top of the
existing credential redaction and clears the credential-bearing
fields:

- ``url`` (the primary leak vector)
- ``spec_path`` (OpenAPI spec URLs that may carry tokens)
- ``static_headers`` / ``extra_headers`` (Authorization)
- ``env`` (arbitrary secrets)
- ``authorization_url`` / ``token_url`` / ``registration_url``

Identity fields (``server_id``, ``alias``, ``mcp_info``, etc.) are
preserved so the UI can still list servers a non-admin's team has
access to.

Apply the new sanitizer in `fetch_all_mcp_servers` and the per-server
fetch path right after the existing virtual-key branch. Update the
existing `test_list_mcp_servers_non_admin_user_filtered` assertions
that previously checked URL visibility.

Frontend defense-in-depth: hide the URL unmask toggle on
`mcp_server_view.tsx` unless the viewer is a proxy admin.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* Fix runtime policy attachment initialization

Mark runtime-created policies and attachments initialized so global policy attachments created from the policy builder apply immediately without requiring a restart.

Co-authored-by: Cursor <cursoragent@cursor.com>

* test(router): cover _try_early_resolve_deployments_for_model_not_in_names

The router_code_coverage CI check requires every function in router.py to
be referenced by at least one test under tests/{local_testing,
router_unit_tests,test_litellm} in a file with "router" in its name.
The recently-extracted helper had no direct test, so the check failed
with "0.45% of functions in router.py are not tested".

Add a focused test that exercises the four return paths: model already
in self.model_names, no fallback applies, pattern-router match, and
default_deployment substitution (also asserting the stored default
isn't mutated).

https://claude.ai/code/session_019AVp1XL7RT9RxRe4qRLkay

* Fix policy registry teardown in tests

Reset the policy ID index during policy engine test cleanup so stale policy versions cannot leak between tests.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(batches): count non-chat tokens, validate batch-file model access (VERIA-39) (#27015)

* fix(batches): count non-chat tokens and validate every model in batch file

Two security control bypasses on POST /v1/batches:

1. `_get_batch_job_input_file_usage` only summed tokens for
   `body.messages` (chat completions). Embedding (`input`) and text
   completion (`prompt`) batches reported zero, letting massive
   non-chat workloads slip past TPM rate limits. Extend the counter
   to handle string and list shapes for both fields.

2. The batch input file was forwarded to the upstream provider
   without inspecting the models named inside the JSONL — only the
   outer `model` query parameter was checked against the caller's
   allowlist. A caller restricted to gpt-3.5 could submit a batch
   targeting gpt-4o and the upstream would execute it under the
   proxy's shared API key.

Add `_get_models_from_batch_input_file_content` (returns the
distinct `body.model` values) and call it from
`_enforce_batch_file_model_access` in the pre-call hook, which runs
each model through `can_key_call_model` so the same allowlist
semantics (wildcards, access groups, all-proxy-models, team aliases)
the proxy enforces on `/chat/completions` apply here too. Any
unauthorized model raises a 403 before the file is forwarded.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(batches): count pre-tokenized prompt/input shapes, classify 403 logs

Two follow-ups from the Greptile review on the batch validation PR:

1. P1 TPM bypass via integer token arrays. The OpenAI batch schema
   accepts ``prompt`` and ``input`` as ``list[int]`` (a single
   pre-tokenized prompt) or ``list[list[int]]`` (multiple) in addition
   to the string and ``list[str]`` shapes. Pre-fix only the string
   shapes were counted, so a caller could submit a batch with hundreds
   of millions of pre-tokenized tokens and the rate limiter would
   record zero. Extract the per-field logic into
   ``_count_prompt_or_input_tokens`` and count each int as one token.

2. P2 access-denial logs were indistinguishable from I/O failures.
   ``count_input_file_usage`` caught every exception under a generic
   "Error counting input file usage" message, so an intentional 403
   from ``_enforce_batch_file_model_access`` looked the same in the
   logs as a missing file or a Prisma timeout. Catch ``HTTPException``
   separately and log 403s at WARNING level with a security-relevant
   message before re-raising.

Tests cover the new shapes: single ``list[int]``, ``list[list[int]]``
(the worst-case bypass vector), and embeddings ``input`` with
pre-tokenized arrays.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(proxy): re-validate user_id after /user/info re-parses query (#27009)

* fix(proxy): re-validate user_id ownership after /user/info re-parses query

The route-level access check in `RouteChecks.non_proxy_admin_allowed_routes_check`
reads `request.query_params.get("user_id")`, which decodes literal `+` to
spaces. The endpoint then re-parses the raw query string with `urllib.unquote`
in `get_user_id_from_request` to preserve `+` characters (so plus-addressed
emails work as user_ids). Those two paths produce different ids: a caller
who registered a user_id containing a literal space could pass the route
check and then read another user's row by sending the encoded `+` form.

Add `_enforce_user_info_access` and call it after `_normalize_user_info_user_id`
returns the final id. Proxy admin / view-only admin still bypass; everyone
else must match the resolved user_id (or have no user_id, which falls back
to the caller's own id later in the handler).

Tests cover the admin bypass, owner-match path, and the cross-user lookup
that this change blocks.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(proxy): apply user_info ownership check to PROXY_ADMIN_VIEW_ONLY

`_enforce_user_info_access` was bypassing both PROXY_ADMIN and
PROXY_ADMIN_VIEW_ONLY, but the upstream route check in
`RouteChecks.non_proxy_admin_allowed_routes_check` only treats
PROXY_ADMIN as a true admin for the `/user/info` route — view-only
admins go through the `user_id == valid_token.user_id` enforcement
along with regular users. Mirroring that asymmetry left the same
encoded-`+` bypass open for view-only admins whose user_id contains a
literal space.

Drop the PROXY_ADMIN_VIEW_ONLY exemption so the post-decode re-check
matches the upstream rule. Update tests: a view-only admin must now
be blocked from cross-user lookups but still allowed to read their
own row.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* feat(spend-logs): opt-in suppression of stack traces in spend-tracking error logs

Adds LITELLM_SUPPRESS_SPEND_LOG_TRACEBACKS env var. When set to true and the
proxy log level is INFO or above, spend-tracking error paths emit a single
ERROR line without the full traceback. Stack traces are preserved at DEBUG
and the Sentry / proxy_logging_obj.failure_handler path is unchanged.

The new spend_log_error helper is wired through the spend write hot path:
  - DBSpendUpdateWriter (update_database, _update_*_db, batch upsert,
    redis-commit fallbacks)
  - _ProxyDBLogger._PROXY_track_cost_callback
  - get_logging_payload exception path
  - update_spend / update_daily_tag_spend / spend logs queue monitor

Resolves LIT-2704.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(spend-logs): preserve no-traceback behavior for update_daily_tag_spend

This call site previously logged a single-line error via verbose_proxy_logger.error()
with no traceback. Switching it to spend_log_error(..., exc=e) caused a full stack
trace to render by default (when LITELLM_SUPPRESS_SPEND_LOG_TRACEBACKS is unset),
which contradicts the PR goal of leaving default behavior unchanged. Revert this
specific site to the original error log call.

* fix(spend-logs): preserve no-traceback behavior for update_daily_tag_spend

Bugbot caught a regression: the previous error log here was a single-line
verbose_proxy_logger.error(...) with no traceback. spend_log_error attaches
the active exception's traceback by default (when the suppression env var
is unset), so swapping it in changed default behavior. Revert this one site
to its original .error() call to keep the PR strictly opt-in.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* feat(spend-logs): suppress traceback in SpendLogs error_information row

Extend LITELLM_SUPPRESS_SPEND_LOG_TRACEBACKS to the failure callback so the
per-row Metadata pane in the UI no longer shows the stack trace when the
opt-in env var is set, matching the existing console-side suppression.

https://claude.ai/code/session_014dztoRbRnRvq54HL9EyHx6

* [Fix] Proxy: Repair Merge Fallout In Router-Override Fallback Auth

Conflict resolution for #26968 dropped the `Iterator` typing import
(NameError at module load), left a dead `fallback_models = cast(...)`
block, and the new tests called `_enforce_key_and_fallback_model_access`
without the now-required `request` kwarg.

* isolate dual OTEL handlers

* harden cloud file compatibility path

* harden cloud file compatibility path

* [Fix] Proxy/Key Management: Align Key-Org Membership Checks On Generate And Regenerate

Mirrors the membership rule on /key/update so that /key/generate and
/key/{key}/regenerate apply the same `_validate_caller_can_assign_key_org`
gate when the caller specifies an `organization_id`. Proxy admins bypass.
The check no-ops when `organization_id` is not being set.

* thread trusted params through vertex file content

* trust only server legacy file flag

* chore(proxy): keep public AI hub unauthenticated

* fix(proxy): preserve low-detail readiness status

* [Test] Anthropic: Replace Legacy Claude-4-Sonnet Alias With Haiku 4.5

Three live-API tests pinned to claude-4-sonnet-20250514, which is a
non-canonical alias of claude-sonnet-4-20250514. Anthropic's main API
no longer resolves the legacy form under freshly issued keys, so the
tests fail with not_found_error. The token counter test pinned to
claude-sonnet-4-20250514 itself (deprecation_date 2026-05-14, two weeks
out) was on borrowed time too.

Bump all four to claude-haiku-4-5-20251001 — capability superset for what
these tests exercise (streaming, parallel tool calling, extended thinking,
token counting), no upcoming deprecation, cheaper per-token.

* chore(proxy): move URL-valued model/file_id guard from SDK to proxy

The previous per-provider guards in HuggingFace, Oobabooga, and Gemini
files lived in the SDK layer, breaking SDK callers who legitimately pass
URL-valued model identifiers. Move the check to the proxy boundary in
add_litellm_data_to_request so SDK users keep working while proxy users
default-deny URL-valued model and file_id, with admin opt-in via
litellm.provider_url_destination_allowed_hosts.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* [Chore] Proxy/UI: Drop stray _experimental/out/chat/index.html

This file is a regenerable UI build artifact that should not be tracked
in source. Removing so the merge into litellm_internal_staging stays clean.

* [Test] Anthropic Passthrough: Bump Streaming Cost-Injection Test To Haiku 4.5

test_anthropic_messages_streaming_cost_injection hits the proxy's
/v1/messages route, which routes via the anthropic/* wildcard to
api.anthropic.com. The 404 surfaced in the test was Anthropic's own
not_found_error propagated back through the proxy (visible from the
x-litellm-model-id hash on the response — the proxy did route).

Same root cause as the prior commit: the legacy claude-4-sonnet-20250514
alias is no longer recognized by Anthropic's main API under the new key.
Swap to claude-haiku-4-5-20251001 — same routing path, canonical model.

* fix(proxy): handle ownership-recording failures after upstream create

If record_container_owner raises after the upstream container is created,
the user previously got a 500 with no usable container — they were billed
for an unreachable resource. Move ownership recording into the create
path's exception handling and split the two failure modes:

- HTTPException from the recorder (auth conflicts) propagates verbatim
  so the client sees the real status code, not a generic LLM error.
- Unexpected exceptions are logged and swallowed; the response is
  returned to the caller so they aren't billed for a container they
  can't address. The DB row stays untracked until an operator reconciles.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(guardrails): close post-call coverage gaps

* fix(types): add /team/permissions_bulk_update to management_routes

The blocklist check in _check_proxy_admin_viewer_access only fires for
routes that match LiteLLMRoutes.management_routes — the bulk-update
endpoint was missing from that list, so the test for view-only admins
on /team/permissions_bulk_update fell through to "allow."

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* [Test] Anthropic Passthrough: Bump Thinking Tests Off Legacy Sonnet 4 Alias

base_anthropic_messages_test.test_anthropic_messages_with_thinking and
test_anthropic_streaming_with_thinking still pinned to
claude-4-sonnet-20250514 — the same legacy alias Anthropic no longer
recognizes under freshly issued keys. The other four tests in this base
class already use claude-sonnet-4-5-20250929; these two were missed.

Bump to claude-haiku-4-5-20251001 (supports_reasoning=true, no upcoming
deprecation). Subclasses including TestAnthropicPassthroughBasic
inherit these methods.

* fix(guardrails): cover multi-choice output variants

* fix(proxy): preserve public ai hub ui setting

* fix(scim): cascade FK cleanup on user delete and surface block status in UI

SCIM DELETE /Users/{id} previously called litellm_usertable.delete without
clearing rows that FK back to the user, so Postgres rejected the delete with
LiteLLM_InvitationLink_user_id_fkey and the SCIM caller saw a 500. Add a
helper to drop invitation_link, organization_membership, and team_membership
rows before the user delete (mirrors /user/delete in internal_user_endpoints).

Also add a Status column to the Virtual Keys and Internal Users tables so
admins can see at a glance which keys are blocked and which users SCIM has
deactivated. SCIM-blocked keys carry a tooltip explaining the origin.

Pin the dashboard's Node version to 20 via .nvmrc to match CI.

* chore: update Next.js build artifacts (2026-05-02 03:21 UTC, node v20.20.2)

* perf(proxy): cache container/skill ownership reads on the hot path

Container ownership and skill rows are looked up on every retrieve /
delete / list / file-content / chat-completion-with-skill call. The new
stores wrapped raw Prisma queries with no cache, putting one DB
round-trip on each request. Add an in-process TTL'd cache mirroring the
_byok_cred_cache pattern in mcp_server/server.py: per-key (value,
monotonic_timestamp), 60s TTL, 10000-entry cap with full-clear on
overflow, invalidated by every write. Negative results (`None`) are
cached too so untracked-resource checks also skip the DB.

Tests cover: cache-after-first-hit, negative caching, write
invalidation, no-caching-on-DB-error, TTL expiry, capacity eviction.
56 tests pass.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* chore: update Next.js build artifacts (2026-05-02 03:39 UTC, node v20.20.2)

* fix: remove traceback key instead of it being ""

* fix: linting error

* fix(scim): preserve scim_active on PUT when client omits the field

A SCIM PUT may legally omit `active` (full-replace with the field
absent). Pydantic fills the SCIMUser.active default of True, so the PUT
handler was overwriting metadata.scim_active with True even when the
client never sent it — silently reactivating a previously SCIM-blocked
user and unblocking their keys.

Use model_fields_set to detect whether the client actually sent
`active`. If omitted, preserve the prior scim_active value and skip
the cascade to virtual keys.

Also drop comments added in this PR that just narrate what the code
does; keep only the docstrings and the SQL-NULL pitfall note that
explain non-obvious behaviour.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(proxy): use set lookup for permitted agent filters

* fix(mcp): redact command fields for non-admin server views

* fix(proxy): forward decoded container ids after ownership checks

* fix(caching): handle stale isolated Redis semantic index

* fix(cloudflare): support response_text in streaming chunk parser

Newer Cloudflare Workers AI models (e.g. Nemotron) emit 'response_text'
instead of 'response' on streamed chunks. The non-streaming path was
already updated to fall back to 'response_text' (#26385), but the
streaming chunk parser still only read 'response', which caused
streaming requests against those models to silently produce empty
content.

Mirror the non-streaming fallback in CloudflareChatResponseIterator.chunk_parser
and add a streaming test for the response_text shape.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* Fix code qa

* Address bugbot: drop dead encode/decode helpers; preserve empty custom_id

- Remove unused _encode_gcp_label_value / _decode_gcp_label_value singular
  helpers; only the _chunks variants are actually called.
- Use 'is not None' check for custom_id so empty-string custom_ids are
  still labeled and round-trip through batch outputs.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* Forward Vertex file content logging context

* test vertex file content logging forwarding

Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>

* Fix Vertex batch output logging mutation

* fix: don't mutate caller's logging_obj in _try_transform_vertex_batch_output_to_openai

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>

* feat: add opt-out flag for Vertex batch output transformation

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.

* fix(anthropic,bedrock): omit thinking/output_config when reasoning_effort="none"

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.

* fix(vertex-ai): set response=null on batch error entries per OpenAI spec

The Vertex batch output transformer was emitting both a populated 'response' and 'error' for failed batch entries. The OpenAI Batch output spec defines them as mutually exclusive: on error 'response' MUST be null. This broke any consumer using 'result["response"] is None' to detect failures.

* test(vertex-ai): cover transformation_error path emits response=null

* fix(security): sandbox jinja2 in gitlab/arize/bitbucket prompt managers

DotpromptManager was hardened to render through
ImmutableSandboxedEnvironment. The three sibling managers (gitlab,
arize, bitbucket) were missed and still instantiate plain
jinja2.Environment(), leaving the same attribute-traversal SSTI
primitive open: a template fetched from a GitLab/BitBucket repo or
Arize Phoenix workspace can reach __class__.__init__.__globals__ and
execute arbitrary Python on the proxy host.

Match the dotprompt pattern by switching all three to
ImmutableSandboxedEnvironment. The sandbox blocks the dunder-traversal
chain while leaving normal {{ var }} substitution intact, so the
template surface is unchanged for legitimate use.

Adds tests/test_litellm/integrations/test_prompt_manager_ssti.py
(18 cases) verifying each manager's jinja_env is a sandbox, that
classic SSTI payloads raise SecurityError, and that ordinary variable
rendering still works.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* chore(proxy): drop client-supplied pricing fields from request bodies

The proxy currently forwards request-body pricing parameters (the fields
on `CustomPricingLiteLLMParams`, plus `metadata.model_info`) into the
core call path. Those fields belong to deployment configuration, not to
per-request input — sending them from a client mutates the request's
recorded cost and, via `litellm.completion` → `register_model`, the
process-wide `litellm.model_cost` map for every later caller in the
worker. Strip them at the boundary.

The strip set is built from `CustomPricingLiteLLMParams.model_fields` so
pricing fields added later are covered automatically. Operators who do
want clients to supply per-request pricing can opt back in per key or
team via `metadata.allow_client_pricing_override = true`, mirroring the
existing `allow_client_mock_response` and
`allow_client_message_redaction_opt_out` flags.

Tests cover the strip set's coverage, root and metadata strips, the
opt-in skip on both key and team metadata, and a regression check that
the global `litellm.model_cost` map is unmutated after a stripped
request.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* chore(proxy): log stripped pricing fields at debug for operator visibility

Operators upgrading would otherwise see client-supplied pricing overrides
silently stop applying with no diagnostic. Emit a debug-level line listing
the dropped fields and pointing at the opt-in flag when any are stripped;
stay silent on the no-op path so the log isn't filled with noise.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(proxy): move pricing strip below the litellm_metadata JSON-string parse

The strip ran before the proxy parses ``litellm_metadata`` from a JSON
string into a dict (a path used by multipart/form-data and ``extra_body``
callers), so ``isinstance(metadata, dict)`` was False and ``model_info``
survived the strip. Move the call to the same post-parse position the
``user_api_key_*`` strip already uses for the same reason. Adds a
regression test exercising the JSON-string ``litellm_metadata`` path.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* test(responses): replace legacy claude-4-sonnet alias in multiturn tool-call test

Anthropic's main API no longer resolves the non-canonical 'claude-4-sonnet-20250514'
alias for freshly issued keys, returning 404 not_found_error. PR #27031 already
swept three other live tests pinned to this alias to claude-haiku-4-5-20251001
but missed test_multiturn_tool_calls in the responses API suite, which is now
failing reliably on PR CI runs (e.g. PR #27074, job 1603363).

Bump the two model references in test_multiturn_tool_calls to the same
claude-haiku-4-5-20251001 snapshot used by PR #27031 -- it covers everything
this test exercises (tool calling, multi-turn) and isn't on a deprecation
schedule.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* chore(proxy): close callback-config and observability-credential side channels

Two related gaps in the proxy's request bouncer:

1. ``is_request_body_safe`` (auth_utils.py) walked the request-body root
   and the ``litellm_embedding_config`` nested dict, but not ``metadata``
   or ``litellm_metadata``. The same fields it bans at root — Langfuse /
   Langsmith / Arize / PostHog / Braintrust / Phoenix / W&B Weave / GCS /
   Humanloop / Lunary credentials and routing — were silently accepted
   when the caller put them inside metadata, retargeting observability
   callbacks to a caller-controlled host with caller-supplied creds.
   Walk both metadata containers (and parse the JSON-string form sent via
   multipart / ``extra_body``) through the same banned-params helper, so
   the existing ``allow_client_side_credentials`` opt-in covers both
   paths consistently.

2. The banned-params list was hand-maintained and lagged the canonical
   ``_supported_callback_params`` allow-list in
   ``initialize_dynamic_callback_params``. Derive the observability bans
   from that allow-list (minus a small ``_SAFE_CLIENT_CALLBACK_PARAMS``
   set for informational fields like ``langfuse_prompt_version`` and
   ``langsmith_sampling_rate``) so future integrations are covered
   automatically; ``_EXTRA_BANNED_OBSERVABILITY_PARAMS`` carries the
   handful of fields integrations read but the allow-list hasn't caught
   up to. A guard test fails CI if a new entry is added to
   ``_supported_callback_params`` without an explicit safe-list decision.

Separately in ``litellm_pre_call_utils.py``: add ``callbacks``,
``service_callback``, ``logger_fn``, and ``litellm_disabled_callbacks``
to ``_UNTRUSTED_ROOT_CONTROL_FIELDS``. The first three are appended to
worker-wide ``litellm.{input,success,failure,_async_*,service}_callback``
lists / ``litellm.user_logger_fn`` from inside ``function_setup`` — one
request poisons every subsequent caller in that worker. The last is the
inverse primitive: the legitimate path reads it from key/team metadata,
the request-body version silently disables admin-configured audit /
observability for the call.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(auth): per-param allow must continue, not return early

A pre-existing logic bug in ``_check_banned_params``: when the
deployment-level ``configurable_clientside_auth_params`` permitted one
banned field, the loop ``return``-ed on the first match instead of
``continue``-ing, so any other banned param later in the same body or
metadata dict was never checked. This PR's metadata walk multiplies the
surface where that bypass matters — a body pairing an allowed
``api_base`` with an observability credential like ``langfuse_host``
would silently pass.

Proxy-wide ``allow_client_side_credentials`` keeps ``return`` (it's a
global opt-in for every banned param). The per-param branch becomes
``continue`` so only the one explicitly-permitted field is skipped.

Adds a regression test that exercises the api_base + langfuse_host pair.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(vector_store): resolve embedding config at request time, never persist creds

The vector store create/update path previously called
``_resolve_embedding_config`` against the admin-configured router/DB
model and persisted the resolved ``litellm_embedding_config`` dict
(``api_key`` / ``api_base`` / ``api_version``) into the
``litellm_managedvectorstorestable.litellm_params`` column. Because the
resolver expanded ``os.environ/...`` references via ``get_secret``, the
DB row carried cleartext provider credentials, and the
``/vector_store/{new,info,update,list}`` responses returned them to any
authenticated caller who could supply a known admin model name.

Move the auto-resolve out of ``create_vector_store_in_db`` and out of
the update path. Persist only the user-supplied ``litellm_embedding_model``
reference. Resolve at request-handling time inside
``_update_request_data_with_litellm_managed_vector_store_registry`` so
the resolved config lives in the per-request ``data`` dict and is
garbage-collected after the response. Legacy rows that were created by
an earlier proxy version and already carry a resolved
``litellm_embedding_config`` skip the re-resolution and pass through
unchanged so embedding calls keep working.

The ``new_vector_store`` response now also runs the existing
``_redact_sensitive_litellm_params`` masker (already used by ``info``,
``update``, and ``list``), defending against caller-supplied cleartext
on the create path and against legacy rows whose persisted credentials
are still in the database.

Existing tests that asserted the old write-time-resolve behaviour are
updated to assert the new persistence shape (no embedding config
stored, just the model reference). Two new tests cover the use-time
path: one asserting fresh resolution happens when a row carries only
the model reference, the other asserting legacy rows with persisted
config skip re-resolution and continue to work.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(vector_store): tighten registry-mutation comment and dedupe test helpers

* fix(vector_store): cache use-time embedding-config resolution

Hold the resolved config in a process-memory TTL cache so the
request-handling path doesn't run litellm_proxymodeltable.find_first
on every vector-store call.

* fix(anthropic,bedrock,vertex): forward output_config.effort + 400 on garbage reasoning_effort

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>

* fix(constants): make ANTHROPIC_MIN_THINKING_BUDGET_TOKENS a plain constant

The documentation CI test (tests/documentation_tests/test_env_keys.py)
asserts every os.getenv() key in the source has a matching entry in the
litellm-docs config_settings.md table. ANTHROPIC_MIN_THINKING_BUDGET_TOKENS
tracks Anthropic's published wire-protocol minimum (1024) — it's not a
user-tunable, so making it env-overridable was wrong anyway. Drop the
os.getenv() wrapper; the value is now a plain literal.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(anthropic,bedrock): correct effort error message and dedupe effort_map

- Remove 'none' from the Bedrock _validate_anthropic_adaptive_effort error
  message; it was listed as a valid value but rejected by the membership
  check, leaving users in a feedback loop if they tried 'none'.
- Hoist the duplicated reasoning_effort -> output_config.effort mapping
  out of AnthropicConfig.map_openai_params and
  AmazonConverseConfig._handle_reasoning_effort_parameter into a single
  AnthropicConfig.REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT class constant
  so the two routes cannot drift.

* fix(anthropic): translate reasoning_effort on /v1/messages route

Closes the remaining QA-sweep gap on PR #27074: Bedrock Invoke
/v1/messages was silently ignoring ``reasoning_effort`` because the
shared param filter only kept native Anthropic keys, so every effort
tier collapsed to the same behavior on the wire (27/231 cells failing
across opus-4-5 / opus-4-6 / sonnet-4-6).

Map ``reasoning_effort`` to native Anthropic ``thinking`` /
``output_config.effort`` at the ``AnthropicMessagesConfig`` layer so
all four /v1/messages routes (direct Anthropic, Azure AI, Vertex AI,
Bedrock Invoke) inherit the same translation:

- Add ``reasoning_effort`` to ``AnthropicMessagesRequestOptionalParams``
  so the param filter in
  ``AnthropicMessagesRequestUtils.get_requested_anthropic_messages_optional_param``
  no longer drops it before the transformation runs.

- Add ``_translate_reasoning_effort_to_anthropic`` and call it from
  ``transform_anthropic_messages_request``. Mirrors
  ``AnthropicConfig.map_openai_params`` on the chat completion path
  (re-uses ``_map_reasoning_effort`` and
  ``REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT``) so the two routes
  cannot drift. Pops ``reasoning_effort`` so it never reaches the wire.

- Caller-supplied native ``thinking`` / ``output_config.effort`` always
  win — same precedence as
  ``_translate_legacy_thinking_for_adaptive_model``.

- Garbage values (``""``, ``"disabled"``, ``"invalid"``) raise
  ``AnthropicError(status_code=400)`` instead of falling through and
  surfacing as 500s from the provider.

- ``"none"`` clears thinking + output_config so callers can opt out
  per request.

Also restores the non-adaptive-model test coverage on Bedrock Invoke
/v1/messages that the previous commit lost when
``test_bedrock_messages_strips_output_config`` was renamed to the
``forwards`` variant on Opus 4.7.

Adds a new test file
``test_reasoning_effort_translation.py`` covering the translation at
the shared config level (adaptive + non-adaptive models, none, garbage,
caller precedence) so all four /v1/messages routes are exercised by a
single suite.

Adds parametrized + behavioral tests on the Bedrock Invoke /v1/messages
suite covering: minimal/low/medium/high/xhigh/max mapping for adaptive
models, thinking-budget mapping for non-adaptive Opus 4.5, ``none``
clears both, garbage raises 400, explicit ``output_config`` wins.

Refs: https://github.com/BerriAI/litellm/pull/27074

* fix(anthropic,bedrock): reject unmapped reasoning_effort at mapping site

Both the chat completion path (AnthropicConfig.map_openai_params) and the
Bedrock Converse path (_handle_reasoning_effort_parameter) used
REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT.get(value, value) which falls
back to the raw input on unmapped keys. Combined with _map_reasoning_effort
returning type='adaptive' for any string on Claude 4.6/4.7, garbage values
(e.g. 'disabled') could leak into optional_params['output_config']['effort']
unvalidated if map_openai_params ran without the downstream transform_request
or _validate_anthropic_adaptive_effort check.

Mirror the /v1/messages pattern: use .get(value) (no fallback) and raise
BadRequestError immediately when the value is unmapped, co-locating
validation with the mapping for defense in depth.

* style: black formatting

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* fix(anthropic): stop class-attr leak; gate xhigh/max on every route

The reasoning-effort mapping dict was a public class attribute on
AnthropicConfig, so BaseConfig.get_config returned it as a request
parameter and every Anthropic-backed call (Anthropic / Azure / Vertex /
Bedrock Invoke) hit a 400 'REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT:
Extra inputs are not permitted' from the provider. Move the mapping
to a module-level constant.

_supports_effort_level only looked the model up under
custom_llm_provider='anthropic', so bedrock-prefixed model ids
(e.g. bedrock/invoke/us.anthropic.claude-opus-4-7) returned False
for both 'max' and 'xhigh' even when the underlying model entry has
the flag set. Strip known provider prefixes and retry the lookup
against litellm.model_cost directly so per-model gating works on
every route.

Mirror the per-model xhigh/max gate from
AnthropicConfig._apply_output_config in
AnthropicMessagesConfig._translate_reasoning_effort_to_anthropic so
the /v1/messages route also raises a clean 400 instead of forwarding
the unsupported tier.

* feat(anthropic,bedrock): strip output_config under drop_params for non-effort models

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

(cherry picked from commit 1233943e78)

* fix(base_llm): filter all _-prefixed class attrs from get_config

The drop_params strip work added `AnthropicConfig._EFFORT_SUPPORTING_MODEL_PATTERNS`
as a private class-level lookup tuple. `BaseConfig.get_config()` only
filtered the `__`-prefixed names plus `_abc` / `_is_base_class`, so
`_EFFORT_SUPPORTING_MODEL_PATTERNS` would have leaked into the request
body the same way `REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT` did before
the previous commit.

Generalize the existing `_abc` / `_is_base_class` carve-outs to skip
every `_`-prefixed name. `AmazonConverseConfig.get_config()` overrides
the base method, so apply the same change there.

Also unblocks future internal helpers from accidentally serialising into
the wire body.

* fix(anthropic): drive output_config.effort support from model map flags

Replace hardcoded _EFFORT_SUPPORTING_MODEL_PATTERNS with a JSON-backed
check that uses supports_*_reasoning_effort flags from the model map.
Add supports_minimal_reasoning_effort: true to opus-4-5 and mythos-preview
entries (which previously only carried supports_reasoning) so the JSON
remains the single source of truth for effort capability.

* fix(anthropic,bedrock,databricks): four reasoning_effort follow-ups

- claude-sonnet-4-6 + reasoning_effort=max no longer 400s. Renamed
  _is_opus_4_6_model to _is_claude_4_6_model at three sites and added
  supports_max_reasoning_effort: true to 12 model entries in the JSON
  cost map (10 sonnet 4.6 ids + OpenRouter opus 4.6/4.7).
- _map_reasoning_effort now raises BadRequestError(400) directly with
  llm_provider, instead of letting Databricks (and similar callers)
  surface its raw ValueError as a 500.
- output_config.effort on Opus 4.5 over Bedrock no longer 400s for
  missing effort-2025-11-24 beta. Flipped JSON to "effort-2025-11-24"
  for bedrock + bedrock_converse and added an auto-attach branch in
  _process_tools_and_beta for non-adaptive Anthropic + output_config
  on Converse.
- reasoning_effort=xhigh / =max on legacy budget-mode models
  (Haiku 4.5, Sonnet 4.5, Opus 4.5) now map to thinking.budget_tokens
  8192 / 16384 instead of returning 400. Added two constants in
  litellm/constants.py.

Tests updated for all four flips. Validated end-to-end via 306-cell
live proxy matrix (6 model families x 3 routes x 17 effort cases),
all pass.

* fix(databricks): validate reasoning_effort and set output_config on adaptive Claude

The Databricks path called `AnthropicConfig._map_reasoning_effort` for
Claude models but never validated the effort string nor set
`output_config.effort` for adaptive models (Claude 4.6/4.7). Since
`_map_reasoning_effort` returns `type=adaptive` for ANY non-None /
non-"none" string on adaptive models (including "disabled",
"invalid", ""), Databricks silently accepted garbage and emitted a
request without an `output_config.effort`, collapsing every adaptive
tier to identical behavior.

Match the Anthropic native, Bedrock Converse, Bedrock Invoke, and
/v1/messages paths: when the resolved `thinking` is non-None on a
4.6/4.7 model, look up the value in
`REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT` and either raise a clean
`BadRequestError` or set `optional_params["output_config"]`.

* fix(azure): omit model from image generation and image edit deployment requests

Azure OpenAI routes image gen/edit by deployment in the URL; sending the
deployment id in model breaks gpt-image-2 (invalid_value). Strip model from
JSON for deployments/.../images/generations and from multipart data for
.../images/edits. Non-deployment URLs (e.g. Azure AI FLUX) unchanged.

Fixes #26316.

Co-authored-by: Cursor <cursoragent@cursor.com>

* test(azure): exercise image gen JSON filter via HTTP client; dedupe image edit URL

- Image generation tests patch HTTPHandler.post / get_async_httpx_client so
  make_*_azure_httpx_request runs and wire json is asserted on call kwargs.
- Azure image edit: strip model in finalize_image_edit_multipart_data using the
  same URL string the handler passes to POST (no second get_complete_url in
  transform). BaseImageEditConfig default finalize is a no-op.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(azure_ai/anthropic): promote output_config out of extra_body so validation runs

`azure_ai` is registered in `litellm.openai_compatible_providers`, so
`add_provider_specific_params_to_optional_params` (litellm/utils.py)
auto-stuffs any non-OpenAI kwarg (e.g. `output_config={"effort": "..."}`)
into `optional_params["extra_body"]`. `AzureAnthropicConfig.transform_request`
then strips `extra_body` entirely on the way out, silently dropping the
param — and `AnthropicConfig._apply_output_config` never sees it, so
`effort="invalid"` / `effort="xhigh"` on a non-supporting model
quietly reaches the model with default behavior instead of returning a
clean 400 (as the native `anthropic` provider does).

Promote the keys back to top-level `optional_params` (using `setdefault`
so explicit top-level values win) before delegating to the parent
`AnthropicConfig`. Apply in both `validate_environment` and
`transform_request` so flag detection (`is_mcp_server_used`, etc.) and
output-config validation both run.

Surfaced by the QA matrix expansion on PR #27074: 20 cells where Azure
returned 200 while `anthropic` returned 400 — all `output_config` mode
across haiku_4_5, sonnet_4_5, opus_4_5, sonnet_4_6, opus_4_6, opus_4_7
families with `effort` in {invalid, xhigh, max, low, medium, high}.

Tests:
* `test_output_config_promoted_from_extra_body`: valid effort reaches data
* `test_invalid_output_config_effort_raises_via_extra_body`: 400 on bad effort
* `test_unsupported_effort_xhigh_raises_via_extra_body`: 400 on xhigh-on-Sonnet-4.6
* `test_extra_body_promotion_does_not_clobber_top_level`: setdefault semantics

* test(image_gen): expect no model in Azure image edit multipart (#26316)

Align test_azure_image_edit_litellm_sdk with deployment-scoped Azure edits.

Co-authored-by: Cursor <cursoragent@cursor.com>

* refactor(anthropic): extract _validate_effort_for_model to prevent drift

The chat completion path (`_apply_output_config`) and the /v1/messages
pass-through (`AnthropicMessagesConfig._translate_reasoning_effort_to_anthropic`)
both gate `max` / `xhigh` per model. The two sites had diverged from
near-identical copies into separately maintained blocks, creating a real
drift risk when a new model tier (e.g. Claude 4.8) lands -- a contributor
could update one site and miss the other.

Centralise the gating in `AnthropicConfig._validate_effort_for_model`,
which returns an error message string or `None`. Each call site keeps
its own provider-appropriate exception type (`BadRequestError` for the
chat path, `AnthropicError` for the /v1/messages pass-through) but the
gating decision now comes from one place. Net -11 LOC.

Adds a parametrised unit test exercising the helper directly across
4.5 / 4.6 / 4.7 model families and `max` / `xhigh` / lower-effort
inputs. Existing tests at both call sites continue to pass unchanged.

Addresses Greptile finding on PR #27074.

* fix(databricks): narrow reasoning_effort_value to str for mypy

`non_default_params.get("reasoning_effort")` returns `Any | None`,
but `REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT.get()` expects `str`.
Mypy flagged this on the strict pass. Narrow with `isinstance` before
the lookup; non-strings fall through to the existing `BadRequestError`
below with a clean validation message, so behavior is unchanged.

Fixes a regression introduced by 1a10746e95 in this PR.

* feat(proxy): add health_check_reasoning_effort for model health checks

Co-authored-by: Cursor <cursoragent@cursor.com>

* test(image_gen): align Azure image gen fixture with body omitting model

Expected JSON matches deployment-scoped Azure POST (#26316).

Co-authored-by: Cursor <cursoragent@cursor.com>

* test(anthropic/chat): force PR-local model_cost map via autouse fixture

CI runs without LITELLM_LOCAL_MODEL_COST_MAP=True, so litellm.model_cost
is loaded from main-branch JSON (default model_cost_map_url) instead of
the PR's checked-out model_prices_and_context_window.json. Tests that
assert per-model flags added in this PR (supports_max_reasoning_effort,
supports_xhigh_reasoning_effort) therefore pass locally but fail in CI
with 'AssertionError: assert False is True' on 5 cases:

  - test_anthropic_model_supports_effort_param_recognizes_supporting_models
    [anthropic.claude-mythos-preview, bedrock/.../mythos-preview,
     claude-opus-4-5-20251101]
  - test_supports_effort_level_handles_provider_prefixes
    [bedrock/invoke/us.anthropic.claude-sonnet-4-6-max-True,
     claude-sonnet-4-6-max-True]

Add an autouse fixture at tests/test_litellm/llms/anthropic/chat/conftest.py
that monkey-patches litellm.model_cost to the PR-local JSON for every test
in this directory. The parent conftest already snapshots+restores
litellm.model_cost per-function, so the mutation is contained.

This is a scoped workaround. The proper fix is to set the env var
globally in the test workflow once the ~10 inline self-set test files
are audited; tracking that as a follow-up issue.

* [Fix] Docker: Pin Wolfi And Uv To Multi-Arch Index Digests

The previous pins resolved to single-platform amd64 manifests, so buildx
pulled the same amd64 base for both linux/amd64 and linux/arm64 targets.
The published OCI index then advertised an arm64 entry whose layers are
byte-identical to amd64 -- arm64 users got an amd64 binary.

Switch all three Dockerfiles to the multi-arch image-index digests:
  - cgr.dev/chainguard/wolfi-base   (index has linux/amd64 + linux/arm64)
  - ghcr.io/astral-sh/uv:0.11.7     (index has linux/amd64 + linux/arm64)

Resolved with `docker buildx imagetools inspect <ref>` -- that returns
the index digest. `docker pull` + `docker inspect` returns the per-host
platform digest, which is what slipped in last time.

* [Fix] Docker: Pin Uv To Multi-Arch Index Digest In Remaining Dockerfiles

Apply the same fix to the three Dockerfiles not in the release pipeline
today (alpine, dev, health_check) so they stay correct if/when they're
built for arm64 in the future.

Wolfi pins are not present in these files; the python:3.11-alpine and
python:3.13-slim digests they already use are multi-arch indexes that
include arm64/v8, so only the uv pin needed swapping.

* fix(xai): fold reasoning_tokens into completion_tokens to satisfy OpenAI invariant

xAI's chat completions API accounts reasoning_tokens separately from
completion_tokens, but rolls them into total_tokens. This breaks the
OpenAI invariant total_tokens == prompt_tokens + completion_tokens
that downstream consumers (including litellm's own _usage_format_tests
in tests/llm_translation/base_llm_unit_tests.py:58) rely on.

Live capture (grok-3-mini-beta, 2026-05-04):
    prompt=14, completion=10, total=336, reasoning=312
    14 + 10 = 24, NOT 336.

OpenAI's o1/o3 reasoning models include reasoning_tokens in
completion_tokens, leaving the prompt+completion=total invariant
intact. xAI deviates. This patch aligns xAI to OpenAI semantics by
folding reasoning_tokens into completion_tokens after the parent
OpenAI parser runs.

The fold is idempotent and defensive:
- Only fires when total_tokens == prompt_tokens + completion_tokens
  + reasoning_tokens (the documented xAI shape). Refuses to fold if
  the gap doesn't match, guarding against silent corruption when xAI
  changes accounting.
- Skips if completion_tokens already covers the gap (already
  normalised — e.g. cost calc replays a previously-folded Usage).

xai.cost_calculator.cost_per_token already added reasoning_tokens to
the visible completion count for billing. Post-fold the Usage block
now satisfies that invariant directly, so the cost calc would
double-bill. Updated cost_per_token to detect the OpenAI-normalised
shape (total == prompt + completion) and skip the reasoning add-on
in that case, falling through to the legacy raw-shape behaviour for
callers that bypass the transformation (e.g. proxy log replay).

Tests:
- Adds TestXAIReasoningTokenFolding covering: gap-explained-fold,
  idempotent-no-double-fold, no-reasoning-skip, gap-mismatch-skip.
- Adds test_already_normalised_usage_does_not_double_count_reasoning
  to lock the cost-calc idempotency.
- Updates 7 pre-existing cost-calc tests whose total_tokens was
  internally inconsistent (used the OpenAI-normalised total but kept
  reasoning_tokens external) to use the documented xAI raw shape
  total = prompt + visible completion + reasoning. Pre-existing
  values masked the missing-fold by accident.

Verified end-to-end against the live xAI API:
    LITELLM_LOCAL_MODEL_COST_MAP=False (CI default) +
    XAI_API_KEY set +
    pytest tests/llm_translation/test_xai.py::TestXAIChat::test_prompt_caching
        -> PASSED in 18.81s (was: AssertionError on
        usage.total_tokens == usage.prompt_tokens + usage.completion_tokens)

20/20 tests in tests/test_litellm/llms/xai/test_xai_cost_calculator.py
and 8/8 in tests/test_litellm/llms/xai/test_xai_chat_transformation.py
pass.

* refactor(bedrock/converse): delegate effort gating to AnthropicConfig._validate_effort_for_model

Removes the duplicated max/xhigh gating logic in
_validate_anthropic_adaptive_effort and the now-unused
_supports_effort_level_on_bedrock helper. Per-model gating now flows
through the centralized AnthropicConfig._validate_effort_for_model
(whose _supports_effort_level already strips Bedrock prefixes), so the
chat completion, /v1/messages, and Bedrock Converse paths can't drift
when a new gated effort tier is added.

* Implement normalize_nonempty_secret_str function to trim whitespace from secrets and treat empty values as unset. Update proxy_server to use this function for Grafana credentials. Enhance tests to validate the new normalization behavior.

* Fix qdrant semantic cache miss metadata

* chore(deps): refresh dependency locks

* chore(deps): authorize pytest license

* fix: preserve tokenizer decode round trips

* refactor(anthropic): drive adaptive-thinking gate via supports_adaptive_thinking flag

Three of greptile's open comments on #27074 (P2 converse:512, P1
databricks:361, and the underlying capability-flag policy rule) flagged
the same pattern: _is_claude_4_6_model(...) or _is_claude_4_7_model(...)
used inline as a runtime 'is this an adaptive-thinking model?' check.
That requires a code release each time a new adaptive Claude lands.

Consolidate the inline gating to AnthropicModelInfo._is_adaptive_thinking_model,
and switch the helper itself to read a new supports_adaptive_thinking
flag from `model_prices_and_context_window.json` via `_supports_factory`,
falling back to the family pattern only when the model-map entry doesn't
carry the flag (preserves OpenRouter / Vercel / Bedrock-prefixed variants
that route through the same code path with non-canonical ids).

Adds `supports_adaptive_thinking: true` to the four 4.6/4.7 anthropic
entries (opus-4-6 + dated, opus-4-7 + dated, sonnet-4-6). Bedrock-prefixed
and Vertex-prefixed entries don't need the flag because both fall back
through the family pattern (the helper short-circuits early on True from
either path) and the bedrock/vertex Claude IDs all match the existing
opus-4-{6,7} / sonnet-4-{6,7} pattern.

Affected call sites:

- `bedrock/chat/converse_transformation.py:_handle_reasoning_effort_parameter`
- `anthropic/chat/transformation.py:_map_reasoning_effort`
- `anthropic/chat/transformation.py:map_openai_params` (output_config branch)
- `databricks/chat/transformation.py:map_openai_params` (output_config branch)

The remaining `_is_claude_4_6_model` / `_is_claude_4_7_model` references
in `AnthropicConfig._validate_effort_for_model` and
`AnthropicConfig.get_supported_openai_params` are intentionally retained:
they're per-model gating fallbacks for variants whose model-map entries
don't yet carry the `supports_max_reasoning_effort` /
`supports_reasoning` flag. Those are documented in-place.

Tests: 537 anthropic/bedrock/databricks/vertex/messages tests pass.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* chore(deps): address dependency review notes

* test(model_prices): add supports_adaptive_thinking to schema

`test_aaamodel_prices_and_context_window_json_is_valid` validates the
model-map JSON against an explicit schema with `additionalProperties`,
so the new `supports_adaptive_thinking` flag added in
98ced0ae43 needs a matching schema entry.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* refactor: remove unnecessary comments from #27074

Strip out the explanatory and historical comments that don't carry
business-logic justification. Comments that simply narrate what code
does — or that explain prior behavior, what was changed, or which PR
introduced a fix — are removed. Docstrings are reduced to a one-line
summary where the long form repeated information already evident from
the code or test data.

No code-behavior changes. All 643 affected unit tests still pass.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* test: keep decode token test local

* chore(deps): align dashboard node engine

* feat: selectively apply routing strategy according to model name

* style: make _model_supports_effort_param more concise

* refactor(anthropic,bedrock): hoist drop_params output_config warning to module constant

Three call sites (anthropic chat, bedrock converse, bedrock invoke messages)
emitted the same '...Effort is only supported on Opus 4.5+, Sonnet 4.6+, and
Mythos Preview' warning verbatim. Extract DROP_UNSUPPORTED_OUTPUT_CONFIG_WARNING
in litellm/llms/anthropic/chat/transformation.py and import it from the bedrock
sites so future copy edits live in one place.

Addresses Michael's review on PR #27074.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* refactor(anthropic,bedrock,databricks): factor BadRequestError for unknown reasoning_effort

Three call sites raised the same BadRequestError("Invalid reasoning_effort:
... Must be one of 'minimal', 'low', ...") block when REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT
returned None: anthropic chat map_openai_params, bedrock converse
_handle_reasoning_effort_parameter, and databricks chat reasoning_effort path.

Extract AnthropicConfig._raise_invalid_reasoning_effort(model, value, llm_provider)
so future copy edits / valid-set changes happen in one place. Typed as NoReturn
so type-checkers correctly narrow control flow at call sites.

Addresses Michael's review on PR #27074.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* Clean up Redis semantic cache isolation fallback

* fix(guardrails): align banned_keywords + azure_content_safety call_type gates with runtime route_type

The hooks gated on ``call_type == "completion"`` but the proxy ingress
passes ``route_type`` straight through as ``call_type`` —
``"acompletion"`` for /v1/chat/completions and ``"aresponses"`` for
/v1/responses. Tests passed because they used the literal sync
``"completion"`` value, masking the gap.

Switch both hooks to ``is_text_content_call_type`` (matches the
canonical runtime values: completion / acompletion / aresponses) and
update existing tests to assert against runtime values, plus parametrize
a regression test that pins the gate.

* fix: remove unused import

* Add semantic cache legacy migration flag

* Treat 0 team_member_budget as no cap

* chore(caching): annotate qdrant quantization_params dict type

Mypy infers the dict's value type from the first branch
(Dict[str, bool]) which clashes with the scalar branch's mixed-type
inner dict. Explicit Dict[str, Any] annotation lifts the inference.

* chore(caching): remove allow_legacy_unscoped_cache_hits opt-in

The flag was an opt-in escape hatch for the cross-tenant leak the rest
of the patch closes — flipping it on (env var or constructor param)
re-enables exactly the VERIA-54 primitive on either backend. There is
no operational need that the secure path doesn't already meet:

- Qdrant: legacy points without ``litellm_cache_key`` payload are
  excluded by the must-clause filter and treated as misses; new sets
  populate the cache key, so cold-start lasts only as long as the
  natural cache rebuild.
- Redis: existing unscoped index can't carry the new schema; the init
  path falls back to ``{name}_isolated`` (and recreates it on stale
  schema), leaving the legacy index untouched.

Drop the constructor param, env-var fallback, ``_using_legacy_unscoped_index``
flag, the legacy-reuse branch in ``_init_semantic_cache``, and the
matching guards in set/get paths. Update tests to drop the legacy-mode
cases and assert the secure-only behaviour.

* fix(container): keep ownership-filter exceptions out of the LLM-error path

filter_container_list_response runs after the upstream call has
already succeeded; treating an ownership-lookup failure as an LLM-API
error fires post_call_failure_hook for a successful upstream call and
returns a misleading provider-shaped error to the client. Run the
filter outside the try/except so genuine LLM errors stay scoped to
the upstream call.

* chore(container,skills): LRU eviction for owner caches; widen file_purpose Literal

Two cleanups from the /simplify pass:

* ``_CONTAINER_OWNER_CACHE`` and ``_SKILL_CACHE`` now LRU-evict via
  ``OrderedDict.popitem(last=False)`` instead of full ``clear()`` at
  capacity. Full clears converted a steady-state cached workload into a
  periodic full-DB-load oscillation as the cache repopulated from zero
  and cleared again. Reads now ``move_to_end`` so the just-touched
  entry survives the next eviction. Mirrors the pre-existing LRU
  pattern in ``_remember_container_owner``.

* ``LiteLLM_ManagedObjectTable.file_purpose`` Literal now includes
  ``"container"`` so Pydantic validation accepts rows written by the
  ownership store.

* chore(container,skills): drop legacy-access opt-out env vars

LITELLM_ALLOW_UNTRACKED_CONTAINER_ACCESS and
LITELLM_ALLOW_UNOWNED_SKILL_ACCESS were operator-toggleable opt-outs
for the cross-tenant access primitive this PR closes — flipping either
on re-enabled exactly the VERIA-20 read path. Default-secure with no
escape hatch matches sibling fixes (vector-store cred isolation, semantic
cache key isolation, user_config strip): all rejected the
opt-out-of-security pattern.

Untracked containers and unowned skills (rows that pre-date this
enforcement) are admin-only. Non-admin owners need to either re-create
via the now-tracked flow or have an admin assign ``created_by`` on the
existing row. Update tests to assert the strict-only behaviour.

* 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.

* fix(proxy): include request-blocked callback params in auth bans

* 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.

* fix(proxy): normalize managed resource team owner field

* 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).

* fix(container): cache list-allow-set, track admin-created containers

Address Greptile P2 follow-ups from the prior round:

* Cache ``_get_allowed_container_ids`` (60s LRU/TTL keyed by sorted
  owner-scope tuple) so ``GET /v1/containers`` doesn't issue a fresh
  ``find_many`` against ``litellm_managedobjecttable`` on every list
  call. Invalidate the caller's own cache entry when they record a
  new owner so the just-created container shows up on their next list.

* Tighten the admin early-return in ``record_container_owner`` to skip
  ONLY when there's literally no container ID to stamp. An admin with
  identity (the master-key path populates ``user_id`` + ``api_key``)
  flows through the normal record path so admin-created containers are
  tracked like any other caller's. The truly-identity-less admin case
  still falls through to the 403 below — correct fail-secure default.

Skill-cache invalidation gap (also flagged by Greptile) is moot: there
is no skill update endpoint exposed; ownership-affecting mutations are
only delete (already invalidates) and create (new ID, no cache entry
to update).

* chore(container): use delete_cache, json-encode scope key, clean test

/simplify follow-ups:

* Replace the two-``pop`` reach into ``cache_dict``/``ttl_dict`` with
  the existing public ``InMemoryCache.delete_cache(key)`` — the same
  idiom used elsewhere in the proxy. Bonus: ``delete_cache`` calls
  ``_remove_key`` which also handles ``expiration_heap`` consistency
  the direct pops were silently leaking.

* JSON-encode the sorted scope list for the cache key instead of
  ``"|".join``. ``user_id`` / ``team_id`` / ``org_id`` / ``api_key``
  are free-form strings and could contain a literal ``|`` — JSON
  quoting escapes any in-string separator unambiguously.

* Extract ``_allowed_container_ids_cache_key()`` so the read and
  invalidation sites compute the key the same way.

* Fix a placeholder-then-overwrite test construction: the
  ``__module__.split(".")[0] and "proxy_admin"`` line evaluated to a
  literal string that was immediately overwritten with the real enum
  value. Hoist the import and construct directly.

* [Fix] Tests: Replace deprecated openrouter/claude-3.7-sonnet with claude-sonnet-4.5

OpenRouter has dropped active endpoints for anthropic/claude-3.7-sonnet,
causing test_reasoning_content_completion to fail with a 404 "No endpoints
found" error. Switch to anthropic/claude-sonnet-4.5, which is current and
supports reasoning streaming.

* feat: routing groups ui

* fix(security): prevent secret_fields from leaking into spend logs

secret_fields (containing raw HTTP headers including Authorization
Bearer tokens) was being included in proxy_server_request['body']
because the body snapshot was a copy.copy(data) of the full request
dict. This body gets serialized and persisted in the LiteLLM_SpendLogs
table, exposing user credentials in the database.

Root cause: data['secret_fields'] was set before the body snapshot at
data['proxy_server_request']['body'] = copy.copy(data), so the full
raw headers (including auth tokens) ended up in the snapshot.

Fix (defense in depth):
1. Exclude 'secret_fields' when creating the body snapshot in
   litellm_pre_call_utils.py (primary fix)
2. Strip 'secret_fields' in _sanitize_request_body_for_spend_logs_payload
   as a secondary safeguard

secret_fields remains available on the live data dict for legitimate
downstream consumers (MCP, Responses API).

Co-authored-by: Krrish Dholakia <krrish-berri-2@users.noreply.github.com>

* chore: update Next.js build artifacts (2026-05-05 02:13 UTC, node v20.20.2)

* [Fix] Proxy: Break managed-resources import cycle on Python 3.13

The Python 3.13 CCI smoke matrix surfaces a partially-initialized-module
ImportError when loading the managed files hook chain:

  litellm.proxy.hooks/__init__ (mid-import)
    -> enterprise.enterprise_hooks
    -> litellm_enterprise.proxy.hooks.managed_files
    -> litellm.llms.base_llm.managed_resources.isolation
    -> litellm.proxy.management_endpoints.common_utils
    -> litellm.proxy.utils  (re-enters litellm.proxy.hooks)

The except ImportError block in hooks/__init__.py silently swallowed the
failure, leaving managed_files unregistered and POST /files returning
500 "Managed files hook not found".

Two-layer fix:
- Inline the 3-line _user_has_admin_view check in isolation.py instead
  of importing it from litellm.proxy.management_endpoints.common_utils.
  litellm.llms.* should not depend on litellm.proxy.* — removing this
  layering violation breaks the cycle at its root.
- Define PROXY_HOOKS and get_proxy_hook before the conditional
  enterprise import in litellm/proxy/hooks/__init__.py, so any future
  re-entry resolves the public names instead of hitting an
  ImportError on a partially-initialized module.

Also fold in two unrelated CCI repairs surfaced in the same staging run:
- tests/otel_tests/test_key_logging_callbacks.py: per-key
  gcs_bucket_name / gcs_path_service_account are now stripped by
  initialize_dynamic_callback_params, so the GCS client falls through
  to the env-only branch. Update the assertion to match the new
  "GCS_BUCKET_NAME is not set" message.
- .circleci/config.yml: tests/pass_through_tests now resolves
  google-auth-library@10.x via the @google-cloud/vertexai 1.12.0 bump,
  which uses dynamic ESM imports Jest 29 cannot load without
  --experimental-vm-modules. Pass that flag in the Vertex JS test step.

Adds tests/test_litellm/proxy/hooks/test_proxy_hooks_init.py as a
regression guard: managed_files / managed_vector_stores must register,
and isolation.py must not transitively import litellm.proxy.utils.

* [Fix] Proxy: Address Greptile feedback on hook-cycle PR

- Move _user_has_admin_view to litellm.proxy._types as
  user_api_key_has_admin_view (single source of truth). common_utils.py
  and isolation.py both import from there now, removing the duplicated
  role-check that could silently diverge if new admin roles are added.
- Add pytest.importorskip("litellm_enterprise") to the two regression
  tests that assert managed_files / managed_vector_stores are registered;
  those keys come from ENTERPRISE_PROXY_HOOKS so the tests would fail
  unconditionally in a checkout without the enterprise extra installed.

* [Fix] Lint: Mark _user_has_admin_view re-export in common_utils

Ruff F401 flagged the aliased import as unused within common_utils.py
because the name is consumed only by external modules (~15 callers
across guardrails, spend tracking, MCP, agents, management endpoints).
Add `# noqa: F401  re-exported` so the alias survives lint while
keeping a single source of truth in litellm.proxy._types.

* 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>

* test(proxy): cover health_check_reasoning_effort for completion mode

Co-authored-by: Cursor <cursoragent@cursor.com>

* [Fix] Tests: Use master key for /otel-spans in test_chat_completion_check_otel_spans

/otel-spans now requires proxy admin (returns 401 'Only proxy admin
can be used to generate, delete, update info for new keys/users/teams.
Route=/otel-spans' for non-admin callers). Switch the GET call to use
the master key sk-1234 while keeping the generated key for the
chat-completion request that produces the spans.

* [Fix] Tests: Pick chat-completion OTEL trace by content, not recency

The /otel-spans endpoint returns process-wide spans and tags
most_recent_parent by max start_time. After tightening that route to
proxy_admin (sk-1234), the GET /otel-spans request itself emits auth
spans that beat the chat-completion spans on start_time, so
most_recent_parent now points at the request's own auth trace
(['postgres', 'postgres']) and the >=5-span assertion fails.

Pick the chat-completion trace by content: it is the only trace whose
span list is a superset of {postgres, redis, raw_gen_ai_request,
batch_write_to_db}. Verified locally end-to-end against
otel_test_config.yaml + OTEL_EXPORTER=in_memory: 3/3 runs green.

* [Fix] CI: Enable VCR replay for test_azure_o_series

The Azure o-series tests were excluded from the conftest's VCR auto-marker
because of a respx/vcrpy transport-patching conflict, but the only respx
reference in the file was an unused `MockRouter` import. Drop the dead
import and remove the file from the conflict set so cassettes record on
first run and replay thereafter, eliminating the 60-95s live Azure latency
that was crashing xdist workers under --timeout=120 thread-mode timeouts.

* [Fix] Tests: Restore /metrics access for prometheus test suite

/metrics now requires auth by default; tests/otel_tests/test_prometheus.py
makes 4+ unauthenticated GETs against http://0.0.0.0:4000/metrics, so
every prometheus test in CI now fails the metric assertion.

Set require_auth_for_metrics_endpoint: false in otel_test_config.yaml
to opt out for this test job, which scrapes /metrics directly. Verified
locally: 8/8 prometheus tests green (one flaky retry on
test_proxy_success_metrics that pre-dates this PR).

Also drop the -x stop-on-first-failure flag from the otel test command
so all failures in the job surface in a single CI run rather than
hiding behind whichever one trips first.

* [Perf] CI: Skip Redundant Playwright Apt Install in E2E UI Job

The cimg/python:3.12-browsers base image already ships every Chromium
system dependency Playwright needs (libnss3, libatk-bridge2.0-0,
libcups2, etc. — the install log shows them all as "already the newest
version"). Passing --with-deps to `npx playwright install` therefore
runs an apt-get update + install for nothing, but pays the full cost of
hitting Ubuntu mirrors. On a recent run those mirrors stalled hard:
apt-get update alone took 6m53s at 81.5 kB/s with several archives
returning connection refused.

Drop --with-deps and persist ~/.cache/ms-playwright alongside
node_modules so the Chromium binary is also reused across runs. Bump
the cache key to v2 so the existing v1 entry (which only contained
node_modules) is not loaded and skipped over the new browser path.

* [Fix] Docker: Remove Hardcoded Prisma Binary Target For Multi-Arch Builds

PRISMA_CLI_BINARY_TARGETS="debian-openssl-3.0.x" was hardcoded in
docker/Dockerfile.non_root by #17695. On a buildx linux/arm64 leg this
forces prisma to download the amd64 schema-engine into an arm64 image,
so 'prisma migrate deploy' fails at startup with 'Could not find
schema-engine binary'.

Removing the env lets prisma auto-detect per build platform: amd64
builds still resolve to debian-openssl-3.0.x (Wolfi falls back to
debian, same binary as before), and arm64 builds now correctly fetch
linux-arm64-openssl-3.0.x. The offline-cache pre-warm goal of #17695 is
preserved — only which binaries fill the cache changes.

Fixes #19458

* [Fix] UI: Clear Admin Session Cookies Before Establishing Invited User's Session (#27227)

The invite-signup form was writing the new user's token via raw
`document.cookie` at `path=/`, while the rest of the auth surface uses
`storeLoginToken` (which writes at `path=/ui` and mirrors to
sessionStorage). After signup the inviter's `path=/ui` cookie kept
winning path-specificity matching, and sessionStorage still held the
inviter's token, so the dashboard rendered as the inviter rather than
the newly created user.

Treat invite signup as a principal-change boundary — clear prior
session cookies first, then store the new token via the canonical
helper.

* test: add 24hr Redis-backed VCR cache to additional test suites (#27159)

* test: add 24hr Redis-backed VCR cache to additional test suites

Extracts the existing llm_translation VCR plumbing into a reusable helper
(tests/_vcr_conftest_common.py) and wires it into the conftest.py files
of the test directories listed in LIT-2787:

  audio_tests, batches_tests, guardrails_tests, image_gen_tests,
  litellm_utils_tests, local_testing, logging_callback_tests,
  pass_through_unit_tests, router_unit_tests, unified_google_tests

The same helper is also adopted by the pre-existing llm_translation and
llm_responses_api_testing conftests to remove the copy-pasted VCR setup.

Each consuming conftest:
- registers the Redis persister via pytest_recording_configure
- auto-marks collected tests with pytest.mark.vcr (skipping respx-using
  files where applicable, since respx and vcrpy both patch httpx)
- gates cassette writes on test success via _vcr_outcome_gate

The cache is opt-in via CASSETTE_REDIS_URL; when unset, VCR is disabled
and tests hit live providers as before. LITELLM_VCR_DISABLE=1 still
forces a bypass for ad-hoc local runs.

Test directories that run LiteLLM proxy in Docker (build_and_test,
proxy_logging_guardrails_model_info_tests, proxy_store_model_in_db_tests)
are intentionally not included: VCR.py patches the in-process httpx
transport and cannot intercept calls made from inside a Docker container.
The installing_litellm_on_python* jobs make no LLM calls and don't
benefit from caching.

https://linear.app/litellm-ai/issue/LIT-2787/add-24hr-caching-to-additional-test-suites

* test(vcr): add safe-body matcher to handle JSONL and binary request bodies

vcrpy's stock body matcher inspects Content-Type and unconditionally
runs json.loads on application/json bodies. JSON Lines payloads (used
by the Bedrock batch S3 PUT and other upload paths) crash that with
json.JSONDecodeError: Extra data, before the matcher can return
'not a match'.

This was the root cause of the batches_testing CI job failing on
test_async_create_file once VCR auto-marking was applied to the
batches_tests directory.

Add a conservative byte-equality body matcher and use it in place of
'body' in the shared match_on tuple. The matcher is strictly more
conservative than vcrpy's default — the only thing it gives up is
'different JSON key order is treated as the same body', which doesn't
apply to deterministic litellm-built request payloads. It can never
produce a false positive that the default would have rejected, so
there is no cross-contamination risk.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* test(vcr): exclude tests that VCR replay actively breaks

A few tests are incompatible with cassette replay and were failing on
the latest CI run after VCR auto-marking was extended to local_testing
and logging_callback_tests:

- test_amazing_s3_logs.py (logging_callback_tests): the test asserts on
  a per-run response_id that should round-trip through a real S3
  PUT/LIST. vcrpy's boto3 stub intercepts the PUT and the LIST replays
  stale keys, so the freshly-generated id is never found.
- test_async_embedding_azure (logging_callback_tests) and
  test_amazing_sync_embedding (local_testing): the failure branches
  deliberately pass api_key='my-bad-key' to assert that the failure
  callback fires. We scrub auth headers from cassettes (so the bad-key
  request matches the prior good-key request), and vcrpy replays the
  recorded 200 — the failure callback never fires.
- test_assistants.py (local_testing): the OpenAI Assistants polling
  APIs mint fresh thread/run IDs every recording session and then poll
  until status=='completed'. Replays of those polled GETs can never
  match a freshly-generated run id, so every CI run effectively
  re-records and the suite blows past the 15m no_output_timeout.

Skip these from VCR auto-marking so they continue to hit live providers
as they did before this change. The remaining tests in each directory
still get cached.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* test(vcr): expand skip lists for second batch of incompatible tests

Followup to the previous commit. After re-running CI on the rebuilt
branch, three more tests surfaced as VCR-replay-incompatible:

- litellm_utils_testing :: test_get_valid_models_from_dynamic_api_key
  Calls GET /v1/models with api_key='123' to assert the result is empty.
  We scrub auth headers, so the bad-key request matches the prior
  good-key cassette and replays the recorded model list.
- litellm_utils_testing :: test_litellm_overhead.py
  Measures litellm_overhead_time_ms as a percentage of total wall-clock
  time. With cached responses the upstream 'network' time collapses to
  microseconds, blowing past the 40%% threshold the test asserts on.
  Skip the whole file (every parametrization is at risk).
- local_testing_part1 :: test_async_custom_handler_completion and
  test_async_custom_handler_embedding
  Same bad-key failure-callback pattern as the already-skipped
  test_amazing_sync_embedding.
- litellm_router_testing :: test_router_caching.py
  Asserts on litellm's own router-level response cache by comparing
  response1.id to response2.id across repeat upstream calls (test
  bypasses litellm cache via ttl=0 and expects upstream to return a
  *new* id). With VCR replay both upstream calls return the same
  cassette body, so the ids are identical. Skip the whole file.
- logging_callback_tests :: test_async_chat_azure (preemptive)
  Same shape as already-skipped test_async_embedding_azure; was masked
  by upstream OpenAI rate-limit failures on baseline.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* test(vcr): use item.path and tighten matcher docstring

- Replace pytest's deprecated item.fspath with item.path in
  apply_vcr_auto_marker_to_items so we don't emit deprecation
  warnings under pytest 8.
- Clarify _safe_body_matcher docstring to reflect actual behavior
  (direct == first, then UTF-8 bytes comparison, no repr fallback).

Addresses Greptile review feedback on PR #27159.

* test(vcr): swallow all RedisError on cassette save/load

Cassette persistence is strictly best-effort: any Redis-side failure
(connection blip, timeout, OutOfMemoryError when the maxmemory cap is
hit, READONLY replicas, etc.) should degrade to 'test passed but
cassette not cached' rather than fail the test on teardown.

Previously the persister only caught ConnectionError and TimeoutError,
so OutOfMemoryError — which Redis Cloud raises when the cassette cache
hits its memory cap and there are no evictable keys — propagated out of
vcrpy's autouse fixture and ERRORed otherwise-passing tests on
teardown. This caused the litellm_utils_testing CircleCI job to fail on
the latest commit's run, even though the underlying test was a unit
test that used mock_response and produced no real upstream traffic
(the cassette was dirtied by a background langfuse callback). The
rerun only succeeded because Redis evictions happened to free enough
room before the SET — i.e. it was timing-dependent flakiness.

Catch redis.exceptions.RedisError (the common base of all server- and
client-side Redis exceptions) on both save and load, and parametrize
the regression tests across ConnectionError, TimeoutError, and
OutOfMemoryError to pin the new behavior.

* test(vcr): surface cassette-cache failures with warnings + session banner

When the persister silently swallows a Redis OOM (or any RedisError) on
save/load there is otherwise no visible signal that the cache is
degraded — tests pass, the cassette just isn't persisted, and the next
session still hits the same Redis at the same near-cap memory.

Add three layers of observability so that failure mode is loud:

1. Per-process health counters ("save_failures", "load_failures", and
   the last error string for each), exposed via cassette_cache_health()
   and reset via reset_cassette_cache_health(). The persister
   increments these in addition to logging.

2. VCRCassetteCacheWarning (UserWarning subclass) emitted via
   warnings.warn() inside the persister's except block. Pytest's
   built-in warnings summary at session end automatically lists every
   such warning, so the failure is visible in CI logs without any
   conftest-level wiring.

3. Session-end banner via emit_cassette_cache_session_banner() and a
   stderr-fallback atexit handler registered from
   register_persister_if_enabled(). Two states:
     - red "VCR CASSETTE CACHE DEGRADED" when save_failures or
       load_failures > 0
     - yellow "VCR CASSETTE CACHE NEAR CAPACITY" (no failures, but
       used_memory >= 85% of maxmemory) so the next session knows
       the Redis is approaching OOM before any SET actually fails

Capacity comes from a best-effort INFO memory probe
(cassette_cache_capacity_snapshot) that returns None on any failure or
when maxmemory is uncapped. The atexit handler skips xdist workers so
only the controller emits.

Tests: parametrize the existing save/load swallow-error tests across
ConnectionError/TimeoutError/OutOfMemoryError, add direct tests for
the health counters and warning emission, and a new
test_vcr_conftest_common_banner.py covering banner output for every
state (silent/red/yellow/disabled/xdist-worker).

* test(vcr): bucket cassettes by API key fingerprint, drop bad-key skips

Tests that deliberately call an LLM API with a bad key (e.g. to assert
that the failure callback fires, or that check_valid_key returns False)
were being silently served the prior good-key cassette: we scrub the
real Authorization / x-api-key header from the cassette before storing
it, so a follow-up bad-key call is byte-identical to the good-key call
under the existing match_on tuple.

Add a 'key_fingerprint' custom matcher that distinguishes requests by
the SHA-256 of their API-key headers. The fingerprint is stamped into
a synthetic 'x-litellm-key-fp' header by a new before_record_request
hook, which then strips the real auth headers (we have to do the
scrubbing here instead of via vcrpy's filter_headers knob, because
filter_headers runs *first* and would erase the value we want to hash).

Bad-key requests now get a different cassette bucket than good-key
requests, so vcrpy will not replay a recorded 200 in place of the
expected 401. The fingerprint is a one-way hash of the secret, so
cassettes never contain the key.

This permanently removes the 'bad-key' category of skips:

- tests/local_testing: dropped ::test_amazing_sync_embedding,
  ::test_async_custom_handler_completion,
  ::test_async_custom_handler_embedding
- tests/logging_callback_tests: dropped ::test_async_chat_azure,
  ::test_async_embedding_azure
- tests/litellm_utils_tests: dropped
  ::test_get_valid_models_from_dynamic_api_key

Coverage: 7 new unit tests in tests/test_litellm/test_vcr_safe_body_matcher.py
covering header stripping, fingerprint determinism, no-auth bucketing,
good-vs-bad key discrimination, x-api-key (Anthropic/Azure) discrimination,
and idempotence under replay.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* test(vcr): drop redundant comments and docstrings

Trim narration of code that is already self-evident from function and
variable names. Keep the two genuinely non-obvious bits:

- ordering constraint between filter_headers and before_record_request,
  which would invite a maintainer to re-introduce the bug if removed
- the per-directory _VCR_INCOMPATIBLE_FILES rationale, since 'why
  exactly is this skipped' is not knowable from the test name alone

Also drop the 40-line commented-out drop-in conftest snippet at the
bottom of _vcr_conftest_common.py — the consuming conftests are the
canonical reference.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* test(vcr): make _before_record_request idempotent

vcrpy invokes before_record_request more than once per request:
can_play_response_for calls it, then __contains__ /
_responses (reached via play_response) call it again on the
result. The second invocation sees a request whose auth headers we
already stripped, so a naive recompute yields "no-key" and
overwrites the real fingerprint stored in the header.

This makes can_play_response_for and play_response disagree on
matchability — the former says "yes, we have a stored response for
this" (matching no-key to no-key) and the latter throws
UnhandledHTTPRequestError because it computes a fresh real
fingerprint that doesn't match the stored no-key.

In CI this manifested as ~30 failing tests across guardrails_testing,
audio_testing, batches_testing, image_gen_testing, llm_responses_api,
litellm_router_unit_testing, etc. Skip the recompute when the header
is already set, so re-applying the hook is a no-op.

Adds a regression test that fires the hook twice on the same dict and
asserts the fingerprint stays put.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* test(vcr): drop more redundant docstrings and headers

* test(vcr): enable 24hr cache for ocr_tests and search_tests

These two directories were the only non-dockerized test suites in the
build_and_test workflow that make live LLM/provider API calls but were
not VCR-enabled by this PR. Together they account for 96 tests:

- tests/ocr_tests/ (31): Mistral OCR, Azure AI OCR, Azure Document
  Intelligence, Vertex AI OCR. Pure-unit tests inside the same files
  (e.g. TestAzureDocumentIntelligencePagesParam) make no HTTP calls
  and become benign VCR NOOPs.
- tests/search_tests/ (65): Brave, DataForSEO, DuckDuckGo, Exa,
  Firecrawl, Google PSE, Linkup, Parallel.ai, Perplexity, SearchAPI,
  Searxng, Serper, Tavily.

Both directories use the canonical minimal conftest pattern from
tests/audio_tests/conftest.py with no skip lists. None of the test
files use respx, none assert on per-call upstream non-determinism
(no response1.id != response2.id, no overhead-as-fraction-of-total,
no live polling), so the default match_on tuple should cache cleanly.
If a flake surfaces during the first cassette-recording CI run, we
can add a targeted skip the same way we did for the other dirs.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

---------

Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>

* [Fix] Team UI: handle legacy dict shape for metadata.guardrails (#27224)

* [Fix] Team UI: handle legacy dict shape for metadata.guardrails

A team can have metadata.guardrails stored as {"modify_guardrails": bool}
(the permission-flag shape introduced in PR #4810) rather than the
expected string[]. The opt-out logic added in PR #25575 calls .filter()
on this field, which throws TypeError on a dict and crashes the team
detail page.

Add a safeGuardrailsList helper that returns [] when the field is not
an array, and route the three read sites through it.

* [Fix] Team UI: inline Array.isArray guards for guardrails metadata

Replace the safeGuardrailsList helper with inline Array.isArray checks
at each call site, and apply the same guard to opted_out_global_guardrails
for consistency. No known legacy dict rows for opted_out_global_guardrails,
but the unguarded `|| []` pattern is the same shape risk.

Six call sites now defended directly: three for metadata.guardrails
and three for metadata.opted_out_global_guardrails.

* chore: update Next.js build artifacts (2026-05-05 22:45 UTC, node v20.20.2) (#27240)

* [Infra] Bump deps (#27157)

* bump: version 0.4.70 → 0.4.71

* bump: version 0.1.39 → 0.1.40

* uv lock

---------

Co-authored-by: Michael Riad Zaky <michaelr@Mac.localdomain>
Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: ryan-crabbe-berri <ryan@berri.ai>
Co-authored-by: user <70670632+stuxf@users.noreply.github.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: shivam <shivam@berri.ai>
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
Co-authored-by: Krrish Dholakia <krrish+github@berri.ai>
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
Co-authored-by: Michael-RZ-Berri <michael@berri.ai>
Co-authored-by: harish-berri <harish@berri.ai>
Co-authored-by: Yassin Kortam <yassinkortam@g.ucla.edu>
Co-authored-by: Michael Riad Zaky <michaelr@Michaels-MacBook-Air.local>
Co-authored-by: Krrish Dholakia <krrish-berri-2@users.noreply.github.com>
2026-05-05 16:15:03 -07:00
Cursor Agent
3f5c589255
fix(bedrock): add 1-hour cache write tier for Claude 4.5/4.6/4.7 (Global, US)
AWS Bedrock pricing publishes a separate 1-hour prompt-cache write rate for
Claude 4.5 / 4.6 / 4.7 (1.6x the 5-minute rate). Without
`cache_creation_input_token_cost_above_1hr`, cost tracking for 1-hour-TTL
prompt caching on Bedrock falls back to the 5-minute rate and undercounts
spend by ~60%.

Adds the field to the spot-checked Global and US-region entries:

- anthropic.claude-opus-4-7         (Global $10.00 / MTok)
- anthropic.claude-opus-4-6-v1      (Global $10.00 / MTok)
- anthropic.claude-opus-4-5-...     (Global $10.00 / MTok)
- anthropic.claude-sonnet-4-6       (Global $6.00 / MTok)
- anthropic.claude-sonnet-4-5-...   (Global $6.00 / MTok regular,
                                     $12.00 / MTok long-context >200K)
- anthropic.claude-haiku-4-5-...    (Global $2.00 / MTok)
- global.anthropic.* mirrors of the above
- us.anthropic.* mirrors at the US +10% premium

Also updates the long-context (>200K) variants of Sonnet 4.5 with
`cache_creation_input_token_cost_above_1hr_above_200k_tokens`.

The mirrored entries in `litellm/model_prices_and_context_window_backup.json`
are updated in lockstep.

EU / AU / APAC / JP / us-gov regional variants are out of scope for this
change pending separate verification against AWS Bedrock pricing for those
regions.

Adds tests/test_litellm/test_bedrock_anthropic_1hr_cache_pricing.py to lock
in the expected values and the 1.6x ratio invariant.

Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
2026-04-29 19:21:57 +00:00
ishaan-berri
4ae2996f08
Add gpt-image-2 support (#26644) (#26705)
* Add gpt-image-2 support

* Address gpt-image-2 PR feedback

Co-authored-by: Emerson Gomes <emerson.gomes@thalesgroup.com>
2026-04-28 20:10:42 -07:00
Liam McDonald
503c3921c8 Fix gpt-5.5-pro pricing 2026-04-27 15:33:59 -07:00
Mateo Wang
319193604c
[Feat] Add azure/gpt-5.5 + azure/gpt-5.5-pro entries (+ dated variants) (#26361)
* feat(azure): add azure/gpt-5.5 + azure/gpt-5.5-pro entries (+ dated variants)

Azure variants of OpenAI's GPT-5.5 family. Microsoft has not yet
shipped GPT-5.5 on Azure OpenAI (latest GA on the Foundry models page
is GPT-5.4 as of 2026-04-24), but adding the entries day-0 mirrors the
established precedent for azure/gpt-5.4* (which were in the cost map
before the Azure rollout) so cost tracking and capability flags work
the moment customers deploy.

Schema follows the existing azure/gpt-5.4* shape:
- Same base/long-context pricing as openai/gpt-5.5*: $5/$30 chat,
  $60/$360 pro per 1M, with priority tier 2x base
- Azure variants drop the flex/batches keys (Azure has no flex tier)
  but keep priority pricing, matching gpt-5.4* precedent
- mode=chat for the thinking model, mode=responses for pro

reasoning_effort capability flags mirror the OpenAI variants exactly
since Azure proxies the same API contract: minimal rejection on both
chat and pro, low/none rejection on pro. Once #26456 (which sets
supports_low_reasoning_effort + minimal=false on openai/gpt-5.5*)
lands, OpenAI and Azure flag profiles align.

Tests pin entry presence + pricing for all four Azure variants and
verify the live-API-derived reasoning_effort flags.

* test: register supports_low_reasoning_effort in cost-map JSON schema

azure/gpt-5.5-pro and azure/gpt-5.5-pro-2026-04-23 added in this branch
carry supports_low_reasoning_effort=false. The strict
'additionalProperties: false' schema in
test_aaamodel_prices_and_context_window_json_is_valid rejected the new
key. Register it alongside the other supports_*_reasoning_effort
entries.

Note: the runtime side of this flag (code that reads it) lands in
#26456. Until that PR merges the flag is inert for both Azure and
OpenAI pro entries, but having the schema accept it lets cost-map
tests pass on either merge order.
2026-04-25 14:19:59 -07:00
Chesars
ebe16072f2 Merge remote-tracking branch 'upstream/litellm_internal_staging' into litellm_staging_03_23_2026
# Conflicts:
#	model_prices_and_context_window.json
#	tests/test_litellm/llms/vertex_ai/multimodal_embeddings/test_vertex_ai_multimodal_embedding_transformation.py
2026-04-25 15:16:13 -03:00
Chesars
384cfdad47 Revert "Merge pull request #24164 from dongyu-turo/feat/update-bedrock-claude-price-above-200k"
This reverts commit b8189ea1de, reversing
changes made to 19c8f3d565.
2026-04-25 15:04:05 -03:00
Krrish Dholakia
70492cee42
feat(proxy): add /v1/memory CRUD endpoints (#26218)
Some checks are pending
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Unit Tests: Proxy DB Operations / guardrails-hooks (push) Blocked by required conditions
Unit Tests: Proxy DB Operations / jwt-and-keys (push) Blocked by required conditions
Unit Tests: Proxy DB Operations / key-generation (push) Blocked by required conditions
Unit Tests: Proxy DB Operations / logging-misc (push) Blocked by required conditions
Unit Tests: Proxy DB Operations / proxy-runtime (push) Blocked by required conditions
Unit Tests: Proxy DB Operations / proxy-server-core (push) Blocked by required conditions
Unit Tests: Proxy DB Operations / schema-migration (push) Blocked by required conditions
Unit Tests: Security / security (push) Waiting to run
* feat(proxy): add /v1/memory CRUD endpoints with user/team scoping

New LiteLLM_MemoryTable stores user/team-scoped key/value entries with
optional JSON metadata. Value is a String (LLM-readable text) and metadata
is an optional Json? envelope, matching the Letta + mem0 hybrid model so
future structured fields can be added without a schema migration.

Endpoints:
  POST   /v1/memory         - create
  GET    /v1/memory         - list (caller-scoped; admins see all)
  GET    /v1/memory/{key}   - fetch one
  PUT    /v1/memory/{key}   - upsert
  DELETE /v1/memory/{key}   - delete

Non-admin callers cannot set a user_id/team_id other than their own.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(proxy/memory): omit metadata field when None on create

Prisma's Python client rejects `metadata=None` on a `Json?` field with
"A value is required but not set" — the field must be omitted from the
`data` dict entirely to store SQL NULL. Build the create payload
conditionally in both `create_memory` and the PUT-create branch of
`upsert_memory`.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* feat(ui): add Memory page to view/manage /v1/memory entries

Adds a new "Memory" sidebar item under Tools so users can see what their
agents have stored. Lists all memories visible to the caller (scoped by
the backend), with a key-search filter, preview column, scope tags, and
view/edit/delete actions. Create modal accepts optional JSON metadata.

- networking.tsx: fetchMemoryList / createMemory / updateMemory / deleteMemory
  wired to the /v1/memory CRUD endpoints.
- MemoryView + MemoryEditModal: new antd-based components (per CLAUDE.md:
  use antd for new UI, not tremor).
- page.tsx + leftnav.tsx: wire the "memory" route + sidebar entry.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* feat(memory): add key_prefix filter + promote Memory to AI GATEWAY nav

Backend:
- GET /v1/memory now accepts `key_prefix` for Redis-style namespace
  scans (e.g. `?key_prefix=user:`). When both `key` and `key_prefix`
  are passed, `key_prefix` wins.
- Prefix filter sits under the visibility filter in the Prisma where
  clause, so it can never leak rows across user/team scopes.
- New tests: prefix match, and cross-scope isolation (another user's
  `user:*` rows must not appear in the caller's results).

UI:
- Memory moved from a Tools submenu to a top-level AI GATEWAY item
  (alongside Agents, MCP Servers, Skills) — it's an API primitive,
  not a tool-management surface.
- Search box now drives prefix search, matching the Redis mental
  model ("type the namespace, see everything under it").

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(memory): enforce unique key per scope by using NULLS NOT DISTINCT

The unique constraint `(key, user_id, team_id)` on LiteLLM_MemoryTable
silently allowed duplicates when user_id or team_id was NULL, because
Postgres treats every NULL as distinct by default (ANSI semantics). A
caller with no team_id could POST the same key three times and get
three rows.

Migration:
1. Dedupe existing rows, keeping the most recent per (key, user_id,
   team_id), using `IS NOT DISTINCT FROM` so NULL == NULL.
2. Drop the old unique index.
3. Recreate it with `NULLS NOT DISTINCT` (Postgres 15+).

No code change: POST already returns 409 on unique-violation error
messages — it just wasn't firing before because the constraint didn't
catch the NULL-team case.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(memory): make key globally unique, 409 on any duplicate

Switches from the compound unique `(key, user_id, team_id)` to a simple
`key @unique`. The compound form silently allowed duplicates when
user_id or team_id was NULL (Postgres treats each NULL as distinct), so
callers could POST the same key repeatedly. Globally-unique key means
one row per key, period — any duplicate create → 409.

- schema.prisma (×3): `key String @unique`, drop `@@unique(...)`.
- initial add_memory_table migration: unique index on (key) only.
- Remove the now-unused follow-up NULLS NOT DISTINCT migration.
- Endpoint error message simplified ("already exists" — no "for this scope").
- Test fake's create() now enforces global key uniqueness.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(ui/memory): full-width layout + user/teams-style columns

- Add `w-full` to the MemoryView outer div so the page fills the
  flex-flex-1 container (was collapsing to intrinsic width).
- Replace the combined "Scope" column with separate User ID / Team ID
  columns, matching the layout of the Users / Teams pages: ID, Name,
  Preview, User ID, Team ID, Updated, Actions.
- IDs render with a truncated mono label + copy-to-clipboard button,
  same pattern as view_users.
- Detail drawer now shows Memory ID / User ID / Team ID as separate
  fields instead of stacked color tags.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(ui/memory): use clean MCP-style ID pill, drop copy icons

The ID / User ID / Team ID columns showed a mono text blob with a
copy-to-clipboard icon next to each value — too busy compared to the
MCP Servers page. Swap the renderer for MCP's pill style:

- Truncated mono ID inside a blue Tailwind pill
  (`font-mono text-blue-600 bg-blue-50 ... rounded-md border`).
- No copy icon. Full ID surfaces via tooltip.
- ID column is a button that opens the detail drawer on click;
  user/team ID pills are static (not clickable).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(memory): address greptile review feedback

Addresses 5 greptile findings (3/5 → higher confidence target):

1. Identity-less orphan rows (P1): non-admin callers with no user_id AND
   no team_id could create rows that the visibility filter would never
   match again. Now rejected up front with 400 — caller must authenticate
   with a scoped key or act as PROXY_ADMIN.

2. Upsert race returning 500 (P1): PUT's check-then-create isn't atomic;
   a concurrent writer could slip a row in between the 404-check and the
   create call. Now catch unique-violation on create, re-read, and fall
   through to update — PUT stays idempotent. If the conflicting row
   belongs to a different scope, surface a 409 instead of 500.

3. PUT-create scope inconsistency (P2): PUT's create branch always used
   the caller's own user_id/team_id, so admins couldn't bootstrap rows
   scoped elsewhere via PUT (only POST). Now PUT-create calls the shared
   `_resolve_scope()` helper, matching POST semantics.

4. Stale schema comment (P2): schema said "Keyed by (key, user_id,
   team_id)" but `key` is globally unique. Updated all three schema
   copies to reflect the actual design.

5. UI silently truncated at 200 (P2): MemoryView fetched pageSize=200
   with no load-more. Swapped to real server-side pagination driven by
   `data.total`; page size is now 50 and the pager is a real AntD
   control.

Also extracts a shared `_resolve_scope()` helper and `_is_unique_violation()`
from create_memory so POST and PUT don't drift on the scope/error logic.

Tests: +3 new (identity-less 400, PUT admin bootstrap, PUT race →
update), 18/18 pass.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(memory): typed Prisma error + explicit-null metadata on PUT

Two more greptile threads from the last review:

- Unique-violation detection was string-matching "Unique"/"UniqueViolation"
  in the exception message, fragile across Prisma/driver versions. Now
  check the typed error `code == "P2002"` first, with string fallback.

- PUT could not distinguish "metadata omitted" from "metadata: null" —
  both parsed as `None`, so callers had no way to clear stored metadata.
  Switch to Pydantic v2's `model_fields_set` to tell which fields the
  caller actually sent; explicit null now clears the column.

New tests:
- explicit null clears metadata
- omitted metadata preserves existing value

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(ui/memory): send explicit null when user clears metadata

Addresses the remaining P1 from the last greptile review:

When the edit modal's metadata textarea was cleared and saved,
`metadataParsed` stayed `undefined`, `JSON.stringify` dropped the key
entirely, and the backend's `model_fields_set` guard therefore left
the stored metadata untouched — UI showed success but nothing changed.

Now: empty textarea on edit → send explicit `null` so the backend
sees `metadata` in `model_fields_set` and clears the column.
Empty textarea on create still maps to `undefined` (field omitted)
to avoid Prisma's `Json? = None` quirk on insert.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(ui/memory): preserve slashes in key path encoding

The backend route `/v1/memory/{key:path}` supports keys with slashes,
but `encodeURIComponent` encoded `/` as `%2F`. Some proxies (nginx
default, CloudFlare, AWS ALB) reject or re-decode `%2F` mid-flight,
so UI update/delete calls on slash-containing keys could fail or
silently misroute.

New helper `encodeMemoryKeyForPath` splits by `/`, URL-encodes each
segment, then rejoins with literal `/`. Every other unsafe char
(spaces, `?`, `#`, `%`) stays encoded per-segment; slashes stay as
path delimiters, matching what the `:path` converter expects.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(ui/memory): drop misleading client-side column sorters

With server-side pagination, client sorters on `key` and `updated_at`
only reorder the current page while pretending to sort the full
dataset — users would see "sorted by name" but only the visible 50
rows would actually be sorted.

Remove the sorters. The backend already returns rows in
`updated_at DESC` order (sensible default for a memory view), and
users can narrow the result with the key-prefix filter.

Greptile also flagged missing `@@map` on the new model as a
"consistency" issue, but only 1 of 59 tables in this repo uses
`@@map` — the dominant pattern is to rely on Prisma's default
(model name == table name). Skipping that finding as a
false-positive on convention.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(memory): compose visibility + key filters via explicit AND

Greptile P1 (filter-fragility): `where.update(vis)` was semantically
correct today, but dict-merging by key meant any future visibility
filter that grew a new top-level "OR" would silently clobber the
existing key filter.

Compose explicitly instead:

    where = {"AND": [key_filter, vis]}

Applied to both `list_memory` and `_find_memory_for_caller`. When
either side is empty (admin has no visibility filter; list has no
key filter), skip the wrapper and use the non-empty side directly
to keep the generated SQL clean.

Test fake's `_matches` now understands top-level `AND` too.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* refactor(ui/memory): wrap write helpers with react-query useMutation

Previously the Memory view read via `useQuery` but called the raw
create/update/delete fetch helpers directly in handlers, tracking
loading state with a local `submitting` flag and invalidating state
via `refetch()`. That mixes two concerns:

- it skips react-query's mutation state (isPending / isError / isSuccess)
- `refetch()` only retouches the currently-mounted query instance, not
  other cached pages, so navigating back to an older page could show
  stale rows

Switch the three write paths to `useMutation`:

- `createMutation`, `updateMutation`, `deleteMutation` — each owns
  the mutation fn, success toast, and error toast.
- Success handlers invalidate the whole `["memoryList", ...]` prefix
  via `queryClient.invalidateQueries`, so every cached page refetches
  (pagination + filter-aware).
- Refresh button now invalidates instead of `refetch()`, keeping all
  behavior consistent.
- handleSave/handleDelete become thin adapters that call `.mutateAsync`;
  their errors are swallowed locally since the mutation's onError has
  already surfaced the toast.

Also tightened the edit modal's key-field tooltip to reflect the
actual global-unique semantics (was "Unique per user/team scope").

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(memory): close cross-user write gap + sanitize 500 errors (Veria)

Addresses two Veria findings:

**High — cross-user memory tampering via team membership.** The
visibility filter uses an OR (`user_id == caller OR team_id == caller`)
so team members can SEE each other's team-scoped rows. That's
intentional for list/get. But because PUT/DELETE used the same filter
to find the target row, any team member could overwrite or delete a
teammate's *personal* row whenever both `user_id` and `team_id` were
stamped on it — broader visibility was being silently treated as
broader authority.

New `_assert_write_access(row, caller)` enforces ownership for
mutations. Non-admin rules:

- The row's `user_id` must match the caller (personal ownership), OR
- The row has no `user_id` and its `team_id` matches the caller's
  team (a "pure team row" intended for shared writes).

Admins bypass the check. The same gate runs in PUT (both regular
and post-race-recovery branches) and DELETE.

**Medium — DB internals leaked through 500 detail.** Every `except`
block was raising `HTTPException(500, detail=str(e))`, which surfaces
Prisma error strings (table/column names, host:port, error class
names) to API callers. New `_internal_error()` helper logs the real
exception server-side and returns a generic, caller-safe `detail`.
Applied to create, list, upsert (general fallthrough), and delete.

Also tightened the race-recovery 409 message to drop the "in a
different scope" wording — the caller never needs to know whose
scope it lives in.

Tests (+5):
- teammate cannot overwrite personal row → 403
- teammate cannot delete personal row → 403
- teammate CAN modify pure team row (no user_id stamped) → 200
- admin bypasses write-auth → 200
- 500 response never echoes Prisma internals (table/host/class names)

25/25 unit tests pass.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(memory): require team admin to modify pure team rows

Tightens the write-authorization rule for "pure team rows" (rows with
no user_id stamped, only team_id) to match the pattern used by
team-management endpoints (`_is_user_team_admin` + `_is_user_org_admin_for_team`):

- Plain team members can READ team rows via the OR visibility filter
  (intentional, unchanged).
- Only PROXY_ADMIN, team admins of the row's team_id, or org admins
  for the team's organization may MODIFY them. Plain members get 403.

`_assert_write_access` is now async and takes the prisma_client so it
can fetch the team and run the existing `_is_user_team_admin` /
`_is_user_org_admin_for_team` helpers from
`litellm.proxy.management_endpoints.common_utils`. The org-admin path
is best-effort: it calls `get_user_object`, which depends on the
proxy_server module being initialized, so any exception there is
treated as "not an org admin" rather than crashing the request.

Tests:
- team admin can modify pure team row → 200
- plain team member cannot modify pure team row → 403
- plain team member cannot delete pure team row → 403

Updates the test fake to add a tiny `litellm_teamtable.find_unique`
implementation and a `_make_team(team_id, admin_user_ids=[...])`
helper.

27/27 unit tests pass.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix: mypy + UI page-metadata sync for memory page

Two CI failures:

1. mypy: `_find_memory_for_caller` had `key_filter` inferred as
   `dict[str, str]` (literal type) and the conditional `{"AND": [key_filter, vis]}`
   returned `dict[str, list[...]]`, so the join site failed
   `dict-item` typing. Annotate both intermediates as `dict` so mypy
   widens the value type.

2. UI test (`page_utils.test.ts > should have descriptions for all
   pages`): every leftnav entry must have a description in
   `page_metadata.ts`, and `memory` was missing. Added a one-line
   description, matching the style of neighboring entries.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* [Feat] Day-0 support for GPT-5.5 and GPT-5.5 Pro (#26449)

* feat(openai): day-0 support for GPT-5.5 and GPT-5.5 Pro

Add pricing + capability entries for the new GPT-5.5 family launched by
OpenAI on 2026-04-24:

- gpt-5.5 / gpt-5.5-2026-04-23 (chat): $5/$30/$0.50 per 1M
  input/output/cached input
- gpt-5.5-pro / gpt-5.5-pro-2026-04-23 (responses-only): $60/$360/$6
  per 1M input/output/cached input

Other fees (long-context >272k, flex, batches, priority, cache
discounts) follow the same ratios as GPT-5.4, with context window
retained at 1.05M input / 128K output.

No transformation / classifier code changes are required:
OpenAIGPT5Config.is_model_gpt_5_4_plus_model() already matches 5.5+ via
numeric version parsing, and model registration is driven from the
JSON. The existing responses-API bridge for tools + reasoning_effort
(litellm/main.py:970) already covers gpt-5.5-pro.

Tests:
- GPT5_MODELS regression list now covers gpt-5.5-pro and dated variants
- New test_generic_cost_per_token_gpt55_pro cost-calc test
- Updated test_generic_cost_per_token_gpt55 for long-context fields

* fix(openai): mirror reasoning_effort flags onto gpt-5.5 dated variants

gpt-5.5-2026-04-23 and gpt-5.5-pro-2026-04-23 were missing the
supports_none_reasoning_effort, supports_xhigh_reasoning_effort, and
supports_minimal_reasoning_effort flags that their non-dated
counterparts define. Reasoning-effort routing in OpenAIGPT5Config is
fully capability-driven from these JSON flags — since an absent flag
is treated as False for opt-in levels (xhigh), users pinning to a
dated snapshot would silently lose xhigh support and diverge from the
base alias on logprobs + flexible temperature handling.

Copy the flags onto both dated variants so every dated snapshot
inherits the base model's reasoning-effort capability profile.

Adds a parametrized regression test that asserts
supports_{none,minimal,xhigh}_reasoning_effort parity between each
dated variant and its non-dated counterpart, preventing future drift
when new snapshots are added.

* fix(schema): close LiteLLM_MemoryTable model brace dropped during merge

The rebase against `litellm_internal_staging` (which added
`LiteLLM_AdaptiveRouterState` / `LiteLLM_AdaptiveRouterSession`) left
the closing brace of `LiteLLM_MemoryTable` missing in all three
schema copies — the next model declaration ended up parsed as a field
of the memory table, surfacing as the CI prisma error:

    error: This line is not a valid field or attribute definition.
      -->  schema.prisma:1250
       |
    1249 | // Per-(router, request_type, model) Beta posterior for the adaptive router.
    1250 | model LiteLLM_AdaptiveRouterState {

Add the missing `}` (and the standard blank line) after the memory
table's `@@index([team_id])` in `schema.prisma`,
`litellm/proxy/schema.prisma`, and
`litellm-proxy-extras/litellm_proxy_extras/schema.prisma`.

`prisma generate --schema litellm/proxy/schema.prisma` now runs clean;
27/27 memory unit tests pass.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
2026-04-24 18:38:07 -07:00
shin-berri
ca443a957c
Merge pull request #24374 from BerriAI/litellm_staging_03_22_2026
Litellm staging 03 22 2026
2026-04-24 12:38:47 -07:00
yuneng-jiang
d73b790cae
Merge pull request #26248 from BerriAI/litellm_anthropic_messages_call_type_fix
fix(proxy): preserve anthropic_messages call type for /v1/messages logging
2026-04-24 09:42:36 -07:00
Yuneng Jiang
55ea431c05
Merge remote-tracking branch 'origin/litellm_internal_staging' into litellm_gpt54_mini_nano_versioned_models 2026-04-24 09:28:54 -07:00
Sameer Kankute
e1466be825
feat(pricing): gemini-embedding-2 GA cost map, blog, and test (#26391)
* feat(pricing): gemini-embedding-2 GA cost map, blog, and test

- Add model_prices entries for gemini-embedding-2 (Gemini + Vertex paths)
- Add docs blog gemini_embedding_2_ga with LiteLLM proxy curl examples
- Add test_gemini_embedding_2_ga_in_cost_map in test_utils

Made-with: Cursor

* Fix greptile reviews
2026-04-24 09:28:18 -07:00
Cesar Garcia
8bd58fb82d
Merge branch 'litellm_internal_staging' into litellm_staging_03_22_2026 2026-04-24 13:12:19 -03:00
Mateo Wang
3950f5ea72
feat: add gpt-5.5 to model cost map (#26345)
* feat: add gpt-5.5 to model cost map

Add gpt-5.5 entry with pricing from OpenAI flagship page:
input $5/1M, cached input $0.50/1M, output $30/1M, 272K context.

* test: add gpt-5.5 coverage for model cost map and gpt-5 routing

- Add gpt-5.5 to GPT5_MODELS parametrized list so both OpenAIGPT5Config
  and AzureOpenAIGPT5Config routing tests cover the new model.
- Add test_generic_cost_per_token_gpt55 verifying the new entry's
  cost-map values ($5/$0.50/$30 per 1M) and that generic_cost_per_token
  returns the expected prompt/completion costs.
2026-04-23 14:05:22 -07:00
Sameer Kankute
d5449f5b1a
Merge pull request #26300 from BerriAI/litellm_oss_staging_04_22_2026
Litellm oss staging 04 22 2026
2026-04-23 18:53:58 +05:30
Zark .
fcf917df6d
Feat(dashscope): add image generation support for qwen-image-2.0 and qwen-image-2.0-pro (#25672)
* feat: add dashscope/qwen-image-2.0 and qwen-image-2.0-pro to model cost map

* feat: implement DashScope image generation transformation class

* feat: register DashScope in ProviderConfigManager for image generation

* feat: add DashScope to image generation provider routing

* feat: auto-route qwen-image /chat/completions requests to /images/generations

* test: add unit tests for DashScope image generation (22 cases)

* refactor: remove proxy-layer qwen-image auto-routing

* feat: auto-redirect image_generation models in acompletion()

* test: add acompletion auto-redirect test for image_generation models

* fix: remove unused Union import in DashScope transformation

* fix: scope acompletion redirect to dashscope and narrow exception handler

* fix: move get_str_from_messages to module-level import and forward n param to aimage_generation

* refactor: remove acompletion image_generation auto-redirect for dashscope

* test: remove acompletion auto-redirect test for dashscope image models

---------

Co-authored-by: zark.lin <zark.lin@thinkchina.com>
2026-04-22 20:03:46 -07:00
Vigilans
b42b86df7a
fix(adapter): normalize reasoning effort with graceful degradation (#26111)
* fix(model-info): include reasoning effort support fields in get_model_info

_get_model_info_helper constructs ModelInfoBase explicitly but never
reads supports_xhigh/minimal/none_reasoning_effort from the cost map
JSON. Add the three fields so get_model_info() returns them correctly.

Also add supports_minimal_reasoning_effort to the ModelInfo TypedDict
(xhigh and none were already declared, minimal was missing).

* fix(model-registry): add missing reasoning effort fields for claude 4.6/4.7

Claude Opus 4.7 supports max reasoning effort (above xhigh).
The field was present for Opus 4.6 but missing for all Opus 4.7
entries (base, dated, Bedrock, Vertex AI, Azure AI).

All Claude 4.6/4.7 models (Opus 4.6, Sonnet 4.6, Opus 4.7) support
minimal reasoning effort via adaptive thinking. Add the field to all
provider variants.

* fix(adapter): map output_config.effort to reasoning_effort (#25079)

Anthropic's adaptive thinking (thinking.type="adaptive") and
output_config.effort were silently dropped when translating to
OpenAI format, resulting in no reasoning_effort on the outgoing
request.

Adapter changes (format translation):
- adapters/transformation.py: add "adaptive" branch to
  translate_anthropic_thinking_to_reasoning_effort(); pass through
  output_config.effort as-is in _translate_thinking_to_openai();
  add "output_config" to translatable_anthropic_params
- adapters/handler.py: extract output_config from extra_kwargs into
  request_data so it reaches the translation layer
- responses_adapters/transformation.py: add "adaptive" branch and
  output_config param to translate_thinking_to_reasoning()

Handler changes (model-aware normalization):
- utils.py: add normalize_reasoning_effort_value() that uses
  get_model_info() to map "max" → "xhigh"/"high" and
  "minimal" → "minimal"/"low" based on model capabilities
- adapters/handler.py: call normalization before responses routing
- responses_adapters/handler.py: call normalization after translation

Relates to BerriAI/litellm#25079

* test(reasoning-effort): add tests for effort capability fields and normalize logic

Test coverage for:
- get_model_info returning supports_minimal/max_reasoning_effort fields
- JSON registry entries for claude 4.6/4.7 across all providers
- normalize_reasoning_effort_value degradation chains and exception fallback
- Adapter translation of adaptive thinking + output_config.effort

* fix: forward custom_llm_provider to normalize_reasoning_effort_value in responses adapter
2026-04-22 19:19:54 -07:00
Cesar Garcia
25c0aa8bfd
Merge pull request #26283 from BerriAI/litellm_internal_staging
Sync litellm_staging_03_22_2026 with litellm_internal_staging
2026-04-22 19:55:27 -03:00
Sameer Kankute
6ebbfe5190
fix(anthropic): allow output_config effort max for Opus 4.7 and model map
- Validate max effort like xhigh: Opus 4.6/4.7 id patterns or supports_max_reasoning_effort
- Set supports_max_reasoning_effort on claude-opus-4-7 entries in model cost JSON
- Update tests and add test_max_effort_accepted_for_opus_47

Made-with: Cursor
2026-04-22 22:06:07 +05:30
ishaan-berri
0e42d4cb08
April 21st Ishaan Branch (#26213)
* fix(otel): preserve Splunk Observability Cloud trace OTLP endpoint (#26183)

* fix(otel): preserve Splunk Observability Cloud trace OTLP URL

Splunk ingest uses /v2/trace/otlp; _normalize_otel_endpoint must not append /v1/traces.

- Return trace endpoints unchanged when they match Splunk OTLP path patterns
- Add unit tests for observability.splunkcloud.com, signalfx.com, and /trace/otlp suffix
- Set OTEL_EXPORTER_OTLP_PROTOCOL in protocol selection tests (from_env precedence over OTEL_EXPORTER)

Made-with: Cursor

* test(otel): use parameterized.expand for Splunk OTLP URL cases

Made-with: Cursor

* fix(otel): narrow Splunk trace URL guard to /v2/trace/otlp only

Made-with: Cursor

* test(otel): cover OTEL_EXPORTER fallback when OTLP protocol env unset

Made-with: Cursor

* Add Openrouter Opus 4.7 Entry (#26130)

---------

Co-authored-by: milan-berri <milan@berri.ai>
Co-authored-by: Matt Greathouse <matt5316@gmail.com>
2026-04-21 20:18:56 -07:00
ishaan-berri
e6897f5510
add moonshot/kimi-k2.6 to model registry (#26203)
* add moonshot/kimi-k2.6 to model registry

* add moonshot/kimi-k2.6 to backup model registry

* add tests for moonshot/kimi-k2.6 model registry

* fix moonshot/kimi-k2.6 pricing and add reasoning support

* fix moonshot/kimi-k2.6 pricing and add reasoning support in backup

* update kimi-k2.6 tests: fix pricing, add tool_choice and reasoning checks

* fix: load kimi-k2.6 registry tests from local backup instead of remote cost map
2026-04-21 19:58:43 -07:00
ishaan-berri
a302613eb5
feat(bedrock): add support for bedrock-mantle endpoint (Claude Mythos Preview) (#26196)
* add anthropic.claude-mythos-preview to model_prices_and_context_window.json

* add mantle route to bedrock common_utils: route detection, chat config, messages config dispatch

* add AmazonMantleConfig for bedrock/mantle /chat/completions endpoint

* add AmazonMantleMessagesConfig for bedrock/mantle /messages endpoint

* register AmazonMantleMessagesConfig in __init__.py and lazy imports registry

* add unit tests for bedrock mantle route and config dispatch

* add e2e tests for bedrock mantle: URL, body, SigV4 header, region routing
2026-04-21 15:41:58 -07:00
Michael-RZ-Berri
4f823cedac
Add supported providers to prompt caching doc (#26124)
* Add supported providers to prompt caching doc

* Move Z.ai / GLM to cache_control marker list

* Mark xAI models as supporting prompt caching

* Narrow xAI prompt caching flag to models with documented cache pricing

* Add prompt caching flag to grok-4, grok-4-0709, grok-4-latest

---------

Co-authored-by: Michael Riad Zaky <michaelr@Michaels-MacBook-Air.local>
2026-04-20 15:25:21 -07:00
Sameer Kankute
d5cfdcc6ee
feat(models): add versioned GPT-5.4 mini and nano aliases
Add dated snapshot entries for GPT-5.4 mini and nano (including Azure-prefixed aliases) so users can pin to the 2026-03-17 model versions.
2026-04-20 21:02:28 +05:30
Sameer Kankute
57eae8d01c
Merge branch 'litellm_internal_staging' into litellm_staging_03_22_2026
Some checks failed
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2026-04-20 19:56:00 +05:30
Sameer Kankute
3ef362289b
Add support for grok-4.20-0309-reasoning model 2026-04-17 08:49:59 +05:30
ishaan-berri
44c992416c
Merge pull request #25867 from BerriAI/litellm_day_0_opus_4.7_support
Litellm day 0 opus 4.7 support
2026-04-16 09:42:11 -07:00
Sameer Kankute
07d863b8e7
Remove max support for opus 4.7 2026-04-16 21:58:03 +05:30
Sameer Kankute
f94c8dda82
Fix model names 2026-04-16 21:47:58 +05:30
Sameer Kankute
0868a82c34
Add support for opus 4.7 with new effort levels 2026-04-16 20:45:45 +05:30
Tim Ren
dd4a41951f
fix(utils): allowed_openai_params must not forward unset params as None (#25777)
* feat(proxy): add NO_OPENAPI env var to disable /openapi.json endpoint (#25696)

* feat(proxy): add NO_OPENAPI env var to disable /openapi.json endpoint - Fixes #25538

* test(proxy): add tests for _get_openapi_url

---------

Co-authored-by: Progressive-engg <lov.kumari55@gmail.com>

* feat(prometheus): add api_provider label to spend metric (#25693)

* feat(prometheus): add api_provider label to spend metric

Add `api_provider` to `litellm_spend_metric` labels so users can
build Grafana dashboards that break down spend by cloud provider
(e.g. bedrock, anthropic, openai, azure, vertex_ai).

The `api_provider` label already exists in UserAPIKeyLabelValues and
is populated from `standard_logging_payload["custom_llm_provider"]`,
but was not included in the spend metric's label list.

* add api_provider to requests metric + add test

Address review feedback:
- Add api_provider to litellm_requests_metric too (same call-site as
  spend metric, keeps label sets in sync)
- Add test_api_provider_in_spend_and_requests_metrics following the
  existing pattern in test_prometheus_labels.py

* fix: ensure `litellm_metadata` is attached to `pre_call` guardrail to align with `post_call` guardrail (#25641)

* fix: ensure `litellm_metadata` is attached to pre_call to align with post_call

* refactor: remove unused BaseTranslation._ensure_litellm_metadata

* refactor: module level imports for ensure_litellm_metadata and CodeQL

* fix: update based off of Codex comment

* revert: undo usage of `_guardrail_litellm_metadata`

* feat: add pricing entry for openrouter/google/gemini-3.1-flash-lite-preview (#25610)

* fix(bedrock): skip synthetic tool injection for json_object with no schema (#25740)

When response_format={"type": "json_object"} is sent without a JSON
schema, _create_json_tool_call_for_response_format builds a tool with an
empty schema (properties: {}). The model follows the empty schema and
returns {} instead of the actual JSON the caller asked for.

This patch:
- Skips synthetic json_tool_call injection when no schema is provided.
  The model already returns JSON when the prompt asks for it.
- Fixes finish_reason: after _filter_json_mode_tools strips all
  synthetic tool calls, finish_reason stays "tool_calls" instead of
  "stop". Callers (like the OpenAI SDK) misinterpret this as a pending
  tool invocation.

json_schema requests with an explicit schema are unchanged.

Co-authored-by: Claude <noreply@anthropic.com>

* fix(utils): allowed_openai_params must not forward unset params as None

`_apply_openai_param_overrides` iterated `allowed_openai_params` and
unconditionally wrote `optional_params[param] = non_default_params.pop(param, None)`
for each entry. If the caller listed a param name but did not actually
send that param in the request, the pop returned `None` and `None` was
still written to `optional_params`. The openai SDK then rejected it as
a top-level kwarg:

    AsyncCompletions.create() got an unexpected keyword argument 'enable_thinking'

Reproducer (from #25697):

    allowed_openai_params = ["chat_template_kwargs", "enable_thinking"]
    body = {"chat_template_kwargs": {"enable_thinking": False}}

Here `enable_thinking` is only present nested inside
`chat_template_kwargs`, so the helper should forward
`chat_template_kwargs` and leave `enable_thinking` alone. Instead it
wrote `optional_params["enable_thinking"] = None`.

Fix: only forward a param if it was actually present in
`non_default_params`. Behavior is unchanged for the happy path (param
sent → still forwarded), and the explicit `None` leakage is gone.

Adds a regression test exercising the helper in isolation so the test
does not depend on any provider-specific `map_openai_params` plumbing.

Fixes #25697

---------

Co-authored-by: lovek629 <59618812+lovek629@users.noreply.github.com>
Co-authored-by: Progressive-engg <lov.kumari55@gmail.com>
Co-authored-by: Ori Kotek <ori.k@codium.ai>
Co-authored-by: Alexander Grattan <51346343+agrattan0820@users.noreply.github.com>
Co-authored-by: Mohana Siddhartha Chivukula <103447836+iamsiddhu3007@users.noreply.github.com>
Co-authored-by: Amiram Mizne <amiramm@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
2026-04-16 19:04:26 +05:30
Chesars
f82ba6ca6b Resolve remaining merge conflicts with upstream/main
- streaming_iterator.py: adopted main's more defensive version of the
  tool-arg queueing check (.get() instead of [], isinstance guard) —
  same logic, same behavior, lower crash surface
- model_prices_and_context_window.json + backup: combined staging's
  search_context_cost_per_query fields (PR #24372) with main's new
  supports_service_tier field — both are independent additions to the
  same Gemini model entries
- test_streaming_handler.py: kept Azure streaming regression test
  (PR #24354) and added main's two new Gemini legacy vertex
  finish_reason normalization tests
- test_gemini_batch_embeddings.py: kept staging's unsupported-params
  filtering tests (PR #24370) and added main's index/order test
2026-04-15 23:05:03 -03:00
Chesars
67e4604284 Merge upstream/main into litellm_staging_03_22_2026
Resolved conflicts:
- streaming_handler.py: combined role check (PR #24354, Azure streaming)
  with reasoning_items check (new in main) — both are independent OR
  conditions in is_chunk_non_empty()
- CI/CD: accepted main's versions throughout
  - Redis tests migrated to CircleCI (PR #25354): removed enable-redis
    from GH Actions workflows
  - E2E UI tests restructured (PR #25365): simplified CircleCI job
  - Coverage via Codecov added to all GH Actions unit test workflows
  - Deleted test-litellm-matrix.yml and test-proxy-e2e-azure-batches.yml
    (removed in main)
2026-04-15 22:54:53 -03:00
Tim
db94b4d55c
fix(cost-map): add us-south1 to vertex qwen3-235b-a22b-instruct-2507-maas (#25382) 2026-04-14 11:59:36 -07:00
Sameer Kankute
b8f7d61400
Merge pull request #25589 from BerriAI/litellm_oss_staging_04_11_2026
Litellm oss staging 04 11 2026
2026-04-14 23:34:25 +05:30
ishaan-berri
4a71583951
Merge pull request #25348 from BerriAI/litellm_gemini-veo-video-resolution-pricing2
feat(gemini): Veo Lite pricing, video resolution usage and tiered cost
2026-04-14 10:23:22 -07:00
Sameer Kankute
ee40da58a2
Merge branch 'main' into litellm_oss_staging_04_11_2026 2026-04-14 20:54:12 +05:30
Sameer Kankute
fa605d85c0
Merge pull request #25616 from BerriAI/main
merge main
2026-04-13 08:43:43 +05:30
csoni-cweave
ee06b9278a
feat(model):add wandb model offerings to include kimi-k2.5 and minimax-m2.5 (#25409) 2026-04-11 19:46:40 -07:00
Sameer Kankute
97f722f558
feat(cost): add baseten model api pricing entries (#25358)
Add Baseten Model API pricing entries for Nemotron, GLM, Kimi, GPT OSS, and DeepSeek models with validated model slugs. Include a focused regression test to assert provider and per-token pricing values.

Made-with: Cursor
2026-04-08 21:39:58 -07:00
Krrish Dholakia
f42ffed2bd
Litellm oss staging 04 02 2026 p1 (#25055)
* fix(vertex_ai): support pluggable (executable) credential_source for WIF auth (#24700)

The WIF credential dispatch in load_auth() only handled identity_pool and
aws credential types. When credential_source.executable was present (used
for Azure Managed Identity via Workload Identity Federation), it fell
through to identity_pool.Credentials which rejected it with MalformedError.

Add dispatch to google.auth.pluggable.Credentials for executable-type
credential sources, following the same pattern as the existing identity_pool
and aws helpers.

Fixes authentication for Azure Container Apps → GCP Vertex AI via WIF
with executable credential sources.

* feat(logging): add component and logger fields to JSON logs for 3rd p… (#24447)

* feat(logging): add component and logger fields to JSON logs for 3rd party filtering

* Let user-supplied extra fields win over auto-generated component/logger, tighten test assertions

* Feat - Add organization into the metrics metadata for org_id & org_alias (#24440)

* Add org_id and org_alias label names to Prometheus metric definitions

* Add user_api_key_org_alias to StandardLoggingUserAPIKeyMetadata

* Populate user_api_key_org_alias in pre-call metadata

* Pass org_id and org_alias into per-request Prometheus metric labels

* Add test for org labels on per-request Prometheus metrics

* chore: resolve test mockdata

* Address review: populate org_alias from DB view, add feature flag, use .get() for org metadata

* Add org labels to failure path and verify flag behavior in test

* Fix test: build flag-off enum_values without org fields

* Gate org labels behind feature flag in get_labels() instead of static metric lists

* Scope org label injection to metrics that carry team context, remove orphaned budget label defs, add test teardown

* Use explicit metric allowlist for org label injection instead of team heuristic

* Fix duplicate org label guard, move _org_label_metrics to class constant

* Reset custom_prometheus_metadata_labels after duplicate label assertion

* fix: emit org labels by default, remove flag, fix missing org_alias in all metadata paths

* fix: emit org labels by default, no opt-in flag required

* fix: write org_alias to metadata unconditionally in proxy_server.py

* fix: 429s from batch creation being converted to 500 (#24703)

* add us gov models (#24660)

* add us gov models

* added max tokens

* Litellm dev 04 02 2026 p1 (#25052)

* fix: replace hardcoded url

* fix: Anthropic web search cost not tracked for Chat Completions

The ModelResponse branch in response_object_includes_web_search_call()
only checked url_citation annotations and prompt_tokens_details, missing
Anthropic's server_tool_use.web_search_requests field. This caused
_handle_web_search_cost() to never fire for Anthropic Claude models.

Also routes vertex_ai/claude-* models to the Anthropic cost calculator
instead of the Gemini one, since Claude on Vertex uses the same
server_tool_use billing structure as the direct Anthropic API.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

* fix(anthropic): pass logging_obj to client.post for litellm_overhead_time_ms (#24071)

When LITELLM_DETAILED_TIMING=true, litellm_overhead_time_ms was null for
Anthropic because the handler did not pass logging_obj to client.post(),
so track_llm_api_timing could not set llm_api_duration_ms. Pass
logging_obj=logging_obj at all four post() call sites (make_call,
make_sync_call, acompletion, completion). Add test to ensure make_call
passes logging_obj to client.post.

Made-with: Cursor

* sap - add additional parameters for grounding

- additional parameter for grounding added for the sap provider

* sap - fix models

* (sap) add filtering, masking, translation SAP GEN AI Hub modules

* (sap) add tests and docs for new SAP modules

* (sap) add support of multiple modules config

* (sap) code refactoring

* (sap) rename file

* test(): add safeguard tests

* (sap) update tests

* (sap) update docs, solve merge conflict in transformation.py

* (sap) linter fix

* (sap) Align embedding request transformation with current API

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) mock commit

* (sap) run black formater

* (sap) add literals to models, add negative tests, fix test for tool transformation

* (sap) fix formating

* (sap) fix models

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) commit for rerun bot review

* (sap) minor improve

* (sap) fix after bot review

* (sap) lint fix

* docs(sap): update documentation

* fix(sap): change creds priority

* fix(sap): change creds priority

* fix(sap): fix sap creds unit test

* fix(sap): linter fix

* fix(sap): linter fix

* linter fix

* (sap) update logic of fetching creds, add additional tests

* (sap) clean up code

* (sap) fix after review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) add a possibility to put the service key by both variants

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) update test

* (sap) update service key resolve function

* (sap) run black formater

* (sap) fix validate credentials, add negative tests for credential fetching

* (sap) fix validate credentials, add negative tests for credential fetching

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) fix after bot review

* (sap) lint fix

* (sap) lint fix

* feat: support service_tier in gemini

* chore: add a service_tier field mapping from openai to gemini

* fix: use x-gemini-service-tier header in response

* docs: add service_tier to gemini docs

* chore: add defaut/standard mapping, and some tests

* chore: tidying up some case insensitivity

* chore: remove unnecessary guard

* fix: remove redundant test file

* fix: handle 'auto' case-insensitively

* fix: return service_tier on final steamed chunk

* chore: black

* feat: enable supports_service_tier to gemini models

* Fix get_standard_logging_metadata tests

* Fix test_get_model_info_bedrock_models

* Fix test_get_model_info_bedrock_models

* Fix remaining tests

* Fix mypy issues

* Fix tests

* Fix merge conflicts

* Fix code qa

* Fix code qa

* Fix code qa

* Fix greptile review

---------

Co-authored-by: michelligabriele <gabriele.michelli@icloud.com>
Co-authored-by: Josh <36064836+J-Byron@users.noreply.github.com>
Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: milan-berri <milan@berri.ai>
Co-authored-by: Alperen Kömürcü <alperen.koemuercue@sap.com>
Co-authored-by: Vasilisa Parshikova <vasilisa.parshikova@sap.com>
Co-authored-by: Lin Xu <lin.xu03@sap.com>
Co-authored-by: Mark McDonald <macd@google.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
2026-04-08 21:37:10 -07:00
Austin Varga
541e81de2f
fix: expose reasoning effort fields in get_model_info + add together_ai/gpt-oss-120b (#25263)
* fix: expose reasoning effort fields in get_model_info and add together_ai/gpt-oss-120b

- litellm/utils.py: pass supports_none_reasoning_effort and
  supports_xhigh_reasoning_effort through _get_model_info_helper so
  get_model_info() returns them (previously silently dropped). Fixes #25096.

- model_prices_and_context_window.json: add together_ai/openai/gpt-oss-120b
  with supports_reasoning: true so reasoning_effort is accepted for this
  model without requiring drop_params. Fixes #25132.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix: consolidate duplicate together_ai/openai/gpt-oss-120b entry and sync backup file

* fix: link commit to GitHub account for CLA verification

---------

Co-authored-by: Austin Varga <austin@knowmi.ai>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-08 21:34:03 -07:00
Sameer Kankute
c68a19b883
feat(gemini): Veo Lite pricing, size→resolution, usage video_resolution for cost tiers
Made-with: Cursor
2026-04-08 19:50:50 +05:30
Shivam Rawat
2bb7387a83
Litellm aws gov cloud mode support (#25254)
* add us gov models

* added max tokens

* greptile fix

---------

Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com>
2026-04-07 08:49:17 -07:00
ishaan-berri
b53cfe729a
Litellm ishaan march30 (#24887) (#25151)
* fix(pricing): add unversioned vertex_ai/claude-haiku-4-5 entry

Missing unversioned entry causes cost tracking to return $0.00 for
all requests using vertex_ai/claude-haiku-4-5. All other Vertex AI
Claude models have both versioned and unversioned entries.

* fix(router): skip misleading tags error when no candidates (e.g. cooldown)

Return early from get_deployments_for_tag when healthy_deployments is empty so
tag-based routing does not raise no_deployments_with_tag_routing after cooldown
filters all deployments. Adds regression test.

Made-with: Cursor

* feat(oci): add embedding support and update model catalog

- Add OCIEmbeddingConfig for OCI GenAI embedding models
- Add 16 new chat models (Cohere, Meta Llama, xAI Grok, Google Gemini)
- Add 8 embedding models (Cohere embed v3.0, v4.0)
- Update documentation with embedding examples
- Update pricing for all new models



* test(oci): add unit tests for OCI embedding support

- 17 unit tests covering OCIEmbeddingConfig
- Tests for URL generation, param mapping, request/response transform
- Tests for model pricing JSON completeness



* style(oci): format with black and ruff

* fix(oci): correct embedding request body format

OCI embedText API expects inputs, truncate, and inputType at the
top level of the request body, not nested under embedTextDetails.
Fixed transformation and updated tests accordingly.

Verified with real OCI API: 3/3 embedding models working.

* docs: clarify tag routing early return and test intent

Made-with: Cursor

* fix(oci): address code review findings from Greptile

- P1: Fix signing URL mismatch with custom api_base by accepting
  api_base parameter in transform_embedding_request
- P2: Remove encoding_format from supported params (OCI does not
  support it, was silently dropped)
- P2: Raise ValueError for token-array inputs instead of silently
  converting to string representation
- Add test for token-list rejection

* fix(mcp): add STS AssumeRole support for MCP SigV4 authentication

MCPSigV4Auth only supported static AWS credentials or the boto3 default
credential chain. Production Kubernetes environments typically authenticate
via IAM role assumption (sts:AssumeRole), which was not possible.

Add aws_role_name and aws_session_name parameters to the MCP SigV4 auth
stack. When aws_role_name is provided, MCPSigV4Auth calls sts:AssumeRole
to obtain temporary credentials before signing requests. Explicit keys,
if also provided, are used as the source identity for the STS call;
otherwise ambient credentials (pod role, instance profile) are used.

* fix: stop logging credential values and add missing redaction patterns

Replaces raw credential values in debug/error log messages with
boolean presence checks or type names. Adds PEM block, GCP token,
JWT, SAS token, and service-account blob patterns to the redaction
filter. Fixes private_key pattern to capture full PEM blocks instead
of stopping at the first whitespace.

Addresses: Vertex AI credential JSON (including RSA private key)
being logged to stderr on health check failures.

* fix: log only field names for UserAPIKeyAuth, not full object

* style: apply black formatting to experimental_mcp_client/client.py

* style: fix black/isort formatting and mypy error in proxy_server.py

- Fix black formatting in experimental_mcp_client/client.py (done in prev commit)
- Fix black/isort formatting in key_management_endpoints.py, proxy_server.py, transformation.py
- Fix mypy: iterate over optional list safely (access_group_ids or []) in proxy_server.py

* fix(test): patch check_migration.verbose_logger directly to fix xdist ordering issue

When test_proxy_cli.py tests run before test_check_migration.py in the same
xdist worker, litellm.proxy.db.check_migration is already in sys.modules.
Patching litellm._logging.verbose_logger has no effect on the already-bound
reference. Patch the correct target (check_migration.verbose_logger) and
import the module before patching so the order doesn't matter.

* fix(mypy): make api_base Optional in PydanticAIProviderConfig to match base class signature

---------

Co-authored-by: Ihsan Soydemir <soydemir.ihsan@gmail.com>
Co-authored-by: Milan <milan@berri.ai>
Co-authored-by: Daniel Gandolfi <danielgandolfi@gmail.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: michelligabriele <gabriele.michelli@icloud.com>
Co-authored-by: user <70670632+stuxf@users.noreply.github.com>
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
2026-04-04 14:44:07 -07:00
ishaan-berri
c6aa3ea452
Litellm ishaan april1 try2 (#25110)
* Litellm ishaan april1 (#25103)

* fix(proxy): enforce upperbound key params on key/update and add custom_key_update hook

The /key/update endpoint did not enforce upperbound_key_generate_params,
allowing users to bypass configured limits (tpm_limit, rpm_limit,
max_budget, duration, budget_duration) by updating an existing key
instead of generating a new one.

Extract the upperbound enforcement logic from _common_key_generation_helper()
into a standalone _enforce_upperbound_key_params() function and call it from
both the generate and update paths. For updates, None values are skipped
(not filled with defaults) since they mean "don't change this field".

Also adds a custom_key_update config option and user_custom_key_update global,
mirroring the existing custom_key_generate pattern, so custom key validation
logic can fire during key updates as well.

* fix(proxy): invoke custom_key_update hook in bulk update path

The user_custom_key_update hook was only called in update_key_fn
(single key update) but not in _process_single_key_update (bulk
update path), allowing custom validation to be bypassed via the
/key/update/bulk endpoint. Mirror the hook invocation in both paths.

* fix(proxy): pass UpdateKeyRequest to hook in bulk path, not BulkUpdateKeyRequestItem

Move the custom_key_update hook invocation to after UpdateKeyRequest
is constructed so the hook receives the same type in both single and
bulk update paths. Previously the bulk path passed
BulkUpdateKeyRequestItem (5 fields only), which would cause
AttributeError for hooks accessing fields like tpm_limit or models.

* fix(bedrock): promote cache usage to message_delta for Claude Code (#24850)

Ensure Bedrock/Anthropic-compatible streaming exposes cache usage where Claude Code reads it by promoting message_stop usage onto message_delta and preserving usage fields in fake-streamed message_delta events.

Made-with: Cursor

* fix(search): Support self-hosted Firecrawl response format in search transform (#24866)

The `transform_search_response` method only handled Firecrawl Cloud (v2)
response format where `data` is a dict with `web`/`news` keys. Self-hosted
Firecrawl (v1) returns `data` as a flat list of result objects, causing an
`AttributeError: 'list' object has no attribute 'get'`.

Detect the response format by checking if `data` is a list (self-hosted)
or dict (cloud) and handle both cases.

Cloud format:  {"data": {"web": [...], "news": [...]}}
Self-hosted:   {"success": true, "data": [{"url": "...", "title": "...", ...}]}

Co-authored-by: Synergy <synergyoclaw@gmail.com>

* feat: add environment and user tracking to prompt management (#24855)

* feat: add environment and user tracking to prompt management

- Add environment (development/staging/production) and created_by columns to LiteLLM_PromptTable
- Update unique constraint to [prompt_id, version, environment]
- All CRUD endpoints support environment filtering and user tracking
- Redesigned prompt detail page with environment tabs and version history
- UI: environment filter on list page, environment selector in editor
- 8 new tests for environment and user tracking

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: Black formatting and add environments to PromptInfoResponse TypeScript type

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: address Greptile review findings

- P1: delete_prompt scopes in-memory cleanup to environment when provided
- P2: dotprompt_content parsed directly regardless of environment flag
- P2: use distinct for environments query
- P2: fix double-fetch on initial mount in prompt_info.tsx
- fix: remove unsupported select kwarg from find_many

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: address remaining Greptile review comments

- Remove unused useCallback import (index.tsx)
- Remove unused ENV_COLORS variable (prompt_info.tsx)
- P1: in-memory fallback in get_prompt_versions now respects environment filter
- P1: reset selectedEnv when promptId changes to avoid stale state
- Cyclic imports are pre-existing pattern, not introduced by this PR

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: scope patch_prompt to environment using primary key

- Add environment query param to patch_prompt endpoint
- Look up target row by composite key (prompt_id + version + environment)
- Update by primary key (id) to target exactly one row
- Fixes Greptile finding: patch with multiple environments no longer ambiguous

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: use actual start_time for failed request spend logs (#24906)

async_post_call_failure_hook set both start_time and end_time to
datetime.now(), making all failed requests show duration=0. Use the
actual start_time from litellm_logging_obj instead, so spend logs
reflect the real request duration on timeout and other failures.

Fixes #24888

* feat(bedrock): add nova canvas image edit support (#24869)

* feat(bedrock): add nova canvas image edit support

* fix(bedrock): support PathLike inputs for nova image edit

* chore: sync schema.prisma copies from root

* fix(mypy): correct type-ignore code for delta_usage arg-type

* fix(mypy): cast status_code to str, suppress intentional str yield

* fix(lint): extract _create_content_block_chunks to fix PLR0915

* fix(lint): extract helpers to fix PLR0915 in prompt endpoints

---------

Co-authored-by: michelligabriele <gabriele.michelli@icloud.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
Co-authored-by: redhelix <amin.lalji@gmail.com>
Co-authored-by: Synergy <synergyoclaw@gmail.com>
Co-authored-by: Talha Anwar <37379131+talhaanwarch@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: madhu19991 <madhu@thunkai.com>
Co-authored-by: Srikanth @adobe <devarakondasrikanth@users.noreply.github.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

* fix(test): update model armor streaming test to handle string or int error code

---------

Co-authored-by: michelligabriele <gabriele.michelli@icloud.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
Co-authored-by: redhelix <amin.lalji@gmail.com>
Co-authored-by: Synergy <synergyoclaw@gmail.com>
Co-authored-by: Talha Anwar <37379131+talhaanwarch@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: madhu19991 <madhu@thunkai.com>
Co-authored-by: Srikanth @adobe <devarakondasrikanth@users.noreply.github.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
2026-04-03 14:57:44 -07:00
mubashir1osmani
d4a3a5e530
fix gpt-5.4 pricing (#24748) 2026-04-02 21:51:21 -07:00
Marty Sullivan
52a596d2a4
Bedrock Model Updates 2026-03-26 (#24645)
* add new bedrock models & remove duplicate vertexai entry

* adding non-regional entry for minimax-2.5
2026-04-02 21:26:04 -07:00
David Chen
b7ccc5b691
[Test Fix] fix gov pricing tests (#25022)
* fix pricing tests

* fix mypy

* fix cost expectation since us based model is used now.

* fix test get model info
2026-04-02 15:55:55 -07:00