dynaprompt 0.1.0

A powerful, lazy-loading prompt configuration manager.

**dynaprompt ** - Lazy-loading prompt configuration manager built directly on Dynaconf's principles.


DynaPrompt is a powerful, lazy-loading prompt configuration manager inspired by Dynaconf. It offers a structured way to manage, version, and render LLM prompts while separating prompt text from configuration metadata.

🚀 30-Second Quickstart

pip install dynaprompt

1. Create a prompts.toml:

[default.greeting]
template = "Hello {{ name }}! You are a helpful assistant."

[production.greeting]
template = "Hello {{ name }}! You are a professional consultant."

2. Use it in Python:

from dynaprompt import DynaPrompt

prompts = DynaPrompt(settings_files=["prompts.toml"])

# Renders: "Hello Emam! You are a helpful assistant."
print(prompts.greeting.render(name="Emam").text)

---

💡 Why DynaPrompt?

1. Lazy Loading is the Core

Most libraries load prompts at import time. This makes environment swapping hard and slows down startup.

The "Old" Way (Hardcoded/Manual):

# Loaded once, forever. Hard to swap for tests/production.
SYSTEM_PROMPT = open("prompts/system.txt").read()

The DynaPrompt Way:

from dynaprompt import prompts
# File is only read NOW. Respects ENV_FOR_DYNAPROMPT=production automatically.
print(prompts.system.render())

2. Why not just use Dynaconf?

Since Dynaconf handles strings, why a new library? DynaPrompt adds prompt-specific logic:

• Jinja2 First-Class: Automatic variable injection, recursive flattening, and secret resolution.
• Schema Auto-loading: Automatically registers Pydantic models from .py files as response schemas.
• Prompt Inheritance: Use extends to share model config (temperature, max_tokens) between templates.
• Render State: Remembers previous variables for precise partial updates via .rerender().

3. Comparison with others

Feature	DynaPrompt	Prompt-Poet / Promptix	f-strings
Boilerplate	Zero (just a folder)	Medium (manual registration)	High
Lazy Loading	✅ Yes	❌ No	❌ No
Env Layers	✅ Native	⚠️ Manual	❌ No
Inheritance	✅ Native	❌ No	❌ No
Schemas	✅ Auto-discovery	⚠️ Manual	❌ No

---

✨ Features

• File-based Prompt Management: Write prompt templates in clean Markdown (.md or .txt) with YAML frontmatter, or group multiple prompts in a prompts.toml file.
• Auto-Discovery & Companion Files: Pass a directory like settings_files=["prompts/"] and DynaPrompt automatically loads all .md files. It also auto-discovers a sibling prompts.toml for managing metadata separately.
• Automatic Name Sanitization: Filenames like Call Analysis.md become prompts.call_analysis.
• Validation & Hooks: Enforce constraints on rendered prompts and intercept rendering with a powerful hook system.

🛠 Usage

Loading from a Directory

DynaPrompt excels at organizing templates as files.

from dynaprompt import DynaPrompt

prompts = DynaPrompt(
    settings_files=["examples/"], # Scans for .md, .toml, .py schemas
    environments=True
)

# Accessing analyzer.md template
rendered = prompts.analyzer.render(user_name="Emam", text="DynaPrompt is great!")

print(rendered.text)
print(rendered.config["model"]) # "gemini-1.5-pro" (from frontmatter or .toml)

# Partial update: keeps "user_name" but changes "text"
updated = prompts.analyzer.rerender(text="It's really fast.")

Schema Integration

DynaPrompt automatically registers Pydantic models found in your settings_files.

# If examples/schemas.py defines class UserProfile(BaseModel):
prompts = DynaPrompt(settings_files=["examples/"])

# Model is available as an attribute
user_schema = prompts.UserProfile

# Use it in rendering (automatically injects JSON schema if referenced in template)
rendered = prompts.fetch_user.render(username="em2m")

🔍 Inspection & Tab-Completion

DynaPrompt is designed for developer productivity.

• Tab-Completion: Use dir(prompts) or hit Tab in your IDE to see all available prompts and schemas.
• History Tracking: Inspect exactly where a prompt was loaded from and how it was merged across layers.

# See loading history for a prompt
print(prompts.inspect("customer_support"))