When value > 1 (e.g., "2mo") and current_month + value > 12,
target_month exceeds valid range (1-12), causing ValueError in
datetime constructor. For example, calling duration_in_seconds("2mo")
in November produces target_month=13.
Use modular arithmetic to correctly wrap months and increment year.
Signed-off-by: JiangNan <1394485448@qq.com>
* add new azure gpt models
* add versionless azure/gpt-5.4 models
* Undated azure/gpt-5.4 alias missing supports_service_tier
* indicate service tier support for azure/gpt-5.3-chat
* fix priority tier pricing for new azure/gpt models
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>
Replace Tremor Button components with antd Button in the user
management flow (Invite User, Bulk Invite Users, Select Users,
Bulk Edit, and onboarding modal copy link button).
The test calls OpenAI's gpt-4o-audio-preview model which sometimes
doesn't return usage data in the streaming response. Fixed by:
- Adding @pytest.mark.flaky(retries=5, delay=2) for retry handling
- Fixing usage_obj loop to check chunk.usage is not None
- Skipping gracefully when OpenAI doesn't return usage data
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The test_create_skill test was consistently failing in CI with a 500 from
Anthropic because the SKILL.md frontmatter always used the same hardcoded
name (test-skill-litellm). Since test_delete_skill is permanently skipped,
skills accumulate in the CI account, and re-creating with a duplicate name
triggers an Internal Server Error on Anthropic's side.
Fix: pass a timestamp-based unique_suffix to create_skill_zip so each run
produces a distinct skill name in the zip's SKILL.md frontmatter.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.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 Claude Agent SDK sends max_tokens=32000 for unrecognized model names
(like "bedrock-nova-pro"), which exceeds Nova Pro's 10,000 limit. Enable
modify_params in the test proxy config so LiteLLM clamps max_tokens to the
model's actual limit. Also swap nova-premier to nova-pro since premier
requires provisioned throughput unavailable in CI.
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>
When retrieving a batch via the unified batch ID path, only unified_batch_id
was set on _hidden_params but model_id was missing. The managed files hook
requires both to encode output_file_id into a managed ID.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The SearXNG search tests were failing in CI because they depend on a live
SearXNG instance that returns results. Since this provider is used by a
very small subset of customers, replace the flaky integration tests with
deterministic unit tests that validate request payloads, URL construction,
response parsing, and header configuration without requiring external infra.
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>
Chocolatey v2.5.1 introduced interactive prompts that block CI. Add
--no-progress, --force flags and CHOCOLATEY_CONFIRM_ALL env var to
fully suppress user input in non-interactive environments.
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>
