🚀 PydanticRPC

PydanticRPC is a Python library that enables you to rapidly expose Pydantic models via gRPC/Connect RPC services without writing any protobuf files. Instead, it automatically generates protobuf files on the fly from the method signatures of your Python objects and the type signatures of your Pydantic models.

Below is an example of a simple gRPC service that exposes a PydanticAI agent:

import asyncio

from openai import AsyncOpenAI
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_rpc import AsyncIOServer, Message


# `Message` is just an alias for Pydantic's `BaseModel` class.
class CityLocation(Message):
    city: str
    country: str


class Olympics(Message):
    year: int

    def prompt(self):
        return f"Where were the Olympics held in {self.year}?"


class OlympicsLocationAgent:
    def __init__(self):
        client = AsyncOpenAI(
            base_url="http://localhost:11434/v1",
            api_key="ollama_api_key",
        )
        ollama_model = OpenAIModel(
            model_name="llama3.2",
            openai_client=client,
        )
        self._agent = Agent(ollama_model)

    async def ask(self, req: Olympics) -> CityLocation:
        result = await self._agent.run(req.prompt())
        return result.data


if __name__ == "__main__":
    # New enhanced initialization API (optional - backward compatible)
    s = AsyncIOServer(service=OlympicsLocationAgent(), port=50051)
    loop = asyncio.get_event_loop()
    loop.run_until_complete(s.run())

And here is an example of a simple Connect RPC service that exposes the same agent as an ASGI application:

import asyncio

from openai import AsyncOpenAI
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_rpc import ASGIApp, Message


class CityLocation(Message):
    city: str
    country: str


class Olympics(Message):
    year: int

    def prompt(self):
        return f"Where were the Olympics held in {self.year}?"


class OlympicsLocationAgent:
    def __init__(self):
        client = AsyncOpenAI(
            base_url="http://localhost:11434/v1",
            api_key="ollama_api_key",
        )
        ollama_model = OpenAIModel(
            model_name="llama3.2",
            openai_client=client,
        )
        self._agent = Agent(ollama_model, result_type=CityLocation)

    async def ask(self, req: Olympics) -> CityLocation:
        result = await self._agent.run(req.prompt())
        return result.data

# New enhanced initialization API (optional - backward compatible)
app = ASGIApp(service=OlympicsLocationAgent())

💡 Key Features

• 🔄 Automatic Protobuf Generation: Automatically creates protobuf files matching the method signatures of your Python objects.
• ⚙️ Dynamic Code Generation: Generates server and client stubs using grpcio-tools.
• ✅ Pydantic Integration: Uses pydantic for robust type validation and serialization.
• 📄 Pprotobuf File Export: Exports the generated protobuf files for use in other languages.
• For gRPC:
  • 💚 Health Checking: Built-in support for gRPC health checks using grpc_health.v1.
  • 🔎 Server Reflection: Built-in support for gRPC server reflection.
  • ⚡ Asynchronous Support: Easily create asynchronous gRPC services with AsyncIOServer.
• For Connect-RPC:
  • 🌐 Full Protocol Support: Native Connect-RPC support via connect-python
  • 🔄 All Streaming Patterns: Unary, server streaming, client streaming, and bidirectional streaming
  • 🌐 WSGI/ASGI Applications: Run as standard WSGI or ASGI applications for easy deployment
• 🛠️ Pre-generated Protobuf Files and Code: Pre-generate proto files and corresponding code via the CLI. By setting the environment variable (PYDANTIC_RPC_SKIP_GENERATION), you can skip runtime generation.
• 🤖 MCP (Model Context Protocol) Support: Expose your services as tools for AI assistants using the official MCP SDK, supporting both stdio and HTTP/SSE transports.

⚠️ Important Notes for Connect-RPC

When using Connect-RPC with ASGIApp:

• Endpoint Path Format: Connect-RPC endpoints use CamelCase method names in the path: /<package>.<service>/<Method> (e.g., /chat.v1.ChatService/SendMessage)
• Content-Type: Set Content-Type: application/json or application/connect+json for requests
• HTTP/2 Requirement: Bidirectional streaming requires HTTP/2. Use Hypercorn instead of uvicorn for HTTP/2 support
• Testing: Use buf curl for testing Connect-RPC endpoints with proper streaming support

For detailed examples and testing instructions, see the examples directory.

📦 Installation

Install PydanticRPC via pip:

pip install pydantic-rpc

For CLI support with built-in server runners:

pip install pydantic-rpc-cli  # Includes hypercorn and gunicorn

🆕 Enhanced Features (v0.10.0+)

Note: All new features are fully backward compatible. Existing code continues to work without modification.

Enhanced Initialization API

All server classes now support optional initialization with services:

# Traditional API (still works)
server = AsyncIOServer()
server.set_port(50051)
await server.run(MyService())

# New enhanced API (optional)
server = AsyncIOServer(
    service=MyService(),
    port=50051,
    package_name="my.package"
)
await server.run()

# Same for ASGI/WSGI apps
app = ASGIApp(service=MyService(), package_name="my.package")

Error Handling with Decorators

Automatically map exceptions to gRPC/Connect status codes:

from pydantic_rpc import error_handler
import grpc

class MyService:
    @error_handler(ValidationError, status_code=grpc.StatusCode.INVALID_ARGUMENT)
    @error_handler(KeyError, status_code=grpc.StatusCode.NOT_FOUND)
    async def get_user(self, request: GetUserRequest) -> User:
        # Exceptions are automatically converted to proper status codes
        if request.id not in users_db:
            raise KeyError(f"User {request.id} not found")
        return users_db[request.id]

🚀 Getting Started

PydanticRPC supports two main protocols:

• gRPC: Traditional gRPC services with Server and AsyncIOServer
• Connect-RPC: Modern HTTP-based RPC with ASGIApp and WSGIApp

🔧 Synchronous gRPC Service Example

from pydantic_rpc import Server, Message

class HelloRequest(Message):
    name: str

class HelloReply(Message):
    message: str

class Greeter:
    # Define methods that accepts a request and returns a response.
    def say_hello(self, request: HelloRequest) -> HelloReply:
        return HelloReply(message=f"Hello, {request.name}!")

if __name__ == "__main__":
    server = Server()
    server.run(Greeter())

⚙️ Asynchronous gRPC Service Example

import asyncio

from pydantic_rpc import AsyncIOServer, Message


class HelloRequest(Message):
    name: str


class HelloReply(Message):
    message: str


class Greeter:
    async def say_hello(self, request: HelloRequest) -> HelloReply:
        return HelloReply(message=f"Hello, {request.name}!")


async def main():
    # You can specify a custom port (default is 50051)
    server = AsyncIOServer(port=50052)
    await server.run(Greeter())


if __name__ == "__main__":
    asyncio.run(main())

The AsyncIOServer automatically handles graceful shutdown on SIGTERM and SIGINT signals.

🌐 Connect-RPC ASGI Application Example

from pydantic_rpc import ASGIApp, Message

class HelloRequest(Message):
    name: str

class HelloReply(Message):
    message: str

class Greeter:
    async def say_hello(self, request: HelloRequest) -> HelloReply:
        return HelloReply(message=f"Hello, {request.name}!")

app = ASGIApp()
app.mount(Greeter())

# Run with uvicorn:
# uvicorn script:app --host 0.0.0.0 --port 8000

🌐 Connect-RPC WSGI Application Example

from pydantic_rpc import WSGIApp, Message

class HelloRequest(Message):
    name: str

class HelloReply(Message):
    message: str

class Greeter:
    def say_hello(self, request: HelloRequest) -> HelloReply:
        return HelloReply(message=f"Hello, {request.name}!")

app = WSGIApp()
app.mount(Greeter())

# Run with gunicorn:
# gunicorn script:app

🏆 Connect-RPC with Streaming Example

PydanticRPC provides native Connect-RPC support via connect-python, including full streaming capabilities and PEP 8 naming conventions. Check out our ASGI examples:

# Run with uvicorn
uv run uvicorn greeting_asgi:app --port 3000

# Or run streaming example
uv run python examples/streaming_connect_python.py

This will launch a connect-python-based ASGI application that uses the same Pydantic models to serve Connect-RPC requests.

Streaming Support with connect-python

connect-python provides full support for streaming RPCs with automatic PEP 8 naming (snake_case):

from typing import AsyncIterator
from pydantic_rpc import ASGIApp, Message

class StreamRequest(Message):
    text: str
    count: int

class StreamResponse(Message):
    text: str
    index: int

class StreamingService:
    # Server streaming
    async def server_stream(self, request: StreamRequest) -> AsyncIterator[StreamResponse]:
        for i in range(request.count):
            yield StreamResponse(text=f"{request.text}_{i}", index=i)
    
    # Client streaming
    async def client_stream(self, requests: AsyncIterator[StreamRequest]) -> StreamResponse:
        texts = []
        async for req in requests:
            texts.append(req.text)
        return StreamResponse(text=" ".join(texts), index=len(texts))
    
    # Bidirectional streaming
    async def bidi_stream(
        self, requests: AsyncIterator[StreamRequest]
    ) -> AsyncIterator[StreamResponse]:
        idx = 0
        async for req in requests:
            yield StreamResponse(text=f"Echo: {req.text}", index=idx)
            idx += 1

app = ASGIApp()
app.mount(StreamingService())

[!NOTE] Please install protoc-gen-connect-python to run the connect-python example.

♻️ Skipping Protobuf Generation

By default, PydanticRPC generates .proto files and code at runtime. If you wish to skip the code-generation step (for example, in production environment), set the environment variable below:

export PYDANTIC_RPC_SKIP_GENERATION=true

When this variable is set to "true", PydanticRPC will load existing pre-generated modules rather than generating theƒm on the fly.

🪧 Setting Protobuf and Connect RPC/gRPC generation directory

By default your files will be generated in the current working directory where you ran the code from, but you can set a custom specific directory by setting the environment variable below:

export PYDANTIC_RPC_PROTO_PATH=/your/path

⚠️ Reserved Fields

You can also set an environment variable to reserve a set number of fields for proto generation, for backward and forward compatibility.

export PYDANTIC_RPC_RESERVED_FIELDS=1

💎 Advanced Features

🌊 Response Streaming (gRPC)

PydanticRPC supports streaming responses for both gRPC and Connect-RPC services. If a service class method's return type is typing.AsyncIterator[T], the method is considered a streaming method.

Please see the sample code below:

import asyncio
from typing import Annotated, AsyncIterator

from openai import AsyncOpenAI
from pydantic import Field
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_rpc import AsyncIOServer, Message


# `Message` is just a pydantic BaseModel alias
class CityLocation(Message):
    city: Annotated[str, Field(description="The city where the Olympics were held")]
    country: Annotated[
        str, Field(description="The country where the Olympics were held")
    ]


class OlympicsQuery(Message):
    year: Annotated[int, Field(description="The year of the Olympics", ge=1896)]

    def prompt(self):
        return f"Where were the Olympics held in {self.year}?"


class OlympicsDurationQuery(Message):
    start: Annotated[int, Field(description="The start year of the Olympics", ge=1896)]
    end: Annotated[int, Field(description="The end year of the Olympics", ge=1896)]

    def prompt(self):
        return f"From {self.start} to {self.end}, how many Olympics were held? Please provide the list of countries and cities."


class StreamingResult(Message):
    answer: Annotated[str, Field(description="The answer to the query")]


class OlympicsAgent:
    def __init__(self):
        client = AsyncOpenAI(
            base_url='http://localhost:11434/v1',
            api_key='ollama_api_key',
        )
        ollama_model = OpenAIModel(
            model_name='llama3.2',
            openai_client=client,
        )
        self._agent = Agent(ollama_model)

    async def ask(self, req: OlympicsQuery) -> CityLocation:
        result = await self._agent.run(req.prompt(), result_type=CityLocation)
        return result.data

    async def ask_stream(
        self, req: OlympicsDurationQuery
    ) -> AsyncIterator[StreamingResult]:
        async with self._agent.run_stream(req.prompt(), result_type=str) as result:
            async for data in result.stream_text(delta=True):
                yield StreamingResult(answer=data)


if __name__ == "__main__":
    s = AsyncIOServer()
    loop = asyncio.get_event_loop()
    loop.run_until_complete(s.run(OlympicsAgent()))

In the example above, the ask_stream method returns an AsyncIterator[StreamingResult] object, which is considered a streaming method. The StreamingResult class is a Pydantic model that defines the response type of the streaming method. You can use any Pydantic model as the response type.

Now, you can call the ask_stream method of the server described above using your preferred gRPC client tool. The example below uses buf curl.

% buf curl --data '{"start": 1980, "end": 2024}' -v http://localhost:50051/olympicsagent.v1.OlympicsAgent/AskStream --protocol grpc --http2-prior-knowledge 

buf: * Using server reflection to resolve "olympicsagent.v1.OlympicsAgent"
buf: * Dialing (tcp) localhost:50051...
buf: * Connected to [::1]:50051
buf: > (#1) POST /grpc.reflection.v1.ServerReflection/ServerReflectionInfo
buf: > (#1) Accept-Encoding: identity
buf: > (#1) Content-Type: application/grpc+proto
buf: > (#1) Grpc-Accept-Encoding: gzip
buf: > (#1) Grpc-Timeout: 119997m
buf: > (#1) Te: trailers
buf: > (#1) User-Agent: grpc-go-connect/1.12.0 (go1.21.4) buf/1.28.1
buf: > (#1)
buf: } (#1) [5 bytes data]
buf: } (#1) [32 bytes data]
buf: < (#1) HTTP/2.0 200 OK
buf: < (#1) Content-Type: application/grpc
buf: < (#1) Grpc-Message: Method not found!
buf: < (#1) Grpc-Status: 12
buf: < (#1)
buf: * (#1) Call complete
buf: > (#2) POST /grpc.reflection.v1alpha.ServerReflection/ServerReflectionInfo
buf: > (#2) Accept-Encoding: identity
buf: > (#2) Content-Type: application/grpc+proto
buf: > (#2) Grpc-Accept-Encoding: gzip
buf: > (#2) Grpc-Timeout: 119967m
buf: > (#2) Te: trailers
buf: > (#2) User-Agent: grpc-go-connect/1.12.0 (go1.21.4) buf/1.28.1
buf: > (#2)
buf: } (#2) [5 bytes data]
buf: } (#2) [32 bytes data]
buf: < (#2) HTTP/2.0 200 OK
buf: < (#2) Content-Type: application/grpc
buf: < (#2) Grpc-Accept-Encoding: identity, deflate, gzip
buf: < (#2)
buf: { (#2) [5 bytes data]
buf: { (#2) [434 bytes data]
buf: * Server reflection has resolved file "olympicsagent.proto"
buf: * Invoking RPC olympicsagent.v1.OlympicsAgent.AskStream
buf: > (#3) POST /olympicsagent.v1.OlympicsAgent/AskStream
buf: > (#3) Accept-Encoding: identity
buf: > (#3) Content-Type: application/grpc+proto
buf: > (#3) Grpc-Accept-Encoding: gzip
buf: > (#3) Grpc-Timeout: 119947m
buf: > (#3) Te: trailers
buf: > (#3) User-Agent: grpc-go-connect/1.12.0 (go1.21.4) buf/1.28.1
buf: > (#3)
buf: } (#3) [5 bytes data]
buf: } (#3) [6 bytes data]
buf: * (#3) Finished upload
buf: < (#3) HTTP/2.0 200 OK
buf: < (#3) Content-Type: application/grpc
buf: < (#3) Grpc-Accept-Encoding: identity, deflate, gzip
buf: < (#3)
buf: { (#3) [5 bytes data]
buf: { (#3) [25 bytes data]
{
 "answer": "Here's a list of Summer"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [31 bytes data]
{
  "answer": " and Winter Olympics from 198"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [29 bytes data]
{
  "answer": "0 to 2024:\n\nSummer Olympics"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [20 bytes data]
{
  "answer": ":\n1. 1980 - Moscow"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [20 bytes data]
{
  "answer": ", Soviet Union\n2. "
}
buf: { (#3) [5 bytes data]
buf: { (#3) [32 bytes data]
{
  "answer": "1984 - Los Angeles, California"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [15 bytes data]
{
  "answer": ", USA\n3. 1988"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [26 bytes data]
{
  "answer": " - Seoul, South Korea\n4."
}
buf: { (#3) [5 bytes data]
buf: { (#3) [27 bytes data]
{
  "answer": " 1992 - Barcelona, Spain\n"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [20 bytes data]
{
  "answer": "5. 1996 - Atlanta,"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [22 bytes data]
{
  "answer": " Georgia, USA\n6. 200"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [26 bytes data]
{
  "answer": "0 - Sydney, Australia\n7."
}
buf: { (#3) [5 bytes data]
buf: { (#3) [25 bytes data]
{
  "answer": " 2004 - Athens, Greece\n"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [20 bytes data]
{
  "answer": "8. 2008 - Beijing,"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [18 bytes data]
{
  "answer": " China\n9. 2012 -"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [29 bytes data]
{
  "answer": " London, United Kingdom\n10."
}
buf: { (#3) [5 bytes data]
buf: { (#3) [24 bytes data]
{
  "answer": " 2016 - Rio de Janeiro"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [18 bytes data]
{
  "answer": ", Brazil\n11. 202"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [24 bytes data]
{
  "answer": "0 - Tokyo, Japan (held"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [21 bytes data]
{
  "answer": " in 2021 due to the"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [26 bytes data]
{
  "answer": " COVID-19 pandemic)\n12. "
}
buf: { (#3) [5 bytes data]
buf: { (#3) [28 bytes data]
{
  "answer": "2024 - Paris, France\n\nNote"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [41 bytes data]
{
  "answer": ": The Olympics were held without a host"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [26 bytes data]
{
  "answer": " city for one year (2022"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [42 bytes data]
{
  "answer": ", due to the Russian invasion of Ukraine"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [29 bytes data]
{
  "answer": ").\n\nWinter Olympics:\n1. 198"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [27 bytes data]
{
  "answer": "0 - Lake Placid, New York"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [15 bytes data]
{
  "answer": ", USA\n2. 1984"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [27 bytes data]
{
  "answer": " - Sarajevo, Yugoslavia ("
}
buf: { (#3) [5 bytes data]
buf: { (#3) [30 bytes data]
{
  "answer": "now Bosnia and Herzegovina)\n"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [20 bytes data]
{
  "answer": "3. 1988 - Calgary,"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [25 bytes data]
{
  "answer": " Alberta, Canada\n4. 199"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [26 bytes data]
{
  "answer": "2 - Albertville, France\n"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [13 bytes data]
{
  "answer": "5. 1994 - L"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [24 bytes data]
{
  "answer": "illehammer, Norway\n6. "
}
buf: { (#3) [5 bytes data]
buf: { (#3) [23 bytes data]
{
  "answer": "1998 - Nagano, Japan\n"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [16 bytes data]
{
  "answer": "7. 2002 - Salt"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [24 bytes data]
{
  "answer": " Lake City, Utah, USA\n"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [18 bytes data]
{
  "answer": "8. 2006 - Torino"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [17 bytes data]
{
  "answer": ", Italy\n9. 2010"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [40 bytes data]
{
  "answer": " - Vancouver, British Columbia, Canada"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [13 bytes data]
{
  "answer": "\n10. 2014 -"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [20 bytes data]
{
  "answer": " Sochi, Russia\n11."
}
buf: { (#3) [5 bytes data]
buf: { (#3) [16 bytes data]
{
  "answer": " 2018 - Pyeong"
}
buf: { (#3) [5 bytes data]
buf: { (#3) [24 bytes data]
{
  "answer": "chang, South Korea\n12."
}
buf: < (#3)
buf: < (#3) Grpc-Message:
buf: < (#3) Grpc-Status: 0
buf: * (#3) Call complete
buf: < (#2)
buf: < (#2) Grpc-Message:
buf: < (#2) Grpc-Status: 0
buf: * (#2) Call complete
%

🪶 Empty Messages

Empty request/response messages are automatically mapped to google.protobuf.Empty:

from pydantic_rpc import AsyncIOServer, Message


class EmptyRequest(Message):
    pass  # Automatically uses google.protobuf.Empty


class GreetingResponse(Message):
    message: str


class GreetingService:
    async def say_hello(self, request: EmptyRequest) -> GreetingResponse:
        return GreetingResponse(message="Hello!")
    
    async def get_default_greeting(self) -> GreetingResponse:
        # Method with no request parameter (implicitly empty)
        return GreetingResponse(message="Hello, World!")

🎨 Custom Serialization

Pydantic's serialization decorators are fully supported:

from typing import Any
from pydantic import field_serializer, model_serializer
from pydantic_rpc import Message


class UserMessage(Message):
    name: str
    age: int
    
    @field_serializer('name')
    def serialize_name(self, name: str) -> str:
        """Always uppercase the name when serializing."""
        return name.upper()


class ComplexMessage(Message):
    value: int
    multiplier: int
    
    @model_serializer
    def serialize_model(self) -> dict[str, Any]:
        """Custom serialization with computed fields."""
        return {
            'value': self.value,
            'multiplier': self.multiplier,
            'result': self.value * self.multiplier  # Computed field
        }

The serializers are automatically applied when converting between Pydantic models and protobuf messages.

⚠️ Limitations and Considerations

1. Nested Message serializers are now supported (v0.8.0+)

class Address(Message):
    city: str
    
    @field_serializer("city")
    def serialize_city(self, city: str) -> str:
        return city.upper()

class User(Message):
    name: str
    address: Address  # ← Address's serializers ARE applied with DEEP strategy
    
    @field_serializer("name")
    def serialize_name(self, name: str) -> str:
        return name.upper()  # ← This IS applied

Serializer Strategy Control: You can control how nested serializers are applied via environment variable:

# Apply serializers at all nesting levels (default)
export PYDANTIC_RPC_SERIALIZER_STRATEGY=deep

# Apply only top-level serializers
export PYDANTIC_RPC_SERIALIZER_STRATEGY=shallow

# Disable all serializers
export PYDANTIC_RPC_SERIALIZER_STRATEGY=none

Performance Impact:

• DEEP strategy: ~4% overhead for simple nested structures
• SHALLOW strategy: ~2% overhead (only top-level)
• NONE strategy: No overhead (serializers disabled)

2. New fields added by serializers are ignored

class ComplexMessage(Message):
    value: int
    multiplier: int
    
    @model_serializer
    def serialize_model(self) -> dict[str, Any]:
        return {
            "value": self.value,
            "multiplier": self.multiplier,
            "result": self.value * self.multiplier  # ← Won't appear in protobuf
        }

Problem: The result field doesn't exist in the Message definition, so it's not in the protobuf schema.

3. Type must remain consistent

class Ba