copilotkit--copilotkit/skills/copilotkit-integrations/references/integrations/a2a.md

A2A (Agent-to-Agent) Integration

CopilotKit supports multi-agent architectures via two A2A patterns: A2A Middleware (orchestrating multiple agents from different frameworks) and A2A + A2UI (agents that render UI components declaratively).

A2A Middleware

The A2A Middleware pattern enables a frontend to communicate with multiple specialized agents built with different frameworks. An orchestrator coordinates the agents, and the middleware injects a send_message_to_a2a_agent tool.

Architecture

Next.js UI (CopilotKit)
    |  AG-UI Protocol
A2A Middleware
    |  A2A Protocol
    +---> Research Agent (LangGraph, port 9001)
    +---> Analysis Agent (ADK, port 9002)
    ^
    |
Orchestrator (ADK, port 9000)

Prerequisites

• Node.js 18+
• Python 3.10+
• Google API key + OpenAI API key

Next.js Route (app/api/copilotkit/...slug/route.ts)

import {
  CopilotRuntime,
  createCopilotHonoHandler,
  InMemoryAgentRunner,
} from "@copilotkit/runtime/v2";
import { HttpAgent } from "@ag-ui/client";
import { A2AMiddlewareAgent } from "@ag-ui/a2a-middleware";
import { handle } from "hono/vercel";

const researchAgentUrl =
  process.env.RESEARCH_AGENT_URL || "http://localhost:9001";
const analysisAgentUrl =
  process.env.ANALYSIS_AGENT_URL || "http://localhost:9002";
const orchestratorUrl = process.env.ORCHESTRATOR_URL || "http://localhost:9000";

// Connect to orchestrator via AG-UI Protocol
const orchestrationAgent = new HttpAgent({ url: orchestratorUrl });

// A2A Middleware wraps orchestrator and injects send_message_to_a2a_agent tool
const a2aMiddlewareAgent = new A2AMiddlewareAgent({
  description: "Research assistant with 2 specialized agents",
  agentUrls: [researchAgentUrl, analysisAgentUrl],
  orchestrationAgent,
  instructions: `
    You are a research assistant that orchestrates between 2 specialized agents.
    - Research Agent (LangGraph): Gathers and summarizes information
    - Analysis Agent (ADK): Analyzes research findings

    When the user asks to research a topic:
    1. Research Agent - gather information
    2. Analysis Agent - analyze the findings
    3. Present the complete research and analysis
  `,
});

const runtime = new CopilotRuntime({
  agents: {
    a2a_chat: a2aMiddlewareAgent,
  },
  runner: new InMemoryAgentRunner(),
});

const app = createCopilotHonoHandler({
  runtime,
  basePath: "/api/copilotkit",
});

export const GET = handle(app);
export const POST = handle(app);
export const PATCH = handle(app);
export const DELETE = handle(app);

Key patterns:

• A2AMiddlewareAgent from @ag-ui/a2a-middleware wraps the orchestrator
• agentUrls lists all A2A-compatible agent endpoints
• orchestrationAgent is the main agent that receives requests from the UI
• instructions guide the orchestrator on how to use the specialized agents
• The middleware automatically injects the send_message_to_a2a_agent tool

The shipped a2a-middleware example subclasses A2AMiddlewareAgent to recreate an isolated middleware agent per run (overriding runAgent/clone) so concurrent threads don't share orchestrator state. The flat construction above is the pedagogical baseline; reach for the per-run subclass pattern when serving multiple concurrent users.

Cross-reference: the runtime skill documents an alternate single-agent A2A path using A2AAgent from @ag-ui/a2a (connecting directly to one A2A server). Use A2AMiddlewareAgent from @ag-ui/a2a-middleware (shown here) when orchestrating multiple A2A agents from one chat.

Adding New Agents

1. Create a new Python agent implementing the A2A protocol
2. Register its URL in agentUrls
3. Update the middleware instructions to describe the new agent
4. Add a dev script to package.json

---

A2A + A2UI

A2UI (Agent-to-UI) enables agents to render UI components declaratively. The agent defines UI components in its prompt, and CopilotKit renders them.

Prerequisites

• Python 3.12+
• Node.js 20+
• Gemini API key

Key Difference from Standard Integrations

In A2A + A2UI, most of the UI is generated by the agent rather than defined in React components. The agent sends declarative component descriptions (calendars, inboxes, forms, etc.) which are rendered by CopilotKit's A2UI renderer.

Frontend

The main page.tsx is minimal -- the agent drives the UI:

// Most UI comes from the agent via A2UI declarative components
// To see/edit the components, look in agent/prompt_builder.py
// Generate new components with the A2UI Composer: https://a2ui-editor.ag-ui.com

Resources

• A2UI + CopilotKit Documentation
• A2UI Specification
• A2UI Composer -- visual tool for creating A2UI components

---

MCP Apps

MCP Apps integrate Model Context Protocol servers as middleware on a BuiltInAgent:

import {
  CopilotRuntime,
  BuiltInAgent,
  createCopilotHonoHandler,
  InMemoryAgentRunner,
} from "@copilotkit/runtime/v2";
import { MCPAppsMiddleware } from "@ag-ui/mcp-apps-middleware";
import { handle } from "hono/vercel";

const middlewares = [
  new MCPAppsMiddleware({
    mcpServers: [
      {
        type: "http",
        url: "http://localhost:3108/mcp",
        serverId: "threejs",
      },
    ],
  }),
];

const agent = new BuiltInAgent({
  model: "openai/gpt-4o",
  prompt: "You are a helpful assistant.",
});

for (const middleware of middlewares) {
  agent.use(middleware);
}

const runtime = new CopilotRuntime({
  agents: { default: agent },
  runner: new InMemoryAgentRunner(),
});

const app = createCopilotHonoHandler({
  runtime,
  basePath: "/api/copilotkit",
});

export const GET = handle(app);
export const POST = handle(app);
export const PATCH = handle(app);
export const DELETE = handle(app);

Key patterns:

• Uses BuiltInAgent from @copilotkit/runtime/v2 (not an external agent)
• MCPAppsMiddleware adds MCP server tools to the agent
• Multiple MCP servers can be added in the mcpServers array
• Each server needs type, url, and serverId