llmlingua
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.
Decision gist · record as of 2026-08-14
Yes. The package solves a real problem—token costs and context limits—with active maintenance, low install friction, and permissive licensing. It integrates cleanly into existing LLM workflows and has been adopted by major frameworks. Install if you work with LLM APIs or long-context tasks and want to reduce costs or fit within token constraints.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.8.0 and a compatible version of torch and transformers; model weights are downloaded on first use.
- Low friction install with a pure Python wheel.
- The package depends on transformers, torch, accelerate, and other standard ML libraries—all widely maintained.
License · maintenance · safety
MIT License (permissive) — MIT License permits unrestricted use, modification, and distribution in both open-source and commercial projects with minimal obligations.
last release 2024-04-09 (857 days) · last repo commit 2026-04-08 · 6,559 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 95,983 downloads/mo, #13,238 on PyPI
Alternatives
Verify before relying
pip install llmlingua
from llmlingua import PromptCompressor
llm_lingua = PromptCompressor()
compressed = llm_lingua.compress_prompt(
prompt="your prompt here",
instruction="",
question="",
target_token=200
)- Whether the package works offline after initial model download or requires network access for inference.
- Memory footprint and GPU requirements for different compression methods (LLMLingua vs. LongLLMLingua vs. LLMLingua-2).
- Compatibility with specific LLM APIs (OpenAI, Anthropic, etc.) beyond the examples shown.
What it is and what it does
LLMLingua is a prompt compression toolkit that uses a small, efficient language model to identify and remove non-essential tokens from prompts before sending them to larger LLMs. It implements three variants—LLMLingua, LongLLMLingua (for long-context scenarios), and LLMLingua-2 (faster, distilled from GPT-4)—each designed to reduce token consumption while preserving task performance. The core use case is lowering costs and latency when working with token-metered LLM APIs or handling long contexts that would otherwise exceed model limits.
The package integrates with popular RAG frameworks like LangChain and LlamaIndex, making it straightforward to drop into existing pipelines. It depends on transformers, torch, accelerate, tiktoken, nltk, and numpy—standard ML infrastructure. The project is actively maintained by Microsoft researchers, with recent releases and integration into production systems.
Use it for
- Reduce API costs when calling GPT-4 or GPT-3.5 by compressing prompts before submission.
- Handle long documents in RAG systems by compressing retrieved context to fit within token budgets.
- Mitigate the 'lost in the middle' problem in long-context LLM tasks by prioritizing key information.
- Accelerate inference latency by reducing KV-cache size and prompt processing overhead.
- Preserve in-context learning and chain-of-thought reasoning while shrinking prompt size.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package solves a real problem—token costs and context limits—with active maintenance, low install friction, and permissive licensing. It integrates cleanly into existing LLM workflows and has been adopted by major frameworks. Install if you work with LLM APIs or long-context tasks and want to reduce costs or fit within token constraints.
Install
llmlingua on PyPI
Before you install
Low friction install with a pure Python wheel. The package depends on transformers, torch, accelerate, and other standard ML libraries—all widely maintained. Maintenance is active with recent commits and a healthy star count.
Requires Python >=3.8.0 and a compatible version of torch and transformers; model weights are downloaded on first use.
License in practice
MIT License permits unrestricted use, modification, and distribution in both open-source and commercial projects with minimal obligations.
Quickstart
pip install llmlingua
from llmlingua import PromptCompressor
llm_lingua = PromptCompressor()
compressed = llm_lingua.compress_prompt(
prompt="your prompt here",
instruction="",
question="",
target_token=200
)Verify before relying
- Whether the package works offline after initial model download or requires network access for inference.
- Memory footprint and GPU requirements for different compression methods (LLMLingua vs. LongLLMLingua vs. LLMLingua-2).
- Compatibility with specific LLM APIs (OpenAI, Anthropic, etc.) beyond the examples shown.
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release >=3.8.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagestransformersacceleratetorchtiktokennltknumpy |
| Maintenance | Actively maintained 857 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 95,983 / month, #13,238 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: Science/ResearchProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: llmlingua-0.2.2-py3-none-any.whl
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