* feat(xai): add native xAI batches and files support
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(xai): tighten batch handler typing and avoid Final redeclaration on star import
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(xai): walk batch result pages iteratively
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(xai): stop paging on empty pagination token and honor litellm.xai_key
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(xai): import NotRequired and TypedDict from typing_extensions for Python 3.10
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(batches): accept image and video endpoints on batch create
* test(xai): lock batch endpoint, auth, and result contracts
The batches test package collided with litellm/batches under pytest prepend, so the All Other Providers shard could not collect the new tests.
* fix(xai): price grok batch usage at xAI's 20 percent batch discount
* fix(xai): map not-found file reads to 404, bill batch reasoning tokens, and add 200k batch tier rates
* refactor(xai): drop routine prose and move tests under tests/unit
* fix(health): hand the resolved provider to list_batches in batch-mode health checks
* test(xai): make tests/unit/llms/xai/batches a package
* fix(xai): walk every page of the files list by pagination_token
---------
Co-authored-by: Mubashir Osmani <mubashir@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* ci: run the unit_selection.sh shard files on every event instead of only fork pull requests
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci: rename fork-flag to unit-flag now that it applies on every event
* test: move tests/test_litellm root and small trees into tests/unit
Pure renames, no content changes. Follow-up commits in this PR fix
references, merge the three files that already existed in tests/unit,
keep live-provider tests in tests/test_litellm and wire CI.
* test: carry tests/test_litellm conftest isolation into tests/unit
Callback lists, routing fallbacks, cached HTTP clients, logger state, AWS,
proxy-URL and keychain env, and session-end client cleanup now reset for
unit tests too. The environment isolation owns its MonkeyPatch so a test's
own monkeypatch is undone before the model-cost teardown runs.
* test: merge, split and prune the moved root and small-tree tests
Merge batches/test_batch_utils.py and the chat_completions and messages
dispatch tests into the files that already existed in tests/unit. Keep
the live Gemini interactions tests, the async image-fetch format test and
the OpenAI embedding scorer test in tests/test_litellm since they need
real network or keys. Put test_router.py under tests/unit/test_router so
the existing package no longer shadows it. Delete eight tests the audit
found superseded by stronger ones kept in this move.
* ci: run the moved root and small-tree tests under their legacy flags
Add the misc and responses-caching-types flags to unit_selection.sh and
CircleCI, extend enterprise-routing and mcp-integration, and point the
legacy GHA shards, Makefile, redis-compat workflow, merge smoke manifest
and change classifier at the new paths.
* test: make the new tests/unit directories packages
tests/unit/test_package_layout.py requires every directory to carry an
__init__.py, and without one the moved and retained
test_litellm_responses_bridge.py modules collide on import.
* test: scope the unit socket block to tests/unit in shared sessions
The GHA shards collect the legacy test-path and the unit selection in one
pytest session. The unit conftest's loopback-only block leaked into legacy
modules that reach the network at import. The legacy conftest now lifts the
restriction at collect and setup time, and the unit conftest re-applies it
when collecting its own modules.
* test: give the shard-script tests their own GITHUB_OUTPUT
They only passed where the runner set it. The CircleCI unit job's env
allowlist drops it, so the script's redirect failed there.
* test: point the router and module-deletion checks at tests/unit
router_code_coverage and code_qa_check_tests only searched tests/test_litellm,
so the moved router tests no longer counted. The two silent-experiment tests
the audit deleted were the only direct callers of those methods; they are
replaced with tests that assert the forwarded shadow request and the
recursion guard.
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(vertex): native batch JSONL passthrough with cost tracking
Add a per-request `passthrough=true` multipart field on `POST /v1/files`
(and the same kwarg on `litellm.create_file`) that uploads a native
Vertex AI batch JSONL to the deployment's GCS bucket unchanged, so rows
using `googleSearch` and other Gemini-only features run as written and
the output, `groundingMetadata` included, comes back untouched.
Passthrough is sticky through the GCS object path
(`litellm-vertex-files/passthrough/...`), so batch create and output
retrieval inherit it without new state. Native output rows are costed
from their `usageMetadata` with the deployment's model and model_info,
in the polling and retrieve paths and for the existing global
`disable_vertex_batch_output_transformation` flag, which billed $0
before.
The proxy requires the target to resolve to vertex_ai deployments only,
refuses `passthrough` with a non-batch purpose, a non-default
`target_storage`, or pre-call guardrails, and validates native rows on
`request` instead of the OpenAI batch keys.
* refactor(vertex): keep native batch row pricing inside the Vertex adapter
Moves native Vertex batch row detection, response parsing, and per-row
pricing from litellm/batches/batch_utils.py into
litellm/llms/vertex_ai/batches/transformation.py, so batch_utils only
aggregates the rows it gets back. Adds tests/test_litellm/files to the
misc unit shard so the new test directory is claimed by a shard.
* fix(files): say what a passthrough batch upload takes when a row is not native
The missing-key 400 listed bare key names, so an OpenAI-shaped row under
passthrough=true read "Each line must be a JSON object with keys request".
The batch line shape now carries its own hint, and the passthrough one says
a passthrough upload takes native Vertex batch rows with a request key
* fix(batches): bill native Vertex embedding batch rows on the native cost path
A native Vertex output row whose response holds an embedding was validated as a
generateContent response, so the documented tokenCount-only shape counted as a failed
row. Price embedding rows from their own usage (promptTokenCount, else tokenCount) with
the helper the transformed embeddings path already used, and drop the prompt-details
helper nothing calls anymore.
* fix(batches): keep modality batch rates on native Vertex embedding rows
An embedding row that carries usageMetadata was billed from promptTokenCount alone, so
its promptTokensDetails no longer reached the audio, image, and video batch rates the
way it did before the native cost path. Run every row with usageMetadata through the
Gemini usage parser and keep the flat tokenCount fallback for embedding rows without it.
* fix(batches): price native Vertex batch rows by modelVersion under a wildcard deployment
A `vertex_ai/*` deployment hands the batch cost path `*` as the deployment model, which
no cost map resolves, so every native (passthrough or flag-on) row was billed at $0. A
wildcard deployment model now defers to the row's own `modelVersion`, the way the
transformed path already prices by the row's `model`.
Also moves the native passthrough tests under tests/test_litellm, the tree codecov
reads, and covers the raw upload chunking, the embedding output translation, the
unpriceable-row path, and the flag-on dispatch.
* fix(batches): keep explicit deployment prices for native Vertex rows without a modelVersion
Under a wildcard deployment a native batch row that carries no modelVersion (an embedding
row, or a generateContent row Vertex returned without one) was billed at $0 even when the
deployment's model_info sets explicit batch prices, because the cost calculator was never
called. The row now falls back to the wildcard name, which the cost calculator prices from
the explicit model_info, and only a row with neither a modelVersion nor a deployment model
is billed at $0 with the warning
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(cost): bill batch prompts above 272K at OpenAI's long-context batch tier
* fix(cost): mirror batch long-context keys on custom pricing params
Register the two *_above_272k_tokens_batches keys on CustomPricingLiteLLMParams so a per-deployment override stays out of the shared backend key, add them to the inline model-info schema and alias-count tests, and build LiteLLM_Params and GenericLiteLLMParams through model_validate at the two dict-splat call sites so basedpyright's reportArgumentType budget ratchets down instead of blocking the new fields.
* fix(cost): add the gpt-5.5-pro batch long-context tier and ignore malformed batch tier keys
* fix(cost): bill cached batch tokens at OpenAI's cached batch rate
Adds cache_read_input_token_cost_batches and
cache_read_input_token_cost_above_272k_tokens_batches for the tiered
OpenAI entries at half the standard cached rate, bills cached batch
tokens at that rate per output line, and parses string-valued batch
rates in deployment-level model_info.
* fix(cost): bill batch cache writes at the batch cache-write rate and carry published batch rates for one-sided deployments
OpenAI's Batch table prices cache writes for gpt-6-astra, gpt-5.6, gpt-5.6-sol, gpt-5.6-terra and gpt-5.6-luna at half the standard cache-write rate, so the cost map gains cache_creation_input_token_cost_batches and its above_272k tier for those entries and batch cost pulls written tokens out of the input bucket at that rate; models without the key keep billing writes at the batch input rate.
A deployment declaring only one side of its batch pricing now carries every published batch rate of the other side (tier, cached, cache write), its own keys win, and a lone tier, cached or cache-write batch key counts as declared pricing instead of being ignored.
* fix(cost): select the batch long-context tier from any batch tier key
A deployment that declares its own flat standard input rate keeps every
published batch rate of the output direction, including the 272K output
tier, but the tier was only ever selected when an input tier key was also
present. Detect the crossed tier from any of the four batch tier keys so
the carried output, cache-read, and cache-write tiers bill at their tier
rate above 272K tokens.
* chore(proxy): keep the OpenAPI snapshot as CI generates it
* fix(cost): pick each batch price component's tier from its own keys
The batch rate picker crossed one threshold for every component, so a
deployment declaring only an output tier also moved its input, cached, and
cache-write rates to that cutoff. Each component now crosses its own
*_above_<N>k_tokens_batches keys and falls back to its flat key.
The JSON schema is regenerated with the generator as it is on main:
cost-map-guard renders the PR's cost map with the base branch's generator,
so the descriptions for the new batch cache keys move to a follow-up.
* chore(proxy): restore the lazy OpenAPI snapshot to what CI's Python 3.12 generates
The merge commit carried a snapshot regenerated on a Python 3.14 venv, which dedents
docstrings at compile time, so one description line differed from the file CI regenerates
on 3.12 and the schema.d.ts sync check went red. The snapshot is byte-identical to main again
