promptslim 0.4.1

AI Prompt Slimming Toolkit — reduce token consumption at the source before every API call. 40+ Chinese & English redundancy patterns, code protection, Anthropic Prompt Caching analysis.

PromptSlim 🪒

AI Prompt Slimming Toolkit — reduce token consumption at the source before every API call.

🌐 English | 中文

🎯 Token Optimization · Cost Saving · Prompt Engineering · LLM Tools

📖 掘金详解 v0.3.0： 给你的 AI 提示词剃得再干净一点

What Problem Does This Solve?

Every word you send to an LLM costs money. Filler words, redundant phrases, and polite fluff silently drain your budget. Most developers don't realize 5-40% of their token spend is waste.

PromptSlim strips redundancy at the prompt level — before it reaches the API. Two lines of Python and you're done.

Quick Start

pip install promptslim

Python SDK

from promptslim import quick_slim

text = "嗯，那个我想说的是，这个功能非常非常好用，对吧？"
report = quick_slim(text)
print(f"Token saved: {report.savings_pct}% | Cost saved: ${report.cost_saved:.6f}/call")

One-Line OpenAI Integration

import promptslim
promptslim.patch_openai()  # 一行代码，自动压缩所有 prompt

from openai import OpenAI
client = OpenAI()
# 所有调用自动压缩 — 无需改动业务代码
response = client.chat.completions.create(model="gpt-4o", messages=[...])

CLI

# Count tokens
promptslim count "Hello world" -m gpt-4o

# Quick slim (rule-based, no API required)
promptslim slim prompt.txt -o slimmed.txt

# Smart compression (LLM-powered, preserves semantics)
promptslim smart long_chat.json -m gpt-4o-mini --max-tokens 512 -o slimmed.txt

# Compare two texts
promptslim compare old.txt new.txt

Demo

快速体验

pip install promptslim
echo "嗯那个我想说的是这个功能非常非常好用对吧" | promptslim slim

Python API

from promptslim import quick_slim
report = quick_slim("嗯那个我想说的是这个功能非常非常好用对吧")
print(f"节省 {report.savings_pct}% Token")

更多用法

# 统计 Token 数
promptslim count -i prompt.txt

# 智能压缩（LLM 二次精简）
promptslim smart -i prompt.txt --api-key YOUR_KEY

# 对比精简前后差异
promptslim compare -i prompt.txt

功能	说明
中英文冗余	40+ 条规则自动检测
代码保护	Python/JS/Go 代码块自动跳过
LLM 语义压缩	调用 LLM 二次精简，再省 30-50%
缓存分析	Anthropic Prompt Caching 命中率预估

Original: In order to basically say that this is really very important
          and actually I think we should definitely consider it.
Slimmed:  say this is important and I think we should consider it.
Saved:    31.3% tokens

Original: 嗯，那个我想说的是，这个功能非常非常非常好用，对吧？你知道吗？
Slimmed:  我想说的是，这个功能好用。
Saved:    40.0% tokens

Real-world Benchmarks

Scenario	Original Tokens	Slimmed	Savings	Semantics
Customer support logs	15,432	9,871	36.0%	Preserved
Code review prompt	2,847	1,923	32.5%	Code protected
Meeting transcript	8,210	5,416	34.0%	Preserved
Multi-turn chat history	4,560	3,102	32.0%	Preserved
Technical documentation	1,200	1,080	10.0%	Preserved

Features

Feature	Description
Redundancy Detection	40+ patterns in Chinese & English — filler words, redundant modifiers, verbose phrases
Smart Compression	LLM-powered semantic compression for chat history before context overflow
Comparison Reports	Before/after token count, cost, and savings percentage at a glance
Multi-Model Tokenizer	Accurate tiktoken counting for GPT / Claude / DeepSeek / Qwen
Python SDK	One-line: from promptslim import quick_slim
OpenAI Patch	promptslim.patch_openai() auto-compresses all calls
Bilingual	Works with both Chinese and English text

vs Alternatives

Feature	PromptSlim	promptfoo	Langfuse	tiktoken
Pre-call compression	Yes	No	No	No
Rule-based dedup	Yes (40+ patterns)	No	No	No
LLM smart compression	Yes	No	No	No
Prompt Caching analysis	Yes (Claude-specific)	No	No	No
Token counting	Yes	Partial	No	Yes
Chinese + English	Yes	Partial	No	No
CLI + SDK	Yes	Yes	Yes	SDK only

Redundancy Patterns

Type	Examples
English fillers	um, uh, hmm, basically, literally, actually
English modifiers	very, really, extremely, absolutely
English verbose phrases	in order to → to, due to the fact that → because
Chinese fillers	嗯, 啊, 哦, 那个, 就是说
Chinese modifiers	非常, 特别, 极其, 十分, 超级
Polite fluff	希望对你有所帮助, 如有问题请随时联系
Repeated punctuation	！！→！, ？？→？

Paired with AI Cost Sentinel

Slim before call → Track after call. Form a complete cost optimization loop.

import openai
from promptslim import quick_slim

# 1. Slim before sending
text = load_prompt()
report = quick_slim(text)

# 2. Send through Sentinel proxy (tracks actual cost)
client = openai.OpenAI(base_url="http://localhost:8000/v1")
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": report.slimmed}]
)

# 3. See estimated savings
print(f"Estimated savings: ${report.cost_per_call_saved:.6f}/call")

Project Structure

promptslim/
├── promptslim/
│   ├── __init__.py        # Public API exports
│   ├── __main__.py        # python -m promptslim entry
│   ├── cache.py           # Anthropic Prompt Caching analysis
│   ├── cli.py             # CLI entry point
│   ├── compressor.py      # 40+ redundancy patterns + LLM compressor
│   ├── patch.py           # OpenAI SDK monkey-patch
│   └── tokenizer.py       # Multi-model token counting
├── tests/
│   └── test_promptslim.py
├── .github/workflows/
│   └── python-test.yml
├── pyproject.toml
└── README.md

Known Limitations

• Rule engine only does safe deletion (fillers, redundant phrases). If your prompt is fundamentally verbose, it won't rewrite your style
• Not suitable for legal/contract text. "Redundant" words in legal docs are often intentional emphasis
• Code blocks have protection logic (looks_like_code), but edge cases (e.g. pseudo-code comments mixing Chinese and English) may be misjudged
• smart_compress depends on LLM quality. Try quick_slim first — it's free and offline
• Currently OpenAI-compatible APIs only

已知限制

• 规则引擎只做安全删除（填充词、冗余句式），不会改写表达风格
• 法律/合同类文本不适用——这些文档的"冗余词"可能是刻意强调
• 代码有保护逻辑，但混合中英文的伪代码注释等边缘情况可能被误判
• smart_compress 依赖 LLM 质量，建议先用免费的 quick_slim 看效果
• 目前只支持 OpenAI 兼容 API

Roadmap

• Web playground (paste text, see savings live)
• VS Code extension (slim on save)
• Custom regex rules
• Batch processing directory
• LangChain / LlamaIndex integration

License

MIT — see LICENSE