A resident AI assistant on DingTalk, Feishu, and Telegram

If your goal is only “ask AI in chat and get a reply back,” then DingTalk bots, Feishu bots, and Telegram bots have existed for years. But if your goal is to get work done — keep context, call tools, run on a schedule, and proactively send the result back into the same chat — you need more than a bot. You need a resident assistant.

That is the role GoWork is designed for inside the OmniGoAI product line. GoWork's assistant can plug into DingTalk, Feishu, and Telegram so that chat becomes the task entry point. A user sends work from the channel they already live in, the assistant continues in the background, and the result comes back to the original conversation.

In other words, channel integration is just the front door; the real product value lives in the execution layer behind it. If you have already read Meet GoWork's assistant: a local AI that does the work, not just the talk, this article answers the next practical question: once an assistant is inside IM, how is it different from a normal chat bot, and where does that difference matter most?

What problem are teams actually trying to solve when they put AI into IM?

When a team says “we want an AI assistant in DingTalk” or “can we connect AI to Feishu?”, the real request is usually not “make it reply when someone mentions it.” It is closer to this:

Let people assign work from the chat tool they already use all day.
Let the assistant understand ongoing context instead of treating every message as a fresh start.
Let it keep doing work after the message arrives — reading files, running commands, checking status, or scheduling reminders.
Let it send the outcome back into the same DingTalk, Feishu, or Telegram conversation.

If you only solve the first point, you still have a question-answer bot. Once you solve the other three, IM turns from a chat surface into a workflow entry point.

Why is “can reply in chat” not the same as “can do the work”?

This is the key distinction.

A typical IM bot is good at three things:

receiving a text message;
calling a model once;
returning another text message.

But real work often sounds more like this:

Check whether today's CI failed in this repository.
Every morning at 9, send me a summary of unresolved tickets.
Watch this page and tell me when the price drops below a threshold.
Send me the screenshot from the previous run.
Continue the task we started last week.

None of those are single-turn chat problems. They depend on state, tools, and continued execution. That is exactly where GoWork's assistant is different: it does not only receive messages, it keeps task context, calls tools, schedules recurring work, and pushes results back to the channel.

What does the workflow look like after GoWork is connected to DingTalk, Feishu, or Telegram?

The simplest model is a four-step loop:

A message arrives from the chat channel.
The assistant interprets the request. It decides whether this is casual chat, a status query, a file task, a web lookup, a scheduled job, or a longer execution chain.
The assistant runs in the background. It uses local tools, workspace files, shell commands, memory, search, or scheduling where needed.
The result goes back to the original conversation. That can be a short answer, a summary, a reminder, or even an image when the task produced one.

Step three is the real differentiator. The quality of the system is not determined by where the message entered. It is determined by whether the assistant can keep moving after the chat window is gone.

Which use cases benefit first from a resident IM assistant?

Scenario 1: daily execution tasks that start in chat

Examples include:

“Summarize today's release notes and send them back here.”
“Check this document and tell me which action items remain.”
“Turn this request into five concrete tasks for the team.”

These tasks all start in IM, but the work happens elsewhere. GoWork fits this pattern because it can accept the request in chat, execute in the background, and return the result in place.

Scenario 2: reminders, recurring jobs, and monitoring

Scheduling is where a resident assistant clearly separates itself from a chat-only bot.

For example:

“Every weekday at 9am, send me my open PR summary.”
“Remind me every evening at 8 to review today's tasks.”
“Check this page every 10 minutes and tell me if anything changes.”

GoWork can translate those natural-language requests into scheduled tasks, run them in the background, and deliver the result back to the same chat channel. If you want the broader capability picture, see the assistant documentation.

Scenario 3: work that needs continuity, not just one answer

A surprising amount of real work sounds like this:

“Continue the task from earlier.”
“Run the same process again.”
“Remember this format from now on.”
“Send me the screenshot from before.”

Without conversation memory, task history, and delivery records, those requests break immediately. GoWork's assistant is built to keep the conversation, the task, the memory, and the delivery channel connected instead of starting from zero every time.

Why do DingTalk, Feishu, and Telegram each matter as entry points?

These three channels represent different working environments:

DingTalk is common in China-based team collaboration and operational messaging.
Feishu fits internal collaboration, documentation, and project communication.
Telegram is often the lightweight control surface for individuals and distributed teams.

So support for multiple channels is not just “more bot APIs.” It means the same assistant can enter the communication environment people already use every day. For many teams, that matters more than any single channel feature.

When do you need GoWork's assistant instead of a normal bot?

Ask four simple questions:

Do you want the system to use tools and do work, not only reply?
Do you want it to remember project context, preferences, or previous runs?
Do you need recurring reminders, monitoring, or scheduled execution?
Do you need results to be pushed back into the same conversation automatically?

If the answer is “yes” to at least two of those, you are usually looking for a resident assistant rather than a chat bot.

A common mistake: treating channel integration as the whole product

Many teams start with “how do we connect AI to DingTalk, Feishu, or Telegram?” But the harder part starts after the connection exists:

how to restrict permissions so the assistant does not touch the wrong file or send the wrong message;
how to manage long-running work in the background;
how to remember user instructions without asking for them again;
how to make scheduled reminders, screenshots, and results return to the correct conversation.

That is why GoWork is not designed as a stand-alone IM bot. It is built as a closed loop of resident assistant + local tool execution + scheduling + channel delivery.

FAQ

FAQ 1: After GoWork is connected to IM, is it still just a chatbot?

No. Chat is the entry point, not the product boundary. The meaningful difference is that it can keep running tasks, preserve state, call tools, and proactively return outcomes.

FAQ 2: Is this only for DingTalk?

No. GoWork's assistant can connect to DingTalk, Feishu, and Telegram. Different channels fit different teams, but they all sit on the same assistant execution layer.

FAQ 3: Why emphasize “resident assistant” instead of “bot”?

Because many tasks depend on continuity: what happened last time, whether a run is still active, whether a reminder exists, and what should happen next. A resident assistant keeps that state. A normal bot usually does not.

FAQ 4: What is the best first scenario to try?

Usually one of two patterns: daily execution tasks that start in chat, or scheduled reminders and monitoring. Both quickly show the value of combining an IM entry point with a real execution layer.

If you are not looking for another bot that can merely talk in a group, but for an assistant that can live inside DingTalk, Feishu, or Telegram and actually get work done, GoWork is the closer match. To try it in practice, start from the GoWork download page, or read assistant capabilities first to see what kinds of tasks it can take over.