Context Compaction

Context Compaction reduces the prompt footprint of a single conversation session by summarizing older turns and keeping the active context smaller.

This page is about session context, not long-term memory storage. If you want to merge or rewrite stored memories in a memory provider, see Bot Memory Management.

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Why It Exists

As a conversation grows, the bot needs to send more prior messages back to the model. That increases:

• token usage
• latency
• pressure on the model's context window
• the chance that older but still important turns will crowd out newer ones

Context compaction helps by replacing older conversational detail with a shorter summary that still preserves enough continuity for the next turns.

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What It Changes

Context compaction affects the active session context only.

It does not:

• delete the bot itself
• change the configured memory provider
• merge long-term memory records
• replace the need for memory search

In practice, it changes how much historical session text is carried into future model calls.

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Automatic Compaction

Configure automatic context compaction from the bot's General tab.

Relevant fields:

Field	Description
Compaction Enabled	Enable or disable automatic context compaction for this bot.
Compaction Threshold	Estimated token threshold that triggers background compaction.
Compaction Ratio	How aggressively the session should be reduced during compaction.
Compaction Model	The model used to summarize old session context.

When enabled, Memoh can compact context in the background after a turn when the estimated input size passes the configured threshold.

Memoh also uses the selected model's context_window to understand how close the session is to the available budget.

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Immediate Compaction

You can trigger compaction immediately for the current session in two ways:

From The Session Status Panel

1. Open the active conversation.
2. Open the session status panel.
3. Click Compact Now.

The status panel also shows the current context usage, cache hit rate, and used skills, which helps you decide whether compaction is useful right now.

From Slash Commands

Run:

/compact

or:

/compact run

This runs synchronous context compaction for the current session and returns a status result to chat.

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Status And Logs

The Compaction tab in the bot detail page provides an audit trail for context compaction runs.

Typical fields include:

• Status — whether the compaction finished successfully or failed
• Summary — the compacted summary text or a summary preview
• Message Count — how many messages were involved
• Started / Completed Time — when the run happened
• Model / Usage — metadata about the model and token usage when available

The log list is useful when you want to verify that automatic compaction is actually running or diagnose a failure.

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Relationship To context_window

Memoh tracks the current session against the selected chat model's context_window.

You can see this in:

• the Web UI session status panel
• the /status slash command

Compaction becomes more valuable as the active session gets closer to the model's context limit. A dedicated compaction model can also be used to summarize more cheaply than the main chat model.

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Context Compaction vs Memory Compaction

These two features sound similar but solve different problems:

Feature	Scope	Trigger	Result
Context Compaction	One active session	Session panel or /compact	Summarizes older chat history for future turns
Memory Compaction	Long-term memory provider	Memory tab	Rewrites stored memory entries

Use Context Compaction when one conversation has become too large.

Use Memory Compaction when the bot's stored memories themselves have become noisy or redundant.

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Next Steps

• To inspect session runtime information, see Sessions.
• To understand slash-triggered compaction, see Slash Commands.
• To manage long-term memory instead of session context, see Bot Memory Management.