Converse adds a json_tool_call tool for response_format, or a dummy tool for
tool history, and appends the operator's tool_config cachePoint after it even
when it drops every listed tool. The cache step now counts that point whenever
Converse sends a tool in its tool config, so under drop_params a later 1h
breakpoint goes out as 5m instead of getting Claude's ttl order 400.
A raw Bedrock cachePoint with a ttl right after a tool breakpoint formed one run
with the breakpoint's cachePoint, and Bedrock rejects a run where two points set
a ttl. The raw cachePoint now stands in for that breakpoint, which is not sent,
and keeps its slot of Claude's four at the raw cachePoint's ttl, the same way a
client's cache_control on a block replaces its breakpoint. It does so only when
the breakpoint has the same ttl or none, or under drop_params, so without
drop_params a breakpoint with another ttl is still sent and gets Bedrock's 400.
The step also orders the tools Converse sends with one stable sort instead of a
scan per tool. The Converse tool checks move into a helper, since completion()
grew past what basedpyright analyzes
When breakpoints join into one mark (a tool result's parts, a message and its last block, a tool and its function), the first usable one stayed. A 30m breakpoint counts as usable because drop_params can send it without its ttl, so it took the place of a valid 1h beside it and the joined mark went out at 5m. The first breakpoint Claude takes as sent now stays, and the 30m one is still checked, so it answers 400 without drop_params as before
The timing test for settling thousands of marks moves from tests/unit, whose guide bans clock reads, to a CodSpeed benchmark
* 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.
