mirror of
https://github.com/CopilotKit/CopilotKit.git
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e1f88ebc12
Addresses review feedback that channel-host.mts is doing too much.
Two changes, both scoped to the starters:
1. Channel construction moves to a new `channels.mts` beside `agent.ts` —
name resolution, `createChannel`, and the `onMessage` handler. That is
also the file to edit to customise a Channel (commands, reactions,
onMention), which previously meant editing the host.
The per-framework agent import moves with it, so `channel-host.mts` is now
byte-identical in all 15 starters rather than 13 + 2.
2. The host no longer stands up an HTTP server. Its comment claimed the
server was what "keeps the lifecycle-owning process alive"; that is false.
An open undici WebSocket holds the event loop on its own — verified with a
standalone repro where a process with no HTTP server and no timers of its
own stayed up indefinitely on a single WebSocket connection. The server was
therefore serving a second, uncalled copy of the runtime API on port 8300
for no reason.
With the server gone, `createCopilotNodeListener` was the wrong factory —
it builds a request listener purely for its activation side effect. The
host now uses `createCopilotRuntimeHandler` + `ready()`, which is the
documented long-running-host pattern (see fetch-handler.ts). This also
drops `node:http`, `basePath`, and the CHANNEL_PORT env var.
Behaviour is unchanged: same Channel, same agent, same status reporting, and
the same non-zero exit on activation failure.
Verified: 14/14 starters with a `typecheck:channel` script pass; mastra has no
such script by design (166dc94691) and its pre-existing Mastra `Memory` type
error is byte-identical before and after. `npm run channel` exercised on both
failure paths — missing channels.json, and missing INTELLIGENCE_API_KEY with a
name supplied — confirming the new `./channels.mjs` specifier resolves under
tsx as well as tsc. `parity:check` output identical to the pre-change baseline.
Refs #6315
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
301 lines
9.0 KiB
Markdown
301 lines
9.0 KiB
Markdown
# A2A + AG-UI Multi-Agent Starter
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A minimal starter template for building multi-agent applications with **A2A Protocol** (Agent-to-Agent) and **AG-UI Protocol** (Agent-UI). This project demonstrates how to coordinate multiple AI agents across different frameworks (LangGraph and Google ADK) to solve tasks collaboratively.
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## Quick Start
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### Prerequisites
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- **Node.js** 18+
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- **Python** 3.10+
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- **Google API Key** - [Get one here](https://aistudio.google.com/app/apikey)
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- **OpenAI API Key** - [Get one here](https://platform.openai.com/api-keys)
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### Installation
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1. **Install frontend dependencies:**
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```bash
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npm install
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```
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2. **Install Python dependencies:**
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```bash
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cd agents
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python3 -m venv .venv
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source .venv/bin/activate # On Windows: .venv\Scripts\activate
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pip install -r requirements.txt
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cd ..
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```
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3. **Set up environment variables:**
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```bash
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cp .env.example .env
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# Edit .env and add your API keys:
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# GOOGLE_API_KEY=your_google_api_key
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# OPENAI_API_KEY=your_openai_api_key
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```
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4. **Start all services:**
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```bash
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npm run dev
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```
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This will start:
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- **UI**: http://localhost:3000
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- **Orchestrator**: http://localhost:9000
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- **Research Agent**: http://localhost:9001
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- **Analysis Agent**: http://localhost:9002
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## Usage
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Try asking:
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- "Research quantum computing"
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- "Tell me about artificial intelligence"
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- "Research renewable energy"
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The orchestrator will:
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1. Send your query to the **Research Agent** to gather information
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2. Pass the research to the **Analysis Agent** for insights
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3. Present a complete summary with both research and analysis
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## Development Scripts
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```bash
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# Start everything
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npm run dev
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# Start individual services
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npm run dev:ui # Next.js UI only
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npm run dev:orchestrator # Orchestrator only
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npm run dev:research # Research agent only
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npm run dev:analysis # Analysis agent only
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# Build for production
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npm run build
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# Lint code
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npm run lint
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# Hold an Intelligence Channel open (see "Running a Channel" below)
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npm run channel
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# Type-check the channel host on its own tsconfig.channel.json
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npm run typecheck:channel
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```
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## Running a Channel
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`channel-host.mts` mounts the orchestrator agent as an Intelligence
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Channel (Slack, Teams). It requires `INTELLIGENCE_API_KEY` and a declared
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Channel in `.copilotkit/channels.json` — set both up with `copilotkit init` or
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`copilotkit channels add`, which write that file and the credentials your
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`.env` needs, then:
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```bash
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npm run channel
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```
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The host reads which Channel to hold from `.copilotkit/channels.json`. If a
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project declares more than one, set `INTELLIGENCE_CHANNEL_NAME` to pick one.
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The host holds no provider credentials and exposes no provider endpoint —
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Intelligence owns the provider edge — so the same file works for every provider.
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The Channel itself is declared in `channels.mts` — that is where to add commands,
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reactions, or an `onMention` handler. `channel-host.mts` only owns the process
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lifetime, and is byte-identical in every starter.
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Once startup finishes, the log reports the truth per Channel:
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- `Channel "<name>" is online.` — the session is up and can send.
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- `Channel "<name>" is declared but no provider is attached yet.` —
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a normal waiting state, not a failure. Run `copilotkit channels status` to
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see what setup remains.
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Neither message proves the provider app is installed, reachable, or that
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anyone can message it — verify that separately (invite the bot, then message
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it) before treating the Channel as working.
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## Customization
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### Adding New Agents
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1. **Create a new Python agent** in `agents/`:
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- Implement A2A Protocol (see existing agents as examples)
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- Choose a port (e.g., 9003)
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- Define agent capabilities and skills
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2. **Register in middleware** (`app/api/copilotkit/route.ts`):
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```typescript
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const newAgentUrl = "http://localhost:9003";
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const a2aMiddlewareAgent = new A2AMiddlewareAgent({
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agentUrls: [
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researchAgentUrl,
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analysisAgentUrl,
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newAgentUrl, // Add here
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],
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// ...
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});
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```
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3. **Add run script** in `package.json`:
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```json
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"dev:newagent": "python3 agents/new_agent.py"
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```
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4. **Update concurrently command** to include your new agent
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### Changing UI
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- **Main page**: Edit `app/page.tsx` for layout and result display
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- **Chat**: Edit `components/chat.tsx` for chat behavior
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- **Styling**: Edit `app/globals.css` and `tailwind.config.ts`
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- **A2A badges**: Edit `components/a2a/` components
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## What This Demonstrates
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This starter shows how specialized agents built with different frameworks can communicate via the A2A protocol:
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### Architecture
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```
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┌──────────────────────────────────────────┐
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│ Next.js UI (CopilotKit) │
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└────────────┬─────────────────────────────┘
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│ AG-UI Protocol
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┌────────────┴─────────────────────────────┐
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│ A2A Middleware │
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│ - Routes messages between agents │
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└──────┬───────────────────────────────────┘
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│ A2A Protocol
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│
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├─────► Research Agent (LangGraph)
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│ - Gathers information
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│ - Port 9001
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│
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└─────► Analysis Agent (ADK)
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- Analyzes findings
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- Port 9002
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▲
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│
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┌──────┴──────────┐
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│ Orchestrator │
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│ (ADK) │
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│ Port 9000 │
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└─────────────────┘
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```
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### Agents
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1. **Orchestrator (ADK + AG-UI Protocol)**
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- Receives requests from the UI
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- Coordinates specialized agents
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- Port: 9000
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2. **Research Agent (LangGraph + A2A Protocol)**
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- Gathers and summarizes information
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- Returns structured JSON
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- Port: 9001
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3. **Analysis Agent (ADK + A2A Protocol)**
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- Analyzes research findings
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- Provides insights and conclusions
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- Port: 9002
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## Project Structure
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```
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starter/
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├── app/
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│ ├── api/copilotkit/route.ts # A2A middleware setup (KEY FILE!)
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│ ├── layout.tsx # Root layout
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│ ├── globals.css # Styles
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│ └── page.tsx # Main UI
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│
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├── components/
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│ ├── chat.tsx # Chat component with A2A visualization
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│ └── a2a/ # A2A message components
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│ ├── agent-styles.ts # Agent branding utilities
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│ ├── MessageToA2A.tsx # Outgoing message badges
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│ └── MessageFromA2A.tsx # Incoming message badges
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│
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├── agents/ # Python agents
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│ ├── orchestrator.py # Orchestrator (ADK + AG-UI) - Port 9000
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│ ├── research_agent.py # Research (LangGraph + A2A) - Port 9001
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│ ├── analysis_agent.py # Analysis (ADK + A2A) - Port 9002
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│ └── requirements.txt # Python dependencies
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│
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├── package.json # Frontend dependencies & scripts
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├── .env.example # Environment variables template
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└── README.md # This file
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```
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## Key Concepts
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### AG-UI Protocol
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The **AG-UI Protocol** standardizes communication between the frontend (CopilotKit) and agents. The orchestrator uses AG-UI to receive messages from the UI.
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### A2A Protocol
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The **A2A Protocol** standardizes agent-to-agent communication. The Research and Analysis agents use A2A to communicate with the orchestrator.
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### A2A Middleware
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The **A2A Middleware** (in `app/api/copilotkit/route.ts`) is the magic that connects everything:
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- Wraps the orchestrator agent
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- Registers A2A agents automatically
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- Injects a `send_message_to_a2a_agent` tool into the orchestrator
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- Routes messages between agents
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## Troubleshooting
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### Agents not connecting?
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- Verify all services are running: `http://localhost:9000-9002`
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- Check console for startup errors
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### Missing API keys?
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- Ensure `.env` file exists with `GOOGLE_API_KEY` and `OPENAI_API_KEY`
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- Restart all services after adding keys
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### Python import errors?
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- Activate virtual environment: `source agents/.venv/bin/activate`
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- Reinstall dependencies: `pip install -r agents/requirements.txt`
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### Port conflicts?
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- Change ports in `.env` file:
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```
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ORCHESTRATOR_PORT=9000
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RESEARCH_PORT=9001
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ANALYSIS_PORT=9002
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```
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## Learn More
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- [AG-UI Protocol Documentation](https://docs.ag-ui.com)
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- [A2A Protocol Specification](https://a2a-protocol.org)
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- [Google ADK Documentation](https://google.github.io/adk-docs/)
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- [LangGraph Documentation](https://langchain-ai.github.io/langgraph/)
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- [CopilotKit Documentation](https://docs.copilotkit.ai)
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## License
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MIT
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