---
id: pydantic-handlebars
version: "0.2.1"
license: MIT
license_treatment: permissive
maintenance: active
---

# pydantic-handlebars — Handlebars template engine for composing LLM prompts, built on Pydantic

License: permissive · Maintenance: active · Downloads: 2.1M/mo

## What it is and what it does

pydantic-handlebars is a Handlebars template engine designed specifically for building LLM system prompts from dynamic, structured data. It bridges templating and type safety by validating templates against Pydantic models at compile time, catching field name typos and schema mismatches before any data is rendered. This prevents a common class of prompt-building bugs where a template references a field that doesn't exist in the data model.

The package supports standard Handlebars syntax—variable interpolation, dot-notation paths, block helpers (each, if, unless), and conditionals—but validates all field references against a Pydantic schema upfront. You compile a template with a model type, then render it with instances of that type. It also offers a lower-level schema-checking API for user-provided templates and a TypedCompiler for caching when compiling multiple templates against the same type.

Use it for:

- Build system prompts for AI agents from structured data (guidelines, examples, context) stored in a database or config without hardcoding.
- Compose few-shot examples dynamically by iterating over lists of Turn objects or similar structured records in a template.
- Validate user-provided prompt templates against a schema before rendering to catch errors early.
- Generate personalized LLM prompts by rendering templates with customer-specific or session-specific Pydantic model instances.
- Catch template typos and missing fields at compile time rather than discovering them during prompt rendering at runtime.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Renders Handlebars templates for composing LLM prompts, with compile-time validation against Pydantic models to catch typos and missing fields before rendering.

Yes, if you build LLM prompts from structured data and want compile-time validation. The single dependency (Pydantic), low install friction, and active maintenance make it a safe choice. Alpha status is appropriate for a focused, single-purpose tool. No known vulnerabilities. MIT license poses no restrictions. Install if type-safe, data-driven prompt composition is a workflow you need; skip if you hand-write prompts or use a different templating approach.

## Install

pip install pydantic-handlebars
uv add pydantic-handlebars
poetry add pydantic-handlebars

## Installing pydantic-handlebars

Before you install: Low friction: pure Python wheel with only Pydantic as a runtime dependency. Active maintenance (last commit 2026-05-25), though still in Alpha status (Development Status :: 3). Supports current Python versions (3.10+).

License in practice: MIT license permits unrestricted use, modification, and distribution in commercial and private projects with minimal obligations.

Quickstart:

pip install pydantic-handlebars

from pydantic import BaseModel
from pydantic_handlebars import compile

class User(BaseModel):
    name: str
    age: int

template = compile('Hello {{name}}, age {{age}}!', User)
print(template.render(User(name='Alice', age=30)))

Requires Python 3.10+ and Pydantic 2.0+.

Verify before relying:

- Performance characteristics with large templates or high-volume rendering.
- Completeness of Handlebars feature coverage relative to the full Handlebars.js specification.
- Error recovery and partial rendering behavior when templates contain invalid syntax.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 2.1M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

handlebars template engine, llm prompt composition, pydantic template validation, dynamic prompt generation, type-safe template rendering, few-shot example templating, structured prompt building, llm-prompts, pydantic-integration, type-safe-templates

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