The test_streaming_mcp_events_validation test was flaky because:
1. It didn't mock the nested aresponses() call inside the iterator's
_create_initial_response_iterator(), causing real API calls that fail
without credentials
2. The iterator silently swallowed exceptions and set phase="finished",
discarding pre-generated MCP discovery events
3. The _execute_tool_calls mock had wrong signature (missing tool_server_map)
Production fix: MCPEnhancedStreamingIterator no longer sets phase="finished"
on LLM call failure — it falls through to emit MCP discovery events first.
Test fix: Added mock for litellm.responses.main.aresponses returning a fake
async streaming iterator, fixed mock signatures, removed try/except that
masked failures.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- test_proxy_e2e_azure_batches: e2e managed batch test with delete retry for batch_processed
- test_fixtures_smoke: smoke test for fixtures
- validate_e2e_setup: setup validation script
Made-with: Cursor
- conftest: mock server + proxy server fixtures, log capture, health check fix
- base_integration_test: fix key_alias format (replace @ and . for API validation)
- test_managed_files_base: S3 callback wait with early exit, delete retry logic
Made-with: Cursor
* feat(proxy): add key_alias, key_hash, requested_model tags to DD APM spans
* refactor(proxy): consolidate DD APM tag helpers into DDSpanTagger class
* refactor(proxy): move DDSpanTagger to its own file litellm/proxy/dd_span_tagger.py
* style(ui/): distinguish agent calls from llm calls on ui
* feat: initial grouping working
* feat: set stable contextid for a2a calls - allows for easily passing to downstream llm/mcp calls
* feat(a2a_endpoints.py): fix tracing to avoid recreating logging objects for the same call
allows stable trace id usage
* fix(guardrail_endpoints): handle string ui_type values in _build_field_dict
_build_field_dict unconditionally called .value on ui_type, which crashes
for guardrail configs that use plain strings (e.g. BlockCodeExecutionGuardrailConfigModel
uses "multiselect" and "percentage"). Now checks with hasattr before calling .value.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: propagate trace/session id from headers in MCP server calls
Cherry-picked mcp_server/server.py fixes from 6feb9bab: adds
get_chain_id_from_headers to extract x-litellm-trace-id /
x-litellm-session-id from raw headers, and uses it in call_tool
and list_tools to keep spend logs and tracing consistent with A2A.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
The route-level auth check was blocking internal_user role (team admins)
from reaching /key/{key}/reset_spend because KEY_RESET_SPEND was missing
from key_management_routes. Added it so team admins pass the route check
and the endpoint's existing _check_proxy_or_team_admin_for_key enforces
actual authorization.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The _resolve_model_for_cost_lookup function was only checking
litellm_params.model when resolving model names from the router.
For Azure custom deployment names (e.g. azure/openai/gpt-5.3-codex),
this deployment name doesn't exist in the model cost map, so cost
returned /bin/zsh.
Now checks model_info.base_model and litellm_params.base_model first,
falling back to litellm_params.model only if no base_model is set.
This matches how the router resolves base_model everywhere else.
The _safe_get_request_headers caching (commit e7175a52) uses
request.state._cached_headers. With Mock(spec=Request), getattr on
state returns a Mock (truthy), causing RedactedDict to receive a Mock
instead of a dict. Using a real starlette State object fixes this.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Address Greptile review: test_resolve_jwks_url_resolves_oidc_discovery_document
also used the inconsistent patch.object pattern.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Vertex AI / Gemini uses Pydantic's model_json_schema() which omits
additionalProperties: False (Gemini rejects it). The test expected
the same schema for all providers.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The patch.object with new_callable=AsyncMock can behave inconsistently
across Python versions, causing mock_response.status_code to return a
MagicMock instead of the assigned value. Direct assignment is simpler
and more reliable.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The CompletionTokensDetailsWrapper type now includes video_tokens field,
but this test's expected dict was not updated to include it.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The Gemini API requires role="user" on function_response content blocks
(added in commit 273cf12afa), but these tests were never updated to match.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Two independent fixes for pre-existing test failures on main:
1. Anthropic streaming: The sync __next__ method used a simple
holding_chunk pattern that lost chunks when multiple events needed
to be returned. Refactored to use the same chunk_queue approach as
the async __anext__ method. Also fixed tests that used ModelResponse
(which defaults finish_reason to 'stop') instead of ModelResponseStream.
2. Azure GPT-5.1 logprobs: The base OpenAI class includes logprobs for
gpt-5.1+ models, but Azure hasn't verified support for gpt-5.1.
Added explicit removal of logprobs/top_logprobs for gpt-5.1 (non-5.2)
models in the Azure config.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
