PolicyEngine API

The PolicyEngine API is a Flask-based REST service that provides tax and benefit calculations for the web app and programmatic users.

For Users

What is the API?

When you use policyengine.org, the API processes your calculations on our servers.

API base: https://api.policyengine.org

What it does:

• Runs tax and benefit calculations
• Stores and retrieves policy reforms
• Computes population-wide impacts
• Serves parameter and variable metadata

Public Access

The API is publicly accessible with rate limits:

• Unauthenticated: 100 requests/minute
• Authenticated: 1,000 requests/minute

Try it:

curl https://api.policyengine.org/us/policy/2

API Documentation

OpenAPI spec: https://api.policyengine.org/docs

Interactive docs: Swagger UI at API docs endpoint

For Analysts

Using the API

Option 1: Python client (recommended)

# Use the policyengine package
# See policyengine-python-client-skill

Option 2: Direct API calls

import requests

# Calculate household impact
response = requests.post(
    "https://api.policyengine.org/us/calculate",
    json={
        "household": household_situation,
        "policy_id": None  # or reform_id
    }
)
result = response.json()

Key Endpoints

Household calculations:

POST /us/calculate
POST /uk/calculate

Policy management:

GET  /us/policy/{policy_id}
POST /us/policy

Economy impacts:

GET /us/economy/{policy_id}/over/{baseline_policy_id}

Metadata:

GET /us/parameters
GET /us/variables
GET /us/parameter/{parameter_name}
GET /us/variable/{variable_name}

Rate Limits and Performance

Rate limits:

• 100 req/min (unauthenticated)
• 1,000 req/min (authenticated - contact team)

Response times:

• Household calculation: ~200-500ms
• Population impact: ~5-30 seconds
• Cached results: <100ms

Optimization:

• Use the same policy_id for multiple requests (caching)
• Batch calculations when possible
• Use webhooks for long-running jobs (population impacts)

For Contributors

Repository

Location: PolicyEngine/policyengine-api

Clone:

git clone https://github.com/PolicyEngine/policyengine-api
cd policyengine-api

Current Architecture

To see current structure:

tree policyengine_api/

# Key directories:
ls policyengine_api/
# - endpoints/     - HTTP endpoint handlers
# - routes/        - Route registration
# - services/      - Business logic
# - compute_api/   - Calculation services
# - economy_api/   - Economy impact calculations
# - utils/         - Helpers (caching, validation)
# - data/          - Static data

Current Implementation Patterns

Reference endpoint (read this first):

cat policyengine_api/endpoints/economy.py

This demonstrates:

• Standard endpoint structure
• Request validation
• Caching pattern
• Error handling
• Response formatting

To find other endpoints:

ls policyengine_api/endpoints/
# - household.py
# - policy.py
# - economy.py
# - metadata.py
# - etc.

Standard Endpoint Pattern (Stable)

from flask import Blueprint, request, jsonify
from policyengine_api.utils import cache

blueprint = Blueprint("my_endpoint", __name__)

@blueprint.route("/us/calculate", methods=["POST"])
def calculate():
    """Standard pattern: validate, cache-check, compute, cache, return."""
    try:
        # 1. Get and validate input
        data = request.json
        if not data:
            return jsonify({"error": "No data provided"}), 400

        # 2. Generate cache key
        cache_key = f"calc_{hash(str(data))}"

        # 3. Check cache
        cached = cache.get(cache_key)
        if cached:
            return jsonify(cached)

        # 4. Compute
        result = perform_calculation(data)

        # 5. Cache result
        cache.set(cache_key, result, expire=3600)

        # 6. Return
        return jsonify(result)

    except Exception as e:
        return jsonify({"error": str(e), "status": "error"}), 500

Current implementation details:

# See actual endpoint for current pattern
cat policyengine_api/endpoints/household.py

Caching Strategy

To see current caching implementation:

# Redis configuration
cat policyengine_api/utils/cache.py

# Find cache usage
grep -r "cache\." policyengine_api/endpoints/

Pattern:

• Redis for caching
• Cache keys based on inputs
• TTL varies by endpoint (1 hour to 1 day)
• Clear cache on parameter changes

Background Jobs

For long-running calculations (population impacts):

To see current implementation:

# RQ (Redis Queue) usage
grep -r "@job" policyengine_api/

# Job patterns
cat policyengine_api/economy_api/

Pattern:

• Use RQ for jobs > 5 seconds
• Return job_id immediately
• Poll for completion
• Cache results

Country Integration

How API loads country packages:

cat policyengine_api/country.py

Pattern:

• Dynamically imports country packages
• Routes by country code (/us/, /uk/)
• Manages multiple model versions

Service Layer

Business logic separated from endpoints:

ls policyengine_api/services/

Pattern:

# endpoints/household.py
from policyengine_api.services import household_service

@app.route("/us/calculate", methods=["POST"])
def calculate():
    result = household_service.calculate(data)
    return jsonify(result)

# services/household_service.py
def calculate(data):
    # Business logic here
    simulation = create_simulation(data)
    return simulation.calculate(...)

Dataset Selection Pattern

When to delegate to policyengine.py:

The API should return None for dataset selection in most cases, allowing policyengine.py to choose the appropriate default dataset. This creates better separation of concerns.

Pattern:

# In economy_service.py _setup_data() method:

# ❌ DON'T: Explicitly specify datasets the API shouldn't control
if region == "ny":
    return "gs://policyengine-us-data/some_dataset.h5"

# ✅ DO: Return None to let policyengine.py choose the default
if region in US_STATES:
    return None  # policyengine.py handles state-specific datasets

# ✅ DO: Only specify datasets for special cases the API needs to control
if region == "nyc":
    return "gs://policyengine-us-data/pooled_3_year_cps_2023.h5"  # NYC exception

Why this matters:

• Keeps dataset logic centralized in policyengine.py where it belongs
• API doesn't need to know about state-specific dataset paths
• Easier to update dataset selection without API changes
• Only special cases (like NYC) should be explicitly specified in the API

When to see this pattern:

• Look at policyengine_api/services/economy_service.py
• Look for _setup_data() method
• Related to microsimulation and state-level calculations

Testing

To see current test patterns:

ls tests/
cat tests/test_household.py

Run tests:

make test

# Specific test
pytest tests/test_economy.py -v

# With coverage
make test-coverage

Development Server

Start locally:

make debug

Test endpoint:

curl http://localhost:5000/us/policy/2

Deployment

To see deployment configuration:

# Google Cloud Platform
cat app.yaml         # App Engine config
cat cloudbuild.yaml  # Cloud Build config

# Environment variables
cat .env.example

Current deployment:

• Google App Engine
• Cloud SQL (PostgreSQL)
• Redis (caching)
• Cloud Build (CI/CD)

API Versions

To see versioning strategy:

grep -r "version" policyengine_api/

Current approach:

• API version in URLs (may add /v1/ prefix)
• Country package versions independent
• Breaking changes rare (backwards compatible)

Architecture Diagrams

Request Flow

User/App → API Gateway → Flask App → Country Package → Core Engine
                              ↓
                            Redis Cache
                              ↓
                          Background Job (if needed)
                              ↓
                          PostgreSQL (storage)

Dependencies

policyengine-core
    ↓
policyengine-us, policyengine-uk, etc.
    ↓
policyengine-api (you are here)
    ↓
policyengine-app (consumes API)

To understand dependencies:

• See policyengine-core-skill for engine patterns
• See policyengine-us-skill for country model usage
• See policyengine-app-skill for how app calls API

Common Development Tasks

Task 1: Add New Endpoint

1. Study reference implementation:

   cat policyengine_api/endpoints/economy.py
   

2. Create new endpoint file:

   # policyengine_api/endpoints/my_endpoint.py
   # Follow the pattern from economy.py
   

3. Register route:

   # See route registration
   cat policyengine_api/routes/__init__.py
   

4. Add tests:

   # Follow test pattern
   cat tests/test_economy.py
   

Task 2: Modify Caching Behavior

See current caching:

cat policyengine_api/utils/cache.py

Common changes:

• Adjust TTL (time to live)
• Change cache key generation
• Add cache invalidation

Task 3: Update Country Package Version

To see how versions are managed:

# Requirements
cat requirements.txt | grep policyengine-

# Update and deploy
# See deployment docs in README

Security and Best Practices

Input Validation

Always validate:

• Country code (us, uk, ca)
• Policy ID format
• Household structure
• Parameter values

See validation examples:

grep -r "validate" policyengine_api/endpoints/

Error Handling

Standard error response:

return jsonify({
    "error": "Error message",
    "details": additional_context,
    "status": "error"
}), status_code

See error patterns:

grep -A 5 "jsonify.*error" policyengine_api/endpoints/

Logging

To see logging configuration:

cat policyengine_api/gcp_logging.py

Pattern:

• Google Cloud Logging
• Log all errors
• Log slow queries (>1s)
• Don't log sensitive data

Related Skills

• policyengine-python-client-skill - Using the API
• policyengine-core-skill - Understanding the engine
• policyengine-us-skill - Country model integration
• policyengine-app-skill - How app consumes API
• policyengine-standards-skill - Code quality
• policyengine-writing-skill - API documentation style

Resources

Repository: https://github.com/PolicyEngine/policyengine-api Live API: https://api.policyengine.org Documentation: https://api.policyengine.org/docs Status: https://status.policyengine.org