* fix(mcp): forward extra_headers for OpenAPI MCP tools
OpenAPI-generated tools only applied static closure headers and BYOK
Authorization via ContextVar. Copy MCPServer.extra_headers from the
incoming MCP request into _request_extra_headers (set in server.py before
local tool dispatch), merge in openapi_to_mcp_generator via a small helper.
OAuth2 M2M: do not forward caller Authorization from raw_headers (same rule
as _prepare_mcp_server_headers for managed MCP).
Adds TestRequestExtraHeaders and clarifies mcp_server_manager registration
comment.
Fixes#26794
Co-authored-by: Cursor <cursoragent@cursor.com>
* refactor(mcp): access has_client_credentials on MCPServer directly
Greptile: getattr default was redundant; property exists on MCPServer and
mcp_server is non-None inside the extra_headers forwarding block.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
The previous detection treated any model with input_cost_per_image
or output_cost_per_image as image generation. Several chat and
embedding models carry those fields to price multimodal vision input,
not generated images:
- gemini-3.1-pro-preview (mode=chat) has output_cost_per_image=0.00012
alongside input/output token pricing.
- azure/gpt-realtime-* (mode=chat) has input_cost_per_image=5e-6.
- amazon.titan-embed-image-v1 (mode=embedding) has
input_cost_per_image=6e-5.
For these models the image-gen branch fired first and reserved a
fraction of a cent per request, short-circuiting the token-priced
path entirely. Long Gemini chats reserved 1 × $0.00012 instead of
the true token cost.
Gate strictly on mode in {"image_generation", "image_edit"}. All 197
real image_generation entries and all 31 image_edit entries
(Flux Kontext, Stability inpaint/outpaint, etc.) carry the right mode,
so the field-presence fallback was unnecessary.
Adds regression tests for the chat-model-with-image-cost-field case
and for image_edit reservation.
Image-generation routes (dall-e-3, flux, etc.) have no per-token output
cost so they fell through to the no-reservation read-time-only path.
Concurrent image requests against a depleted budget could all pass
common_checks (counter exactly at max_budget passes the strict-`>`
gate) and reach the provider before reconciliation caught up.
Add per-image reservation in _estimate_request_max_cost_for_model:
when the model has a per-image cost field, reserve `n × cost_per_image`
upfront. The atomic counter increment serializes concurrent admissions,
so the second request sees the post-first-reservation counter and
raises BudgetExceededError instead of silently leaking through.
Both `output_cost_per_image` and `input_cost_per_image` are honored —
naming is inconsistent across providers (OpenAI dall-e-3 uses
input_cost_per_image, aiml/dall-e-3 uses output_cost_per_image for
the same per-generated-image price).
Per-pixel pricing (DALL-E 2 size variants) and TTS/STT routes still
fall through to read-time enforcement; those are follow-ups.
- Introduce RoutingPrismaWrapper that transparently routes read operations (find_*, count, group_by, query_raw, query_first) to a reader endpoint while writes remain on the writer, enabling Aurora-style reader/writer endpoint splits
- Add IAMEndpoint dataclass and parse_iam_endpoint_from_url() to capture static connection fields from a reader URL so only the IAM token needs to rotate, avoiding the need for separate DATABASE_HOST_READ_REPLICA/etc. env vars
- Enhance PrismaWrapper with per-instance knobs (db_url_env_var, iam_endpoint, recreate_uses_datasource, log_prefix) so writer and reader wrappers are independent: the reader writes its fresh URL to DATABASE_URL_READ_REPLICA and passes datasource override to Prisma since Prisma only auto-reads DATABASE_URL
- Fix deadlock in PrismaWrapper.__getattr__: when called from inside a running event loop, schedule the token refresh as a background task instead of blocking with run_coroutine_threadsafe + future.result(), which would deadlock the loop thread waiting for a coroutine that needs the loop to run
- Fix botocore crash when DATABASE_PORT is unset by defaulting to "5432" in both proxy_cli.py and PrismaWrapper.get_rds_iam_token(); passing None caused botocore to embed the literal string "None" in the presigned URL
- Implement graceful reader degradation: reader connect/recreate failures are non-fatal; wrapper sets _reader_unavailable=True and silently routes reads to the writer to keep the proxy serving traffic during transient reader outages
- Add PrismaClient.writer_db property so the reconnect smoke-test always validates the writer engine specifically; query_raw on the routing wrapper would route to the reader and not verify the newly-recreated writer
- Expose DATABASE_URL_READ_REPLICA in Helm chart (values.yaml + deployment.yaml) via both plain value and secret key reference, and document the field in docker-compose.yml
- Add 887-line test suite covering routing logic, IAM token refresh paths, reader degradation scenarios, datasource override behavior, and the deadlock regression
Co-authored-by: Yassin Kortam <yassinkortam@g.ucla.edu>
reserve_budget_for_request fell back to reserving the entire remaining
team/key/user headroom whenever a request omitted max_tokens, which
pinned the spend counter at max_budget for the duration of the
in-flight request and false-positive-blocked every concurrent or
back-to-back request until the success callback reconciled. Surfaced
as an integration-test team being budget-blocked at its $2000 cap
while DB spend was $0.144.
Switch the missing-max_tokens path to a fixed default of 16384 output
tokens (mirrors parallel_request_limiter_v3's DEFAULT_MAX_TOKENS_ESTIMATE
precedent), and clamp explicit max_tokens at the model's
max_output_tokens for reservation accounting only. The outbound request
body is unchanged, so providers see whatever the caller actually sent;
only the local integer used to compute reservation cost is bounded.
This also prevents a hostile max_tokens=999999999 from inflating one
request's reservation up to the entire team headroom.
For Opus 4.7 (output $25/M, max_output 128K) on a $2000 budget the
worst-case per-request reservation drops from "everything left" to
$3.20, raising admittable concurrency from 1 to ~625.
Operators upgrading past 35bbca60b0 (which made /metrics auth
default-on) see "Malformed API Key passed in. Ensure Key has 'Bearer '
prefix." with no hint that
litellm_settings.require_auth_for_metrics_endpoint: false restores the
previous unauthenticated behavior. Append that discovery hint to the
existing 401 body so a Prometheus scraper that breaks after upgrade
has a clear migration path. No behavior change.
* feat(sso): show full IdP claims in /sso/debug/callback
The debug callback only displayed the proxy-parsed OpenID summary, so
customers couldn't verify what custom claims (team_id, team_alias, roles,
etc.) the IdP was actually returning. Render two new sections — Raw
Claims (userinfo) and Access Token Claims (decoded JWT) — alongside the
existing parsed view. Strip bearer tokens defense-in-depth in case a
non-conforming IdP places them in its userinfo response.
Resolves LIT-2838
* Update litellm/proxy/management_endpoints/ui_sso.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix(sso): hoist json.dumps out of f-string for py3.10 ruff
---------
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Mirrors the system-message skip in PR #25481 for tool-role messages.
Adds a global litellm.skip_tool_message_in_guardrail flag and a
per-guardrail litellm_params.skip_tool_message_in_guardrail override,
applied in the OpenAI and Anthropic chat translation handlers.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(auth): pass team_id in member-level model access check
_check_team_member_model_access calls _can_object_call_model without
team_id, so access groups defined via model_info.access_groups cannot
resolve for team-scoped DB models (their internal router name is
model_name_<team>_<uuid>, not the public name). The team-level check
already passes team_id; this mirrors that.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* test(auth): add tests for member-level access group resolution with team_id
Eight tests covering _can_object_call_model and
_check_team_member_model_access with team-scoped DB models:
- access group resolves when team_id is passed
- access group fails without team_id (pre-fix behavior)
- literal model name still works with team_id (no regression)
- denied model still denied with team_id
- second model in group also reachable
- end-to-end member access via access group (mocked membership)
- end-to-end member denied for model not in allowed list
- no-override member inherits team-level check
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* proxy: hot-reload config YAML when --reload is set
Uvicorn's --reload only watches *.py by default, so editing the
--config YAML did not restart the proxy. _get_reload_options() now
extends reload_dirs/reload_includes with the config file's directory
and basename when --config is provided.
* proxy: qualify reload_includes with absolute config path
Address Greptile review on PR #27274. When the --config file lives
outside cwd, reload_includes previously stored only the basename, which
meant uvicorn/watchfiles would also reload on edits to any same-named
file inside cwd. Use the absolute config path as the include pattern in
that case so only the actual proxy config triggers a restart.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* fix(proxy): use basename for reload_includes config pattern
Uvicorn's resolve_reload_patterns() calls pathlib.Path.glob(), which
raises NotImplementedError on absolute patterns (uvicorn discussion
2156). Passing config_abs (an absolute path) when the config file lived
outside cwd crashed startup under --reload. The config_dir is already
added to reload_dirs, so using just the basename as the include pattern
is sufficient to match the specific config file.
* fix: make it reload app when yaml changes
* style: remove unneeded comments
---------
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>
* Include model name + configured TPM/RPM in priority rate-limit 429 errors (#27215)
* Include model name + configured TPM/RPM in priority rate-limit 429 errors
The current 429 message ('Priority-based rate limit exceeded. Priority: prod,
Rate limit type: tokens, Remaining: -664145, Model saturation: 86.3%') doesn't
tell the operator which model was hit or what the configured limit is, so they
can't tell whether the priority allocation needs tuning or the model TPM is
just too small.
Add Model, Model TPM, and Model RPM to both the priority-based 429 and the
sibling Model-capacity 429 in dynamic_rate_limiter_v3._check_rate_limits.
Pure error-message change — no behavior or schema impact.
* test: assert priority 429 includes model name + configured TPM/RPM
Adds a regression test for the new fields in the priority-based 429 detail
('Model:', 'Model TPM:', 'Model RPM:'). Verified locally that the test
fails against the unpatched dynamic_rate_limiter_v3.py and passes after
the patch.
---------
Co-authored-by: shin-watcher <ext-agent-shin@berri.ai>
* Update litellm/proxy/hooks/dynamic_rate_limiter_v3.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* Update litellm/proxy/hooks/dynamic_rate_limiter_v3.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---------
Co-authored-by: shin-watcher <ext-agent-shin@berri.ai>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Several tests parametrized over (model, api_key, ...) tuples or raw
token strings, causing pytest to embed those values in the test ID
and print them in CI logs. Refactored each affected test to keep the
same coverage without putting key material into parametrize.
- audio_tests/test_audio_speech.py: split env-var keys into separate
azure/openai test functions sharing a helper; sync_mode parametrize
preserved.
- audio_tests/test_whisper.py: split into openai_whisper /
azure_whisper functions sharing a helper; response_format parametrize
preserved.
- local_testing/test_embedding.py: single-case parametrize inlined.
- proxy_unit_tests/test_user_api_key_auth.py: 5 header parametrize
cases split into 5 named tests sharing an _assert helper.
- proxy_unit_tests/test_proxy_utils.py: 4 api_key_value cases split
into 4 named tests.
- test_litellm/proxy/auth/test_user_api_key_auth.py: 5 key-prefix
cases (Bearer / Basic / lowercase bearer / raw / AWS SigV4) split
into 5 named tests.
Verified: black clean; 14 refactored unit tests pass; pytest collects
audio/embedding tests with safe IDs (no key material in test IDs).
* fix(anthropic, mcp): sanitize tool names to match Anthropic's `^[a-zA-Z0-9_-]{1,128}$`
Tool names with characters like `/` or `.` (commonly produced by the
OpenAPI -> MCP generator from `operationId`s such as
`actions/download-job-logs-for-workflow-run`) caused Anthropic to reject
requests with `tools.N.custom.name: String should match pattern
'^[a-zA-Z0-9_-]{1,128}$'`.
Two layers of fix:
1. Anthropic transformation: build a per-request forward map (original ->
sanitized, disambiguated by suffix on collisions) and a reverse map
(only for names actually rewritten). Forward map is applied to tool
defs, `tool_choice`, and historical assistant tool_calls in messages.
Reverse map is threaded through both the non-streaming and streaming
response paths so callers continue to see their original tool names
in `tool_use` blocks.
2. OpenAPI -> MCP generator: sanitize `operationId` (and the
method+path fallback) at registration time so generated MCP tools are
valid for any strict-name provider, not just Anthropic. The dashboard
preview endpoint applies the same sanitization for parity.
Includes unit tests covering: collision disambiguation between
`foo_bar` and `foo/bar` in the same request, reverse-map only firing
for actually-rewritten names, message rewrite for historical tool_calls,
streaming chunk_parser reverse-mapping, and sanitization of OpenAPI
operationIds plus the preview endpoint output.
Made-with: Cursor
* fix(anthropic): build tool-name maps in transform_request, not optional_params
The previous patch stashed the per-request forward and reverse tool-name
maps under ``optional_params["_anthropic_tool_name_forward_map"]`` and
``optional_params["_anthropic_tool_name_map"]``. ``optional_params`` is
the dict that becomes the JSON body via ``data = {**optional_params}``,
so those internal keys leaked over the wire and Anthropic 400'd with:
_anthropic_tool_name_forward_map: Extra inputs are not permitted
Worse, this meant *every* request whose tool list contained any name with
an invalid character (the exact case the patch was meant to fix) regressed
into a confusing meta-error pointing at LiteLLM's internal map instead of
the offending tool.
Fix: move all tool-name sanitization into ``transform_request``, which is
the single chokepoint already shared by ``AnthropicConfig``,
``AmazonAnthropicConfig`` (Bedrock invoke), ``VertexAIAnthropicConfig``,
and ``AzureAnthropicConfig`` (all call ``super().transform_request`` /
``AnthropicConfig.transform_request(self, ...)``). New static helper
``_sanitize_tool_names_in_request`` walks the already-Anthropic-shaped
``optional_params["tools"]`` (only ``type=="custom"`` entries -- hosted
tool names are reserved by Anthropic and must not be touched), builds
the per-request forward/reverse maps, and applies the forward map in
place to ``tools[*].name`` and ``tool_choice.name``. The reverse map is
stashed exclusively on ``litellm_params`` (which is never serialized to
a provider) under ``_anthropic_tool_name_map`` for the response paths
to consume.
Side effect of this restructure: ``map_openai_params`` is now a pure
OpenAI->Anthropic param translator with no side-channel state, which
matches its contract everywhere else in the codebase.
Tests: replaced the now-incorrect "stashes maps in optional_params"
tests with regressions that assert no underscore-prefixed keys appear
in either ``optional_params`` after ``map_openai_params`` or in the
final ``transform_request`` body. Added end-to-end coverage for:
sanitization in ``transform_request``, ``tool_choice`` rewriting,
historical ``tool_calls`` rewriting in messages, and hosted-tool
passthrough.
Made-with: Cursor
* fix(anthropic): always sanitize empty text content blocks
Anthropic 400s on `{"role": "user", "content": ""}` with:
"messages: text content blocks must be non-empty"
LiteLLM already had `_sanitize_empty_text_content` to rewrite empty text
to a placeholder, but it was gated behind `litellm.modify_params=True`.
With that flag off (default), empty content from upstream agent
frameworks (e.g. pydantic-ai) flowed straight through and tripped the
Anthropic validator.
Fix:
- Always run `_sanitize_empty_text_content` at the top of
`anthropic_messages_pt`, independent of `modify_params`. There is no
way to "pass through" an empty text block, so this is non-optional.
The richer tool-call sanitizations (Cases A/B/D, which actually
mutate conversation structure) remain gated on `modify_params`.
- Extend `_sanitize_empty_text_content` to also handle list-of-blocks
content (`[{"type": "text", "text": ""}]`), not just string content.
Adds 3 regression tests covering string content, list-of-blocks
content, and the no-op case (non-empty messages with modify_params off).
Made-with: Cursor
* fix(anthropic): drop dead tool-name forward-map params, fix mypy + caller-mutation
- remove unused `name_forward_map` param from `_map_tool_choice`,
`_map_tool_helper`, `_map_tools` and the `_apply_anthropic_tool_name_forward`
helper. Production sanitization runs in `_sanitize_tool_names_in_request`
at `transform_request`; these params were never threaded through.
- handler.py: use `ANTHROPIC_TOOL_NAME_REVERSE_MAP_KEY` constant instead of
the hardcoded `"_anthropic_tool_name_map"` string.
- fix mypy `"object" has no attribute "__iter__"` in
`_rewrite_tool_names_in_messages` by guarding `tool_calls` with
`isinstance(..., list)`.
- `_sanitize_tool_names_in_request`: build a new tools list with copy-on-
change entries (and copy `tool_choice` on rewrite) so a caller reusing
the same tool list/dicts across requests doesn't see its inputs
permanently rewritten.
- doc-comment `_build_request_tool_name_maps` clarifying it operates on
OpenAI-format tools (vs `_sanitize_tool_names_in_request` which runs
on Anthropic-format tools post-`_map_tools`).
- tests: drop 3 tests pinning the now-removed param paths; add coverage
for tool_calls + None function_call rewrite and caller-dict immutability.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(mcp): inherit stored credentials in test/tools/list for edit flow
When editing an existing MCP server, the Tool Configuration preview
calls POST /mcp-rest/test/tools/list with server_id but no credentials
(management API redacts them). The endpoint now calls
_inherit_credentials_from_existing_server() so stored bearer tokens
and OAuth2 M2M credentials are loaded from global_mcp_server_manager
automatically — tools load without re-entering credentials.
New servers (no server_id) and requests with explicit credentials are
unaffected (function is a no-op in both cases).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(mcp): show all tools in edit panel, not just allowed tools
Edit flow was passing externalTools (from GET /tools/list, filtered by
allowed_tools) to MCPToolConfiguration, disabling the internal hook.
Remove the external props so the internal hook fires via
POST /test/tools/list, which returns all tools unfiltered. Combined
with the credential inheritance fix, tools load automatically without
re-entering credentials and all tools are visible for re-configuration.
existingAllowedTools still pre-checks previously allowed tools.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Fix order-dependent collision in _build_anthropic_tool_name_maps
Use a two-pass approach: first pre-register all already-valid tool names
in the 'used' set, then sanitize/disambiguate names that need rewriting.
This ensures valid names always have priority regardless of input order,
preventing duplicate tool names on the wire when e.g. 'foo/bar' appears
before 'foo_bar' in the tool list.
Add regression test for the reversed ordering case.
* Fix OpenAPI tool name collision: disambiguate sanitized names with numeric suffixes
sanitize_openapi_tool_name replaces all invalid chars with '_', but when
two operationIds differ only by sanitized characters (e.g. 'foo/list' and
'foo.list' both become 'foo_list'), the second registration silently
overwrites the first in the tool registry.
Add collision disambiguation in register_tools_from_openapi that appends
_2, _3, ... suffixes when a sanitized name is already taken, mirroring
the existing logic in _build_anthropic_tool_name_maps.
* Fix preview endpoint missing collision disambiguation for tool names
Add used_names tracking and _2/_3 suffix disambiguation to
_preview_openapi_tools, matching the logic in register_tools_from_openapi.
Without this, two operationIds that sanitize to the same string (e.g.
'foo/list' and 'foo.list' both becoming 'foo_list') would show duplicate
names in the preview while registration would disambiguate them.
* Align preview HTTP method order with register_tools_from_openapi
The preview endpoint and register_tools_from_openapi both use
order-dependent collision disambiguation (_2, _3 suffixes). When the
iteration order differs, two operations on the same path with sanitized
names that collide get different suffixes in preview vs registration,
so the dashboard shows names that don't match what actually got
registered.
Also adds a regression test that fails on the swapped order.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* Skip duplicate originals in _build_anthropic_tool_name_maps
If the same invalid tool name appeared twice in original_names (e.g.
['foo/bar', 'foo/bar']), the second occurrence overwrote the forward
map entry with a freshly-suffixed name (foo_bar_2), leaving foo_bar
orphaned in 'used' with no reverse mapping. _sanitize_tool_names_in_request
then rewrote both tool entries to foo_bar_2, and Anthropic 400'd on
duplicate tool names.
Skip the rewrite if forward already has the original mapped.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
- 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.
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.
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>
