Fastloom – The Open Foundation for Building Event-Driven Services

Fastloom is a lightweight, batteries-included foundation for building modern backends. Define your settings, schemas, and endpoints; Fastloom wires up the rest: FastAPI, Mongo (Beanie), Rabbit (FastStream), metrics/traces/logs/errors, and more.

Think of it as the glue for your stack: web, messaging, caching, DB, observability, and integrations with best-in-class tools.

Why Fastloom

No boilerplate: minimal scaffolding/templating; most wiring is handled inside the library.
Composable: opt into only what you need via extras (fastapi, rabbit, kafka, mongo, redis, mcp, celery, openai).
Pydantic-first: type-safe models, validators, and clear input/output contracts.
Multi-tenant by design: tenant context flows through DI and storage.
AuthN/Z via DI: OIDC token introspection and pluggable PDP (ABAC/RBAC/ReBAC) hooks.
Event-driven ready: publish/subscribe with routing keys and health.
Observability-native: metrics, traces, logs from day one.
Self-hostable: production parity with a cloud/aaS setup.

Integrated Services (the platform)

Fastloom plugs into a family of self-hostable services:

IAM → OIDC/SSO, authN/Z, RBAC/ABAC/ReBAC.
Notify → realtime notifications, Pusher-compatible API.
Pulse → user activity + event tracking with OpenTelemetry hooks.
File → object storage on MinIO (S3-compatible).
Finance, Subscription, SMS/Email, Meet, Persona → optional services you can wire in.

Each service is:

self-hostable (Docker Compose or Helm),
BaaS-available.

Quick start

# Install fastloom with the extras you need
poetry add fastloom -E fastapi -E mongo -E rabbit

A minimal service is two files at the project root — settings.py and app.py — plus a tenants.yaml for defaults:

# settings.py
from fastloom.db.settings import MongoSettings
from fastloom.launcher.settings import LauncherSettings
from fastloom.observability.settings import ObservabilitySettings
from fastloom.settings.general import BaseGeneralSettings
from fastloom.signals.settings import RabbitmqSettings


class Settings(
    BaseGeneralSettings,
    LauncherSettings,
    MongoSettings,
    RabbitmqSettings,
    ObservabilitySettings,
): ...

# app.py
from fastapi import APIRouter

from fastloom.launcher.schemas import App

from my_service import models, signals

router = APIRouter()


@router.get("/ping")
async def ping() -> dict[str, str]:
    return {"pong": "ok"}


app = App(
    routes=[(router, "", "Health")],
    models_module=models,
    signals_module=signals,
)

# tenants.yaml
default:
    ENVIRONMENT: development
    PROJECT_NAME: my_service
    MONGO_URI: mongodb://localhost:27017
    MONGO_DATABASE: my_service
    RABBIT_URI: amqp://guest:guest@localhost:5672/

Run with the bundled CLI (registered as launch via [project.scripts]):

launch

See docs/quickstart.md for a fuller walkthrough.

What you get out of the box

App orchestrator (fastloom.launcher)
- Discovers your routes, models, signals, and healthchecks from app.py / settings.py
- Exposes settings and health endpoints (public toggle via LauncherSettings.SETTINGS_PUBLIC)
FastAPI-native
- Dependency-injected request/tenant context and guards
- Clear routing, OpenAPI, and dependency injection patterns
Auth & Access
- DI-based guards with OIDC / OAuth2 token introspection
- Optional IAM sidecar for introspection + ACL
Multi-tenancy
- Tenant-aware DI context across web, DB, and messaging
- Per-tenant settings endpoint backed by DB + cache, with tenants.yaml defaults
Database layer (MongoDB via Beanie)
- Created/updated mixins, pagination utilities, typed helpers
- Auto model discovery for DB init via App.models_module
Signals / Messaging (Rabbit + Kafka via FastStream)
- Rabbit: event-driven publish/subscribe with retries and DLX-based backoff
- Kafka: subscriber/publisher wiring for consuming topics (e.g. Debezium CDC)
- Subscriber wiring and healthchecks
- Auto-streamed BaseDocumentSignal Beanie models
Observability
- OpenTelemetry distro + OTLP exporter, Logfire, Sentry (errors + profiling)
I18N
- Exception handler and template utils with Babel/Jinja2
Healthchecks
- Automatic app/DB/messaging/cache checks + system routes
MCP
- Optional FastMCP mount with bearer auth forwarding
Pydantic-native schemas and validators
- Schema In/Out validation for request/response contracts
- Common types and validators (fastloom.types)

Dive deeper in the docs below.

Documentation

Quickstart → docs/quickstart.md
Conventions → docs/conventions.md
Launcher & App model → docs/launcher.md
Settings & Configs → docs/settings.md
Tenant → docs/tenant.md
Auth → docs/auth.md
DB (Mongo/Beanie) → docs/db.md
Signals (Rabbit / Kafka) → docs/signals.md
Cache (Redis) → docs/cache.md
Healthchecks → docs/healthcheck.md
Observability → docs/observability.md
File storage → docs/file.md
I18N → docs/i18n.md
MCP → docs/mcp.md
Testing → docs/test.md
Internal testing (fastloom's own suite) → docs/internal-testing.md

Claude Code integration

If you build services on top of fastloom and use Claude Code, you can install the fastloom-sdk plugin. It bundles:

Scaffolding skills — scaffold-fastloom-service, add-fastloom-route, add-rabbit-subscriber.
Audit skill — audit-fastloom-settings flags misuse in your settings.py / tenants.yaml.
Reference skill — fastloom-reference ships the full docs/ so Claude can ground its answers in the canonical documentation instead of guessing from training data.

/plugin marketplace add aradng/FastLoom
/plugin install fastloom-sdk@fastloom

Skills auto-activate from context (e.g. asking "how does fastloom auth work?" triggers fastloom-reference); explicit invocation is /fastloom-sdk:<skill-name>. The plugin is opt-in per user and doesn't ship via PyPI — it's distributed through the marketplace in this same repo.

Roadmap

More CLI scaffolds and blueprints.
Automatic pydantic-ai agentic tool creation from APIs.
Migrate PDP to OPAL / opa based.

Metadata

Release files for fastloom 0.4.50

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Source distribution for fastloom 0.4.50
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