A2A and gRPC both come from Google's ecosystem, but they solve different layers. gRPC is a high-performance RPC framework with protobuf serialization -- fast, typed, battle-tested. A2A is an agent-native protocol built on top of HTTP with JSON-RPC, designed for task delegation, agent discovery, and multi-turn workflows. One optimizes bytes on the wire; the other optimizes agent collaboration.
| Dimension | Option A | Option B |
|---|---|---|
| Primary Purpose | Agent-to-agent task delegation and collaboration | High-performance remote procedure calls |
| Serialization | JSON (human-readable, ~2-5x larger) | Protobuf (binary, compact, ~50-80% smaller) |
| Task Lifecycle | Built-in: submitted, working, completed, failed, canceled | None -- implement your own state machine |
| Agent Discovery | Agent Cards at /.well-known/agent.json with capabilities | Service reflection (optional), no standard discovery |
| Streaming | SSE for task progress updates | Bidirectional streaming with backpressure |
| Latency (p50) | ~30-60ms (HTTP + JSON parsing) | ~5-15ms (HTTP/2 + protobuf) |
| Browser Support | Full (standard HTTP + JSON) | Requires grpc-web proxy |
| Schema Evolution | JSON Schema (flexible, no codegen required) | Protobuf (strict, requires codegen, backward-compatible) |
| Multi-Agent Orchestration | Native: task routing, delegation, artifacts | Manual: build orchestration on top of RPC calls |
| Throughput (msgs/sec) | ~2,000-5,000 on single node | ~15,000-50,000 on single node |
A2A doesn't just send messages -- it models tasks. When Agent A delegates work to Agent B, A2A tracks the task through submitted, working, completed, failed, and canceled states. Each state transition can carry artifacts (results, intermediate outputs) and metadata. gRPC gives you a function call with a request and response. For agent systems where tasks take seconds to minutes and involve multiple steps, A2A's lifecycle model eliminates the need to build your own state machine on top of raw RPC.
Protobuf serialization is 5-10x faster than JSON parsing and produces payloads 50-80% smaller. gRPC's HTTP/2 multiplexing handles thousands of concurrent streams on a single connection. For latency-sensitive pipelines -- real-time trading signals, video frame analysis, sensor data processing -- gRPC's raw throughput is unmatched. A2A's JSON overhead is acceptable for agent-to-agent delegation (10-50 calls per workflow), but falls behind for high-frequency communication patterns.
A2A agents publish an Agent Card at /.well-known/agent.json describing their capabilities, supported input/output types, and authentication requirements. Any agent can discover another by fetching this URL. gRPC has server reflection, but it's opt-in, not standardized, and returns protobuf service descriptors rather than semantic capability descriptions. For multi-agent systems where agents need to find and evaluate each other at runtime, A2A's discovery mechanism is fundamentally more useful.
A2A uses Server-Sent Events for streaming task updates -- simple, browser-compatible, one-directional. gRPC supports full bidirectional streaming with backpressure control, making it ideal for real-time data flows where both sides send concurrently. If your agents need to exchange a steady stream of updates simultaneously, gRPC wins. If you need progress reporting on a delegated task, A2A's SSE is simpler to implement and debug.
The best agent architectures use both. A2A handles the orchestration layer -- task delegation, agent discovery, progress tracking, artifact exchange. gRPC handles the performance-critical data plane -- moving large payloads, streaming sensor data, or executing high-frequency internal RPCs. Delx uses A2A for inter-agent communication and could use gRPC for internal microservice calls where latency matters. This split gives you agent semantics where they matter and raw speed where it matters.
A2A is JSON over HTTP -- any language, any HTTP client, zero codegen. You can test A2A calls with curl. gRPC requires .proto files, code generation, and language-specific stubs. The initial setup cost is higher, but the generated types catch errors at compile time. For rapid prototyping and polyglot agent ecosystems, A2A's simplicity wins. For large teams with strict API contracts, gRPC's type safety and codegen are worth the setup cost.
Moving from gRPC to A2A means converting .proto service definitions to Agent Card capabilities and replacing protobuf messages with JSON task payloads. The reverse (A2A to gRPC) requires defining .proto schemas for each task type and building lifecycle tracking on top of streaming RPCs. Most teams don't fully migrate -- they add A2A as an orchestration layer on top of existing gRPC services, keeping both.
No. A2A is a higher-level protocol that adds agent semantics (task lifecycle, discovery, artifacts) on top of HTTP. gRPC is a lower-level RPC framework. They operate at different abstraction layers.
The A2A spec uses HTTP + JSON-RPC. You could theoretically implement A2A semantics over gRPC, but you'd lose browser compatibility and JSON simplicity -- the two main reasons teams choose A2A.
At 100 calls/minute, both are effectively identical in practice. A2A adds ~20-40ms overhead per call vs gRPC. That's 2-4 seconds total per minute -- irrelevant for most agent workflows.
Delx currently supports MCP, A2A, REST, and CLI. gRPC isn't exposed directly, but internal services could use it. The agent-facing protocols are MCP (tool execution) and A2A (agent communication).
Agent Cards are semantic -- they describe what an agent can do in natural language plus structured capabilities. Protobuf reflection returns raw method signatures. Agent Cards are designed for AI agents to read; reflection is designed for developer tooling.
gRPC. Its bidirectional streaming with backpressure is designed for high-throughput real-time data. A2A's SSE is for task progress updates, not continuous data streams.