Comprehensive prompt management and testing framework for production LLM workflows with automated git versioning
Project description
๐ llmhq-promptops
Git blame for prompts. When prod breaks, know exactly which prompt version was running, in seconds. No SaaS, no vendor lock-in โ all in your own git.
llmhq-promptops is a prompt management framework that records every deploy, builds an immutable snapshot at CI time, and lets you ask promptops blame --at <timestamp> to find out what was running when an incident happened. Built for teams who already use git for everything else.
โจ Key Features
- ๐ Incident archaeology โ
promptops blame --at <ts>resolves "what prompt was running in prod at that moment" by composing the deploy log with git history. - ๐ณ Production runtime without
.git/โpromptops snapshot buildwrites a self-contained.promptops/snapshot.json. Ship that in your Docker image;AutoResolverpicks it up automatically. - ๐ Deploy event log โ append-only
.promptops/deploys.jsonlrecords every deploy with provenance (env, commit, actor, metadata). Committed to git alongside your prompts. - ๐ Automated git versioning โ Pre-commit hook detects prompt changes, bumps semver in YAML metadata, re-stages.
- ๐ Version-aware testing โ
:unstaged,:working,:latest,commit-<sha>, or any tag. - ๐ Python SDK โ
PromptManager(resolver=AutoResolver())works the same in dev (uses git) and prod (uses snapshot).
โก Quick Start
Two paths depending on what you want to do.
Path A โ 60-second demo on a sample repo
Walk through the incident-archaeology flow on a pre-baked repo, no setup of your own.
pip install llmhq-promptops
git clone https://github.com/llmhq-hub/promptops.git
cd promptops/examples/incident-archaeology-demo
./setup.sh # builds a tmp git repo with pre-baked history
cd /tmp/promptops-demo
# 1. Look at what's been deployed
promptops deploy list
# 2. "Production broke at 10:00 UTC. What was running?"
promptops blame --at 2026-05-20T10:00:00Z
# 3. Same question, full text of one prompt
promptops blame --at 2026-05-20T10:00:00Z --prompt summarizer
That's the hero use case. Three commands, one provable answer.
Full walkthrough + screencast script:
examples/incident-archaeology-demo/README.md
Path B โ Try it on your own repo
For when you want to add this to a real project.
pip install llmhq-promptops
cd <your-project> # any git repo
promptops init repo # creates .promptops/ directory
promptops hooks install # opt-in: auto-version on commit
# Write a prompt
promptops create prompt user-onboarding
# Render it
python -c "from llmhq_promptops import get_prompt; print(get_prompt('user-onboarding', {'name': 'World'}))"
# Record a deploy and look it up later
promptops deploy event --env prod -m release=v0.1.0
promptops blame --at "$(date -u +%Y-%m-%dT%H:%M:%SZ)"
๐ Usage Examples
Recommended production setup
from llmhq_promptops import PromptManager, AutoResolver
# Same code in dev (uses .git/) and prod (uses .promptops/snapshot.json)
manager = PromptManager(resolver=AutoResolver(repo_path="."))
prompt = manager.get_prompt("user-onboarding", {"name": "Alice"})
Resolving with full provenance
When you need to know which version actually ran (for logging, observability):
resolved = manager.resolve("user-onboarding")
# resolved.text โ raw YAML
# resolved.version โ e.g. "v1.2.3" or "commit-abc12345"
# resolved.commit โ full 40-char SHA
# resolved.source โ "git" or "snapshot"
# resolved.resolved_at โ tz-aware UTC datetime
log_to_observability({
"prompt": resolved.prompt_id,
"version": resolved.version,
"commit": resolved.commit,
"source": resolved.source,
})
Specific versions
from llmhq_promptops import get_prompt
prompt = get_prompt("user-onboarding") # smart default
prompt = get_prompt("user-onboarding:v1.2.1") # tagged version
prompt = get_prompt("user-onboarding:commit-abc12345") # untagged commit
prompt = get_prompt("user-onboarding:unstaged") # working tree
prompt = get_prompt("user-onboarding:working") # HEAD (committed)
Using with LLM frameworks
from llmhq_promptops import get_prompt
prompt = get_prompt("user-onboarding:working", {"name": "John"})
# OpenAI
import openai
openai.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
)
# Anthropic
import anthropic
anthropic.Anthropic().messages.create(
model="claude-3-sonnet-20240229",
messages=[{"role": "user", "content": prompt}],
)
๐ง CLI Commands
| Command group | What it does |
|---|---|
promptops init repo |
Create .promptops/ directory structure |
promptops hooks install |
Opt-in auto-versioning hooks (pre-commit + post-commit) |
promptops create prompt <id> |
Scaffold a new prompt YAML |
promptops render prompt <file> |
Render a prompt with variables |
promptops test status / test diff / test runtest |
Version-aware prompt testing |
promptops deploy event --env <e> |
Append a deploy event to .promptops/deploys.jsonl |
promptops deploy list |
Show recent deploys, newest first |
promptops snapshot build |
Write .promptops/snapshot.json (production-runtime artifact) |
promptops snapshot inspect |
Print contents of a snapshot |
promptops blame --at <ts> |
Incident archaeology: what was running when? |
promptops backfill-deploys --from-git-log |
Seed the deploy log from existing git history |
promptops migrate tag-history |
Create per-prompt git tags for historical commits |
Full help: promptops --help or promptops <command> --help.
๐ Project Structure
.promptops/
โโโ prompts/ # YAML prompt templates (auto-versioned)
โโโ deploys.jsonl # Append-only deploy event log (committed to git)
โโโ snapshot.json # Production-runtime snapshot (built at CI time)
โโโ config.yaml # Hook + tool configuration
โโโ tests/ # Test datasets
โโโ results/ # Test reports
โโโ logs/ # Audit logs
โโโ reports/ # Auto-generated version change notes
๐ Prompt Schema
# .promptops/prompts/user-onboarding.yaml
metadata:
id: user-onboarding
version: "1.2.0" # Auto-incremented by pre-commit hook
description: "User onboarding welcome message"
tags: ["onboarding", "welcome"]
models:
default: gpt-4-turbo
supported: [gpt-4-turbo, claude-3-sonnet, llama2-70b]
template: |
Welcome {{ user_name }}!
Available features:
{% for feature in features %}
- {{ feature }}
{% endfor %}
variables:
user_name: {type: string, required: true}
features: {type: list, default: ["Browse", "Purchase"]}
tests:
- dataset: .promptops/tests/onboarding-data.json
metrics: {max_tokens: 150, min_relevance: 0.8}
๐ Automated Versioning
Semantic version rules applied by the pre-commit hook:
- PATCH (1.0.0 โ 1.0.1) โ template content changes only
- MINOR (1.0.0 โ 1.1.0) โ new variables added (backwards compatible)
- MAJOR (1.0.0 โ 2.0.0) โ required variables removed (breaking change)
Workflow:
- Edit a prompt โ changes in working tree
promptops test --prompt name:unstaged(test before commit)git add+git commitโ pre-commit hook bumps version and re-stages- Post-commit hook tags the new version and generates a changelog entry
Zero manual version management.
๐ Version References
| Reference | Resolves to | Use case |
|---|---|---|
prompt-name |
Smart default (unstaged if different, else working) | Development |
:unstaged |
Uncommitted working-tree content | Testing changes before commit |
:working / :latest / :head |
Latest committed (HEAD) | Production |
:v1.2.3 |
Specific semver tag | Reproducible builds |
:commit-abc12345 |
Immutable commit reference for untagged commits | Incident archaeology |
๐ ๏ธ Requirements
- Python 3.8+
- Git (for versioning at dev time โ not required at production runtime if you ship
snapshot.json) - YAML + Jinja2 (auto-installed)
๐ Dependencies
- Core: Typer (CLI), Jinja2 (templates), PyYAML (parsing), GitPython (git access)
- Compatibility: typing_extensions (Python 3.8)
๐ค Contributing
See CONTRIBUTING.md.
git clone https://github.com/llmhq-hub/promptops.git
cd promptops
python -m venv venv
source venv/bin/activate
pip install -e .
pip install pytest
pytest tests/ --ignore=tests/test_versioning.py --ignore=tests/test_langchain.py
๐ License
MIT โ see LICENSE.
๐ Support
- Issues: github.com/llmhq-hub/promptops/issues
- Discussions: github.com/llmhq-hub/promptops/discussions
- Releases & changelog: github.com/llmhq-hub/promptops/releases
Made with โค๏ธ for teams shipping LLMs in production.
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