fastgrpcpy 0.1.0

FastAPI-style gRPC for Python

fastgrpcpy

FastAPI-style gRPC for Python.
Define services with type-annotated Python. Get the protobuf schema, generated stubs, and a running server — automatically.

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Overview

fastgrpcpy treats your Python type hints as the source of truth for a gRPC service. The framework derives the .proto schema from your code, compiles the gRPC stubs, wires your handlers into the generated servicer, and runs the server — sync or async, with hot reload in development.

from dataclasses import dataclass
from fastgrpcpy import rpc, service

@dataclass
class User:
    id: int
    name: str

@service
class UserService:
    @rpc
    async def get_user(self, user_id: int) -> User:
        return User(id=user_id, name="FastgRPC")

fastgrpcpy dev main.py

Call it from any gRPC client. With server reflection enabled in dev mode, grpcurl works with no extra setup:

grpcurl -plaintext -d '{"user_id": 42}' \
  127.0.0.1:50051 fastgrpcpy.UserService/GetUser
# {"id": "42", "name": "Ashesh"}

That single command performs introspection, code generation, compilation, and serving in one step. No .proto files to write or maintain by hand.

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Features

• Zero-boilerplate service definition — decorate a class with @service, decorate methods with @rpc. fastgrpcpy handles the rest.
• Hot reload in development, including transitive Python imports, powered by a Rust-backed file watcher.
• Automatic protobuf generation from Python type hints, including nested dataclasses, Optional, list[T], dict[str, T], and AsyncIterator[T].
• Wire compatibility guarantees — field numbers are pinned in a lock file (.fastgrpcpy.lock), preventing accidental wire-breaking changes.
• Sync and async servers, auto-selected from your handler signatures (grpc.aio for async, grpc.server for sync).
• All four streaming modes — unary-unary, unary-stream, stream-unary, stream-stream.
• gRPC server reflection enabled in dev mode, so grpcurl, Postman, and BloomRPC work without any configuration.
• Multi-file projects — split services, business logic, and models freely; reload follows imports.

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Installation

Requires Python 3.11 or later.

pip install fastgrpcpy

For local development:

pip install -e '.[dev]'

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Command-line interface

Command	Purpose
fastgrpcpy dev <file>	Start the development server with hot reload and reflection
fastgrpcpy run <file>	Start the production server (no reload, no reflection)
fastgrpcpy proto <file>	Print the generated .proto schema to stdout
fastgrpcpy compile <file>	Write the .proto and compiled stubs to a directory

fastgrpcpy dev

The development server selects the appropriate gRPC stack based on your handler signatures:

• Any async def handler → grpc.aio.server()
• All def handlers → grpc.server(ThreadPoolExecutor)
• Mixed sync and async → fails fast with a ValidationError

fastgrpcpy dev examples/complex/main.py
fastgrpcpy dev examples/complex/main.py --host 0.0.0.0 --port 50051

On every file save the framework executes the following pipeline:

1. The Rust-backed watcher detects a .py change in the project tree.
2. User modules are evicted from sys.modules so transitive imports reload cleanly.
3. The entry-point file is re-imported, re-running the @service and @rpc decorators.
4. The pipeline runs: inspector → field-number lock → validator → proto writer → protoc compiler.
5. The previous server is gracefully stopped; a new one is built with the rewired handlers.

Reflection is enabled, so grpcurl -plaintext localhost:50051 list works out of the box.

fastgrpcpy run

Production server. No watcher, no reflection.

fastgrpcpy run main.py --host 0.0.0.0 --port 50051

fastgrpcpy proto

Print the generated .proto schema to stdout. Suitable for piping into a clients repository or copying into another build pipeline.

fastgrpcpy proto main.py
fastgrpcpy proto main.py > schemas/user.proto
fastgrpcpy proto main.py --package myco.users

The command consults .fastgrpcpy.lock to keep field numbers stable across runs.

fastgrpcpy compile

Produce a complete client bundle: the .proto source plus pre-compiled _pb2.py and _pb2_grpc.py modules.

fastgrpcpy compile main.py --out ./client/

client/
├── fastgrpcpy.proto
├── fastgrpcpy_pb2.py
└── fastgrpcpy_pb2_grpc.py

The output directory can be checked in to your clients repository or published as its own package.

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Watch mode

fastgrpcpy dev runs a continuous build-and-serve loop:

edit main.py / business.py / models.py
        ↓ (Rust file watcher fires)
purge user modules from sys.modules
        ↓
re-import main.py — decorators re-register services
        ↓
inspector → lock → validator → proto writer → protoc compile
        ↓
stop the running server, start a new one with rewired handlers

Behavior worth knowing:

• Transitive imports reload. Editing business.py or models.py triggers a full rebuild. Without the explicit sys.modules purge, Python would serve cached versions.
• Framework modules persist. fastgrpcpy, grpc, and google.protobuf are excluded from the purge to keep reload latency low; re-importing the generated grpc stubs every keystroke would be prohibitively slow.
• .py files trigger reload. Generated .proto and _pb2.py artifacts are filtered out by the watcher to prevent reload loops.
• Reload errors are isolated. A failed rebuild (syntax error, validation failure, etc.) does not kill the running server; the previous server keeps serving traffic and the error is logged.

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Project layout

A typical fastgrpcpy service:

my_service/
├── main.py                    # @service classes
├── business.py                # business logic, imported by main.py
├── models.py                  # dataclasses, imported by both
├── .fastgrpcpy/                 # gitignored, ephemeral build artifacts
│   ├── fastgrpcpy.proto         # generated schema, refreshed every reload
│   ├── fastgrpcpy_pb2.py        # compiled message classes
│   └── fastgrpcpy_pb2_grpc.py   # compiled servicer base + stub
└── .fastgrpcpy.lock             # committed; tracks protobuf field numbers

Everything inside .fastgrpcpy/ is regenerated on every fastgrpcpy dev reload. To export the schema for client teams, use fastgrpcpy proto (stdout) or fastgrpcpy compile --out <dir> (writes a clean bundle wherever you choose).

Field-number stability and .fastgrpcpy.lock

Protobuf identifies fields by number, not by name. If field numbers shift between releases — for example because a new field was inserted in the middle of a dataclass — every existing client will silently misinterpret the wire payload.

.fastgrpcpy.lock is the authoritative record of every field number ever assigned. New fields receive the next available number; removed fields are tombstoned and never reused.

[User]
id = 1
name = 2

[User.removed]
legacy_email = 3   # tombstoned — number 3 is permanently retired

Commit .fastgrpcpy.lock and review changes in pull requests, the same way you would treat a lock file or a database migration history.

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Examples

The examples/ directory contains progressively larger services:

Folder	Demonstrates
hello_world/	Minimal async service
async_basic/	Async handler with await asyncio.sleep
sync/	Sync handler running on the grpc thread-pool server
complex/	Multi-file project: main.py, business.py, models.py, two services, nested dataclasses
streaming/	Server, client, and bidirectional streaming
middleware/	Interceptor pattern (logging example)

fastgrpcpy dev examples/complex/main.py --port 50065

grpcurl -plaintext 127.0.0.1:50065 list
grpcurl -plaintext -d '{"user_id": 1}' \
  127.0.0.1:50065 fastgrpcpy.UserService/GetUser

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Architecture

src/fastgrpcpy/
├── decorators.py        # @service, @rpc, the registry
├── app.py               # App configuration, interceptor registry
├── exceptions.py        # gRPC-status-mapped exception classes
├── codegen/
│   ├── ir.py            # ProtoFile / ProtoMessage / ProtoField (dataclasses)
│   ├── inspector.py     # Python types → IR
│   ├── lock.py          # field-number stability via .fastgrpcpy.lock
│   ├── validator.py     # IR and registry sanity checks
│   ├── proto_writer.py  # IR → .proto source string
│   └── compiler.py      # invokes grpcio-tools / protoc
├── server/
│   ├── runner.py        # build pipeline + lifecycle
│   ├── wiring.py        # servicer synthesis, sync/async detection
│   ├── converter.py     # dataclass ↔ protobuf recursive conversion
│   ├── watcher.py       # watchfiles-based hot reload
│   └── reflection.py    # gRPC server reflection setup
└── cli/
    └── main.py          # Typer CLI: dev, run, proto, compile

The codegen layer is a sequence of pure functions over an in-memory ProtoFile IR. The IR itself is never persisted; only the final .proto and .fastgrpcpy.lock are.

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Development

pip install -e '.[dev]'
pytest                # unit + integration tests
ruff check .
mypy src

The test suite includes end-to-end integration tests that spin up a real gRPC server (both sync and async) and exercise it over the wire.

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Project status

Alpha. The core pipeline — code generation, lock-file management, sync and async server runtimes, hot reload, multi-file projects — is implemented and covered by tests. Known gaps include TLS support, OpenTelemetry tracing, and HTTP/JSON transcoding.

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License

MIT