* 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>
Build one ClientConfig that names ring and loads the native roots once, and hand it to every tokio-tungstenite dial as its connector instead of installing a process-wide default from the dial path. Each of the three dial sites gets a wss:// test that reproduces the panic if its connector is dropped.
The two tests that shipped with the fix both called ensure_crypto_provider
themselves, so deleting the call from connect_upstream left the whole suite
green, and swapping ring for aws-lc-rs did too.
Adds an integration test, which gets its own process, that dials wss:// at a
local plain-TCP listener through the public Responses WebSocket entrypoint and
asserts an Err plus an installed provider. Without the install in the dial it
panics with the original CryptoProvider message. A unit test now compares the
installed provider's cipher suites and key-exchange groups against ring's, so
the choice of backend is pinned rather than assumed.
Also names tls12 in the workspace rustls features: it already arrives through
reqwest and tokio-rustls, so the graph is unchanged, but a direct dependency
should say it needs TLS 1.2 rather than inherit it.
The gateway's dependency graph turns on two rustls crypto backends at once:
reqwest's rustls-tls pulls in ring, and litellm-core's bedrock-auth pulls in
aws-lc-rs through aws-config. rustls 0.23 refuses to guess between them, so
ClientConfig::builder panics, and that is exactly how tokio-tungstenite builds
its TLS config. Every outbound WebSocket dial killed its tokio worker and the
client saw the socket vanish with no close frame.
reqwest and the AWS SDK both pick a provider explicitly, so only the tungstenite
path was affected. Route all three dial sites through one helper that installs
ring once per process before connecting.
The image had two independent breaks. The Dockerfile pinned rust 1.90 while
the repo pins 1.98 in rust-toolchain.toml and never copied it in, so the first
cargo call died on crates needing a newer rustc. The runtime stage then ran
pip install on the root pyproject, which builds with maturin against the
python-bridge crate, so metadata generation failed with no Cargo manifest and
no Rust toolchain in that stage.
Copy rust-toolchain.toml into the builder so every cargo call uses the pinned
channel, build the wheel in the builder stage where cargo and python3-dev
already live, and have the runtime stage install that artifact instead of
compiling anything. Add the ai-gateway image job to the rust workflow so a
broken build fails a PR instead of surfacing on a release.
Clippy never links, so python-config's pyo3/auto-initialize needs no
libpython and the gateway clippy step can cover every feature at once.
The test step stays on --features server because cargo test does link
and this job installs no Python.
The check list existed in three places that had already drifted apart;
CLAUDE.md is now the only copy and the other two point at it.
The Dockerfile asked cargo for --features python-config, which cannot
select the litellm-ai-gateway bin target: that target carries
required-features = ["server"], so cargo silently built nothing and the
later COPY of /build/litellm-rust/target/release/litellm-ai-gateway had
no file to copy. Turn the server feature on and name the bin explicitly
so a future required-features drift fails at the cargo step instead of
silently producing an empty release dir.
litellm-ai-gateway's server feature is off by default and nothing in the workspace turns it on, so the workspace clippy and test steps never compiled src/auth, src/routes, src/state, src/realtime or the gateway binary. 43 tests ran instead of 57.
Adds the two steps CLAUDE.md already documents as the local gate, and fixes the three collapsible_if violations that had accumulated behind the flag.
* 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
