llmtrim-uniffi

UniFFI bindings over llmtrim-core: one Rust definition, idiomatic in-process bindings for Python, Ruby, Swift and Kotlin. The compression runs natively in the caller's process (no server, no async).

API

A deliberately flat surface over the engine:

fn compress(
    input: String,                 // a provider-shaped request body (JSON)
    provider: Option<Provider>,    // OpenAi | Anthropic | Google, or None to auto-detect
    preset: Option<String>,        // "aggressive" | "agent" | "code" | "rag" | "safe" | …
                                   // None = config from the environment / config file
) -> Result<CompressOutput, LlmtrimError>

CompressOutput carries the compressed request_json, the resolved provider/model, the tokenizer label/exactness, and the before/after/frozen input-token counts. Embedders that need the full rehydration plan or per-stage reports should depend on llmtrim-core directly in Rust.

In-process vs. the proxy

llmtrim has two integration routes:

The proxy (HTTPS_PROXY=127.0.0.1:8788 llmtrim) intercepts your existing traffic and compresses it in flight. Nothing in your code changes, but the client has to route through the proxy and trust its CA.
These bindings compress in your process. You call compress() on the request body, then send the result with your own HTTP client. No proxy, no CA, no env-var setup.

Use the in-process path when the proxy can't run:

Sandboxed / serverless functions where you can't set a process-wide HTTPS_PROXY or run a side process.
Certificate-pinned clients that reject a MITM CA, so the proxy's interception fails.
Anywhere you'd rather not add a network hop or an extra moving part.

It replaces a per-framework adapter: instead of wiring a hook into each SDK, you compress the body once and POST it yourself. Runnable end-to-end examples (compress, then send with your own client) are in examples/.

Python

# Build a self-contained wheel (cdylib + generated glue):
crates/llmtrim-uniffi/scripts/build-wheel.sh --release
pip install target/wheels/llmtrim-*.whl

import llmtrim, json

req = json.dumps({"model": "gpt-4o",
                  "messages": [{"role": "user", "content": "…"}]})
out = llmtrim.compress(req, llmtrim.Provider.OPEN_AI, "aggressive")
print(out.input_tokens_before, "->", out.input_tokens_after)
# send out.request_json to the provider

Why build-wheel.sh and not plain maturin build: maturin's bindings = "uniffi" auto-packaging is sensitive to the maturin↔uniffi version pair. With maturin 1.14 + uniffi 0.31 it builds the native library into the wheel but omits the generated Python glue (empty package __init__.py). The script runs maturin, then injects the freshly generated bindings and repacks the wheel with valid RECORD hashes. Remove it once the auto path packages cleanly.

Ruby / Swift / Kotlin

All targets generate from the same built library, no extra Rust. The generated glue is a build artifact (its checksums are pinned to the library ABI), so it is regenerated per release rather than committed:

crates/llmtrim-uniffi/scripts/generate-bindings.sh out/   # python, ruby, swift, kotlin

Generation needs an unstripped library. Library-mode uniffi-bindgen reads metadata symbols from the cdylib, but the workspace release profile sets strip = true. The script therefore generates from the (unstripped) debug build; the native library you ship can be a stripped cargo build --release -p llmtrim-uniffi cdylib; the glue loads it by name.

Ruby (verified). This is the raw generated binding (module LlmtrimFfi), for a source build with libllmtrim_ffi.so on the load path. The published gem aliases it to Llmtrim (require "llmtrim" then Llmtrim.compress(...)); see packaging/ruby.

require_relative "llmtrim_ffi"
require "json"
out = LlmtrimFfi.compress(
  JSON.generate({model: "gpt-4o", messages: [{role: "user", content: "…"}]}),
  LlmtrimFfi::Provider::OPEN_AI, "aggressive")
puts "#{out.input_tokens_before} -> #{out.input_tokens_after}"

Swift emits llmtrim_ffi.swift + an FFI header and modulemap; Kotlin emits uniffi/.../llmtrim_ffi.kt (which loads the cdylib via JNA). CI compiles and runs a smoke for both: Swift on macOS (swiftc against the modulemap), Kotlin on a JVM (kotlinc + JNA), so a binding break is caught in all four languages (see tests/swift, tests/kotlin and the bindings* jobs in .github/workflows/ci.yml).

Publishable packages

Each ships the compiled engine bundled, so consumers need no Rust toolchain:

Target	Build	Package	Verified
Python (PyPI)	`scripts/build-wheel.sh`	wheel	locally
Ruby (gem)	`scripts/build-gem.sh`	`packaging/ruby/`	locally
Kotlin/JVM (Maven)	`scripts/build-maven.sh`	`packaging/kotlin/`	locally
Swift (SwiftPM)	`scripts/build-xcframework.sh`	`packaging/swift/`	macOS CI only

Each packaging/<lang>/README.md has the usage + publish details.

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