spring-boot-channels-chat

Omnichannel AI Chat — one @Agent, every channel

Same AI assistant, every channel — built with a single @Agent. The persona lives in a skill file; the framework wires that one agent to the web console and to every configured messaging platform (Telegram, Slack, Discord, WhatsApp, Messenger). No per-channel delivery code.

How it works

OmnichannelChat is annotated @Agent(name = "omnichannel", skillFile = "skill:omnichannel-chat"). The skill file META-INF/skills/omnichannel-chat/SKILL.md carries the system prompt and a ## Channels section. When atmosphere-channels is on the classpath, the framework registers the agent's pipeline with ChannelAiBridge, which routes inbound messages from every listed channel to the agent and sends the reply back through the originating platform.

## Channels        <-- in SKILL.md: telegram, slack, discord, whatsapp, messenger

Quick start (web only)

./mvnw spring-boot:run -pl samples/spring-boot-channels-chat

Open http://localhost:8080/atmosphere/console/ — AI chat with streaming responses via WebSocket. (Demo mode answers with no API key; set LLM_API_KEY for a real LLM.)

Adding Telegram

1. Create a bot via @BotFather and copy the token
2. Start a tunnel: ngrok http 8080
3. Register the webhook:

   curl -X POST "https://api.telegram.org/bot<TOKEN>/setWebhook" \
     -H "Content-Type: application/json" \
     -d '{"url": "https://<NGROK_URL>/webhook/telegram", "secret_token": "my-secret"}'

4. Run:

   TELEGRAM_BOT_TOKEN=<TOKEN> TELEGRAM_WEBHOOK_SECRET=my-secret \
     ./mvnw spring-boot:run -pl samples/spring-boot-channels-chat

5. Message your bot on Telegram — the same agent that answers in the browser answers here

Adding Slack

1. Create a Slack app at https://api.slack.com/apps
2. Enable Events API, subscribe to message.channels
3. Set the webhook URL to https://<NGROK_URL>/webhook/slack
4. Run:

   SLACK_BOT_TOKEN=xoxb-... SLACK_SIGNING_SECRET=... \
     ./mvnw spring-boot:run -pl samples/spring-boot-channels-chat

Adding more channels

Set the env vars for any channel and it auto-activates:

Channel	Required env vars
Telegram	TELEGRAM_BOT_TOKEN, TELEGRAM_WEBHOOK_SECRET
Slack	SLACK_BOT_TOKEN, SLACK_SIGNING_SECRET
Discord	DISCORD_BOT_TOKEN, DISCORD_PUBLIC_KEY
WhatsApp	WHATSAPP_PHONE_NUMBER_ID, WHATSAPP_ACCESS_TOKEN, WHATSAPP_APP_SECRET
Messenger	MESSENGER_PAGE_TOKEN, MESSENGER_APP_SECRET

With a real LLM

LLM_API_KEY=your-gemini-key LLM_MODEL=gemini-2.5-flash \
  TELEGRAM_BOT_TOKEN=... TELEGRAM_WEBHOOK_SECRET=... \
  ./mvnw spring-boot:run -pl samples/spring-boot-channels-chat

Key code

File	Purpose
OmnichannelChat.java	The @Agent — @Prompt streams every message through the pipeline
META-INF/skills/omnichannel-chat/SKILL.md	System prompt + ## Channels the agent serves
ConsoleEndpointAlias.java	Aliases the agent handler to /atmosphere/ai-chat for the console
application.yaml	LLM settings (atmosphere.ai.*) and channel credentials (atmosphere.channels.*)

Architecture

Web browser ──WebSocket──┐
Telegram ─────webhook────┤
Slack ────────webhook────┤   @Agent(name = "omnichannel")
Discord ──────gateway────┤   skill:omnichannel-chat
WhatsApp ─────webhook────┤            │
Messenger ────webhook────┘     one AI pipeline
                                      │
                          ChannelAiBridge (channels) +
                          WebSocket handler (web)
                                      │
                              streaming response
                                      v
                       back through the originating surface

The same agent and skill serve every surface. Inbound channel messages are routed to the agent's pipeline by ChannelAiBridge (from atmosphere-channels), which handles platform format differences (max length, reply threading, markdown) transparently. The ## Channels list in the skill file is the agent's channel allow-list — only the listed platforms reach this agent.