llmpromptvault 0.1.1

Version, compare, and manage your LLM prompts. No API keys required. Bring your own model.

LLMPromptVault 🔐

Version and compare your LLM prompts. No API key required. Bring your own model.

---

Why LLMPromptVault Exists

Prompt engineering is iterative.

You tweak wording.
You test outputs.
You switch models.
You measure cost.
You forget what worked.

LLMPromptVault gives structure to that chaos.

It helps you:

• Track prompt versions
• Log LLM responses with metrics
• Compare prompt variants side-by-side
• Analyze performance over time

---

What LLMPromptVault Does

LLMPromptVault is a prompt lifecycle management library — not an LLM client.

It does three core things:

1. Version your prompts — track every change like Git
2. Log responses — store outputs with latency and token usage
3. Compare prompts — see side-by-side differences with metrics

⚠️ LLMPromptVault never calls any LLM.

You call your own model — OpenAI, Claude, Gemini, Ollama, local models — and pass the response to LLMPromptVault.

---

Installation

pip install llmpromptvault

Only one dependency: pyyaml.
Everything else uses Python’s standard library.

---

Quick Start

from llmpromptvault import Prompt, Compare

# 1️⃣ Define two versions of a prompt
v1 = Prompt("summarize", template="Summarize this: {text}", version="v1")
v2 = Prompt("summarize", template="Summarize in 3 bullet points: {text}", version="v2")

# 2️⃣ YOU call your LLM (any model, any way you like)
r1 = your_llm(v1.render(text="Some article content..."))
r2 = your_llm(v2.render(text="Some article content..."))

# 3️⃣ Compare and log results
cmp = Compare(v1, v2)
cmp.log(r1, r2)
cmp.show()

Example Output:

────────────────────────────────────────────────────────────
  LLMPROMPTVAULT COMPARISON
────────────────────────────────────────────────────────────
  Prompt A                       summarize (v1)
  Prompt B                       summarize (v2)
────────────────────────────────────────────────────────────

  ── Response A ──
  Here is a summary of the article...

  ── Response B ──
  • Key point one
  • Key point two
  • Key point three

────────────────────────────────────────────────────────────
  Metric                           Prompt A     Prompt B
  ──────────────────────────── ──────────── ────────────
  Word count                             12           18
  Char count                             68          112
  Latency (ms)                        820.0        950.0
  Tokens                                 45           62
────────────────────────────────────────────────────────────

---

Core API

Prompt — Define and Version Prompts

from llmpromptvault import Prompt

p = Prompt(
    name="classify",
    template="Classify this text as positive or negative: {text}",
    version="v1",
    description="Sentiment classifier",
    tags=["classify", "sentiment"],
)

# Variables required by template
p.variables()      # ['text']

# Render prompt (no LLM call happens here)
rendered = p.render(text="I love this product!")

# You call your LLM
response = your_llm(rendered)

# Log run metadata
p.log(
    rendered_prompt=rendered,
    response=response,
    model="gpt-4o-mini",
    latency_ms=820,
    tokens=45,
)

# Aggregate statistics
p.stats()

# Raw run history
p.runs(last_n=10)

---

Versioning

# Create a new version — v1 automatically preserved
v2 = p.update(
    new_template="You are a sentiment expert. Classify as positive/negative/neutral: {text}"
)

# View full history
p.history()

Versioning is explicit and controlled — nothing happens automatically.

---

Save & Load YAML

Export prompts as human-readable YAML:

p.save("prompts/classify.yaml")

Load anywhere:

p = Prompt.load("prompts/classify.yaml")

Example YAML:

name: classify
version: v1
description: Sentiment classifier
template: "Classify this text as positive or negative: {text}"
tags:
  - classify
  - sentiment

---

Compare — Side-by-Side Prompt Evaluation

from llmpromptvault import Compare

cmp = Compare(v1, v2)

cmp.log(
    response_a=response_v1,
    response_b=response_v2,
    model="gpt-4o",
    latency_ms_a=820,
    latency_ms_b=950,
    tokens_a=45,
    tokens_b=62,
)

cmp.show()
cmp.diff()
cmp.summary()

You can:

• Compare output length
• Compare token usage
• Compare latency
• Aggregate results across multiple runs

---

Registry — Share Prompts Across Projects

from llmpromptvault import Registry

reg = Registry("./shared_prompts")

reg.push(v1)
reg.push(v2)

reg.list()
reg.versions("classify")

latest = reg.pull("classify")
specific = reg.pull("classify", "v1")

reg.delete("classify", "v1")

Registry is useful for:

• Team collaboration
• Shared prompt libraries
• Reproducible experiments

---

Works With Any LLM

Because LLMPromptVault never calls an LLM itself, it works with anything.

OpenAI

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": rendered}]
)

p.log(rendered, response.choices[0].message.content, model="gpt-4o")

Anthropic

import anthropic

client = anthropic.Anthropic(api_key="...")

response = client.messages.create(
    model="claude-haiku-4-5-20251001",
    max_tokens=1024,
    messages=[{"role": "user", "content": rendered}]
)

p.log(rendered, response.content[0].text, model="claude-haiku-4-5-20251001")

Ollama (Local)

import requests

response = requests.post(
    "http://localhost:11434/api/generate",
    json={"model": "llama3", "prompt": rendered, "stream": False}
)

p.log(rendered, response.json()["response"], model="llama3")

---

Typical Project Structure

your_project/
├── prompts/
│   └── classify.yaml
├── .promptvault/
│   ├── history.json
│   └── runs.db
└── main.py

Add .promptvault/ to .gitignore to keep run logs local, or commit it to share analytics with your team.

---

License

MIT © LLMPromptVault Contributors