Text the same AI from WhatsApp, Telegram, and Slack, and it remembers

You can message the same AI agent from WhatsApp on your phone, from Telegram on your laptop, and from Slack at work. With those channel integrations configured, the agent can retrieve relevant saved context through Vilix AI. The key is that memory is not tied to a particular channel. Vilix AI provides a shared store the agent can read and write after you set up its MCP connection and memory loop.

The channel is not the brain

WhatsApp, Telegram, Slack, Discord, Signal, and iMessage are channels of communication between you and the agent. Its channel integrations define what history and events it can read, while Vilix AI provides a separate shared memory store.

When developers set up always-on agents, they often bind a channel to a harness, such as OpenClaw or Hermes. The harness manages incoming messages, routes them to a model, and sends the reply back to the channel. OpenClaw and Hermes already have persistent local memory; whether it crosses channels is up to the developer’s configuration, while Vilix AI adds shared context from the other tools bound to your account.

Vilix AI tackles the problem at the memory level: connect it to the agent as a custom MCP server at api.vilix.ai/mcp, authenticate, and configure the read-and-save loop. Successful writes make those exchanges available to later context requests from clients connected to the same account.

How Vilix AI wires into an always-on agent

Vilix AI is an MCP-native persistent memory layer. Set up the agent to follow this pattern and verify both tool calls:

• Before replying, the agent calls get_context, which returns available recent messages, saved memories, user rules such as "reply in bullet points when I am clearly on mobile", and relevant project and task state.

• After composing the reply, before sending it, the agent calls save_turn with the exact exchange, then sends the saved reply. Reuse chat_id within the conversation.

Memory is stored server-side in your Vilix AI account rather than tied to a particular app or device. If an exchange from WhatsApp at 9 pm is saved successfully, the agent can retrieve it when relevant to your Telegram conversation at 9 am the next day.

The same pattern applies to using Vilix AI with other tools, such as ChatGPT and Claude. Here, the agent harness receives messages from the configured channels and calls the Vilix AI memory tools. You can read more about the technical details in the post about memory for any agent with the Vilix AI MCP.

A concrete example: Telegram today, WhatsApp tomorrow

Suppose you are planning a product launch. On Tuesday afternoon, you are at your desk, and you open Telegram to ask your configured agent to lay out a list of things to do before the launch date. In this example, the agent retrieves the relevant Vilix AI project context and drafts a checklist. It saves the exact exchange before sending its reply.

On Wednesday morning, you are on the road and open WhatsApp to ask the agent to remind you of that checklist. In this example, the agent calls get_context, finds the saved turn from Tuesday, and replies with the list of tasks.

If you also created the checklist items as tasks in Vilix AI, you can tell the agent to "mark the copy review as done" and have it update that task. After the update succeeds, the new task state is available to later reads from your configured agent.

You can also hand the agent work through a connected client. The Vilix AI Agent Inbox allows you to send a task to your own running agent from your phone. The queued message appears on the agent’s next memory read; it does not require a new chat session.

What channels are supported

Vilix AI works with supported tools and agents that can use its remote MCP connection. The channels you can connect depend on your agent harness. OpenClaw and Hermes can be configured with Vilix AI over MCP and can route messages from WhatsApp, Telegram, Slack, and other platforms according to their channel support and configuration.

If you are building your own agent harness, add Vilix AI as a remote MCP server, authenticate, and set up the read-and-save loop. Relevant saved context can then be retrieved across the channel integrations you configure. See the post on same AI memory across devices for more detail on how account-based storage works.

User rules and project context travel with you

Context in Vilix AI is not just about conversation history. There are three main surfaces that are useful for always-on agents:

• User rules are short personal instructions returned when the agent requests context. "Keep replies under 100 words when I am clearly in a hurry." "Always respond in Spanish." Connected agents can receive the same rules, though you should still check that their replies follow them.

• Projects and Tasks provide the agent with a lightweight project manager. Context requests can return relevant task status and project notes, reducing what you need to explain again. A successful task update becomes available to subsequent reads.

• Semantic and keyword search lets the agent look for relevant details in saved conversations beyond the immediate exchange. A saved constraint from three weeks ago can surface when relevant; search features and history access depend on the plan.

This is the same memory layer that powers context across ChatGPT, Claude, Cursor, Codex, and other tools. An always-on messaging agent is just another surface that consumes the same context store.

Setting it up

For OpenClaw or Hermes, add Vilix AI as a remote MCP server, authenticate, and set up the read-and-save loop. Verify both calls in a real conversation. The harness takes care of channel routing; Vilix AI provides shared memory.

For custom agents, add the Vilix AI MCP endpoint at api.vilix.ai/mcp as a custom connector. The two tools used in the memory loop are get_context and save_turn. Tell the agent to retrieve context, compose a reply, save the exact exchange, and then send the saved reply. Reuse chat_id within the conversation and test that the client actually invokes both tools.

Vilix AI has a free tier to get you started. Try Vilix AI free and connect your first agent channel in a few minutes.

Frequently asked questions

Does Vilix AI integrate directly with WhatsApp or Telegram?

No. WhatsApp, Telegram, Slack, and similar are message channels; Vilix AI is an assistant memory layer. You connect Vilix AI to the AI agent that powers the bot, not to the channels themselves. The agent is what processes the messages and routes them to and from the channels.

What agent harnesses work with Vilix AI for messaging bots?

OpenClaw and Hermes support remote MCP connections that can be configured for Vilix AI. Their channel integrations handle WhatsApp, Telegram, Slack, and other platforms. A custom agent with compatible remote MCP support can also connect, authenticate, and use the read-and-save loop.

Will the agent remember a conversation from WhatsApp when I text from Telegram?

Yes, a configured agent can retrieve a relevant conversation saved through one channel while responding through another. Both integrations must use the intended Vilix AI account and memory loop. The account holds the saved exchanges, while each context request returns relevant available information rather than identical history every time.

Can I set rules that apply across all channels?

Yes. Vilix AI user_rules are set in your account and are available to connected agents when they request context. They are short personal instructions, such as "reply in bullet points when I am clearly on mobile". Sharing a rule makes it available across configured channels; it does not guarantee that every reply follows it.

Is there a free plan?

Yes. Vilix AI has a free tier with no time limit, plus a Pro plan at $19.99 per month with a 7-day full-Pro trial. You can get started for free without a credit card.