A2A vs gRPC: Protocol Comparison for Agent Communication

gRPC is the go-to protocol for high-performance service communication. It's fast, type-safe, and has excellent streaming support. So when you're building a system where agents need to talk to each other, gRPC is a reasonable consideration.

But A2A and gRPC solve different problems at different layers. gRPC is a general-purpose RPC framework. A2A is a purpose-built protocol for AI agent interoperability. The right choice depends on what you're optimizing for.

Transport

gRPC uses HTTP/2 exclusively. This gives you multiplexed streams over a single connection, header compression, and binary framing. It's efficient for high-throughput, low-latency communication between services in the same datacenter.

A2A uses HTTP/1.1 or HTTP/2 with JSON-RPC 2.0. Streaming uses SSE (Server-Sent Events) over a standard HTTP response. It works with any HTTP infrastructure — proxies, CDNs, load balancers, API gateways — without special configuration.

gRPC:
  Client <-- HTTP/2 + Protobuf --> Server
  Binary frames, multiplexed streams

A2A:
  Client -- HTTP POST (JSON-RPC) --> Server
  Client <-- SSE (text/event-stream) -- Server
  Standard HTTP, works everywhere

gRPC's HTTP/2 requirement can be a problem. Many API gateways and reverse proxies don't fully support HTTP/2 end-to-end. Cloud load balancers sometimes terminate HTTP/2 at the edge and proxy as HTTP/1.1 internally, breaking gRPC streaming. A2A avoids this entirely — SSE works over HTTP/1.1.

Serialization

gRPC uses Protocol Buffers (protobuf). Messages are defined in .proto files and compiled to language-specific code. Binary encoding is compact and fast. Schema evolution is handled through field numbers.

// agent_service.proto
syntax = "proto3";

service AgentService {
  rpc SendTask(TaskRequest) returns (TaskResponse);
  rpc StreamTask(TaskRequest) returns (stream TaskUpdate);
}

message TaskRequest {
  string task_id = 1;
  Message message = 2;
}

message Message {
  string role = 1;
  repeated Part parts = 2;
}

message Part {
  string kind = 1;
  string text = 2;
  bytes data = 3;
}

message TaskResponse {
  string task_id = 1;
  TaskStatus status = 2;
  repeated Artifact artifacts = 3;
}

message TaskUpdate {
  oneof update {
    TaskStatusUpdate status_update = 1;
    TaskArtifactUpdate artifact_update = 2;
  }
}

A2A uses JSON. Messages are self-describing, human-readable, and need no compilation step. The tradeoff is size — JSON is roughly 2-10x larger than protobuf for the same data — and parsing speed.

{
  "jsonrpc": "2.0",
  "id": "req-1",
  "method": "message/send",
  "params": {
    "id": "task-001",
    "message": {
      "role": "user",
      "parts": [{ "kind": "text", "text": "Analyze this dataset" }]
    }
  }
}

For AI agents, the serialization overhead rarely matters. The bottleneck is the LLM inference, not JSON parsing. A 50ms JSON parse is noise when the agent takes 3 seconds to generate a response.

Streaming

Both protocols support streaming. The mechanics differ significantly.

gRPC supports four streaming modes:

Unary (request-response)
Server streaming (one request, stream of responses)
Client streaming (stream of requests, one response)
Bidirectional streaming (stream in both directions)

Bidirectional streaming is gRPC's killer feature. Client and server can send messages independently, interleaved on the same connection.

A2A supports server-side streaming via SSE through the message/stream method. The client sends a request, and the server streams back events. For bidirectional communication (like multi-turn conversations), A2A uses sequential request-response cycles with the same task ID.

# gRPC bidirectional streaming
async def chat(self, request_iterator, context):
    async for request in request_iterator:
        # Process each message as it arrives
        response = await self.process(request)
        yield response

# A2A multi-turn (sequential request-response)
# Turn 1: Client sends message, server streams response
# Server responds with input-required
# Turn 2: Client sends follow-up, server streams response
# Server responds with completed

gRPC's bidirectional streaming is more flexible in theory. In practice, most agent interactions follow the request-then-stream pattern that A2A's SSE handles well. True bidirectional streaming (both sides sending independently) is rare in agent communication.

Discovery

This is where the comparison gets interesting.

gRPC has server reflection — a running gRPC server can describe its available services and methods. This tells you the API shape (what RPCs exist, what their request/response types are), but not what the service is capable of in a semantic sense.

# gRPC reflection: tells you the methods available
grpcurl -plaintext localhost:50051 list
# Output: AgentService

grpcurl -plaintext localhost:50051 describe AgentService
# Output: method signatures

A2A has Agent Cards — JSON metadata at /.well-known/agent-card.json that describes skills, capabilities, examples, and auth requirements in a way that another agent (or an LLM) can reason about.

# A2A discovery: tells you what the agent CAN DO
curl https://agent.example.com/.well-known/agent-card.json

{
  "name": "Data Analysis Agent",
  "description": "Analyzes datasets for trends, anomalies, and insights",
  "skills": [
    {
      "id": "trend-analysis",
      "name": "Trend Analysis",
      "description": "Identifies trends and patterns in time-series data",
      "tags": ["data", "trends", "time-series"],
      "examples": ["Find revenue trends in this quarterly data"]
    }
  ]
}

gRPC reflection answers "what methods can I call?" A2A Agent Cards answer "what tasks can this agent handle?" For agent orchestration — where an orchestrator needs to match a task to the right agent — capability-level discovery is what matters.

Type safety

gRPC is strongly typed end-to-end. Protobuf schemas are compiled into language-specific types. If you change a message field, the compiler catches mismatches. This is excellent for large teams where API contracts matter.

A2A uses JSON with a documented schema. You get runtime validation (e.g., Pydantic in Python, Zod in TypeScript) but not compile-time guarantees. The A2A Python SDK provides typed models:

# A2A SDK types provide runtime validation
from a2a.types import (
    AgentCard,
    Message,
    Part,
    TaskStatus,
    TaskState,
)

# Validation at runtime, not compile time
card = AgentCard(
    name="My Agent",
    description="Does things",
    version="1.0.0",
    url="https://agent.example.com",
    skills=[],
)

For agent communication, the lack of compile-time types is less painful than it sounds. Agent messages are mostly natural language text, not complex structured data. The "schema" is: a message has a role and parts, parts contain text or data. That's stable.

Performance

gRPC wins on raw throughput. Binary protobuf, HTTP/2 multiplexing, connection reuse, header compression. If you're making thousands of RPC calls per second between services, gRPC is measurably faster.

For AI agents, throughput is almost never the bottleneck. An agent call involves:

HTTP overhead: ~1-5ms (gRPC) vs ~2-10ms (HTTP/JSON)
Serialization: ~0.1ms (protobuf) vs ~1-5ms (JSON)
LLM inference: 500ms - 30,000ms

The LLM dominates. The difference between gRPC and A2A transport is in the noise.

Where performance does matter is streaming efficiency. gRPC's HTTP/2 framing is more efficient for high-frequency small updates. If an agent streams tokens at 50+ per second, gRPC has lower per-message overhead. A2A's SSE is fine for typical agent streaming rates (a few events per second).

Ecosystem and tooling

gRPC:

Mature client libraries in 11+ languages
Production-grade load balancing (client-side and proxy-based)
Interceptors for auth, logging, tracing
gRPC-web for browser clients (with limitations)
Deep integration with Kubernetes, Istio, Envoy

A2A:

Official Python SDK (a2a-python)
Growing framework support (Google ADK, LangGraph, CrewAI, Spring Boot)
Works with any HTTP tooling — curl, Postman, fetch, httpx
Browser-native SSE support (no special client needed)
Agent directory for discovering compatible agents

gRPC has a deeper ecosystem for infrastructure. A2A has a wider reach for interoperability — any HTTP client is an A2A client.

When to use gRPC

High-throughput internal services. Thousands of calls per second between co-located services. gRPC's binary protocol and HTTP/2 are built for this.
Strict API contracts. Teams that need compile-time schema validation and backward-compatible evolution. Protobuf's field numbering system handles this better than JSON schemas.
Bidirectional streaming. If you genuinely need both client and server to push messages independently (real-time collaboration, live data feeds), gRPC's bidirectional streams are the right tool.
Existing gRPC infrastructure. If your organization already runs gRPC with Envoy/Istio service mesh, adding another gRPC service is simpler than introducing a new protocol.

When to use A2A

Agent interoperability. Agents from different frameworks, languages, and organizations need to communicate. A2A is the shared protocol.
Capability-based discovery. You need orchestrators to find and select agents dynamically based on what they can do, not what methods they expose.
Browser and edge clients. A2A works everywhere HTTP works. No special client libraries, no HTTP/2 requirements, no proxy headaches.
Multi-turn conversations. A2A's input-required pattern and task ID continuity handle conversational flows naturally. With gRPC, you'd build this yourself.
Cross-boundary communication. A2A is designed for agents that cross team, organization, and cloud boundaries. gRPC is optimized for services within a single infrastructure.

Using both together

You can run gRPC internally and expose A2A externally. This is a legitimate architecture for teams that want gRPC's performance between co-located services and A2A's interoperability for external agent communication:

# Internal: gRPC between services
class InternalAnalysisService(AnalysisServicer):
    async def Analyze(self, request, context):
        # Fast binary protocol for internal calls
        return AnalysisResponse(
            summary=await self.analyze(request.text),
        )

# External: A2A agent wrapping the internal gRPC service
class ExternalAnalysisAgent:
    def __init__(self):
        self.grpc_channel = grpc.aio.insecure_channel("internal-service:50051")
        self.grpc_stub = AnalysisStub(self.grpc_channel)

    async def handle_task(self, context):
        user_text = context.current_message["parts"][0]["text"]

        # Call internal gRPC service
        grpc_response = await self.grpc_stub.Analyze(
            AnalysisRequest(text=user_text)
        )

        await context.add_artifact(
            parts=[{"kind": "text", "text": grpc_response.summary}]
        )
        await context.set_status(state="completed")

gRPC and A2A aren't competing protocols. gRPC is infrastructure plumbing — optimized for speed between services you control. A2A is an agent interaction protocol — optimized for discovery, interoperability, and conversational workflows across boundaries. Use gRPC where latency matters. Use A2A where interoperability matters. Use both when you need both.

Explore the agent directory to see A2A agents in action, or read the A2A vs REST comparison for another angle on protocol choice.