OACP vs MCP vs A2A: When to Use Each Protocol

MCP, ACP, A2A, and OACP solve different problems at different layers. Here’s how to choose.

The protocol stack

Four protocols are shaping multi-agent development. Each operates at a different layer:

┌─────────────────────────────────────────────┐
│  A2A — Agent discovery & remote messaging   │  internet-scale
├─────────────────────────────────────────────┤
│  OACP — Async workflow messaging            │  local filesystem
├─────────────────────────────────────────────┤
│  ACP — Client ↔ agent sessions              │  IDE / editor
├─────────────────────────────────────────────┤
│  MCP — Agent-to-tool integration            │  tool access
└─────────────────────────────────────────────┘

At a glance

MCP

Gives agents access to tools and data sources.

• JSON-RPC over stdio or HTTP
• Connect agents to APIs, databases, files
• One MCP server per tool integration

ACP

Connects IDE clients to coding agents.

• JSON-RPC over stdio (HTTP draft)
• Agent lifecycle, streaming, permissions
• Adopted by Zed, JetBrains, Neovim

A2A

Agent discovery and messaging over the internet.

• HTTP/HTTPS with TLS and auth
• Cross-org, internet-routable agents
• 150+ organizations, Linux Foundation

OACP

Async workflow coordination for agent teams.

• Filesystem transport (YAML)
• Task dispatch, review loops, handoffs
• Zero infrastructure, crash-resilient

Side-by-side comparison

Protocol comparison: MCP, ACP, A2A, and OACP

	MCP	ACP	A2A	OACP
Solves	Tool access	Client ↔ agent sessions	Agent discovery + networking	Async workflow coordination
Transport	JSON-RPC (stdio/HTTP)	JSON-RPC (stdio; HTTP draft)	HTTP/HTTPS	Filesystem (YAML)
Best for	Connecting agents to APIs, DBs, files	IDE ↔ coding agent interaction	Cross-org, internet-routable agents	Local teams, dev machines, CI
Infrastructure	MCP server per tool	ACP-capable client + agent	TLS, auth, HTTP endpoints	A shared directory
Message persistence	None (synchronous calls)	None (session-scoped)	Optional (push notifications)	Native — YAML files wait in inbox
Setup	Install MCP server	Use ACP-capable client + agent	Deploy servers + networking	oacp init my-project

When to use MCP

MCP (Model Context Protocol) defines how an agent calls a tool. Use it when you need agents to:

• Query databases, call APIs, or access file systems through a standard interface
• Share tool integrations across multiple agents or clients
• Give an LLM structured access to external data sources

MCP is the tool-access layer. Every agent that uses external tools benefits from MCP, regardless of what other protocols it speaks.

Tip

If your agents only need to call tools and you don’t need agents talking to each other, MCP alone may be all you need.

When to use ACP

ACP (Agent Client Protocol, by Zed Industries) defines how an IDE or client communicates with a coding agent. Use it when you need:

• IDE agent lifecycle management — start, stop, stream responses
• Structured tool permissions and file access between client and agent
• Direct, synchronous client-to-agent sessions over JSON-RPC

ACP is adopted by Zed, JetBrains, Neovim, and 28+ agents in its registry. It covers the IDE-to-agent boundary: prompt/response streaming, terminal operations, and agent registration.

Note

ACP does not cover agent-to-agent communication, task dispatch, review loops, or the coordinator pattern. It’s the client-to-agent layer, not the agent-to-agent layer.

When to use A2A

A2A (Agent-to-Agent Protocol, under the Linux Foundation) defines how agents discover and message each other over the internet. Use it when you need:

• Cross-organization agent communication over HTTP
• Service discovery — finding agents by capability across a network
• Enterprise-grade networking with TLS, auth, and HTTP endpoints

A2A is backed by 150+ organizations and designed for internet-scale agent networking. Both agents must be running and reachable for messages to be delivered.

When to use OACP

OACP (Open Agent Coordination Protocol) is the async coordination layer for multi-agent workflows. Use it when you need:

• Multiple AI agents (Claude, Codex, Cursor, Gemini) to coordinate on engineering tasks
• Typed workflow messages — task dispatch, code review, handoff, brainstorm
• Durable async messaging — messages persist as YAML files in the filesystem and wait in agent inboxes
• A coordinator pattern — a human or agent dispatches work and tracks progress
• Zero infrastructure — no servers, no databases, just a shared directory

Tip

OACP’s messages are durable YAML files that persist in the filesystem. Agents don’t need to be running at the same time — messages wait in inbox directories until the recipient reads them. No network, no server, no message broker.

Using them together

These protocols are complementary, not competing. A single agent can use all four at different layers — and in production, most multi-agent setups do.

Full stack example

A coding agent in a typical multi-agent session might:

1. MCP — call a GitHub API via an MCP server to fetch PR data
2. ACP — stream its analysis back to the IDE client
3. OACP — check its inbox for a code review request from a coordinator agent
4. A2A — discover and message a remote security-scanning agent

Each protocol handles one layer. No overlap, no conflict.

Local team (no network)

For a local multi-agent team on one machine:

1. MCP — agents access tools (databases, APIs, files)
2. OACP — coordinator dispatches tasks, agents exchange reviews

No ACP needed (no IDE integration). No A2A needed (same machine). Two protocols cover the full workflow.

Enterprise (internet-scale)

For agents spanning multiple organizations:

1. MCP — each agent accesses its own tools
2. A2A — agents discover each other and communicate over HTTPS
3. OACP — within each org, local agents coordinate via filesystem inboxes

A gateway between OACP inboxes and A2A endpoints is a natural bridge — and A2A’s own community is exploring inbox patterns that validate this direction.

Which protocol fits your situation?

1. Do your agents need to call external tools (APIs, databases, files)?

   Yes → Use MCP for tool access. This is the foundation layer most agents need.

2. Do your agents need to talk to each other?

   If across the internet → Use A2A for discovery and networking. If on the same machine → Use OACP for async coordination. If only IDE ↔ agent → Use ACP for client sessions.

3. Do you need structured workflows (task dispatch, code reviews, handoffs)?

   Yes → Use OACP. It’s the only protocol with typed workflow messages and a coordinator pattern. No infrastructure required — just a shared directory.

Get started with OACP Installation guide

Frequently asked questions

What is the difference between MCP and A2A?

MCP gives agents access to tools and data sources via JSON-RPC. A2A lets agents discover and message each other across the internet via HTTP. MCP is the tool-access layer; A2A is the networking layer. They operate at different levels and are complementary.

How do I coordinate multiple AI agents?

OACP coordinates multiple AI agents through typed workflow messages — task dispatch, code review, handoff — over a shared filesystem. Messages persist as YAML files in agent inboxes, requiring no servers or databases. Get started with pip install oacp-cli.

Can I use MCP, A2A, and OACP together?

Yes. These protocols are complementary, not competing. A single agent can use MCP for tool access, ACP for IDE integration, A2A for internet-scale agent discovery, and OACP for local multi-agent coordination — all in the same session.

What is the best multi-agent workflow tool?

It depends on your needs. For tool access, use MCP. For IDE-to-agent communication, use ACP. For cross-organization agent networking, use A2A. For coordinating multiple AI agents on a local machine or CI with no infrastructure, use OACP. Most production setups combine multiple protocols at different layers.

Does OACP replace MCP or A2A?

No. OACP operates at the workflow coordination layer — it handles task dispatch, reviews, and handoffs between agents. MCP handles tool access. A2A handles internet-scale agent networking. They solve different problems and work together naturally.