pleasedodisturb/awesome-llm-token-optimization

Local proxy that compresses your LLM API requests so you pay less, with no change to the answers. Trims wasted tokens from prompts, history, tool output, and code before they're sent: -31% input / -74% output, measured live. Any provider, no extra model calls. Also an MCP server and embeddable library (Rust, Python, Ruby, Kotlin, Swift, JS/TS).

Drop-in prompt compression for production LLM apps. Cut your token bill 40-60% without changing your code. Python SDK, LLMLingua-2, MIT.

[EMNLP'23, ACL'24] To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss.

A curated list on AI token economics: what tokens cost, where they get wasted, and how to cut the bill. Tools, benchmarks, papers, and copy-paste configs for the token economy of LLMs and coding agents.

A framework for serving and evaluating LLM routers - save LLM costs without compromising quality

Find the ghost tokens. Fix them. Survive compaction. Avoid context quality decay.

One tiny lib for LLM token + cost math

Optimizing inference proxy for LLMs

Spend fewer tokens on the same work. A local CLI that finds wasted tokens in Claude Code, Codex and your own API calls.