guardrails-api 0.4.3

Guardrails API

Guardrails API

A FastAPI server that hosts Guardrails AI in your own environment, providing OpenAI-compatible endpoints for applying guards to LLM interactions.

Installation

Requirements: Python 3.10–3.13

pip install guardrails-api

For development:

git clone https://github.com/guardrails-ai/guardrails-api.git
cd guardrails-api
pip install -e ".[dev]"

Quick Start

1. Install Guardrails Hub validators

guardrails hub install hub://guardrails/detect_pii

2. Set up your guards

Option A — Config file (in-memory, no database required)

Create a config.py that defines your guards:

from guardrails import Guard
from guardrails.hub import DetectPII

guard = Guard(name="pii-guard")
guard.use(DetectPII(pii_entities=["EMAIL_ADDRESS", "PHONE_NUMBER"]))

Add any additional server settings to your .env file:

PORT=8000
GUARDRAILS_LOG_LEVEL=INFO

Guards are loaded at startup and the API is read-only. Suitable for local development and static deployments.

Option B — PostgreSQL (persistent, full CRUD)

Add database credentials to your .env file using individual variables:

PGHOST=localhost
PGPORT=5432
PGDATABASE=guardrails
PGUSER=postgres
PGPASSWORD=password

Or a single connection URL:

DB_URL=postgresql://postgres:password@localhost:5432/guardrails

When a database is configured, schema migrations run automatically on startup and guards can be created, updated, and deleted via the API.

3. Start the server

guardrails-api start --env .env

The server will be available at http://localhost:8000.

• Swagger UI: http://localhost:8000/docs
• Health check: http://localhost:8000/health-check

CLI Reference

guardrails-api start

Start the API server.

guardrails-api start [OPTIONS]

Option	Default	Description
--env	.env	Path to environment file
--config	""	Path to config file defining guards
--port	8000	Port to listen on
--middleware	""	Path to middleware file
--env-override	False	Override existing env vars with values from --env

Examples:

# Basic startup
guardrails-api start

# Custom port and config
guardrails-api start --port 9000 --config ./my_guards.py

# Custom env file with override
guardrails-api start --env ./production.env --env-override

guardrails-api db upgrade

Upgrade the database schema (PostgreSQL only).

guardrails-api db upgrade [REVISION] [OPTIONS]

Argument/Option	Default	Description
revision	head	Target revision
--env	.env	Path to environment file
--env-override	False	Override existing env vars

guardrails-api db upgrade
guardrails-api db upgrade abc123ef

guardrails-api db downgrade

Downgrade the database schema (PostgreSQL only).

guardrails-api db downgrade [REVISION] [OPTIONS]

Argument/Option	Default	Description
revision	-1	Target revision (use -1 to roll back one step)
--env	.env	Path to environment file
--env-override	False	Override existing env vars

guardrails-api db downgrade
guardrails-api db downgrade -2

guardrails-api --version

Print the installed version.

guardrails-api --version

Configuration

Environment Variables

Variable	Default	Description
PORT	8000	Server port
HOST	http://localhost	Host address
GUARDRAILS_LOG_LEVEL	INFO	Log level for Guardrails
LOGLEVEL	INFO	Application log level
GUARDRAILS_API_KEY	—	API key for authenticating requests
APP_ENVIRONMENT	local	Deployment environment label

PostgreSQL (optional)

By default the server uses in-memory storage. To enable persistence, set database connection variables:

# Individual variables
PGHOST=localhost
PGPORT=5432
PGDATABASE=guardrails
PGUSER=postgres
PGPASSWORD=password

# Or a full connection URL
DB_URL=postgresql://postgres:password@localhost:5432/guardrails

# Optional connection extras
DB_EXTRAS=?sslmode=verify-ca
PG_POOL_SIZE=5
PG_POOL_MAX_OVERFLOW=10
PG_POOL_TIMEOUT=30

When PGHOST (or DB_URL) is set, the server will automatically run schema migrations on startup and enable full CRUD operations on guards via the API.

Custom Middleware

Pass a middleware file to register custom Starlette middleware:

# middleware.py
from starlette.middleware.base import BaseHTTPMiddleware

class AuthMiddleware(BaseHTTPMiddleware):
    async def dispatch(self, request, call_next):
        # add custom auth logic here
        return await call_next(request)

guardrails-api start --middleware middleware.py

Running in Production

For production, run directly with uvicorn or gunicorn:

# uvicorn
uvicorn --factory 'guardrails_api.app:create_app' --host 0.0.0.0 --port 8000 --workers 4

# gunicorn
gunicorn -k uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000 "guardrails_api.app:create_app()"

API Endpoints

Method	Path	Description
GET	/health-check	Server health status
GET	/guards	List all guards
POST	/guards	Create a guard (requires PostgreSQL)
GET	/guards/{guard_name}	Get a guard by name
PUT	/guards/{guard_name}	Update a guard (requires PostgreSQL)
DELETE	/guards/{guard_name}	Delete a guard (requires PostgreSQL)
POST	/guards/{guard_name}/validate	Run validation against a guard
POST	/guards/{guard_name}/openai/v1/chat/completions	OpenAI ChatCompletion compatiable endpoint for guarded LLM interactions.

Storage Modes

In-memory (default): Guards are loaded from config.py at startup. The API is read-only — guards cannot be created or updated via the API.

PostgreSQL: Full CRUD via the API. Guards defined in config.py are seeded on startup. Schema migrations run automatically.