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A high-performance Python API framework powered by Rust

Project description

FastrAPI (Fast + Rust + API)

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FastRAPI GIF FastrAPI is a high-performance web framework that supercharges your Python APIs with the power of Rust. Built on Axum and PyO3, it delivers unmatched speed, type safety, and developer-friendly Python syntax. Create robust, async-ready APIs with minimal overhead and maximum throughput. FastrAPI is your drop-in replacement for FastAPI, offering familiar syntax with up to 33x faster performance.

Key Features

  • Lightning Speed: Powered by Rust and Axum, FastrAPI delivers up to 33x faster performance than FastAPI, making your APIs scream.
  • Python-First: Write clean, familiar Python code, no Rust knowledge needed. FastrAPI handles the heavy lifting behind the scenes.
  • Ironclad Type Safety: Inherits Rust's robust type system for rock-solid reliability, catching errors before they hit production.
  • Pydantic Powered: Seamless integration with Pydantic for effortless request and response validation, keeping your data in check.
  • Async Native: Built on Tokio's async runtime, FastrAPI maximizes concurrency for handling thousands of requests with ease.
  • Ultra Lightweight: Minimal runtime overhead with maximum throughput.
  • Drop-in Replacement: Drop-in compatibility with FastAPI's beloved decorator syntax, so you can switch without rewriting your codebase.

Is it as fast as claimed?

Yes. Powered by Rust and Axum, FastrAPI outperforms FastAPI by up to 33x in real-world benchmarks, with no compromises on usability. Check it out here

FastRAPI vs other frameworks comparision

Do I need to know Rust?

Nope. FastrAPI lets you write 100% Python code while leveraging Rust's performance under the hood.

Can it handle complex APIs?

Absolutely. With full Pydantic integration and async support, FastrAPI scales effortlessly for small projects and enterprise-grade APIs alike.

Will it keep up with FastAPI updates?

Yes. FastrAPI mirrors FastAPI's decorator-based syntax, ensuring compatibility and instant access to familiar workflows.

Installation

uv

uv install fastrapi

pip

pip install fastrapi

Quick Start

from fastrapi import FastrAPI
app = FastrAPI()

@app.get("/hello")
def hello():
    return {"Hello": "World"}

@app.post("/echo")
def echo(data):
    return {"received": data}

if __name__ == "__main__":
    app.serve("127.0.0.1", 8080)

Now, test it with:

curl http://127.0.0.1:8080/hello

For the POST endpoint:

curl --location 'http://127.0.0.1:8080/echo' \
--header 'Content-Type: application/json' \
--data '{"foo": 123, "bar": [1, 2, 3]}'
Show Pydantic example
from pydantic import BaseModel
from fastrapi import FastrAPI

api = FastrAPI()

class User(BaseModel):
    name: str
    age: int

@api.post("/create_user")
def create_user(data: User):
    return {"msg": f"Hello {data.name}, age {data.age}"}

api.serve("127.0.0.1", 8080)
Show ResponseTypes Example
from fastrapi import FastrAPI
from fastrapi.responses import HTMLResponse, JSONResponse

api = FastrAPI()

@api.get("/html")
def get_html() -> HTMLResponse:
    return HTMLResponse("<h1>Hello</h1>")

api.serve("127.0.0.1", 8080)
Show Middleware Example
from fastrapi import FastrAPI
from fastrapi.responses import JSONResponse

from fastrapi.middleware import (
    CORSMiddleware,
    TrustedHostMiddleware,
    GZipMiddleware,
    SessionMiddleware
)

app = FastrAPI()

# TrustedHost Middleware
app.add_middleware(
    TrustedHostMiddleware, 
    allowed_hosts=["127.0.0.1", "localhost", "127.0.0.1:8000"],
    www_redirect=True
)

# CORS Middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["GET", "POST"],
    allow_headers=["*"],
    allow_credentials=False
)

# 3. GZip Middleware
app.add_middleware(
    GZipMiddleware, 
    minimum_size=500,
    compresslevel=9
)

# 4. Session Middleware
app.add_middleware(
    SessionMiddleware,
    secret_key="super-duper-secret-key-change-this-in-prod-pwease-uwu-BUT-MAKE-IT-LONGER-NOW",
    session_cookie="fastrapi_session",
    max_age=3600,
    https_only=False
)

# ROUTES
# WARNING: ALWAYS return JSONResponse if it's JSON, to ensure proper serialization
@app.get("/")
def index() -> JSONResponse:
    return JSONResponse({"status": "running"})

@app.get("/heavy")
def heavy_data() -> JSONResponse:
    # response large enough to trigger GZip compression
    large_data = "x" * 1000
    return JSONResponse({
        "data": large_data,
        "note": "Check content-encoding header!"
    })

# Session Test: Increment a counter stored in the cookie
@app.get("/counter")
def session_counter(request) -> JSONResponse:
    # For now, this verifies the Middleware sets the cookie correctly.
    return JSONResponse({"message": "Session cookie should be set"})

if __name__ == "__main__":
    app.serve("127.0.0.1", 8000)

# Test with:
# curl -v -H "Host: 127.0.0.1" http://127.0.0.1:8000/
# curl -v -H "Origin: http://example.com" http://127.0.0.1:8000/

Performance

Benchmarks using k6 show it outperforms FastAPI + Guvicorn across multiple worker configurations.

🖥️ Test Environment

  • Kernel: 6.16.8-arch3-1
  • CPU: AMD Ryzen 7 7735HS (16 cores, 4.83 GHz)
  • Memory: 15 GB
  • Load Test: 20 Virtual Users (VUs), 30s

⚡ Benchmark Results

Framework Avg Latency (ms) Median Latency (ms) Requests/sec P95 Latency (ms) P99 Latency (ms)
FASTRAPI 0.59 0.00 31360 2.39 11.12
FastAPI + Guvicorn (workers: 1) 21.08 19.67 937 38.47 93.42
FastAPI + Guvicorn (workers: 16) 4.84 4.17 3882 10.22 81.20

TLDR; FASTRAPI handles thousands of requests per second with ultra-low latency , making it ~33× faster than FastAPI + Guvicorn with 1 worker.

Current Limitations

Some advanced features are still in development like:

  • Logging/metrics
  • A nice logging tool
  • Better error handling (currently shows Rust errors)
  • Rate limiter (even FastAPI doesn't have it)
  • Websockets
  • Dependency injection
  • Static file serving
  • Background tasks
  • Testing support
  • GraphQL support

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

  • Fork the repository
  • Create your feature branch (git checkout -b feature/amazing-feature)
  • Commit your changes (git commit -m 'Add some amazing feature')
  • Push to the branch (git push origin feature/amazing-feature)
  • Open a Pull Request

Check out CONTRIBUTING.md for more details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Inspired by FastAPI Built with PyO3 and Axum

Star History

Star History Chart

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