promptlock 0.2.0

Prompt versioning, output validation, and run logging for LLM pipelines

promptlock

Prompt versioning, output validation, and run logging for LLM pipelines.

Most LLM engineering pain comes from three places: prompts that drift without anyone noticing, outputs that break downstream logic, and no record of what ran when. promptlock fixes all three — no cloud account, no dashboard, no framework lock-in.

pip install promptlock

---

Why promptlock

Problem	What promptlock gives you
Prompts change silently and break things	Version-controlled prompt registry backed by a local YAML file
LLM output shape is unpredictable	Output contracts that validate structure, length, and patterns
No record of what ran in production	SQLite run logger with filtering and summary stats

---

Install

pip install promptlock
# or with uv
uv add promptlock

Requires Python 3.11+. Only one external dependency: pyyaml.

---

Quickstart

from promptlock import PromptRegistry, OutputContract, RunLogger
from promptlock.exceptions import ContractViolation

# 1. Save and load versioned prompts
registry = PromptRegistry("prompts.yaml")
registry.save("summarizer", "v1.0", "Summarize this document: {doc}\nLanguage: {lang}")

template = registry.load("summarizer", version="latest")
rendered = template.render(doc="AI is transforming healthcare.", lang="English")

# 2. Validate the LLM output
contract = OutputContract(
    required_fields=["summary", "keywords"],
    max_length=500,
    min_length=20,
)

llm_output = {"summary": "AI aids diagnostics.", "keywords": ["ai", "health"]}

try:
    contract.validate(llm_output)
    validated = True
    error = None
except ContractViolation as e:
    validated = False
    error = str(e)

# 3. Log the run
logger = RunLogger("runs.db")
logger.log(
    prompt_name="summarizer",
    version="v1.0",
    model="gpt-4o",
    input=rendered,
    output=llm_output,
    validated=validated,
    error=error,
)

---

Modules

PromptRegistry

Store and retrieve prompt versions from a local YAML file.

from promptlock import PromptRegistry

registry = PromptRegistry("prompts.yaml")

# save a prompt version
registry.save("classifier", "v1.0", "Classify the following text: {text}")
registry.save("classifier", "v1.1", "Classify this as positive/negative/neutral: {text}")

# load a specific version
template = registry.load("classifier", version="v1.0")

# load the most recent version
template = registry.load("classifier", version="latest")

# list all prompts and versions
registry.list_prompts()
# {'classifier': ['v1.0', 'v1.1']}

# delete a version or all versions
registry.delete("classifier", version="v1.0")
registry.delete("classifier")

---

PromptTemplate

Render prompt strings with named placeholders.

from promptlock import PromptTemplate

template = PromptTemplate("Translate this to {lang}: {text}")

print(template.variables)
# ['lang', 'text']

rendered = template.render(lang="French", text="Hello world")
# 'Translate this to French: Hello world'

# missing variables raise TemplateRenderError
template.render(lang="French")
# TemplateRenderError: Missing required template variables: ['text']

---

OutputContract

Define and validate the expected shape of an LLM output.

from promptlock import OutputContract
from promptlock.exceptions import ContractViolation

# validate a JSON output
contract = OutputContract(
    required_fields=["summary", "keywords"],
    max_length=500,
    min_length=20,
)
contract.validate({"summary": "Short summary.", "keywords": ["ai"]})  # passes

# validate a plain string
sentiment_contract = OutputContract(
    allowed_values=["positive", "negative", "neutral"]
)
sentiment_contract.validate("positive")   # passes
sentiment_contract.validate("unknown")    # raises ContractViolation

# validate with regex
contract = OutputContract(regex_patterns=[r"\d{4}"])
contract.validate("Report from 2025")   # passes
contract.validate("No year here")       # raises ContractViolation

Available rules:

Rule	Type	Description
required_fields	list[str]	Keys that must exist in a JSON output
max_length	int	Maximum character length of the output
min_length	int	Minimum character length of the output
regex_patterns	list[str]	Patterns the output must match (all must pass)
allowed_values	list[str]	Output must be one of these exact strings

---

RunLogger

Log every LLM run to a local SQLite file.

from promptlock import RunLogger

logger = RunLogger("runs.db")

logger.log(
    prompt_name="summarizer",
    version="v1.1",
    model="gpt-4o",
    input="Summarize this: ...",
    output={"summary": "AI is evolving.", "keywords": ["ai"]},
    validated=True,
)

# retrieve runs with filters
logger.get_runs(prompt_name="summarizer", validated="failed", limit=10)

# quick summary of pass/fail counts
logger.summary("summarizer")
# {'passed': 42, 'failed': 3, 'not_checked': 5}

---

Exceptions

All exceptions inherit from PromptlockError so you can catch broadly or specifically.

from promptlock.exceptions import (
    PromptlockError,       # base exception
    ContractViolation,     # output failed validation
    PromptNotFound,        # prompt name/version not in registry
    TemplateRenderError,   # missing variable during render
)

---

Project structure

src/promptlock/
├── __init__.py       # public API
├── registry.py       # PromptRegistry
├── template.py       # PromptTemplate
├── contract.py       # OutputContract
├── logger.py         # RunLogger
└── exceptions.py     # custom exceptions

---

Contributing

Pull requests are welcome. For major changes, please open an issue first.

git clone https://github.com/NorthCommits/Promptlock
cd Promptlock
uv sync
uv run pytest -v

---

License

MIT