starlite VS fastapi

For the past one and a half years 😁. Checkout our docs https://starliteproject.dev/

#1286 is the issue tracking the removal of our last internal Pydantic datastructure, and PR #1382 has the addition of attrs based signature modelling. Once those are merged you will be able to use Starlite without Pydantic.

You can go to Open an Issue and open an issue for a feature request/enhancement and describe basically the behavior you are wanting to see.

Falcon FastAPI Sanic Starlite (disclosure: I do work here)

for server side , starliteproject.dev/, only python async framwork worth looking into.

Well, I'm not objective but I'd say Starlite (https://github.com/starlite-api/starlite) is a might fine codebase. You can also learn a lot about tooling and typing going through it.

I have mentioned your concerns to the Starlite discussions board - https://github.com/starlite-api/starlite/discussions/1183

You can find the complete changelog on our v1.51.0 GitHub release page.

This project was inspired by FastAPI and aims to provide a similar developer-friendly experience

[FastAPI] https://fastapi.tiangolo.com/) for the web server

🔗 New to FastAPI? Check out FastAPI's official documentation.

Last year I wrote a series of articles explaining how FastAPI works. It was a rewarding experience, since I was able to both share my knowledge and learn more about it.

The advancement of AI has empowered businesses to incorporate intelligent automation into their applications. A serverless Generative AI (GenAI) API enables developers to harness cutting-edge AI models without the burden of infrastructure management. This guide walks you through building a scalable and cost-effective GenAI API using FastAPI, a high-performance Python framework with built-in async support and seamless AWS integration. By deploying FastAPI on AWS Lambda with AWS API Gateway, you can create a fully managed, pay-per-use architecture that eliminates server maintenance.

Finally, whatever file launches your application (in this case, mine is main.py), it is conventional to include that if __name__ == "main": block at the bottom (shown above), to specify the entrypoint to your application. This isn't strictly necessary in many use cases—for instance, if you're writing a REST API using FastAPI and launching it via uvicorn, you don't need this block. But it also doesn't hurt to have it regardless. It's a helpful reminder of where things begin.

**Conclusion — **Deploying a FastAPI application to Render is a straightforward and efficient process. With just a few configuration steps, you can easily go from development to a live, scalable web service.

FastAPI Documentation

FastAPI

LangServe leverages the power of FastAPI and pydantic to create a robust and efficient serving layer for your LangChain applications. It essentially wraps your LangChain chains or agents, turning them into FastAPI endpoints.