# FastStream — Async Python Framework for Event-Driven Applications

> An asynchronous Python framework for building event-driven microservices with message broker integration, dependency injection, validation, and automatic AsyncAPI documentation.

## Install

Save as a script file and run:

# FastStream — Async Python Framework for Event-Driven Applications

## Quick Use
```bash
pip install "faststream[kafka]"
```
```python
from faststream import FastStream
from faststream.kafka import KafkaBroker

broker = KafkaBroker("localhost:9092")
app = FastStream(broker)

@broker.subscriber("orders")
async def handle_order(data: dict):
    print(f"Processing order: {data}")
```

## Introduction
FastStream is a Python framework for building event-driven applications that consume and produce messages through brokers like Kafka, RabbitMQ, NATS, and Redis. It brings FastAPI-like developer experience to message processing with type validation and dependency injection.

## What FastStream Does
- Connects to multiple message brokers with a unified subscriber/publisher API
- Validates incoming messages using Pydantic models automatically
- Provides dependency injection for shared resources like database connections
- Generates AsyncAPI documentation from your code annotations
- Includes an in-memory test broker for unit testing without infrastructure

## Architecture Overview
FastStream wraps broker client libraries behind a common interface. Subscribers are async functions decorated with routing metadata. The framework handles connection lifecycle, message deserialization, validation through Pydantic, and error handling. A middleware stack allows cross-cutting concerns like logging and retry logic.

## Setup & Configuration
- Install with broker extras: pip install faststream[kafka|rabbit|nats|redis]
- Define a broker instance pointing to your message infrastructure
- Decorate async functions as subscribers with topic/queue configuration
- Configure consumer groups, acknowledgment modes, and retry policies
- Deploy as a long-running service via Docker, systemd, or Kubernetes

## Key Features
- FastAPI-inspired syntax with decorators, type hints, and auto-validation
- Built-in testing utilities with in-memory broker simulation
- Automatic AsyncAPI specification generation for documentation
- Multiple broker support in a single application for bridge patterns
- OpenTelemetry integration for distributed tracing across services

## Comparison with Similar Tools
- **Celery** — task queue focused on job scheduling; FastStream handles real-time event streams
- **Faust** — Kafka-only stream processing; FastStream supports multiple brokers
- **nameko** — RPC-focused microservice framework; FastStream is event-driven with modern async
- **Dramatiq** — simple task queue; FastStream provides richer validation and documentation
- **Apache Kafka Streams** — JVM-native; FastStream brings similar patterns to Python

## FAQ
**Q: Can I use FastStream with existing FastAPI applications?**
A: Yes. FastStream provides a FastAPI integration that lets you run both HTTP and message handlers in one app.

**Q: How does error handling work for failed messages?**
A: FastStream supports configurable retry policies, dead-letter queues, and custom error handlers per subscriber.

**Q: Does it support exactly-once processing?**
A: It supports at-least-once delivery with idempotency patterns; exactly-once depends on broker capabilities.

**Q: Can I process messages in batches?**
A: Yes. Batch consumption is supported for Kafka and NATS with configurable batch sizes and timeouts.

## Sources
- https://github.com/ag2ai/faststream
- https://faststream.airt.ai

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