* feat(extraction): add Clairic extraction types and Cloudflare provider config
* feat(extraction): add the /v1/extraction route, router wiring, and Clairic billing
* fix(extraction): refuse or drop stream, pass unknown keys, run guardrails, keep the requested model name
Clairic does not stream, so stream=true answers 400 and is dropped and billed under drop_params. Keys LiteLLM does not know pass through to Cloudflare. Guardrails scan state, field instructions and text answers. Frozen decision and extraction responses now carry the requested model name. List fields must name their item type. /health/test_connection and the Add Model page accept the extraction mode.
* fix(extraction): keep upstream model names on decisions, scan images, keep typed values, refuse OpenAI params
Scope the frozen-response model stamp to extraction so /v1/decisions and /v1/systemone return the upstream name as on main. Pass request images to guardrails as data URLs and drop answer spans when a response guardrail rewrites state_text. Field spec values keep their JSON type, so a wrong type answers 400 instead of being coerced. OpenAI generation params answer 400 unless drop_params. The Model Hub shows a /v1/extraction snippet for extraction models.
* fix(extraction): send only Clairic's request fields, count extraction tokens, keep typed answers typed, drop spans a request guardrail made stale, and honor --max_tokens
* fix(extraction): scan number answers in response guardrails, rewrite guardrail fields in linear time, and leave --max_tokens as on main
* fix(extraction): scan float answers as whole numbers so fraction digits never trip a response guardrail
* fix(extraction): round only mean and median for response guardrails so fractional values keep their decimal text
* fix(extraction): count state and fields for budget reservations and scan estimate quantiles in response guardrails
* fix(extraction): send each repeated text to guardrails once and keep the context-window budget reservation
* test(extraction): type request_data in the guardrail test helpers
* fix(anthropic): keep user messages with None or empty list content so requests do not end on an assistant turn
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(anthropic): avoid mutable empty-list construction in sanitizer
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(anthropic): avoid rebinding sanitizer input
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(anthropic): keep a trailing user turn whose content key was dropped
litellm.completion strips None fields from every message before the provider
transform, so a user message sent with content null reaches the Anthropic
converter with no content key and raised KeyError. Read the content with get
so that turn gets the same placeholder as an empty list.
* fix(anthropic): only fill user turns with empty content and keep their cache_control
* fix(anthropic): fill each empty user message in place like an empty string
* fix(anthropic): add the placeholder only for an empty turn or a cache breakpoint, in linear time
* test(anthropic): cover the tool-result guard with cache_control and widen the linear-time bound
* test(anthropic): cover empty user turns through the proxy on chat, responses and messages
* test(anthropic): type the empty user turn tests and move the linear-time check to a benchmark
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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>
Guard the per-request CPU cost of the chat completion, MCP tool and A2A
message transforms against regressions on every commit. All benchmarks are
pure in-process work with no network I/O so they stay deterministic under
CodSpeed's simulation mode, and they import under the base dependency set the
benchmark job installs.
Inference covers the full SDK overhead via mock_response (simple, multi-turn,
tools, streaming) plus convert_to_model_response_object as a deterministic
anchor. MCP covers the client-side tool translation and the proxy server-side
tool-name prefix round-trip. A2A covers the client request/response transforms
and the proxy server-ingress message conversion.
Adds the mcp and a2a-sdk packages to the benchmark run since those transform
modules need them, and broadens the workflow triggers to litellm_internal_staging
so the internal branch flow is benchmarked too.
