Forwarded client headers on bridged /v1/responses calls were serialized into the
OpenAI JSON body as extra_headers when EXPERIMENTAL_OPENAI_BASE_LLM_HTTP_HANDLER
was set, and OpenAI rejected the request with unknown_parameter. The headers are
already merged into the outgoing HTTP headers, so only set the SDK-style
optional param on the SDK client path
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The rebuild's tool-call selection and its text-only fast path only looked at
choice 0 of each chunk, so a chunk that packs several choices (Gemini with
candidateCount above 1) lost a tool call carried by a later candidate, and a
chunk whose later choice had no tool calls at all made the rebuild raise.
Both now consider every choice in the chunk.
* fix(streaming): keep usage-only chunks from crashing streams with empty stream_options
The usage-only chunk branch in CustomStreamWrapper.chunk_creator indexed stream_options["include_usage"] directly, so a caller passing stream_options={} hit a KeyError that surfaced as MidStreamFallbackError. Streaming mock_response with an admission input_tokens count (#40637) now always emits such a chunk, which made the crash reachable. Reuse the send_stream_usage policy computed at init instead. Also annotate the #40637 test bindings with Final.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(streaming): report admitted zero prompt tokens instead of recounting in mock streams
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* perf(mock): emit admission-time usage chunk on streaming mock_response
Streaming mock_response chunks carried no usage, so the chunk builder re-tokenized the whole prompt in Python after the stream ended even when budget reservation had already counted it at admission. The mock streaming generators now yield a final usage-only chunk carrying the admission prompt count (same completion count as the non-streaming path). Without an admission count the old tokenizer fallback stays.
* fix(mock): type the mock stream generators and keep the usage chunk on the content stream id
Review follow-up: the usage-only chunk was built with a fresh id, so CustomStreamWrapper switched response_id for the finish-reason and usage chunks. It now copies the content stream id. The generators also get full parameter and return annotations.
---------
Co-authored-by: yassin <yassin@berri.ai>
* feat(mock): report admission-time input token count in mock_response usage
Mock completions always reported prompt_tokens=10, so spend tracking, TPM metrics, budgets and the tokens-per-minute autoscaling signal saw 10 tokens for a 100k-token request. Budget reservation now carries the admission-time input token count in the reservation record, and mock_completion reads it back so mock traffic exercises the same spend and TPM paths as real traffic without any extra tokenizer work.
* fix(mock): keep a zero admission input token count instead of falling back to 10
---------
Co-authored-by: yassin <yassin@berri.ai>
Narrows the no-choices guard so a dict, string, or None still raises the APIError while an empty list passes through,
guards the non-stream Anthropic bridge against indexing an empty choices list, and repairs test_completion_missing_role,
whose raw-response mock was patched in as the create() callable itself so the handler only ever saw a MagicMock
The auto-bridge that moves gpt-5.4+ requests carrying function tools and no
reasoning_effort onto /v1/responses only fired when the resolved api_base was
the literal https://api.openai.com/v1, so a deployment pointed at an OpenAI
PrivateLink hostname (<region>.privatelink.api.openai.com) or a port-qualified
or trailing-slash default stayed on Chat Completions and got OpenAI's 400 back.
Gate on the resolved URL's hostname instead: api.openai.com or any subdomain of
it bridges, every other custom base still stays on chat
Review turned up two real problems in the TTS path.
Router.aspeech forwarded voice=None whenever the caller omitted it, which overwrote a
voice set in the deployment's litellm_params, so a configured fallback voice was
ignored on voice-less requests. It now leaves the key alone when no voice is passed.
get_complete_url also fell back to MISTRAL_API_BASE, but speech() always receives a
non-null api_base from get_llm_provider, whose mistral branch only reads
MISTRAL_AZURE_API_BASE and otherwise hardcodes the public host. That branch could
never run, and its unit test asserted a behavior the real path does not have. The
working override is api_base on the deployment, now pinned by an end-to-end test
Resolves two conflicts:
- tests/test_litellm/vector_stores/test_main.py: staging moved search() to a
RouterVectorStoreEmbeddingExecutor while this branch parametrized the same
test over query; keep both the executor assertions and the parametrize.
- tests/logging_callback_tests/test_bedrock_knowledgebase_hook.py: staging
carries a duplicate embedding_executor kwarg that makes the file a
SyntaxError; drop the trailing duplicate.
Resolves the tests/test_litellm/test_main.py collision, where both sides appended a
new test at the end of the file, by keeping both.
Also carries the one-line fix from #39502: staging arrived with a duplicate
embedding_executor kwarg in the Bedrock KB fake handler, which ruff rejects as a
syntax error, so every commit here would otherwise fail lint. The change is byte
identical to #39502, so that PR merges cleanly once it lands.
Restate xAI's usage.cost_in_usd_ticks as usage.cost on chat and responses
replies, streamed ones included, then let the cost calculator own the
figure: a deployment with its own input_cost_per_token and
output_cost_per_token keeps that price, cost margins apply on chat streams
as they already did on non-streamed calls, and only OpenRouter's usage
cost becomes the llm_provider-x-litellm-response-cost header, so xAI
streams no longer skip the calculator through the header or the
stream_chunk_builder hidden response_cost.
Streamed responses through the proxy previously exposed no usable cost:
the x-litellm-response-cost header is unreadable mid-stream and the final
usage chunk carried only tokens, priced against an alias model name the
client cannot resolve. The include_cost_in_streaming_usage flag existed
but was off by default and only fixed the wire, not SDK clients.
Stamp usage.cost into the joined streaming response by default wherever a
final usage object is built: the chat-completions stream_chunk_builder,
the native /v1/responses RESPONSE_COMPLETED event, and synthetic response
events. Provider-reported cost always wins over the computed value, and
only positive computed costs are stamped so unpriceable alias responses
keep deferring to the logging object's own calculation. Per-chunk SSE
cost injection (/v1/messages, generateContent, passthrough) stays behind
the flag.
Also normalize non-litellm usage objects in stream_chunk_builder: openai
CompletionUsage lacks Usage.__contains__, so membership probes silently
returned False and client-side rebuilds dropped the wire cost and
recounted token usage locally. Wire token counts and cost now survive.
Resolves LIT-6427
Router._add_deployment called get_llm_provider without the deployment's api_base, so a config entry with a bare model plus a known OpenAI-compatible endpoint failed startup validation with LLM Provider NOT provided and the proxy returned 400 no healthy deployments for that model group. acompletion had the same gap at request time: it forwarded only base_url into its get_llm_provider call, dropping the api_base kwarg the router passes. Both now forward api_base so endpoint matching resolves the provider the same way sync completion already does
Five of the tests could pass without the behavior they guard being
correct. The passthrough spend tests derived their expected spend in
setup_method from whatever cost map was live rather than the pinned
checked-in one. The test_main.py cost fixture cleared only one of the
two price caches, leaving billing to read stale prices while the
assertions read the pinned map. The gpt-5.6 bridge test parametrized
over two suffixes the version check discards, so both cases were
identical. The anthropic flush helper swallowed the loop-binding
RuntimeError it exists to report. The cache-write test pinned a literal
1.25 rate ratio unrelated to the bug it guards.
* test: add regression coverage for twelve closed issues
Adds targeted regression tests for behavior that was fixed but left ungated,
so the fixes cannot silently regress:
- #33772 openai cache_write_tokens cost
- #34309 Responses API cache cost_breakdown
- #35363 /v1/responses batch spend
- #36619 auto-router api_base/api_key leak on a shared model name
- #35359 batch fallbacks within the owning model group
- #36523 passthrough streamed Responses spend log
- #36646 passthrough embeddings spend log
- #37147 non-object metadata on create_batch is a 400
- #35362 unscoped list files reads the managed-file store
- #33221 gpt-5.6 bridges to Responses on function tools alone
- #34487 LLM complexity classifier runs for every caller metadata shape
- #35124 streamed /v1/messages emits success logging on both bridges
Cost assertions read rates from litellm.model_cost rather than hardcoding
dollar amounts, so they do not drift on repricing.
* fix: stop the new regression tests polluting and tripping over shared global state
Two shard failures, both from global state the new tests share with their
neighbours rather than from the behaviour under test.
test_main.py's local_cost_map pinned litellm.model_cost but left the
get_model_info lru_cache warm, so completion_cost billed at whatever prices
were cached earlier in the process while the assertions read the pinned map.
Clear the cache on both sides of the fixture, matching the local_model_cost_map
fixture in tests/test_litellm/conftest.py.
The anthropic messages streaming tests called GLOBAL_LOGGING_WORKER.flush()
on whatever queue happened to be around. A queue left non-empty by an earlier
test is still bound to that test's loop, so join() either hangs or raises
"bound to a different event loop". Rebind to the running loop before the call
and wait for the captured payload instead of a fixed sleep.
* test: drop the cwd-relative sys.path.insert calls from the test suite
TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.
Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.
Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.
* test: drop the duplicate imports the sys.path sweep exposed to F811
* test(pre-call-utils): restore the os import the new bedrock tests need
