* 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(redis): authenticate sync clusters with IAM credential providers
Signed-off-by: Silu Panda <31051721+SiluPanda@users.noreply.github.com>
* test(redis): exercise IAM cluster authentication over TCP
Run Azure and GCP regressions against a real local cluster with only
cloud token issuance stubbed. Build a checksum-verified Redis server
in the compatibility workflow and report its coverage.
Signed-off-by: Silu Panda <31051721+SiluPanda@users.noreply.github.com>
* test(redis): separate unit and cluster integration coverage
Keep the mapped test tree mock-only. Run the live cluster cases
from the existing local caching integration file, selected by
explicit node IDs in the Redis compatibility workflow.
Signed-off-by: Silu Panda <31051721+SiluPanda@users.noreply.github.com>
* test(ci): isolate workflow coverage audit fixtures
Replace the stale unrun caching-file assumption with isolated workflow
fixtures for file and node-ID selectors. Keep the unnamed-file negative
check and clarify which live caching cases remain outside CI.
Signed-off-by: Silu Panda <31051721+SiluPanda@users.noreply.github.com>
---------
Signed-off-by: Silu Panda <31051721+SiluPanda@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>
* ci: add dashboard and core smoke checks across supported Python versions
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci: tighten merge smoke harness and keep mapped test diffs additive
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci: terminate proxy on readiness timeout and use contextlib.suppress in teardown
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: yuneng <yuneng@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(autoroute): wait for a valid fuzzy selection index and cancel the prompt on driver failure
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(autoroute): read the fuzzy selection through the public InquirerPy property
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): inject the HIBP client into change_password so the breached-password test never touches the network
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(mcp): only use the 200ms read timeout in the silent mode of the transport completion test
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): record HIBP requests so the ordering test asserts no lookup happened
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci(codeql): filter the weak-sensitive-data-hashing false positive on the HIBP k-anonymity lookup
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(e2e): move the compat-matrix populator from a GCE VM to a Render cron job
The daily Claude Code compatibility-matrix job ran as a systemd timer on
the litellm-compatibility-matrix-populator VM in the vertex-check GCP
project. Replace that with a Render Docker cron job built from a new
Dockerfile in tests/e2e/claude_code/cron_vm: pinned and checksummed
debian base, gh, uv, and Claude Code CLI, a non-root populator user, and
run_daily.sh as the entrypoint. run_daily.sh now clones a fresh blobless
checkout per run (Render cron disks are ephemeral), reads the publish PAT
from the github-token secret file under CREDENTIALS_DIRECTORY, and its
comments no longer describe systemd. The .service and .timer units are
gone; README.md and the env example describe the Render service, its
secret files, and the local docker build instead.
* docs(e2e): name the plan and trigger route Render's cron-job API accepts
Render answers a bare 404 for the legacy pro_max plan name on a cron job
(4c-16g is the same 4 CPU / 16 GB size) and the manual trigger route is
/v1/cron-jobs, not /v1/cronjobs.
* fix(e2e): install the published litellm wheel instead of building the tag from source
The tag builds a Rust extension through maturin, which needs a C and Rust
toolchain the cron image does not carry, so the first Render run failed at
uv sync with "linker cc not found". Sync the locked dependencies with
--no-install-project, install the PyPI wheel (what users run) with
--no-build, and pass --no-sync to every uv run so uv never puts the source
build back.
* fix(e2e): keep the SKIP_PUBLISH matrix where a Render run can read it
The validation run wrote the matrix into the image checkout, which nobody
can read once the container exits. Save it under HOME and print it at the
end of the log instead.
* fix(e2e): let the stale compat-matrix PR sweep see past the newest 100 docs PRs
The docs repo has a few hundred open PRs, so a 100-item list never
reached the week-old compat-matrix PR and the sweep left it open on
every run.
* docs(e2e): say the Render cron needs a manual deploy after each merge
Pushes never started a deploy during setup because Render only hears
about them through its GitHub app, which the org does not have, so the
README now carries the deploy command and the wait-for-live rule
* ci: build the compat-matrix cron image on pull requests
The CI coverage gate requires every Dockerfile to be built by a job, and
building this one on each PR that touches it also catches a broken pin
or checksum before Render does
* fix(e2e): shim the whole tests/e2e tree into the compat-matrix worktree
The five-file helper allowlist missed fixture_mode, which e2e_config now
imports, so the first Render run died at conftest load with
ModuleNotFoundError. Copy the image's whole tests/e2e tree instead and
keep pytest from loading the EKS-harness conftest with --confcutdir
* fix(e2e): scope the compat-matrix sweep to the publishing account's own PRs
The stale-PR sweep selected every open docs PR whose head branch starts
with compat-matrix/, so a contributor's fork PR under that name would
have been closed once a newer matrix PR existed. The sweep now resolves
the publishing login from the token and only closes same-repo PRs that
account opened
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* feat(logger): add shared Rust diagnostics and Python logging bridge
* feat(logger): dispatch diagnostic processing through Rust
* chore: regenerate Cargo.lock after rebase
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test: allowlist bounded logging tree walkers in recursive detector
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* perf(logger): skip decoding plain access arguments
* test(logger): skip embedded-python logger test when litellm deps are absent
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style: cargo fmt
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test: expect NativeDiagnosticProcessor in the native public surface
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(stub): export NativeDiagnosticProcessor via __new__ in _native.pyi
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(tracing): rename logger crate and document host sink contract
* test(logger): cover exc, stack, and nested extras in the diagnostic filter
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(logger): keep rendered redacted line when template scan flags a key pattern
The blanket REDACTED for a changed msg/color template discarded lines
whose rendered form was already redacted by the same pipeline, e.g.
'password=%s' became 'REDACTED' instead of 'password=REDACTED'. Only
fall back to REDACTED when the rendered form did not change either,
which is where interpolation can mangle the key pattern the scrub
would otherwise see.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci(rust): install python deps so the logger bridge test runs
The end-to-end bridge test skipped silently when litellm's Python deps
were absent. uv sync --no-install-project installs them without a
maturin build, and PYTHONPATH makes them visible to the embedded
interpreter
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Yujong Lee <yujong@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(ci): close open pull requests superseded by a merged fix on their linked issue
* fix(ci): replace the mixed-anchor release-line regex with a plain predicate
* fix(ci): recover a half-done close, page linked pull requests, trust only the workflow's marker
* fix(ci): close superseded pull requests whatever closed the issue and fail the job when the script throws
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Eight directories the misc shard named moved to tests/unit on 2026-09-20, and one
missing path makes pytest-xdist collect [0 items] for the whole shard, which the
exit-5 tolerance turned into a green required check running nothing. The shared
Run tests step now drops a path that does not exist with a :⚠️: and runs
pytest over the rest, keeping option tokens verbatim.
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* ci: benchmark and gate an installed release wheel
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci: simplify installed-wheel benchmark check
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(rust): add native tokenizer codec
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(tokenizer): route Python tokenization through the Rust extension
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style(lint): format tokenizer call
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(packaging): restore runtime dependencies and native images
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(tokenizer): preserve Python SDK behavior with Rust tokenizers
* fix(tokenizer): restore compatibility paths
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(tokenizer): count custom tokenizers directly
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(tokenizer): preserve caller-supplied Python tokenizer counts
* fix(tokenizer): reuse packaged vocabularies in the native wheel
* refactor(rust_bridge): route token counting through the catalog as RUST_OPT_IN
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(spend_tracking): compare tokenizer groups by value
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(deps): re-resolve filelock under the <4.0 pin
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(llms): align transformation override signatures with base configs
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* build(rust): use fat LTO to keep the native wheel under the 35 MB limit
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(tokenizer): preserve Python defaults with opt-in Rust dispatch
* test(proxy): tolerate missing litellm.utils.Tokenizer when patching it
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): patch the tokenizer dispatch function instead of the removed alias
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(tokenizer): give the Rust wrappers the tiktoken and tokenizers surface
Callers of litellm.encoding and litellm.create_tokenizer must see the same
read-only API whichever backend the catalog selects.
- OpenAIEncoding mirrors tiktoken.Encoding: n_vocab, max_token_value,
token_byte_values, encode_single_token, encode_with_unstable,
encode_to_numpy, decode_with_offsets, is_special_token, repr; the Rust
tiktoken crate keeps a Vocabulary beside each CoreBPE and reports the
requested encoding name (gpt2 stays gpt2).
- HuggingFaceTokenizer mirrors the read-only tokenizers.Tokenizer surface
(token_to_id, id_to_token, get_vocab, get_vocab_size,
get_added_tokens_decoder, num_special_tokens_to_add, padding, truncation,
encode_special_tokens, from_buffer); HuggingFaceEncoding gains the
char/word/token lookups, pad, truncate, set_sequence_id and merge.
Mutators stay on the Python tokenizer.
- from_json/from_pretrained claim the fork gate only when the huggingface
feature is compiled in; the surrogate fallback matches on the Codec.
- Tokenizer caching is keyed on the same catalog Context the dispatch runs
on; rust_tokenizer reads the encoding name without loading an encoding;
LITELLM_RUST parsing is cached.
- Drop the unused tiktoken_encoding_for_model export and Error::Download.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
* fix(tokenizer): close the exhaustive matches with assert_never
CodeQL reads a `match` over a Literal with no default arm as an implicit
`None` return. `assert_never` makes the exhaustiveness explicit for both the
HuggingFace tokenizer loader and the Rust token-counter factory.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
* feat(tokenizer): derive the fast counter from the shared tokenizer
The count-only counter (`fast` feature) and the codec each parsed the same
artifact: TokenCounter took the Anthropic JSON and the tiktoken rank files
from Python while Tokenizer loaded them again. One parse now serves both.
- FastTokenizer builds from a model another loader holds: `from_shared`
takes the Arc<tokenizers::Tokenizer> the HF codec keeps, and
`from_*_pairs` take the ranks the tiktoken vocabulary already parsed.
- `FastCounter::fast_counter` in the core crate derives it from either codec;
encodings the fast scanner does not reproduce are refused.
- Native `Tokenizer.count(text, fast=False)` opts into that counter, built
once per tokenizer on first use; `TokenCounter.from_tokenizer(tokenizer,
fast=False)` replaces the JSON and rank-file constructors.
- The Python route counts over the native tokenizers the codec path shares
(`native_encoding`, `native_anthropic`) and no longer reads rank files;
the packaged Anthropic tokenizer has one loader, `tokenizer_dispatch.anthropic`.
- Public wrappers gain `count(text, fast=False)`.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---------
Co-authored-by: Yujong Lee <yujong@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com>
The Buildkite ephemeral stack runs the gateway in another pod, so it cannot reach the pytest host's provider edge. The GitHub changed-e2e lane runs gateways on the runner and sets E2E_PROVIDER_EDGE_HOST_REACHABLE
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The changed-test gate booted its stage-mirror stack without files_settings
or finetune_settings, so every raw upload with a custom_llm_provider hit a
500, and it exported the whole provider env into the gateways, so the
AWS_ROLE_NAME the assume-role test needs made the GovCloud deployment run
an AssumeRole with its static keys. The gate also deleted its pytest output,
so a red run left nothing to read. The mirror config now carries the
openai, azure, and vertex_ai file settings, gateways start without
AWS_ROLE_NAME, and the workflow uploads the pass logs and junit files with
every secret value, every field of a JSON-valued secret, and their
XML-escaped forms replaced before the raw files are removed.
