LALancetnik·Jun 1, 2026Submission
FastStream 0.7: MQTT support – in-memory tests, AsyncAPI generation and more
4 points · Open discussionag2ai
faststream
Python2023–2026
ag2ai / faststream
faststream
Asynchronous Python framework for event-driven services. A thin client for Kafka, RabbitMQ, NATS, Redis and MQTT with full access to native broker features, plus AsyncAPI docs, in-memory tests and observability out of the box.
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3Distinct people
12Mentions
0Explicit recommenders
5.4KGitHub stars
What to know before exploring
What it is
Asynchronous Python framework for event-driven services. A thin client for Kafka, RabbitMQ, NATS, Redis and MQTT with full access to native broker features, plus AsyncAPI docs, in-memory tests and observability out of the box.
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
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Also in these discussions
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year12 dated mentions
Observed mentions: 2023-09-26 11:46:30 UTC to 2026-06-01 20:49:30 UTC.
* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 5 | 1 | 4 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 4 | 3 | 1 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 2 | 1 | 1 |
| 2026 (partial) | 2026-01-01 00:00:00 UTC | 2027-01-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
12 sourcesFastStream 0.6 Release unlocks support for new event brokers
2 points · Open discussionWe’ve finally released version 0.6, and that means we can start adding support for new brokers again
This update brings major internal improvements, a cleaner API, and sets the stage for expanding the ecosystem even further.
Check out the full release notes: https://github.com/ag2ai/faststream/releases/tag/0.6.0
And the documentation of course:
https://faststream.ag2.ai/0.6/getting-started/
Exciting things ahead
In FastStream 0.6 Release unlocks support for new event brokersShow HN:FastStream - A powerful library for building services with event streams
FastStream (https://github.com/airtai/faststream) simplifies the process of writing producers and consumers for message queues, handling all the parsing, networking and documentation generation automatically. It is a new package based on the ideas and experiences gained from FastKafka and Propan. By joining our forces, we picked up the best from both packages and created a unified way to write services capable of processing streamed data regardless of the underlying protocol. We'll continue to maintain both packages, but new development will be in this project.
Making streaming microservices has never been easier. Designed with junior developers in mind, FastStream simplifies your work while keeping the door open for more advanced use cases. Here's a look at the core features that make FastStream a go-to framework for modern, data-centric
Read the rest
microservices.
- Multiple Brokers: FastStream provides a unified API to work across multiple message brokers (Apache Kafka, RabbitMQ, NATS and Redis)
- Pydantic Validation: Leverage Pydantic's validation capabilities to serialize and validate incoming messages
- Automatic Docs: Stay ahead with automatic AsyncAPI documentation
- Intuitive: Full-typed editor support makes your development experience smooth, catching errors before they reach runtime
- Powerful Dependency Injection System: Manage your service dependencies efficiently with FastStream's built-in DI system
- Testable: Supports in-memory tests, making your CI/CD pipeline faster and more reliable
- Extendable: Use extensions for lifespans, custom serialization and middleware
- Integrations: FastStream is fully compatible with any HTTP framework you want (FastAPI especially)
- Observability: Add OpenTelemetry or Prometheus support to your services.
FastStream v0.4.0: Introducing Confluent Kafka Integration with Async Support
5 points · Open discussionFastStream releases a new minor version 0.4.0 today
This release adds support for Confluent's Python Client for Apache Kafka™. Confluent's Python Client for Apache Kafka does not natively support async functions, and its integration with modern async-based services is a bit trickier. That was the reason why our initial support for Kafka broker used aiokafka. However, that choice was less fortunate as it is not as well maintained as the Confluent version. After receiving numerous requests, we finally decided to bite the bullet and create an async wrapper around Confluent's Python Client and add full support for it in FastStream.
You can find the release here - https://github.com/airtai/faststream/releases/tag/0.4.0
Please take a look at it, play with it, and if you are satisfied, then go ahead use it in your projects: https://faststream.ai
Read the rest
Show HN: Confluent Kafka support added to FastStream v0.4.0rc0
FastStream - https://github.com/airtai/faststream, a stream processing framework, already supports Kafka stream processing using the aiokafka library, as well as other brokers such as Redis, RabbitMQ, and NATS.
Responding to popular demand, the latest 0.4.0rc0 version introduces support for Kafka stream processing using Confluent Kafka's Python library - https://github.com/confluentinc/confluent-kafka-python.
Please take a look at it and let us know what you think: https://faststream.airt.ai/0.4/confluent/
Wondering why and how we created FastStream? We've documented that in this short blog post - https://dev.to/airtai/how-we-deprecated-two-successful-projects-and-joined-forces-…
5 points · Open discussionfaststream-gen(https://github.com/airtai/faststream-gen/) uses GPT models to automatically generate microservices using the FastStream(https://github.com/airtai/faststream) framework for Apache Kafka, RabbitMQ and NATS. Simply describe your microservice in plain English, and it will generate a production-level FastStream application ready to deploy in a few minutes and under $1 cost, together with unit and integration tests, documentation and Docker images.
In Generating production-level streaming microservices using GPTfaststream-gen(https://github.com/airtai/faststream-gen/) uses GPT models to automatically generate microservices using the FastStream(https://github.com/airtai/faststream) framework for Apache Kafka, RabbitMQ and NATS. Simply describe your microservice in plain English, and it will generate a production-level FastStream application ready to deploy in a few minutes and under $1 cost, together with unit and integration tests, documentation and Docker images.
See the full (video and detailed step-by-step textual) tutorial here - https://faststream-gen.airt.ai/Tutorial/Cryptocurrency_Tutor...
In [dead]Sure, by default it installs PydanticV2, but you can downgrade it manually - all works fine. We are testing it with bith versions https://github.com/airtai/faststream/actions/runs/6312083287...
In FastStream: Effortless Kafka/RabbitMQ Event Stream Integration for MicroservicesIt is an interesting feature request, I create an issue that you can follow the progress:
https://github.com/airtai/faststream/issues/758
In FastStream: Effortless Kafka/RabbitMQ Event Stream Integration for MicroservicesNew FOSS Python Framework for Writting Apache Kafka and RabbitMQ Services
11 points · Open discussion