prompt-vcs

Git-native prompt management for Python applications. Keep prompts in code or YAML, pin production versions with a lockfile, and review every change through Git. No database or hosted service is required.

中文文档

Why prompt-vcs

• Code-first workflow with p() and @prompt.
• Single-file prompts.yaml by default, with a split-file mode for larger projects.
• Explicit version pinning through .prompt_lock.json; invalid or missing locked versions fail instead of silently falling back.
• CLI support for extraction, migration, version switching, history, testing, validation, A/B experiments, and export.
• Typed Python API, Jinja sandboxing, atomic state writes, and automatic cache refresh after file changes.

Install

pip install prompt-vcs

Optional dependencies:

pip install "prompt-vcs[validation]"  # JSON Schema validation
pip install "prompt-vcs[analysis]"    # Welch's t-test for small A/B samples

prompt-vcs requires Python 3.10 or newer.

Quick start

Initialize a project:

pvcs init

Use a prompt directly in Python:

from prompt_vcs import p

message = p("user_greeting", "Hello {name}", name="Ada")

The string in code is the development default. To manage versions in YAML, add them to prompts.yaml:

user_greeting:
  description: Greeting shown to a signed-in user
  versions:
    v1:
      template: "Hello {{ name }}"
    v2:
      template: "Welcome back, {{ name }}"

Pin a version and inspect the result:

pvcs switch user_greeting v2
pvcs status
pvcs diff user_greeting v1 v2

pvcs switch records the selected version in .prompt_lock.json. Commit that file with prompts.yaml so the application and its prompt versions move through Git together.

For prompts already stored in YAML, the code can omit the default string:

message = p("user_greeting", name="Ada")

Storage modes

The default single-file layout keeps the project compact:

your-project/
|-- .prompt_lock.json
|-- prompts.yaml
`-- src/

For a larger prompt collection, use pvcs init --split to store versions under prompts/<id>/<version>.yaml.

Main workflows

Run pvcs --help or pvcs <command> --help for all options.

Prompt tests render templates and apply deterministic assertions. Output validation supports substring, regex, length, JSON Schema, and custom rules. A/B experiments select prompt variants and store application-provided scores. These tools do not call an LLM or judge model quality on their own.

See Validation and testing for configuration examples.

Existing codebases

Preview an automatic migration before changing source files:

pvcs migrate src/ --dry-run

Then apply changes interactively or without prompts:

pvcs migrate src/
pvcs migrate src/ --yes

Use --clean to move templates into YAML and leave only prompt IDs and variables in code. Migration is implemented with LibCST so Python syntax and formatting remain structured.

Offline example

The repository includes a customer-support example covering rendering, version switching, YAML test suites, output validation, and Python tests without an API key:

python -m pip install -e ".[dev]"
powershell -ExecutionPolicy Bypass -File .\examples\customer-support-demo\run_all.ps1

See the example walkthrough for the files, commands, and expected output.

Development

python -m pip install -e ".[dev]"
npm --prefix vscode-extension ci
python scripts/verify.py --quick

Use python scripts/verify.py for the full local suite or python scripts/verify.py --release to also build and smoke-test distribution artifacts.

License

MIT