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Posted on Oct 11

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

#agents #architecture #mcp #systemdesign

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

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.

Prerequisites
Why multi-agent systems need open protocols
The two layers: agent-to-tool vs agent-to-agent
MCP (Model Context Protocol)
A2A (Agent2Agent Protocol)
ACP, ANP, and AG-UI: the alternatives
MCP vs A2A vs ACP: side-by-side comparison
How to choose: a decision guide
Using MCP and A2A together
Common mistakes
Conclusion

Prerequisites

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

[ ] Basic familiarity with LLM agents and tool calling
[ ] Comfort reading JSON and short Python snippets
[ ] A rough idea of HTTP APIs (REST, JSON-RPC, or SSE streaming)
[ ] Python 3.10+ if you want to run the MCP example (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.

Why multi-agent systems need open protocols

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

N agents × M tools = N×M integrations if every pairing is custom.
Vendor lock-in: an agent built on one framework can't easily collaborate with one built on another.
No shared discovery: how does Agent A learn that Agent B exists and what it can do?
Security gaps: ad-hoc auth between agents is hard to audit.

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

The two layers: agent-to-tool vs agent-to-agent

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.

MCP (Model Context Protocol)

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:

Tools: functions the model can call
Resources: read-only data the model can pull in
Prompts: reusable prompt templates

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

A minimal MCP server

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

# 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

Huge and growing ecosystem of ready-made servers
Simple mental model: one agent, many tools
Supported by major AI clients and IDEs

Limitations

Designed around a client-to-server relationship, not peer-to-peer agent collaboration
Doesn't define long-running task lifecycles between autonomous agents
Local stdio servers need careful security review, since you're running third-party code

A2A (Agent2Agent Protocol)

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:

Agent Card: a JSON "business card" describing the agent's skills, endpoint, and auth requirements, usually served from a well-known URL
Task: a unit of work with a lifecycle (submitted, working, input-required, completed, failed)
Message / Artifact: the conversation turns and outputs of a task
Streaming & push notifications: for long-running work

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

An example Agent Card

{
  "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.

Strengths

Purpose-built for agent-to-agent delegation and long-running tasks
Built-in discovery via Agent Cards
Framework-agnostic and vendor-neutral governance

Limitations

Younger ecosystem than MCP, so fewer production references
More moving parts (task states, auth schemes) than a simple tool call
Overkill if all your "agents" live in one process

ACP, ANP, and AG-UI: the alternatives

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

ACP (Agent Communication Protocol)

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 (Agent Network Protocol)

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 (Agent-User Interaction Protocol)

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.

Agent Protocol and framework-native messaging

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 vs A2A vs ACP: side-by-side comparison

	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

How to choose: a decision guide

Start with the problem, not the protocol:

"My agent needs to call APIs, databases, or files." → Use MCP.
"I have several agents in one codebase and one framework." → Use your framework's native orchestration. Don't add a network protocol you don't need yet.
"My agents come from different teams, vendors, or frameworks and must collaborate." → Use A2A.
"Agents from different organizations need to discover and trust each other." → Evaluate A2A with strong auth first. Watch ANP if you need decentralized identity.
"I need a live, streaming UI for my agent." → Add AG-UI.
"I already run ACP agents." → Plan a migration path to A2A.

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

Using MCP and A2A together

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.

Common mistakes

Treating MCP and A2A as either/or. They solve different layers.
Adopting a network protocol too early. In-process agents don't need discovery and task lifecycles.
Skipping auth design. Agent Cards and MCP servers both expose capabilities, so decide who may call what before you go to production.
Trusting third-party MCP servers blindly. Review code and permissions like any other dependency.
Ignoring spec versions. Pin protocol and SDK versions, since these specs are still moving.

Conclusion

Key takeaways:

MCP standardizes agent-to-tool connections and has the most mature ecosystem.
A2A standardizes agent-to-agent collaboration and is the clearest path for cross-vendor multi-agent systems.
ACP is converging into A2A, ANP is worth watching for decentralized scenarios, and AG-UI covers the frontend layer.
Most real systems end up using more than one, so choose by layer, not by hype.

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.

DEV Community

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

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

Table of Contents

Prerequisites

Why multi-agent systems need open protocols

The two layers: agent-to-tool vs agent-to-agent

MCP (Model Context Protocol)

A minimal MCP server

A2A (Agent2Agent Protocol)

An example Agent Card

ACP, ANP, and AG-UI: the alternatives

ACP (Agent Communication Protocol)

ANP (Agent Network Protocol)

AG-UI (Agent-User Interaction Protocol)

Agent Protocol and framework-native messaging

MCP vs A2A vs ACP: side-by-side comparison

How to choose: a decision guide

Using MCP and A2A together

Common mistakes

Conclusion

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