promptci 1.0.0

Prompt versioning with CI/CD regression gates — version, test, diff, and deploy prompts with quality gates, schema evolution, PII scrubbing, and full observability

promptregistry

Prompt versioning with CI/CD regression gates — for production LLM engineering.

Version, diff, test, and deploy prompts with quality gates, schema migration, PII scrubbing, and full observability.

pip install promptregistry

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Why promptregistry?

In 2026, prompt changes are the most common cause of silent LLM regressions. Teams edit prompts in Google Docs, paste them into code, and ship — with zero version control, no diff visibility, and no quality gates. When output quality degrades, nobody knows which prompt change caused it.

promptregistry brings software engineering discipline to prompt management.

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Quickstart

from promptregistry import (
    PromptRegistry, PromptVersion, PromptStatus,
    LengthGate, VariableGate, InjectionRiskGate, RegressionGatePipeline,
)

# Create and register a versioned prompt
registry = PromptRegistry()

prompt_v1 = PromptVersion(
    name="summarize",
    version="1.0.0",
    content="Summarize the following text: {{text}}. Be concise.",
    tags=["summarization"],
    status=PromptStatus.ACTIVE,
)
registry.register(prompt_v1)

# Render with variable substitution
rendered = registry.render("summarize", {"text": "The quick brown fox..."})
print(rendered)

# Run CI gates before deploying v2
pipeline = (
    RegressionGatePipeline()
    .add_gate(LengthGate(min_chars=20, max_chars=5000))
    .add_gate(VariableGate(required=["text"]))
    .add_gate(InjectionRiskGate())
)
report = pipeline.run(prompt_v1)
print(f"Gates passed: {report.overall_passed}")

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Built-in Regression Gates

Gate	Description
LengthGate	Min/max character length check
VariableGate	Required {{variable}} presence
InjectionRiskGate	Blocks injection patterns (ignore previous instructions, etc.)
KeywordCoverageGate	Required keywords coverage fraction

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Prompt Diff

from promptregistry import diff_prompts, diff_to_unified

diff = diff_prompts(prompt_v1, prompt_v2)
print(diff.summary())        # ScoreDiff summary
print(diff.changed)          # True / False
print(diff.char_delta)       # Character delta

print(diff_to_unified(prompt_v1, prompt_v2))   # Unified diff string

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Advanced Features

Caching (LRU + TTL + SHA-256)

from promptregistry.advanced import PromptCache

cache = PromptCache(max_size=1000, ttl=600)
memoized_render = cache.memoize(registry.render)
rendered = memoized_render("summarize", "1.0.0", {"text": "hello"})
print(cache.stats())

Prompt Pipeline

from promptregistry.advanced import PromptPipeline

pipeline = (
    PromptPipeline()
    .map("strip_trailing", lambda p: p.model_copy(update={"content": p.content.strip()}))
    .filter("active_only", lambda p: p.status == PromptStatus.ACTIVE)
    .with_retry("strip_trailing", retries=2)
)
result = pipeline.run(prompt_v1)
print(pipeline.audit_log)

Declarative Validation + Schema Evolution

from promptregistry.advanced import PromptValidator, PromptRule, SchemaEvolver

validator = (
    PromptValidator()
    .add_rule(PromptRule("non_empty", lambda p: len(p.content) > 0, "Content required"))
    .add_rule(PromptRule("has_tags", lambda p: len(p.tags) > 0, "At least one tag required"))
)
violations = validator.validate(prompt_v1)

evolver = SchemaEvolver()
evolver.register_migration("1.0.0", "2.0.0", lambda p: p.model_copy(update={"description": "v2 prompt"}))
prompt_v2 = evolver.migrate(prompt_v1, "2.0.0")

PII Scrubbing

from promptregistry.advanced import PIIScrubber

scrubber = PIIScrubber()
clean = scrubber.scrub("Contact: [e-mail], SSN: 123-45-6789")
# → "Contact: [EMAIL], SSN: [SSN]"

Async Batch Registration

from promptregistry.advanced import abatch_register, batch_register
import asyncio

asyncio.run(abatch_register(prompt_list, registry.register))
batch_register(prompt_list, registry.register, max_workers=8)

Rate Limiter (sync + async)

from promptregistry.advanced import RateLimiter

limiter = RateLimiter(rate=50, capacity=50)
if limiter.acquire():
    rendered = registry.render("summarize", {"text": "..."})

Observability

from promptregistry.advanced import PromptProfiler, DriftDetector, CIReportExporter

profiler = PromptProfiler()
profiled_render = profiler.profile(registry.render)
profiled_render("summarize", {"text": "hi"})
print(profiler.report())

detector = DriftDetector(threshold=0.05)
detector.set_baseline(report_v1)
drifts = detector.detect(report_v2)

exporter = CIReportExporter(report)
print(exporter.to_json())
print(exporter.to_csv())
print(exporter.to_markdown())

Streaming

from promptregistry.advanced import stream_versions, versions_to_ndjson

for pv in stream_versions(prompt_list):
    print(pv.name, pv.version)

for line in versions_to_ndjson(prompt_list):
    print(line)

Regression Tracking

from promptregistry.advanced import PromptRegressionTracker, ScoreTrend

tracker = PromptRegressionTracker(window=20)
tracker.record(report_v1)
tracker.record(report_v2)
print(tracker.trend())               # "improving" / "declining" / "stable"
print(tracker.latest_regression())

trend = ScoreTrend(window=10)
for score in [0.7, 0.8, 0.9]:
    trend.record(score)
print(trend.trend(), trend.volatility())

Audit Log + Cost Ledger + Model Router

from promptregistry.advanced import AuditLog, CostLedger, ModelRouter

log = AuditLog()
log.log("deploy", {"name": "summarize", "version": "2.0.0"})

ledger = CostLedger()
ledger.record("summarize", "2.0.0", tokens=500, cost_usd=0.01)
print(ledger.summary())

router = ModelRouter(cheap_threshold=200)
model = router.route(prompt_v1)   # "gpt-4o-mini" or "gpt-4o"

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Persistence

registry.save("registry.json")
registry.load("registry.json")

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Installation

pip install promptregistry

Python 3.8+ · No external dependencies (stdlib + pydantic)

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License

MIT