CheckBatchCost poller updated the status column but not the file_object
JSON column. The list_batches endpoint reads status from file_object,
so batches appeared stuck in "validating" even after Azure reported
them as completed. Now update file_object alongside status in the
per-job DB write.
Function calls not supported with reasoning_effort != 'none' on gpt-5.4.
Drop reasoning_effort when tools are in the request (small minority of volume).
Made-with: Cursor
The OpenAI chat completion API expects reasoning_effort as a string
('none', 'low', 'medium', 'high', 'xhigh'). Config/deployments may pass
the Responses API format: {'effort': 'high', 'summary': 'detailed'}.
Fix BadRequestError when model config uses dict format by extracting
the 'effort' value before passing to the API.
Made-with: Cursor
When forward_llm_provider_auth_headers=true, Authorization: Bearer <litellm-key> was
being forwarded to Anthropic if it looked like an OAuth key, causing auth failures.
Now checked against authenticated_with_header: if Authorization was used to authenticate
with the proxy, it is always stripped before forwarding to the LLM provider.
Made-with: Cursor
- Add forward_llm_provider_auth_headers support from litellm_settings
- When enabled, client x-api-key takes precedence over deployment keys
- Forward x-api-key when x-litellm-api-key or Authorization used for auth
- Fix duplicate patch lines in test_byok_oauth_endpoints.py
- Add Claude Code BYOK documentation with /login and ANTHROPIC_CUSTOM_HEADERS
- Add unit tests for clean_headers x-api-key forwarding logic
- Sync model_prices backup (pre-commit hook)
Made-with: Cursor
- Pass request_model to Azure AI cost calculator to detect router requests
- Add router flat cost ($0.14/M input tokens) even when Azure returns actual model in response
- Add test for router flat cost with response containing actual model
- Update docs with cost calculation flow and configuration requirements
Made-with: Cursor
- Shift from hardcoded model checks to dynamic lookup via _supports_factory
- Add supports_none_reasoning_effort for gpt-5.1/5.2/5.4 chat variants
- Add supports_xhigh_reasoning_effort for gpt-5.1-codex-max, gpt-5.2, gpt-5.4+
- Update model_prices_and_context_window.json and backup
- Add ProviderSpecificModelInfo types for new fields
- Fix Azure: use _supports_reasoning_effort_level instead of removed is_model_gpt_5_1_model
Made-with: Cursor
Support passing duration=null on /key/update to reset a key's expiry to never expires, alongside the existing "-1" magic string (kept for backward compat).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The /team/daily/activity endpoint used Prisma pagination (page_size=1000)
but the UI only fetched page 1. Teams with many keys/models easily exceed
1000 rows in LiteLLM_DailyTeamSpend, causing truncated totals.
Switches the endpoint to use SQL GROUP BY via get_daily_activity_aggregated
with include_entity_breakdown=True, returning all data in a single response
while preserving per-team breakdown. Also adds timezone parameter support.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When the messages or response JSON fields in spend logs are truncated
before being written to the database, the truncation marker now includes
a note explaining:
- This is a DB storage safeguard
- Full, untruncated data is still sent to logging callbacks (OTEL, Datadog, etc.)
- The MAX_STRING_LENGTH_PROMPT_IN_DB env var can be used to increase the limit
Also emits a verbose_proxy_logger.info message when truncation occurs in
the request body or response spend log paths.
Adds 3 new tests:
- test_truncation_includes_db_safeguard_note
- test_response_truncation_logs_info_message
- test_request_body_truncation_logs_info_message
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Add CRUD endpoints for managing Hashicorp Vault configuration via the
proxy admin API, with background sync, env var management, and
connection testing. Fix pre-existing bug where premium check ran after
global state mutation, and guard DELETE against clearing non-Vault
secret managers.
Previously, model_dump(exclude_none=True) included all bool fields (since
False != None), causing a partial PATCH to overwrite every other setting to
its default. Fix uses exclude_unset=True and reads the existing DB record
before merging, giving proper PATCH semantics.
This was a pre-existing bug but is fixed here since we're touching this code.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- rbac_utils.py: change feature_name from str to Literal["agents", "vector_stores"]
so typos are caught by type checkers at import time
- proxy_setting_endpoints.py: extract _RUNTIME_GENERAL_SETTINGS_FLAGS as a module-level
constant, replacing duplicated inline lists in get_ui_settings and update_ui_settings
- test_vector_store_rbac.py: remove try/except pattern that silently swallowed non-403
HTTPExceptions; tests now let any unexpected exception propagate as a test failure
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- rbac_utils.py: remove duplicated _check_if_team_admin/_is_user_team_admin_for_any_team;
delegate to _user_has_admin_privileges from management_endpoints/common_utils with the
shared user_api_key_cache (fixes no-op DualCache and missing org admin coverage)
- test_rbac_utils.py: update patch target to match new delegation path
- SidebarProvider.tsx: pass allowAgentsForTeamAdmins and allowVectorStoresForTeamAdmins
props to Sidebar
- leftnav.tsx: add useTeams hook + isTeamAdmin memo; exempt team admins from sidebar
filtering when allow_*_for_team_admins is enabled (fixes frontend/backend inconsistency)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Claude Code v2.1.69+ sends `custom: {defer_loading: true}` on tool
definitions. Anthropic's API accepts this field, but Bedrock rejects it
with "Extra inputs are not permitted", causing ~90% of requests to fail.
Strip the `custom` field from each tool in the request body before
sending to Bedrock, in both the Messages API and Chat API invoke paths.
Fixes#22847
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
* fix(proxy): readiness check returns 200 when database is unreachable
_db_health_readiness_check() catches health_check() exceptions but
never updates db_health_cache to "disconnected" and never re-raises.
The caller health_readiness() always returns 200 with "db": "connected"
hardcoded, regardless of actual DB state.
In Kubernetes, this means pods with dead database connections stay in
the Service endpoints and continue receiving traffic they cannot serve.
Changes:
- Set db_health_cache to "disconnected" and re-raise the exception on
health_check failure so health_readiness() returns 503
- Use actual db_health_status["status"] in the response instead of
hardcoding "db": "connected"
- Reduce cache TTL from 2 minutes to 15 seconds. The 2-minute window
is too wide for readiness probes (typically 10-15s intervals) and
means a pod can report healthy for up to 2 minutes after the DB dies
- Only serve cached results when status is "connected". The previous
condition (status != "unknown") would also cache "disconnected" for
2 minutes, delaying recovery detection after a DB comes back
* fix(proxy): add DB connection self-healing to readiness check
When the Prisma query engine's internal TCP connection pool holds dead
connections (caused by network blips, Cloud SQL proxy restarts, or
node-level issues), health_check() fails with httpx.ConnectError.
The engine never recovers on its own because nothing triggers a
disconnect/connect cycle to restart the subprocess with fresh
connections.
This leaves pods permanently failing readiness checks until they are
manually restarted, even after the underlying DB becomes reachable
again.
Add a reconnect attempt to _db_health_readiness_check() when
health_check() fails:
1. disconnect() - kills the query engine subprocess and closes all
connections (has built-in backoff retry: 3 tries, 10s max)
2. connect() - starts a new engine with fresh TCP connections (has
built-in backoff retry: 3 tries, 10s max)
3. health_check() - verifies the new connection works (has built-in
backoff retry: 3 tries, 10s max)
If reconnect succeeds, the pod immediately returns to service (200).
If it fails, the original exception is re-raised (503). Reconnect
attempts are rate-limited by probe frequency (~10-15s), so a
permanently unreachable DB gets one attempt per cycle with no retry
loops.
This uses the same disconnect/connect mechanism that
PrismaWrapper.recreate_prisma_client() uses for IAM token refresh,
and aligns with the community-documented pattern for Prisma connection
recovery in long-running processes (prisma/prisma#24718, #27024).
* Add poetry lock and modify test_health_endpoints
* Address allow_requests_on_db_unavailable regression
* Address comments
* resolve greptile issue
* Restore accidentally deleted UI HTML files
These were removed in an earlier commit but still exist on main.
Restoring to keep the PR diff clean.
* Guard reconnect with is_database_transport_error
Only attempt disconnect/connect/health_check cycle for transport-level
failures (unreachable DB, dropped connection). Data-layer errors like
UniqueViolationError indicate the DB is reachable, so reconnecting
would be pointless churn.
* Address greptile's comments
* Fix module alias after rebase and add adversarial test coverage
- Unify module alias to _health_endpoints_module after rebase conflict
- Add test for non-transport error with flag on (exercises is_database_transport_error guard)
- Add test for disconnect() failure during reconnect cycle
- Split non-transport error test into flag-off (re-raises) and flag-on (skips reconnect) variants
* Remove stale UI HTML files reintroduced during rebase
* fix: don't close HTTP/SDK clients on LLMClientCache eviction
Removing the _remove_key override that eagerly called aclose()/close()
on evicted clients. Evicted clients may still be held by in-flight
streaming requests; closing them causes:
RuntimeError: Cannot send a request, as the client has been closed.
This is a regression from commit fb72979432. Clients that are no longer
referenced will be garbage-collected naturally. Explicit shutdown cleanup
happens via close_litellm_async_clients().
Fixes production crashes after the 1-hour cache TTL expires.
* test: update LLMClientCache unit tests for no-close-on-eviction behavior
Flip the assertions: evicted clients must NOT be closed. Replace
test_remove_key_closes_async_client → test_remove_key_does_not_close_async_client
and equivalents for sync/eviction paths.
Add test_remove_key_removes_plain_values for non-client cache entries.
Remove test_background_tasks_cleaned_up_after_completion (no more _background_tasks).
Remove test_remove_key_no_event_loop variant that depended on old behavior.
* test: add e2e tests for OpenAI SDK client surviving cache eviction
Add two new e2e tests using real AsyncOpenAI clients:
- test_evicted_openai_sdk_client_stays_usable: verifies size-based eviction
doesn't close the client
- test_ttl_expired_openai_sdk_client_stays_usable: verifies TTL expiry
eviction doesn't close the client
Both tests sleep after eviction so any create_task()-based close would
have time to run, making the regression detectable.
Also expand the module docstring to explain why the sleep is required.
* docs(AGENTS.md): add rule — never close HTTP/SDK clients on cache eviction
* docs(CLAUDE.md): add HTTP client cache safety guideline
* Include user_email in new user creation within get_user_object
Enhance the get_user_object function to include user_email in the parameters when creating a new user. This change is accompanied by a new test to verify that user_email is correctly included during the upsert process.
* Improve error handling in test_get_user_object by logging exceptions
Updated the test_get_user_object_upsert_includes_user_email function to log exceptions when they occur, enhancing the visibility of potential issues during testing. This change helps in diagnosing failures related to the mock LiteLLM_UserTable.
* fix(passthrough): raise_for_status in _async_streaming to propagate Azure 429s
* address greptile review feedback (greploop iteration 1)
Guard data/json args when content is provided to avoid httpx ValueError
* address greptile review feedback (greploop iteration 2)
Use bare raise to preserve original traceback in _async_streaming exception handler
* address greptile review feedback (greploop iteration 3)
Close httpx streaming response on error to prevent connection pool exhaustion
* address greptile review feedback (greploop iteration 4)
Guard aclose() call to prevent masking original exception; add explicit test for content param forwarding
* address greptile review feedback (greploop iteration 5)
Pass content to sign_request so AWS body-hash signing is correct when content is the sole body source
* revert sign_request content change - request_data expects dict, not bytes
Bedrock's sign_request calls json.dumps(request_data) — passing content bytes
would TypeError. sign_request should only receive data/json (dict), not raw bytes.
- Update translate_thinking_for_model (3rd code path) to inject
summary="detailed" by default, consistent with the other two paths
- Add disable_default_reasoning_summary flag check via shared helper
- Add tests for flag enabled/disabled and user-provided summary
- Document disable_default_reasoning_summary in reasoning_content.md
- Extract duplicated summary_disabled evaluation from handler.py and
transformation.py into a shared is_default_reasoning_summary_disabled()
helper in utils.py to prevent future divergence.
- Add test_summary_excluded_when_env_var_set to handler test class to
close env-var test coverage gap flagged by Greptile.
Add `litellm.disable_default_reasoning_summary` flag (default False) and
env var `LITELLM_DISABLE_DEFAULT_REASONING_SUMMARY` to allow users to
opt out of the automatic `summary="detailed"` injection when routing
Anthropic thinking requests to OpenAI's Responses API.
Default behavior is preserved (summary="detailed" is always added),
but users who don't want to pay for summary tokens can now disable it.
https://claude.ai/code/session_01VJU9EwVvgvmeCe3Yu1aULa
For full-replace PUT semantics, always include static_headers and extra_headers
in update_data, defaulting to {} and [] when not supplied. Previously,
omitting these fields left stale DB values intact (e.g. auth headers).
Made-with: Cursor
gemini/gemini-live-2.5-flash-preview-native-audio-09-2025 uses mode='realtime'
but the schema in test_aaamodel_prices_and_context_window_json_is_valid did
not include 'realtime' as a valid enum value, causing a ValidationError.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Vertex AI does not support the output_config parameter in its API.
This parameter is being added by Anthropic/Gemini transformations but needs
to be removed before sending requests to Vertex AI endpoints.
This fix addresses the "Extra inputs are not permitted" error (issue #22312)
when using Claude models with structured outputs on Vertex AI.
Changes:
- Drop output_config in Gemini model transformation
- Drop output_config in Anthropic partner model transformation
- Drop output_config in Anthropic experimental pass-through transformation
- Add comprehensive tests to verify output_config is dropped
Fixes: #22312
Made-with: Cursor
