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Compaction

Every model has a context window — the maximum number of tokens it can process. When a conversation approaches that limit, OpenClaw compacts older messages into a summary so the chat can continue.

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How it works

  1. Older conversation turns are summarized into a compact entry.
  2. The summary is saved in the session transcript.
  3. Recent messages are kept intact.
When OpenClaw splits history into compaction chunks, it keeps assistant tool calls paired with their matching toolResult entries. If a split point lands inside a tool block, OpenClaw moves the boundary so the pair stays together and the current unsummarized tail is preserved. The full conversation history stays on disk. Compaction only changes what the model sees on the next turn.

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Auto-compaction

Auto-compaction is on by default. It runs when the session nears the context limit, or when the model returns a context-overflow error (in which case OpenClaw compacts and retries). Typical overflow signatures include request_too_large, context length exceeded, input exceeds the maximum number of tokens, input token count exceeds the maximum number of input tokens, input is too long for the model, and ollama error: context length exceeded.
Before compacting, OpenClaw automatically reminds the agent to save important notes to memory files. This prevents context loss.

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Manual compaction

Type /compact in any chat to force a compaction. Add instructions to guide the summary: 
/compact Focus on the API design decisions

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Using a different model

By default, compaction uses your agent’s primary model. You can use a more capable model for better summaries: 
{
  agents: {
    defaults: {
      compaction: {
        model: "openrouter/anthropic/claude-sonnet-4-6",
      },
    },
  },
}

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Compaction start notice

By default, compaction runs silently. To show a brief notice when compaction starts, enable notifyUser: 
{
  agents: {
    defaults: {
      compaction: {
        notifyUser: true,
      },
    },
  },
}
When enabled, the user sees a short message (for example, “Compacting context…”) at the start of each compaction run.

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Compaction vs pruning

CompactionPruning
What it doesSummarizes older conversationTrims old tool results
Saved?Yes (in session transcript)No (in-memory only, per request)
ScopeEntire conversationTool results only
Session pruning is a lighter-weight complement that trims tool output without summarizing.

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Troubleshooting

Compacting too often? The model’s context window may be small, or tool outputs may be large. Try enabling session pruning. Context feels stale after compaction? Use /compact Focus on <topic> to guide the summary, or enable the memory flush so notes survive. Need a clean slate? /new starts a fresh session without compacting. For advanced configuration (reserve tokens, identifier preservation, custom context engines, OpenAI server-side compaction), see the Session Management Deep Dive.
  • Session — session management and lifecycle
  • Session Pruning — trimming tool results
  • Context — how context is built for agent turns
  • Hooks — compaction lifecycle hooks (before_compaction, after_compaction)