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Code generator for FastStream¤

faststream-gen is a Python library that uses generative AI to automatically generate FastStream applications. Simply describe your application requirements, and faststream-gen will generate a production-grade FastStream project that is ready to deploy in no time.

Downloads PyPI -
Version GitHub Workflow
Status GitHub


Source Code:

Getting Started¤

The code generator for FastStream is a Python library that automates the process of creating FastStream applications. It works by taking your application requirements and swiftly turning them into a ready-to-deploy FastStream application.

The key features are:

  • Automatic FastStream project generation: faststream-gen enables you to easily generate complete FastStream application with minimal effort. This library allows you to outline your application requirements, and it will quickly transform them into a fully-fledged FastStream project.
  • Tested code: faststream-gen provides dependable code through rigorous testing, including pre-implemented integration tests, ensuring stability and functionality, saving development time, and preventing common bugs.
  • Script Templates: Streamline the deployment of your FastStream application using faststream-gen’s built-in scripts, tailored for initiating, subscribing to Kafka topic and shutting down the local Kafka broker.
  • GitHub workflow files: faststream-gen integrates seamlessly with your version control and continuous integration pipeline through its GitHub workflow files. These predefined configuration files are optimized for FastStream projects, enabling smooth integration with GitHub Actions. You can automate tasks such as code validation, testing, and deployment, ensuring that your FastStream application remains in top shape throughout its development lifecycle.

Quick start¤

The following quick start guide will walk you through installing and configuring the faststream-gen library, demonstrating the creation of a new FastStream project in seconds.


faststream-gen is published as a Python package and can be installed with pip:

pip install faststream-gen

If the installation was successful, you should now have the faststream-gen installed on your system. Run the below command from the terminal to see the full list of available commands:

faststream_gen --help
 Usage: faststream_gen [OPTIONS] [DESCRIPTION]

 Effortlessly create a new FastStream project based on the app description.

╭─ Arguments ──────────────────────────────────────────────────────────────────╮
│   description      [DESCRIPTION]  Summarize your FastStream application in a │
│                                   few sentences!                             │
│                                                                              │
│                                   Include details about messages, topics,    │
│                                   servers, and a brief overview of the       │
│                                   intended business logic.                   │
│                                                                              │
│                                   The simpler and more specific the app      │
│                                   description is, the better the generated   │
│                                   app will be. Please refer to the below     │
│                                   example for inspiration:                   │
│                                                                              │
│                                   Create a FastStream application using      │
│                                   localhost broker for testing and use the   │
│                                   default port number.  It should consume    │
│                                   messages from the "input_data" topic,      │
│                                   where each message is a JSON encoded       │
│                                   object containing a single attribute:      │
│                                   'data'.  For each consumed message, create │
│                                   a new message object and increment the     │
│                                   value of the data attribute by 1. Finally, │
│                                   send the modified message to the           │
│                                   'output_data' topic.                       │
│                                   [default: None]                            │
╭─ Options ────────────────────────────────────────────────────────────────────╮
│ --input_file          -i      TEXT                   The path to the file    │
│                                                      with the app            │
│                                                      desription. This path   │
│                                                      should be relative to   │
│                                                      the current working     │
│                                                      directory.              │
│                                                      If the app description  │
│                                                      is passed via both a    │
│                                                      --input_file and a      │
│                                                      command line argument,  │
│                                                      the description from    │
│                                                      the command line will   │
│                                                      be used to create the   │
│                                                      application.            │
│                                                      [default: None]         │
│ --output_path         -o      TEXT                   The path to the output  │
│                                                      directory where the     │
│                                                      generated project files │
│                                                      will be saved. This     │
│                                                      path should be relative │
│                                                      to the current working  │
│                                                      directory.              │
│                                                      [default: .]            │
│ --model               -m      [gpt-3.5-turbo-16k|gp  The OpenAI model that   │
│                               t-4]                   will be used to create  │
│                                                      the FastStream project. │
│                                                      For better results, we  │
│                                                      recommend using         │
│                                                      'gpt-4'.                │
│                                                      [default:               │
│                                                      gpt-3.5-turbo-16k]      │
│ --verbose             -v                             Enable verbose logging  │
│                                                      by setting the logger   │
│                                                      level to INFO.          │
│ --dev                 -d                             Save the complete logs  │
│                                                      generated by            │
│                                                      faststream-gen inside   │
│                                                      the output_path         │
│                                                      directory.              │
│ --install-completion                                 Install completion for  │
│                                                      the current shell.      │
│ --show-completion                                    Show completion for the │
│                                                      current shell, to copy  │
│                                                      it or customize the     │
│                                                      installation.           │
│ --help                                               Show this message and   │
│                                                      exit.                   │

Generate new project¤

The faststream-gen library uses OpenAI’s model to generate FastStream projects. In order to use the library, you’ll need to create an API key for OpenAI.

Once you have your API key, store it in the OPENAI_API_KEY environment variable. This is a necessary step for the library to work.

We’re now ready to create a new FastStream application with the faststream-gen library.

Simply run the following command to create a new FastStream application in the my-awesome-project directory:

faststream_gen "Create a FastStream application using localhost broker for testing and use the default port number. It should consume messages from the 'input_data' topic, where each message is a JSON encoded object containing a single attribute: 'data'. While consuming from the topic, increment the value of the data attribute by 1. Finally, send message to the 'output_data' topic." -o "./my-awesome-project"
✨  Generating a new FastStream application!
 ✔ Application description validated. 
 ✔ FastStream app skeleton code generated. akes around 15 to 45 seconds)...
 ✔ The app and the tests are generated.  around 30 to 90 seconds)...
 ✔ New FastStream project created. 
 ✔ Integration tests were successfully completed. 
 Tokens used: 9398
 Total Cost (USD): $0.02865
✨  All files were successfully generated!

Here’s a look at the directory hierarchy:

├── .github
│   └── workflows
│       ├── deploy_docs.yml
│       └── test.yml
├── .gitignore
├── app
│   ├──
│   └──
├── dev_requirements.txt
├── requirements.txt
├── scripts
│   ├── services.yml
│   ├──
│   ├──
│   └──
└── tests

5 directories, 14 files

Let’s take a quick look at the generated application and test code.

from faststream import FastStream, Logger
from faststream.kafka import KafkaBroker

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

to_output_data = broker.publisher("output_data")

async def on_input_data(msg: dict, logger: Logger) -> None:"{msg=}")
    incremented_data = msg["data"] + 1
    await to_output_data.publish({"data": incremented_data})

import pytest

from faststream import Context
from faststream.kafka import TestKafkaBroker

from app.application import broker, on_input_data

async def on_output_data(msg: dict, key: bytes = Context("message.raw_message.key")):

async def test_data_was_incremented():
    async with TestKafkaBroker(broker):
        await broker.publish({"data": 1}, "input_data")
        on_input_data.mock.assert_called_with({"data": 1})
        on_output_data.mock.assert_called_with({"data": 2})

Start localhost Kafka broker¤

In order for FastStream applications to publish and consume messages from the Kafka broker, it is necessary to have a running Kafka broker.

Along with application and test, faststream-gen also generated scripts directory. You can start local Kafka broker (inside docker container) by executing following commands:

cd my-awesome-project
# make all shell scripts executable
chmod +x scripts/*.sh
# start local kafka broker

Start application¤

To start the FastKafka application, run the following command:

faststream run  app.application:app

Stop application¤

To stop the FastKafka application, run the following command:


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This project is licensed under the terms of the Apache License 2.0