The Rust bridge imported httpx to construct the provider error response, which
fails in the isolated wheel check where httpx is absent. Rust now raises
RustUpstreamError with a headers attribute and the Python lifecycle wraps it in
a typed UpstreamFailure carrying the httpx.Response before legacy mapping.
Test helpers gained call_native so pytest.raises blocks hold a single call
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Delete litellm/ocr/input.py and the native _ocr_file_document, _ocr_upload_document
and _ocr_mime_type helpers. File documents now project to a typed OcrDocumentInput
and the core lifecycle reads local paths, encodes bytes and asks the host to read
file-like objects through a ReadDocument operation before the provider request
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Move each route's bridge module under litellm/rust_bridge/<route>/ so a folder
means a Rust implementation exists while the catalog row says whether it is
used. OCR now keeps the Python implementation in litellm/ocr/main.py and the
Rust selection in litellm/ocr/rust.py, removing litellm/ocr/legacy.py
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Split the Rust workflow into fmt, clippy, nextest and wheel jobs so they run in parallel, replace manual actions/cache with Swatinem/rust-cache, and install a pinned checksum-verified cargo-nextest. Make two python-bridge tests self-contained so they pass when nextest runs each test in its own process.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(rust): count tiktoken cl100k_base admission tokens in Rust
The Rust admission token counter only had the Anthropic tokenizer, so every
other model (OpenAI gpt-4 family, Azure, Gemini, Bedrock non-Claude, Mistral)
tokenized with tiktoken on the Python inference worker.
Add an exact cl100k_base counter to litellm-token-counter: the vendored rank
file (base64 token / rank lines, the bytes Python's tiktoken uses) is parsed
into a byte-level BPE model and the cl100k split pattern is a handwritten
scanner over the shared Unicode classes, so no regex engine runs per request.
Both tokenizers share the message, tool and reply-priming accounting.
The PyO3 TokenCounter gains a from_cl100k_ranks constructor; Python reads the
rank file and passes it in, the way claude_json_str already works. The bridge
selects the counter through the same predicates litellm.token_counter uses
(huggingface_tokenizer_kind, openai_tokenizer_encoding), declines o200k_base,
downloaded HuggingFace and custom tokenizers to Python, and budget reservation
counts once per distinct tokenizer a request names.
The legacy gpt-3.5-turbo-0301 message accounting (4 per message, -1 per name)
stays in Python: the selector declines it through the predicate token_counter
itself uses.
* feat(rust): count tiktoken o200k_base admission tokens in Rust (#40794)
Add a handwritten o200k_base split scanner and TokenCounter::from_o200k_ranks
next to the cl100k_base counter, sharing MergeRanks and the request
accounting. The Python bridge selects it when openai_tokenizer_encoding
names o200k_base, so gpt-4o, gpt-4.1, gpt-5, o1/o3/o4 and chatgpt-4o
requests stop tokenizing on the Python worker under LITELLM_RUST=true
Co-authored-by: yassin <yassin@berri.ai>
---------
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(rust_bridge): count budget-check input tokens in Rust on all LLM routes
Rust counts input tokens from the raw JSON body with the GIL released inside the existing budget reservation, covering every LLM route the auth dependency guards. It only fires for models on the Anthropic tokenizer when a budget is set, and Python counts whenever Rust is off, missing, or declines a body shape.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* perf(rust): count byte-level BPE tokens without the GPT-2 split regex (#40594)
The oniguruma run of the ByteLevel pre-tokenizer regex is about 90% of
encode_fast on a 100k token body (100 ms of the ~110 ms Rust admission
count in the gateway pod). A hand-written scanner that yields the same
pieces, then feeds the model directly, counts the same text in 10 ms.
It only engages for tokenizers with the Anthropic shape (optional NFKC,
ByteLevel without prefix space, no post-processor) and falls back to the
full encoder when the text contains an added token. Parity with
encode_fast is tested on random texts, the pieces are compared with the
real pre-tokenizer, and the \p{L}/\p{N}/\s tables are checked against
oniguruma for every code point.
NFKC runs through unicode-normalization-alignments, the crate and
Unicode tables NormalizedString::nfkc already uses, so the fast path
normalizes exactly what the full encoder would. Using the newer
unicode-normalization crate changed the count for 171 code points that
gained compatibility decompositions after Unicode 9 (U+32FF, U+A7F1..).
The fast normalizer is compared with the tokenizer's for every scalar
value and on random texts.
The scanner is built without mutable state: byte_char and mapped_len replace the const table builders and the reusable mapped buffer, and iter::successors replaces the stateful piece iterator. byte_chars_match_the_byte_level_alphabet checks the byte mapping against ByteLevel for every scalar value.
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(rust_bridge): bound concurrent token-count encodes and share the Anthropic tokenizer predicate
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>
* refactor(python-bridge): split non-streaming bridge modules
* refactor(python-bridge): bring shared function tracing into route layer
* feat(dev): list Python route functions and call sites
* feat(dev): list Rust route functions and call sites
* docs(dev): record OCR parity gaps across Python and Rust
* feat(dev): list executed SDK calls with runtime tracing
* feat(dev): report Python vs Rust SDK pipeline steps in one CLI
* feat(dev): side-by-side pipeline step report in compare CLI
* fix(dev): drop invalid Final annotations in compare cell loop
* feat(dev): blue python-only and yellow rust-only steps in compare CLI
* feat(dev): vertical layout with section spacing in compare CLI
* fix(dev): validate SDK trace stages across sync and async routes
* refactor(rust): align SDK route call structure with Python
* refactor(python-bridge): share sync and async route call wrappers
* refactor(dev): split compare CLI into fixtures, runtime, and report modules
* fix(ci): run SDK trace tests and satisfy test lint
Adds a chat_completions route module to litellm-core, mirroring the messages
route, plus Anthropic Messages and Bedrock Converse provider configs. The
per-model `rust: true` opt-in now covers /chat/completions for both providers.
The core accepts an allowlisted subset (text conversations, non-streaming) and
returns CoreError::Unsupported for anything else, so tool calls, multimodal
content and streaming fall back to the Python path transparently.
Resolves LIT-5698
* feat(messages): route Azure Anthropic /messages through Rust behind rust:true
Adds an opt-in Rust path for non-streaming Azure Anthropic Messages. A
deployment sets rust: true in litellm_params to route litellm.messages()
and the proxy /v1/messages endpoint through the native Rust bridge; a
missing flag or rust: false keeps the existing Python path, and non-Azure
providers, streaming, an unavailable bridge, or a None result all fall
back to Python. Rust-backed responses carry an x-litellm-rust: true
response header so callers can see which path served the request.
Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* test(docs): exclude LITELLM_USE_RUST_MESSAGES rollout flag from env-doc check
Mirrors the existing LITELLM_USE_RUST_OCR entry; the flag is an internal
rollout toggle that is intentionally not in the public environment settings
docs yet.
Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* fix(rust_bridge): isolate OCR enable flag and drop dead messages global toggle
use_litellm_rust only mutates the OCR enabled flag when configuring OCR (or
called with no bridge kwargs, preserving the legacy contract), so configuring
only the messages bridge no longer flips OCR state.
Remove the vestigial global enabled/env state from the messages bridge. Routing
is controlled per deployment by rust:true in the shared handler gate, so the
messages module never consulted the global toggle; drop it rather than leave a
no-op switch.
Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* refactor(rust/messages): split Anthropic config into its own provider file and type the request/response contract
Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* feat(messages): route eligible Azure Anthropic streaming through Rust via buffered fake-stream
Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* fix(messages): fold system-role messages for Azure Anthropic and fall back to Python on Rust bridge errors
Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* fix(rust_bridge): use Python::attach for amessages after pyo3 bump
Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* test(proxy): mock get_configured_token_limits in model_info tests
Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* ci: run rust_bridge unit tests in misc shard
Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* Revert "ci: run rust_bridge unit tests in misc shard"
This reverts commit c86d861a03.
* test(anthropic): move rust messages bridge tests into misc-shard dir
Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
pyo3 0.23.5 hard-caps the interpreter at Python 3.13, so building the
native bridge against a 3.14 interpreter aborts inside pyo3-ffi's build
script before anything links. This raises pyo3 and pyo3-async-runtimes
to 0.29 (currently the newest line, and the range starting at 0.26 that
supports 3.14) and migrates the three call sites whose APIs were renamed
across that range: Python::with_gil is now Python::attach and
Python::allow_threads is now Python::detach. On a GIL-enabled interpreter
those are pure renames with identical semantics, so behavior on 3.10
through 3.13 is unchanged
Verified by compiling the native module for cp313 and cp314 and driving
it directly on both interpreters: gil_stats reports exactly one GIL
release per sync OCR call and the async path completes, matching the
0.23.5 baseline. cargo fmt, clippy, and the workspace tests pass on both
3.13 and 3.14 with the lockfile locked, and the lock churn is confined to
the pyo3 crates
Part of #26343; addresses the pyo3 build failure reported in #33116
* refactor(litellm-rust): move provider transforms into litellm-core + crate allowlist test
* feat(litellm-rust): ai-gateway absorbs route I/O (io/) with lib+server feature split
* refactor(litellm-rust): point python-bridge at litellm-ai-gateway
* build(litellm-rust): macOS pyo3 dynamic_lookup linker flag for cdylib builds
* docs(litellm-rust): 3-crate map in README/AGENTS + refresh CLAUDE boundary
* refactor(litellm-rust): update workspace members to the three crates
