The Pitch

You already have a FastAPI app. FastGRPC reads it at startup and gives you a fully working gRPC server — same handlers, same Pydantic models, zero new files to write or maintain.

from fastgrpc import FastGRPC

# Your existing FastAPI app, unchanged
app = FastAPI()

@app.get("/items/{item_id}", response_model=Item)
async def get_item(item_id: int) -> Item:
    ...

# One line to add gRPC on port 50051
FastGRPC(app, grpc_port=50051)

That's the entire migration.

https://github.com/barakplasma/FastGRPC

Performance

Benchmarked on the same FastAPI app, same handler logic, 30,000 requests over ~1 minute with persistent connections (how production clients work):

aarch64, Python 3.13, httpx keep-alive vs grpc.aio persistent channel

Why gRPC is faster:

• Binary encoding — protobuf is more compact and faster to parse than JSON, especially for numbers and nested objects
• HTTP/2 multiplexing — one persistent TCP connection handles many concurrent requests with no head-of-line blocking
• No header overhead — HPACK compression; HTTP/1.1 resends headers verbatim every request

Gotcha: grpcurl and curl spawn a new OS process per call, adding ~30–40 ms of overhead. Always benchmark with persistent clients to see true protocol performance.

Installation

uv add git+https://github.com/barakplasma/FastGRPC.git
# or
pip install git+https://github.com/barakplasma/FastGRPC.git

Requires Python 3.10+. Installs grpcio, grpcio-tools, and grpcio-reflection automatically.

Supports grpc reflection and generates .proto files for you too from the built in fastapi OpenAPI spec