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 # Low-level Guidance (llguidance)
 
-This controller implements a context-free grammar parser with Earley's algorithm
-on top of a lexer which uses [derivatives of regular expressions](https://github.com/microsoft/derivre).
-
-It's to be used by next-generation [Guidance](https://github.com/guidance-ai/guidance) grammars.
-See how it works in [plan.md](./plan.md).
+This library implements constrained decoding (also called constrained sampling or
+structured outputs) for Large Langauge Models (LLMs).
+It can enforce arbitrary context-free grammar on the output of LLM
+and is fast (on the order of 1ms of CPU time per token
+(for 100k tokenizer) with negligible startup costs).
+
+Following grammar formats are supported:
+- `llguidance` - [internal (JSON-based) format](./parser/src/api.rs)
+- regular expressions (following Rust regex crate [syntax](https://docs.rs/regex/latest/regex/#syntax))
+- a large subset of JSON schemas
+- context-free grammars in (a [subset](./parser/src/lark/README.md) of) [Lark](https://github.com/lark-parser/lark) format
+
+The internal format is most powerful and can be generated by the following libraries:
+- [Guidance](https://github.com/guidance-ai/guidance) (Python)
+- [guidance.ts](https://github.com/mmoskal/guidance-ts) (TypeScript)
+- hopefully more to come!
 
-This is now available in `main` branch of Guidance.
-Guidance PR: https://github.com/guidance-ai/guidance/pull/951
+The library can be used from:
+- [Rust](./parser/README.md), [sample](./sample_parser/src/sample_parser.rs)
+- [C and C++](./parser/llguidance.h), [sample](./c_sample/c_sample.cpp)
+- [Python](./python/llguidance/_lib.pyi)
+
+The library is currently integrated in:
+- [Guidance](https://github.com/guidance-ai/guidance) - library for interacting with LLMs;
+  uses either llama.cpp or HF Tranformers
+- [LLGTRT](https://github.com/guidance-ai/llgtrt) - OpenAI-compatible REST server using NVIDIA's [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM)
+
+The integration is ongoing in:
+- onnxruntime-genai - [draft PR](https://github.com/microsoft/onnxruntime-genai/pull/1038)
+- mistral.rs - [preliminary PR](https://github.com/EricLBuehler/mistral.rs/pull/899)
+- llama.cpp - [branch](https://github.com/mmoskal/llama.cpp/tree/llg);
+  note that llama.cpp is fully integrated in Guidance above
+  via Python bindings
+
+Given a context-free grammar, a tokenizer, and prefix of tokens,
+llguidance computes a token mask (set of tokens from the tokenizer)
+that when added to current prefix of token can lead to a valid string in
+the language of the grammar.
+Computing a mask takes on the order of 1ms of single-core CPU time 
+for a tokenizer with 100k tokens.
+While this depends on the exact grammar, it holds eg. for grammars resulting from JSON schemas.
+There is also no significant startup cost.
+
+The library implements a context-free grammar parser with Earley's algorithm
+on top of a lexer which uses [derivatives of regular expressions](https://github.com/microsoft/derivre).
 
 Grammars are normally [JSON-serialized](./parser/src/api.rs).
 The following libraries produce llguidance grammars:
 
-- [guidance](https://github.com/guidance-ai/guidance) (Python)
-- [guidance.ts](https://github.com/mmoskal/guidance-ts) (TypeScript)
-- hopefully more to come!
-
 ## Building
 
 - [install rust](https://www.rust-lang.org/tools/install); 1.75 or later