---
title: "Caveman vs LLMLingua for prompt compression"
description: "Choose Caveman for a coding agent that needs smaller tool results and a way to fetch the original bytes. Choose LLMLingua when you are building a prompt-compression pipeline and want to evaluate a lea"
canonical: https://caveman.so/compare/llmlingua
last-updated: 2026-09-07
---

# Caveman vs LLMLingua for prompt compression

Choose Caveman for a coding agent that needs smaller tool results and a way to fetch the original bytes. Choose LLMLingua when you are building a prompt-compression pipeline and want to evaluate a learned compressor directly in your application.

The two approaches sit at different levels. [Microsoft's LLMLingua project](https://github.com/microsoft/LLMLingua) provides prompt-compression methods including LLMLingua, LongLLMLingua, and LLMLingua-2. Caveman packages content-specific compression with agent launchers, a local store, and recovery tooling.

## What you are adopting

| Question | Caveman | LLMLingua |
| --- | --- | --- |
| Main integration | Wrap a supported agent or use the local engine | Call a prompt-compression library in your application |
| Compression decision | Content detection and a matching compressor | Model-based prompt compression with configurable targets |
| Original bytes | Stored before a lossy view is published | Your application must define its own original-data retention and retrieval contract |
| Extra model inference | Core content detection and compressor selection run locally without an LLM call | Include the compression model's runtime in your benchmark |
| Best first trial | Repeated logs, JSON, diffs, or tool results | Your document or prompt corpus with a fixed quality task |

LLMLingua's research discusses information preservation. That is different from retrieving the exact original file. A model reconstructing important information from compressed text does not prove byte identity.

## Where Caveman removes integration work

An agent can receive a compact result, decide it needs detail, and call the recovery tool. Caveman supplies that loop for supported paths. You do not need to invent a new marker format and teach every tool how to resolve it.

The original store also changes how failure is handled. If a lossy view cannot be stored safely, Caveman keeps the source. If the compressed result is not smaller, the original stays in the request. The local engine's supported input formats and parser builds still matter; unsupported content is not guaranteed to compress.

For a team that runs coding agents on developer machines, those behaviors are often more valuable than a compression ratio on a document benchmark. They determine whether the optimizer can stay enabled during unfamiliar work.

## Where learned compression deserves a test

If your application assembles retrieval passages or long prose prompts, you control the place where a compressor runs. You can tune a learned method for that corpus and evaluate it against your answer-quality checks.

That control comes with operational work. Measure compressor latency and resource use, preserve the source documents, and decide how the model can recover omitted information. Keep structured identifiers and exact text requirements in the grader. A useful summary can still be wrong for a task that needs an exact clause or number.

Avoid adopting a target ratio as a quality guarantee. Your chosen model, language mix, and downstream task decide how much information can be removed.

## A fair comparison

Use the same captured input in both candidates. Keep the downstream model fixed. Compare the finished answer and the total time and cost, including compression inference where used. Test sources that contain rare values and contradictory passages, because deleting either can produce an easy-looking but incorrect answer.

For an agent task, add the cost of follow-up questions and recovery. A method that creates the shortest first prompt can still produce the most expensive completed run. The [measurement guide](/guides/measure-agent-cost) gives a common result format.

We do not publish a Caveman-versus-LLMLingua performance number here because we have not established a matched benchmark for this page. The architectural differences are documented; a ranking on your corpus requires a run.

## Try Caveman on a recorded payload

The [switching guide](/switch/llmlingua) starts with an offline payload and adds recovery before changing the application. That lets you evaluate Caveman without removing LLMLingua from the production path.

Sources: [LLMLingua repository and papers](https://github.com/microsoft/LLMLingua), [Caveman engine](https://docs.caveman.so/docs/proxy/engine), and [Caveman recovery](https://docs.caveman.so/docs/proxy/recoverable).