- Fix case-insensitive tool name matching in _tool_name_matches() so that
OpenAPI operationIds (camelCase) match lowercase registered tool names
when filtering by allowed_tools
- Fix get_base_url() to resolve relative server URLs (e.g. /api/v3) by
deriving full base URL from spec_path when OpenAPI spec has relative URLs
- Add tests for case-insensitive matching and filter_tools_by_allowed_tools
Made-with: Cursor
`all_models = user_api_key_dict.models` was creating an alias, so
`_get_models_from_access_groups` (which uses `.pop()`/`.extend()`) would
mutate the cached object in-place. Now both `.models` and `.team_models`
assignments create copies via `list()`.
Added test to verify the input is not mutated.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds dedup to get_key_models and get_team_models to prevent duplicate
entries when access group member models overlap with proxy_model_list.
Removes dead assignment of all_models in get_team_models.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When a team has "all-proxy-models", the model list expansion now includes
model access group names so they appear in the UI key creation form.
Also fixes get_key_models not forwarding include_model_access_groups to
_get_models_from_access_groups, and removes unused _unfurl_all_proxy_models.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The SageMaker embedding handler was not using _load_credentials(),
which meant aws_role_name and aws_session_name parameters were
ignored. This prevented cross-account role assumption for embeddings
while it worked for completions.
Changes:
- Replace direct boto3 client creation with _load_credentials() call
- Create boto3.Session with assumed credentials
- Add comprehensive unit tests for role assumption
This aligns the embedding handler behavior with the completion handler,
which already supports role assumption via the BaseAWSLLM.get_credentials()
method.
Fixes cross-account SageMaker embedding access where users need to
assume a role in another account to invoke endpoints.
Any param in DEFAULT_CHAT_COMPLETION_PARAM_VALUES that arrives via
completion(**kwargs) is now automatically forwarded to
get_optional_params(), even if it's not a named parameter of
completion().
Previously, get_non_default_completion_params() excluded params in
OPENAI_CHAT_COMPLETION_PARAMS (assuming they'd be forwarded via the
named-param path), while optional_param_args only contained explicitly
named params. Params like 'store' that were in the known-params list
but not named params fell through both paths and were silently dropped.
The fix adds a 7-line loop after building optional_param_args that
forwards any kwargs present in DEFAULT_CHAT_COMPLETION_PARAM_VALUES.
This means new OpenAI params only need to be added to the constants
dict — no boilerplate changes to 3+ function signatures required.
Fixes#23087
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
* feat(charity_engine): add Charity Engine provider
Charity Engine is a crowdsourced distributed computing platform that
donates processing power to charitable causes. Its inference API
provides OpenAI-compatible chat, completions, and embeddings endpoints.
* test(charity_engine): add provider config and resolution tests
Verify JSONProviderRegistry config, provider list membership,
model routing for charity_engine/<model>, and Router compatibility.
* feat(charity_engine): add Charity Engine to LlmProviders enum
Enables provider_list membership and LlmProviders.CHARITY_ENGINE
resolution required by the provider and test suite.
* fix(charity_engine): remove api_base_env to fix non-deterministic test
The CHARITY_ENGINE_API_BASE env var could override the base_url in CI,
causing test_charity_engine_provider_resolution to fail intermittently.
* fix(charity_engine): remove trailing slash from base_url
Claude's API returns assistant messages with empty text blocks
({"type": "text", "text": ""}) alongside tool_use blocks during
multi-turn tool-use conversations. These blocks are rejected when
sent back to the API with "text content blocks must be non-empty".
Sanitization already exists for other code paths (/v1/chat/completions
for both Anthropic and Bedrock), but NOT for the /v1/messages native
path. This adds the same treatment by stripping empty text blocks
from messages in async_anthropic_messages_handler before they are
forwarded to the provider.
Fixes#22930
_get_openai_compatible_provider_info already returns an api_base
ending in /v1, but get_models prepended another /v1, producing
.../inference/v1/v1/accounts/... which 404s.
Strip the trailing /v1 from api_base before re-adding it so that
both the default and any user-supplied base work correctly.
Add parametrized tests covering the default URL, trailing-slash,
custom base with /v1, and custom base without /v1.
Fixes#23106
The bug occurred when user data inadvertently contained reserved Python
keywords like 'self', 'params', or '__class__' as keys. When such a dict
was unpacked via **kwargs to LiteLLM_Params() or GenericLiteLLMParams(),
Python raised TypeError because 'self' was passed both implicitly and
as a keyword argument.
The fix:
- Add a Pydantic model_validator(mode='before') to GenericLiteLLMParams
that filters out reserved keys ('self', 'params', '__class__') before
validation
- Move the max_retries str-to-int conversion into the same validator
- Remove the custom __init__ methods from both GenericLiteLLMParams and
LiteLLM_Params, since the validator now handles the preprocessing
- Clean up unused VERTEX_CREDENTIALS_TYPES import
This fix applies to all classes that inherit from GenericLiteLLMParams,
including LiteLLM_Params and updateLiteLLMParams.
Added comprehensive tests in tests/test_litellm/test_litellm_params_reserved_keys.py
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Add input_fidelity parameter ("high"/"low") to the image edit pipeline,
allowing users to control how much effort the model exerts to match
input image style and features. Fixes#22813.
The tag daily activity endpoint used compute_tag_metadata_totals which
deduplicates by request_id, but LiteLLM_DailyTagSpend stores aggregated
daily records where request_id is either NULL or stale. This caused all
metadata totals (total_spend, total_requests, etc.) to be 0 in the UI
cost panel. Now uses the same standard totals as every other entity type.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The test was flaky under pytest-xdist parallel execution because it used
async acompletion (which runs completion() in a thread pool via
run_in_executor) and relied on shared global state (known_tokenizer_config,
iam_token_cache, module_level_client) that could be modified by other tests
running in parallel. Failures were silently swallowed by a broad try/except,
causing mock_post.call_count to remain 0.
Fix:
- Convert from async acompletion to sync completion, matching every other
test in the file. The test's intent is verifying prompt transformation,
not async behavior.
- Use monkeypatch.setitem for known_tokenizer_config to ensure proper
teardown isolation.
- Remove unnecessary mock layers (async template fetchers, iam_token_cache
pre-population, mock completion response) that were only needed for the
async code path.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The gemini-3.1-flash-image-preview model introduced a new pricing field
that was missing from the test's validation schema and cost_fields list.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(agentcore): handle JSON responses from agents using sync return
BedrockAgentCoreApp agents that use synchronous `return` (instead of
async `yield`) respond with Content-Type: application/json instead of
text/event-stream. The streaming parser only handles SSE format, silently
discarding the JSON body and returning empty content to the client.
This adds Content-Type detection in both sync and async streaming
wrappers — when application/json is received, the response is parsed
and converted to a single-chunk stream. Also extends _parse_json_response
with a fallback chain supporting multiple agent response schemas (standard
AgentCore, Strands framework, plain string, raw JSON fallback).
* fix(agentcore): add dict-type guard to _parse_json_response
Prevent AttributeError when json.loads() returns a non-dict
(e.g. JSON array or primitive) by adding an isinstance check
at the top of _parse_json_response. Non-dict values fall back
to raw JSON string content.
* fix(agentcore): handle malformed JSON and split streaming chunks
- Wrap json.loads() in try/except in both sync and async streaming
wrappers so malformed JSON bodies raise a structured BedrockError
instead of a raw JSONDecodeError
- Split the JSON-fallback streaming path into two chunks (content
chunk with finish_reason=None, then stop sentinel with empty delta)
to match the SSE path convention
* fix(agentcore): catch IO errors in streaming JSON path + async error test
- Broaden except clause to catch both json.JSONDecodeError and IO-level
exceptions (httpx.ReadError, etc.) from response.read()/aread(), so
all failures surface as structured BedrockError
- Add async malformed-JSON test to mirror the sync test coverage
Audit logs (CRUD events on keys, teams, users, models) were only stored in
the Prisma DB. This adds a pluggable callback system so audit logs can be
forwarded to external services like S3 for ingestion into security monitoring
tools.
New config key `audit_log_callbacks` under `litellm_settings` reuses the
existing callback infrastructure. Any CustomLogger subclass can opt in by
overriding `async_log_audit_log_event()`. S3Logger (s3_v2) is implemented
as the first handler, storing audit logs under `audit_logs/{date}/` prefix.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat(proxy): add Prisma DB pool and engine health metrics to Prometheus
Add a PrismaMetricsCollector that periodically queries pg_stat_activity
and the Prisma engine process to expose connection pool and engine health
as Prometheus gauges/counters. Auto-enabled when prometheus_system is in
service_callback.
New metrics:
- litellm_db_pool_active_connections (Gauge)
- litellm_db_pool_idle_connections (Gauge)
- litellm_db_pool_total_connections (Gauge)
- litellm_db_pool_waiting_connections (Gauge)
- litellm_db_engine_up (Gauge)
- litellm_db_engine_restarts_total (Counter)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: address Greptile review feedback
- Only increment engine_restarts counter on heavy reconnects (engine
actually dead), not lightweight network-blip reconnects
- Fix potential KeyError in _get_or_create_gauge/counter fallback path
when REGISTRY._names_to_collectors is absent
- Rename litellm_db_pool_waiting_connections to
litellm_db_pool_lock_waiting_connections to clarify it measures lock
contention, not pool slot queuing
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: warn when prometheus_system enabled but watchdog disabled
Log a warning when users have prometheus_system in service_callback
but PRISMA_HEALTH_WATCHDOG_ENABLED=false, since DB pool and engine
metrics won't be collected in that configuration.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* ci: retrigger CI checks
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: use labeled gauge for DB pool connection metrics
Replace 3 separate pool gauges (active, idle, total) with a single
`litellm_db_pool_connections` gauge using a `state` label. This is more
Prometheus-idiomatic and exposes all pg_stat_activity states (active,
idle, idle in transaction, etc.) without ambiguity about what "total"
includes.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: address Greptile review — stale labels and fallback re-registration
- Zero out known pg_stat_activity states that are absent from the current
query result, preventing stale gauge values from persisting.
- Simplify _get_or_create_gauge/counter by removing the fallback loop
that could re-register an already-registered metric (ValueError).
- Add test for stale label clearing across collection cycles.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: include "unknown" in _PG_STATES for stale label clearing
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: collect immediately on start and consolidate into single query
- Move sleep to end of loop so metrics appear on /metrics immediately
after startup instead of after a 30s delay.
- Combine pool state and lock waiting queries into a single SQL query
using conditional aggregation, halving per-cycle DB overhead.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: prevent tight spin loop on collection error
Move asyncio.sleep outside the try/except so it always executes even
when _collect_engine_health() or _collect_pool_metrics() raises.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: add multiprocess_mode to _get_or_create_gauge initialization
- Include `multiprocess_mode` parameter to properly support multiprocessing in Gauge creation.
- Ensure consistent behavior for labeled and unlabeled Gauges.
* fix: handle invalid env var and document watchdog prerequisite
- Add try/except ValueError for PRISMA_METRICS_COLLECTION_INTERVAL_SECONDS
to prevent proxy startup crash on non-numeric values (e.g. "30s")
- Document that DB metrics require both prometheus_system callback and
PRISMA_HEALTH_WATCHDOG_ENABLED=true
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: use defensive null coalescing for query_raw row values
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* test: add invalid env var fallback test and fix mock signature
- Add test for non-numeric PRISMA_METRICS_COLLECTION_INTERVAL_SECONDS
- Add **kwargs to mock _patched_get_or_create_gauge for forward compat
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
When both enable_jwt_auth and enable_oauth2_auth are True, the proxy now
routes tokens based on their format:
- JWT tokens (3 dot-separated parts) -> JWT auth handler
- Opaque tokens -> OAuth2 auth handler
This enables using JWT for human users and OAuth2 for M2M (machine) clients
on the same LiteLLM instance. Previously, enabling OAuth2 would intercept
all tokens on LLM API routes before JWT auth could run.
When only one auth method is enabled, behavior is unchanged (backward compatible).
- Add _get_effort_level() to extract effective effort from string or dict
- Use effective_effort for xhigh validation, tool-drop, sampling, temperature guards
- Preserve dict format when it has summary/generate_summary for Responses API
- Add tests: xhigh-dict validation, none-dict for tools/sampling/temperature
- Update tests: dict-with-summary now preserved (not normalized)
Made-with: Cursor
When reasoning_effort is passed as a dict with additional fields like 'summary' or 'generate_summary', preserve the full dict format instead of normalizing it to a string. This ensures that when requests are routed to the OpenAI Responses API, all reasoning parameters are correctly included.
The normalization to string format now only happens for simple dicts with just the 'effort' key, which is appropriate for the Chat Completions API.
Fixes issue where summary field was being dropped when routing gpt-5.4+ requests with tools + reasoning to Responses API.
Made-with: Cursor
gpt-5.4-pro and gpt-5.4-pro-2026-03-05 do not support the
/v1/chat/completions endpoint — OpenAI returns a 404 with
"This is not a chat model". These models are responses-only,
like o3-pro and o1-pro.
Changes:
- Set mode from "chat" to "responses" for both model entries
- Update supported_endpoints to ["/v1/responses", "/v1/batch"]
- Add regression test for responses API bridge routing
FixesBerriAI/litellm#23014
PR #20950 added extra_body forwarding to Vertex AI Gemini. LiteLLM-internal
keys (cache, tags) were being merged into the request body, causing Vertex AI
to reject with 400: 'Unknown name "cache": Cannot find field.'
- Add _LITELLM_INTERNAL_EXTRA_BODY_KEYS frozenset (cache, tags)
- Skip these keys in _pop_and_merge_extra_body before merging
- Add regression tests for cache and tags stripping
Fixes regression from 1.79.3 → 1.81.12 when using proxy cache with
extra_body={"cache": {"use-cache": True, "ttl": 86400}}
Made-with: Cursor
Addresses Greptile review:
- dict(_input_schema) before mutation prevents cross-provider state leakage
- Test asserts original tool parameters dict is unchanged after call
Anthropic requires exactly one tool_result per tool_use. When
conversation history (e.g. from session resume/checkpoint restore)
contains duplicate tool result messages with the same tool_call_id,
the API rejects with: 'each tool_use must have a single result.
Found multiple tool_result blocks with id: <id>'.
This is already handled for Bedrock via _deduplicate_bedrock_tool_content()
but was missing from the Anthropic direct and Vertex AI partner paths,
which share sanitize_messages_for_tool_calling().
Fix: Add Case D to sanitize_messages_for_tool_calling() — after the
existing orphan detection passes, scan for duplicate tool_call_ids
and keep only the last occurrence (most complete result).
Added 3 unit tests: dedup with duplicates, no-op with unique IDs,
and behavior when modify_params=False.
Related issues: #11804, #11029, #6836, #1782, #151
Anthropic's API requires all tool input_schema to have type:'object'
at the root level. When OpenAI-format tools have parameters with a
missing or non-'object' type field (common with MCP tool servers),
the schema was passed through unchanged, causing Anthropic to reject
with: 'tools.N.custom.input_schema.type: Input should be object'.
The existing default handles the case where parameters is entirely
missing, but does not normalize schemas that ARE provided with a
wrong or absent type field.
Fix: After extracting _input_schema in _map_tool_helper(), ensure
type is set to 'object' and properties exists. This matches the
normalization already done implicitly by the Bedrock handler.
Added 4 unit tests covering: missing type, wrong type, valid schema
(no-op), and entirely missing parameters.
Related issues: #12020, #64, #1671
