> For the complete documentation index, see [llms.txt](https://docs.cherryai.com.cn/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.cherryai.com.cn/docs/en-us/advanced-basic/chat/context-queue.md).

# Long Conversations, Context, and Queued Messages

The longer the conversation, the more history the model needs to read. When context usage approaches the limit, earlier content may no longer be able to participate in the response. Instead of repeatedly adding a single “continue,” it is better to periodically organize the conclusions and unresolved questions.

<figure><img src="/files/0448a345943a8e022b65316b708432bcc5f6f9cf" alt="助手高级设置中的模型温度和上下文管理选项"><figcaption><p>Adjust advanced settings only when you need to change the response style or how long conversations are handled; if unsure, keep the current value.</p></figcaption></figure>

## Managing long conversations

{% stepper %}
{% step %}

### 1. Watch the context prompt

When the interface indicates increasing context pressure, first stop adding large attachments and check which parts of the history are still relevant to the current goal.
{% endstep %}

{% step %}

### 2. Let the model generate a handoff summary

Ask it to separately list “confirmed facts, current conclusions, issues to be resolved, constraints that must not be lost.” This is better suited for continued work than a plain “summarize it.”
{% endstep %}

{% step %}

### 3. Start a new topic to continue

Put the handoff summary and necessary files into a new topic, and state in the first message which goal will be handled next. Keep the original topic for reference.
{% endstep %}
{% endstepper %}

## Use the message queue

While the model is still replying, you can add the next request to the queue. This is suitable for appending a clear follow-up action, such as “After finishing, organize it into three conclusions.” If the new message will change the direction of the ongoing task, stop the current generation first, then restate the goal.

<figure><img src="/files/2d7e082c4cba72f769a6c049855aeac33c3d77ec" alt="消息队列中两条待发送消息和恢复自动发送按钮"><figcaption><p>While paused, you can first check the results; after resuming, queued messages will continue to be sent from top to bottom.</p></figcaption></figure>

In the figure: ① Two queued messages in the current topic; ② Resume automatic sending. After resuming, messages will continue to be sent from top to bottom.

{% hint style="info" %}
The queue is not an automation plan. It only handles follow-up messages in the current topic; if you need something to run at a fixed time, use \[Scheduled Task].
{% endhint %}

### Example application: reviewing a long report

First upload the report and ask for issues to be listed by section. While the model is processing, add “organize the risk list after finishing” and “generate a checklist at the end” to the queue in order. If you need to verify the first round of results first, you can pause automatic sending; after confirming everything is correct, resume it. This way, you do not need to stay in the conversation and send messages one by one, and you can also avoid the next request being carried out before the review is finished.

<details>

<summary>Why did the model suddenly forget the requirements it mentioned earlier?</summary>

First check whether the conversation is too long, whether the model was switched, and whether the key requirements appeared only once. Write stable rules into the explicit instructions for the current task; if you need long-term reuse, hand it over to an Agent prompt or skill.

</details>


---

# Agent Instructions
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## Querying This Documentation
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```

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