# MCP vs A2A vs ACP: Open Protocols for Multi-Agent Systems, Compared

> Source: <https://dev.to/botsailorofficial/mcp-vs-a2a-vs-acp-open-protocols-for-multi-agent-systems-compared-1m9i>
> Published: 2026-10-11 04:31:13+00:00

If you're building a multi-agent system, you've hit this question: **how should agents talk to tools, and how should they talk to each other?** Wire everything by hand and every new agent adds another custom integration. Pick the wrong protocol and you may rewrite your architecture in six months.

This article compares the main open protocols for multi-agent systems: **MCP, A2A, ACP, ANP, and AG-UI**. It's an MCP vs A2A comparison first, but it also covers the alternatives, because no single protocol solves every layer. By the end you'll know what each protocol does, where they overlap, where they don't, and which combination fits your stack.

You don't need to have built an agent system, but this will be easier if you have:

`pip install "mcp[cli]"`)
**Note:** Agent protocols are evolving quickly. Spec details below are accurate as of writing, but always check each protocol's official repository for the current version before you build.

A single agent with a few tools works fine with hardcoded glue code. Multi-agent systems break that approach:

Open protocols turn N×M into N+M. Each agent or tool implements the standard once and interoperates with everything else that does.

Most confusion in the MCP vs A2A debate comes from treating them as competitors. They mostly solve **different layers**:

| Layer | Question it answers | Main protocols | 
|---|---|---|
| Agent ↔ Tool/Data | "How does my agent call a database, API, or file system?" | MCP | 
| Agent ↔ Agent | "How does my agent delegate work to another agent?" | A2A, ACP, ANP | 
| Agent ↔ User/UI | "How does my agent stream state to a frontend?" | AG-UI | 

Keep this table in mind. It resolves most "which one should I use?" questions.

**Created by:** Anthropic (announced November 2024), now an open standard with broad industry adoption and Linux Foundation-backed governance.

**What it does:** MCP standardizes how an LLM application connects to external tools, data sources, and prompts. An **MCP client** (inside your agent or host app) talks to **MCP servers** that expose capabilities.

**Core primitives:**

**Wire format:** JSON-RPC 2.0, over `stdio` (local) or Streamable HTTP (remote).

Here's a small server exposing one tool, using the official Python SDK:

``` python
# inventory_server.py
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("inventory")

STOCK = {"SKU-001": 42, "SKU-002": 0}

@mcp.tool()
def check_stock(sku: str) -> dict:
    """Return the current stock level for a SKU."""
    qty = STOCK.get(sku)
    if qty is None:
        return {"sku": sku, "error": "unknown SKU"}
    return {"sku": sku, "in_stock": qty > 0, "quantity": qty}

if __name__ == "__main__":
    mcp.run(transport="stdio")
```

Any MCP-compatible client can now discover and call `check_stock`, with no custom integration code.

**Strengths**

**Limitations**

`stdio` servers need careful security review, since you're running third-party code
**Created by:** Google (announced April 2025), later donated to the Linux Foundation, with many industry partners contributing.

**What it does:** A2A lets independent agents, possibly built on different frameworks by different vendors, **discover each other, delegate tasks, and exchange results** without sharing internal state, memory, or tools.

**Core concepts:**

**Wire format:** JSON-RPC 2.0 over HTTP(S), with Server-Sent Events for streaming (recent versions also add gRPC support).

```
{
  "name": "Refund Agent",
  "description": "Evaluates and processes customer refund requests.",
  "url": "https://agents.example.com/refunds",
  "version": "1.0.0",
  "capabilities": { "streaming": true },
  "defaultInputModes": ["text/plain"],
  "defaultOutputModes": ["application/json"],
  "skills": [
    {
      "id": "evaluate-refund",
      "name": "Evaluate refund",
      "description": "Checks order history and policy to decide refund eligibility.",
      "tags": ["support", "payments"]
    }
  ]
}
```

Another agent fetches this card, learns what the Refund Agent can do, and sends it a task. It never needs to know what model, framework, or tools sit behind it.

**Tip:** The key design idea in A2A is that agents are **opaque**. They collaborate through declared capabilities, not by sharing internals. That's what makes cross-vendor collaboration realistic.

An honest comparison includes the options that aren't the headline names.

Originated by IBM Research and the BeeAI project, ACP took a **REST-first** approach to agent interoperability, aiming for simple HTTP semantics without requiring a specialized SDK. In 2025 the ACP team announced it was **merging into A2A** under the Linux Foundation. If you're starting fresh, treat A2A as the successor. If you have existing ACP agents, check the migration guidance from the BeeAI project.

ANP targets an **open, decentralized "internet of agents"**, using decentralized identifiers (DIDs) for identity and trust between agents that don't belong to the same organization. It's the most ambitious option for cross-organization discovery, and also the least mature in terms of production adoption.

AG-UI isn't an agent-to-agent protocol. It standardizes how an agent **streams events to a frontend** (messages, tool calls, state updates). It's complementary: you might use MCP for tools, A2A for agent delegation, and AG-UI to render it all in a web app.

Other options include the earlier **Agent Protocol** (a simple REST API for running agent tasks) and framework-specific messaging inside tools like LangGraph, CrewAI, or AutoGen. These work well *inside* a single framework. They're not interoperability standards, so you'll still need an open protocol at your system's boundary.

|  | **MCP** | **A2A** | **ACP** | **ANP** | **AG-UI** | 
|---|---|---|---|---|---|
| **Primary layer** | Agent ↔ tool/data | Agent ↔ agent | Agent ↔ agent | Agent ↔ agent (decentralized) | Agent ↔ user/UI | 
| **Origin** | Anthropic | Google → Linux Foundation | IBM / BeeAI → merging into A2A | Open community | CopilotKit / open source | 
| **Transport** | stdio, Streamable HTTP | HTTP + SSE (gRPC in newer versions) | REST over HTTP | HTTP + DID-based identity | Event streaming | 
| **Discovery** | Client config / registries | Agent Cards | Agent manifests | DID documents | N/A | 
| **Long-running tasks** | Limited | First-class | Supported | Varies | Streaming events | 
| **Ecosystem maturity** | High | Growing fast | Folding into A2A | Early | Growing | 
| **Best for** | Giving agents tools | Cross-team / cross-vendor agent delegation | Legacy BeeAI setups | Open-internet agent networks | Agent-powered frontends | 

Start with the problem, not the protocol:

**Rule of thumb:** MCP is how an agent *uses* things. A2A is how an agent *works with* other agents.

In production, these are complementary. A typical layered setup looks like this:

```
User ──(AG-UI)──► Orchestrator Agent
                      │
          ┌───────────┼────────────┐
        (A2A)       (A2A)        (MCP)
          ▼           ▼            ▼
   Refund Agent  Shipping Agent  Inventory DB tool
          │           │
        (MCP)       (MCP)
          ▼           ▼
    Payments API  Carrier API
```

The orchestrator delegates to specialist agents over A2A. Each specialist uses MCP to reach its own tools. Neither protocol needs to know about the other, and that separation is why the combination works.

Key takeaways:

The right setup depends on your architecture, not on which protocol is trending. Start with MCP for tools, add A2A when agents truly need to cross boundaries, and keep each layer swappable.

**What are you using in your multi-agent stack today: MCP, A2A, something custom? Share your experience in the comments.**